Tunnel construction safety monitoring system based on Internet of Things

Abstract

The invention discloses a tunnel construction safety monitoring system based on the Internet of Things, and belongs to the technical field of tunnel construction safety monitoring. The system comprises a data acquisition module, a data processing and mapping module, an early warning information processing module, a data analysis and comparison module and a real-time monitoring and notification module. The data acquisition module is responsible for acquiring tunnel construction monitoring data and monitoring point position data; the data processing and mapping module is used for mapping historical tunnel construction monitoring data and historical monitoring point position data to construct a historical data set; the early warning information processing module divides the early warning information into false early warning information and real early warning information; the data analysis and comparison module performs further data analysis and comparison on the false early warning information and the real early warning information; and the real-time monitoring and notification module obtains real-time tunnel construction monitoring data and real-time monitoring point position data, and notifies related personnel to process or adjust.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction safety monitoring, and specifically to an Internet of Things-based tunnel construction safety monitoring system. Background Art

[0002] With the rapid development of the Internet of Things, its applications in various fields are becoming increasingly widespread. It has extended from the initial computer networks and communication fields to multiple fields such as industry, home, medical, transportation, and agriculture. Currently, tunnel construction safety monitoring also benefits from the continuous progress of Internet of Things technology. In the field of tunnel construction safety monitoring, Internet of Things technology plays an important role. It can help relevant personnel to grasp various data during the tunnel construction process in real time, thereby effectively preventing the occurrence of safety accidents.

[0003] Although the existing tunnel construction safety monitoring systems can meet the current needs to a certain extent, there are still certain defects, specifically manifested as follows: During the tunnel construction process, data monitoring points are usually determined in advance. However, when new risk points appear or the monitoring points need to be adjusted during the actual construction process, the traditional tunnel construction safety monitoring systems cannot respond flexibly. Relevant staff need to adjust the monitoring points, which often takes a lot of time and there are certain human errors. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet of Things-based tunnel construction safety monitoring system to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] An Internet of Things-based tunnel construction safety monitoring system, the system includes: a data acquisition module, a data processing and mapping module, an early warning information processing module, a data analysis and comparison module, and a real-time monitoring and notification module;

[0007] The data acquisition module is responsible for obtaining tunnel construction monitoring data and monitoring point location data; the data processing and mapping module maps the historical tunnel construction monitoring data and historical monitoring point location data one by one to construct a historical data set; the early warning information processing module divides the early warning information into false early warning information and true early warning information according to the subsequent processing results of the early warning information recorded in the historical tunnel construction safety log; the data analysis and comparison module conducts further data analysis and comparison on the false early warning information and true early warning information to obtain the difference index between the false early warning information and the true early warning information of the same type, and obtains the monitoring point adjustment data of the false early warning information according to the difference index; the real-time monitoring and notification module obtains the real-time tunnel construction monitoring data and real-time monitoring point location data, and calculates the similarity between the early warning category of the early warning information and the historical early warning information; according to the calculation result, it notifies the relevant personnel to handle it or adjusts according to the monitoring point adjustment data.

[0008] Furthermore, the data acquisition module includes a sensor and monitoring equipment unit and a data communication unit;

[0009] The sensor and monitoring equipment unit is responsible for collecting tunnel construction monitoring data and monitoring point location data; the data communication unit is responsible for transmitting the collected data to the data processing and mapping module; among them, the tunnel construction monitoring data includes displacement and deformation data, hydrographic and water quality data, temperature and humidity data, pressure data, vibration data, and environmental pollution data, etc.

[0010] The data processing and mapping module includes a data cleaning unit, a data storage unit, and a mapping and association unit;

[0011] The data cleaning unit preprocesses and cleans the collected raw data, removes interference factors such as outliers and noise, and ensures the accuracy and consistency of the data; the data storage unit stores the cleaned data in the database for subsequent data analysis and query operations; the mapping and association unit maps the historical tunnel construction monitoring data and historical monitoring point location data one by one to construct a historical data set.

