CN118071040B - Safety inspection evaluation method and system for highway construction - Google Patents
Safety inspection evaluation method and system for highway construction Download PDFInfo
- Publication number
- CN118071040B CN118071040B CN202410502927.9A CN202410502927A CN118071040B CN 118071040 B CN118071040 B CN 118071040B CN 202410502927 A CN202410502927 A CN 202410502927A CN 118071040 B CN118071040 B CN 118071040B
- Authority
- CN
- China
- Prior art keywords
- data
- traffic
- security
- report
- algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000010276 construction Methods 0.000 title claims abstract description 72
- 238000011156 evaluation Methods 0.000 title claims abstract description 32
- 238000007689 inspection Methods 0.000 title abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 126
- 238000007726 management method Methods 0.000 claims abstract description 80
- 238000005516 engineering process Methods 0.000 claims abstract description 72
- 238000005457 optimization Methods 0.000 claims abstract description 42
- 238000005192 partition Methods 0.000 claims abstract description 40
- 238000012300 Sequence Analysis Methods 0.000 claims abstract description 29
- 238000003066 decision tree Methods 0.000 claims abstract description 16
- 230000007613 environmental effect Effects 0.000 claims abstract description 16
- 230000004927 fusion Effects 0.000 claims abstract description 15
- 238000013480 data collection Methods 0.000 claims abstract description 8
- 230000008859 change Effects 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 81
- 238000004458 analytical method Methods 0.000 claims description 57
- 238000012502 risk assessment Methods 0.000 claims description 36
- 238000013179 statistical model Methods 0.000 claims description 16
- 238000007621 cluster analysis Methods 0.000 claims description 15
- 238000003909 pattern recognition Methods 0.000 claims description 15
- 238000012544 monitoring process Methods 0.000 claims description 14
- 238000007781 pre-processing Methods 0.000 claims description 13
- 231100000279 safety data Toxicity 0.000 claims description 13
- 238000007405 data analysis Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 9
- 238000007727 cost benefit analysis Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000005065 mining Methods 0.000 claims description 6
- 238000010223 real-time analysis Methods 0.000 claims description 5
- 238000013079 data visualisation Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000013468 resource allocation Methods 0.000 claims description 4
- 238000010206 sensitivity analysis Methods 0.000 claims description 4
- 238000002922 simulated annealing Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000010191 image analysis Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000013138 pruning Methods 0.000 claims description 3
- 238000011425 standardization method Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 2
- 241001123248 Arma Species 0.000 claims 2
- 230000000694 effects Effects 0.000 abstract description 5
- 238000007418 data mining Methods 0.000 abstract description 4
- 238000012731 temporal analysis Methods 0.000 description 5
- 238000000700 time series analysis Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000008676 import Effects 0.000 description 4
- 238000007796 conventional method Methods 0.000 description 3
- 230000007812 deficiency Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005406 washing Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 244000153665 Ficus glomerata Species 0.000 description 1
- 235000012571 Ficus glomerata Nutrition 0.000 description 1
- 208000025174 PANDAS Diseases 0.000 description 1
- 208000021155 Paediatric autoimmune neuropsychiatric disorders associated with streptococcal infection Diseases 0.000 description 1
- 240000000220 Panda oleosa Species 0.000 description 1
- 235000016496 Panda oleosa Nutrition 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003370 grooming effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of traffic control systems, in particular to a safety inspection evaluation method and a system for highway construction, comprising the following steps: the dynamic navigation system thought based on the GIS adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, integrates multi-source data by using a data fusion algorithm, analyzes traffic conditions in real time and generates a real-time traffic data set. In the invention, traffic data is intelligently processed by using graph theory and path optimization algorithm, construction area is avoided, congestion and accident risk are reduced, partition association evaluation and data mining algorithm are applied to improve safety risk management efficiency, K-means or hierarchical clustering is used for finely classifying and grading risks, a dynamic safety threshold is set by a time sequence analysis and statistics model, decision support is provided by a decision tree and expert system, safety standard is dynamically adjusted according to environmental change, data driving decision is flexibly provided, and safety management effect is enhanced.
Description
Technical Field
The invention relates to the technical field of traffic control systems, in particular to a safety inspection evaluation method and system for highway construction.
Background
The traffic control system is a field focused on ensuring road traffic safety and efficiency through various technical means. Traffic control system technologies generally include aspects such as traffic monitoring, accident prevention, traffic management, and traffic information delivery. In a highway construction scenario, special safety management and control measures are required because of temporary traffic pattern changes, construction equipment and personnel intervention, all of which affect normal traffic.
The road construction safety inspection and evaluation method is a safety management method specially designed for road construction scenes. The method aims to comprehensively evaluate and monitor traffic conditions in a construction area, prevent accidents and ensure the safety of constructors and passing vehicles. The aim of this method is to increase the safety of road construction by reducing traffic accidents in the construction area, while minimizing the impact of construction on the surrounding traffic flow. To achieve these effects, a number of means and measures are typically employed, including installing temporary traffic lights, setting construction warning signs and barriers, deploying traffic monitoring cameras, and implementing traffic grooming schemes. In addition, advanced techniques such as traffic flow analysis software, accident prediction models, and real-time data analysis tools are employed to monitor and evaluate the safety conditions within the construction area in real time and adjust traffic control strategies as needed. Through the comprehensive measures, the traffic safety during highway construction can be effectively improved, traffic jam and accidents caused by construction are reduced, and smooth operation of both construction and traffic is ensured.
In the actual use process of the existing road construction safety inspection evaluation method, the conventional method generally adopts a simpler algorithm in the aspects of path optimization and safety risk evaluation, which limits the application effect in a complex traffic environment. In terms of risk identification and classification, conventional methods often lack advanced data mining and intelligent cluster analysis tools, making risk assessment less careful, and often ignoring certain key security items. Due to the lack of flexible safety threshold settings and the lack of data driven decision support systems, conventional approaches suffer from deficiencies in terms of adaptability and accuracy in coping with construction progress and environmental changes. These deficiencies have limited the efficiency and effectiveness of conventional methods in ensuring traffic safety in the construction area and its surroundings.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a safety inspection and evaluation method and system for highway construction.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a safety inspection and evaluation method for highway construction comprises the following steps:
S1: the dynamic navigation system thought based on the GIS adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, integrates multi-source data by using a data fusion algorithm, and carries out real-time analysis on traffic conditions to generate a real-time traffic data set;
s2: based on the real-time traffic data set, adopting a graph theory algorithm and a path optimization model to process traffic data, calculating an optimal alternative route avoiding a construction area, and generating an optimized traffic route scheme;
S3: based on the optimized traffic route scheme, a partition association evaluation system is adopted, and an Apriori or FP-Growth algorithm is utilized to analyze security data among different partitions, identify potential risks and generate a partition security risk assessment report;
S4: based on the partition security risk assessment report, adopting a security item intelligent cluster analysis tool, and carrying out intelligent classification and grading on security risks by using a K-means or hierarchical clustering algorithm to generate an intelligent classification security risk report;
S5: based on the intelligent classified security risk report, a dynamic security threshold setting system is used, a time sequence analysis and statistical model is applied, security standards are dynamically adjusted according to environmental changes and construction progress, and a dynamic security threshold adjustment scheme is generated;
S6: based on the dynamic safety threshold adjustment scheme, a safety management decision support system is utilized, and a decision tree analysis and expert system technology are combined to provide a data-driven decision scheme to generate a safety management decision support report.
