A large-span spatial structure health monitoring system and method

Abstract

The present application relates to the technical field of building structure health monitoring, and provides a system and method for monitoring the health of a large-span spatial structure, the system comprising: a first data acquisition terminal, a second data acquisition terminal, a data transmission module, a data analysis module, and a client application terminal, wherein the data analysis module is used to analyze the first current health state and the first predicted health state of the large-span spatial structure based on strain rate, deformation rate, strain data, and deformation data, and when it is determined that the first current health state or the first predicted health state is abnormal, a first warning message is generated and sent to the client application terminal; based on the theoretical value of the deformation data and the theoretical value of the strain data, the second current health state and the second predicted health state are analyzed, and a second warning message is generated and sent to the client application terminal. Based on this, the complexity of the system is reduced, and the dependence on the analysis algorithm is reduced, thereby improving the stability and reliability of the system.

Description

Technical Field

[0001] The present application relates to the technical field of building structure health monitoring, and in particular to a system and method for monitoring the health of a large-span spatial structure. Background Art

[0002] During the long-term service of large-span spatial structures, it is inevitable that the structure will degrade due to various factors such as environmental erosion, material aging, fatigue effects, etc. Therefore, long-term tracking and monitoring of large-span spatial structures throughout the entire service stage and analyzing the force variation laws of special components and key parts based on the measured data are of great significance for grasping the working status of the overall structure and further predicting the service life of the structure.

[0003] Due to the diverse forms, huge space, and complex force characteristics of large-span spatial structures, the overall response of the structure is different under different external loads, such as wind load, snow load, temperature effect, etc. Therefore, the current monitoring system usually introduces relevant environmental parameters to correct the structural data when analyzing the health status of the structure. For example, in the technical solution disclosed in the patent disclosure text CN109682417A, when analyzing the health risk probability, environmental influencing factors such as temperature, humidity, corrosion, wind speed, and sunshine are introduced.

[0004] It can be seen that traditional monitoring systems require the deployment of a large number of sensors of various types, which not only makes equipment management more difficult, but also highly dependent on analysis algorithms, increasing the difficulty and complexity of monitoring system development and requiring high equipment maintenance capabilities, thus having a certain negative impact on the overall reliability of the system. Summary of the invention

[0005] In order to ensure the reliability of the system, an embodiment of the present application provides a large-span spatial structure health monitoring system, including: a first data acquisition terminal, used to collect deformation data of large-span spatial structural members; a second data acquisition terminal, used to collect strain data of rods at supports of large-span spatial structures; a data transmission module, used to send the deformation data and the strain data to a data analysis module; the data analysis module is used to calculate the strain rate and the deformation rate according to the strain data and the deformation data, respectively, and analyze a first current health state and a first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain data, and the deformation data; when it is determined that the first current health state or the first predicted health state is abnormal, a first warning message is generated and sent to the client application end; and when judging the first current health state or the first predicted health state When the health status needs to be corrected, a second current health status and a second predicted health status are obtained based on the theoretical value of the deformation data and the theoretical value of the strain data, a second warning information is generated based on the second current health status and the second predicted health status, and the second warning information is sent to the client application end; wherein, the second warning information includes the first current health status, the first predicted health status, the second current health status, the second predicted health status and the reason for the status correction; the method for determining the theoretical value of the deformation data includes selecting a target parameter based on the similarity of meteorological data, and calculating the theoretical value of the deformation data based on the target parameter or compensating the deformation data based on the compensation value to obtain the theoretical value of the deformation data; the client application end is used to receive and send the first warning information and the second warning information.

[0006] Based on the above technical solution, the deformation data and strain data of the relevant components are obtained by deploying the first data acquisition terminal and the second data acquisition terminal, and the health status analysis and prediction are performed directly based on the deformation data and strain data, so as to quickly identify the abnormal situation, and give priority to issuing the first warning information through the client application end to notify the management personnel to pay attention to the abnormal situation in time. At the same time, by correcting the first current health state and the first predicted health state, other abnormal situations can be obtained more comprehensively and accurately, and the second warning information is sent to the management personnel so that the management personnel can further learn more comprehensive and accurate analysis. In addition, the above technical solution can realize the monitoring of the health status of the large-span spatial structure by directly based on the collected deformation data and strain data. The system only needs to connect to two data acquisition terminals to complete data acquisition. The equipment management is simple, the data processing complexity is low, and the system operation pressure is small, thereby improving the system reliability.

[0007] In one implementation, the system further includes a system management module for determining a layout of the first data acquisition terminal and the second data acquisition terminal according to structural characteristics of the large-span spatial structure.

[0008] Based on the above technical solution, workers can install various data collection terminals with the help of the installation instructions output by the system, which can provide operating standards for construction and acceptance.

[0009] In one embodiment, the system also includes a system management module, which is communicatively connected to the first data acquisition terminal, the second data acquisition terminal and the data transmission module respectively; after sending a data acquisition instruction to the first data acquisition terminal and the second data acquisition terminal and sending a data reporting instruction to the data transmission module, it determines whether the operating status of the first data acquisition terminal, the second data acquisition terminal and the data transmission module is normal according to the reception status of the acquisition completion notification, the reception completion notification and the reporting completion notification.

[0010] Based on the above technical solution, the system management module monitors the completion notifications corresponding to each instruction to determine whether the operating status of each data acquisition terminal and data transmission model is normal, thereby realizing status monitoring of each component of the system to ensure the normal operation of the system.

[0011] In one implementation, the first data acquisition terminal is a static level, and the system further includes a third data acquisition terminal for acquiring liquid temperature data of the static level.

[0012] In one embodiment, the outer cover of the sensor, liquid pipe and liquid reservoir of the static level is provided with a protective structure, and the third data acquisition terminal is arranged corresponding to the sensor, the liquid pipe and the liquid reservoir, and is installed in the gap between the protective structure and the sensor, liquid pipe and liquid reservoir of the static level.

