Bridge safety early warning method, device, electronic equipment and storage medium

Abstract

The present application discloses a bridge safety early warning method, device, electronic device and storage medium, including: obtaining m bridge key indicators corresponding to m types of metering sensors; collecting m target metering data sets of the target bridge through m types of metering sensors; collecting k bridge images through a camera module; analyzing the m target metering data sets according to the m bridge key indicators to obtain m key indicator values; determining the first health value of the target bridge according to the m key indicator values; performing feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determining the second health value of the target bridge according to the k bridge feature data; determining the target health value according to the first health value and the second health value; when the target health value is lower than the preset health value, executing the corresponding target alarm event according to the target health value through the alarm module. The embodiment of the present application can accurately monitor the health status of the bridge.

Description

Technical Field

[0001] The present application relates to the technical field of bridge monitoring, and in particular to a bridge safety early warning method, device, electronic equipment and storage medium. Background Art

[0002] The basic connotation of bridge health monitoring is to monitor and evaluate the condition of bridge structures, send out early warning signals when the bridge is under special climate and traffic conditions or when the bridge operation condition is abnormally serious, and provide a basis and guidance for bridge maintenance and management decisions.

[0003] At present, the main method for monitoring the health status of bridges is manual monitoring, that is, human field monitoring data and the monitoring personnel judge the health status of the bridge based on their own experience. This method cannot detect the status of the bridge in real time and comprehensively, resulting in inaccurate monitoring results of the bridge health status monitoring. Therefore, how to accurately monitor the health status of the bridge has become a problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a bridge safety early warning method, device, electronic device and storage medium, which can accurately monitor the health status of the bridge.

[0005] In a first aspect, an embodiment of the present application provides a bridge safety early warning method, which is applied to a bridge monitoring system, wherein the bridge monitoring system includes: a control module, a metering module, a camera module, and an alarm module, wherein the metering module includes m types of metering sensors, where m is an integer greater than 1; the method includes:

[0006] Obtaining m key bridge indicators corresponding to the m types of measurement sensors;

[0007] The target metrological data of the target bridge within a preset time period are collected by the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge;

[0008] The camera module collects bridge images of the target bridge within the preset time period to obtain k bridge images, where k is an integer greater than 1;

[0009] Analyzing the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; and each key indicator value corresponds to a bridge key indicator;

[0010] Determine a first health value of the target bridge according to the m key indicator values;

[0011] Performing feature recognition on the k bridge images by using image recognition technology to obtain k bridge feature data;

[0012] Determine a second health value of the target bridge according to the k bridge characteristic data;

[0013] determining a target health value according to the first health value and the second health value;

[0014] When the target health value is lower than a preset health value, the alarm module executes a corresponding target alarm event according to the target health value.

[0015] In a second aspect, an embodiment of the present application provides a bridge safety early warning device, which is applied to a bridge monitoring system. The bridge monitoring system includes: a control module, a metering module, a camera module, and an alarm module. The metering module includes m types of metering sensors, where m is an integer greater than 1; the device includes: a collection unit, a health assessment unit, and an early warning unit, wherein:

[0016] The acquisition unit is used to obtain m key bridge indicators corresponding to the m types of metrological sensors; the target metrological data of the target bridge within a preset time period are collected by the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge; the bridge image of the target bridge within the preset time period is collected by the camera module to obtain k bridge images; k is an integer greater than 1;

[0017] The health assessment unit is used to analyze the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; each key indicator value corresponds to a bridge key indicator; determine a first health value of the target bridge according to the m key indicator values; perform feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determine a second health value of the target bridge according to the k bridge feature data; determine a target health value according to the first health value and the second health value;

[0018] The early warning unit is used to execute a corresponding target alarm event according to the target health value through the alarm module when the target health value is lower than a preset health value.

[0019] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory is used to store one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the present application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the present application.

[0021] In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package.

[0022] The implementation of this application has the following beneficial effects:

[0023] It can be seen that the bridge safety early warning method described in the present application includes: obtaining m key bridge indicators corresponding to the m types of metering sensors; collecting target metering data of the target bridge within a preset time period through the m types of metering sensors to obtain m target metering data sets; collecting bridge images of the target bridge within a preset time period through the camera module to obtain k bridge images; analyzing the m target metering data sets according to the m key bridge indicators to obtain m key indicator values; determining the first health value of the target bridge according to the m key indicator values; performing feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determining the second health value of the target bridge according to the k bridge feature data; determining the target health value according to the first health value and the second health value; when the target health value is lower than the preset health value, executing the corresponding target alarm event according to the target health value through the alarm module. On the one hand, the structural performance and mechanical state of the bridge are analyzed through the metering data, and on the other hand, the appearance characteristics and potential damage signs of the bridge are analyzed through the image data. The target health value of the bridge is determined by combining these two types of data. This fusion of multi-source data can comprehensively reflect the health status of the bridge and avoid the limitations of a single data source, thereby accurately monitoring the health status of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0025] Figure 1is a structural schematic diagram of a bridge monitoring system provided in an embodiment of the present application;

[0026] Figure 2 is a flow chart of a bridge safety early warning method provided by an embodiment of the present application;

[0027] Figure 3 This is a functional unit composition block diagram of a bridge safety early warning device provided in an embodiment of the present application;

[0028] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] The electronic device described in the embodiments of the present application may include a bridge monitoring system.

[0033] The following is an explanation of some professional terms involved in this application:

[0034] The health status of a bridge is a comprehensive description of the safety, applicability and durability of a bridge structure in a specific period of time. In this application, the health value of a bridge is used to quantify the health status of the bridge. The larger the health value, the better the health status of the bridge.

[0035] Bridge health monitoring: is a technical means of real-time, continuous monitoring and evaluation of bridge structures. It refers to the monitoring and evaluation of the condition of bridge structures, issuing early warning information for bridges under special climate and traffic conditions or when the bridge operation conditions are abnormally serious, and providing a basis and guidance for bridge maintenance and management decisions.

[0036] See also Figure 1 , Figure 1 is a schematic diagram of the structure of a bridge monitoring system provided in an embodiment of the present application, such as Figure 1 As shown, the bridge monitoring system includes: a metering module, a camera module, a control module, and an alarm module. The control module is physically and / or communicatively connected with the metering module, the camera module, and the alarm module respectively.

[0037] Among them, the metering module includes m types of metering sensors, which are used to collect physical quantity data related to the state of the bridge structure in real time, and provide quantitative basis for evaluating the health of the bridge. These data can reflect the response of the bridge under different loads, and help the staff understand the stress and deformation of the bridge. For example, strain sensors and displacement sensors are installed on the beams, piers, supports and other parts of the bridge to monitor the strain and displacement changes of these parts under different loads, so as to judge whether the bridge has potential problems such as excessive deformation and crack expansion. The m types of metering sensors can include at least one of the following: strain sensors, displacement sensors, pressure sensors, acceleration sensors, temperature sensors, etc., which are not limited here.