As a further aspect of the invention, the real-time traffic data set includes traffic flow density, speed and traffic pattern, the optimized traffic route scheme includes alternative paths and strategies to improve congestion, the zoned security risk assessment report includes risk hotspots and potential influencing factors, the intelligent classified security risk report includes risk levels and key security items, the dynamic security threshold adjustment scheme includes updated security parameters and thresholds, and the security management decision support report includes decision schemes and countermeasures.
As a further scheme of the invention, a dynamic navigation system thought based on GIS adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, and integrates multi-source data by using a data fusion algorithm to analyze traffic conditions in real time, and the steps of generating a real-time traffic data set are as follows:
s101: based on the construction area and the periphery, adopting a real-time traffic monitoring technology, collecting data of traffic flow, speed and state through a traffic camera and a sensor network, and preprocessing the data to generate a preliminary traffic data set;
s102: based on the preliminary traffic data set, adopting a data cleaning algorithm to detect abnormal values and carry out data smoothing treatment, removing error data and abnormal values, and carrying out data standardization to generate a cleaned traffic data set;
S103: based on the cleaned traffic data set, a time sequence analysis method is adopted to identify the time variation trend of traffic flow and speed, trend prediction is carried out, and a time sequence analysis result is generated;
S104: based on the time sequence analysis result, integrating data from different sources by adopting a data fusion algorithm to form a unified traffic condition view and generate a real-time traffic data set;
The real-time traffic monitoring technology comprises video image analysis and a wireless sensing network, the data cleaning algorithm comprises outlier analysis and a moving average method, the time sequence analysis method specifically refers to an autoregressive moving average model, and the data fusion algorithm comprises a weighted average method and Kalman filtering.
As a further scheme of the invention, based on the real-time traffic data set, adopting a graph theory algorithm and a path optimization model to process traffic data, calculating an optimal alternative route avoiding a construction area, and generating an optimized traffic route scheme specifically comprises the following steps:
s201: based on the real-time traffic data set, adopting a graph theory algorithm to perform structural modeling on a traffic network, and performing network topology analysis to generate a traffic network model;
S202: based on the traffic network model, a traffic load of multiple road sections is estimated by adopting a traffic distribution algorithm, traffic flow distribution is carried out, and a road section load analysis result is generated;
s203: based on the road section load analysis result, calculating an optimal alternative route bypassing the construction area by adopting a route optimization model, and carrying out route feasibility analysis to generate an optimal alternative route scheme;
S204: based on the optimal alternative route scheme, a cost benefit analysis method is adopted to evaluate the practicability and efficiency of the proposed route scheme, and scheme optimization is carried out to generate an optimized traffic route scheme;
the graph theory algorithm specifically refers to Dijkstra algorithm and A-type algorithm, the flow distribution algorithm comprises a gravity model and a user balanced distribution model, the path optimization model comprises a genetic algorithm and a simulated annealing algorithm, and the cost benefit analysis method comprises a sensitivity analysis and scene simulation technology.
As a further scheme of the invention, based on the optimized traffic route scheme, a partition association evaluation system is adopted, and the Apriori or FP-Growth algorithm is utilized to analyze security data among different partitions, identify potential risks and generate a partition security risk evaluation report, wherein the method specifically comprises the following steps:
S301: based on the optimized traffic route scheme, a data preprocessing technology is adopted to prepare for association rule analysis, and preprocessed safety data is generated;
S302: based on the preprocessed safety data, analyzing the mode and association of the safety data among the partitions by adopting an association rule mining algorithm to generate a frequent item set;
s303: based on the frequent item set, a strong association rule generation technology is adopted, and important rules are screened out by calculating the confidence and support of the item set, so that a potential risk rule set is generated;
S304: based on the potential risk rule set, carrying out risk grade assessment and influence analysis by adopting a risk assessment method, and generating a partition security risk assessment report;
The data preprocessing technology comprises a Z score standardization method and a K neighbor filling method, the Apriori algorithm comprises a term set generation step and a pruning step, the strong association rule generation technology comprises a confidence threshold setting step and a rule extraction step, and the risk assessment method comprises a risk matrix method and an influence assessment model.
As a further scheme of the invention, based on the partition security risk assessment report, a security item intelligent cluster analysis tool is adopted, and a K-means or hierarchical clustering algorithm is used for carrying out intelligent classification and grading on security risks, so that the steps for generating an intelligent classified security risk report are specifically as follows:
s401: based on the partition security risk assessment report, adopting a data normalization technology to perform standardized processing on the risk data to generate normalized risk data;
S402: based on the normalized risk data, adopting a K-means algorithm to perform preliminary classification on the security risk, and generating a preliminary classification result;
s403: based on the preliminary classification result, carrying out refinement risk category classification and grade assessment by adopting a hierarchical clustering algorithm to generate refinement classification and grade classification results;
S404: based on the refined classification and grading results, adopting a report generation technology to integrate analysis results and generate an intelligent classification security risk report;
The data normalization technology comprises a maximum-minimum standardization method and a variance scaling method, the K-means algorithm comprises centroid initialization and clustering iteration, the hierarchical clustering algorithm comprises distance measurement selection and clustering tree construction, and the report generation technology comprises data visualization and report template design.
As a further scheme of the invention, based on the intelligent classified security risk report, a dynamic security threshold setting system is used, a time sequence analysis and statistical model is applied, security standards are dynamically adjusted according to environmental changes and construction progress, and the step of generating a dynamic security threshold adjustment scheme is specifically as follows;
s501: based on the intelligent classified security risk report, performing risk pattern recognition by adopting a time sequence analysis method to generate a risk pattern recognition report;
s502: based on the risk pattern recognition report, carrying out security risk quantitative prediction by applying a statistical model ARMA to generate a security risk prediction report;
S503: dynamically adjusting the safety standard by using a weighted moving average method according to the safety risk prediction report and the environmental change data to generate a real-time adjustment safety standard;
S504: based on the real-time adjustment safety standard and the construction progress, generating a dynamic safety threshold adjustment scheme by adopting a linear programming algorithm;
The time sequence analysis method comprises an autoregressive model and a moving average model, wherein the statistical model ARMA combines autoregressive and moving average characteristics, and the linear programming algorithm is used for resource allocation and optimization.