[0013] Based on the above technical solution, by setting up a protective structure, physical protection of the components of the static level can be achieved, reducing the corrosion and interference of the external environment on the equipment and extending the service life. The third data acquisition terminal is installed in the gap, so that the collected liquid temperature data is closer to the actual liquid temperature, and the third data acquisition terminal is also protected.

[0014] In one implementation, the sensor of the static level is fixed to the large-span spatial structure through a mounting bracket.

[0015] Based on the above technical solution, the installation height of each sensor can be adjusted by the installation bracket to ensure that each sensor is located in the same horizontal plane.

[0016] Based on the same inventive concept, an embodiment of the present application also provides a large-span spatial structure health monitoring method, which is applied to the above-mentioned system and executed by the data analysis module, including: in response to a health monitoring request, respectively calculating the strain rate and the deformation rate according to the strain data and the deformation data; obtaining a first current health state and a first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain data, and the deformation data analysis; when it is determined that the first current health state or the first predicted health state is abnormal, generating a first warning message and sending it to the client application end; judging whether the first current health state and the first predicted health state need to be corrected; and when judging whether correction is needed In the positive case, a second current health state and a second predicted health state are obtained based on the theoretical value of the deformation data and the theoretical value of the strain data; a second warning information is generated based on the second current health state and the second predicted health state and sent to the client application; wherein the second warning information includes the first current health state, the first predicted health state, the second current health state, the second predicted health state and the reason for state correction. The method for determining the theoretical value of the deformation data includes selecting a target parameter based on the similarity of meteorological data, and calculating the theoretical value of the deformation data based on the target parameter, or compensating the deformation data based on a compensation value to obtain the theoretical value of the deformation data.

[0017] In one implementation, compensating the deformation data based on the compensation value to obtain the theoretical value of the deformation data includes: acquiring liquid temperature data, and constructing a liquid temperature image corresponding to the deformation data based on the liquid temperature data; the liquid temperature data includes all liquid temperature data collected by a third data acquisition terminal at the moment of collecting the deformation data; obtaining the compensation value of the deformation data based on the liquid temperature image and the deformation data; judging whether to correct the first current health state and the first predicted health state based on the compensation value; and if correction is determined to be required, obtaining the second current health state and the second predicted health state based on the compensation value analysis of the deformation data.

[0018] Based on the above technical solution, the compensation value of the deformation data is calculated by using the liquid temperature image. Compared with only using the temperature at the monitoring point for compensation, the influence of the overall liquid temperature on the sensor reading is taken into account, providing more complete liquid temperature data for the compensation value calculation, improving the accuracy of the compensation value calculation, and avoiding the one-sidedness of compensation based on a single point temperature.

[0019] In one implementation, the liquid temperature data also includes all liquid temperature data collected by the third data acquisition terminal within a preset time period before the deformation data collection moment; the construction of the liquid temperature image corresponding to the deformation data based on the liquid temperature data includes: constructing corresponding liquid temperature pictures according to each collection moment of the liquid temperature data; and integrating each of the liquid temperature pictures into a dynamic image according to the sequence of the collection moments as the liquid temperature image.

[0020] Based on the above technical solution, the liquid temperature data before the acquisition time is further introduced to construct the liquid temperature image, so that the interference of environmental changes on the liquid flow rate can be captured through the model, providing more comprehensive data support for the calculation of the compensation value.

[0021] In one implementation, the calculation formula of the deformation rate f is: f=(y2-y1) / T, wherein y2 is the current accumulated deformation; y1 is the accumulated deformation in the previous stage; and T is the time interval between the collection moments corresponding to y1 and y2. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic structural diagram of a large-span spatial structure health monitoring system provided by an embodiment of the present application is shown.

[0025] Figure 2 A flow chart of a large-span spatial structure health monitoring method provided by an embodiment of the present application is shown.

[0026] Figure 3 A schematic structural diagram of a large-span spatial structure health monitoring system provided by another embodiment of the present application is shown.

[0027] Figure 4 A flow chart of a large-span spatial structure health monitoring method provided by another embodiment of the present application is shown.

[0028] Figure 5 A flow chart of a compensation value acquisition method in another embodiment of the present application is shown.

[0029] Figure 6 A structural schematic diagram of a large-span spatial structure health monitoring system provided by another embodiment of the present application is shown.

[0030] Figure 7 A schematic diagram of the installation of a static level in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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.

[0032] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, and "first", "second" and various digital numbers are only distinguished for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0033] The features, structures or characteristics in this application may be combined in one or more embodiments in any suitable manner. In various embodiments of this application, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0034] Some optional features in the embodiments of the present application may be implemented independently in some scenarios without relying on other features to solve corresponding technical problems and achieve corresponding effects. They may also be combined with other features in some scenarios as needed.

[0035] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships. The implementation methods of this application do not constitute a limitation on the scope of protection of this application.

[0036] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0037] Please refer to Figure 1The large-span spatial structure health monitoring system provided in the embodiment of the present application includes: a first data acquisition terminal 11, a second data acquisition terminal 12, a data transmission module 13, a data analysis module 14 and a client application terminal 15, wherein the data transmission module 13 is respectively communicated with the first data acquisition terminal 11, the second data acquisition terminal 12 and the data analysis module 14, and the data analysis module 14 is communicated with the client application terminal 15.

[0038] Specifically, the first data acquisition terminal 11 is used to collect deformation data of large-span spatial structural members. The large-span spatial structural members are key components in the large-span spatial structure, generally spanning the upper part of the structure, and are the main monitoring objects. In the embodiment of the present application, the first data acquisition terminal 11 is a static level, and a differential pressure static level can be specifically selected.

