Engineering measurement intelligent management method, system and medium based on Internet of Things

Abstract

The present application relates to the technical field of intelligent management, and in particular to an intelligent management method, system and medium for engineering measurement based on the Internet of Things. By constructing an Internet of Things system containing monitoring nodes, gateways and cloud platforms, the present application can obtain the structural deformation data of each node in the engineering project area in real time, analyze the deformation data on the cloud platform, extract the change trend of the deformation parameters, compare the deformation parameters with the pre-set thresholds and historical data, so as to determine whether the deformation exceeds the limit or is abnormal, and then generate a monitoring report to achieve intelligent evaluation and early warning of the structural safety status.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent management, and in particular to an intelligent management method, system and medium for engineering measurement based on the Internet of Things. Background Art

[0002] At present, traditional engineering structure safety monitoring mainly relies on manual regular observation and detection. This method has a long monitoring time interval and cannot grasp the health status of the structure in real time. In recent years, the development of structural health monitoring systems has utilized sensors and data analysis technology to realize automatic monitoring of structural deformation such as subway foundation pits and bridges. However, the existing structural health monitoring system cannot realize intelligent evaluation of deformation at multiple points within the entire project range, and this situation needs to be further improved. Summary of the invention

[0003] In order to solve the problem that the existing structural health monitoring system cannot realize intelligent evaluation of deformation at multiple points within the entire project range, this application provides an engineering measurement intelligent management method, system and medium based on the Internet of Things, which adopts the following technical solutions:

[0004] In a first aspect, the present application provides an intelligent management method for engineering measurement based on the Internet of Things, which is applied to an Internet of Things network, wherein the Internet of Things network includes a plurality of monitoring nodes, a gateway, and a cloud management system arranged in an engineering project area, wherein the plurality of monitoring nodes monitor engineering deformation data in real time and upload the data to the gateway, wherein the gateway collects the engineering deformation data and uploads the data to the cloud management system, and wherein the cloud management system performs the following steps:

[0005] When engineering deformation data monitored and uploaded by a target monitoring node in real time is extracted, the deformation data of the target monitoring node is calculated to obtain a data change trend of the deformation parameter, wherein the target monitoring node is any one of the multiple monitoring nodes;

[0006] Obtaining a deformation parameter threshold corresponding to the target monitoring node, and judging whether the deformation parameter exceeds a limit according to the data change trend and the deformation parameter threshold;

[0007] Acquire historical deformation data corresponding to the target monitoring node, and determine whether the deformation parameter is abnormal according to the data change trend and the historical deformation data;

[0008] Generate a monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormal judgment result.

[0009] By adopting the above-mentioned technical solution, this application constructs an Internet of Things system containing monitoring nodes, gateways and cloud platforms, and can obtain the structural deformation data of each node in the engineering project area in real time. By analyzing the deformation data on the cloud platform, the changing trend of the deformation parameters is extracted, and the deformation parameters are compared with the pre-set thresholds and historical data, it is possible to determine whether the deformation exceeds the limit or is abnormal, and then generate a monitoring report to achieve intelligent assessment and early warning of the structural safety status.

[0010] Optionally, calculating the deformation data of the target monitoring node to obtain the data change trend of the deformation parameter specifically includes the following steps:

[0011] Performing denoising and filtering processing on the engineering deformation data to obtain processed engineering deformation data;

[0012] Setting a deformation parameter calculation time window, taking the processed engineering deformation data in time sequence, and calculating the deformation parameters, wherein the deformation parameter calculation time window is determined according to the deformation frequency, deformation amplitude range and deformation mode of the engineering project;

[0013] The deformation parameters calculated in each time window are recorded, and a curve chart showing the deformation parameters changing with time is drawn to obtain the data change trend of the deformation parameters.

[0014] By adopting the above-mentioned technical scheme, the present application performs denoising and filtering on the engineering deformation data to reduce random noise in the signal and improve the accuracy of parameter calculation. By determining and setting the deformation parameter calculation time window according to the deformation frequency, deformation amplitude range and deformation mode of the engineering project, the time window can be adapted to the parameter characteristics of different deformation modes. By drawing a time curve, the evolution trend of the deformation parameters can be clearly presented, providing a basis for subsequent judgment of over-limit and abnormalities.