[0012] Furthermore, the mapping and association unit includes:

[0013] Obtain historical tunnel construction monitoring data and historical monitoring point location data from the original tunnel construction safety monitoring system, randomly number the historical monitoring point location data, and map the historical tunnel construction monitoring data and the historical monitoring point location data one-to-one to form a historical dataset A, and A = {a1, a2,..., an}, where a1 represents the historical monitoring point location data numbered 1 and the corresponding historical tunnel construction monitoring data, a2 represents the historical monitoring point location data numbered 2 and the corresponding historical tunnel construction monitoring data, and so on. an represents the historical monitoring point location data numbered n and the corresponding historical tunnel construction monitoring data, and n represents the number of historical monitoring points.

[0014] The historical dataset A contains the corresponding relationship between the historical tunnel construction monitoring data and the monitoring point location data, providing basic data for subsequent analysis and processing; by mapping the historical tunnel construction monitoring data and the monitoring point location data one-to-one, the warning information can be associated with the corresponding historical data, so as to better understand and analyze the warning information; constructing the historical dataset A can help determine the differences between false warning information and true warning information, and provide corresponding comparison data and monitoring point adjustment data, which can be used to assist the staff in processing and adjusting the warning information, improving the response efficiency and accuracy of the warning information.

[0015] Furthermore, the warning information processing module includes a warning information classification unit and a historical data association unit;

[0016] The warning information classification unit includes: obtaining the historical tunnel construction safety log to obtain the warning information of the original tunnel safety monitoring system, and classifying the warning information into false warning information and true warning information according to the subsequent processing results of the warning information recorded in the historical tunnel construction safety log;

[0017] The historical data association unit includes: obtaining the time point when the warning information is issued, selecting the time period T as the time period when the warning information is issued according to the time point, and obtaining the corresponding warning occurrence area according to the warning information; obtaining the historical tunnel construction monitoring data corresponding to the historical monitoring point location data in the warning occurrence area during the time period T, and associating the warning information with the corresponding historical tunnel construction monitoring data.

[0018] By obtaining the time point when the warning information is issued and the corresponding warning occurrence area, the historical monitoring point location data and tunnel construction monitoring data related to the warning can be obtained, which can provide background information to help better understand and analyze the cause and scope of influence of the warning information; associating the warning information with the historical monitoring data can conduct a more in-depth analysis, and potential abnormal patterns or trends can be found through comparison with historical data, providing more clues for the interpretation of the warning information.

[0019] Further, the analysis and comparison module includes a monitoring point quantity comparison unit, a data analysis unit, and a monitoring point adjustment unit;

[0020] The monitoring point quantity comparison unit includes: further classifying false warning information and true warning information respectively according to the warning category, and the warning category of the true warning information includes the warning category of the false warning information; obtaining the corresponding warning category for each false warning information, finding the true warning information with the same warning category according to the warning category, and comparing the number of historical monitoring points corresponding to the false warning information and the true warning information; wherein, the number of true warning information with the same warning category is more than one;

[0021] The data analysis unit analyzes according to the comparison result of the monitoring point quantity comparison unit and calculates the difference index;

[0022] The monitoring point adjustment unit includes: arranging the calculated difference indexes in ascending order, extracting the historical monitoring point position data of the true warning information with the difference index accounting for the top a% as the control data, where a is a set percentage threshold; obtaining the adjusted monitoring point position data for the false warning information as the template data according to the historical tunnel construction safety log record; calculating the matching degree P between the control data and the template data, and the calculation formula is:

[0023]

[0024] wherein, σ represents the adjustment parameter, G represents the difference index between the control data and the template data, and the value of the difference index is related to the number of corresponding monitoring points of the control data and the template data. When the number of monitoring points of the control data and the template data is not equal, the difference index is G1. When the number of monitoring points of the control data and the template data is equal, the difference index is G2; selecting the one with the largest matching degree as the monitoring point adjustment data and associating the monitoring point adjustment data with the corresponding false warning information.