[0038] Among them, the camera module is used to collect image information of the bridge and intuitively display the appearance of the bridge. Through image recognition technology, defects such as cracks, rust, and damage on the surface of the bridge can be detected, and environmental changes around the bridge can be monitored. For example, regular image acquisition of the bridge can promptly detect newly emerging cracks or rusted areas, providing a basis for bridge maintenance. The camera module can be installed at key locations of the bridge, such as the bridge deck, piers, and both sides of the bridge to obtain clear images of the bridge. The camera module may include at least one of the following: a fixed camera, a rotatable camera, a pan-tilt camera, etc., which are not limited here; the camera in the camera module may have different viewing angles and focal lengths to meet different monitoring needs.

[0039] The control module, as the core of the bridge monitoring system, is responsible for coordinating the work of each module, processing and analyzing the collected data, judging the health of the bridge, and triggering the alarm module as needed. For example, the control module receives data from the metering module and the camera module, performs real-time analysis and processing, and when an abnormal situation is found, the control module controls the alarm module to execute the alarm event.

[0040] The alarm module is used to execute corresponding alarm events when the health status of the bridge is abnormal, and remind relevant personnel to take measures. The alarm module can execute different types of alarm events according to different situations, such as sound and light alarm, SMS alarm, email alarm, etc. The alarm module can include at least one of the following: sound and light alarm, SMS module, etc., which are not limited here.

[0041] See also Figure 2 , Figure 2 The present invention provides a flow chart of a bridge safety early warning method provided in an embodiment of the present invention. The method is applied to a bridge monitoring system. The bridge monitoring system includes: a control module, a metering module, a camera module, and an alarm module. The metering module includes m types of metering sensors, where m is an integer greater than 1. The method includes but is not limited to the following steps:

[0042] S201. Obtain m key bridge indicators corresponding to the m types of measurement sensors.

[0043] In the embodiment of the present application, the key indicators of the bridge are important parameters for evaluating the health status of the bridge structure, which can reflect the performance and status of the bridge in different aspects, for example, the stress indicators of the bridge.

[0044] In a specific embodiment, m key bridge indicators corresponding to m types of metering sensors can be obtained, one key bridge indicator corresponding to each type of metering sensor. Specifically, the sensor type of each of the m types of metering sensors can be obtained first to obtain m sensor types, and then, the m key bridge indicators can be determined according to the m sensor types. For example, a mapping relationship between preset sensor types and key bridge indicators can be pre-stored, and the m key bridge indicators corresponding to the m sensor types can be determined based on the mapping relationship.

[0045] S202. Target metrological data of a target bridge within a preset time period are collected by means of the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge.

[0046] In the embodiment of the present application, the preset time period can be preset in advance or defaulted.

[0047] In a specific embodiment, target metering data of a target bridge within a preset time period can be collected by m types of metering sensors to obtain m target metering data sets. Specifically, appropriate collection parameters can be set for the m types of metering sensors according to the sensor types of the metering sensors and the bridge monitoring requirements. For example, for strain sensors, parameters such as sampling frequency, range, and resolution can be set. Then, within the preset time period, target metering data of the target bridge can be collected by m types of metering sensors, thereby obtaining m target metering data sets.

[0048] Optionally, step S202, collecting target metering data of the target bridge within a preset time period by using the m types of metering sensors to obtain m target metering data sets, may include the following steps:

[0049] S21, acquiring metering data of the target bridge collected by a first metering sensor within the preset time period to obtain a first metering data set; the first metering sensor is any metering sensor among the m metering sensors;

[0050] S22, obtaining initial device attribute parameters corresponding to the first type of measurement sensor;

[0051] S23, obtaining target constituent materials of the target bridge, and obtaining at least one target constituent material;

[0052] S24, determining an influencing parameter corresponding to each component material in the at least one target component material to obtain at least one influencing parameter;

[0053] S25, adjusting the initial device attribute parameter according to the at least one influencing parameter to obtain a target device attribute parameter;

[0054] S26, determining a target fine-tuning coefficient corresponding to the target device attribute parameter;

[0055] S27: Adjust the first metrology data set according to the target fine-tuning coefficient to obtain a target metrology data set corresponding to the first metrology sensor.

[0056] In the embodiment of the present application, the device attribute parameters may include at least one of the following: measurement range, measurement accuracy, resolution, etc., which are not limited here.

[0057] In a specific embodiment, the metering data of the target bridge collected by the first metering sensor within a preset time period can be obtained to obtain a first metering data set; then, the initial device attribute parameters corresponding to the first metering sensor can be obtained. Specifically, the first sensor type corresponding to the first metering sensor can be obtained, and the initial device attribute parameters can be determined according to the first sensor type. For example, a mapping relationship between a preset sensor type and device attribute parameters can be pre-stored, and the initial device attribute parameters corresponding to the first sensor type can be determined based on the mapping relationship; then, the target component material of the target bridge can be obtained to obtain at least one target component material. Specifically, the control module can access a bridge database corresponding to the target bridge, the bridge database stores a list of materials used in the bridge construction process, and the list of materials records the types, quantities and specifications of various materials used, and obtains at least one target component material from the bridge database.

[0058] Further, the influencing parameter corresponding to each component material in at least one target component material can be determined to obtain at least one influencing parameter. Specifically, a mapping relationship between preset target component materials and influencing parameters can be pre-stored, and at least one influencing parameter corresponding to at least one target component material is determined based on the mapping relationship. The value range of the influencing parameter can be -0.12~0.12; then, the initial device attribute parameter can be adjusted according to the at least one influencing parameter. The specific calculation formula is as follows:

[0059] Target device attribute parameter = initial device attribute parameter × (1 + influencing parameter a) × ... × (1 + influencing parameter s);

[0060] Among them, the influencing parameter a is the first influencing parameter among the at least one influencing parameter, and the influencing parameter s is the last influencing parameter among the at least one influencing parameter. According to the above formula, the target device attribute parameter can be obtained; then, the target fine-tuning coefficient corresponding to the target device attribute parameter can be determined. Specifically, the mapping relationship between the preset device attribute parameter and the fine-tuning coefficient can be pre-stored, and the target fine-tuning coefficient corresponding to the target device attribute parameter can be determined based on the mapping relationship. The value range of the target fine-tuning coefficient can be -0.2~0.2; finally, the first metering data set can be adjusted according to the target fine-tuning coefficient. The specific calculation formula is as follows:

[0061] Target measurement data set b = first measurement data set × (1 + target fine-tuning coefficient);

[0062] According to the above formula, the target metrology data set b can be obtained, wherein the target metrology data set b is the target metrology data set corresponding to the first type of metrology sensor.

[0063] In this way, by obtaining a first metrological data set corresponding to a first metrological sensor; obtaining initial device property parameters corresponding to the first metrological sensor; obtaining target component materials of a target bridge to obtain at least one target component material; determining an influence parameter corresponding to each component material in at least one target component material to obtain at least one influence parameter; adjusting the initial device property parameters according to at least one influence parameter to obtain target device property parameters; determining a target fine-tuning coefficient corresponding to the target device property parameters; adjusting the first metrological data set according to the target fine-tuning coefficient to obtain a target metrological data set corresponding to the first metrological sensor, by determining the influence parameter corresponding to each component material and adjusting the initial device property parameters, the state of the bridge in actual use can be more accurately reflected, thereby improving the accuracy of the metrological data.