[0015] Optionally, the deformation parameter includes a displacement parameter, a tilt parameter and a strain parameter; the step of obtaining a deformation parameter threshold value corresponding to the target monitoring node and judging whether the deformation parameter exceeds a limit according to the data change trend and the deformation parameter threshold value specifically includes the following steps:

[0016] Determine the displacement limit, tilt limit and strain limit of the target monitoring node according to the structural type, material parameters and bearing load of the target monitoring node;

[0017] Determine the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node according to the data change trend;

[0018] The real-time displacement parameter, the tilt parameter and the strain parameter are compared with their corresponding limit values ​​respectively. When any real-time parameter exceeds the corresponding limit value, it is determined that the deformation parameter exceeds the limit.

[0019] By adopting the above-mentioned technical scheme, the present application determines the displacement limit, inclination limit and strain limit of the target detection node according to the structural type, material parameters and bearing load of the target monitoring node, so that the limit judgment is more accurate, and through the over-limit judgment of different types of deformation parameters, the structural status is monitored more comprehensively. When the real-time deformation exceeds the limit, the deformation parameter is judged to be out of limit, so as to quickly issue an over-limit warning and improve the accident response speed.

[0020] Optionally, after comparing the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node with their respective corresponding limit values, the method also includes the following: when any parameter among the real-time displacement parameter, tilt parameter and strain parameter is close to the limit value, increasing the monitoring frequency of the target monitoring node.

[0021] By adopting the above-mentioned technical scheme, the present application realizes dynamic adjustment of the monitoring frequency according to the degree to which the parameters are close to exceeding the limit. For the monitoring nodes where the key parameters are approaching the limit value, the deformation state can be captured more sensitively by increasing the monitoring frequency, thereby improving the monitoring responsiveness to accelerated or sudden deformation, and at the same time realizing on-demand allocation of monitoring resources to avoid waste of system resources due to excessive monitoring.

[0022] Optionally, the acquiring of historical deformation data corresponding to the target monitoring node and judging whether the deformation parameter is abnormal according to the data change trend and the historical deformation data specifically includes the following steps:

[0023] Extracting a deformation parameter data set of the target monitoring node in a historical time period from a deformation monitoring database to obtain historical deformation data, wherein the historical time period is a time period that is the same as the current time period;

[0024] Determine the probability distribution of deformation parameters in the historical time period according to the historical deformation data;

[0025] Determining a current deformation parameter of the target monitoring node according to the data change trend;

[0026] Comparing the current deformation parameter with the deformation parameter probability distribution, and judging whether the current deformation parameter deviates from a normal range according to the comparison result;

[0027] If the current deformation parameter deviates from the normal range, the deformation parameter is determined to be abnormal.

[0028] By adopting the above technical solution, the present application obtains the deformation parameter data set of the historical period of the target monitoring node in the same period, analyzes the historical data to determine the probability distribution of the deformation parameter, calculates the current deformation parameter, and compares the current deformation parameter with the historical distribution to determine whether it is abnormal. Using the probability distribution, the parameter abnormality can be automatically determined.

[0029] Optionally, after generating the monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormality judgment result, the method further includes the following steps:

[0030] According to the monitoring reports corresponding to each monitoring node, a correlation matrix of multiple monitoring nodes in the project area is constructed;

[0031] Identifying the degree of correlation between deformation parameters of monitoring nodes in the correlation matrix;

[0032] According to the correlation degree, the deformation propagation path and the key monitoring nodes where the key influencing factors are located are classified and determined; wherein the monitoring nodes whose correlation degree is higher than the degree threshold are determined to be nodes on the same deformation propagation path; and the initial node of the deformation propagation path is determined to be the key monitoring node;

[0033] Increase the monitoring frequency of the key monitoring nodes.

