Engineering safety intelligent monitoring cloud platform

Abstract

The invention relates to the technical field of engineering monitoring, in particular to an engineering safety intelligent monitoring cloud platform which comprises a data acquisition and transmission module, a data screening and processing module, a data processing and analysis module, a monitoring report generation module, a multi-platform interaction and integration module and a data storage module. Through the functions of data preliminary screening, secondary re-screening and multi-project intelligent analysis, platform data is subjected to multi-layer screening and intelligent analysis, a large amount of data input by a monitoring platform is screened, error and abnormal information and data in the large amount of data are removed, it is guaranteed that the data are real and reliable, and the data processing efficiency is improved. The influence of data abnormity on subsequent engineering safety judgment is avoided; according to the method, the connection data is transmitted and analyzed to obtain the transmission state and the interaction log, and when the interaction log corresponds to an error, the error report is generated based on the corresponding error item, so that interaction and integration of data of multiple platforms are facilitated, abnormal information of platform interaction is analyzed autonomously, and the rejection efficiency of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering monitoring, and particularly to an intelligent monitoring cloud platform for engineering safety. Background Technique

[0002] With the development of cities, it is becoming increasingly common to carry out underground engineering construction such as subways, civil air defenses, and basements of high-rise buildings in complex environments with dense building clusters and crisscross pipelines. All sectors of society have also put forward higher requirements for engineering safety. Currently, most monitoring data is stored in paper form. However, with the continuous progress of technology, various monitoring platforms have emerged like mushrooms after a spring rain, and various monitoring platforms are similar, and their basic functions mainly include data upload, data calculation, data storage, report generation, etc.

[0003] Traditional engineering safety monitoring platforms lack the function of intelligent data analysis. A large amount of data is not analyzed after storage, making it difficult to ensure the authenticity and reliability of the data. When the data is abnormal, it will affect the subsequent judgment of engineering safety. Moreover, in large-scale engineering construction or regional engineering monitoring, multiple different monitoring platforms may be used simultaneously, and it is difficult to conduct data interaction and integration between the platforms. Summary of the Invention

[0004] The present invention provides an intelligent monitoring cloud platform for engineering safety to solve the technical problems mentioned in the above background technique.

[0005] The present invention provides an intelligent monitoring cloud platform for engineering safety, including a data acquisition and transmission module, a data screening and processing module, a data processing and analysis module, a monitoring report generation module, a multi-platform interaction and integration module, and a data storage module.

[0006] The data acquisition and transmission module monitors project data through a data upload unit and transmits the acquired monitoring project data to the data storage module; the data upload unit supports mobile APP Bluetooth upload, automated upload, serial port upload, result method upload, and original file upload; the monitoring project data includes, but is not limited to, monitoring data of horizontal displacement, vertical displacement, underground water level, various stresses, anchor cable tension, deep horizontal displacement, cracks, and vacuum degree generated within the engineering project.

[0007] The data screening and processing module is used to obtain the monitoring project data, perform data screening and analysis on the monitoring project data to obtain engineering abnormal information, identify engineering abnormal points from the engineering abnormal information, generate corresponding abnormal information to be verified according to the engineering abnormal point information, and mark the remaining corresponding monitoring project data as preferred engineering monitoring data.

[0008] As a further improvement of the present invention, data screening and analysis are performed on the monitoring project data, specifically as follows: Data screening includes primary data screening and secondary re-screening. By performing primary data screening on the monitoring project data, the corresponding data compliance in the monitoring project data is obtained. When the data compliance of the monitoring project data corresponds to non-compliance, the monitoring project data corresponding to the non-compliance is marked as the initially selected information to be verified. For the monitoring project data with data compliance corresponding to compliance, secondary re-screening is performed to obtain data deviation. When the data deviation corresponds to data deviation, the corresponding re-screening information to be verified is generated. The initially selected information to be verified and the re-screening information to be verified are combined to obtain the abnormal information to be verified.

[0009] As a further improvement of the present invention, primary data screening is performed on the monitoring project data, and the specific primary screening method is as follows: Based on the monitoring project data, data measurement information is obtained. The data measurement information includes instrument accuracy, calibration data, inclinometer data, and equipment usage information; the pre-set standard measurement accuracy intervals corresponding to each monitoring project data are obtained, and the instrument accuracy corresponding to each monitoring project data is matched with the corresponding standard measurement accuracy interval. When the instrument accuracy corresponding to the monitoring project data exceeds the standard measurement accuracy interval, accuracy abnormal information is generated for the monitoring project data with accuracy abnormality; According to the calibration data, the calibration period and data measurement time points of each instrument and equipment are obtained, and the calibration status of each instrument is identified to obtain the current calibration status of each instrument. The calibration status includes normal calibration and out of calibration period. The data measurement time points of each monitoring project data are matched with the calibration period to obtain the corresponding calibration status. When the calibration status corresponds to out of calibration period, calibration abnormal information is generated for the monitoring project data with calibration abnormality; Based on the inclinometer data, the inclinometer hole depth values of each engineering period are obtained, and the inclinometer hole depth values corresponding to two adjacent engineering periods are compared in depth. If the inclinometer hole depth values are inconsistent, the corresponding inclinometer abnormal information is generated for the monitoring project data with inclinometer abnormality; The equipment usage information of the uploading equipment corresponding to each monitoring project data is obtained. The equipment usage information includes the uploading address and equipment number of the input equipment corresponding to each monitoring project data. The uploading addresses corresponding to the monitoring project data with the same equipment number are compared for coincidence to obtain the types of uploading addresses. The pre-set warning line for address types is obtained. When the types of uploading addresses corresponding to each uploading equipment exceed the warning line for address types, equipment abnormal information is generated for the monitoring project data with equipment abnormality; The monitoring project data with accuracy abnormality, calibration abnormality, inclinometer abnormality, and equipment abnormality are combined, and the corresponding monitoring project data are marked as non-compliant data.