[0025] Further, the data analysis unit includes:

[0026] Use the monitoring point quantity comparison unit to compare the false warning information with the number of historical data monitoring points corresponding thereto; if the number of historical data monitoring points corresponding to the false warning information is not equal to the number of historical data monitoring points corresponding to the true warning information, it indicates that the excessive or insufficient number of historical data monitoring points is the main reason for the false warning information, and the false information analyzed here excludes the influence of monitoring equipment failures; then analyze the historical monitoring point position data of the false warning information and the true warning information; according to the historical monitoring point position data of the false warning information and the true warning information, calculate the historical monitoring point monitoring ranges of the false warning information and the true warning information respectively, denoted as S1 and S2; calculate the difference index G1 between the historical monitoring point position data of the false warning information and the true warning information of the same warning category, and the specific calculation formula is:

[0027]

[0028] Among them, N1 represents the number of historical monitoring points in the warning occurrence area of the false warning information, N 2i represents the number of historical monitoring points in the warning occurrence area of the i-th true warning information of the same warning category as the false warning information, S2i represents the historical monitoring point monitoring range of the i-th true warning information of the same warning category as the false warning information, α and β represent different parameters, and m represents the number of true warning information of the same warning category as the false warning information.

[0029] Furthermore, the data analysis unit further includes:

[0030] If the number of historical data monitoring points corresponding to the false warning information is equal to the number of historical data monitoring points corresponding to the true warning information, it indicates that the historical monitoring point positions and the historical tunnel construction monitoring data together cause the false warning information, and the false warning information analyzed here also excludes the influence of monitoring equipment failures; then analyze the historical monitoring point position data and the historical tunnel construction monitoring data of the false warning information and the true warning information; according to the historical monitoring point position data of the false warning information and the true warning information, calculate the distances between adjacent historical monitoring points of the false warning information and the true warning information respectively, denoted as L1 and L2; according to the historical tunnel construction monitoring data of the false warning information and the true warning information, draw the curve graphs of the historical tunnel construction monitoring data changing with time within the time period T, denoted as Q1 and Q2 respectively; calculate the difference index G2 between the historical monitoring point position data and the historical tunnel construction monitoring data of the false warning information and the true warning information of the same warning category, and the specific calculation formula is:

[0031]

[0032] Among them, γ and μ represent parameters, L 1jDenote the distance between adjacent historical monitoring points of the j-th false warning message, L 2j Denote the distance between adjacent historical monitoring points of the j-th true warning message, M denotes the data number of the distance between adjacent historical monitoring points, S Q1 Denote the area enclosed by the curve Q1 and the coordinate axes, S Q2 Denote the area enclosed by the curve Q2 and the coordinate axes, S Q2_k Denote the area enclosed by the k-th Q2 curve and the coordinate axes, K denotes the number of Q2 curves of true warning messages with the same warning category as the false warning message.

[0033] Furthermore, the real-time monitoring and notification module includes:

[0034] Obtain real-time tunnel construction monitoring data and real-time monitoring point location data. When the tunnel construction safety monitoring system issues a warning message, obtain the warning category of the current warning message and calculate the similarity with historical warning messages of the same warning category; this helps to quickly and accurately judge the credibility and urgency of the current warning message; if the one with the maximum similarity belongs to the true warning message in the historical warning messages, notify the relevant staff to take corresponding actions, which can timely take actions to ensure the safety of the construction site and avoid the occurrence or further deterioration of accidents; if the one with the maximum similarity belongs to the false warning message in the historical warning messages, output the corresponding template data and monitoring point adjustment data to the relevant staff, and the relevant staff make corresponding adjustments to the current monitoring points; this helps to reduce the misjudgment of false warning messages, provide correct reference data and adjustment plans, and avoid unnecessary emergency handling and resource waste.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: Through the data acquisition module and the data processing and mapping module, the system can collect and process various tunnel construction monitoring data in real time and accurately. Compared with the traditional manual monitoring method, the system can obtain data more comprehensively and meticulously, eliminate human errors, and improve the accuracy and reliability of the monitoring data. Through the data processing and mapping module, the system can convert the monitoring data into an intuitive graphical display, which is convenient for users to understand and analyze. This can help users better grasp the situation and trend of tunnel construction and make decisions and adjustments in a timely manner. Moreover, through the construction and comparative analysis of the historical dataset A, the system can identify false warning information and distinguish it from real warning information. This can reduce the interference caused by false warnings to the staff and improve the timeliness and accuracy of real warning information. Through the warning information processing module and the real-time monitoring and notification module, the system can analyze the monitoring data in real time and automatically send out warning information, reducing the subjectivity and delay of human judgment. This can promptly identify potential risks and problems and take corresponding measures, improving the accuracy and timeliness of warning information. Through the data analysis and comparison module and the monitoring point adjustment function, the system can conduct in-depth analysis and comparison of the monitoring data and adjust the monitoring points according to historical data, improving the matching degree and accuracy of the data. This can reduce the false alarm rate and missed alarm rate and improve the management efficiency of construction safety. By calculating indicators such as the difference index and the matching degree, the system can provide data for adjusting the positions of the monitoring points to the staff to help them reasonably adjust the current monitoring points. This can minimize the generation of false warning information and improve the accuracy and effectiveness of the monitoring points. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used in conjunction with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0037] Figure 1 is a schematic diagram of the modules of the tunnel construction safety monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] Please refer to Figure 1 , the present invention provides the following technical solutions:

