CN118333245B - Public safety video monitoring point location layout method and system based on space big data - Google Patents
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Abstract
The invention discloses a public safety video monitoring point position layout method and a public safety video monitoring point position layout system based on space big data, which relate to the technical field of image monitoring, and the method comprises the following steps: constructing a multi-source heterogeneous database. And constructing a configuration three-dimensional fitting model by utilizing the database. And performing basic importance analysis based on the database. And establishing the importance degree of the predicted position. Integrating the basic importance degree and the predicted importance degree, constructing the comprehensive importance degree, and constructing the fitness evaluation function based on the three-dimensional fitting model. And executing optimizing initialization to construct an initial population and a particle swarm. And evaluating the initial population and the particle swarm through the fitness evaluation function, and performing iterative optimization. And establishing an information sharing channel, carrying out data information sharing when the iterative optimization meets a preset threshold, and carrying out iterative optimization. And (5) continuing iteration until the termination condition is met, and outputting the monitoring point position layout scheme with the highest fitness score. Thereby achieving the technical effects of improving the layout balance and coverage and improving the monitoring efficiency.
Description
Technical Field
The invention relates to the technical field of image monitoring, in particular to a public safety video monitoring point location layout method and system based on spatial big data.
Background
In recent years, intelligent video image front-end and front-end networks have an important role in improving public safety management level. In the construction of the front end of the intelligent video image and the front end network, the scientificity level of the front end position layout planning is a key factor influencing the front end equipment to exert the collection benefit.
The conventional point location layout method which depends on experience and basic geographic information data is greatly influenced by individual subjective factors, lacks systematicness and scientificity, is difficult to cover key areas comprehensively, and is easy to generate monitoring blind spots. There are technical problems of unbalanced layout, low coverage, and influence on monitoring efficiency.
Disclosure of Invention
The invention provides a public safety video monitoring point location layout method and a public safety video monitoring point location layout system based on space big data, which are used for solving the technical problems of unbalanced layout, low coverage and influence on monitoring efficiency in the prior art, and realizing the technical effects of improving the layout uniformity and the coverage and improving the monitoring efficiency.
In a first aspect, the present invention provides a public safety video monitoring point location layout method based on spatial big data, wherein the method comprises:
Constructing a multi-source heterogeneous database, wherein the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photography mapping data, trip analysis data and view pedestrian traffic statistical data.
And constructing a scene of the target monitoring area through the multi-source heterogeneous database, and configuring a three-dimensional fitting model based on a scene construction result.
And analyzing the basic importance of the target monitoring area based on the multi-source heterogeneous database, and establishing the basic position importance.
And extracting people stream data, historical security event data and environment data of the target monitoring area, and carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data to establish the importance degree of the predicted position.
Integrating the basic position importance and the predicted position importance, constructing the comprehensive position importance, and constructing an adaptability evaluation function of the monitoring point based on the comprehensive position importance and the three-dimensional fitting model.
And executing optimizing initialization, wherein the optimizing initialization comprises the steps of constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme.
And carrying out adaptability evaluation on the initial population and the initial particle population through a adaptability evaluation function, and carrying out iterative optimization according to a adaptability evaluation result.
And establishing an information sharing channel, and carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold value, and carrying out iterative optimization according to a data information sharing result.
And (5) continuously performing iterative optimization, and outputting a monitoring point position layout scheme corresponding to the highest fitness score when the termination condition is met.
In a second aspect, the present invention further provides a public safety video monitoring point location layout system based on spatial big data, wherein the system comprises:
the multi-source heterogeneous database construction module is used for constructing a multi-source heterogeneous database, and the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photography mapping data, trip analysis data and view pedestrian traffic flow statistical data.
The three-dimensional fitting model configuration module is used for constructing a scene of the target monitoring area through the multi-source heterogeneous database and configuring a three-dimensional fitting model based on a scene construction result.
And the position importance degree establishing module is used for analyzing the basic importance degree of the target monitoring area based on the multi-source heterogeneous database and establishing the basic position importance degree.
The prediction position importance degree establishing module is used for extracting people stream data, historical security event data and environment data of the target monitoring area, and carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data to establish the prediction position importance degree.
And the evaluation function construction module is used for integrating the basic position importance degree and the predicted position importance degree, constructing the comprehensive position importance degree and constructing an adaptability evaluation function of the monitoring point position based on the comprehensive position importance degree and the three-dimensional fitting model.
The optimizing and initializing module is used for executing optimizing and initializing, the optimizing and initializing comprises constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme.
The fitness evaluation module is used for evaluating the fitness of the initial population and the initial particle swarm through a fitness evaluation function and performing iterative optimization according to a fitness evaluation result.
And the sharing optimization module is used for establishing an information sharing channel, carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold, and carrying out iterative optimization according to a data information sharing result.
And the iteration output module is used for continuously carrying out iteration optimization, and outputting a monitoring point location layout scheme corresponding to the highest fitness score when the termination condition is met.