[0064] S203. Capture bridge images of the target bridge within the preset time period through the camera module to obtain k bridge images; k is an integer greater than 1.

[0065] In the embodiment of the present application, within a preset time period, the target bridge can be photographed by the camera module at preset time intervals to obtain k bridge images, wherein the preset time interval can be preset in advance or defaulted.

[0066] S204. Analyze the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; and each key indicator value corresponds to a bridge key indicator.

[0067] In the embodiment of the present application, the key indicator value is a specific value obtained by analyzing the target measurement data set and can reflect the health status of specific key aspects of the bridge. The value range of the key indicator value can be 0~100. For example, the stress key indicator value (one of m key indicator values) represents the stress magnitude of the bridge structure under various loads. The stress key indicator value can reflect whether the bridge structure is in a safe stress state. If the stress value exceeds the normal range, it means that the bridge is at risk of structural damage.

[0068] In a specific embodiment, the control module may analyze m target metering data sets according to m key bridge indicators to obtain m key indicator values.

[0069] Optionally, step S204, analyzing the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values, may include the following steps:

[0070] S41, obtaining a first bridge key indicator and a first target measurement data set; the first bridge key indicator is any bridge key indicator among the m bridge key indicators; the first target measurement data set is a target measurement data set corresponding to the first bridge key indicator among the m target measurement data sets;

[0071] S42, determining a first average measurement value and a first mean square error corresponding to the first target measurement data set;

[0072] S43, determining a reference first key indicator value corresponding to the first average measurement value;

[0073] S44, determining a first optimization factor corresponding to the first mean square error;

[0074] S45, sampling the first target metering data set to obtain a plurality of sampled metering data; each sampled metering data corresponds to a sampling time;

[0075] S46, performing straight line fitting according to the plurality of sampled measurement data and the sampling time corresponding to each sampled measurement data to obtain a first measurement straight line;

[0076] S47, obtaining a first slope corresponding to the first measurement straight line;

[0077] S48, determining a second optimization factor corresponding to the first slope;

[0078] S49. Optimize the reference first key indicator value according to the first optimization factor and the second optimization factor to obtain a key indicator value corresponding to the first bridge key indicator.

[0079] In an embodiment of the present application, the first bridge key indicator and the first target metering data set can be obtained first; then, the first average metering value of the first target metering data set can be calculated according to the average value calculation formula, and similarly, the first mean square deviation of the first target metering data set can be calculated according to the mean square deviation calculation formula; then, the reference first key indicator value corresponding to the first average metering value can be determined; then, the mapping relationship between the preset mean square deviation and the optimization factor can be pre-stored, and the first optimization factor corresponding to the first mean square deviation can be determined based on the mapping relationship.

[0080] Next, the first target metering data set can be sampled equidistantly to obtain a plurality of sampled metering data, and then the first sampling time of each sampled metering data in the plurality of sampled metering data can be obtained to obtain a plurality of first sampling times; a plurality of first coordinate points can be obtained by combining the plurality of sampled metering data and the corresponding first sampling time in the plurality of first sampling times, and then a straight line fitting method (e.g., least square method) can be used to fit the plurality of first coordinate points to obtain a first metering straight line; further, a first straight line equation y=ax+b of the first metering straight line can be obtained, wherein y is the metering value, x is the time point, a is the first slope, and b is the intercept, and the first slope can be obtained according to the straight line equation; then, a second optimization factor corresponding to the first slope can be determined, and specifically, a mapping relationship between a preset slope and an optimization factor can be pre-stored, and the second optimization factor corresponding to the first slope can be determined based on the mapping relationship; wherein the value range of the first optimization factor and the second optimization factor can be -0.3~0.3; finally, the reference first key indicator value can be optimized according to the first optimization factor and the second optimization factor, and the specific calculation formula is as follows:

[0081] Key indicator value c = reference first key indicator value × (1 + first optimization factor) × (1 + second optimization factor);

[0082] According to the above formula, the key indicator value c can be obtained, wherein the key indicator value c is the key indicator value corresponding to the key indicator of the first bridge.

[0083] In this way, by determining the first average measurement value and the first mean square error of the first target measurement data set, we can have a more comprehensive understanding of the centralization trend and dispersion of the data. The average measurement value reflects the overall level of the data, while the mean square error reflects the fluctuation of the data. Incorporating these two parameters into the calculation of the key indicator values ​​allows the calculation results to more accurately reflect the actual situation of the bridge, thereby improving the accuracy of the key indicator values.

[0084] Optionally, step S43, determining a reference first key indicator value corresponding to the first average measurement value may include the following steps:

[0085] S431, obtaining a normal measurement value set corresponding to the target bridge;

[0086] S432, determining a first normal measurement value corresponding to the first bridge key indicator in the normal measurement value set;

[0087] S433, obtaining the target operating time corresponding to the target bridge;

[0088] S434, determining a target attenuation factor corresponding to the target operating time;

[0089] S435, adjusting the first normal measurement value according to the target attenuation factor to obtain a second normal measurement value;

[0090] S436: Determine a difference between the first average measurement value and the second normal measurement value to obtain a target measurement difference;

[0091] S437. Determine the reference first key indicator value according to the target measurement difference.

[0092] In an embodiment of the present application, the normal measurement value set represents a series of measurement value sets that can reflect various physical quantities or performance indicators of the target bridge under normal conditions. Specifically, the normal measurement value set may include m or more normal measurement values, and each normal measurement value corresponds to a key bridge indicator.

[0093] In a specific embodiment, a normal metering value set corresponding to the target bridge can be obtained first. Specifically, when the target bridge is just put into use, various key bridge indicators of the target bridge (for example, the m key bridge indicators mentioned in the present application) can be detected manually or by machine to obtain target detection data. Then, the average value of each of the m key bridge indicators can be calculated based on the target detection data to obtain m average values, and these m average values ​​constitute the normal metering value set; then, the normal metering value corresponding to the first key bridge indicator can be screened out from the normal metering value set to obtain the first normal metering value; then, the target operating time corresponding to the target bridge can be obtained. Specifically, the current time can be obtained first, and then the time when the target bridge is put into use can be obtained. The target operating time can be obtained by subtracting the use time from the current time.