[0010] As a further improvement of the present invention, secondary re-screening is performed on the monitoring project data corresponding to data compliance, and the specific re-screening method is as follows: Based on the monitoring project data corresponding to compliance, obtain the deep horizontal displacement data and the support axial force data corresponding to each monitoring project data; obtain the inclinometer probe length and the inclinometer tilt angle according to the deep horizontal displacement data, divide the inclinometer tube into multiple inclinometer measurement segments, and normalize the inclinometer probe length and the inclinometer tilt angle corresponding to a certain inclinometer measurement segment and use the linear displacement method formula to calculate the horizontal displacement of the building within the depth range of the inclinometer tube ; where, L represents the inclinometer probe length; represents the inclinometer tilt angle of the i-th inclinometer measurement segment; the value of n is a positive integer; identify the horizontal displacement to obtain the displacement direction at the top of the inclinometer tube, and generate corresponding inclinometer deviation information when the displacement direction of the inclinometer tube corresponds to moving outward from the foundation pit.

[0011] Based on the support axial force data, obtain the elastic moduli of concrete and steel bars, the cross-sectional areas of concrete and steel bars, and the self-vibration frequencies measured by the strain gauges, and use the concrete support axial force calculation formula to calculate the concrete support axial force ; where, represents the support axial force; represents the elastic moduli of concrete and steel bars; represents the cross-sectional areas of concrete and steel bars; represents the steel string type reinforcement meter / strain gauge constant; and both represent the self-vibration frequencies measured by the strain gauges; obtain the support axial force warning value given by the design, compare the concrete support axial force with the support axial force warning value, and generate corresponding axial force abnormal information when the concrete support axial force exceeds the support axial force warning value.

[0012] Obtain the single measurement change value corresponding to each compliance monitoring project data, and obtain the expected rate given by the design. Calculate the exceeding multiple of the single measurement change value exceeding the expected rate through a divider. When the exceeding multiple is greater than the preset multiple upper limit, generate corresponding data change abnormal information.

[0013] Set the inclinometer deviation information, the axial force abnormal information, and the data change abnormal information as output signals. When any one or more output signals appear in the monitoring project data corresponding to each compliance, mark the corresponding compliance monitoring project data as deviation monitoring project data, and mark the remaining compliance monitoring project data after removing the deviation monitoring project data part as preferred engineering monitoring data.

[0014] The data processing and analysis module divides the engineering points into multiple monitoring profiles, obtains the optimized engineering monitoring data, identifies the data acquisition locations of the corresponding monitoring data points for the optimized engineering monitoring data, matches the data acquisition locations corresponding to each monitoring data point with the positions of the monitoring profiles to obtain the position distances between the monitoring data points and the monitoring profiles, obtains the monitoring data points corresponding to the designed distance ranges for each monitoring profile, aggregates the monitoring data points within the corresponding distance ranges for each monitoring profile to form a profile point group, and performs multi-project monitoring analysis on the optimized engineering monitoring data of the profile point group corresponding to the monitoring profile to obtain safety alarm information.

[0015] As a further improvement of the present invention, the multi-project monitoring analysis is performed on the optimized engineering monitoring data of the profile point group corresponding to the monitoring profile, and the specific analysis steps are as follows: A1: Based on the optimized engineering monitoring data, perform a comprehensive analysis of the deep horizontal displacement and the horizontal displacement at the slope top. According to the schematic diagram of the horizontal displacement points and the corner points, assume the measuring points A (XA, YA) and B (XB, YB), DM1 and DM2 are the slope corner points, set a virtual section, and the monitoring points set in each straight line segment should be divided into the same section. For example, A and B are within the range of the DM1-DM2 section, and C, D, and E are within the range of the DM2-DM3 section; the DM1-DM2 section can be represented by the straight line equation and the distance from the monitoring point to the virtual section is calculated through the formula The distance from the monitoring point to the virtual section is the horizontal displacement distance at the slope top. Based on the formula in the above analysis calculate the deep horizontal displacement distance, input the horizontal displacement distance at the slope top and the deep horizontal displacement distance into a comparator for comparison to obtain the displacement distance difference, and generate displacement abnormal state information when the displacement distance difference exceeds the designed distance difference threshold.

