CN117824750A - A health monitoring system and method for bridge substructure - Google Patents

Abstract

The embodiment of the specification provides a health maintenance monitoring system and method for a bridge substructure, wherein the system comprises a raw material monitoring module, a process monitoring module, a health maintenance regulation module, a remote communication module, a data synchronization module, an early warning module and a processor; the raw material monitoring module is configured to collect raw material monitoring data; the process monitoring module includes a sensor; the health-preserving regulation and control module is configured to carry out health-preserving regulation and control; the remote communication module is configured to enable remote communication; the data synchronization module is configured to synchronously upload the transmitted communication data to the cloud platform; the early warning module is configured to control an alarm device at the position of the corresponding bridge substructure to carry out audible and visual alarm based on the early warning parameters; the processor is configured to: controlling raw material storage and distribution parameters according to the raw material monitoring data; regulating and controlling health maintenance parameters of the lower structure of the bridge according to the raw material storage and distribution parameters and the process monitoring data; and determining early warning parameters, and controlling the early warning module to perform early warning based on the early warning parameters.

Description

A health monitoring system and method for bridge substructure

Technical Field

The present specification relates to the field of concrete operations, and in particular to a health monitoring system and method for a bridge substructure.

Background technique

Bridge structures have a profound impact on traffic. If the quality of the bridge structure is not good, it may affect traffic flow and driving safety, leading to major accidents such as bridge collapse, threatening people's daily travel safety. Therefore, the status of the bridge structure should be monitored in real time, and corresponding maintenance measures should be taken to extend the service life of the bridge structure.

In order to monitor the status of bridge structures, CN102979307B provides a temperature-controlled anti-cracking construction method for concrete structures, which generates a preset construction plan by monitoring the temperature of the concrete casting and various parameters of the construction environment. However, this method does not continuously monitor the concrete casting. During the maintenance of the bridge structure, if the state of the concrete casting changes due to environmental factors and other issues, it is impossible to dynamically update the subsequent maintenance plan according to the changed state of the concrete casting and take remedial measures in time, which may cause the maintenance quality of the concrete casting to be unstable and cause quality risks to the bridge substructure.

Therefore, it is urgent to propose a health monitoring system for the bridge substructure to monitor the bridge substructure in real time, discover problems in time, and take remedial measures.

Summary of the invention

One or more embodiments of the present specification provide a health monitoring system for a bridge substructure, including a raw material monitoring module, a process monitoring module, a health regulation module, a remote communication module, a data synchronization module, an early warning module, and a processor; the raw material monitoring module is configured to collect raw material monitoring data corresponding to the concrete of the bridge substructure; the process monitoring module includes a sensor, and the sensor is configured to monitor the process monitoring data of the curing process of the concrete; the health regulation module is configured to perform health regulation on the bridge based on the health parameters; the remote communication module is configured to realize remote communication among the raw material monitoring module, the process monitoring module, the health regulation module, the data synchronization module, the early warning module, and the processor; the data synchronization module is configured to synchronously upload the communication data transmitted by the remote communication module to a cloud platform, so as to send the communication data to at least one user terminal based on the cloud platform; the early warning module is configured to control the alarm device at the corresponding position of the bridge substructure to give an audible and visual alarm based on the early warning parameters; the processor is configured to: control the raw material storage and distribution parameters according to the raw material monitoring data; regulate the health parameters of the bridge substructure according to the raw material storage and distribution parameters and the process monitoring data; and determine the early warning parameters, and control the early warning module to give an early warning based on the early warning parameters.

One of the embodiments of the present specification provides a maintenance monitoring method for a bridge substructure, which is executed based on a processor of the aforementioned bridge substructure maintenance monitoring system, including: controlling raw material storage and distribution parameters according to raw material monitoring data; regulating the maintenance parameters of the bridge substructure according to the raw material storage and distribution parameters and process monitoring data; and determining early warning parameters to achieve early warning based on the early warning parameters.

One or more embodiments of the present specification provide a health monitoring device for a bridge substructure, including a processor, wherein the processor is used to execute a health monitoring method for a bridge substructure.

One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for monitoring the maintenance of a bridge substructure.

BRIEF DESCRIPTION OF THE DRAWINGS

This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

FIG1 is a schematic diagram of system modules of a health monitoring system for a bridge substructure according to some embodiments of this specification;

FIG2 is an exemplary flow chart of a health monitoring method for a bridge substructure according to some embodiments of this specification;

FIG3 is a schematic diagram of a sequence prediction model according to some embodiments of the present specification;

FIG. 4 is a schematic diagram of determining warning parameters according to some embodiments of this specification.

Detailed ways

In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, without paying creative work, this specification can also be applied to other similar scenarios based on these drawings. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

It should be understood that the "system", "device", "unit" and/or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and/or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

FIG. 1 is a schematic diagram of system modules of a health monitoring system for a bridge substructure according to some embodiments of the present specification.

In some embodiments, the health monitoring system 100 for the bridge substructure may include a raw material monitoring module 110 , a process monitoring module 120 , a health control module 130 , a remote communication module 140 , a data synchronization module 150 , an early warning module 160 , and a processor 170 .

The raw material monitoring module is a module used to collect raw material monitoring data corresponding to the concrete of the bridge substructure.

The bridge substructure refers to the structure below the bridge, for example, the different construction locations of the piers.

Raw material monitoring data refers to data obtained by monitoring concrete raw materials, such as temperature and humidity of concrete raw materials. Concrete raw materials may include sand, stone, water, etc. Concrete raw materials are stored in a raw material storage and distribution device.