The invention discloses a public safety video monitoring point position layout method and a public safety video monitoring point position layout system based on space big data, comprising the following steps: constructing a multi-source heterogeneous database, wherein the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photography mapping data, trip analysis data and view pedestrian traffic statistical data. And constructing a scene of the target monitoring area by using the database, and configuring a three-dimensional fitting model. And (5) analyzing the basic importance based on the database, and establishing the basic position importance. And extracting people stream data, historical security event data and environment data of the target area, carrying out position security prediction, and establishing the importance degree of the predicted position. Integrating the basis and the predicted position importance, constructing the comprehensive position importance, and constructing an adaptability evaluation function of the monitoring point based on the three-dimensional fitting model. And carrying out optimizing initialization, constructing an initial population and a particle swarm, wherein each individual and each particle represent a monitoring point position layout scheme. And evaluating the initial population and the particle swarm through the fitness evaluation function, and performing iterative optimization according to the result. And establishing an information sharing channel, and carrying out data information sharing based on the information sharing channel and carrying out iterative optimization when the iterative optimization meets a preset threshold value. And (5) continuing iteration until the termination condition is met, and outputting the monitoring point position layout scheme with the highest fitness score. The public safety video monitoring point location layout method and system based on the space big data solve the technical problems of unbalanced layout, low coverage and influence on monitoring efficiency, and achieve the technical effects of improving the layout balance and coverage and improving the monitoring efficiency.
Drawings
Fig. 1 is a schematic flow chart of a public safety video monitoring point location layout method based on space big data.
Fig. 2 is a schematic structural diagram of a public safety video monitoring point location layout system based on space big data.
Reference numerals illustrate: the system comprises a multi-source heterogeneous database construction module 11, a three-dimensional fitting model configuration module 12, a position importance establishment module 13, a predicted position importance establishment module 14, an evaluation function construction module 15, an optimizing initialization module 16, an adaptability evaluation module 17, a sharing optimization module 18 and an iteration output module 19.
Detailed Description
The technical scheme provided by the embodiment of the invention aims to solve the technical problems of unbalanced layout, low coverage and influence on monitoring efficiency in the prior art, and adopts the following overall thought:
First, a multi-source heterogeneous database is constructed containing a plurality of data types including internet map vector image data, POI point of interest data, AOI interest surface data, aero-photogrammetry data, trip analysis data, and view traffic statistics data. Then, a scene of the target monitoring area is constructed by using the multi-source heterogeneous database, and a three-dimensional fitting model is configured based on the result of the scene construction. Then, a basic importance analysis is performed on the target monitoring area to establish the importance of the basic position. Then, people stream data, historical security event data and environmental data are extracted from the target monitoring area, and position security prediction is performed based on the data so as to establish importance of the predicted position. And then integrates the importance of the basic position and the importance of the predicted position to construct the importance of a comprehensive position. And then, constructing an adaptability evaluation function of the monitoring point by using the importance of the comprehensive position and the three-dimensional fitting model. Further, optimization initialization is performed, including constructing an initial population and an initial population of particles. Each individual in the initial population and each particle in the initial population represents a layout scheme for the monitoring point. And then, carrying out fitness evaluation on the initial population and the initial particle swarm through a fitness evaluation function, and carrying out iterative optimization according to an evaluation result. And simultaneously, an information sharing channel is established, when the iterative optimization meets a preset threshold, data information sharing is carried out based on the channel, and the iterative optimization is carried out according to a sharing result. And finally, continuing to perform iterative optimization until the termination condition is met, and outputting a monitoring point position layout scheme corresponding to the highest fitness score.
The foregoing aspects will be better understood by reference to the following detailed description of the invention taken in conjunction with the accompanying drawings and detailed description. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the exemplary embodiments used only to explain the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention. It should be noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
Fig. 1 is a flow chart of a public safety video monitoring point location layout method based on space big data, wherein the method comprises the following steps:
Constructing a multi-source heterogeneous database, wherein the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photography mapping data, trip analysis data and view pedestrian traffic statistical data.
Optionally, first, a multi-source heterogeneous database is constructed, which contains a plurality of data from different data sources, specifically including: internet map vector image data, POI point of interest data, AOI interest surface data, aerial photogrammetry data, trip analysis data, and view volume statistics.
Optionally, a multi-level data fusion is performed by a data processing and fusion method based on the data source structure characteristics of the multi-source data, so as to obtain the multi-source heterogeneous database with high data accuracy and reliability.