[0094] As the target bridge is in operation, it will gradually age and decay, thus causing the normal measurement value set detected at the beginning to be inaccurate. Therefore, it is necessary to determine the target attenuation factor corresponding to the target operating time. For example, a mapping relationship between a preset operating time and an attenuation factor can be pre-stored, and the target attenuation factor corresponding to the target operating time can be determined based on the mapping relationship. The value range of the target attenuation factor is -0.3~0; then, the first normal measurement value can be adjusted according to the target attenuation factor. The specific calculation formula is as follows:

[0095] The second normal measurement value = the first normal measurement value × (1 + target attenuation factor);

[0096] According to the above formula, the second normal measurement value can be obtained; then, the difference between the first average measurement value and the second normal measurement value can be calculated, and the specific calculation formula is as follows:

[0097] Target measurement difference = first average measurement value - second normal measurement value;

[0098] According to the above formula, the target metering difference can be obtained; then, the reference first key indicator value can be determined according to the target metering difference. Specifically, the mapping relationship between the preset metering difference and the key indicator value can be pre-stored, and the reference key indicator value corresponding to the target metering difference can be determined based on the mapping relationship. Then, the deviation between the target metering difference and the second normal metering value can be calculated. The specific calculation formula is as follows:

[0099] Target deviation = target measurement difference / second normal measurement value × 100%;

[0100] According to the above formula, the target deviation can be obtained. Then, the target fine-tuning parameter corresponding to the target deviation can be determined. Specifically, the mapping relationship between the preset deviation and the fine-tuning parameter can be pre-stored, and the target fine-tuning parameter corresponding to the target deviation can be determined based on the mapping relationship. The value range of the target fine-tuning parameter is -0.25~0.25; the reference key indicator value is adjusted according to the target fine-tuning parameter. The specific calculation formula is as follows:

[0101] Reference first key indicator value = reference key indicator value × (1 + target fine-tuning parameter);

[0102] According to the above formula, the reference first key indicator value can be obtained.

[0103] In this way, by obtaining the target operating time, the time factor can be incorporated into the evaluation of the bridge status, which can more accurately reflect the actual situation of the bridge at different stages of use. This is because different operating time corresponds to different degrees of bridge performance degradation and maintenance requirements. Fully considering the impact of the time factor can make the final evaluation result more in line with the actual service status of the bridge.

[0104] S205. Determine a first health value of the target bridge according to the m key indicator values.

[0105] In the embodiment of the present application, the control module may calculate the first health value based on m key indicator values.

[0106] Optionally, step S205, determining the first health value of the target bridge according to the m key indicator values, may include the following steps:

[0107] A1. Obtain m key indicator thresholds corresponding to the m key indicators of the bridge; each key indicator threshold corresponds to a key indicator of the bridge;

[0108] A2. Determine a key indicator value among the m key indicator values ​​that is greater than a corresponding key indicator threshold among the m key indicator thresholds, and obtain i key indicator values; i is a natural number less than or equal to m;

[0109] A3. Determine the key indicator thresholds corresponding to the i key indicator values ​​among the m key indicator thresholds to obtain i key indicator thresholds;

[0110] A4. Determine the difference between the i key indicator values ​​and the corresponding key indicator thresholds among the i key indicator thresholds to obtain i first difference values;

[0111] A5. Obtain a first weight set corresponding to the i first difference values; the first weight set includes i weight values; each weight value corresponds to a first difference value;

[0112] A6. Determine a target difference value according to the first weight set and the i first differences;

[0113] A7. Determine the first health value corresponding to the target difference.

[0114] In the embodiment of the present application, the key indicator threshold represents the maximum key indicator value allowed for the target bridge under normal use and various loads.

[0115] In a specific embodiment, m key indicator thresholds corresponding to m bridge key indicators can be obtained first. For example, a mapping relationship between preset bridge key indicators and key indicator thresholds can be pre-stored, and the m key indicator thresholds corresponding to the m bridge key indicators can be determined based on the mapping relationship; then, a key indicator value greater than a corresponding key indicator threshold in the m key indicator thresholds can be determined among the m key indicator values ​​to obtain i key indicator values. For example, assuming that the m key indicator values ​​include a stress indicator value, a stress indicator threshold in the m key indicator thresholds can be obtained, and the size relationship between the stress indicator value and the stress indicator threshold can be compared. If the stress indicator value is greater than the stress indicator threshold, it means that the stress indicator value is too large and is an abnormal value, and it is determined that the stress indicator value is one of the above i key indicator values; conversely, if the stress indicator value is less than or equal to the stress indicator threshold, it means that the stress indicator value does not exceed the normal range and is a normal value.

[0116] Further, the key indicator thresholds corresponding to the i key indicator values ​​in the m key indicator thresholds can be determined to obtain i key indicator thresholds; then, the difference between the i key indicator values ​​and the i key indicator thresholds can be determined, and the specific calculation formula is as follows:

[0117] First difference d3=key indicator value d1-key indicator threshold d2;

[0118] Among them, the key indicator value d1 is any key indicator value among the i key indicator values, the key indicator threshold d2 is the key indicator threshold corresponding to the key indicator value d1, and the first difference d3 is the first difference corresponding to the key indicator value d1; i first differences can be obtained by calculating i times according to the above formula; then, the first weight set corresponding to the i first differences can be obtained; the specific calculation formula is as follows according to the first weight set and the i first differences:

[0119] Target difference = first difference a × first weight a + ... + first difference i × first weight i;

[0120] Among them, the first difference a is the first first difference among the i first differences, and the first weight a is the first weight corresponding to the first difference a; the target difference can be obtained according to the above formula; finally, the first health value corresponding to the target difference can be determined. For example, the mapping relationship between the preset difference and the health value can be pre-stored, and the first health value corresponding to the target difference can be determined based on the mapping relationship.

[0121] In this way, by determining the key indicator values ​​that are greater than the corresponding key indicator threshold among the m key indicator values, we can accurately find out the indicators with problems, focus on analyzing those key indicators that exceed the safety range, avoid indiscriminate analysis of all indicators, and improve the efficiency of data analysis.

[0122] Optionally, step A5, obtaining the first weight set corresponding to the i first differences, may include the following steps:

[0123] B1. Determine i reference weights according to the i first differences; the larger the first difference, the larger the reference weight;

[0124] B2. Obtain target measurement data sets corresponding to the i first difference values ​​in the m target measurement data sets to obtain i target measurement data sets;

[0125] B3, determining the data volume of each target measurement data set in the i target measurement data sets to obtain i data volumes;

[0126] B4. Determine the adjustment coefficient corresponding to each of the i data amounts to obtain i adjustment coefficients;

[0127] B5. Adjust corresponding reference weights among the i reference weights according to the i adjustment coefficients to obtain the i weights.

[0128] In an embodiment of the present application, i reference weights can be determined based on i first difference values. For example, a mapping relationship between preset difference values ​​and reference weights can be pre-stored, and i reference weights corresponding to the i first difference values ​​are determined based on the mapping relationship. Then, the target metering data sets corresponding to the i first difference values ​​in the m target metering data sets can be obtained to obtain i target metering data sets. Specifically, each first difference value can correspond to a unique identifier, and i unique identifiers corresponding to the i first difference values ​​can be obtained. Then, the corresponding i target metering data sets are selected from the m target metering data sets according to the i unique identifiers.

[0129] Furthermore, the data volume of each target metering data set in the i target metering data sets can be determined to obtain i data volumes. Specifically, the i target metering data sets can be stored in the system database of the bridge monitoring system. The quantity information of the i target metering data sets can be obtained by querying the system table of the system database or using a specific database function to obtain i quantity information, that is, i data volumes.