[0016] A2: Based on the optimized engineering monitoring data, perform a comprehensive analysis of the axial force and the horizontal displacement at the slope top. According to the monitoring schematic diagram of the horizontal displacement points A and B at the slope top, obtain the optimized engineering monitoring data corresponding to the two monitoring points A and B and calculate the support axial force of the monitoring points through the support axial force calculation formula of the displacement method. The support axial force calculation formula of the displacement method is expressed as to calculate the support axial force of the monitoring points ; where, L0 represents the initial length of the concrete support beam; Ln represents the support length measured for the nth time; represents the concrete stress. When the concrete strength grade is less than or equal to C50, ; when the concrete strength grade is greater than C50, , where represents the standard value of the concrete cube compressive strength. It is expressed as the average value of the axial compressive strength; It is expressed as the concrete compressive strain when the concrete compressive stress reaches When calculating the value is less than 0.002, it is taken as 0.002; Based on the monitoring schematic diagram of the horizontal displacement at the top of the slope at points A and B, the displacements at both ends of the support are consistent with the horizontal displacements at both ends of the slope top. The axial force of the linear displacement method support is calculated through the linear displacement method formula in the above analysis. The difference between the axial force of the support at the monitoring point corresponding to the displacement calculation method and the axial force of the linear displacement method support is calculated to obtain the calculation difference value. When the calculation difference value is greater than the upper limit of the calculation difference given in the design, the corresponding abnormal axial force state information is generated.

[0017] A3: Based on the data storage module, the groundwater level data and the surface settlement data are obtained and analyzed. The groundwater level change signal of the groundwater level is identified from the groundwater level data. The groundwater level change signal includes the groundwater level rising signal, the groundwater level falling signal, and the groundwater level unchanged signal. Similarly, the corresponding surface settlement signal is identified from the surface settlement data. The surface settlement signal includes the surface rising signal, the surface falling signal, and the surface unchanged signal. The groundwater level change signal and the surface settlement signal are combined to obtain the combined state signal and are respectively marked as SX and DX. The combined state signal is expressed as (SX, DX). The pre-designed abnormal combined state signal is obtained. When the abnormal combined state information corresponds to the different change trends of the groundwater level change signal and the surface settlement signal, the corresponding surface settlement groundwater level abnormal information is generated.

[0018] A4: The displacement abnormal state information, the axial force abnormal state information, and the surface settlement groundwater level abnormal information are aggregated to generate the safety alarm information.

[0019] The monitoring report generation module is used to obtain the abnormal information to be verified, the non-compliant data, the deviation monitoring item data, and the safety alarm information, and identify the corresponding monitoring abnormal information from the abnormal information to be verified, the non-compliant data, the deviation monitoring item data, and the safety alarm information. The monitoring abnormal information includes the above abnormal information and the abnormal acquisition information corresponding to the abnormal data. The corresponding abnormal verification report is generated according to the abnormal acquisition information.

[0020] The multi-platform interaction integration module is used to obtain the connection data between the current cloud platform and the other platforms, and perform transmission analysis on the connection data to obtain the transmission state. An interaction log is generated according to the transmission state. When the interaction log corresponds to an error, an error report is generated based on the corresponding error item.

[0021] As a further improvement of the present invention, the transmission analysis of the connection data is carried out, and the specific transmission analysis steps are as follows: The connection data is decoded by a data decoder to obtain the connection information between the current platform and other platforms. The connection information includes connection parameters and data transmission parameters. The connection matching degree of the connection parameters between the current platform and other platforms is identified by a sensor. When the connection matching degree is less than the pre-designed matching lower limit value, corresponding matching exception information is generated.

[0022] The data transmission volume and transmission frequency between the current platform and other platforms are obtained according to the data transmission parameters. Based on the data transmission volume, the corresponding pre-transmission data volume and post-transmission data volume before and after data transmission are obtained. The transmission volume difference between the pre-transmission data volume and the post-transmission data volume is calculated by a subtractor. When the transmission volume difference is greater than the pre-given transmission volume difference threshold, a corresponding transmission exception signal is generated. Based on the transmission frequency, the normal transmission frequency range during the transmission between the current platform and other platforms is obtained. The transmission frequency between the current platform and other platforms is matched with the corresponding normal transmission frequency range. The transmission duration corresponding to the transmission frequency exceeding the normal transmission frequency range is marked as the abnormal transmission duration. When the proportion of the abnormal transmission duration in the total transmission duration exceeds the pre-designed threshold, corresponding frequency exception information is generated. The matching exception information, transmission exception signal, and frequency exception information are aggregated to generate a transmission status of transmission error alarm.

[0023] A data storage module stores the information and data generated by the above-mentioned modules through a random access memory.

[0024] In the technical solution provided by the present invention, compared with the prior art, the beneficial effects are as follows: 1. Through the functions of data preliminary screening, secondary re-screening, and multi-item intelligent analysis, the present invention conducts multi-layer screening and intelligent analysis on platform data, screens a large amount of data input by the monitoring platform, removes incorrect and abnormal information and data in the massive data, ensures the authenticity and reliability of the data, and avoids the impact of data anomalies on subsequent judgment of project safety.

[0025] 2. By obtaining the connection data between the current cloud platform and other platforms, analyzing the transmission of the connection data to obtain the transmission status, generating an interaction log according to the transmission status, and generating an error report based on the corresponding error item when the interaction log corresponds to an error, the present invention facilitates the interaction and integration of data of multiple platforms, autonomously analyzes the abnormal information of platform interaction, and improves the rejection efficiency of users. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following drawings used in the description of the embodiments will be briefly introduced. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present application.