Illustratively, the internet map facilitator vector image is used to provide underlying geographic information data to construct a base map of the monitoring point layout. The POI interest point data are used for extracting key target units, identifying missing objects and providing references for selecting video monitoring points. The AOI interest surface data can be used for extracting the area of the community, identifying the access opening which needs to be closed by combining the logistics forwarding data, and optimizing the community monitoring point position layout. The aerial photogrammetry data has higher resolution, can provide high-precision geographic information, and can be used for analyzing the visual field and the acquisition blind area of monitoring equipment. The trip analysis data can comprise user heat information of a communication service platform, a trip service platform and the like, such as connection quantity data of a communication base station, trip thermodynamic diagrams and the like, and the crowd space distribution and trip rules are analyzed through the trip analysis data so as to guide street surface blind supplement and ensure that monitoring points cover high people flow dense areas. The view pedestrian traffic flow statistical data is used for providing pedestrian traffic flow distribution conditions and ensuring that monitoring points cover traffic major roads and important nodes.
And constructing a scene of the target monitoring area through the multi-source heterogeneous database, and configuring a three-dimensional fitting model based on a scene construction result.
Optionally, firstly, based on the scene construction granularity requirement of the target monitoring area, extracting relevant data of the target monitoring area from a database, including vector image data, POI interest point data, AOI interest surface data and the like, performing multi-scale data fusion, and then, constructing a three-dimensional scene of the target monitoring area on a GIS platform based on the fusion data, including topography, buildings, roads, existing monitoring points and the like.
Optionally, a three-dimensional fitting model is configured based on a scene construction result, and firstly, the interactive multi-source heterogeneous database acquires aerial photogrammetry data and three-dimensional oblique photogrammetry data to provide high-precision three-dimensional information. Then, three-dimensional object features of the target monitoring area are extracted from the three-dimensional information, wherein the three-dimensional object features comprise topographic relief features, building features, vegetation features and the like. And fusing the three-dimensional data with the scene model to form a preliminary three-dimensional fitting model, wherein the preliminary three-dimensional fitting model comprises a three-dimensional visual three-dimensional model which represents the terrain change, the building outline dimension, the spatial position of a road, vegetation and the like in the scene construction result.
Optionally, configuring the three-dimensional fitting model further comprises mapping travel analysis data and view pedestrian traffic statistical data to corresponding spatial positions of the three-dimensional fitting model through a spatial registration method, so that synchronous visual representation of various data in the multi-source heterogeneous database is realized, the acquired three-dimensional fitting model can accurately and finely represent a target monitoring area, and subsequent analysis layout is facilitated.
And analyzing the basic importance of the target monitoring area based on the multi-source heterogeneous database, and establishing the basic position importance.
Optionally, POI interest point data, AOI interest surface data, trip analysis data and view traffic flow statistic data of the target monitoring area are integrated. And combining the monitoring task requirement of the target monitoring area, analyzing the basic importance degree of the target monitoring area, and establishing the basic position importance degree.
Specifically, first, POI data is analyzed to identify places of great significance, such as business centers, transportation hubs, and the like. The AOI data is analyzed to determine the extent of important areas such as a city center, industrial park, logistic park, transportation hub, etc. And analyzing travel data, knowing the concentrated areas of people and traffic, counting the flow data of personnel and vehicles, and evaluating the activity degree and congestion condition of each area. Then, based on the monitoring task requirement of the target monitoring area, corresponding attention weights are distributed to a plurality of key target units, a plurality of target surfaces, the traffic flow and other monitoring elements of the target monitoring area; then, the plurality of monitor elements are weighted and evaluated based on the attention weight. Illustratively, the greater the number of points of interest or faces of interest, the greater the importance of the points of interest or faces of interest, and the greater the level of traffic or flow of traffic, the greater the importance of the underlying location of the target monitored area.
And extracting people stream data, historical security event data and environment data of the target monitoring area, and carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data to establish the importance degree of the predicted position.
Optionally, the people stream data is people stream data with time sequence marks, including historical and real-time people stream, density distribution, time change law and the like. The people stream data reflects the migration and change characteristics of the people stream state of the target monitoring area along with time, in other words, the people stream data comprises the change trend of the people stream of the target area.
Optionally, the historical security event data includes event type, time of occurrence, location, event frequency, severity, and the like. The historical security event data contains the distribution characteristics of the security events of the target monitoring area in the time dimension, the severity dimension and the space dimension.
Optionally, the environmental data is used to describe data information of environmental conditions within the monitored area, including natural and human factors such as weather conditions, lighting conditions, topography, infrastructure conditions, and the like.
In some embodiments, establishing the predicted location importance includes:
Constructing a position safety prediction model, wherein the position safety prediction model is trained by big data; and after extracting the characteristics of the people stream data, the historical security event data and the environment data, inputting the characteristics into the position security prediction model to generate the predicted position importance.
Optionally, a position safety prediction model is built based on big data training, firstly, people stream data, historical safety event data and environment data of a target monitoring area are collected, and preprocessing steps such as cleaning, normalization and denoising are carried out on the collected data, so that data quality and consistency are ensured. Then, time sequence features such as a people flow change trend, a peak time period and the like are extracted from the people flow data. Features such as event frequency, type, severity, etc. are extracted from the historical security event data. And extracting the characteristics of weather conditions, illumination conditions, topography, and the like from the environmental data. And then, combining the extracted various features into a feature vector, wherein the feature vector is used as input data, and the formed feature vector contains a dynamic mapping relation of the variation in the target area.