The raw material storage and distribution device is a device for storing raw materials, which can store concrete raw materials based on raw material storage and distribution parameters. The raw material storage and distribution parameters may include the temperature and humidity of the raw material reserves. Based on the regulation of the raw material storage and distribution parameters of the raw material storage and distribution device, the state management of the concrete raw material storage space can be achieved.

In some embodiments, the raw material monitoring data may be obtained based on sensors, such as temperature sensors, humidity sensors, etc.

The process monitoring module is a module for acquiring process monitoring data. In some embodiments, the process monitoring module may include a sensor.

The sensor may include a temperature sensor, a humidity sensor, etc. There may be multiple sensors. In some embodiments, the sensor may be used to monitor process monitoring data of the curing process of concrete.

In some embodiments, the location of the sensor can be adjusted according to the network environment of the construction site. For example, when the sensor is in a high-altitude work area, the location of the sensor should be adjusted as the construction progresses. For example, when pouring concrete on a 60-meter-high bridge pier, the higher the pouring height, the higher the sensor height should be adjusted. For example, the user adjusts the location of the sensor every 3 meters of concrete pouring.

The curing process refers to the process of curing the construction site after pouring concrete. Curing can include covering, watering, moistening, windproofing, heat preservation and other curing measures to ensure that the newly poured concrete at the construction site can harden normally or accelerate hardening and achieve strength growth.

Process monitoring data refers to the data obtained by monitoring concrete products during the curing process. Concrete products refer to products cast from concrete raw materials. For example, precast beams, etc. Process monitoring data may include concrete product temperature, ambient temperature and humidity, etc.

The health regulation module is a module used to regulate the health of the bridge based on health parameters.

Curing parameters refer to parameters related to curing control of concrete products. For example, curing parameters include parameters in the curing process of a bridge. In some embodiments, curing parameters may include parameters such as the amount of water sprinkled during water curing, the power of heat preservation or cooling equipment, etc.; and parameters such as the amount of steam during steam curing, the power of heat preservation or cooling equipment, etc. In some embodiments, curing parameters may be preset based on prior knowledge.

Health regulation refers to the process of adjusting health parameters. Health regulation can include the adjustment direction and adjustment value of health parameters. The adjustment direction can include increasing or decreasing the parameters.

The remote communication module is a module used to realize remote communication between the raw material monitoring module, the process monitoring module, the health control module, the data synchronization module, the early warning module, and the processor.

In some embodiments, the mode of remote communication can be adjusted based on the network environment of the construction site. For example, when the network of the construction site is poor and remote communication is difficult to achieve, the user can go to the vicinity of the sensor regularly and collect monitoring data obtained by the sensor through Bluetooth and other methods. For another example, when the construction site achieves full network coverage, the user can adjust the monitoring points based on the construction progress. For example, when pouring a bridge pier with a total height of 60 meters, the user can adjust the monitoring point higher for every 3 meters of pouring to ensure smooth communication.

The data synchronization module is a module used to synchronously upload the communication data transmitted by the remote communication module to the cloud platform, so as to send the communication data to at least one user terminal based on the cloud platform.

In some embodiments, data synchronization can also be achieved based on sensors and user terminals. For example, users can use sensors with built-in traffic cards (e.g., NBIOT sensors), which can upload monitoring data to the cloud platform through the network. Users can view monitoring data through user terminals based on the cloud platform. For example, users can view monitoring data in real time through computer web pages, mobile applets, or mobile software, and can also configure warning parameters through user terminals, and receive or send warning-related information.

Communication data refers to the data transmitted between modules. Communication data can include raw material monitoring data, process monitoring data, health parameters and early warning parameters, etc.

A cloud platform refers to a platform that provides computing, network and storage capabilities based on services of hardware resources and software resources. In some embodiments, a cloud platform can be used to send communication data to at least one user terminal. A cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc. or any combination thereof.

The user terminal refers to one or any combination of devices such as mobile devices with input and/or output functions. In some embodiments, the user terminal may be used by one or more users, including users who directly use the service, and other related users.

In some embodiments, the user terminal can receive communication data sent by the cloud platform.

The early warning module is a module for controlling the alarm device at the position of the corresponding bridge substructure to give an audible and visual alarm based on the early warning parameters. In some embodiments, the early warning module can be installed at each preset early warning position. For a detailed description of the early warning position, please refer to Figure 4 and its related content.

Warning parameters are parameters related to the warning issued by the alarm device. Warning parameters may include warning location, warning frequency and warning method. Warning methods may include separate sound warning, separate light effect warning and sound and light warning, etc. In some embodiments, the warning parameters can be determined based on historical data. The processor can analyze historical data and use the historical warning parameters with the highest historical frequency as the current warning parameters. For example, when the construction time is during the day, sound warnings are often used with horn sound effects at a fixed frequency; when the construction time is at night, due to low visibility, it is necessary to further improve the obviousness of the warning, and red lights are often used to flash at a fixed frequency to warn users.

In some embodiments, the processor can be used to control raw material storage and distribution parameters based on raw material monitoring data; adjust the health parameters of the bridge substructure based on the raw material storage and distribution parameters and process monitoring data; and determine early warning parameters and control the early warning module to issue early warnings based on the early warning parameters.

For details on controlling the parameters for raw material storage and distribution, please refer to Figure 2 and its related contents. For details on regulating the parameters for health preservation, please refer to Figure 2 and Figure 3 and their related contents. For details on early warning, please refer to Figure 4 and its related contents.