[0027] Figure 1It is the principle block diagram of the present invention; Figure 2 It is the schematic diagram of the horizontal displacement points and corner points of the present invention; Figure 3 It is the schematic diagram of monitoring the horizontal displacements at points A and B at the slope top of the present invention. Specific embodiments

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0029] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Please refer to Figures 1-3 , in an embodiment of the present invention, an embodiment of an engineering safety intelligent monitoring cloud platform includes: The data acquisition and transmission module monitors the data of the monitoring items through the data upload unit and transmits the acquired monitoring item data to the data storage module; the data upload unit supports data upload via mobile APP Bluetooth, automated upload, serial port upload, result method upload, and original file upload, providing flexible and variable data upload modes; the monitoring item data includes, but is not limited to, the monitoring data of horizontal displacement, vertical displacement, groundwater level, various stresses, anchor cable tension, deep horizontal displacement, cracks, and vacuum degree generated within the engineering project.

[0030] The data screening and processing module is used to obtain the monitoring item data, perform data screening and analysis on the monitoring item data to obtain engineering anomaly information, identify engineering anomaly points from the engineering anomaly information, generate corresponding anomaly to-be-verified information according to the engineering anomaly point information, and mark the remaining corresponding monitoring item data as preferred engineering monitoring data.

[0031] Performing data screening and analysis on the monitoring item data specifically includes: data screening includes primary data screening and secondary re-screening. By performing primary data screening on the monitoring item data, the data compliance corresponding to the monitoring item data is obtained. When the data compliance corresponding to the monitoring item data is non-compliant, the non-compliant corresponding monitoring item data is marked as primary to-be-verified information. Secondary re-screening is performed on the monitoring item data with compliant data compliance to obtain data deviation. When the data deviation corresponds to data deviation, corresponding re-screening to-be-verified information is generated. The primary to-be-verified information and the re-screening to-be-verified information are combined to obtain anomaly to-be-verified information.

[0032] Performing primary data screening on the monitoring item data, and its specific primary screening method is as follows: Data measurement information is obtained based on the data of monitoring items, and the data measurement information includes instrument accuracy, calibration data, inclinometer data, and equipment usage information; the preset standard measurement accuracy intervals corresponding to the data of each monitoring item are obtained, and the instrument accuracy corresponding to the data of each monitoring item is matched with the corresponding standard measurement accuracy interval. When the instrument accuracy corresponding to the monitoring item data exceeds the standard measurement accuracy interval, accuracy anomaly information is generated for the monitoring item data with accuracy anomaly. The calibration periods and data measurement time points of each instrument and equipment are obtained according to the calibration data, the calibration status of each instrument is identified by the calibration period of each instrument, and the calibration status includes normal calibration and out of calibration period. The data measurement time points of the data of each monitoring item are matched with the calibration period to obtain the corresponding calibration status. When the calibration status corresponds to out of calibration period, calibration anomaly information is generated for the monitoring item data with calibration anomaly. Based on the inclinometer data, the inclinometer hole depth values of each engineering period are obtained, and the inclinometer hole depth values corresponding to two adjacent engineering periods are compared in depth. If the inclinometer hole depth values are inconsistent, corresponding inclinometer anomaly information is generated for the monitoring item data with inclinometer anomaly. The equipment usage information of the uploading equipment corresponding to the data of each monitoring item is obtained, and the equipment usage information includes the uploading address and equipment number of the input equipment corresponding to the data of each monitoring item. The uploading addresses corresponding to the data of each monitoring item with the same equipment number are compared for coincidence to obtain the types of uploading addresses. The preset warning line for address types is obtained. When the types of uploading addresses corresponding to each uploading equipment exceed the warning line for address types, equipment anomaly information is generated for the monitoring item data with equipment anomaly. The monitoring item data with accuracy anomaly, calibration anomaly, inclinometer anomaly, and equipment anomaly are aggregated, and the corresponding monitoring item data are marked as non-compliant data.

[0033] The monitoring item data corresponding to data compliance are subjected to secondary re-screening, and the specific re-screening method is as follows: Based on the monitoring item data corresponding to compliance, the deep horizontal displacement data and support axial force data corresponding to the data of each monitoring item are obtained; according to the deep horizontal displacement data, the inclinometer probe length and inclinometer tilt angle are obtained, the inclinometer tube is divided into multiple inclinometer measurement segments, and the inclinometer probe length and inclinometer tilt angle corresponding to a certain inclinometer measurement segment are normalized and the linear displacement method formula is used to calculate the horizontal displacement of the building within the depth range of the inclinometer tube ; where, L represents the inclinometer probe length; The inclination angle of the inclinometer represented as the i-th inclinometer measurement section; n takes positive integer values; the displacement direction at the top of the inclinometer tube is identified for the horizontal displacement, and when the displacement direction of the inclinometer tube corresponds to moving outward from the foundation pit, the corresponding inclinometer deviation information is generated; when conducting inclinometer measurements, the bottom of the inclinometer tube is not completely stable and may have a certain offset, resulting in a deviation in the measurement result and an overall offset.