Further, a suitable position safety prediction model is selected, including decision trees, random forests, support vector machines, neural networks and the like, and a large data platform (such as Hadoop and Spark) is used for model training so as to process a large-scale data set and improve model training efficiency. And inputting the constructed feature vector into a model for training, performing supervised learning through historical data, and adjusting model parameters to optimize performance so as to obtain a position safety prediction model.
Optionally, the processed people stream data, the historical security event data and the environmental data feature vector are input into a trained location security prediction model. The position safety prediction model predicts the data value of the people stream data, the historical safety event data and the environmental data at a certain preset future time node through the feature vector, and fuses and updates the prediction result with POI interest point data, AOI interest surface data, trip analysis data and view people traffic flow statistical data, so as to generate the predicted position importance according to the same method principle of the basic importance analysis.
For example, if the people stream data, the historical security event data and the environmental data show that the people flow in the target monitoring area is in an ascending trend, there is continuous population migration, the environmental condition is complex, the security event is frequent or the occurrence rate is ascending, the importance of the predicted position of the target monitoring area is higher, which represents that the target monitoring area needs a higher level of monitoring attention in the future.
And the safety prediction of the target monitoring area is realized by constructing and training a position safety prediction model and combining comprehensive analysis of people stream data, historical safety event data and environment data. And a prospective basis is provided for regional security management and resource allocation.
Integrating the basic position importance and the predicted position importance, constructing the comprehensive position importance, and constructing an adaptability evaluation function of the monitoring point based on the comprehensive position importance and the three-dimensional fitting model.
Optionally, based on the monitoring task requirement of the target monitoring area, a fusion weight of the basic position importance and the predicted position importance is defined, specifically, the design service life and the plan upgrading period in the monitoring task requirement of the target monitoring area are obtained, and the basic position importance and the predicted position importance are configured by a professional technician or an expert system in combination with the area development updating speed. In other words, in a rapidly evolving area, the predicted location importance may need to be a greater weight to accommodate future changes. In an area which is basically stable, the importance of the basic position may occupy a larger proportion so as to be close to the actual requirement, and the cost surge and the resource waste caused by excessive construction are avoided.
The importance level of the target monitoring area can be accurately reflected by the comprehensive position importance level so as to guide the subsequent monitoring point position layout.
In some embodiments, constructing the fitness evaluation function of the monitoring point location based on the integrated location importance and the three-dimensional fitting model further includes:
an evaluation formula of the fitness evaluation function is constructed as follows:
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Wherein, To monitor the total number of points,Characterization of the first embodimentThe size of the area under the individual monitoring points that is not effectively monitored,In order to calibrate the size of the blind area,Characterization of the first embodimentThe comprehensive position importance of each monitoring point location,Is the firstThe monitoring efficiency of the individual monitoring points is that,Is the firstThe monitoring coverage rate of the monitoring points,Is the weight coefficient of the index of the dead zone,To synthesize the weight coefficient of the position importance index,To monitor the weight coefficient of the device status indicator,In order to monitor the weight coefficient of the point location quantity index,、、Performing the first based on the three-dimensional fitting modelAnd obtaining fitting of the monitoring points.
The area size B i which is not effectively monitored is a negative index, and the larger the value is, the larger the blind area is, the worse the monitoring effect is. B m is a constant for normalizing the blind spot size. Monitoring efficiencyIs a forward index and indicates the working efficiency of the monitoring equipment. The larger the value is, the better the working state of the equipment is (such as high monitoring on-line rate, good monitoring resolution, etc.). Monitoring coverageAnd the forward index is used for indicating the coverage range of the monitoring point. The larger the value, the wider the coverage.
By constructing the fitness evaluation function, objective and efficient evaluation and optimization of the monitoring point position layout are facilitated, and the high efficiency and the coverage comprehensiveness of the monitoring system are ensured. The fitness evaluation function comprehensively considers factors such as the size of the blind area, the importance of the position, the monitoring efficiency, the coverage rate and the like, so that the evaluation result is more comprehensive and accurate.
And executing optimizing initialization, wherein the optimizing initialization comprises the steps of constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme.
Alternatively, the initial population and the initial particle population are two sets of candidate solutions generated either randomly or based on heuristic rules, respectively. And the initial population and the initial particle swarm are created in different ways. Wherein each candidate solution (individual) represents a monitoring point placement scheme. The method includes the steps of carrying out random fluctuation by taking a current monitoring point position layout scheme and an alternative monitoring point position layout scheme as reference schemes respectively, wherein the random fluctuation comprises the steps of moving monitoring point positions, rotating monitoring point position directions, adding and deleting monitoring points and modulating monitoring points Jiao Duandeng, and constructing an initial population and an initial particle swarm.