As a further aspect of the present invention, based on the dynamic security threshold adjustment scheme, a security management decision support system is utilized, and a decision tree analysis and expert system technology are combined to provide a data-driven decision scheme, and the step of generating a security management decision support report specifically includes:
S601: based on the dynamic safety threshold adjustment scheme, different safety management strategies are evaluated by adopting a decision tree analysis method, and a safety management strategy evaluation report is generated;
S602: based on the security management policy evaluation report, performing deep analysis and optimization by using an expert system technology, and generating an optimized security management policy;
S603: inputting the optimized security management strategy into a security management decision support system, and applying a cluster analysis algorithm to perform iterative optimization to generate a data-driven decision scheme;
S604: based on the data-driven decision scheme, adopting a natural language generation technology to compile report items, and generating a security management decision support report;
the decision tree analysis method comprises the steps of constructing a tree structure model and visualizing a decision path, wherein the expert system technology comprises an industry expert knowledge base and rule-based decision support, the cluster analysis algorithm is used for data classification and pattern recognition, and the natural language generation technology is used for generating readable text from data.
The system comprises a real-time traffic data analysis module, a traffic route optimization module, a safety risk identification module, a safety risk classification module, a dynamic safety threshold adjustment module, a safety management strategy module and a decision support report generation module.
The real-time traffic data analysis module is used for collecting data, preprocessing the data and predicting trend based on traffic conditions of a construction area and the periphery by adopting a real-time traffic monitoring technology, and generating a cleaned and predicted traffic data set;
The traffic route optimization module processes traffic data by adopting a graph theory algorithm and a path optimization model based on the cleaned and predicted traffic data set, calculates an optimal alternative route and generates an optimized traffic route scheme;
The safety risk identification module analyzes safety data and identifies potential risks by adopting an association rule mining algorithm based on an optimized traffic route scheme, and generates a subarea safety risk assessment report;
The security risk classification module performs intelligent classification and classification on security risks by using a clustering algorithm based on the partition security risk assessment report to generate an intelligent classification security risk report;
the dynamic safety threshold adjustment module dynamically adjusts safety standards according to environmental changes by adopting a time sequence analysis and statistical model based on the intelligent classification safety risk report to generate a dynamic safety threshold adjustment scheme;
The security management policy module evaluates differentiated security management policies based on a dynamic security threshold adjustment scheme and combines a decision analysis technology to generate optimized security management policies;
The decision support report generation module inputs the optimized security management strategy to a decision support system, applies data analysis and report generation technology, and generates a security management decision support report.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the method, the traffic data can be intelligently processed by using a graph theory algorithm and a path optimization model, and an optimal alternative route is provided for avoiding a construction area, so that the risks of traffic jam and related accidents are reduced. By applying the partition association evaluation system and the advanced data mining algorithm, potential safety risks can be identified more effectively, and the accuracy and efficiency of safety risk management are improved. By using the K-means or hierarchical clustering algorithm to conduct intelligent clustering analysis of the safety items, the method can conduct finer classification and grading on risks, and therefore a more targeted scheme is provided for safety management. The dynamic safety threshold setting system combining the time sequence analysis and the statistical model and the safety management decision support system of the decision tree analysis and the expert system technology enable the method to dynamically adjust the safety standard and provide a data-driven decision scheme according to the environment change and the construction progress, and improve the flexibility and the effect of safety management.
Drawings
FIG. 1 is a schematic workflow diagram of the present invention;
FIG. 2 is a S1 refinement flowchart of the present invention;
FIG. 3 is a S2 refinement flowchart of the present invention;
FIG. 4 is a S3 refinement flowchart of the present invention;
FIG. 5 is a S4 refinement flowchart of the present invention;
FIG. 6 is a S5 refinement flowchart of the present invention;
FIG. 7 is a S6 refinement flowchart of the present invention;
Fig. 8 is a system flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1, the present invention provides a technical solution: a safety inspection and evaluation method for highway construction comprises the following steps:
S1: the dynamic navigation system thought based on the GIS adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, integrates multi-source data by using a data fusion algorithm, and carries out real-time analysis on traffic conditions to generate a real-time traffic data set;
S2: based on a real-time traffic data set, adopting a graph theory algorithm and a path optimization model to process traffic data, calculating an optimal alternative route avoiding a construction area, and generating an optimized traffic route scheme;
s3: based on an optimized traffic route scheme, a partition association evaluation system is adopted, and an Apriori or FP-Growth algorithm is utilized to analyze security data among different partitions, identify potential risks and generate a partition security risk assessment report;
s4: based on the partition security risk assessment report, adopting a security item intelligent cluster analysis tool, and carrying out intelligent classification and grading on security risks by using a K-means or hierarchical clustering algorithm to generate an intelligent classification security risk report;
s5: based on the intelligent classified security risk report, a dynamic security threshold setting system is used, a time sequence analysis and statistics model is applied, security standards are dynamically adjusted according to environmental changes and construction progress, and a dynamic security threshold adjustment scheme is generated;
s6: based on the dynamic safety threshold adjustment scheme, a safety management decision support system is utilized, and a decision tree analysis and expert system technology are combined to provide a data-driven decision scheme to generate a safety management decision support report.
The real-time traffic data set comprises traffic flow density, speed and traffic mode, the optimized traffic route scheme comprises alternative paths and strategies for improving congestion, the partitioned security risk assessment report comprises risk hot spots and potential influencing factors, the intelligent classified security risk report comprises risk levels and key security items, the dynamic security threshold adjustment scheme comprises updated security parameters and thresholds, and the security management decision support report comprises decision schemes and countermeasures.
The traffic flow and the traffic state are monitored in real time by utilizing the GIS-based dynamic navigation and real-time data collection technology, so that the accuracy and the response capability of traffic management are enhanced. Then, through a graph theory algorithm and path optimization, the method effectively plans the optimal alternative route avoiding the construction area, obviously reduces traffic jam and optimizes the whole traffic flow line. In addition, the safety data of different areas are accurately analyzed by adopting partition association evaluation and advanced data mining algorithms such as Apriori or FP-Growth, potential risks are timely identified and dealt with, and the safety risk management capacity is improved. The intelligent classification and grading of the security risks further enhance the accuracy and efficiency of risk assessment by means of K-means or hierarchical clustering algorithm. Meanwhile, by means of the dynamic safety threshold setting system, the safety standard is dynamically adjusted according to the environment and the construction progress, and the instantaneity and the adaptability of safety management are ensured. Finally, the method combines decision tree analysis and expert system technology to provide data-driven decision support, so that safety management is more scientific and efficient.