[0034] Based on the support axial force data, obtain the elastic moduli of concrete and steel bars, the cross-sectional areas of concrete and steel bars, and the self-vibration frequencies measured by the strain gauges, and use the concrete support axial force calculation formula Calculate the concrete support axial force ; where Is represented as the support axial force; Is represented as the elastic moduli of concrete and steel bars; Is represented as the cross-sectional areas of concrete and steel bars; Is represented as the vibrating wire type steel bar gauge / strain gauge constant; And Are both represented as the self-vibration frequencies measured by the strain gauges; obtain the support axial force warning value given by the design, compare the concrete support axial force with the support axial force warning value, and when the concrete support axial force exceeds the support axial force warning value, generate the corresponding axial force abnormal information.

[0035] Obtain the single measurement change value corresponding to the data of each compliance monitoring item, and obtain the expected rate given by the design. Calculate the exceeding multiple of the single measurement change value exceeding the expected rate through a divider. When the exceeding multiple is greater than the preset multiple upper limit, generate the corresponding data change abnormal information.

[0036] Set the inclinometer deviation information, axial force abnormal information, and data change abnormal information as output signals. When any one or more output signals appear in the data of each compliance monitoring item, mark the corresponding compliance monitoring item data as deviation monitoring item data, and mark the remaining compliance monitoring item data after removing the deviation monitoring item data part as preferred engineering monitoring data.

[0037] The data processing and analysis module divides the engineering points into multiple monitoring profiles, obtains the preferred engineering monitoring data and identifies the data acquisition positions of the corresponding monitoring data points for the preferred engineering monitoring data. Match the data acquisition positions corresponding to each monitoring data point with the positions of the monitoring profiles to obtain the position distances between the monitoring data points and the monitoring profiles. Obtain the monitoring data points corresponding to the design-given distance ranges for each monitoring profile. Combine the monitoring data points within the distance ranges corresponding to each monitoring profile to obtain a profile point group. Conduct multi-item monitoring analysis on the preferred engineering monitoring data of the profile point group corresponding to the monitoring profile to obtain safety alarm information.

[0038] Perform multi-project monitoring analysis on the preferred engineering monitoring data corresponding to the monitoring section's corresponding section point group. The specific analysis steps are as follows: A1: Conduct a comprehensive analysis of the deep horizontal displacement and the horizontal displacement at the slope crest based on the preferred engineering monitoring data. According to the schematic diagrams of the horizontal displacement points and the corner points, assume the measuring point A (XA, YA) and the measuring point B (XB, YB), and DM1 and DM2 are the slope corner points. Set a virtual section. The monitoring points set in each straight line segment should be divided into the same section. For example, A and B are within the range of the DM1-DM2 section, and C, D, and E are within the range of the DM2-DM3 section; the DM1-DM2 section can be represented by a straight-line equation and calculate the distance from the monitoring point to the virtual section through the formula The distance from the monitoring point to the virtual section is the horizontal displacement distance at the slope crest; Based on the formula in the above analysis calculate the deep horizontal displacement distance. Input the horizontal displacement distance at the slope crest and the deep horizontal displacement distance into a comparator for comparison to obtain the displacement distance difference. When the displacement distance difference exceeds the distance difference threshold given in the design, generate displacement anomaly status information.

[0039] A2: Conduct a comprehensive analysis of the axial force and the horizontal displacement at the slope crest based on the preferred engineering monitoring data. According to the monitoring schematic diagrams of the horizontal displacement points A and B at the slope crest, obtain the preferred engineering monitoring data corresponding to the two monitoring points A and B and calculate the support axial force of the monitoring point through the calculation formula of the support axial force by the displacement method; The calculation formula of the support axial force by the displacement method is expressed as Calculate the support axial force of the monitoring point ; where, L0 represents the initial length of the concrete support beam; Ln represents the support length measured for the nth time; represents the concrete stress. When the concrete strength grade is less than or equal to C50, ; when the concrete strength grade is greater than C50, , where, represents the standard value of the concrete cube compressive strength; represents the average value of the axial compressive strength; represents the concrete compressive strain when the concrete compressive stress reaches . When the calculated value is less than 0.002, take it as 0.002; the concrete strength grade C50 means that the standard value of the concrete cube compressive strength is 50 MPa; Based on the monitoring schematic diagram of the horizontal displacements at points A and B on the slope top, the displacements at both ends of the support are consistent with the horizontal displacements at both ends of the slope top. The axial force of the support by the linear displacement method is calculated through the linear displacement method formula in the above analysis. The difference between the axial force of the support at the monitoring point corresponding to the displacement calculation method and the axial force of the support by the linear displacement method is calculated to obtain the calculation difference value. When the calculation difference value is greater than the upper limit of the calculation difference given in the design, the corresponding abnormal axial force status information is generated.

[0040] A3: Based on the data storage module, the groundwater level data and surface settlement data are obtained for analysis. The groundwater level change signal of the groundwater level is identified from the groundwater level data. The groundwater level change signal includes the water level rising signal, the water level falling signal, and the water level unchanged signal. Similarly, the corresponding surface settlement signal is identified from the surface settlement data. The surface settlement signal includes the surface rising signal, the surface falling signal, and the surface unchanged signal. The water level change signal and the surface settlement signal are combined to obtain the combined status signal and are respectively marked as SX and DX. The combined status signal is expressed as (SX, DX). The pre-designed abnormal combined status signal is obtained. When the abnormal combined status information corresponds to the different change trends of the water level change signal and the surface settlement signal, the corresponding surface settlement and groundwater level abnormal information is generated. During the process of the groundwater level dropping, the effective stress of the soil increases accordingly. At this time, the effective stress of the soil will increase, resulting in surface settlement. And during the process of engineering excavation, the supporting structure will undergo lateral deformation, resulting in a "settlement trough" on the surface. Therefore, under normal circumstances, if the groundwater level drops while the surface rises, a measurement error should have occurred.