[0034] By adopting the above-mentioned technical scheme, the present application constructs a correlation matrix between monitoring nodes through the monitoring reports corresponding to each monitoring node, and then identifies the degree of correlation between the deformation parameters of the monitoring nodes, determines the deformation propagation path and key influencing factors according to the degree of correlation, determines the initial node of the deformation propagation path as the key monitoring node, and increases the monitoring frequency of the key monitoring nodes, so as to intelligently identify the main influencing factors and key areas of structural deformation, realize key detection of key nodes, and discover signals of structural deformation development in advance.

[0035] Optionally, after classifying and determining the deformation propagation path and the key monitoring nodes where the key influencing factors are located according to the correlation degree, the method further includes the following steps:

[0036] Establishing a deformation propagation model based on data of a plurality of monitoring nodes in the deformation propagation path;

[0037] The deformation state in a future time period is predicted according to the deformation propagation model, and the predicted deformation state is sent to the engineering management personnel so that the engineering management personnel can manage the engineering project.

[0038] By adopting the above technical solution, the present application establishes a deformation propagation model based on the data of multiple monitoring nodes in the deformation propagation path, predicts the deformation state in the future time period based on the deformation propagation model, and sends the predicted deformation state to the engineering management personnel. It can deeply simulate and analyze the propagation law of structural deformation, realize the prediction and early warning of the structural deformation process, and the engineering management personnel can take measures in advance according to the prediction results to improve the safety of project operation; at the same time, combined with the determination of key node monitoring, it realizes the active prevention and control of structural safety, and improves the automation, intelligence and foresight of structural health monitoring and early warning.

[0039] Optionally, the method further comprises the steps of:

[0040] Setting alarm thresholds for the key monitoring nodes;

[0041] When the deformation parameter of the key monitoring node exceeds the alarm threshold, an over-threshold alarm message is sent to the engineering management personnel.

[0042] By adopting the above technical solution, the present application sets an alarm threshold for the key monitoring nodes. When the deformation parameters of the key monitoring nodes exceed the alarm threshold, an over-threshold alarm message is sent to the engineering management personnel, so that an alarm can be issued in time when the deformation exceeds the limit, thereby improving the response speed.

[0043] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for intelligent management of engineering measurements based on the Internet of Things when executing the computer program.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned engineering measurement intelligent management method based on the Internet of Things.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] 1. This application constructs an Internet of Things system containing monitoring nodes, gateways and cloud platforms, which can obtain the structural deformation data of each node in the project area in real time. By analyzing the deformation data on the cloud platform, the change trend of the deformation parameters is extracted, and the deformation parameters are compared with the pre-set thresholds and historical data to determine whether the deformation is excessive or abnormal, and then generate a monitoring report to achieve intelligent evaluation and early warning of the structural safety status;

[0047] 2. This application reduces random noise in the signal and improves the accuracy of parameter calculation by removing noise and filtering the engineering deformation data. By determining and setting the deformation parameter calculation time window according to the deformation frequency, deformation amplitude range and deformation mode of the engineering project, the time window can adapt to the parameter characteristics of different deformation modes. By drawing the time curve, the evolution trend of the deformation parameters can be clearly presented, providing a basis for subsequent judgment of over-limit and abnormality;

[0048] 3. This application determines the displacement limit, tilt limit and strain limit of the target detection node according to the structural type, material parameters and bearing load of the target monitoring node, so as to make the limit judgment more accurate. By judging the over-limit of different types of deformation parameters, the structural state is monitored more comprehensively. When the real-time deformation exceeds the limit, the deformation parameter is judged to be over-limit, so as to quickly issue an over-limit warning and improve the accident response speed;

[0049] 4. This application constructs a correlation matrix between monitoring nodes through the monitoring reports corresponding to each monitoring node, and then identifies the degree of correlation between the deformation parameters of the monitoring nodes, determines the deformation propagation path and key influencing factors based on the degree of correlation, determines the initial node of the deformation propagation path as the key monitoring node, and increases the monitoring frequency of key monitoring nodes, so as to intelligently identify the main influencing factors and key areas of structural deformation, realize key detection of key nodes, and discover signals of structural deformation development in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is an exemplary flow chart of an engineering measurement intelligent management method based on the Internet of Things according to an embodiment of the present application;

[0051] Figure 2 is another exemplary flow chart of an engineering measurement intelligent management method based on the Internet of Things according to an embodiment of the present application;