Specifically, first, the number of individuals in a population, that is, the population size (particle group size) is set. Then, a random monitoring point placement scheme is generated for each individual in the population (particle swarm) to obtain an initial population (initial particle swarm).
And carrying out adaptability evaluation on the initial population and the initial particle population through a adaptability evaluation function, and carrying out iterative optimization according to a adaptability evaluation result.
In some embodiments, performing iterative optimization according to the fitness evaluation result further includes:
Dividing the initial population into a first individual set and a second individual set based on the fitness evaluation result, wherein the first individual set is an optimal solution individual set; cross mutation is carried out on the first individual set, a child individual set is established, random search and update are carried out on the second individual set, and a random individual set is established; updating the initial population based on the set of child individuals and the set of random individuals to complete iterative optimization.
Alternatively, first, each individual (monitoring point placement scheme) in the initial population is subjected to fitness evaluation based on a fitness evaluation function, a fitness value of each individual is calculated, and then, the initial population is divided into two individual sets according to a fitness evaluation result. Wherein the first set of individuals (the optimal solution set of individuals) comprises individuals with higher fitness values, typically the first p% of individuals are selected. The second population comprises the remaining individuals, typically the latter 1-p% of individuals.
Optionally, the first individual set (optimal solution individual set) is subjected to cross mutation to generate a new offspring individual set. Wherein crossing comprises randomly selecting two individuals for crossing to generate a new individual. Illustratively, the crossover may be performed at the genetic level, such as exchanging the position, orientation, jiao Duandeng parameters of the monitoring points. The mutation operation comprises randomly adjusting certain parameters of the individual, such as randomly adjusting the position, the direction or the focal segment of a certain monitoring point, so as to increase the diversity of the population.
Optionally, the second set of individuals is subjected to random search update, that is, individual parameters in the second set of individuals are randomly adjusted, so as to generate new individuals. Where it is necessary to ensure that new individuals are within reasonable limits to maintain the validity of the solution.
Further, the offspring individual sets and the random individual sets are combined, the initial population is updated, and one round of iteration is completed. The updated initial population has the same population size as the initial population. And then, performing iterative optimization, performing fitness evaluation on the new population, calculating the fitness value of each individual, selecting a new optimal solution individual set and a second individual set according to the fitness value, and performing the next iteration.
In some implementations, the method further comprises:
Setting a following section, and calculating a local optimal position in the particle following section based on an adaptability evaluation result to generate a first update constraint; acquiring a global optimal position of an initial particle swarm, and generating a second update constraint based on the global optimal position; and updating the particles of the initial particle swarm through the first updating constraint and the second updating constraint, and completing iterative optimization according to an updating result.
Optionally, a following interval is defined for each particle, which interval is used to limit the position update range of the particle. The following interval may be a certain neighborhood space of the particle, and the following interval is set according to specific characteristics of the problem, for example, a larger following interval may be set in an initial iteration stage.
Optionally, based on the fitness evaluation result, a plurality of new fitness evaluation results of a plurality of positions are calculated in the following section of each particle, and the position with the best performance of the new fitness evaluation results is selected as the local optimal position. Then, a first update constraint is generated based on the locally optimal position, representing that the update direction and speed of each particle is affected by its locally optimal position.
Optionally, a global optimal position in the initial particle swarm, that is, a position with the highest fitness value in all the particles, is obtained. A second update constraint is then generated based on the global optimum, which characterizes the update direction and speed of each particle as also affected by the global optimum.
The update direction of each particle is illustratively the vector sum of its first update constraint and its second update constraint, where the first update constraint and the second update constraint are represented as vectors pointing to a locally optimal position or a globally optimal position from the current position of the particle.
And establishing an information sharing channel, and carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold value, and carrying out iterative optimization according to a data information sharing result.
In some embodiments, an information sharing channel is established, when iterative optimization meets a preset threshold, data information sharing is performed based on the information sharing channel, and iterative optimization is performed according to a data information sharing result, including:
Configuring trigger constraints of an information sharing channel, wherein the trigger constraints comprise periodic trigger constraints and transition trigger constraints; and after the triggering constraint is activated, activating the information sharing channel, and executing optimal solution sharing in the current population and the particle swarm based on the information sharing channel to complete data information sharing.
The periodic triggering constraint refers to triggering constraint based on iteration time or iteration algebra, and the triggering constraint comprises data information sharing after iteration is performed for a specific time or a specific algebra; the transition triggering constraint is a triggering constraint based on the fitness configuration of the solution, and when the fitness lifting proportion or the absolute value of the lifted fitness of a certain solution is larger than the preset proportion or absolute value of the fitness, the data information sharing is carried out,
Optionally, at each iteration, checking whether the periodic trigger constraint or the transition trigger constraint is satisfied, and if any trigger condition is satisfied, activating the information sharing channel to perform optimal solution sharing in the current population and the particle swarm. Illustratively, the particles are selected from the current population and the population of particles, respectively. And introducing the optimal solution in the particle swarm into the population to replace the inferior solution individual, and introducing the optimal solution in the population into the particle swarm to replace the inferior solution particle.