[0041] A4: The displacement abnormal status information, the axial force abnormal status information, and the surface settlement and groundwater level abnormal information are aggregated to generate the safety alarm information.

[0042] The monitoring report generation module is used to obtain the abnormal information to be verified, non-compliant data, deviation monitoring project data, and safety alarm information, and identify the corresponding monitoring abnormal information from the abnormal information to be verified, non-compliant data, deviation monitoring project data, and safety alarm information. The monitoring abnormal information includes the above abnormal information and the abnormal acquisition information corresponding to the abnormal data. The corresponding abnormal verification report is generated according to the abnormal acquisition information.

[0043] The multi-platform interaction integration module obtains the connection data between the current cloud platform and other platforms, and conducts transmission analysis on the connection data to obtain the transmission status. An interaction log is generated according to the transmission status. When the interaction log corresponds to an error, an error report is generated based on the corresponding error item. For example, the error report corresponds to that the connection matching between the current platform and other platforms is too low and the matching strategy needs to be replaced, the transmission volume is abnormal and there is a transmission blockage, and the transmission frequency is too low corresponding to a connection failure.

[0044] Perform transmission analysis on the connection data. The specific steps of the transmission analysis are as follows: Decode the connection data through a data decoder to obtain the connection information between the current platform and other platforms. The connection information includes connection parameters and data transmission parameters; the connection parameters include, but are not limited to, network connection addresses, port numbers, and transmission protocols. Identify the connection matching degree of the connection parameters between the current platform and other platforms through sensors. When the connection matching degree is less than the pre-designed matching lower limit value, generate corresponding matching exception information.

[0045] Obtain the data transmission volume and transmission frequency between the current platform and other platforms according to the data transmission parameters; based on the data transmission volume, obtain the pre-transmission data volume and post-transmission data volume corresponding before and after data transmission, and calculate the transmission volume difference between the pre-transmission data volume and the post-transmission data volume through a subtractor. When the transmission volume difference is greater than the pre-given transmission volume difference threshold, generate a corresponding transmission exception signal; based on the transmission frequency, obtain the normal transmission frequency range when the current platform and other platforms transmit, match the transmission frequency between the current platform and other platforms with the corresponding normal transmission frequency range, and mark the transmission duration corresponding to the transmission frequency exceeding the normal transmission frequency range as an abnormal transmission duration. When the proportion of the abnormal transmission duration in the total transmission duration exceeds the pre-designed threshold, generate corresponding frequency exception information; collect the matching exception information, transmission exception signal, and frequency exception information to generate a transmission status of a transmission error alert.

[0046] The data storage module stores the information and data generated by the above-mentioned modules through a random access memory; the storage includes, but is not limited to, monitoring item data, abnormal information to be verified, non-compliant data, deviation monitoring item data, and security alert information.

[0047] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An engineering safety intelligent monitoring cloud platform, including a data acquisition and transmission module and a data storage module, characterized in that: Also includes: The data screening and processing module is used to obtain the monitoring project data and perform data screening and analysis on it to obtain engineering abnormality information, identify the engineering abnormality information to obtain engineering abnormality points, generate corresponding abnormal information to be verified according to the engineering abnormality point information, and mark the corresponding remaining monitoring project data as preferred engineering monitoring data; The data processing and analysis module divides the engineering point into multiple monitoring sections, obtains the preferred engineering monitoring data and identifies the corresponding data collection position of the monitoring data point, matches the data collection position corresponding to each monitoring data point with the position of the monitoring section to obtain the position distance between the monitoring data point and the monitoring section, obtains the corresponding monitoring data point within the distance range given by the corresponding design of each monitoring section, gathers the monitoring data points within the corresponding distance range of each monitoring section to obtain a section point group, and performs multi-project monitoring and analysis on the preferred engineering monitoring data of the monitoring section corresponding to the section point group to obtain safety alarm information; The monitoring report generation module is used to obtain abnormal information to be verified, non-compliant data, deviation monitoring project data and safety alarm information and identify and obtain corresponding monitoring abnormal information. The monitoring abnormal information includes each abnormal information and abnormal collection information corresponding to the abnormal data, and generates a corresponding abnormal verification report according to the abnormal collection information; The multi-platform interaction integration module is used to obtain the connection data between the current cloud platform and other platforms, and perform transmission analysis on the connection data to obtain the transmission status, generate an interaction log based on the transmission status, and generate an error report based on the corresponding error item when the interaction log corresponds to an error.

2. According to claim 1, an engineering safety intelligent monitoring cloud platform is characterized in that: The data acquisition and transmission module monitors the project data through the data upload unit, and transmits the collected monitoring project data to the data storage module; the data upload unit supports mobile phone APP Bluetooth upload, automatic upload, serial port upload, result method upload and original file upload; the monitoring project data includes the monitoring data of horizontal displacement, vertical displacement, groundwater level, various stresses, anchor cable tension, deep horizontal displacement, cracks and vacuum degree generated in the engineering project.