[0040] Tunnel construction safety monitoring system based on the Internet of Things, the system includes: a data acquisition module, a data processing and mapping module, an early warning information processing module, a data analysis and comparison module, and a real-time monitoring and notification module;

[0041] The data acquisition module is responsible for obtaining tunnel construction monitoring data and monitoring point location data; the data processing and mapping module maps the historical tunnel construction monitoring data and historical monitoring point location data one by one to construct a historical data set; the early warning information processing module divides the early warning information into false early warning information and true early warning information according to the subsequent processing results of the early warning information recorded in the historical tunnel construction safety log; the data analysis and comparison module conducts further data analysis and comparison for the false early warning information and true early warning information of the same type to obtain the difference index between the false early warning information and the true early warning information of the same type, and obtains the monitoring point adjustment data of the false early warning information according to the difference index; the real-time monitoring and notification module obtains the real-time tunnel construction monitoring data and real-time monitoring point location data, and calculates the similarity between the early warning category of the early warning information and the historical early warning information; notifies relevant personnel for processing according to the calculation result or makes adjustments according to the monitoring point adjustment data.

[0042] The data acquisition module includes a sensor and monitoring equipment unit and a data communication unit;

[0043] The sensor and monitoring equipment unit is responsible for collecting tunnel construction monitoring data and monitoring point location data; the data communication unit is responsible for transmitting the collected data to the data processing and mapping module; among them, the tunnel construction monitoring data includes displacement and deformation data, hydrographic and water quality data, temperature and humidity data, pressure data, vibration data, and environmental pollution data, etc.;

[0044] The data processing and mapping module includes a data cleaning unit, a data storage unit, and a mapping and association unit;

[0045] The data cleaning unit preprocesses and cleans the collected raw data, removes interference factors such as outliers and noise, and ensures the accuracy and consistency of the data; the data storage unit stores the cleaned data in the database for subsequent data analysis and query operations; the mapping and association unit maps the historical tunnel construction monitoring data and historical monitoring point location data one by one to construct a historical data set.

[0046] The mapping and association unit includes:

[0047] Obtain historical tunnel construction monitoring data and historical monitoring point location data from the original tunnel construction safety monitoring system, randomly number the historical monitoring point location data, map the historical tunnel construction monitoring data and the historical monitoring point location data one-to-one to form a historical dataset A, and A = {a1, a2,..., an}, where a1 represents the historical monitoring point location data numbered 1 and the corresponding historical tunnel construction monitoring data, a2 represents the historical monitoring point location data numbered 2 and the corresponding historical tunnel construction monitoring data, and so on, an represents the historical monitoring point location data numbered n and the corresponding historical tunnel construction monitoring data, and n represents the number of historical monitoring points.

[0048] The historical dataset A contains the corresponding relationship between the historical tunnel construction monitoring data and the monitoring point location data, providing basic data for subsequent analysis and processing; by mapping the historical tunnel construction monitoring data and the monitoring point location data one-to-one, the warning information can be associated with the corresponding historical data, so as to better understand and analyze the warning information; constructing the historical dataset A can help determine the difference between false warning information and true warning information, and provide corresponding comparison data and monitoring point adjustment data, which can be used to assist the staff in processing and adjusting the warning information, improving the response efficiency and accuracy of the warning information.