[0039] The second data acquisition terminal 12 is used to collect strain data of the rods at the supports of the large-span spatial structure. The damage forms of the large-span spatial structure can be divided into two basic forms: overall collapse and local deformation damage. Overall collapse usually occurs at the supports. Before overall collapse, the internal force of the rods at the supports will increase significantly. By monitoring the stress value of the rods at the supports, it can be intuitively judged whether the large-span spatial structure has an overall collapse risk. In the embodiment of the present application, the second data acquisition terminal 12 is a stress gauge.

[0040] The data transmission module 13 is deployed on the network device, and is used to receive the deformation data and strain data collected by the first data acquisition terminal 11 and the second data acquisition terminal 12, and send the deformation data and the strain data to the data analysis module 14. In implementation, the data transmission module 13 can also be used to detect the operating status of the first data acquisition terminal 11 and the second data acquisition terminal 12, so as to timely discover equipment communication abnormalities and notify maintenance.

[0041] Specifically, when the data transmission module 13 reaches the preset data reporting node, it sends data collection requests to the first data collection terminal 11 and the second data collection terminal 12 in sequence according to the preset time intervals, and after receiving the response message, it records the receiving time of each response message, and then calculates the data transmission duration based on the receiving time and the sending time, and records it.

[0042] It is worth noting that each data collection terminal performs data collection according to a preset data collection node. The time corresponding to the data collection node is set before the data reporting node. That is to say, before each data reporting, each data collection terminal has completed the data collection and is in a waiting state for reporting. In this way, by staggering the time of data collection and data reporting, it can be ensured that each time the data is reported, each data collection terminal can respond directly based on local data. The receiving time of the response message can directly reflect the data transmission efficiency and eliminate the interference of data collection processing.

[0043] When each data collection terminal is in a dormant state, that is, when no data collection or data reporting is being performed, the data transmission module 13 sends a detection request to the first data collection terminal 11 and the second data collection terminal 12 at a preset time interval, and calculates the detection response duration according to the reception of the detection response message, and compares the detection response duration with the data transmission duration to analyze the communication status of each data collection terminal. When the detection response duration is greater than the data transmission duration and exceeds the response threshold, it can be determined that the corresponding data collection terminal is abnormal, and an abnormal recovery reminder is generated and sent to the administrator through the client application terminal 15. The response threshold can be determined according to the historical network communication status, such as the maximum difference between the historical detection response duration and the data transmission duration under normal conditions.

[0044] At the same time, in order to ensure the normal data collection, when the first data acquisition terminal 11 where the abnormality occurs is a static level, the data transmission module 13 can also send a first control instruction to each sensor in the static level to instruct each sensor to send deformation data directly to the data transmission module 13 through its own wireless network port at the next data reporting node. When the abnormality is recovered, a second control instruction can be sent to each sensor to instruct each sensor to send data to the data acquisition module based on the signal line. The data acquisition module is a component in the static level for uniformly sending and receiving collected data from each sensor. By default, each sensor is connected to the data acquisition module through a signal line and transmits the collected data to the data acquisition module, and the data acquisition module reports the collected data to the data transmission module 13 in a unified manner.

[0045] In one implementation, when the data transmission module 13 determines that the static level is abnormal, it can send wireless detection signals to each sensor on the abnormal static level based on the wireless local area network, and determine whether each sensor can communicate normally according to each received detection response message. For example, the sensor with missing detection response message is determined as a communication abnormality, and the corresponding abnormal information is added to the abnormal recovery notification, so that the management personnel can uniformly restore the components of the static level. Among them, the wireless detection signal can be sent together in the first control instruction to save communication energy consumption.

[0046] The data analysis module 14 is deployed on the control center server, which can be a cloud server or a local computer room, and is used to execute the large-span spatial structure health monitoring method provided in the embodiment of the present application. Please refer to Figure 2 , the method specifically includes the following steps.

[0047] S201, in response to a health monitoring request, respectively calculating a strain rate and a deformation rate according to strain data and deformation data.

[0048] In implementation, the health monitoring request may be issued by a user or automatically by the system.

[0049] When receiving a health monitoring request from the user, the data analysis module 14 immediately sends a real-time data collection request to the data transmission module 13. The data transmission module 13 first determines whether there is data to be reported. If so, the data to be reported is sent to the data analysis module 14. Otherwise, a real-time data collection request is directly sent to each data collection terminal to receive and forward the collected deformation data and strain data.

[0050] When receiving a health monitoring request automatically issued by the system, the data analysis module 14 directly performs monitoring and analysis based on the latest deformation data and strain data reported by the data transmission module 13 .

[0051] After acquiring the deformation data and strain data for this monitoring and analysis, the data analysis module 14 calculates the strain rate and deformation rate respectively.

[0052] In one example, the deformation variable y corresponding to each monitoring point can be calculated based on the following formula: x :

[0053] y x =y x测 -(y x1 +y x2 ) / 2

[0054] Among them, y x测 is the deformation data collected by the sensor at the monitoring point; x1 ,y x2 It is the deformation data collected by the sensors on both sides of the monitoring point. Based on the above formula, the deformation amount of the structure at the monitoring point relative to the monitoring points on both sides can be calculated, so it can better reflect the degree of local structural deformation.

[0055] Get the deformation variable y corresponding to the monitoring point x After that, the deformation rate f is calculated based on the following formula:

[0056] f=(y2-y1) / T

[0057] Among them, y1 is the cumulative deformation of the previous stage, which can be directly read from the database, and y1 is also calculated based on the above formula; y2 is the deformation of this time, which is calculated in real time based on the above formula. T is the time interval between the collection times corresponding to y1 and y2, and the collection time interval of the corresponding deformation data.

[0058] The second data acquisition terminal is a strain gauge, which can directly confirm the strain amount based on the collected data, obtain the change value of the strain amount by the difference between the current collected data and the most recent collected data, and calculate the strain rate by dividing the change value of the strain amount by the time interval.