[0130] Then, the adjustment coefficient corresponding to each of the i data amounts can be determined to obtain i adjustment coefficients. Specifically, a mapping relationship between a preset data amount and an adjustment coefficient can be pre-stored, and the i adjustment coefficients corresponding to the i data amounts can be determined based on the mapping relationship; then, the corresponding reference weights in the i reference weights can be adjusted according to the i adjustment coefficients. The specific calculation formula is as follows:

[0131] Weight e3 = reference weight e1 × (1 + adjustment coefficient e2);

[0132] Among them, the reference weight e1 is any reference weight among the i reference weights, the adjustment coefficient e2 is the adjustment coefficient corresponding to the reference weight e1 in the i adjustment coefficients, and the weight e3 is the weight corresponding to the reference weight e1; i weights can be obtained by calculating i times according to the above formula.

[0133] In this way, the weights are determined by comprehensively considering multiple factors such as the first difference and the amount of data, avoiding the one-sidedness of determining the weights by a single factor. By combining the interaction of multiple factors, it is possible to more comprehensively evaluate the impact of various key indicators on the health status of the bridge, thereby improving the scientificity and reliability of the bridge health assessment.

[0134] S206 , performing feature recognition on the k bridge images by using image recognition technology to obtain k bridge feature data.

[0135] In an embodiment of the present application, the control module uses image recognition technology to perform feature recognition on k bridge images to obtain k bridge feature data. Specifically, image recognition technology (for example, grayscale co-occurrence matrix, local binary pattern and other methods) is used to extract texture features from the k bridge images to obtain k texture feature data, that is, k bridge feature data. For example, the grayscale co-occurrence matrix is ​​a method for describing texture features by calculating the grayscale value distribution of pixel pairs in an image. It can extract texture features such as image contrast, correlation, and energy.

[0136] S207: Determine a second health value of the target bridge according to the k bridge characteristic data.

[0137] In the embodiment of the present application, the control module may analyze and process the k bridge characteristic data to obtain the second health value.

[0138] Optionally, step S207, determining the second health value of the target bridge according to the k bridge characteristic data, may include the following steps:

[0139] S71, obtaining a preset bridge characteristic database corresponding to the target bridge; the preset bridge characteristic database includes x standard bridge characteristic data; x is an integer not less than k;

[0140] S72, extracting corresponding standard bridge characteristic data from the preset bridge characteristic database according to the k bridge characteristic data, to obtain k standard bridge characteristic data; each standard bridge characteristic data corresponds to one bridge characteristic data;

[0141] S73, calculating the similarity between the k bridge characteristic data and the corresponding standard bridge characteristic data in the k standard bridge characteristic data according to a preset similarity algorithm to obtain k similarities; each similarity corresponds to one bridge characteristic data;

[0142] S74, determining an average similarity corresponding to the k similarities;

[0143] S75. Determine the second health value corresponding to the average similarity.

[0144] In the embodiment of the present application, the preset bridge feature database and the preset similarity algorithm can be preset or defaulted in advance, wherein the preset bridge feature database is a pre-established collection of various feature data of the target bridge under normal conditions.

[0145] In a specific embodiment, a preset bridge feature database corresponding to the target bridge can be obtained. Specifically, a mapping relationship between a preset bridge and a bridge feature database can be pre-stored, and the preset bridge feature database corresponding to the target bridge can be determined based on the mapping relationship; then, corresponding standard bridge feature data can be extracted from the preset bridge feature database based on k bridge feature data to obtain k standard bridge feature data. Specifically, the data type of each bridge feature data in the k bridge feature data can be determined first to obtain k data types, and then, k standard bridge feature data can be extracted from the preset bridge feature database based on the k data types. For example, assuming that one of the k data types is color data, then the color data in the preset bridge feature database, that is, the standard color data, can be extracted.

[0146] Furthermore, the similarity between k bridge feature data and corresponding standard bridge feature data in k standard bridge feature data can be calculated according to a preset similarity algorithm to obtain k similarities. Specifically, the k bridge feature data and the k standard bridge feature data can be first converted into corresponding feature vectors to obtain k feature vectors and k standard feature vectors. For example, for image features, the color, texture, shape and other features of the image can be extracted to form a multi-dimensional feature vector. For another example, for numerical features, it can be directly used as a vector. Then, the preset similarity algorithm can be a cosine similarity algorithm. The similarity between k feature vectors and corresponding standard feature vectors in k standard feature vectors is calculated by the cosine similarity algorithm to obtain k similarities. The cosine similarity algorithm is a conventional technology and will not be described in detail here. Then, the average similarity of the k similarities can be calculated according to the average value calculation formula. Finally, the second health value corresponding to the average similarity can be determined. For example, a mapping relationship between a preset similarity and a health value can be pre-stored, and the second health value corresponding to the average similarity can be determined based on the mapping relationship.

[0147] In this way, by calculating k bridge characteristic data and calculating the similarity with k standard bridge characteristic data, the bridge can be evaluated from multiple angles. Different bridge characteristic data may reflect different aspects of the bridge, such as structural integrity, surface condition, material properties, etc. Comprehensive consideration of these different characteristics can provide a more comprehensive understanding of the overall health status of the bridge, avoid the limitations of a single feature evaluation, and thus accurately evaluate the health status of the bridge.

[0148] S208. Determine a target health value according to the first health value and the second health value.

[0149] In the embodiment of the present application, the control module can calculate the first health value and the second health value to obtain the target health value.

[0150] Optionally, step S208, determining a target health value according to the first health value and the second health value, may include the following steps:

[0151] S81, obtaining a first device accuracy parameter corresponding to the metering module;

[0152] S82, obtaining a second device accuracy parameter corresponding to the camera module;

[0153] S83, determining a reference first weight corresponding to the first device precision parameter, and determining a reference second weight corresponding to the second device precision parameter;

[0154] S84, determining a reference health value according to the reference first weight, the reference second weight, the first health value, and the second health value;

[0155] S85, obtaining target weather data of the target bridge within the preset time period;

[0156] S86, determining a first interference coefficient of the target weather data on the metering module;

[0157] S87, determining a second interference coefficient of the target weather data on the camera module;

[0158] S88. Adjust the reference health value according to the first interference coefficient and the second interference coefficient to obtain the target health value.

[0159] In the embodiment of the present application, the first device accuracy parameter may include at least one of the following: measurement accuracy, sampling frequency, range, etc., which are not limited here; the second device accuracy parameter may include at least one of the following: image resolution, focal length parameter, color reproduction, shooting frame rate, etc., which are not limited here.