[0049] In this embodiment, assume there are 5 monitoring point location data, which are respectively:

[0050] Monitoring point 1: (10, 20, 5),

[0051] Monitoring point 2: (15, 25, 6),

[0052] Monitoring point 3: (12, 22, 7),

[0053] Monitoring point 4: (18, 30, 8),

[0054] Monitoring point 5: (22, 35, 9);

[0055] At the same time, there are corresponding tunnel construction monitoring data, including data such as ground settlement and groundwater level.

[0056] These monitoring point location data can be randomly numbered, and then they are mapped one-to-one with the corresponding tunnel construction monitoring data to form the historical dataset A.

[0057] For example, the numbered monitoring point location data and the corresponding tunnel construction monitoring data are as follows:

[0058] a1: (Monitoring point 1 location data, Tunnel construction monitoring data 1),

[0059] a2: (Location data of monitoring point 2, Tunnel construction monitoring data 2),

[0060] a3: (Location data of monitoring point 3, Tunnel construction monitoring data 3),

[0061] a4: (Location data of monitoring point 4, Tunnel construction monitoring data 4),

[0062] a5: (Location data of monitoring point 5, Tunnel construction monitoring data 5).

[0063] The early warning information processing module includes an early warning information classification unit and a historical data association unit;

[0064] The early warning information classification unit includes: obtaining the historical tunnel construction safety log to get the early warning information of the original tunnel safety monitoring system, and classifying the early warning information into false early warning information and true early warning information according to the subsequent processing results of the early warning information recorded in the historical tunnel construction safety log;

[0065] The historical data association unit includes: obtaining the time point when the early warning information is issued, selecting the time period T as the time period when the early warning information is issued according to the time point, and obtaining the corresponding early warning occurrence area according to the early warning information; obtaining the historical tunnel construction monitoring data of the historical monitoring point location data in the warning occurrence area corresponding to the time period T, and associating the warning information with the corresponding historical tunnel construction monitoring data.

[0066] By obtaining the time point when the early warning information is issued and the corresponding early warning occurrence area, the historical monitoring point location data and tunnel construction monitoring data related to the early warning can be obtained. These data can provide background information to help better understand and analyze the cause and scope of influence of the early warning information; associating the early warning information with the historical monitoring data can conduct a more in-depth analysis. By comparing with the historical data, potential abnormal patterns or trends can be found, providing more clues for the interpretation of the early warning information.

[0067] The analysis and comparison module includes a monitoring point quantity comparison unit, a data analysis unit, and a monitoring point adjustment unit;

[0068] The monitoring point quantity comparison unit includes: further classifying the false early warning information and true early warning information according to the early warning category respectively, and the early warning category of the true early warning information includes the early warning category of the false early warning information; obtaining the corresponding early warning category for each false early warning information, finding the true early warning information with the same early warning category according to the early warning category, and comparing the number of historical monitoring points corresponding to the false early warning information and the true early warning information; among them, the number of true early warning information with the same early warning category is more than one;

[0069] The data analysis unit analyzes according to the comparison result of the monitoring point quantity comparison unit and calculates the difference index;

[0070] The monitoring point adjustment unit includes: arranging the calculated difference indexes in ascending order, and extracting the historical monitoring point position data of the true warning information with the difference indexes accounting for the top a% as the control data, where a is a set percentage threshold; obtaining the adjusted monitoring point position data for false warning information as the template data according to the historical tunnel construction safety log records; calculating the matching degree P between the control data and the template data, and the calculation formula is:

[0071]

[0072] where σ represents the adjustment parameter, G represents the difference index between the control data and the template data, and the value of the difference index is related to the number of corresponding monitoring points of the control data and the template data. When the number of monitoring points of the control data and the template data is not equal, the difference index is G1. When the number of monitoring points of the control data and the template data is equal, the difference index is G2; select the one with the largest matching degree as the monitoring point adjustment data, and associate the monitoring point adjustment data with the corresponding false warning information.