[0052] Figure 3 This is a module diagram of an engineering measurement intelligent management system based on the Internet of Things according to an embodiment of the present application;

[0053] Figure 4 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0055] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0056] In geotechnical engineering surveys, manual regular observation and testing are mainly relied on. This method has a long monitoring time interval and cannot grasp the health status of the structure in real time. In recent years, the development of structural health monitoring systems has utilized sensors and data analysis technology to realize automatic monitoring of structural deformations such as subway foundation pits and bridges, but it is unable to intelligently evaluate deformations at multiple points throughout the entire project, and the degree of intelligence is insufficient.

[0057] The present application provides an intelligent management method for engineering measurement based on the Internet of Things. By constructing an Internet of Things system containing monitoring nodes, gateways and cloud platforms, the structural deformation data of each node in the engineering project area can be obtained in real time. By analyzing the deformation data on the cloud platform, the change trend of the deformation parameters is extracted, and the deformation parameters are compared with pre-set thresholds and historical data to determine whether the deformation exceeds the limit or is abnormal. Then, a monitoring report is generated to achieve intelligent evaluation and early warning of the structural safety status.

[0058] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0059] The embodiment of the present application provides an engineering measurement intelligent management method based on the Internet of Things, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the terminal device is an electronic device, but is not limited to this, and can also be a smart tablet, a computer, etc. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application does not limit this.

[0060] Reference Figure 1, Figure 1 It is an exemplary flow chart of an intelligent management method for engineering measurement based on the Internet of Things in an embodiment of the present application.

[0061] An intelligent management method for engineering measurement based on the Internet of Things is applied to the Internet of Things network. The Internet of Things network includes multiple monitoring nodes, gateways and cloud management systems deployed in the engineering project area. The multiple monitoring nodes monitor engineering deformation data in real time and upload them to the gateway. The gateway collects engineering deformation data and uploads it to the cloud management system. The cloud management system performs the following steps:

[0062] S110 , when engineering deformation data monitored in real time and uploaded by the target monitoring node is extracted, the deformation data of the target monitoring node is calculated to obtain a data change trend of the deformation parameter.

[0063] Among them, the target monitoring node is any one of the multiple monitoring nodes. The monitoring node is provided with a sensor, including one or more of a displacement sensor, an inclinometer and a strain gauge, which is used to monitor one or more of the displacement parameters, inclination parameters and strain parameters of the monitoring node. In this embodiment, in the engineering scenario of the subway foundation pit, displacement sensors are arranged at positions such as the foundation pit supporting wall and the frame beam to monitor the lateral displacement of the wall or beam; inclinometers are arranged at positions such as the top of the supporting wall to monitor the inclination deformation of the wall, or inclination sensors are arranged on the road surface to detect the settlement of the road surface; strain gauges are buried in the concrete of the supporting wall to monitor the internal stress of the concrete. The sensor then collects the tiny deformation of the monitoring node in real time, and the collected raw signal is uploaded to the gateway at a preset frequency, such as 20 Hz per minute.

[0064] Specifically, step S110 specifically includes the following sub-steps:

[0065] S111, performing denoising and filtering processing on the engineering deformation data to obtain processed engineering deformation data.

[0066] Among them, random noise is eliminated through denoising, and then the signal is smoothed through low-pass filtering. The processed data is stored in the database for subsequent analysis and calculation.

[0067] S112, setting a deformation parameter calculation time window, obtaining the processed engineering deformation data in chronological order, and calculating the deformation parameters.

[0068] Among them, the deformation parameter calculation time window is determined according to the deformation frequency, deformation amplitude range and deformation mode of the engineering project. By determining the main frequency component of the deformation of the engineering structure, the first low-order vibration frequency of the structure is taken as the main frequency of the deformation, and the integer period of this frequency is taken as the time window length; by determining the maximum deformation that may occur in the structure, set a time window that contains the maximum deformation peak as much as possible; by analyzing the deformation mode of the engineering structure, the time correlation of periodic deformation is stronger, and a shorter window is taken, and a longer window is used for non-periodic deformation to cover various changes. The processed deformation data is taken in chronological order, the extracted signal segments are analyzed, and the displacement, tilt and strain parameters within the time period are calculated.