[0160] In a specific embodiment, the first device accuracy parameter corresponding to the metering module can be obtained first. Specifically, the first device accuracy parameter can be the metering accuracy. Since the metering module includes m types of metering sensors, the metering accuracy of each metering sensor can be obtained to obtain m metering accuracy, and the average metering accuracy of the m metering accuracy is calculated, and the average metering accuracy is used as the first device accuracy parameter; then, the second device accuracy parameter corresponding to the camera module can be obtained. Specifically, the device technical document of the device module can be queried. The device technical document will clearly list various device information of the device (for example, device accuracy parameters), and the second device accuracy parameter can be obtained from the device technical document; then, the reference first weight corresponding to the first device accuracy parameter can be determined, and the reference second weight corresponding to the second device accuracy parameter can be determined. Specifically, the above-mentioned reference first weight and reference second weight can meet the following conditions:

[0161] First device accuracy parameter / second device accuracy parameter=reference first weight value / reference second weight value;

[0162] 1 = reference first weight + reference second weight;

[0163] According to the above two formulas, the reference first weight and the reference second weight can be calculated; then, according to the reference first weight, the reference second weight, the first health value and the second health value, the specific calculation formula is as follows:

[0164] Reference health value = first health value × reference first weight + second health value × reference second weight;

[0165] The reference health value can be obtained according to the above formula; then, the target weather data of the target bridge within the preset time period can be obtained. Specifically, the control module can be connected to the Internet to obtain the weather data of the area where the target bridge is located, that is, the target weather data, through the Internet; then, the first interference coefficient of the target weather data on the metering module can be determined. Specifically, the target temperature data of the target bridge can be determined according to the target weather data, and the first interference coefficient corresponding to the target temperature data can be determined. For example, the mapping relationship between the preset temperature data and the interference coefficient can be pre-stored, and the first interference coefficient corresponding to the target temperature data can be determined based on the mapping relationship, wherein the value range of the first interference coefficient is -0.3~0; then, the second interference coefficient of the target weather data on the camera module is determined. Specifically, the target visibility of the target bridge can be determined according to the target weather data, and the first interference coefficient corresponding to the target visibility can be determined. For example, the mapping relationship between the preset visibility and the interference coefficient can be pre-stored, and the first interference coefficient corresponding to the target visibility can be determined based on the mapping relationship, wherein the value range of the first interference coefficient is -0.5~0; finally, the reference health value can be adjusted according to the first interference coefficient and the second interference coefficient. The specific calculation formula is as follows:

[0166] Target health value = reference health value × (1 + first interference coefficient) × (1 + second interference coefficient);

[0167] The target health value can be obtained according to the above formula.

[0168] In this way, the precision parameters of the metering module and the camera module respectively reflect their accuracy and reliability in the measurement and image acquisition processes. By considering the weights corresponding to these parameters, the weight of high precision is large, and the weight of low precision is correspondingly reduced. This can avoid evaluation deviations caused by equipment accuracy problems, thereby improving the evaluation accuracy of the target health value of the bridge.

[0169] S209: When the target health value is lower than a preset health value, the alarm module executes a corresponding target alarm event according to the target health value.

[0170] In the embodiment of the present application, the preset health value can be preset or defaulted in advance, and the target alarm event can include at least one of the following: broadcast alarm, light alarm, email alarm, etc., which are not limited here.

[0171] In a specific embodiment, when the target health value is lower than the preset health value, it indicates that the health status of the target bridge is not good and needs to be repaired or inspected. The corresponding target alarm event can be executed according to the target health value through the alarm module. Specifically, a mapping relationship between the preset health value and the alarm event can be pre-set, and the target alarm event corresponding to the target health value can be determined based on the mapping relationship.

[0172] The implementation of this application has the following beneficial effects:

[0173] It can be seen that the bridge safety early warning method described in the present application includes: obtaining m key bridge indicators corresponding to the m types of metering sensors; collecting target metering data of the target bridge within a preset time period through the m types of metering sensors to obtain m target metering data sets; collecting bridge images of the target bridge within a preset time period through the camera module to obtain k bridge images; analyzing the m target metering data sets according to the m key bridge indicators to obtain m key indicator values; determining the first health value of the target bridge according to the m key indicator values; performing feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determining the second health value of the target bridge according to the k bridge feature data; determining the target health value according to the first health value and the second health value; when the target health value is lower than the preset health value, executing the corresponding target alarm event according to the target health value through the alarm module. On the one hand, the structural performance and mechanical state of the bridge are analyzed through the metering data, and on the other hand, the appearance characteristics and potential damage signs of the bridge are analyzed through the image data. The target health value of the bridge is determined by combining these two types of data. This fusion of multi-source data can comprehensively reflect the health status of the bridge and avoid the limitations of a single data source, thereby accurately monitoring the health status of the bridge.

[0174] See also Figure 3 , Figure 3 This is a functional unit composition block diagram of a bridge safety early warning device 300 provided in an embodiment of the present application, which is applied to a bridge monitoring system. The bridge monitoring system includes: a control module, a metering module, a camera module, and an alarm module. The metering module includes m types of metering sensors, where m is an integer greater than 1; the bridge safety early warning device 300 includes: a collection unit 301, a health assessment unit 302, and an early warning unit 303, wherein:

[0175] The acquisition unit 301 is used to obtain m key bridge indicators corresponding to the m types of metrological sensors; the target metrological data of the target bridge within a preset time period are collected by the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge; the bridge image of the target bridge within the preset time period is collected by the camera module to obtain k bridge images; k is an integer greater than 1;

[0176] The health assessment unit 302 is used to analyze the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; each key indicator value corresponds to a bridge key indicator; determine the first health value of the target bridge according to the m key indicator values; perform feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determine the second health value of the target bridge according to the k bridge feature data; determine the target health value according to the first health value and the second health value;

[0177] The early warning unit 303 is used to execute a corresponding target alarm event according to the target health value through the alarm module when the target health value is lower than a preset health value.

[0178] Optionally, in the aspect of analyzing the m target metering data sets according to the m bridge key indicators to obtain m key indicator values, the health assessment unit 302 is specifically used for:

[0179] Acquire a first bridge key indicator and a first target metering data set; the first bridge key indicator is any bridge key indicator among the m bridge key indicators; the first target metering data set is a target metering data set corresponding to the first bridge key indicator among the m target metering data sets;

[0180] Determine a first average measurement value and a first mean square error corresponding to the first target measurement data set;

[0181] Determine a reference first key indicator value corresponding to the first average measurement value;

[0182] Determining a first optimization factor corresponding to the first mean square error;

[0183] Sampling the first target metering data set to obtain a plurality of sampled metering data; each sampled metering data corresponds to a sampling time;

[0184] Perform straight line fitting according to the plurality of sampled metering data and the sampling time corresponding to each sampled metering data to obtain a first metering straight line;

[0185] Obtaining a first slope corresponding to the first measurement straight line;

[0186] determining a second optimization factor corresponding to the first slope;

[0187] The reference first key indicator value is optimized according to the first optimization factor and the second optimization factor to obtain a key indicator value corresponding to the first bridge key indicator.

[0188] Optionally, in determining the reference first key indicator value corresponding to the first average measurement value, the health assessment unit 302 is specifically configured to:

[0189] Obtaining a normal measurement value set corresponding to the target bridge;

[0190] Determine a first normal measurement value corresponding to the first bridge key indicator in the normal measurement value set;

[0191] Obtaining a target operating time corresponding to the target bridge;

[0192] Determine a target attenuation factor corresponding to the target operating time;

[0193] Adjusting the first normal measurement value according to the target attenuation factor to obtain a second normal measurement value;

[0194] Determine a difference between the first average metering value and the second normal metering value to obtain a target metering difference;

[0195] The reference first key indicator value is determined according to the target measurement difference.