Referring to fig. 2, a GIS-based dynamic navigation system concept adopts a real-time data collection technology to collect traffic flow, speed and traffic state in a construction area and surrounding areas, integrates multi-source data by using a data fusion algorithm, performs real-time analysis on traffic conditions, and specifically, generates a real-time traffic data set by the steps of:
s101: based on the construction area and the periphery, adopting a real-time traffic monitoring technology, collecting data of traffic flow, speed and state through a traffic camera and a sensor network, and preprocessing the data to generate a preliminary traffic data set;
S102: based on the preliminary traffic data set, adopting a data cleaning algorithm to detect abnormal values and smooth the data, removing error data and abnormal values, and carrying out data standardization to generate a cleaned traffic data set;
s103: based on the cleaned traffic data set, a time sequence analysis method is adopted to identify the time variation trend of traffic flow and speed, trend prediction is carried out, and a time sequence analysis result is generated;
S104: based on the time sequence analysis result, integrating data from different sources by adopting a data fusion algorithm to form a unified traffic condition view and generate a real-time traffic data set;
the real-time traffic monitoring technology comprises video image analysis and a wireless sensor network, the data cleaning algorithm comprises an outlier analysis and a moving average method, the time sequence analysis method specifically refers to an autoregressive moving average model, and the data fusion algorithm comprises a weighted average method and Kalman filtering.
In S101, data is collected by a real-time traffic monitoring technique in an effort to acquire detailed information about construction areas and surrounding traffic conditions. First, a target area is covered by deploying a traffic camera and a sensor network. These devices will constantly capture data of traffic flow, speed and traffic status. The raw data must be pre-processed before it is transmitted to the central server, which includes removing noise from the data, performing data complementation to fill any missing data points, and ensuring timestamp synchronization for all data. This series of operations will generate a preliminary traffic data set that provides the basis for subsequent analysis.
In S102, the preliminary traffic data set will be processed to ensure its accuracy and consistency. First, outliers are detected and culled using data cleansing algorithms, including outlier analysis, to prevent negative impact on the data. Then, the moving average method is used to smooth the data, and the fluctuation of the data is reduced, so that a more stable result is obtained. And finally, data standardization is carried out to ensure that the units and the dimensions of the data of different sensors and cameras are consistent, and a cleaned traffic data set is generated.
In S103, the time series analysis method is used to study the traffic data set after washing. In particular, an autoregressive moving average model (ARMA) or other time series analysis method is employed to identify time-varying trends in traffic flow and speed. By analyzing the historical data, the traffic situation in a future period of time is predicted. This will generate the results of the time series analysis, including trend graphs and predictive data, providing important information for real-time navigation.
In S104, data from different sources is integrated using a data fusion algorithm to create a unified traffic situation view. This includes real-time monitoring of data, data after washing, and results of time series analysis. By using data fusion techniques such as weighted averaging and kalman filtering, it is ensured that the obtained real-time traffic data set is accurate and reliable. This integrated data set will be used in a navigation system to provide real-time traffic information and road conditions.
Referring to fig. 3, based on a real-time traffic data set, a graph theory algorithm and a path optimization model are adopted to process traffic data, calculate an optimal alternative route avoiding a construction area, and the steps of generating an optimized traffic route scheme are specifically as follows:
S201: based on a real-time traffic data set, adopting a graph theory algorithm to perform structural modeling on a traffic network, and performing network topology analysis to generate a traffic network model;
S202: based on a traffic network model, a traffic load of multiple road sections is estimated by adopting a traffic distribution algorithm, traffic flow distribution is carried out, and a road section load analysis result is generated;
s203: based on the road section load analysis result, calculating an optimal alternative route bypassing the construction area by adopting a route optimization model, and carrying out route feasibility analysis to generate an optimal alternative route scheme;
S204: based on the optimal alternative route scheme, a cost benefit analysis method is adopted to evaluate the practicability and efficiency of the proposed route scheme, and scheme optimization is carried out to generate an optimized traffic route scheme;
The graph theory algorithm specifically refers to Dijkstra algorithm and A algorithm, the flow distribution algorithm comprises a gravity model and a user balance distribution model, the path optimization model comprises a genetic algorithm and a simulated annealing algorithm, and the cost benefit analysis method comprises sensitivity analysis and scene simulation technology.
In S201, a graph theory algorithm is employed to structurally model the traffic network based on the real-time traffic data set. Firstly, elements such as roads, intersections, road sections and the like are represented in the form of nodes and edges, and a traffic network model is built according to the road connection relation. Then, network topology analysis is performed to determine the connection relationship and distance between the nodes. This will generate an accurate traffic network model that will provide the basis for subsequent analysis.
In S202, traffic loads of a plurality of road segments are estimated using a flow distribution algorithm. A gravity model or a user balanced distribution model is employed to simulate the flow of vehicles in the network. This will produce road segment load analysis results showing traffic flow and congestion conditions for each road segment.
In S203, an optimal alternative route avoiding the construction area is calculated using the path optimization model based on the road section load analysis result. And (3) adopting an optimization algorithm such as a genetic algorithm or a simulated annealing algorithm, and the like, and finding an optimal bypass path by considering factors such as traffic jam, construction area and the like. At the same time, path feasibility analysis is performed, ensuring that the generated alternative route is viable.
In S204, a cost-benefit analysis method is used to evaluate the practicality and efficiency of the proposed route scheme. Sensitivity analysis is performed to consider the scheme performance under different variables and scenarios to determine the optimal route. Scene simulation techniques are performed, if necessary, to further verify the feasibility and benefits of the scheme. And finally, generating an optimized traffic route scheme for the user.
Referring to fig. 4, based on an optimized traffic route scheme, a partition association evaluation system is adopted, and an Apriori or FP-Growth algorithm is utilized to analyze security data between differentiated partitions, identify potential risks, and generate a partition security risk evaluation report specifically includes the steps of:
S301: based on an optimized traffic route scheme, a data preprocessing technology is adopted to prepare for association rule analysis, and preprocessed safety data is generated;
S302: based on the preprocessed safety data, analyzing the mode and association of the safety data among the partitions by adopting an association rule mining algorithm to generate a frequent item set;
S303: based on frequent item sets, a strong association rule generation technology is adopted, and important rules are screened out by calculating the confidence and support of the item sets, so that a potential risk rule set is generated;
S304: based on the potential risk rule set, adopting a risk assessment method to carry out risk grade assessment and influence analysis, and generating a partition security risk assessment report;
The data preprocessing technology comprises a Z score standardization and a K neighbor filling method, the Apriori algorithm comprises a step of item set generation and pruning, the strong association rule generation technology comprises a confidence threshold setting and rule extraction, and the risk assessment method comprises a risk matrix method and an influence assessment model.