[0059] S202, obtaining a first current health state and a first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain amount, and the deformation amount.

[0060] In implementation, the first current health state may be determined based on the strain rate threshold, the deformation rate threshold, the strain threshold, and the deformation threshold, respectively. For example, when all of the above values ​​do not exceed the corresponding thresholds, the first current health state is determined to be normal, otherwise, the first current health state is determined to be abnormal.

[0061] The first predicted health state is determined in the following manner: first, based on the strain rate and the deformation rate, the time required to reach the corresponding deformation threshold or strain threshold is predicted when deformation continues at the current rate.

[0062] For example, the time P required to reach the deformation threshold can be obtained based on the following formula:

[0063] P=(y ts -y x ) / f

[0064] Among them, y ts is the deformation threshold.

[0065] Then, according to the time length P, a first predicted health state is determined.

[0066] Specifically, the first predicted health state can be determined according to the size of the P value corresponding to each monitoring point. For example, when the number of monitoring points with a P value less than the predicted health threshold exceeds the warning threshold, the first predicted health state is determined to be abnormal; otherwise, it is normal. In one example, the initial values ​​of the predicted health threshold and the warning threshold are pre-set by the system, and can be adjusted accordingly as the service life of the large-span spatial structure increases. For example, the longer the service life, the larger the predicted health threshold and the smaller the warning threshold; in one example, the predicted health threshold can be determined based on the average abnormal repair time, that is, the historical average time from the discovery of the abnormality to the completion of the abnormal repair or elimination.

[0067] In another example, clustering can also be performed based on the P values ​​corresponding to all monitoring points, and then the first predicted health state can be determined in combination with the location clustering degree and number of similar monitoring points. For example, monitoring points with the same P value or a difference not exceeding a preset range are classified into one category, and the average distance from the center point of the same type of monitoring points to each edge point is used to characterize the degree of clustering. The smaller the average distance, the higher the degree of clustering. At the same time, the number of similar monitoring points is combined to determine the parameters used to judge the first predicted health state. For example, when clustering is performed according to the P value, if the number of clusters is less than the first threshold, the first predicted health state is determined to be abnormal; when the number of clusters is greater than the first threshold, but a certain degree of clustering exceeds the second threshold and the number of the same type exceeds the third threshold, the first predicted health state is determined to be abnormal. Based on this, the trend of local deformation and overall deformation risk can be predicted in a timely manner.

[0068] In the embodiment of the present application, the above thresholds can be obtained from system configuration information, which can be directly input by the user or set by the system default. The specific needs are determined according to the situation of the structure. It is understandable that the management personnel can adjust these thresholds according to the monitoring needs.

[0069] S203: When it is determined that the first current health state or the first predicted health state is abnormal, first warning information is generated and sent to the client application end.

[0070] Among them, the first warning information includes the abnormal type and the corresponding data display information, such as the deformation trend graph, the deformation rate change graph, etc.

[0071] Based on the above steps, by simply processing the collected deformation data and strain data, it is possible to quickly determine whether there are any abnormalities in the first current health state and the first predicted health state, and when an abnormality is identified, a first warning message is promptly issued through the client application terminal 15, so that management personnel can obtain the abnormality type and corresponding data information in a timely and intuitive manner through the client application terminal 15.

[0072] S204: Determine whether the first current health state and the first predicted health state need to be corrected.

[0073] In the embodiment of the present application, after determining the first current health state and the first predicted health state, the data analysis module 14 may continue to execute this step.

[0074] If it is determined that correction is needed, step S205 is executed; if it is determined that correction is not needed, the monitoring task is completed and a monitoring report corresponding to the monitoring task is sent to the client application terminal 15 for the user to view at any time.

[0075] In implementation, whether correction is needed can be determined by analyzing whether the strain data and deformation data conform to the corresponding historical data change trend. Specifically, whether correction is needed can be determined based on the difference between the theoretical value of the strain data, the theoretical value of the deformation data and the corresponding true value. Among them, the method for determining the theoretical value of the deformation data includes selecting a target parameter based on the similarity of the meteorological data, and calculating the theoretical value of the deformation data based on the target parameter.

[0076] In one example, a historical linear relationship between deformation data and time can be constructed based on historical deformation data and historical meteorological data of multiple historical periods. The parameters of the linear relationship of the current period are predicted based on the changing trends of the parameters in each historical linear relationship. The predicted parameter values ​​are corrected based on the similarity between the historical meteorological data and the current meteorological data to obtain historical corrected parameters. Then, according to the current meteorological data, target parameters are selected from the historical corrected parameters and the current parameters in the linear relationship constructed based on the deformation data of the current period. The theoretical value of the deformation data is calculated based on the target parameters. When the difference between the theoretical value and the measured value exceeds the preset error range, it is determined that the first current health state and the first predicted health state need to be corrected.

[0077] For example, based on the deformation data of August 2021, August 2022 and August 2023, three sets of linear relationships between deformation data and time are constructed: y=a1x+b1, y=a2x+b2, y=a3x+b3. According to the change trends of a1, a2, a3 and b1, b2, b3, the values ​​of parameters a0 and b0 corresponding to August 2024 are predicted respectively, and the correction values ​​n and m of a0 and b0 are obtained according to the difference between the mean of meteorological factors such as temperature, humidity, wind speed in each period and the mean corresponding to the current period. The larger the difference, the larger the correction value. At the same time, based on the deformation data collected in August 2024, the linear relationship between deformation data and time is constructed to obtain parameters a4 and b4. The similarity between the current meteorological data and the mean of the historical period and the mean of the current period is analyzed respectively. If the similarity with the mean of the current meteorological data is higher, a4 and b4 are selected to calculate the theoretical value of the deformation data. If the similarity with the historical mean is higher, a0n and b0m are selected to calculate the theoretical value of the deformation data. The relevant meteorological data is obtained by accessing the third-party platform interface, such as the meteorological information platform.