And the data information sharing nodes are judged through triggering constraint, and the optimal solution sharing in the current population and the particle swarm is carried out, so that the overall optimization effect is improved.
And (5) continuously performing iterative optimization, and outputting a monitoring point position layout scheme corresponding to the highest fitness score when the termination condition is met.
Optionally, based on the population and the particle swarm, respectively performing iterative optimization and performing iterative data information sharing based on triggering constraint until the obtained fitness evaluation result of the optimal solution meets a preset fitness threshold, or the number of times of iterative optimization and data information sharing meets a preset iteration number, and outputting the optimal solution in the iterative optimization as a monitoring point location layout scheme.
Further, the method further comprises:
Performing area monitoring of a target monitoring area, and acquiring monitoring feedback; generating layout preference constraints of the monitoring points through the monitoring feedback; and carrying out subsequent monitoring point location distribution control optimization through the layout preference constraint.
Optionally, area monitoring is performed based on the current situation monitoring point placement scheme, and monitoring feedback of the target monitoring area is obtained, wherein the feedback data comprise monitoring coverage rate, monitoring blind area, monitoring quality and the like. The monitoring feedback data is then analyzed (e.g., to identify insufficient monitoring coverage and quality of monitoring issues) to generate layout preference constraints. The method includes the steps of preferentially distributing new monitoring points in a monitoring blind area and a low monitoring coverage rate area in monitoring feedback data, and optimizing points with poor monitoring quality. And finally, updating the comprehensive position importance of the target monitoring area based on layout preference constraint, and performing subsequent monitoring point position distribution control optimization.
In summary, the public safety video monitoring point location layout method based on the spatial big data provided by the invention has the following technical effects:
By constructing a multi-source heterogeneous database, the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photography mapping data, trip analysis data and view traffic flow statistical data. And constructing a scene of the target monitoring area through the multi-source heterogeneous database, and configuring a three-dimensional fitting model based on a scene construction result. And (3) analyzing the basic importance of the target monitoring area based on the multi-source heterogeneous database, and establishing the basic position importance. And extracting people stream data, historical security event data and environment data of the target monitoring area, carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data, and establishing the importance degree of the predicted position. Integrating the importance degree of the basic position and the importance degree of the predicted position, constructing the importance degree of the comprehensive position, and constructing an adaptability evaluation function of the monitoring point based on the importance degree of the comprehensive position and the three-dimensional fitting model. And executing optimizing initialization, wherein the optimizing initialization comprises the steps of constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme. And carrying out adaptability evaluation on the initial population and the initial particle population through a adaptability evaluation function, and carrying out iterative optimization according to a adaptability evaluation result. And establishing an information sharing channel, and carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold value, and carrying out iterative optimization according to a data information sharing result. And (5) continuously performing iterative optimization, and outputting a monitoring point position layout scheme corresponding to the highest fitness score when the termination condition is met. Thereby realizing the technical effects of improving the layout balance and coverage and improving the monitoring efficiency.
Example two
Fig. 2 is a schematic structural diagram of the public safety video monitoring point location layout system based on space big data. For example, the flow diagram of the public security video monitoring point location layout method based on the spatial big data in fig. 1 can be implemented by the structure shown in fig. 2.
Based on the same conception as the public safety video monitoring point location layout method based on the space big data in the embodiment, the public safety video monitoring point location layout system based on the space big data further provided by the invention comprises the following steps:
the multi-source heterogeneous database construction module 11 is configured to construct a multi-source heterogeneous database, where the multi-source heterogeneous database includes internet map vector image data, POI interest point data, AOI interest surface data, aerial photography mapping data, trip analysis data, and view traffic statistics data.
The three-dimensional fitting model configuration module 12 is configured to perform scene construction of the target monitoring area through the multi-source heterogeneous database, and configure a three-dimensional fitting model based on a scene construction result.
And the position importance degree establishing module 13 is used for analyzing the basic importance degree of the target monitoring area based on the multi-source heterogeneous database and establishing the basic position importance degree.
The predicted position importance level establishing module 14 is configured to extract people stream data, historical security event data, and environmental data of the target monitoring area, and perform position security prediction of the target monitoring area based on the people stream data, the historical security event data, and the environmental data, so as to establish a predicted position importance level.
And the evaluation function construction module 15 is used for integrating the basic position importance degree and the predicted position importance degree, constructing the comprehensive position importance degree, and constructing the adaptability evaluation function of the monitoring point position based on the comprehensive position importance degree and the three-dimensional fitting model.
The optimizing initialization module 16 is configured to perform optimizing initialization, where the optimizing initialization includes constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point layout scheme.
And the fitness evaluation module 17 is used for evaluating the fitness of the initial population and the initial particle population through a fitness evaluation function and performing iterative optimization according to the fitness evaluation result.