[0069] S113, recording the deformation parameters calculated in each time window, drawing a curve chart of the deformation parameters changing with time, and obtaining the data change trend of the deformation parameters.

[0070] Specifically, the deformation parameters calculated in each time window are recorded, arranged in chronological order, and graphs showing the changes of parameters over time are drawn, such as displacement-time and tilt-time graphs. Then, the changing trends of the parameters are analyzed through waveform curves to clearly present the evolution trend of the deformation parameters, providing a basis for subsequent judgment of over-limit and abnormalities.

[0071] S120: Obtain a deformation parameter threshold corresponding to the target monitoring node, and determine whether the deformation parameter exceeds a limit according to the data change trend and the deformation parameter threshold.

[0072] Specifically, step S120 includes the following sub-steps:

[0073] S121. Determine the displacement limit, tilt limit and strain limit of the target monitoring node according to the structural type, material parameters and bearing load of the target monitoring node.

[0074] Specifically, the structural type, material parameters, and bearing load of the monitoring node are used to determine the displacement limit, inclination limit, and strain limit. For example, according to the concrete material of the foundation pit supporting wall, the displacement limit of 30mm, the maximum tensile strain limit and the maximum compressive strain limit of 2000με and 1500με are obtained by checking the relevant specifications. According to the height and soil pressure, the inclination limit of 1 / 500 at the top of the wall is calculated.

[0075] S122. Determine the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node according to the data change trend.

[0076] Specifically, the corresponding vertical coordinates are read according to the horizontal coordinates at the current moment through the displacement-time, tilt-time and strain-time curves to determine the real-time displacement parameters, tilt parameters and strain parameters.

[0077] S123, comparing the real-time displacement parameter, the tilt parameter and the strain parameter with their corresponding limit values ​​respectively. When any real-time parameter exceeds the corresponding limit value, it is determined that the deformation parameter exceeds the limit.

[0078] Specifically, when one of the real-time displacement parameter, tilt parameter and strain parameter exceeds the corresponding limit value, it is determined that the deformation parameter is out of limit, and an early warning notification may be issued at this time.

[0079] Furthermore, when any of the real-time displacement parameters, tilt parameters, and strain parameters are close to the limit, the monitoring frequency of the target monitoring node is increased. By increasing the monitoring frequency of the monitoring nodes where the key parameters are close to the limit, the deformation state is captured more sensitively, the monitoring responsiveness to deformation acceleration or sudden changes is improved, and the monitoring resources are allocated on demand to avoid excessive monitoring and waste of system resources.

[0080] S130: Obtain historical deformation data corresponding to the target monitoring node, and determine whether the deformation parameter is abnormal based on the data change trend and the historical deformation data.

[0081] Specifically, step S130 includes the following sub-steps:

[0082] S131. Extracting a deformation parameter data set of a target monitoring node within a historical time period from a deformation monitoring database to obtain historical deformation data.

[0083] The deformation monitoring database stores historical deformation monitoring data. The historical time period is the same time period as the current time period, and the data of the same period can be compared according to the year, month, etc.

[0084] S132. Determine the probability distribution of deformation parameters in a historical time period according to the historical deformation data.

[0085] Specifically, historical displacement, tilt, and strain monitoring datasets are extracted to obtain the probability distribution of deformation parameters.

[0086] S133. Determine the current deformation parameter of the target monitoring node according to the data change trend.

[0087] Specifically, according to the current moment, the real-time displacement, tilt, and strain values ​​are read from the deformation parameter time curve to obtain the current deformation parameters of the target monitoring node.

[0088] S134: Compare the current deformation parameter with the deformation parameter probability distribution, and determine whether the current deformation parameter deviates from a normal range based on the comparison result.

[0089] Specifically, the deformation parameter probability distribution of the current deformation parameter is compared with that of the historical parameters of the same period, and the quantile of the current parameter in the deformation parameter probability distribution is calculated. If the quantile is at the edge of the distribution, it is determined that the current parameter deviates from the normal range.