[0078] Based on this, the data analysis module 14 first determines the theoretical value of the deformation data, and judges whether to correct the first current health state and the first predicted health state based on the difference between the theoretical value of the deformation data and the current deformation data measurement value, thereby avoiding misjudgment caused by environmental factors affecting the measurement accuracy of the data acquisition terminal.

[0079] The calculation method of the theoretical value of the strain data and the method for judging whether correction is needed based on the theoretical value of the strain data are the same as the above method and will not be described in detail. The preset error range can be determined based on the reasonable error range of the corresponding data acquisition terminal. If the difference between the theoretical value and the measured value is within the reasonable error range of the corresponding data acquisition terminal, it indicates that the measured value is within the normal range and no correction is required.

[0080] S205 , analyzing and obtaining a second current health state and a second predicted health state based on the theoretical value of the deformation data or the theoretical value of the strain data.

[0081] Specifically, the second current health state and the second predicted health state may be obtained by analyzing the deformation data theoretical value or the strain data theoretical value used in step S204 to determine that the first current health state or the first predicted health state needs to be repaired.

[0082] In one implementation, the second current health state and the second predicted health state can be obtained based on the theoretical value of the deformation data and the theoretical value of the strain data. The analysis process and method can refer to the first current health state and the first predicted health state, and will not be repeated here. Based on this, the second current health state and the second predicted health state can be obtained by re-analyzing the theoretical value, which can minimize the situation where the abnormal state cannot be discovered in time due to inaccurate data collection.

[0083] S206: Generate second warning information based on the second current health status and the second predicted health status and send it to the client application.

[0084] The second warning information includes the first current health state, the first predicted health state, the second current health state, the second predicted health state, and the state correction reason. This allows the user to obtain the corresponding results of the entire analysis process and possible abnormal conditions of the data acquisition terminal through the client application terminal 15, so as to take timely countermeasures. For example, when the management personnel receive the second warning information, they can know that the data acquisition terminal may have a problem of poor monitoring accuracy through the state correction reason, so that the data acquisition terminal can be repaired in time.

[0085] It is understandable that when the current health status or predicted health status of a large-span spatial structure is abnormal, the location and type of the abnormality will be displayed in both the first warning information and the second warning information to facilitate management personnel to quickly locate the abnormal situation.

[0086] It can be seen that based on the above technical solution, the data analysis module 14 first quickly evaluates and predicts the health status of the large-span spatial structure according to the deformation data and strain data collected by the first data acquisition terminal and the second data acquisition terminal, and promptly issues a first warning message when an abnormality is found. At the same time, by combining the analysis of historical data, the health status is evaluated and predicted again based on the theoretical value to avoid omissions caused by inaccurate collected data. Since the entire analysis process is based only on deformation data and strain data, a more intuitive data presentation can be achieved, and since the calculation of other factors is not introduced, the deployment cost of other sensors is saved, the difficulty of equipment management is low, and there is no need to design algorithms to adapt to other characteristics of different large-span spatial structures. It has wide applicability and better scalability.

[0087] In another embodiment of the present application, a method for determining the theoretical value of deformation data is also provided, that is, when the first data acquisition terminal is a static level, the theoretical value can be calculated by obtaining the compensation value of the deformation data. Figure 3 The large-span spatial structure health monitoring system also includes a third data acquisition terminal 16.

[0088] The third data acquisition terminal 16 is in communication connection with the data transmission module 13 and is used for acquiring the liquid temperature data of the static level, and may be a temperature sensor.

[0089] In the embodiment of the present application, in order to reduce the impact of the external environment on the static level and extend the life of the equipment, the sensor, liquid pipe and external cover of the liquid reservoir of the static level are provided with a protective structure, and the protective structure can be made of a high-strength, corrosion-resistant and heat-insulating material. The third data acquisition terminal is arranged corresponding to the sensor, liquid pipe and liquid reservoir, and is installed in the gap between the protective structure and the sensor, liquid pipe and liquid reservoir of the static level. Preferably, the third data acquisition terminal is fixed on the protective structure, and the detection element is close to the liquid to be measured.

[0090] Please refer to Figure 4 Another embodiment of this embodiment provides a large-span spatial structure health monitoring method, which is applied to Figure 3 The system shown specifically includes the following steps:

[0091] S401, in response to a health monitoring request, respectively calculating a strain rate and a deformation rate according to strain data and deformation data.

[0092] S402, obtaining a first current health state and a first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain amount, and the deformation amount.

[0093] S403: When it is determined that the first current health state or the first predicted health state is abnormal, first warning information is generated and sent to the client application end.

[0094] S404: Determine whether to modify the first current health state and the first predicted health state based on the theoretical value of the deformation data.

[0095] In this embodiment, the theoretical value of the deformation data is calculated based on the compensation value. Please refer to Figure 5 , the method for obtaining the compensation value includes the following steps:

[0096] S501, acquiring liquid temperature data, and constructing a liquid temperature image corresponding to the deformation data based on the liquid temperature data.

[0097] The liquid temperature data includes all liquid temperature data collected by the third data collection terminal at the deformation data collection moment.

[0098] In one implementation, the third data acquisition terminal collects liquid temperature data based on the same collection node of the hydrostatic level and caches it locally, and upon receiving a request from the data transmission module 13, sends the cached data to the data transmission module 13. Based on this, it can be ensured that the liquid temperature data collected by the third data acquisition terminal is collected at the same time as the deformation data.

[0099] In one example, the construction of the liquid temperature image can be implemented based on the matplotlib library of Python.