Some embodiments of this specification monitor the maintenance process of the bridge substructure in real time through the joint operation of multiple system modules, which can promptly detect problems that arise during the maintenance process, remind users to take remedial measures, and prevent maintenance failures that may cause hidden dangers such as future bridge structure collapse.

It should be understood that the system and its modules shown in FIG. 1 may be implemented in various ways.

It should be noted that the above description of the health monitoring system and its modules for the bridge substructure is only for the convenience of description and does not limit this specification to the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, the raw material monitoring module, process monitoring module, health regulation module, remote communication module, data synchronization module, early warning module, and processor disclosed in Figure 1 can be different modules in a system, or a module can realize the functions of two or more of the above modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

Fig. 2 is an exemplary flow chart of a bridge substructure health monitoring method according to some embodiments of the present specification. In some embodiments, the process 200 may be executed by a processor of the bridge substructure health monitoring system 100.

Step 210, controlling raw material storage and distribution parameters according to raw material monitoring data.

For detailed description of raw material monitoring data and raw material storage and distribution parameters, please refer to Figure 1 and its related content.

In some embodiments, the processor may determine the raw material storage and distribution parameters based on the raw material monitoring data and the standard storage and distribution data. The standard storage and distribution data refers to the preset standard temperature and humidity of the raw materials during storage. The processor may adjust the value of a certain indicator in the raw material storage and distribution parameters in response to the value of the indicator in the raw material monitoring data being higher than the corresponding indicator in the standard storage and distribution data. For example only, if the temperature monitored in the raw material monitoring data is higher than the temperature in the standard storage and distribution data, the temperature item in the raw material storage and distribution parameters is adjusted lower.

Step 220, adjusting the curing parameters of the bridge substructure according to the raw material storage and distribution parameters and the process monitoring data.

For detailed description of process monitoring data, bridge substructure and curing parameters, please refer to Figure 1 and its related content.

In some embodiments, the processor can adjust the curing parameters of the bridge substructure according to the raw material storage and distribution parameters and process monitoring data through vector matching.

In some embodiments, the processor may construct a feature vector based on current raw material storage and distribution parameters and current process monitoring data.

In some embodiments, the processor can cluster the historical raw material storage parameters, historical process monitoring data, and historical health parameters with good final maintenance effects (for example, some evaluation indicators of the maintenance effects meet preset requirements) in the historical data through a clustering algorithm, and construct multiple reference vectors based on the historical raw material storage parameters and historical process monitoring data corresponding to the multiple cluster centers formed by clustering, and each reference vector corresponds to a historical health parameter. There can be multiple types of clustering algorithms, for example, the clustering algorithm can include K-Means clustering, density-based clustering method (DBSCAN), etc.

In some embodiments, the processor can calculate the similarity between the feature vector and at least one reference vector to determine the target health parameters. The target health parameters refer to the health parameters obtained after regulation. The similarity between the feature vector and the reference vector can be represented by the vector distance between the feature vector and the reference vector. The similarity is negatively correlated with the vector distance. The processor can use the reference vector with the smallest vector distance to the feature vector as the target vector, and the health parameters corresponding to the target vector as the target health parameters. The processor can adjust the current health parameters to the target health parameters to complete the regulation.

In some embodiments, the processor can estimate the matching degree of the preset health parameters based on the raw material storage and preparation parameters and the process monitoring data; in response to the matching degree not meeting the preset matching conditions, the preset health parameters are adjusted.

The preset health parameters refer to the preset health parameters that need to be executed. The preset health parameters can be determined based on historical data or prior knowledge. For example, the health parameters most commonly used in history can be used as the preset health parameters.

The matching degree refers to the degree of adaptability between the preset curing parameters and the construction conditions. The construction conditions may include the construction time, construction location and construction progress.

In some embodiments, the processor can estimate the matching degree of the current preset health parameters in a variety of ways based on the raw material storage parameters and process monitoring data. For example, the processor can construct a feature vector based on the current raw material storage parameters and process monitoring data, and construct a standard vector based on the preset standard raw material storage parameters and preset standard process monitoring data corresponding to the preset health parameters, and determine the matching degree by calculating the cosine value between the feature vector and the standard vector. Among them, the matching degree is positively correlated with the cosine value between the feature vector and the standard vector.

The preset standard raw material storage and distribution parameters and preset standard process monitoring data corresponding to the preset curing parameters can be determined based on historical data or historical experience. For example, the raw material storage and distribution parameters and process monitoring data corresponding to the historical use of the preset curing parameters when the curing effect is better can be averaged and used as the preset standard raw material storage and distribution parameters and preset standard process monitoring data under the preset curing parameters, respectively.

Exemplarily, the degree of matching can be determined based on the following formula (1):

γ＝k×cos(α _i ，α ₀ ) (1)

Among them, γ is the matching degree of the preset health parameters, k is a preset parameter greater than 0, α _i represents the characteristic vector, and α ₀ represents the standard vector.

In some embodiments, the matching degree also includes a matching degree sequence of preset health parameters in the future.

The matching degree sequence refers to a sequence of matching degrees corresponding to preset health parameters at multiple reference time points in a future period of time. In some embodiments, the length of the future period of time and the multiple reference time points in the future period of time can be based on manual preset.

In some embodiments, the processor may also divide the length of a future period of time into multiple sub-periods of equal time, and use the end time of each sub-period as a reference time point. For example, when the future period of time is 16:30-17:00, the processor may divide the 30 minutes from 16:30-17:00 into three sub-periods of 16:30-16:40, 16:40-16:50, and 16:50-17:00 at intervals of 10 minutes, wherein 16:40, 16:50, and 17:00 are the end times of the three sub-periods, i.e., three reference time points.