[0196] Optionally, in determining the first health value of the target bridge according to the m key indicator values, the health assessment unit 302 is specifically configured to:

[0197] Obtaining m key indicator thresholds corresponding to the m key indicator of the bridge; each key indicator threshold corresponds to a key indicator of the bridge;

[0198] Determine a key indicator value among the m key indicator values ​​that is greater than a corresponding key indicator threshold among the m key indicator thresholds, to obtain i key indicator values; i is a natural number less than or equal to m;

[0199] Determine the key indicator thresholds corresponding to the i key indicator values ​​among the m key indicator thresholds to obtain i key indicator thresholds;

[0200] Determine the difference between the i key indicator values ​​and the corresponding key indicator thresholds among the i key indicator thresholds to obtain i first difference values;

[0201] Obtaining a first weight set corresponding to the i first difference values; the first weight set includes i weight values; each weight value corresponds to a first difference value;

[0202] Determine a target difference value according to the first weight set and the i first differences;

[0203] Determine the first health value corresponding to the target difference.

[0204] Optionally, in the aspect of obtaining the first weight set corresponding to the i first differences, the health assessment unit 302 is specifically configured to:

[0205] Determine i reference weights according to the i first differences; the larger the first difference, the larger the reference weight;

[0206] Obtaining target metrology data sets corresponding to the i first difference values ​​in the m target metrology data sets, to obtain i target metrology data sets;

[0207] Determine the data volume of each target measurement data set in the i target measurement data sets to obtain i data volumes;

[0208] Determine an adjustment coefficient corresponding to each of the i data amounts to obtain i adjustment coefficients;

[0209] Corresponding reference weights among the i reference weights are adjusted according to the i adjustment coefficients to obtain the i weights.

[0210] Optionally, in determining the second health value of the target bridge according to the k bridge characteristic data, the health assessment unit 302 is specifically configured to:

[0211] Acquire a preset bridge feature database corresponding to the target bridge; the preset bridge feature database includes x standard bridge feature data; x is an integer not less than k;

[0212] Extracting corresponding standard bridge characteristic data from the preset bridge characteristic database according to the k bridge characteristic data to obtain k standard bridge characteristic data; each standard bridge characteristic data corresponds to one bridge characteristic data;

[0213] Calculating similarities between the k bridge characteristic data and corresponding standard bridge characteristic data among the k standard bridge characteristic data according to a preset similarity algorithm to obtain k similarities; each similarity corresponds to one bridge characteristic data;

[0214] Determine an average similarity corresponding to the k similarities;

[0215] Determine the second health value corresponding to the average similarity.

[0216] Optionally, in the aspect of collecting the target metering data of the target bridge within a preset time period by using the m types of metering sensors to obtain m target metering data sets, the collecting unit 301 is specifically used for:

[0217] Acquire metering data of the target bridge collected by a first metering sensor within the preset time period to obtain a first metering data set; the first metering sensor is any metering sensor among the m metering sensors;

[0218] Obtaining initial device attribute parameters corresponding to the first type of measurement sensor;

[0219] Obtaining target component materials of the target bridge to obtain at least one target component material;

[0220] Determine an influence parameter corresponding to each component material in the at least one target component material to obtain at least one influence parameter;

[0221] Adjusting the initial device attribute parameter according to the at least one influencing parameter to obtain a target device attribute parameter;

[0222] Determine a target fine-tuning coefficient corresponding to the target device attribute parameter;

[0223] The first metering data set is adjusted according to the target fine-tuning coefficient to obtain a target metering data set corresponding to the first metering sensor.

[0224] Optionally, in determining the target health value according to the first health value and the second health value, the health assessment unit 302 is specifically configured to:

[0225] Obtaining a first device accuracy parameter corresponding to the metering module;

[0226] Obtaining a second device accuracy parameter corresponding to the camera module;

[0227] Determining a reference first weight corresponding to the first device precision parameter, and determining a reference second weight corresponding to the second device precision parameter;

[0228] Determine a reference health value according to the reference first weight, the reference second weight, the first health value and the second health value;

[0229] Acquire target weather data of the target bridge within the preset time period;

[0230] Determining a first interference coefficient of the target weather data on the metering module;

[0231] Determining a second interference coefficient of the target weather data on the camera module;

[0232] The reference health value is adjusted according to the first interference coefficient and the second interference coefficient to obtain the target health value.

[0233] In a specific implementation, the bridge safety warning device 300 described in the embodiment of the present invention may also execute other implementations described in the bridge safety warning method provided in the above embodiment of the present invention, which will not be described in detail here.

[0234] See also Figure 4 , Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application, the electronic device includes a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface are interconnected through a bus, the one or more programs are stored in the memory and are configured to be executed by the processor, the one or more programs include instructions for executing other implementations described in the bridge safety warning method provided in the above embodiment of the present invention, which will not be repeated here.

[0235] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0236] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0237] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0238] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0239] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0240] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0241] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0242] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0243] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A bridge safety early warning method, characterized in that: Applied to a bridge monitoring system, the bridge monitoring system comprises: a control module, a metering module, a camera module, and an alarm module, the metering module comprises m types of metering sensors, m being an integer greater than 1; the method comprises: Obtaining m key bridge indicators corresponding to the m types of measurement sensors; The target metrological data of the target bridge within a preset time period are collected by the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge; The camera module collects bridge images of the target bridge within the preset time period to obtain k bridge images, where k is an integer greater than 1; Analyzing the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; and each key indicator value corresponds to a bridge key indicator; Determine a first health value of the target bridge according to the m key indicator values; Performing feature recognition on the k bridge images by using image recognition technology to obtain k bridge feature data; Determine a second health value of the target bridge according to the k bridge characteristic data; determining a target health value according to the first health value and the second health value; When the target health value is lower than a preset health value, executing a corresponding target alarm event according to the target health value through the alarm module; The m target measurement data sets are analyzed according to the m bridge key indicators to obtain m key indicator values, including: Acquire a first bridge key indicator and a first target metering data set; the first bridge key indicator is any bridge key indicator among the m bridge key indicators; the first target metering data set is a target metering data set corresponding to the first bridge key indicator among the m target metering data sets; Determine a first average measurement value and a first mean square error corresponding to the first target measurement data set; Determine a reference first key indicator value corresponding to the first average measurement value; Determining a first optimization factor corresponding to the first mean square error; Sampling the first target metering data set to obtain a plurality of sampled metering data; each sampled metering data corresponds to a sampling time; Perform straight line fitting according to the plurality of sampled metering data and the sampling time corresponding to each sampled metering data to obtain a first metering straight line; Obtaining a first slope corresponding to the first measurement straight line; determining a second optimization factor corresponding to the first slope; Optimizing the reference first key indicator value according to the first optimization factor and the second optimization factor to obtain a key indicator value corresponding to the first bridge key indicator; The step of determining a reference first key indicator value corresponding to the first average measurement value includes: Obtaining a normal measurement value set corresponding to the target bridge; Determine a first normal measurement value corresponding to the first bridge key indicator in the normal measurement value set; Obtaining a target operating time corresponding to the target bridge; Determine a target attenuation factor corresponding to the target operating time; Adjusting the first normal measurement value according to the target attenuation factor to obtain a second normal measurement value; Determine a difference between the first average metering value and the second normal metering value to obtain a target metering difference; The reference first key indicator value is determined according to the target measurement difference.