[0100] For example, a CSV file or a Pandas DataFrame is first generated based on the coordinate points of the third data collection terminal and the corresponding liquid temperature data, the CSV file or the DataFrame is read using the Python pandas library, and then a thermal map is drawn by calling the matplotlib library as a liquid temperature image. The liquid temperature image is used to characterize the liquid temperature when the deformation data is collected, and can be used to calculate the compensation value for each deformation data collected at the same time.

[0101] In one example, the liquid temperature image of the deformation data of the monitoring point is constructed based on all the liquid temperature data of the static level that collects the deformation data. Accordingly, the liquid temperature image can be used to calculate the compensation value of each deformation data collected by the static level.

[0102] In another example, the liquid temperature image of the deformation data of the monitoring point is constructed based on the liquid temperature data collected by all third data collectors connected to the system. Accordingly, the liquid temperature image can be used to calculate the compensation value of all deformation data currently collected by the system.

[0103] In another implementation, the liquid temperature data also includes the liquid temperature data collected by the third data collection terminal within a preset time period before the deformation data collection moment. Accordingly, constructing the liquid temperature image corresponding to the deformation data based on the liquid temperature data includes: constructing the corresponding liquid temperature pictures according to each collection moment of the liquid temperature data, that is, constructing the corresponding liquid temperature pictures based on the liquid temperature data with the same collection moment, wherein the picture construction method is the same as the above-mentioned image construction method; integrating each of the liquid temperature pictures into a dynamic image according to the order of the collection moments as a liquid temperature image for calculating the compensation value of each deformation data. Based on this, the influence of temperature on the liquid can be more accurately simulated by acquiring the liquid temperature data before collection, and then the corresponding compensation value can be more accurately calculated.

[0104] It is understandable that during the monitoring process of the static level, changes in liquid temperature may cause deviations in the measurement results. By calculating and applying the temperature compensation value, the error caused by temperature changes can be effectively reduced, thereby improving the accuracy of the monitoring data. In an embodiment of the present application, by using the liquid temperature image to calculate the compensation value of each deformation data, the influence of the liquid temperature on the sensor performance can be more accurately considered, and the error introduced by inaccurate or insufficient representativeness of single-point temperature measurement can be reduced. At the same time, since the liquid temperature image covers a plurality of all relevant liquid temperature data, the calculation result is more stable and reliable. This stability helps to reduce the system instability caused by single-point temperature fluctuations or anomalies, and improves the stability and reliability of the entire measurement system. Furthermore, uniformly calculating the compensation value of each deformation data based on the liquid temperature image can better maintain the consistency of the data.

[0105] S502, obtaining a compensation value of the deformation data based on the liquid temperature image and the deformation data.

[0106] In one example, the liquid temperature image and the deformation data may be input into a compensation value calculation model to obtain a compensation value of the deformation data.

[0107] Specifically, deep learning models with time series processing capabilities such as recurrent neural networks (RNN), long short-term memory networks (LSTM), and gated recurrent units (GRU) can be selected to build a compensation value calculation model to capture long-term dependencies in time series data, which is particularly suitable for calculations of liquid temperature images with dynamic images.

[0108] The input layer of the model is the liquid temperature image and deformation data. The hidden layer adopts a multi-layer neural network structure. Each layer contains a certain number of neurons, and selects the linear rectification function (ReLU), sigmoid function or tanh function as the activation function. The output layer is the compensation value. The loss function can select the mean square error MSE and introduce the influence of the use time of the static level on the acquisition accuracy to evaluate the difference between the model prediction value and the actual compensation value. During the model training process, optimization algorithms such as Adam or SGD can be used to train the model and adjust the model parameters to minimize the loss function.

[0109] It is understandable that since the effect of liquid temperature on the static level is measurable and not limited to the type of spatial structure, the trained model can be reused in the monitoring of different large-span spatial structures, saving development costs.

[0110] After obtaining the corresponding compensation value, it can be determined whether the first current health state and the first predicted health state need to be corrected based on whether the deformation amount indicated by the theoretical value obtained after compensating the deformation data based on the compensation value exceeds the deformation threshold. When there is a situation where the deformation threshold is exceeded, it is determined that correction is required and step S405 is executed; otherwise, no correction is required, the monitoring task is completed, and the monitoring report corresponding to the monitoring task is sent to the client application end 15 for the user to view at any time.

[0111] S405, obtaining a second current health state and a second predicted health state based on theoretical value analysis of the deformation data.

[0112] Specifically, the compensation value and the deformation data can be calculated to obtain the compensated deformation data theoretical value, and the corresponding deformation rate can be calculated based on the theoretical value, and then the second current monitoring state and the second predicted health state can be analyzed based on the method described above.

[0113] S406: Generate second warning information based on the second current health status and the second predicted health status and send it to the client application.

[0114] Among them, in the method provided in this embodiment, the specific implementation method of steps S401, S402, S403 and S406 can refer to Figure 2 The corresponding steps in the method shown are not repeated here.

[0115] Based on the above technical solution, since all liquid-related components of the static level are protected by a protective structure, a third data acquisition terminal is arranged in the protective structure of the static level to obtain the liquid temperature data of the static level, and then the compensation value of each deformation data is calculated according to the liquid temperature image constructed by the liquid temperature data, so that a more accurate compensation value can be obtained, and then based on the compensated deformation data, it is determined whether the first current health state and the first predicted health state need to be corrected. In this way, the deformation risk can be fully obtained, and then the second current health state and the second predicted health state that are closer to the actual situation can be obtained based on the compensated deformation data analysis, while avoiding unnecessary repeated calculations. The second warning information is fed back to the user through the client application end, so that the user can not only obtain the abnormal state, but also know the output content of each analysis stage.