3. According to claim 1, an engineering safety intelligent monitoring cloud platform is characterized in that: The data screening and analysis of the monitoring project data is specifically as follows: data screening includes primary data screening and secondary re-screening, and the corresponding data compliance in the monitoring project data is obtained by performing primary data screening on the monitoring project data; when the data compliance of the monitoring project data corresponds to non-compliance, the monitoring project data corresponding to the non-compliance is marked as primary information to be verified, and the monitoring project data whose data compliance corresponds to compliance is secondary screened to obtain data deviation; when the data deviation corresponds to data deviation, the corresponding re-screened information to be verified is generated, and the primary information to be verified and the re-screened information to be verified are combined to obtain abnormal information to be verified.

4. The engineering safety intelligent monitoring cloud platform according to claim 3 is characterized in that: The specific initial screening method for the monitoring project data is as follows: Based on the monitoring project data, data measurement information is obtained; the pre-set standard measurement accuracy interval corresponding to each monitoring project data is obtained, and the instrument accuracy corresponding to each monitoring project data is overlapped and matched with the corresponding standard measurement accuracy interval. When the instrument accuracy corresponding to the monitoring project data exceeds the standard measurement accuracy interval, accuracy abnormality information is generated as the accuracy abnormality monitoring project data; According to the calibration data, the calibration cycle and data measurement time point of each instrument are obtained, the calibration cycle of each instrument is identified to obtain the current calibration status of each instrument, and the data measurement time point of each monitoring project data is matched with the calibration cycle to obtain the corresponding calibration status. When the calibration status corresponds to exceeding the calibration period, calibration abnormality information is generated as calibration abnormality monitoring project data; Based on the inclinometer data, the depth value of the inclinometer hole of each engineering period is obtained, and the depth comparison of the inclinometer hole depth values ​​corresponding to two adjacent engineering periods is performed. If the inclinometer hole depth values ​​are inconsistent, the corresponding inclinometer abnormality information is generated as the inclinometer abnormality monitoring project data; Obtain the equipment usage information of the uploading device corresponding to each monitoring project data, compare the upload addresses of each monitoring project data corresponding to the same equipment number to obtain the upload address type, obtain the pre-set address type warning line, and when the upload address type corresponding to each uploading device exceeds the address type warning line, generate equipment abnormality information as equipment abnormality monitoring project data; collect the accuracy abnormality monitoring project data, calibration abnormality monitoring project data, inclination abnormality monitoring project data and equipment abnormality monitoring project data, and mark the corresponding monitoring project data as non-compliant data.

5. The engineering safety intelligent monitoring cloud platform according to claim 1 is characterized in that: The data compliance corresponds to the monitoring project data that is compliant. The specific re-screening method is as follows: Based on the compliant monitoring project data, the deep horizontal displacement data and support axial force data corresponding to each monitoring project data are obtained; the inclinometer probe length and inclination angle are obtained according to the deep horizontal displacement data, the inclinometer tube is divided into multiple inclinometer measurement sections, and the inclinometer probe length and inclinometer inclination angle corresponding to a certain inclinometer measurement section are normalized and the linear displacement method formula is used to calculate the inclinometer probe length and inclinometer inclination angle. Calculate the horizontal displacement of the building within the depth range of the inclinometer tube ; Wherein, L represents the length of the inclinometer probe; It is represented as the inclination angle of the inclinometer in the i-th inclinometer measurement section; the value of n is a positive integer; the horizontal displacement is identified to obtain the displacement direction of the top of the inclinometer tube, and when the displacement direction of the inclinometer tube corresponds to moving out of the foundation pit, the corresponding inclinometer deviation information is generated; According to the support axial force data, the elastic modulus of concrete and steel bars, the cross-sectional area of ​​concrete and steel bars, and the natural frequency measured by strain gauges are obtained. The concrete support axial force calculation formula is used Calculate the concrete support axial force ;in, Expressed as support axial force; Expressed as the elastic modulus of concrete and steel; It is expressed as the cross-sectional area of ​​concrete and the cross-sectional area of ​​steel bars; Expressed as a steel string rebar gauge / strain gauge constant; and Both represent the natural frequency measured by the strain gauge; obtain the support axial force warning value given by the design, compare the concrete support axial force with the support axial force warning value, and generate corresponding axial force abnormality information when the concrete support axial force exceeds the support axial force warning value; Obtain the single measurement change value corresponding to the data of each compliance monitoring item, and obtain the expected rate given by the design, calculate the single measurement change value and the expected rate through a divider to obtain the excess multiple of the single measurement change value exceeding the expected rate, and generate corresponding data change abnormal information when the excess multiple is greater than the preset multiple upper limit; The inclination deviation information, axial force abnormality information and data change abnormality information are all set as output signals. When any one or more output signals appear in each compliant monitoring project data, the corresponding compliant monitoring project data will be marked as deviation monitoring project data, and the remaining compliant monitoring project data after removing the deviation monitoring project data will be marked as preferred engineering monitoring data.