The preset matching condition refers to the condition that the matching degree meets the requirements. The preset matching condition may include that the matching degree is greater than the matching threshold. The matching threshold refers to the minimum value of the matching degree when the current preset curing parameters and the current construction situation can be considered to match. The matching threshold can be based on manual preset.

In some embodiments, in response to the matching degree satisfying a preset matching condition, that is, the matching degree is greater than a matching threshold, the processor may not adjust the preset health parameters.

In some embodiments, the processor adjusts the preset health parameters in response to the matching degree not satisfying the preset matching condition, that is, the matching degree is not greater than the matching threshold.

In some embodiments, the processor may adjust the preset health parameters in a variety of ways. For example, the processor may send a reminder to the user terminal, prompting the user that the current preset health parameters are not suitable, and reminding the user to re-enter the preset health parameters, so as to complete the adjustment of the preset health parameters based on the preset health parameters input by the user.

In some embodiments, the processor can determine the target future time point based on the matching degree sequence of the preset health parameters in the future period of time, and the matching degree of the target future time point does not meet the preset matching conditions; and adjust the preset health parameters in advance based on the first target future time point.

The target future time point refers to at least one time point in the future when the matching degree of the preset health parameters does not meet the preset matching conditions. In some embodiments, the processor can calculate the matching degree of the preset health parameters at each time point in the future (see FIG. 3 and its related description for the specific calculation method), and compare it with the preset matching conditions, and use the time point that does not meet the preset matching conditions as the target future time point. The first target future time point refers to the first time point in the future that does not meet the preset matching conditions.

In some embodiments, the processor can send a reminder to the user terminal in advance based on the first target future time point, reminding the user to re-enter the preset health parameters, and control the health regulation module to perform maintenance according to the newly input preset health parameters before the first target future time point.

In some embodiments, the processor can also adjust the preset health parameters based on weather data, raw material storage parameters and process monitoring data. For a detailed description of this part, please refer to Figure 3 and its related descriptions.

Some embodiments of the present specification estimate and judge the matching degree of preset health parameters in the future period in advance, and timely screen the preset health parameters that do not meet the requirements, so that the staff can check and adjust in advance and prepare for relevant maintenance and adjustment.

Some embodiments of this specification estimate the matching degree of preset curing parameters based on raw material storage and distribution parameters and process monitoring data, with reference to multiple groups of historical data, which can improve the accuracy of the estimated matching degree of preset curing parameters and ensure that the maintenance process of the bridge substructure can proceed stably.

Step 230, determining warning parameters to implement warning based on the warning parameters.

For detailed description of warning parameters, please refer to Figure 1 and its related content.

In some embodiments, the processor can determine the warning parameters corresponding to the current construction stage based on the historical data based on the current construction stage. For example, the warning parameters for different construction stages can be preset in advance based on historical experience, and the processor can determine the warning parameters based on the current construction stage, and implement the warning based on the warning parameters.

In some embodiments, the processor may also determine the warning parameters based on the process monitoring data and the ideal data. For a detailed description of this part, please refer to FIG. 4 and its related contents.

In some embodiments, the processor may issue a warning to the user in response to the warning parameter. The warning method may include sound, light effect, etc.

Some embodiments of this specification regulate the curing parameters of the bridge substructure according to the raw material storage and distribution parameters and process monitoring data, and monitor not only the state of the concrete product during the curing process, but also the state of the raw materials before pouring, to ensure that the quality of the concrete product obtained after each construction is the same, so as to achieve higher quality construction. In addition, some embodiments of this specification can dynamically regulate the curing parameters based on the real-time state of the concrete before and during curing, and can achieve adaptive adjustment for different construction conditions to ensure smooth construction. In addition, some embodiments of this specification can also achieve early warning based on early warning parameters, so as to inform the user of possible faults in a timely manner, remedy the problems in a timely manner, and ensure the normal progress of construction.

FIG. 3 is a schematic diagram of a sequence prediction model according to some embodiments of the present specification.

In some embodiments, the processor can estimate the matching degree sequence 350 for a period of time in the future based on weather data 310, raw material storage and distribution parameters 320 and process monitoring data 330 through a sequence prediction model 340; in response to the matching degree sequence not meeting the preset matching conditions, the preset health parameters are adjusted.

For detailed descriptions of raw material storage and distribution parameters and process monitoring data, matching degree sequence for a period of time in the future, preset matching conditions and preset health parameters, please refer to Figures 1 and 2 and their related contents.

Weather data refers to data related to the weather. Weather data may include temperature, humidity, etc. within a period of time in the future. In some embodiments, weather data may be obtained based on a third-party platform. For example, the processor may obtain weather data through a weather forecast based on a network platform.

The sequence prediction model is a model used to estimate the matching degree sequence in the future. The sequence prediction model can be a machine learning model, for example, a deep neural network (DNN) model.

The input of the sequence prediction model may include process monitoring data, raw material storage and distribution parameters, and weather data; the output may include a matching degree sequence for a period of time in the future. For example, the matching degree sequence includes the matching degree of preset health parameters at each monitoring time point in the future.

In some embodiments, the input of the sequence prediction model also includes ideal data at multiple monitoring time points in the future.

The monitoring time point refers to the time point at which the concrete-related data is monitored. A future period of time may include multiple monitoring time points. The monitoring time point may be based on manual presets. For example, the monitoring time point may be the same as the reference time point. For an explanation of the reference time point, see the corresponding content of FIG. 2.