[0116] It is worth noting that in the present embodiment, the method for determining the theoretical value of the deformation data is different from that in the above-mentioned embodiments. In other embodiments of the present application, different theoretical values ​​can be obtained based on two determination methods, and whether to make corrections can be determined based on the two theoretical values ​​at the same time. When it is determined that corrections are needed, the second current health state and the second predicted health state can be obtained based on the analysis of the theoretical values ​​used to determine the corrections.

[0117] In other embodiments of this application, please refer to Figure 6 The large-span spatial structure health monitoring system also includes a system management module 17, which is used to determine the layout of the first data acquisition terminal and the second data acquisition terminal according to the structural characteristics of the large-span spatial structure.

[0118] Specifically, the system management module 17 can first analyze the overall layout, structural form, stress characteristics and possible deformation sensitive areas of the large-span spatial structure, and identify the areas in the structure that may produce large deformations by combining historical monitoring data of the same structure, including but not limited to stress concentration, weak material properties or parts that are greatly affected by external loads, and then determine each monitoring target and the corresponding monitoring method. The monitoring targets cover as many key parts and deformation sensitive areas of the structure as possible to fully reflect the overall state of the structure. According to the coordinate information of each monitoring target, combined with the structural model, the distribution map of each monitoring target on the model is obtained, and the layout method of the first data acquisition terminal and the second data acquisition terminal is determined based on the distribution map, so that the first data acquisition terminal and the second data acquisition terminal can monitor each monitoring target.

[0119] In one example, the monitoring target is a large-span spatial structure. The number of sensors of the static level can be determined according to the span value of the large-span spatial structure. The larger the span value, the more sensors of the static level are installed. The coordinates of each monitoring point are determined according to the number of sensors installed. Among them, the large-span spatial structure is usually a symmetrical structure, and the force and deformation distribution is also relatively symmetrical. Therefore, multiple sets of static levels can be deployed symmetrically, and at least one set of static levels is installed on each structural member. It is worth noting that since the sensors of the static level need to be arranged at the same height, that is, each sensor is located on the same horizontal plane, and the relevant structural members of the existing large-span spatial structure are flat and curved, it is necessary to adaptively adjust the installation method.

[0120] For a planar large-span spatial structure, several sets of static levels can be directly arranged symmetrically and evenly in different directions of the structural members, and several strain gauges can be arranged on the rods at the support positions.

[0121] The curved large-span space structure can arrange several static levels symmetrically and evenly in different directions of the structure. At the same time, the vertical mounting bracket can be used to adjust the installation height of each sensor to the same height, and several strain gauges can be arranged on the rods at the support position. The installation diagram of the static level can be specifically referred to Figure 7 Examples, such as Figure 7 As shown, a set of static level is installed on the arc-shaped structure 70, and the sensors 710 of the static level are evenly arranged on the lower surface of the arc-shaped structure 70, and a mounting bracket 72 is provided to keep the sensors 710 at the same horizontal position.

[0122] The system management module 17 can simulate the installation methods of various static levels and strain gauges by constructing models, and output corresponding installation instructions for user reference and implementation.

[0123] In addition, the system management module 17 can also be used to monitor the operating status of each component of the system.

[0124] Specifically, the system creates a task queue, which stores health monitoring tasks and corresponding execution times. When the system time reaches the execution time, the system management module 17 executes the corresponding health monitoring task: sends a data acquisition instruction to each data acquisition terminal to instruct each data acquisition terminal to collect data according to the issued data acquisition node and save it locally, and after completing the data acquisition, sends a collection completion notification to the system management module 17; sends a data reporting instruction to the data transmission module 13 to instruct the data transmission module 13 to request each data acquisition terminal to collect data according to the data reporting node, and sends the collected deformation data and strain data to the data analysis module 14, and after receiving the collected data and completing the data reporting, sends a reception completion notification and a reporting completion notification to the system management module 17 respectively; sends a health monitoring request to the data analysis module 14 to instruct the data analysis module 14 to perform health monitoring and analysis on the large-span spatial structure based on the collected data.

[0125] Among them, the collection node and the reporting node are determined according to the task execution time, and there is a certain time interval between the collection node and the reporting node. The length of the time interval is determined based on the historical data collection time and the collection data transmission time, and is generally set to be greater than the sum of the collection time and the collection data transmission time. This ensures that before the reporting node arrives, the data collection terminal has enough time to complete data collection, and the data transmission module 13 has enough time to obtain the collected data from the data collection terminal.

[0126] It is worth noting that in one example, after sending the corresponding instruction, the system management module 17 monitors the corresponding notification message, and determines whether the operation status of the corresponding terminal or module is normal based on the situation of each notification message received. Specifically, whether the operation status of each terminal and module is correct can be determined based on whether each notification message is received normally. At the same time, the interaction between the data acquisition terminal and the data transmission module 13 and whether the data acquisition processing status is normal can be determined by comparing the amount of collected data indicated in the collection completion notification, the reception completion notification and the reporting completion notification.

[0127] For example, the amount of collected data indicated in the current collection completion notification can be compared with the standard amount of collected data to determine whether the data amounts are consistent. If they are consistent, it indicates that it is normal. If they are inconsistent, it indicates that there is data missing or data redundancy, thereby determining that the data collection terminal is abnormal. The system management module 17 can feedback the abnormal information through the client application terminal 15, where the standard amount of collected data is the amount of collected data corresponding to each data collection terminal in a normal working state obtained based on the test.

[0128] For example, by determining whether the amount of collected data indicated in the collection completion notification, the reception completion notification, and the reporting completion notification are consistent, it can be determined whether there is an abnormality in the data transmission module 13, including communication abnormality or data processing abnormality with the data collection terminal. When the amount of collected data in the collection completion notification is greater than that in the reception completion notification, it indicates that data transmission is lost and positioning communication is abnormal; when the amount of collected data in the reception completion notification is inconsistent with the amount of collected data in the reporting completion notification, it indicates that the data processing capability of the data transmission module 13 is abnormal.