The sharing optimization module 18 is configured to establish an information sharing channel, perform data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold, and perform iterative optimization according to a data information sharing result.
And the iteration output module 19 is used for continuously carrying out iteration optimization, and outputting a monitoring point location layout scheme corresponding to the highest fitness score when the termination condition is met.
Wherein the predicted position importance level establishing module 14 includes:
The safety prediction model building unit is used for building a position safety prediction model which is trained by big data;
and the prediction output unit is used for extracting the characteristics of the people stream data, the historical security event data and the environment data, inputting the characteristics into the position security prediction model and generating the predicted position importance.
Further, the evaluation function construction module 15 includes:
The function definition unit is used for constructing an adaptability evaluation function of the monitoring point based on the comprehensive position importance degree and the three-dimensional fitting model, and further comprises:
an evaluation formula of the fitness evaluation function is constructed as follows:
；
Wherein, To monitor the total number of points,Characterization of the first embodimentThe size of the area under the individual monitoring points that is not effectively monitored,In order to calibrate the size of the blind area,Characterization of the first embodimentThe comprehensive position importance of each monitoring point location,Is the firstThe monitoring efficiency of the individual monitoring points is that,Is the firstThe monitoring coverage rate of the monitoring points,Is the weight coefficient of the index of the dead zone,To synthesize the weight coefficient of the position importance index,To monitor the weight coefficient of the device status indicator,In order to monitor the weight coefficient of the point location quantity index,、、Performing the first based on the three-dimensional fitting modelAnd obtaining fitting of the monitoring points.
Further, the fitness evaluation module 17 includes:
The quality dividing unit is used for dividing the initial population into a first individual set and a second individual set based on the fitness evaluation result, wherein the first individual set is a best solution individual set;
The mutation unit is used for carrying out cross mutation on the first individual set, establishing a child individual set, carrying out random search updating on the second individual set and establishing a random individual set;
and the iterative optimization unit is used for updating the initial population based on the child individual set and the random individual set so as to complete iterative optimization.
In some implementations, the iterative optimization in the fitness evaluation module 17 includes
The first updating constraint unit is used for setting a following interval, calculating the local optimal position in the particle following interval based on the fitness evaluation result, and generating a first updating constraint;
the second updating constraint unit is used for acquiring the global optimal position of the initial particle swarm and generating a second updating constraint based on the global optimal position;
And the particle updating unit is used for updating the particles of the initial particle swarm through the first updating constraint and the second updating constraint, and completing iterative optimization according to the updating result.
Further, the sharing optimization module 18 includes:
the shared trigger constraint unit is used for configuring trigger constraints of the information sharing channel, wherein the trigger constraints comprise periodic trigger constraints and transition trigger constraints;
And the data information sharing unit is used for activating the information sharing channel after the triggering constraint is activated, and executing optimal solution sharing in the current population and the particle swarm based on the information sharing channel so as to complete data information sharing.
Further, the system further comprises a monitoring feedback and optimizing unit for:
Performing area monitoring of a target monitoring area, and acquiring monitoring feedback; generating layout preference constraints of the monitoring points through the monitoring feedback; and carrying out subsequent monitoring point location distribution control optimization through the layout preference constraint.
It should be understood that the embodiments mentioned in this specification focus on differences from other embodiments, and the specific embodiment in the first embodiment is equally applicable to the public safety video monitoring point location layout system based on spatial big data described in the second embodiment, and further development is not performed here for brevity of the specification.
It is to be understood that both the foregoing description and the embodiments of the present invention enable one skilled in the art to utilize the present invention. While the invention is not limited to the embodiments described above, it should be understood that: modifications of the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof may be still performed by those skilled in the art; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (6)
1. The public safety video monitoring point location layout method based on the space big data is characterized by comprising the following steps of:
Constructing a multi-source heterogeneous database, wherein the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aerial photography mapping data, trip analysis data and view pedestrian traffic statistical data;
Performing scene construction of a target monitoring area through the multi-source heterogeneous database, and configuring a three-dimensional fitting model based on a scene construction result;
Performing basic importance analysis of the target monitoring area based on the multi-source heterogeneous database, and establishing basic position importance;
Extracting people stream data, historical security event data and environment data of a target monitoring area, and carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data to establish prediction position importance;
Integrating the basic position importance and the predicted position importance, constructing the comprehensive position importance, and constructing an adaptability evaluation function of the monitoring point based on the comprehensive position importance and the three-dimensional fitting model;
Executing optimizing initialization, wherein the optimizing initialization comprises the steps of constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme;
Performing fitness evaluation on the initial population and the initial particle population through a fitness evaluation function, and performing iterative optimization according to a fitness evaluation result;
establishing an information sharing channel, and carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold value, and carrying out iterative optimization according to a data information sharing result;
Performing iterative optimization continuously, and outputting a monitoring point location layout scheme corresponding to the highest fitness score when the termination condition is met;
the method for constructing the fitness evaluation function of the monitoring point based on the comprehensive position importance and the three-dimensional fitting model further comprises the following steps:
an evaluation formula of the fitness evaluation function is constructed as follows:
；
Wherein, To monitor the total number of points,Characterization of the first embodimentThe size of the area under the individual monitoring points that is not effectively monitored,In order to calibrate the size of the blind area,Characterization of the first embodimentThe comprehensive position importance of each monitoring point location,Is the firstThe monitoring efficiency of the individual monitoring points is that,Is the firstThe monitoring coverage rate of the monitoring points,Is the weight coefficient of the index of the dead zone,To synthesize the weight coefficient of the position importance index,To monitor the weight coefficient of the device status indicator,In order to monitor the weight coefficient of the point location quantity index,、、Performing the first based on the three-dimensional fitting modelFitting and obtaining the monitoring points;
the iterative optimization is performed according to the fitness evaluation result, and the method further comprises the following steps:
dividing the initial population into a first individual set and a second individual set based on the fitness evaluation result, wherein the first individual set is an optimal solution individual set;
Cross mutation is carried out on the first individual set, a child individual set is established, random search and update are carried out on the second individual set, and a random individual set is established;
Updating the initial population based on the set of child individuals and the set of random individuals to complete iterative optimization.