Ideal data refers to ideal parameters during the maintenance process. Ideal data may include ideal temperature and ideal humidity, etc. In some embodiments, the ideal data may be preset by the user. The processor may obtain the ideal data preset by the user based on the cloud platform or the user terminal. For example, the user may set the ideal temperature during the maintenance process to 36°C, etc.

Some embodiments of this specification add ideal data to the input of the sequence prediction model, referring to the user's expectations of the maintenance process, and can obtain a more humane and user-friendly matching degree, thereby improving the user experience.

In some embodiments, the processor may train a sequence prediction model based on a large number of first samples with first labels. The first sample may include sample process monitoring data of sample concrete at a first historical time, sample raw material storage parameters, and sample weather data at a second historical time. The first label may include an actual matching degree sequence corresponding to the sample curing parameters corresponding to the first sample. The first sample and the first label may be acquired based on historical data. The first historical time is before the second historical time.

In some embodiments, the actual matching degree sequence corresponding to the sample curing parameters may be determined based on the actual performance of the sample concrete in the second historical time.

In some embodiments, the processor may determine a mismatch degree sequence based on the first sample and the actual maintenance condition corresponding to the first sample. The mismatch degree sequence includes the mismatch degree of the preset health parameters at each monitoring time point in a future period of time.

In some embodiments, the processor may determine a matching degree sequence based on a mismatching degree sequence. For example, a mismatching time point in a mismatching degree sequence is first determined, and a mismatching time point may refer to a time point in a mismatching degree sequence at which the mismatching degree is greater than a preset threshold, and then a monitoring time point other than a mismatching time point in a mismatching degree sequence is used as a matching time point, and a matching degree value of a time point corresponding to a matching time point in a matching degree sequence is set to a value greater than or equal to a matching threshold, which may be determined based on a time interval between the matching time point and its nearest mismatching time point, such as a larger interval, a larger matching degree value; and a matching degree value of a time point corresponding to a mismatching time point in a matching degree sequence is set to a value less than the matching threshold, and negatively correlated to the mismatching degree.

The mismatch refers to the degree of deviation between the actual environmental conditions and the preset health parameters. In some embodiments, the processor can evaluate the mismatch based on the severity of the problems that occur during the maintenance process and the degree of environmental deviation. The problems that occur during the maintenance process can include blistering, collapse, collapse at blistering, and the number of other types of problems.

Environmental deviation refers to the degree of deviation between the actual environmental parameters that cause various problems and the preset environmental parameters. The actual environmental parameters can be obtained based on actual measurements, and the preset environmental parameters can be determined based on preset health parameters. Due to the influence of factors such as weather or environment, there may be a certain difference between the actual environmental parameters and the preset environmental parameters, and the degree of difference can be understood as the environmental deviation. In addition, there may be certain differences in the actual environmental parameters of different pier construction sites.

The environmental deviation can be expressed as a percentage. The environmental deviation can include the environmental deviation of temperature and the environmental deviation of humidity, as well as the common environmental deviation of temperature and humidity (which can be collectively referred to as environmental deviation at this time). For example, the common environmental deviation of temperature and humidity (i.e., environmental deviation) can be the weighted sum or mean of the environmental deviation of temperature and the environmental deviation of humidity. Just as an example, when bubbles are generated, the actual temperature at the bubble location is 18°C, the preset temperature is 20°C, and the actual temperature is offset by 10% compared to the preset temperature. It can be considered that the environmental deviation of the temperature corresponding to the bubble location is 10%.

The processor can determine the environmental deviation and severity corresponding to the problem by querying the first preset table based on the problem that occurs during the maintenance process. The first preset table includes the corresponding relationship between the problems that occur during the maintenance process and the environmental deviation and severity. The first preset table can be determined based on historical data or prior knowledge. Among them, the severity of the problem can be represented by a number from 0 to 10. As an example only, the first preset table can store the occurrence of bubbling problems, the number of bubbles does not exceed 10, and the environmental deviation does not exceed 10%, and the corresponding severity of the bubbling problem is 2.

In some embodiments, the processor may determine the mismatch degree sequence in a variety of ways. For example, the processor may determine based on the following formula (2):

β＝sum{θ×D _i ×S _i } (2)

Among them, β is the mismatch degree, θ is a preset parameter greater than 0, for example, the value of θ can be 1; _Di is the environmental deviation degree corresponding to problem i, and S _i is the severity corresponding to problem i.

For example, a certain historical time point in the second historical time (such as the first monitoring time point) has a blistering problem due to temperature deviation, and the environmental deviation corresponding to the blistering problem is 10%, and the severity is 2. For example, θ is 1; then the mismatch corresponding to the first monitoring time point is (10%×2)=0.2.

Two problems occurred at another historical time point in the second historical time (such as the second monitoring time point). The first problem was that due to the deviation of temperature and humidity, collapse occurred at the bubbling place. The corresponding environmental deviation of this problem was 5%, and the severity was 9. The second problem was that due to the deviation of humidity, collapse occurred at the non-bubbling place. The corresponding environmental deviation of humidity was 10%, and the severity was 8. The mismatch degree corresponding to the second monitoring time point is (5%×9+10%×8)=1.25.