[0129] Based on this, the system management module 17 can monitor the working status of each component without increasing additional communication costs, so as to assist in maintaining the normal operation of each component of the system.

[0130] It is worth noting that the health monitoring tasks and corresponding execution times in the task queue include those automatically generated periodically and those temporarily created by the data analysis module 14 according to the calculated deformation rate or strain rate. Among them, the system can automatically generate corresponding health monitoring tasks according to the fixed monitoring period set by the user.

[0131] In implementation, after each deformation rate or strain rate is calculated, the data analysis module 14 can adjust the monitoring period based on the growth rate of the deformation rate or strain rate, wherein the greater the growth rate, the shorter the monitoring period. Furthermore, the data analysis module 14 can adjust the length of the monitoring period in combination with the real-time meteorological environmental factors and the changing rules of the deformation rate and strain rate corresponding to historical safety accidents. For example, if extreme weather or a safety accident occurs, the monitoring period is shortened.

[0132] Those skilled in the art can understand that all or part of the steps in the above-mentioned implementation method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of each implementation method of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0133] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A large-span spatial structure health monitoring system, characterized in that: The system comprises: The first data acquisition terminal is used to collect deformation data of large-span spatial structural members; The second data acquisition terminal is used to collect strain data of rods at the supports of the large-span spatial structure; A data transmission module, used for sending the deformation data and the strain data to a data analysis module; The data analysis module is used to calculate the strain rate and the deformation rate according to the strain data and the deformation data, respectively, analyze the first current health state and the first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain data, and the deformation data, and when it is determined that the first current health state or the first predicted health state is abnormal, generate a first warning message and send it to the client application end; and when it is determined that the first current health state or the first predicted health state needs to be corrected, analyze the second current health state and the second predicted health state based on the theoretical value of the deformation data and the theoretical value of the strain data, generate a second warning message based on the second current health state and the second predicted health state, and send the second warning message to the client application end; wherein the second warning message includes the first current health state, the first predicted health state, the second current health state, the second predicted health state and the state correction reason; the method for determining the theoretical value of the deformation data includes selecting a target parameter based on the similarity of meteorological data, and calculating the theoretical value of the deformation data based on the target parameter or compensating the deformation data based on the compensation value to obtain the theoretical value of the deformation data; The client application is used to receive and send the first warning information and the second warning information; A system management module is used to determine the layout of the first data acquisition terminal and the second data acquisition terminal according to the structural characteristics of the large-span spatial structure; the first data acquisition terminal is a static level, and the system also includes a third data acquisition terminal, which is used to collect liquid temperature data of the static level; the sensor, liquid pipe and liquid reservoir of the static level are provided with a protective structure on the outer cover, and the third data acquisition terminal is provided corresponding to the sensor, the liquid pipe and the liquid reservoir, and is installed in the gap between the protective structure and the sensor, the liquid pipe and the liquid reservoir of the static level; the sensor of the static level is fixed to the large-span spatial structure through a mounting bracket.

2. The system according to claim 1, characterized in that The system management module is respectively connected to the first data acquisition terminal, the second data acquisition terminal and the data transmission module for communication; Used to determine whether the operating status of the first data acquisition terminal, the second data acquisition terminal and the data transmission module is normal according to the reception status of the acquisition completion notification, the reception completion notification and the reporting completion notification after sending a data acquisition instruction to the first data acquisition terminal and the second data acquisition terminal and sending a data reporting instruction to the data transmission module.

3. A method for monitoring the health of a large-span spatial structure, characterized in that: The method is applied to the system according to claim 1 or 2 and is executed by the data analysis module, comprising: In response to a health monitoring request, respectively calculating a strain rate and a deformation rate according to the strain data and the deformation data; Obtaining a first current health state and a first predicted health state of the large-span spatial structure based on the strain rate, the deformation rate, the strain data, and the deformation data analysis; When it is determined that the first current health state or the first predicted health state is abnormal, generating first warning information and sending it to the client application end; determining whether the first current health state and the first predicted health state need to be corrected; In the case where correction is determined to be necessary, obtaining a second current health state and a second predicted health state based on the theoretical value of the deformation data and the theoretical value of the strain data; A second warning message is generated based on the second current health state and the second predicted health state and sent to the client application end; wherein the second warning message includes the first current health state, the first predicted health state, the second current health state, the second predicted health state and the reason for the state correction; the method for determining the theoretical value of the deformation data includes selecting a target parameter based on the similarity of meteorological data, and calculating the theoretical value of the deformation data based on the target parameter, or compensating the deformation data based on a compensation value to obtain the theoretical value of the deformation data.

4. The method according to claim 3, characterized in that: The compensating the deformation data based on the compensation value to obtain the theoretical value of the deformation data includes: Acquire liquid temperature data, and construct a liquid temperature image corresponding to the deformation data based on the liquid temperature data; the liquid temperature data includes all liquid temperature data collected by the third data collection terminal at the deformation data collection time; obtaining the compensation value based on the liquid temperature image and the deformation data; The deformation data is compensated according to the compensation value to obtain a theoretical value of the deformation data.

5. The method according to claim 4, characterized in that The liquid temperature data also includes all liquid temperature data collected by the third data collection terminal within a preset time period before the deformation data collection moment; and constructing the liquid temperature image corresponding to the deformation data based on the liquid temperature data includes: According to each collection moment of the liquid temperature data, corresponding liquid temperature pictures are constructed respectively; According to the order of the acquisition moments, the liquid temperature pictures are integrated into a dynamic image as the liquid temperature image.

6. The method according to claim 3, characterized in that The calculation formula of the deformation rate f is: f=(y2-y1) / T Among them, y2 is the current accumulated deformation; y1 is the accumulated deformation in the previous stage; T is the time interval between the acquisition moments corresponding to y1 and y2.

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