2. The method according to claim 1, characterized in that The determining the first health value of the target bridge according to the m key indicator values ​​includes: Obtaining m key indicator thresholds corresponding to the m key indicator of the bridge; each key indicator threshold corresponds to a key indicator of the bridge; Determine a key indicator value among the m key indicator values ​​that is greater than a corresponding key indicator threshold among the m key indicator thresholds, to obtain i key indicator values; i is a natural number less than or equal to m; Determine the key indicator thresholds corresponding to the i key indicator values ​​among the m key indicator thresholds to obtain i key indicator thresholds; Determine the difference between the i key indicator values ​​and the corresponding key indicator thresholds among the i key indicator thresholds to obtain i first difference values; Obtaining a first weight set corresponding to the i first difference values; the first weight set includes i weight values; each weight value corresponds to a first difference value; Determine a target difference value according to the first weight set and the i first differences; Determine the first health value corresponding to the target difference.

3. The method according to claim 2, characterized in that The obtaining of the first weight set corresponding to the i first differences includes: Determine i reference weights according to the i first differences; the larger the first difference, the larger the reference weight; Obtaining target metrology data sets corresponding to the i first difference values ​​in the m target metrology data sets, to obtain i target metrology data sets; Determine the data volume of each target measurement data set in the i target measurement data sets to obtain i data volumes; Determine an adjustment coefficient corresponding to each of the i data amounts to obtain i adjustment coefficients; Corresponding reference weights among the i reference weights are adjusted according to the i adjustment coefficients to obtain the i weights.

4. The method according to any one of claims 1 to 3, characterized in that: The determining the second health value of the target bridge according to the k bridge characteristic data comprises: Acquire a preset bridge feature database corresponding to the target bridge; the preset bridge feature database includes x standard bridge feature data; x is an integer not less than k; Extracting corresponding standard bridge characteristic data from the preset bridge characteristic database according to the k bridge characteristic data to obtain k standard bridge characteristic data; each standard bridge characteristic data corresponds to one bridge characteristic data; Calculating similarities between the k bridge characteristic data and corresponding standard bridge characteristic data among the k standard bridge characteristic data according to a preset similarity algorithm to obtain k similarities; each similarity corresponds to one bridge characteristic data; Determine an average similarity corresponding to the k similarities; Determine the second health value corresponding to the average similarity.

5. The method according to any one of claims 1 to 3, characterized in that: The target metering data of the target bridge within a preset time period are collected by the m types of metering sensors to obtain m target metering data sets, including: Acquire metering data of the target bridge collected by a first metering sensor within the preset time period to obtain a first metering data set; the first metering sensor is any metering sensor among the m metering sensors; Obtaining initial device attribute parameters corresponding to the first type of measurement sensor; Obtaining target component materials of the target bridge to obtain at least one target component material; Determine an influence parameter corresponding to each component material in the at least one target component material to obtain at least one influence parameter; Adjusting the initial device attribute parameter according to the at least one influencing parameter to obtain a target device attribute parameter; Determine a target fine-tuning coefficient corresponding to the target device attribute parameter; The first metrology data set is adjusted according to the target fine-tuning coefficient to obtain a target metrology data set corresponding to the first type of metrology sensor.

6. The method according to any one of claims 1 to 3, characterized in that: The determining a target health value according to the first health value and the second health value includes: Obtaining a first device accuracy parameter corresponding to the metering module; Obtaining a second device accuracy parameter corresponding to the camera module; Determining a reference first weight corresponding to the first device precision parameter, and determining a reference second weight corresponding to the second device precision parameter; Determine a reference health value according to the reference first weight, the reference second weight, the first health value and the second health value; Acquire target weather data of the target bridge within the preset time period; Determining a first interference coefficient of the target weather data on the metering module; Determining a second interference coefficient of the target weather data on the camera module; The reference health value is adjusted according to the first interference coefficient and the second interference coefficient to obtain the target health value.

7. A bridge safety early warning device, characterized in that: Applied to a bridge monitoring system, the bridge monitoring system comprises: a control module, a metering module, a camera module, and an alarm module, the metering module comprises m types of metering sensors, m being an integer greater than 1; the device comprises: a collection unit, a health assessment unit, and an early warning unit, wherein: The acquisition unit is used to obtain m key bridge indicators corresponding to the m types of metrological sensors; the target metrological data of the target bridge within a preset time period are collected by the m types of metrological sensors to obtain m target metrological data sets; each target metrological data set corresponds to a metrological sensor; the m target metrological data sets include at least one of the following: a load data set of the target bridge, a stress data set of the target bridge, a support displacement data set of the target bridge, and an environmental data set of the target bridge; the bridge image of the target bridge within the preset time period is collected by the camera module to obtain k bridge images; k is an integer greater than 1; The health assessment unit is used to analyze the m target measurement data sets according to the m bridge key indicators to obtain m key indicator values; each bridge key indicator corresponds to a target measurement data set; each key indicator value corresponds to a bridge key indicator; determine a first health value of the target bridge according to the m key indicator values; perform feature recognition on the k bridge images through image recognition technology to obtain k bridge feature data; determine a second health value of the target bridge according to the k bridge feature data; determine a target health value according to the first health value and the second health value; The early warning unit is used to execute a corresponding target alarm event according to the target health value through the alarm module when the target health value is lower than a preset health value; Wherein, in the aspect of analyzing the m target measurement data sets according to the m key indicators of the bridge to obtain the m key indicator values, the health assessment unit is specifically used for: Acquire a first bridge key indicator and a first target metering data set; the first bridge key indicator is any bridge key indicator among the m bridge key indicators; the first target metering data set is a target metering data set corresponding to the first bridge key indicator among the m target metering data sets; Determine a first average measurement value and a first mean square error corresponding to the first target measurement data set; Determine a reference first key indicator value corresponding to the first average measurement value; Determining a first optimization factor corresponding to the first mean square error; Sampling the first target metering data set to obtain a plurality of sampled metering data; each sampled metering data corresponds to a sampling time; Perform straight line fitting according to the plurality of sampled metering data and the sampling time corresponding to each sampled metering data to obtain a first metering straight line; Obtaining a first slope corresponding to the first measurement straight line; determining a second optimization factor corresponding to the first slope; Optimizing the reference first key indicator value according to the first optimization factor and the second optimization factor to obtain a key indicator value corresponding to the first bridge key indicator; Wherein, in determining the reference first key indicator value corresponding to the first average measurement value, the health assessment unit is specifically used for: Obtaining a normal measurement value set corresponding to the target bridge; Determine a first normal measurement value corresponding to the first bridge key indicator in the normal measurement value set; Obtaining a target operating time corresponding to the target bridge; Determine a target attenuation factor corresponding to the target operating time; Adjusting the first normal measurement value according to the target attenuation factor to obtain a second normal measurement value; Determine a difference between the first average metering value and the second normal metering value to obtain a target metering difference; The reference first key indicator value is determined according to the target measurement difference.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the program includes instructions for executing the steps in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 6.

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