If the first monitoring time point and the second monitoring time point are the first two monitoring time points in the second historical time, and the second historical time includes a total of 5 monitoring time points, then the mismatch degree sequence is {0.2, 1.25, 0, 0, 0}. If the preset threshold is set to 0.1, the first monitoring time point and the second monitoring time point are mismatch time points, and the third monitoring time point, the fourth monitoring time point, and the fifth monitoring time point are matching time points. Correspondingly, if the matching threshold is 0.6, according to the aforementioned specific method for determining the matching degree sequence based on the mismatch degree sequence, it can be known that in the first label, the actual matching degree sequence corresponding to the sample health parameters of the first sample can be {0.1, 0, 0.7, 0.8, 0.9}.

Among them, the conversion relationship between the matching degree value of the mismatching time point in the matching degree sequence and its mismatching degree in the mismatching degree sequence is based on a preset, for example, the matching degree value is negatively correlated with the mismatching degree; the conversion relationship between the matching degree value of the matching time point in the matching degree sequence and the time interval between the matching time point and its most recent mismatching time point is based on a preset, for example, the matching degree value is positively correlated with the time interval.

In some embodiments, the processor may adjust the preset health parameters in response to the matching degree sequence not satisfying the preset matching condition. The matching degree sequence not satisfying the preset matching condition may refer to the presence of elements in the matching degree sequence that do not satisfy the preset matching condition, such as the presence of a matching degree at a monitoring time point that is less than a matching threshold. In some embodiments, the processor may send the time point that does not satisfy the preset matching condition to the user terminal, and the user terminal re-enters the health parameters of the corresponding time point to adjust the preset health parameters.

In some embodiments, the processor may determine a comprehensive matching degree based on the matching degree sequence; in response to the comprehensive matching degree not satisfying a preset matching condition, the preset health parameters are adjusted.

The comprehensive matching degree is a parameter used to measure the matching degree sequence. The higher the comprehensive matching degree, the higher the overall matching degree of the preset health parameters in the future period. In some embodiments, the comprehensive matching degree can be determined based on the matching degree sequence. The comprehensive matching degree can be positively correlated with each matching degree in the matching degree sequence.

In some embodiments, the comprehensive matching degree can be determined based on the following formula (3):

in, is the comprehensive matching degree, n is the total number of monitoring time points in the matching degree sequence, γ _i is the matching degree of monitoring time point i in the matching degree sequence, and Δt refers to the time length corresponding to monitoring time point i in the matching degree sequence.

The time length corresponding to the monitoring time point can be determined based on the time point i, the current time point, and the fixed time length. As an example only, the time length corresponding to the monitoring time point can be determined based on the following formula (4):

Among them, Δt refers to the time length corresponding to time point i, _ti refers to time point i, _t0 refers to the current time point, Refers to a fixed time length. If the time length corresponding to time point i is less than 1, the processor sets the time length corresponding to time point i to 1. The fixed time length is the time length corresponding to the preset reliable time period, that is, the reliability of the prediction during this period is high, which is reflected in the fact that the predicted values during this period can be fully retained.

In some embodiments, the processor may compare the comprehensive matching degree with the preset matching condition, and in response to the comprehensive matching degree not satisfying the preset matching condition, adjust the preset health parameters corresponding to the future period of time. The specific method of adjusting the health parameters can be seen in Figure 2 and its related content.

Since the longer the time, the more variables there are, and the lower the accuracy of the estimated matching degree, some embodiments of the present specification further consider the credibility of the matching degree by considering the time length factor in the comprehensive matching degree. The longer the time, the lower the prediction accuracy may be, which is reflected in the comprehensive matching degree as the smaller the value, that is, the more likely it is that regulation is needed to ensure the accuracy of the matching degree in a future period of time and ensure the normal progress of construction.

Some embodiments of the present specification predict the matching degree sequence based on weather data, raw material storage and distribution parameters and process monitoring data based on machine learning technology. Based on more and richer historical data, the estimated matching degree sequence can have higher accuracy and better ensure the smooth progress of the maintenance process.

FIG. 4 is a schematic diagram of determining warning parameters according to some embodiments of this specification.

In some embodiments, the processor can obtain ideal data 410 for multiple future monitoring time points from the cloud platform; determine the monitoring frequency 430 and the number of monitoring points 440 for process monitoring based on the interval 420 between the construction completion time of the bridge substructure and the current time; obtain process monitoring data 330 through the process monitoring module based on the monitoring frequency 430 and the number of monitoring points 440; and determine early warning parameters 460 in response to the process monitoring data 330 and the ideal data 410 satisfying the first preset condition 450.

For detailed descriptions of the cloud platform, bridge substructure, process monitoring model, process monitoring data, and early warning parameters, please refer to Figure 1 and its related contents. For detailed descriptions of monitoring time points and ideal data, please refer to Figure 3 and its related descriptions.

In some embodiments, the processor may obtain ideal data for multiple monitoring time points preset by the user through a cloud platform or a user terminal.

Monitoring frequency refers to the frequency and number of times monitoring data is obtained. The number of monitoring points refers to the number of monitoring points used to arrange the monitoring data. There can be multiple monitoring points. The number of monitoring points refers to the number of monitoring points enabled in this monitoring.

In some embodiments, the processor may determine the monitoring frequency and the number of monitoring points by querying a second preset table based on the interval between the construction completion time of the current bridge substructure and the current time. The second preset table includes the corresponding relationship between the interval between the construction completion time and the current time with better monitoring effect in the historical data and the monitoring frequency and the number of monitoring points. The second preset table may be determined based on historical data.

In some embodiments, the processor may determine the monitoring frequency and the number of monitoring points for process monitoring based on the first target future time point, the interval between the construction completion time of the bridge substructure and the current time.