In S301 the process of the present invention,
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.impute import KNNImputer
Let # assume df is DATAFRAME containing secure data
Normalized # Z score
scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
Processing missing values by using # K neighbor filling method
imputer = KNNImputer(n_neighbors=5)
df_imputed = pd.DataFrame(imputer.fit_transform(df_scaled), columns=df_scaled.columns)
In S302, a code example using Apriori algorithm:
from mlxtend.frequent_patterns import apriori, association_rules
# convert data to a format suitable for Apriori algorithm
# Assume df imputed is pre-processed DATAFRAME
df_apriori = df_imputed.applymap(lambda x: 1 if x > 0 else 0)
Execution Apriori algorithm
frequent_itemsets = apriori(df_apriori, min_support=0.5, use_colnames=True)
# Generating association rules
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
In S303 this step is actually already included in the example of the Apriori algorithm above. The association_rules function generates strong association rules from frequent item sets.
In S304, risk assessment is typically a more qualitative process, but may be performed based on the results of the association rules. The following is a simplified example:
Suppose that rule is the generated association rule set
for index, rule in rules.iterrows():
support = rule['support']
confidence = rule['confidence']
lift = rule['lift']
Evaluation of risk level
risk_level = 'High' if (support > 0.5 and confidence > 0.7 and lift > 1.2) else 'Low'
print(f"Rule: {rule['antecedents']} -> {rule['consequents']}")
print(f"Support: {support}, Confidence: {confidence}, Lift: {lift}, R
Referring to fig. 5, based on the partition security risk assessment report, the security risk is intelligently classified and graded by using a security item intelligent cluster analysis tool and a K-means or hierarchical clustering algorithm, and the steps for generating the intelligent classified security risk report are specifically as follows:
S401: based on the partition security risk assessment report, adopting a data normalization technology to perform standardized processing on the risk data to generate normalized risk data;
S402: based on normalized risk data, adopting a K-means algorithm to perform preliminary classification on the security risk, and generating a preliminary classification result;
S403: based on the preliminary classification result, adopting a hierarchical clustering algorithm to carry out refinement risk category classification and grade assessment, and generating refinement classification and grade classification results;
S404: based on the refined classification and grading results, adopting a report generation technology to integrate the analysis results and generate an intelligent classification security risk report;
The data normalization technology comprises a maximum-minimum normalization method and a variance scaling method, the K-means algorithm comprises centroid initialization and clustering iteration, the hierarchical clustering algorithm comprises distance measurement selection and clustering tree construction, and the report generation technology comprises data visualization and report template design.
In S401, based on the data of the partition security risk assessment report, a data normalization technique is used to normalize the risk data to ensure that the values of different indicators are on the same scale. Raw risk data is converted to data in the range of 0 to 1 or standard normal distribution using a max-min normalization method or a variance scaling method. This will generate normalized risk data, providing consistent metrics for subsequent analysis.
In S402, based on normalized risk data, a K-means algorithm is adopted to conduct preliminary security risk classification. First, an appropriate number of cluster centers (K values) are selected, and then centroid initialization is performed. During the clustering iteration, the data points are assigned to the closest centroid and then the centroid position is updated until convergence. This will generate a preliminary classification result, classifying the risk data into different categories.
In S403, based on the preliminary classification result, a hierarchical clustering algorithm is adopted to further refine risk classification and rating. This includes selecting an appropriate distance metric method and building a cluster tree to more precisely categorize risk categories and determine the ranking of each category. This step will generate refined classification and ranking results, providing finer information for risk assessment.
In S404, the analysis results are integrated using report generation techniques to generate an intelligent classified security risk report. This includes data visualization, presenting classification and ranking information in graphical and graphical form for easier understanding and analysis by the user. Meanwhile, a report template is designed, the format and structure of the report are ensured to meet the requirements, and clear risk information and suggestions are provided. The generated report will provide detailed information about the security risk to the decision maker and stakeholders to support risk management and decision making.
Referring to fig. 6, based on the intelligent classified security risk report, using a dynamic security threshold setting system, applying a time sequence analysis and statistical model, dynamically adjusting security standards according to environmental changes and construction progress, and generating a dynamic security threshold adjustment scheme specifically includes the steps of;
S501: based on the intelligent classified security risk report, performing risk pattern recognition by adopting a time sequence analysis method, and generating a risk pattern recognition report;
s502: based on the risk pattern recognition report, carrying out security risk quantitative prediction by applying a statistical model ARMA, and generating a security risk prediction report;
S503: dynamically adjusting the safety standard by using a weighted moving average method according to the safety risk prediction report and the environmental change data to generate a real-time adjustment safety standard;
s504: based on real-time adjustment of safety standards and construction progress, a linear programming algorithm is adopted to generate a dynamic safety threshold adjustment scheme;
The time sequence analysis method comprises an autoregressive model and a moving average model, wherein a statistical model ARMA combines autoregressive and moving average characteristics, and a linear programming algorithm is used for resource allocation and optimization.
In S501, risk pattern recognition is performed on data based on the intelligent classification security risk report using a time-series analysis method. Specifically, the patterns and trends of the historical risk data are analyzed by adopting an autoregressive model, a moving average model and the like. This will generate a risk pattern recognition report providing information about the risk variations.
In S502, based on the risk pattern recognition report, a statistical model ARMA (autoregressive moving average model) is applied to quantitatively predict the security risk. The ARMA model combines autoregressive and moving average features to more accurately predict future risk trends. By analyzing the historical data, a security risk prediction report is generated providing an estimate of future risk levels.
In S503, the security standard is dynamically adjusted using a weighted moving average method based on the security risk prediction report and the environmental change data. This includes determining new security criteria based on the outcome of risk prediction and changes in environmental factors. The weighted moving average method allows the historical and current data to be weighted according to the latest information to more accurately reflect the risk situation. This will generate a real-time adjusted safety standard to accommodate changing environments and risks.
In S504, a linear programming algorithm is used to generate a dynamic safety threshold adjustment scheme based on the real-time adjusted safety standard and the construction progress data. The linear programming algorithm can optimize resource allocation and adjustment of safety standards to ensure maximum maintenance of safety under different environments and construction schedules. This scheme will provide a series of dynamic safety thresholds to guide construction and risk management decisions.