[0090] S135. If the current deformation parameter deviates from the normal range, it is determined that the deformation parameter is abnormal.

[0091] Specifically, if the current deformation parameter is determined to deviate from the normal range, it can be determined that the parameter is currently in an abnormal state, triggering a corresponding abnormal processing and response mechanism.

[0092] S140. Generate a monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormality judgment result.

[0093] Specifically, by generating monitoring reports, engineering managers can view and conduct engineering management, and realize intelligent assessment and early warning of the structural safety status.

[0094] Further, see Figure 2 In some embodiments, after generating the monitoring report, the method further comprises the following steps:

[0095] S210: Construct a correlation matrix of multiple monitoring nodes in the project area according to the monitoring reports corresponding to each monitoring node.

[0096] Specifically, based on the monitoring report corresponding to each monitoring node, the data change trend of the deformation parameter of each monitoring node is obtained, and by analyzing the data change trend of each monitoring node, a correlation matrix between nodes is established to represent the deformation correlation between nodes.

[0097] Furthermore, for the monitoring point with the largest latest change and the monitoring point with the largest latest cumulative change value, an early warning can be issued simultaneously, so that engineering management personnel can pay more attention.

[0098] S220, identifying the correlation degree between the deformation parameters of the monitoring nodes in the association matrix.

[0099] Specifically, a correlation algorithm is used to quantify the deformation correlation between nodes. For example, the Pearson correlation coefficient method is used to calculate the linear correlation of the parameter time series between nodes.

[0100] S230. According to the degree of correlation, classify and determine the key monitoring nodes where the deformation propagation paths and key influencing factors are located.

[0101] Among them, monitoring nodes with correlation degrees higher than the degree threshold are determined as nodes on the same deformation propagation path, and the initial node of the deformation propagation path is determined as the key monitoring node. For example, if two nodes are deformed and the deformation time is the same or similar, they can be determined as nodes on the same deformation propagation path.

[0102] S240. Increase the monitoring frequency of key monitoring nodes.

[0103] Specifically, the monitoring frequency of key monitoring nodes is increased to achieve timely monitoring of deformation propagation.

[0104] Furthermore, a deformation propagation model can be established based on the data of multiple monitoring nodes in the deformation propagation path, and the deformation state in the future time period can be predicted based on the deformation propagation model. The predicted deformation state can be sent to engineering management personnel to facilitate engineering management personnel to manage the engineering projects. Combined with the determination of key node monitoring, active prevention and control of structural safety can be achieved, and the automation, intelligence and foresight of structural health monitoring and early warning can be improved.

[0105] Furthermore, different levels of alarm thresholds are set for key monitoring nodes. When the deformation parameters of key monitoring nodes exceed the alarm threshold, corresponding over-threshold alarm information is sent to engineering management personnel, such as notification through voice, text message, phone, sirens, etc., to improve response speed.

[0106] In some implementation examples, the present application enables engineering management personnel to manage through mini-programs. Users are authorized by administrators to directly call the deformation monitoring database to achieve synchronous real-time updates and synchronous push of warning information, report information and patrol information.

[0107] The implementation principle of an intelligent management method for engineering measurement based on the Internet of Things in an embodiment of the present application is as follows: by constructing an Internet of Things system containing monitoring nodes, gateways and a cloud platform, the present application can obtain the structural deformation data of each node in the engineering project area in real time, analyze the deformation data on the cloud platform, extract the change trend of the deformation parameters, and compare the deformation parameters with pre-set thresholds and historical data to determine whether the deformation exceeds the limit or is abnormal, and then generate a monitoring report to achieve intelligent evaluation and early warning of the structural safety status.

[0108] In the second aspect, the present application provides an engineering measurement intelligent management system based on the Internet of Things. The following describes the engineering measurement intelligent management system based on the Internet of Things in the present application in combination with the above-mentioned engineering measurement intelligent management method based on the Internet of Things. Figure 3 , Figure 3 This is a module diagram of an engineering measurement intelligent management system based on the Internet of Things in an embodiment of the present application.