6. An engineering safety intelligent monitoring cloud platform according to claim 1 or 5, characterized in that: The preferred engineering monitoring data of the monitoring section corresponding to the section point group is subjected to multi-project monitoring analysis, and the specific analysis is as follows: Based on the monitoring data of the selected project, the deep horizontal displacement and the slope top horizontal displacement are comprehensively analyzed to obtain the abnormal displacement status information; Based on the monitoring data of the selected project, the axial force and the horizontal displacement of the slope top are comprehensively analyzed to obtain the abnormal state information of the axial force; Based on the data storage module, the groundwater level data and the surface settlement data are obtained for analysis to obtain abnormal information of the surface settlement water level; The abnormal displacement status information, abnormal axial force status information and abnormal surface subsidence water level information are combined to generate safety alarm information.

7. The engineering safety intelligent monitoring cloud platform according to claim 6 is characterized in that: The comprehensive analysis of deep horizontal displacement and slope top horizontal displacement based on the optimal engineering monitoring data is as follows: According to the schematic diagram of horizontal displacement points and turning points, assuming that measuring point A (XA, YA), measuring point B (XB, YB), DM1 and DM2 are slope turning points, set a virtual segment surface, and the monitoring points set in each straight line segment should be divided into the same segment surface. Set A and B as the DM1-DM2 segment surface range, C, D, E as the DM2-DM3 segment surface range; the DM1-DM2 segment surface is calculated using the straight line equation Expressed by the formula The distance from the monitoring point to the virtual segment surface is calculated, and the distance from the monitoring point to the virtual segment surface is the horizontal displacement distance of the slope top; Based on the formula The deep horizontal displacement distance is calculated, and the slope top horizontal displacement distance and the deep horizontal displacement distance are input into the comparator for comparison to obtain the displacement distance difference. When the displacement distance difference exceeds the distance difference threshold given by the design, the displacement abnormal state information is generated.

8. The engineering safety intelligent monitoring cloud platform according to claim 6 is characterized in that: The comprehensive analysis of axial force and horizontal displacement of slope top based on the optimal engineering monitoring data is as follows: According to the monitoring diagram of the slope top horizontal displacement points A and B, the optimal engineering monitoring data corresponding to the two monitoring points A and B are obtained, and the supporting axial force of the monitoring point is calculated by the supporting axial force calculation formula of the displacement method; The calculation formula of the support axial force by displacement method is expressed as Calculate the support axial force of the monitoring point ; Wherein, L0 represents the initial length of the concrete support beam; Ln represents the support length measured for the nth time; Expressed as concrete stress, when the concrete strength grade is less than or equal to C50, ; When the concrete strength grade is greater than C50, ,in, Expressed as the standard value of concrete cube compressive strength; Expressed as the average value of axial compressive strength; Expressed as concrete compressive stress When the concrete compressive strain is calculated When the value is less than 0.002, it is taken as 0.002; Based on the fact that the displacements of the supports at both ends of the horizontal displacement points A and B at the top of the slope are consistent with the horizontal displacements of the tops of the slopes at both ends, the linear displacement method support axial force is calculated using the linear displacement method formula. The difference between the monitoring point support axial force corresponding to the displacement calculation method and the linear displacement method support axial force is calculated to obtain the calculated difference value. When the calculated difference value is greater than the upper limit of the calculated difference given by the design, the corresponding axial force abnormal state information is generated.

9. The engineering safety intelligent monitoring cloud platform according to claim 6 is characterized in that: The data storage module is used to obtain groundwater level data and surface settlement data for analysis, which is specifically as follows: The groundwater level data is identified to obtain the groundwater level change signal; the surface settlement data is identified to obtain the corresponding surface settlement signal; the water level change signal and the surface settlement signal are combined to obtain the combined state signal and marked as SX and DX respectively. The combined state signal is expressed as (SX, DX), and the abnormal combined state signal given in advance is obtained. The abnormal combined state information corresponds to the generation of corresponding surface settlement water level abnormality information when the change trends of the water level change signal and the surface settlement signal are different.

10. The engineering safety intelligent monitoring cloud platform according to claim 1 is characterized in that: The transmission analysis of the connection data is performed, and the specific transmission analysis steps are as follows: The connection data is decoded by a data decoder to obtain the connection information between the current platform and the other platforms, and the connection information includes connection parameters and data transmission parameters; the connection matching degree of the connection parameters between the current platform and the other platforms is identified by a sensor, and when the connection matching degree is less than the matching lower limit value given in advance, the corresponding matching exception information is generated; According to the data transmission parameters, the data transmission volume and transmission frequency of the current platform and the other platforms are obtained; based on the data transmission volume, the corresponding pre-transmission data volume and post-transmission data volume before and after the data transmission are obtained, and the transmission volume difference between the pre-transmission data volume and the post-transmission data volume is calculated by a subtractor. When the transmission volume difference is greater than a predetermined transmission volume difference threshold, a corresponding transmission abnormality signal is generated; based on the transmission frequency, the normal transmission frequency interval when the current platform and the other platforms are transmitted is obtained, and the transmission frequency of the current platform and the other platforms is matched with the corresponding normal transmission frequency interval, and the transmission duration corresponding to the transmission frequency exceeding the normal transmission frequency interval is marked as abnormal transmission duration, and when the proportion of the abnormal transmission duration to the total transmission duration exceeds the threshold given by the design, the corresponding frequency abnormality information is generated; the matching abnormality information, the transmission abnormality signal and the frequency abnormality information are combined to generate a transmission status as a transmission error alarm.

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