Referring to fig. 7, based on the dynamic security threshold adjustment scheme, the security management decision support system is utilized to provide a data-driven decision scheme in combination with decision tree analysis and expert system technology, and the steps for generating the security management decision support report are specifically as follows:
s601: based on a dynamic safety threshold adjustment scheme, different safety management strategies are evaluated by adopting a decision tree analysis method, and a safety management strategy evaluation report is generated;
s602: based on the security management policy evaluation report, performing deep analysis and optimization by using an expert system technology, and generating an optimized security management policy;
s603: inputting the optimized security management strategy into a security management decision support system, and applying a cluster analysis algorithm to perform iterative optimization to generate a data-driven decision scheme;
S604: based on a data-driven decision scheme, adopting a natural language generation technology to compile report items, and generating a security management decision support report;
the decision tree analysis method comprises the steps of constructing a tree structure model and visualizing a decision path, wherein the expert system technology comprises an industry expert knowledge base and rule-based decision support, the cluster analysis algorithm is used for data classification and pattern recognition, and the natural language generation technology is used for generating readable texts from data.
In S601, based on the dynamic security threshold adjustment scheme, a decision tree analysis method is adopted to evaluate the effects of different security management policies. First, a tree-structured model is built to represent different options for various security management policies. Decision path visualizations are then generated by analyzing the risk and benefit of each option to help the decision maker better understand the potential impact of the various strategies. This will generate security management policy evaluation reports providing information about the different policies.
In S602, based on the security management policy evaluation report, the expert system technology is used for deep analysis and optimization. This includes using industry expert knowledge bases and rule-based decision support systems to help identify potential problems and improve strategies. The expert system will provide advice on how to improve and optimize the strategy to make it more in line with the actual needs and goals. This will generate an optimized security management policy.
In S603, inputting the optimized security management strategy into a security management decision support system, and performing iterative optimization by applying a cluster analysis algorithm. The cluster analysis algorithm can classify and pattern-identify the data according to different conditions, thereby generating a data-driven decision scheme. The system will automatically adjust and optimize the strategy to adapt to changing environments and risks according to actual conditions.
In S604, based on the data-driven decision scheme, a report item is compiled using a natural language generation technique, and a security management decision support report is generated. This report will include details of the detailed data analysis, the optimized policy suggestions, and the decision-making scheme. Natural language generation techniques will generate readable text from the data to help decision makers better understand and adopt suggestions. This report will provide a powerful support for decision making.
Referring to fig. 8, a highway construction safety inspection evaluation system is used for executing the highway construction safety inspection evaluation method, and the system comprises a real-time traffic data analysis module, a traffic route optimization module, a safety risk identification module, a safety risk classification module, a dynamic safety threshold adjustment module, a safety management strategy module and a decision support report generation module.
The real-time traffic data analysis module is used for collecting data, preprocessing the data and predicting trend based on traffic conditions of a construction area and the periphery by adopting a real-time traffic monitoring technology, and generating a cleaned and predicted traffic data set;
The traffic route optimization module processes traffic data by adopting a graph theory algorithm and a path optimization model based on the cleaned and predicted traffic data set, calculates an optimal alternative route and generates an optimized traffic route scheme;
The safety risk identification module analyzes safety data and identifies potential risks by adopting an association rule mining algorithm based on an optimized traffic route scheme, and generates a subarea safety risk assessment report;
The security risk classification module performs intelligent classification and classification on security risks by using a clustering algorithm based on the partition security risk assessment report to generate an intelligent classification security risk report;
The dynamic safety threshold adjustment module dynamically adjusts safety standards according to environmental changes by adopting a time sequence analysis and statistical model based on the intelligent classification safety risk report to generate a dynamic safety threshold adjustment scheme;
the security management policy module evaluates differentiated security management policies based on a dynamic security threshold adjustment scheme and combines a decision analysis technology to generate optimized security management policies;
the decision support report generation module inputs the optimized security management strategy to a decision support system, applies data analysis and report generation technology, and generates a security management decision support report.
By monitoring and predicting the real-time traffic data analysis module, the system effectively reduces the risks of traffic jam and accidents, optimizes traffic flow and improves road utilization rate. And secondly, through intelligent path planning of the traffic route optimization module, negative influence on the environment is reduced, and meanwhile, the traffic efficiency during construction is improved. The combination of the safety risk identification and classification module enables the system to accurately identify and classify potential safety risks and effectively prevent accidents. The dynamic safety threshold adjusting module adjusts the safety standard according to the actual situation, ensures that the construction safety measures are always synchronous with the environment, and improves the adaptability and instantaneity of safety management. And finally, generating a security management strategy and a decision support report, so that a powerful data support and decision tool is provided for a manager, and the overall security management level is improved.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (1)
1. The system is characterized by comprising a real-time traffic data analysis module, a traffic route optimization module, a safety risk identification module, a safety risk classification module, a dynamic safety threshold adjustment module, a safety management strategy module and a decision support report generation module;
the real-time traffic data analysis module is used for collecting data, preprocessing the data and predicting trend based on traffic conditions of a construction area and the periphery by adopting a real-time traffic monitoring technology, and generating a cleaned and predicted traffic data set;
The traffic route optimization module processes traffic data by adopting a graph theory algorithm and a path optimization model based on the cleaned and predicted traffic data set, calculates an optimal alternative route and generates an optimized traffic route scheme;
The safety risk identification module analyzes safety data and identifies potential risks by adopting an association rule mining algorithm based on an optimized traffic route scheme, and generates a subarea safety risk assessment report;
The security risk classification module performs intelligent classification and classification on security risks by using a clustering algorithm based on the partition security risk assessment report to generate an intelligent classification security risk report;
the dynamic safety threshold adjustment module dynamically adjusts safety standards according to environmental changes by adopting a time sequence analysis and statistical model based on the intelligent classification safety risk report to generate a dynamic safety threshold adjustment scheme;
The security management policy module evaluates differentiated security management policies based on a dynamic security threshold adjustment scheme and combines a decision analysis technology to generate optimized security management policies;