For detailed description of the target future time point, please refer to Figure 2 and its related descriptions.

The monitoring frequency and the number of monitoring points are positively correlated with the interval between the construction completion time of the concrete product (e.g., bridge substructure) and the current time. For example, the monitoring frequency and the number of monitoring points can be determined by the following formulas (5-1) and (5-2), respectively:

Where, f is the monitoring frequency, _f0 is the preset standard monitoring frequency, is the interval between the construction completion time of the bridge substructure and the current time, t _g1 is the first target future time point, t ₀ is the current time point, Δt ₀ is the preset standard time interval, j is the number of monitoring points, and p is the preset coefficient.

In some embodiments, the processor can determine the estimated risk through a risk estimation model based on the first target future time point, the interval between the completion time of the bridge substructure construction and the current time, the candidate monitoring frequency and the number of candidate monitoring points; and determine the monitoring frequency and the number of monitoring points for process monitoring based on the estimated risk.

The candidate monitoring frequencies and the number of candidate monitoring points refer to the monitoring frequencies and the number of monitoring points that may be determined as the final monitoring frequencies and the number of monitoring points. The candidate monitoring frequencies and the number of candidate monitoring points can be determined based on the monitoring frequencies and the number of monitoring points with better monitoring effects in historical data.

The risk estimation model is a model used to determine the estimated risk. The risk estimation model can be a machine learning model, for example, a deep neural network (DNN) model.

Estimated risk refers to the comprehensive risk of all possible problems of concrete products during the curing process, which can be expressed based on numerical values. All possible problems of concrete products may include bubbling, collapse, etc.

In some embodiments, the input of the risk prediction model may include the first target future time point, the interval between the completion time of the bridge substructure construction and the current time, the candidate monitoring frequency and the number of candidate monitoring points; the output may include the estimated risk corresponding to the candidate monitoring frequency and the number of candidate monitoring points.

In some embodiments, the processor may train a risk prediction model based on a second sample with a second label. The second sample may include a first target future time point of the sample, an interval between the construction completion time of the sample bridge substructure and the current time, a sample candidate monitoring frequency, and a number of sample candidate monitoring points. The second label may include an actual risk corresponding to the sample bridge substructure of the second sample. The second sample and the second label may be acquired based on historical data.

The actual risk can be determined by the severity of the actual problem of the sample bridge substructure. The method for determining the severity of the actual problem can be implemented by, for example, querying the first preset table, for details, see FIG3 and its related content. The processor can add up the severity of all the actual problems of the sample bridge substructure to determine the actual risk.

In some embodiments, the processor may use the candidate monitoring frequency and the number of candidate monitoring points corresponding to the minimum estimated risk as the target monitoring frequency and the number of monitoring points.

Some embodiments of this specification estimate the risks of different problems occurring during the maintenance process to the overall maintenance process, and determine a more accurate monitoring frequency and number of monitoring points for process monitoring through machine learning, thereby ensuring the quality of process monitoring as much as possible, allowing users to promptly detect signs of problems, determine remedial measures, ensure maintenance quality, and improve maintenance efficiency.

Some embodiments of the present specification determine the monitoring frequency and the number of monitoring points for process monitoring based on the interval between the first target future time point, the construction completion time of the bridge substructure and the current time. When the maintenance parameters do not match the environmental conditions, the monitoring frequency and the number of monitoring points for process monitoring can be adjusted in time so that the maintenance parameters can be adjusted as soon as possible to improve the matching degree and ensure the maintenance efficiency.

In some embodiments, the processor may obtain process monitoring data through the process monitoring module, according to the determined monitoring frequency and number of monitoring points, through sensors at the monitoring points.

In some embodiments, the processor may determine an early warning parameter in response to the process monitoring data and the ideal data satisfying a first preset condition.

The first preset condition is that the deviation between the process monitoring data and the ideal data is greater than the deviation threshold. The deviation threshold is the maximum value when the deviation between the process monitoring data and the ideal data is within an acceptable range.

In some embodiments, the deviation between the process monitoring data and the ideal data is negatively correlated with the cosine value of the process monitoring data and the ideal data. For example, the deviation between the process monitoring data and the ideal data can be the difference between 1 and the cosine value of the process monitoring data and the ideal data.

In some embodiments, in response to the process monitoring data and the ideal data satisfying the first preset condition, the processor can determine the warning frequency based on the deviation between the process monitoring data and the ideal data. The warning frequency is positively correlated with the deviation between the process monitoring data and the ideal data. For example, the warning frequency can be determined based on the following formula (6):

Among them, _fw is the warning frequency, m is the preset parameter, _fw0 is the preset warning frequency, which can be determined manually, _e0 is the deviation threshold, and _ew is the deviation between the process monitoring data and the ideal data.

In some embodiments, the processor can determine the warning mode based on the warning frequency and the first warning threshold and the second warning threshold. The first warning threshold is the critical warning frequency of using light effect warning and sound warning. The second warning threshold is the critical warning frequency of using sound warning and using sound and light warning.

In response to the warning frequency being less than the first warning threshold, the processor may control the warning module to issue a light effect warning. For example, the light effect warning may include emitting a laser or performing continuous flashing.

In response to the warning frequency being not less than the first warning threshold and not greater than the second warning threshold, the processor may control the warning module to issue a sound warning. For example, the sound warning may include issuing a specific sound clip.

In response to the warning frequency being greater than the second warning threshold, the processor may control the warning module to issue an acoustic and visual warning. An acoustic and visual warning refers to an alarm in which an acoustic warning and a light effect warning occur simultaneously. For example, an acoustic and visual warning may include a sound clip being emitted while a light flashes.