[0073] In this embodiment, it is assumed that if the number of historical data monitoring points corresponding to the false warning information is not equal to the number of historical data monitoring points corresponding to the true warning information, the difference index G1 is calculated. It is assumed that the historical monitoring point position data of the true warning information with the difference indexes accounting for the top 30% are extracted as the control data, and the number of control data is three, namely A, B, and C; then calculate the difference index between the template data and the control data. It is assumed that the number of monitoring points of A and B is equal to the number of monitoring points of the template data, then G2 is calculated; for C and the template data, G1 is calculated; then substitute the calculated values into the matching degree calculation formula. It is assumed that the matching degree of A is the largest, then select A as the monitoring point adjustment data, and associate the monitoring point adjustment data with the corresponding false warning information; and when the ones with the largest matching degree are not unique, all the ones with the largest matching degree are used as the monitoring point adjustment data.

[0074] The data analysis unit includes:

[0075] The monitoring point number comparison unit is used to compare the number of false warning information and the corresponding historical data monitoring points; if the number of historical data monitoring points corresponding to the false warning information is not equal to the number of historical data monitoring points corresponding to the real warning information, it means that the main reason why the warning information is false warning information is that there are too many or too few historical data monitoring points, and the false information analyzed here excludes the influence of monitoring equipment failure; then the historical monitoring point location data of the false warning information and the real warning information are analyzed; according to the historical monitoring point location data of the false warning information and the real warning information, the historical monitoring point monitoring ranges of the false warning information and the real warning information are calculated respectively, which are expressed as S1 and S2 respectively; the difference index G1 of the historical monitoring point location data of the false warning information and the real warning information of the same warning category is calculated, and the specific calculation formula is:

[0076]

[0077] Among them, N1 represents the number of historical monitoring points in the warning area where false warning information occurs, and N 2i It represents the number of historical monitoring points in the warning occurrence area of the ith real warning information of the same warning category as the false warning information, S2i represents the monitoring range of the historical monitoring points of the ith real warning information of the same warning category as the false warning information, α and β represent different parameters, and m represents the number of real warning information of the same warning category as the false warning information.

[0078] The data analysis unit also includes:

[0079] If the number of historical data monitoring points corresponding to the false warning information is equal to the number of historical data monitoring points corresponding to the true warning information, it means that the historical monitoring point location and the historical tunnel construction monitoring data jointly caused the warning information to be false warning information. Similarly, the false warning information analyzed here excludes the influence of monitoring equipment failure; then the historical monitoring point location data and historical tunnel construction monitoring data of the false warning information and the true warning information are analyzed; according to the historical monitoring point location data of the false warning information and the true warning information, the distances between adjacent historical monitoring points of the false warning information and the true warning information are calculated, respectively, and are expressed as L1 and L2; according to the historical tunnel construction monitoring data of the false warning information and the true warning information, a curve graph of the change of historical tunnel construction monitoring data over time in time period T is drawn, which are expressed as Q1 and Q2 respectively; the difference index G2 between the historical monitoring point location data and the historical tunnel construction monitoring data of the false warning information and the true warning information of the same warning category is calculated, and the specific calculation formula is:

[0080]

[0081] Among them, γ and μ represent parameters, L 1jDenote the distance between adjacent historical monitoring points of the j-th false warning message, L 2j Denote the distance between adjacent historical monitoring points of the j-th true warning message, M represents the data number of the distance between adjacent historical monitoring points, S Q1 Denote the area enclosed by the curve Q1 and the coordinate axes, S Q2 Denote the area enclosed by the curve Q2 and the coordinate axes, S Q2_k Denote the area enclosed by the k-th Q2 curve and the coordinate axes, K represents the number of Q2 curves of true warning messages with the same warning category as the false warning message.

[0082] The real-time monitoring and notification module includes:

[0083] Obtain real-time tunnel construction monitoring data and real-time monitoring point location data. When the tunnel construction safety monitoring system issues a warning message, obtain the warning category of the current warning message and calculate the similarity with historical warning messages of the same warning category; this helps to quickly and accurately judge the credibility and urgency of the current warning message; if the one with the maximum similarity belongs to the true warning message in the historical warning messages, notify the relevant staff to take corresponding actions, which can timely take actions to ensure the safety of the construction site and avoid the occurrence or further deterioration of accidents; if the one with the maximum similarity belongs to the false warning message in the historical warning messages, output the corresponding template data and monitoring point adjustment data to the relevant staff, and the relevant staff make corresponding adjustments to the current monitoring points; this helps to reduce the misjudgment of false warning messages, provide correct reference data and adjustment plans, and avoid unnecessary emergency handling and resource waste.