the decision support report generation module inputs the optimized security management strategy to a decision support system, applies data analysis and report generation technology, and generates a security management decision support report;
The system is based on a GIS dynamic navigation system thought, adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, integrates multi-source data by using a data fusion algorithm, and performs real-time analysis on traffic conditions to generate a real-time traffic data set;
Based on the real-time traffic data set, adopting a graph theory algorithm and a path optimization model to process traffic data, calculating an optimal alternative route avoiding a construction area, and generating an optimized traffic route scheme;
Based on the optimized traffic route scheme, a partition association evaluation system is adopted, and an Apriori or FP-Growth algorithm is utilized to analyze security data among different partitions, identify potential risks and generate a partition security risk assessment report;
Based on the partition security risk assessment report, adopting a security item intelligent cluster analysis tool, and carrying out intelligent classification and grading on security risks by using a K-means or hierarchical clustering algorithm to generate an intelligent classification security risk report;
Based on the intelligent classified security risk report, a dynamic security threshold setting system is used, a time sequence analysis and statistical model is applied, security standards are dynamically adjusted according to environmental changes and construction progress, and a dynamic security threshold adjustment scheme is generated;
Based on the dynamic safety threshold adjustment scheme, a safety management decision support system is utilized, and a decision tree analysis and expert system technology are combined to provide a data-driven decision scheme to generate a safety management decision support report;
Based on the real-time traffic data set, adopting a graph theory algorithm and a path optimization model to process traffic data, calculating an optimal alternative route avoiding a construction area, and generating an optimal traffic route scheme specifically comprises the following steps:
based on the real-time traffic data set, adopting a graph theory algorithm to perform structural modeling on a traffic network, and performing network topology analysis to generate a traffic network model;
based on the traffic network model, a traffic load of multiple road sections is estimated by adopting a traffic distribution algorithm, traffic flow distribution is carried out, and a road section load analysis result is generated;
Based on the road section load analysis result, calculating an optimal alternative route bypassing the construction area by adopting a route optimization model, and carrying out route feasibility analysis to generate an optimal alternative route scheme;
Based on the optimal alternative route scheme, a cost benefit analysis method is adopted to evaluate the practicability and efficiency of the proposed route scheme, and scheme optimization is carried out to generate an optimized traffic route scheme;
The graph theory algorithm specifically refers to Dijkstra algorithm and A-type algorithm, the flow distribution algorithm comprises a gravity model and a user balanced distribution model, the path optimization model comprises a genetic algorithm and a simulated annealing algorithm, and the cost benefit analysis method comprises a sensitivity analysis and scene simulation technology;
Based on the optimized traffic route scheme, a partition association evaluation system is adopted, and the Apriori or FP-Growth algorithm is utilized to analyze security data among different partitions, identify potential risks and generate a partition security risk assessment report, wherein the method specifically comprises the following steps:
based on the optimized traffic route scheme, a data preprocessing technology is adopted to prepare for association rule analysis, and preprocessed safety data is generated;
based on the preprocessed safety data, analyzing the mode and association of the safety data among the partitions by adopting an association rule mining algorithm to generate a frequent item set;
based on the frequent item set, a strong association rule generation technology is adopted, and important rules are screened out by calculating the confidence and support of the item set, so that a potential risk rule set is generated;
Based on the potential risk rule set, carrying out risk grade assessment and influence analysis by adopting a risk assessment method, and generating a partition security risk assessment report;
The data preprocessing technology comprises a Z score standardization and a K neighbor filling method, the Apriori algorithm comprises a item set generation and pruning step, the strong association rule generation technology comprises a confidence threshold setting and rule extraction, and the risk assessment method comprises a risk matrix method and an influence assessment model;
The real-time traffic data set comprises traffic flow density, speed and traffic mode, the optimized traffic route scheme comprises alternative paths and strategies for improving congestion, the partitioned security risk assessment report comprises risk hot spots and potential influencing factors, the intelligent classified security risk report comprises risk levels and key security items, the dynamic security threshold adjustment scheme comprises updated security parameters and thresholds, and the security management decision support report comprises decision schemes and countermeasures;
The dynamic navigation system thought based on GIS adopts a real-time data collection technology to collect traffic flow, speed and traffic state of a construction area and the periphery, integrates multi-source data by using a data fusion algorithm, carries out real-time analysis on traffic conditions, and specifically comprises the following steps of:
based on the construction area and the periphery, adopting a real-time traffic monitoring technology, collecting data of traffic flow, speed and state through a traffic camera and a sensor network, and preprocessing the data to generate a preliminary traffic data set;
Based on the preliminary traffic data set, adopting a data cleaning algorithm to detect abnormal values and carry out data smoothing treatment, removing error data and abnormal values, and carrying out data standardization to generate a cleaned traffic data set;
based on the cleaned traffic data set, a time sequence analysis method is adopted to identify the time variation trend of traffic flow and speed, trend prediction is carried out, and a time sequence analysis result is generated;
Based on the time sequence analysis result, integrating data from different sources by adopting a data fusion algorithm to form a unified traffic condition view and generate a real-time traffic data set;
The real-time traffic monitoring technology comprises video image analysis and a wireless sensing network, the data cleaning algorithm comprises outlier analysis and a moving average method, the time sequence analysis method specifically refers to an autoregressive moving average model, and the data fusion algorithm comprises a weighted average method and Kalman filtering;
Based on the partition security risk assessment report, a security item intelligent cluster analysis tool is adopted, and a K-means or hierarchical clustering algorithm is used for carrying out intelligent classification and grading on security risks, so that the steps for generating an intelligent classified security risk report are specifically as follows:
based on the partition security risk assessment report, adopting a data normalization technology to perform standardized processing on the risk data to generate normalized risk data;
Based on the normalized risk data, adopting a K-means algorithm to perform preliminary classification on the security risk, and generating a preliminary classification result;
Based on the preliminary classification result, carrying out refinement risk category classification and grade assessment by adopting a hierarchical clustering algorithm to generate refinement classification and grade classification results;
Based on the refined classification and grading results, adopting a report generation technology to integrate analysis results and generate an intelligent classification security risk report;
The data normalization technology comprises a maximum-minimum standardization method and a variance scaling method, the K-means algorithm comprises centroid initialization and clustering iteration, the hierarchical clustering algorithm comprises distance measurement selection and clustering tree construction, and the report generation technology comprises data visualization and report template design;