[0109] An engineering measurement intelligent management system based on the Internet of Things, comprising:

[0110] The data change trend acquisition module 310 is used to calculate the deformation data of the target monitoring node and obtain the data change trend of the deformation parameter when the engineering deformation data monitored and uploaded by the target monitoring node in real time is extracted, wherein the target monitoring node is any one of the multiple monitoring nodes;

[0111] An over-limit judgment module 320 is used to obtain a deformation parameter threshold value corresponding to a target monitoring node and judge whether the deformation parameter exceeds the limit according to a data change trend and the deformation parameter threshold value;

[0112] The abnormality judgment module 330 is used to obtain the historical deformation data corresponding to the target monitoring node, and judge whether the deformation parameter is abnormal according to the data change trend and the historical deformation data;

[0113] The monitoring report generating module 340 is used to generate a monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormality judgment result.

[0114] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a smart management method for engineering measurement based on the Internet of Things is implemented.

[0115] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0117] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0118] 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. An intelligent management method for engineering measurement based on the Internet of Things, characterized in that: Applied to an Internet of Things network, the Internet of Things network includes multiple monitoring nodes, a gateway and a cloud management system deployed in the project area, the multiple monitoring nodes monitor the engineering deformation data in real time and upload it to the gateway, the gateway collects the engineering deformation data and uploads it to the cloud management system, and the cloud management system performs the following steps: When engineering deformation data monitored and uploaded by a target monitoring node in real time is extracted, the deformation data of the target monitoring node is calculated to obtain a data change trend of the deformation parameter, wherein the target monitoring node is any one of the multiple monitoring nodes; Obtaining a deformation parameter threshold corresponding to the target monitoring node, and judging whether the deformation parameter exceeds a limit according to the data change trend and the deformation parameter threshold; Acquire historical deformation data corresponding to the target monitoring node, and determine whether the deformation parameter is abnormal according to the data change trend and the historical deformation data; Generate a monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormal judgment result; The deformation parameters include displacement parameters, tilt parameters and strain parameters; the step of obtaining the deformation parameter threshold corresponding to the target monitoring node and judging whether the deformation parameter exceeds the limit according to the data change trend and the deformation parameter threshold specifically includes the following steps: Determine the displacement limit, tilt limit and strain limit of the target monitoring node according to the structural type, material parameters and bearing load of the target monitoring node; Determine the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node according to the data change trend; The real-time displacement parameter, the tilt parameter and the strain parameter are respectively compared with their corresponding limit values, and when any real-time parameter exceeds the corresponding limit value, it is determined that the deformation parameter exceeds the limit; The acquiring of the historical deformation data corresponding to the target monitoring node and judging whether the deformation parameter is abnormal according to the data change trend and the historical deformation data specifically includes the following steps: Extracting a deformation parameter data set of the target monitoring node in a historical time period from a deformation monitoring database to obtain historical deformation data, wherein the historical time period is a time period that is the same as the current time period; Determine the probability distribution of deformation parameters in the historical time period according to the historical deformation data; Determining a current deformation parameter of the target monitoring node according to the data change trend; Comparing the current deformation parameter with the deformation parameter probability distribution, and judging whether the current deformation parameter deviates from a normal range according to the comparison result; If the current deformation parameter deviates from the normal range, then the deformation parameter is judged to be abnormal; According to the monitoring reports corresponding to each monitoring node, a correlation matrix of multiple monitoring nodes in the project area is constructed; Identifying the degree of correlation between deformation parameters of monitoring nodes in the correlation matrix; According to the correlation degree, the deformation propagation path and the key monitoring nodes where the key influencing factors are located are classified and determined; wherein the monitoring nodes whose correlation degree is higher than the degree threshold are determined to be nodes on the same deformation propagation path; and the initial node of the deformation propagation path is determined to be the key monitoring node; Increase the monitoring frequency of the key monitoring nodes.

2. The engineering measurement intelligent management method based on the Internet of Things according to claim 1 is characterized in that: The calculation of the deformation data of the target monitoring node to obtain the data change trend of the deformation parameter specifically includes the following steps: Performing denoising and filtering processing on the engineering deformation data to obtain processed engineering deformation data; Setting a deformation parameter calculation time window, taking the processed engineering deformation data in time sequence, and calculating the deformation parameters, wherein the deformation parameter calculation time window is determined according to the deformation frequency, deformation amplitude range and deformation mode of the engineering project; The deformation parameters calculated in each time window are recorded, and a curve chart showing the deformation parameters changing with time is drawn to obtain the data change trend of the deformation parameters.