[0084] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0085] Finally, it should be noted that: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Tunnel construction safety monitoring system based on the Internet of Things, characterized in that: The system includes: a data acquisition module, a data processing and mapping module, an early warning information processing module, a data analysis and comparison module, and a real-time monitoring and notification module; The data acquisition module is responsible for obtaining tunnel construction monitoring data and monitoring point location data; the data processing and mapping module maps the historical tunnel construction monitoring data and the historical monitoring point location data one by one to construct a historical data set; the early warning information processing module divides the early warning information into false early warning information and true early warning information according to the subsequent processing results of the early warning information recorded in the historical tunnel construction safety log; the data analysis and comparison module performs further data analysis and comparison on the false early warning information and the true early warning information of the same type to obtain the difference index between the false early warning information and the true early warning information of the same type, and obtains the monitoring point adjustment data of the false early warning information according to the difference index; the real-time monitoring and notification module obtains the real-time tunnel construction monitoring data and the real-time monitoring point location data, and calculates the similarity between the early warning category of the early warning information and the historical early warning information; notifies the relevant personnel to handle it according to the calculation result or makes adjustments according to the monitoring point adjustment data.

2. The tunnel construction safety monitoring system based on the Internet of Things according to claim 1, wherein: The data acquisition module includes a sensor and monitoring equipment unit and a data communication unit; The sensor and monitoring equipment unit is responsible for collecting tunnel construction monitoring data and monitoring point location data; the data communication unit is responsible for transmitting the collected data to the data processing and mapping module; The data processing and mapping module includes a data cleaning unit, a data storage unit, and a mapping and association unit; The data cleaning unit preprocesses and cleans the collected raw data; the data storage unit stores the cleaned data in a database; The mapping and association unit maps the historical tunnel construction monitoring data and the historical monitoring point location data one by one to construct a historical data set.

3. The tunnel construction safety monitoring system based on the Internet of Things according to claim 2, characterized in that: The mapping and association unit includes: Obtain the historical tunnel construction monitoring data and the historical monitoring point location data from the original tunnel construction safety monitoring system, randomly number the historical monitoring point location data, map the historical tunnel construction monitoring data and the historical monitoring point location data one by one to form a historical data set A, and A = {a1, a2,..., an}, where a1 represents the historical monitoring point location data numbered 1 and the corresponding historical tunnel construction monitoring data, a2 represents the historical monitoring point location data numbered 2 and the corresponding historical tunnel construction monitoring data, and so on, an represents the historical monitoring point location data numbered n and the corresponding historical tunnel construction monitoring data, and n represents the number of historical monitoring points.

4. The tunnel construction safety monitoring system based on the Internet of Things according to claim 1, wherein: The early warning information processing module includes an early warning information classification unit and a historical data association unit; The early warning information classification unit includes: obtaining the historical tunnel construction safety log to obtain the early warning information of the original tunnel safety monitoring system, and dividing the early warning information into false early warning information and true early warning information according to the subsequent processing results of the early warning information recorded in the historical tunnel construction safety log; The historical data association unit includes: obtaining the time point when the warning information is issued, selecting the time period T as the time period when the warning information is issued according to the time point, and obtaining the corresponding warning occurrence area according to the warning information; obtaining the historical tunnel construction monitoring data corresponding to the historical monitoring point position data in the warning occurrence area during the time period T, and associating the warning information with the corresponding historical tunnel construction monitoring data.