2. The public safety video monitoring point location layout method based on space big data according to claim 1, wherein the method further comprises:
Setting a following section, and calculating a local optimal position in the particle following section based on an adaptability evaluation result to generate a first update constraint;
acquiring a global optimal position of an initial particle swarm, and generating a second update constraint based on the global optimal position;
and updating the particles of the initial particle swarm through the first updating constraint and the second updating constraint, and completing iterative optimization according to an updating result.
3. The public safety video monitoring point location layout method based on space big data according to claim 2, wherein the establishing an information sharing channel, when the iterative optimization meets a preset threshold, performs data information sharing based on the information sharing channel, and performs iterative optimization according to a data information sharing result, includes:
configuring trigger constraints of an information sharing channel, wherein the trigger constraints comprise periodic trigger constraints and transition trigger constraints;
and after the triggering constraint is activated, activating the information sharing channel, and executing optimal solution sharing in the current population and the particle swarm based on the information sharing channel to complete data information sharing.
4. The method for laying out a public safety video monitoring point location based on spatial big data according to claim 1, wherein the establishing the predicted position importance degree further comprises:
Constructing a position safety prediction model, wherein the position safety prediction model is trained by big data;
And after extracting the characteristics of the people stream data, the historical security event data and the environment data, inputting the characteristics into the position security prediction model to generate the predicted position importance.
5. The public safety video monitoring point location layout method based on space big data according to claim 1, wherein the method further comprises:
performing area monitoring of a target monitoring area, and acquiring monitoring feedback;
Generating layout preference constraints of the monitoring points through the monitoring feedback;
and carrying out subsequent monitoring point location distribution control optimization through the layout preference constraint.
6. Public safety video monitoring point position layout system based on space big data, which is characterized in that the system is used for executing the public safety video monitoring point position layout method based on space big data according to any one of claims 1-5, and the system comprises:
The multi-source heterogeneous database construction module is used for constructing a multi-source heterogeneous database, and the multi-source heterogeneous database comprises internet map vector image data, POI interest point data, AOI interest surface data, aero-photographic mapping data, trip analysis data and view pedestrian traffic flow statistical data;
The three-dimensional fitting model configuration module is used for constructing a scene of the target monitoring area through the multi-source heterogeneous database and configuring a three-dimensional fitting model based on a scene construction result;
The position importance degree establishing module is used for analyzing the basic importance degree of the target monitoring area based on the multi-source heterogeneous database and establishing basic position importance degree;
The prediction position importance degree establishing module is used for extracting people stream data, historical security event data and environment data of the target monitoring area, and carrying out position security prediction of the target monitoring area based on the people stream data, the historical security event data and the environment data to establish the prediction position importance degree;
The evaluation function construction module is used for integrating the basic position importance degree and the predicted position importance degree, constructing the comprehensive position importance degree and constructing an adaptability evaluation function of the monitoring point position based on the comprehensive position importance degree and the three-dimensional fitting model;
The optimizing and initializing module is used for executing optimizing and initializing, the optimizing and initializing comprises constructing an initial population and an initial particle swarm, and each individual in the initial population and each particle in the initial particle swarm represent a monitoring point position layout scheme;
the fitness evaluation module is used for evaluating the fitness of the initial population and the initial particle swarm through a fitness evaluation function and performing iterative optimization according to a fitness evaluation result;
The sharing optimization module is used for establishing an information sharing channel, carrying out data information sharing based on the information sharing channel when the iterative optimization meets a preset threshold, and carrying out iterative optimization according to a data information sharing result;
and the iteration output module is used for continuously carrying out iteration optimization, and outputting a monitoring point location layout scheme corresponding to the highest fitness score when the termination condition is met.
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