3. The engineering measurement intelligent management method based on the Internet of Things according to claim 1 is characterized in that: After comparing the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node with their respective corresponding limit values, the method also includes the following steps: when any parameter among the real-time displacement parameter, tilt parameter and strain parameter is close to the limit value, increasing the monitoring frequency of the target monitoring node.

4. The engineering measurement intelligent management method based on the Internet of Things according to claim 1 is characterized in that: After classifying and determining the deformation propagation path and the key monitoring nodes where the key influencing factors are located according to the correlation degree, the method further includes the following steps: Establishing a deformation propagation model based on data of a plurality of monitoring nodes in the deformation propagation path; The deformation state in a future time period is predicted according to the deformation propagation model, and the predicted deformation state is sent to the engineering management personnel so that the engineering management personnel can manage the engineering project.

5. The engineering measurement intelligent management method based on the Internet of Things according to claim 1 is characterized in that: The method further comprises the steps of: Setting alarm thresholds for the key monitoring nodes; When the deformation parameter of the key monitoring node exceeds the alarm threshold, an over-threshold alarm message is sent to the engineering management personnel.

6. An engineering measurement intelligent management system based on the Internet of Things, applying the engineering measurement intelligent management method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes: A data change trend acquisition module is used to calculate the deformation data of the target monitoring node and obtain the data change trend of the deformation parameter when the engineering deformation data monitored and uploaded by the target monitoring node in real time is extracted, wherein the target monitoring node is any one of the multiple monitoring nodes; An over-limit judgment module is used to obtain a deformation parameter threshold value corresponding to the target monitoring node, and judge whether the deformation parameter exceeds the limit according to the data change trend and the deformation parameter threshold value; An abnormality judgment module, used for obtaining the historical deformation data corresponding to the target monitoring node, and judging whether the deformation parameter is abnormal according to the data change trend and the historical deformation data; A monitoring report generation module, used to generate a monitoring report corresponding to the target monitoring node according to the over-limit judgment result and the abnormal judgment result; The deformation parameters include displacement parameters, tilt parameters and strain parameters; the over-limit judgment module performs the following steps: Determine the displacement limit, tilt limit and strain limit of the target monitoring node according to the structural type, material parameters and bearing load of the target monitoring node; Determine the real-time displacement parameter, tilt parameter and strain parameter of the target monitoring node according to the data change trend; The real-time displacement parameter, the tilt parameter and the strain parameter are respectively compared with their corresponding limit values, and when any real-time parameter exceeds the corresponding limit value, it is determined that the deformation parameter exceeds the limit; The abnormality judgment module performs the following steps: Extracting a deformation parameter data set of the target monitoring node in a historical time period from a deformation monitoring database to obtain historical deformation data, wherein the historical time period is a time period that is the same as the current time period; Determine the probability distribution of deformation parameters in the historical time period according to the historical deformation data; Determining a current deformation parameter of the target monitoring node according to the data change trend; Comparing the current deformation parameter with the deformation parameter probability distribution, and judging whether the current deformation parameter deviates from a normal range according to the comparison result; If the current deformation parameter deviates from the normal range, then the deformation parameter is judged to be abnormal; The system also performs the following steps: According to the monitoring reports corresponding to each monitoring node, a correlation matrix of multiple monitoring nodes in the project area is constructed; Identifying the degree of correlation between deformation parameters of monitoring nodes in the correlation matrix; According to the correlation degree, the deformation propagation path and the key monitoring nodes where the key influencing factors are located are classified and determined; wherein the monitoring nodes whose correlation degree is higher than the degree threshold are determined to be nodes on the same deformation propagation path; and the initial node of the deformation propagation path is determined to be the key monitoring node; Increase the monitoring frequency of the key monitoring nodes.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the engineering measurement intelligent management method based on the Internet of Things described in any one of claims 1-5 are implemented.

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