5. The tunnel construction safety monitoring system based on the Internet of Things according to claim 1, characterized in that: The analysis and comparison module includes a monitoring point quantity comparison unit, a data analysis unit, and a monitoring point adjustment unit; The monitoring point quantity comparison unit includes: further classifying false warning information and true warning information according to the warning category respectively, and the warning categories of true warning information include the warning categories of false warning information; obtaining the corresponding warning category for each false warning information, finding the true warning information with the same warning category according to the warning category, and comparing the number of historical monitoring points corresponding to the false warning information and the true warning information; The data analysis unit analyzes according to the comparison result of the monitoring point quantity comparison unit and calculates the difference index; The monitoring point adjustment unit includes: arranging the calculated difference indexes in ascending order, extracting the historical monitoring point position data of the true warning information with the difference indexes accounting for the top a% as the reference data, where a is a set percentage threshold; obtaining the adjusted monitoring point position data for the false warning information as the template data according to the historical tunnel construction safety log record; calculating the matching degree P between the reference data and the template data, and the calculation formula is: where, σ represents the adjustment parameter, and G represents the difference index between the reference data and the template data; selecting the one with the largest matching degree as the monitoring point adjustment data, and associating the monitoring point adjustment data with the corresponding false warning information.

6. The tunnel construction safety monitoring system based on the Internet of Things according to claim 5, characterized in that: The data analysis unit includes: using the monitoring point quantity comparison unit to compare the number of historical data monitoring points corresponding to the false warning information; If the number of historical data monitoring points corresponding to the false warning information is not equal to the number of historical data monitoring points corresponding to the true warning information, then analyze the historical monitoring point position data of the false warning information and the true warning information; respectively calculate the historical monitoring point monitoring ranges of the false warning information and the true warning information according to the historical monitoring point position data of the false warning information and the true warning information, which are respectively represented as S1 and S2; calculate the difference index G1 between the historical monitoring point position data of the false warning information and the true warning information with the same warning category, and the specific calculation formula is: Among them, N1 represents the number of historical monitoring points in the warning occurrence area of the false warning information, N 2i represents the number of historical monitoring points in the warning occurrence area of the i-th true warning information of the same warning category as the false warning information, S2i represents the historical monitoring point monitoring range of the i-th true warning information of the same warning category as the false warning information, α and β represent different parameters, and m represents the number of true warning information of the same warning category as the false warning information.

7. The tunnel construction safety monitoring system based on the Internet of Things according to claim 5, characterized in that: The data analysis unit also includes: If the number of historical data monitoring points corresponding to the false warning information is equal to the number of historical data monitoring points corresponding to the true warning information, analyze the historical monitoring point location data and historical tunnel construction monitoring data of the false warning information and the true warning information; calculate the distances between adjacent historical monitoring points of the false warning information and the true warning information respectively according to the historical monitoring point location data of the false warning information and the true warning information, which are represented as L1 and L2 respectively; draw the curve graphs of the historical tunnel construction monitoring data changing with time within the time period T according to the historical tunnel construction monitoring data of the false warning information and the true warning information, which are represented as Q1 and Q2 respectively; calculate the difference index G2 of the historical monitoring point location data and historical tunnel construction monitoring data between the false warning information and the true warning information of the same warning category. The specific calculation formula is: where γ and μ represent parameters, L 1j represents the distance between adjacent historical monitoring points of the j-th false warning message, L 2j represents the distance between adjacent historical monitoring points of the j-th true warning message, M represents the data number of the distance between adjacent historical monitoring points, S Q1 represents the area enclosed by the curve Q1 and the coordinate axes, S Q2 represents the area enclosed by the curve Q2 and the coordinate axes, S Q2_k represents the area enclosed by the k-th Q2 curve and the coordinate axes, K represents the number of Q2 curves of true warning messages with the same warning category as the false warning message.

8. The tunnel construction safety monitoring system based on the Internet of Things according to claim 1, characterized in that: The real-time monitoring and notification module includes: Obtain the real-time tunnel construction monitoring data and real-time monitoring point location data. When the tunnel construction safety monitoring system issues a warning information, obtain the warning category of the current warning information and calculate the similarity with the historical warning information of the same warning category; if the one with the maximum similarity belongs to the true warning information in the historical warning information, notify the relevant staff to make corresponding handling. If the one with the maximum similarity belongs to the false warning information in the historical warning information, output the corresponding template data and monitoring point adjustment data to the relevant staff, and the relevant staff make corresponding adjustments to the current monitoring points.

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