In some embodiments, the processor may also determine the warning location based on the process monitoring data. The processor may use the source location of the process monitoring data as the warning location.

Some embodiments of the present specification determine warning parameters based on the determined monitoring frequency and number of monitoring points, process monitoring data and ideal data, and can evaluate the gap between the data in the maintenance process and the user's expectations, and promptly issue warnings to the user, reminding the user to take corresponding measures to adjust the health parameters, etc.; in addition, different warnings can be issued based on the degree of gap, and different situations can be displayed more intuitively, so that users can judge the current situation more quickly, make adjustments as soon as possible, and improve maintenance efficiency.

Some embodiments of the present specification provide a health monitoring device for a bridge substructure, comprising at least one memory and at least one processor, wherein the memory is used to store computer instructions; and the processor is used to execute the above-mentioned health monitoring method for the bridge substructure.

Some embodiments of the present specification provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned bridge substructure health monitoring method.

The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and/or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values is as accurate as possible within the feasible range.

Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and/or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and/or use of terms in this specification shall prevail.

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims (10)

1. A health maintenance monitoring system of a bridge lower structure comprises a raw material monitoring module, a process monitoring module, a health maintenance regulation module, a remote communication module, a data synchronization module, an early warning module and a processor;

The raw material monitoring module is configured to collect raw material monitoring data corresponding to concrete of the lower structure of the bridge;

the process monitoring module includes a sensor configured to monitor process monitoring data of a curing process of the concrete;

the health-preserving regulation module is configured to carry out health-preserving regulation on the bridge based on health-preserving parameters;

the remote communication module is configured to realize remote communication among the raw material monitoring module, the process monitoring module, the health maintenance regulation module, the data synchronization module, the early warning module and the processor;

the data synchronization module is configured to synchronously upload communication data transmitted by the remote communication module to a cloud platform so as to issue the communication data to at least one user terminal based on the cloud platform;

the early warning module is configured to control an alarm device at the position of the corresponding bridge substructure to carry out audible and visual alarm based on early warning parameters;

the processor is configured to:

controlling raw material storage and distribution parameters according to the raw material monitoring data;

regulating and controlling health maintenance parameters of the bridge substructure according to the raw material storage parameters and the process monitoring data; and

And determining early warning parameters, and controlling the early warning module to perform early warning based on the early warning parameters.

2. The system of claim 1, the processor further configured to:

estimating the matching degree of preset health preserving parameters according to the raw material storage parameters and the process monitoring data;

and responding to the matching degree not meeting a preset matching condition, and regulating and controlling the preset health maintenance parameters.

3. The system of claim 2, wherein the degree of matching comprises a sequence of degrees of matching of the preset health parameters over a period of time in the future;

the processor is further configured to:

according to weather data, the raw material storage parameters and the process monitoring data, estimating the matching degree sequence of the future period of time through a sequence prediction model; the sequence prediction model is a machine learning model;

and responding to the matching degree sequence not meeting the preset matching condition, and regulating and controlling the preset health maintenance parameters.

4. The system of claim 1, the processor further configured to:

acquiring ideal data of a plurality of monitoring time points in the future from the cloud platform;

determining monitoring frequency and monitoring point position number of process monitoring based on the interval between the construction completion time and the current time of the bridge substructure;

Acquiring the process monitoring data by the process monitoring module based on the monitoring frequency and the monitoring point position number;

and determining the early warning parameter in response to the process monitoring data and the ideal data meeting a first preset condition.

5. A method of health monitoring of a bridge substructure, based on processor execution of the health monitoring system of a bridge substructure of claim 1, comprising:

controlling raw material storage and distribution parameters according to the raw material monitoring data;

regulating and controlling health maintenance parameters of the bridge lower structure according to the raw material storage and distribution parameters and the process monitoring data; and

and determining early warning parameters to realize early warning based on the early warning parameters.

6. The method of claim 5, wherein adjusting the health parameters of the bridge substructure based on the raw material storage parameters and process monitoring data comprises:

estimating the matching degree of preset health preserving parameters according to the raw material storage parameters and the process monitoring data;

and responding to the matching degree not meeting a preset matching condition, and regulating and controlling the preset health maintenance parameters.

7. The method of claim 6, wherein the degree of matching comprises a sequence of degrees of matching of the preset health parameters over a period of time in the future;

According to the raw material storage and distribution parameters and the process monitoring data, regulating and controlling the health maintenance parameters of the bridge substructure comprises:

according to weather data, the raw material storage parameters and the process monitoring data, estimating the matching degree sequence of the future period of time through a sequence prediction model; the sequence prediction model is a machine learning model;

and responding to the matching degree sequence not meeting the preset matching condition, and regulating and controlling the preset health maintenance parameters.

8. The method of claim 5, the determining an early warning parameter comprising:

acquiring ideal data of a plurality of monitoring time points in the future from the cloud platform;

determining monitoring frequency and monitoring point position number of process monitoring based on the interval between the construction completion time and the current time of the bridge substructure;

acquiring the process monitoring data by the process monitoring module based on the monitoring frequency and the monitoring point position number;

and determining the early warning parameter in response to the process monitoring data and the ideal data meeting a first preset condition.

9. A health monitoring device for a bridge substructure, comprising a processor, wherein the processor is configured to perform the health monitoring method for a bridge substructure according to any of claims 5-8.

10. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the method of health monitoring of a bridge substructure according to any of claims 5 to 8.