Based on the intelligent classified security risk report, a dynamic security threshold setting system is used, a time sequence analysis and a statistical model are applied, security standards are dynamically adjusted according to environmental changes and construction progress, and the step of generating a dynamic security threshold adjustment scheme is specifically as follows;
based on the intelligent classified security risk report, performing risk pattern recognition by adopting a time sequence analysis method to generate a risk pattern recognition report;
based on the risk pattern recognition report, carrying out security risk quantitative prediction by applying a statistical model ARMA to generate a security risk prediction report;
Dynamically adjusting the safety standard by using a weighted moving average method according to the safety risk prediction report and the environmental change data to generate a real-time adjustment safety standard;
Based on the real-time adjustment safety standard and the construction progress, generating a dynamic safety threshold adjustment scheme by adopting a linear programming algorithm;
The time sequence analysis method comprises an autoregressive model and a moving average model, wherein the statistical model ARMA combines autoregressive and moving average characteristics, and the linear programming algorithm is used for resource allocation and optimization;
Based on the dynamic safety threshold adjustment scheme, a safety management decision support system is utilized, and a decision tree analysis and expert system technology are combined to provide a data-driven decision scheme, and the step of generating a safety management decision support report comprises the following steps:
based on the dynamic safety threshold adjustment scheme, different safety management strategies are evaluated by adopting a decision tree analysis method, and a safety management strategy evaluation report is generated;
based on the security management policy evaluation report, performing deep analysis and optimization by using an expert system technology, and generating an optimized security management policy;
Inputting the optimized security management strategy into a security management decision support system, and applying a cluster analysis algorithm to perform iterative optimization to generate a data-driven decision scheme;
Based on the data-driven decision scheme, adopting a natural language generation technology to compile report items, and generating a security management decision support report;
the decision tree analysis method comprises the steps of constructing a tree structure model and visualizing a decision path, wherein the expert system technology comprises an industry expert knowledge base and rule-based decision support, the cluster analysis algorithm is used for data classification and pattern recognition, and the natural language generation technology is used for generating readable text from data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410502927.9A CN118071040B (en) | 2024-04-25 | 2024-04-25 | Safety inspection evaluation method and system for highway construction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410502927.9A CN118071040B (en) | 2024-04-25 | 2024-04-25 | Safety inspection evaluation method and system for highway construction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118071040A CN118071040A (en) | 2024-05-24 |
| CN118071040B true CN118071040B (en) | 2024-07-12 |
Family
ID=91095871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410502927.9A Active CN118071040B (en) | 2024-04-25 | 2024-04-25 | Safety inspection evaluation method and system for highway construction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118071040B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575332A (en) * | 2024-01-12 | 2024-02-20 | 唐山伟仁建筑工程有限公司 | Road construction safety monitoring method and system |
| CN117952439A (en) * | 2024-03-26 | 2024-04-30 | 营口威辰建筑工程有限公司 | Intelligent prediction method and system for road construction environment influence |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6662141B2 (en) * | 1995-01-13 | 2003-12-09 | Alan R. Kaub | Traffic safety prediction model |
| SE512895C2 (en) * | 1998-08-07 | 2000-05-29 | Dinbis Ab | Method and device for route control of traffic |
| CN101140168A (en) * | 2007-10-12 | 2008-03-12 | 宇龙计算机通信科技(深圳)有限公司 | Traffic route selection method, device and system |
| PL2515195T3 (en) * | 2011-04-18 | 2014-10-31 | Joseph Voegele Ag | System for controlling construction site vehicles |
| US20130117057A1 (en) * | 2011-10-28 | 2013-05-09 | Peter Van Moltke | Systems, Methods and Devices for Generating Alternate Itineraries |
| US10024684B2 (en) * | 2014-12-02 | 2018-07-17 | Operr Technologies, Inc. | Method and system for avoidance of accidents |
| GB201503227D0 (en) * | 2015-02-26 | 2015-04-15 | Tomtom Int Bv | Methods and systems for generating routing policies and routes |
| US11017475B1 (en) * | 2015-10-06 | 2021-05-25 | United Services Automobile Association (Usaa) | Systems and methods for analyzing and visualizing traffic accident risk |
| CN108960647A (en) * | 2018-07-11 | 2018-12-07 | 宿州云宏建设安装有限公司 | A kind of highway construction security risk assessment system |
| CN113434931A (en) * | 2021-06-23 | 2021-09-24 | 安徽省交通控股集团有限公司 | BIM technology-based highway construction safety management method |
| CN114282839A (en) * | 2021-12-30 | 2022-04-05 | 贵州省交通规划勘察设计研究院股份有限公司 | Mountain region highway construction safety risk management system |
| CN116988405A (en) * | 2023-08-10 | 2023-11-03 | 中国水利水电第九工程局有限公司 | Protective equipment for slope falling rocks for mountain highway |
| CN117238145A (en) * | 2023-11-14 | 2023-12-15 | 山东纵云信息技术有限公司 | Intelligent traffic management method and system based on big data |
| CN117711185B (en) * | 2024-02-05 | 2024-04-09 | 中国水利水电第九工程局有限公司 | Multi-source data-based early warning and monitoring system and method for highway construction |
-
2024
- 2024-04-25 CN CN202410502927.9A patent/CN118071040B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575332A (en) * | 2024-01-12 | 2024-02-20 | 唐山伟仁建筑工程有限公司 | Road construction safety monitoring method and system |
| CN117952439A (en) * | 2024-03-26 | 2024-04-30 | 营口威辰建筑工程有限公司 | Intelligent prediction method and system for road construction environment influence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118071040A (en) | 2024-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116129366B (en) | Digital twinning-based park monitoring method and related device | |
| CN106530715B (en) | Road network traffic state prediction method based on fuzzy Markov process | |
| CN117671992A (en) | Intelligent bus dispatching method and system | |
| CN117807914A (en) | Real-time bridge stress detection method and system | |
| CN117494905B (en) | Construction method and system for digital counterweight balance arch bridge | |
| CN117436727A (en) | Intelligent water conservancy dispatching optimization system | |
| CN117830029B (en) | Centralized control platform management system based on optimization algorithm | |
| CN117892882A (en) | Huang Heba bank monitoring alarm risk reporting system and method based on network communication platform | |
| CN117787711A (en) | Arch bridge construction single-end management system based on big data | |
| CN117892887A (en) | Land use optimizing system based on big data | |
| CN117806355A (en) | Control method and system for electric power line inspection unmanned aerial vehicle | |
| CN116796043A (en) | Intelligent park data visualization method and system | |
| CN117666367A (en) | Remote control method and system for underground coal mine machinery | |
| CN117853078A (en) | Unmanned aerial vehicle-based dynamic safety alarm system and method for construction site of building engineering | |
| CN116758762A (en) | Control method based on big data | |
| CN118485311B (en) | Cloud-based numerical control equipment management system and method | |
| CN118095867B (en) | Fire-fighting equipment detection evaluation system and method based on video processing and deep learning | |
| CN117952439A (en) | Intelligent prediction method and system for road construction environment influence | |
| CN118134172B (en) | Urban public resource management and allocation system | |
| CN117975713A (en) | Intelligent traffic guiding management system for industrial upstairs | |
| CN118134268A (en) | Multi-dimensional data analysis method for road construction safety monitoring | |
| CN118446513A (en) | Industrial park environment quality monitoring system | |
| CN118071040B (en) | Safety inspection evaluation method and system for highway construction | |
| CN118333245B (en) | Public safety video monitoring point location layout method and system based on space big data | |
| CN118298620B (en) | Logistics station vehicle exit/entrance scheduling method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |