A safety early warning alarm system based on subway underground excavation construction environment

Abstract

The present invention discloses a safety early warning alarm system based on a subway underground excavation construction environment, which relates to the field of early warning alarm technology, including a safety early warning platform, wherein the safety early warning platform is communicatively connected with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module, and a visual report module, wherein the modules are connected by electrical signals. The present invention monitors the status information of key parts, and performs data processing and analysis in real time, timely captures subtle changes that are prone to potential safety hazards, and can quickly discover potential safety hazards. Once the monitoring data exceeds the preset alarm threshold, the system will immediately trigger the alarm mechanism, and send the alarm information to relevant personnel in a variety of ways such as text messages, emails, or system notifications, which greatly shortens the risk response time, provides decision-making time for managers, and effectively reduces the risk of safety accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning alarms, and in particular to an early warning alarm system based on the safety of a subway underground excavation construction environment. Background Art

[0002] As an important part of the urban public transportation system, the construction process of the subway involves multiple technical fields, including civil engineering, architectural modeling and decoration, electromechanical operating equipment and other systems. The particularity of the subway construction environment, such as the small underground working space, complex and changeable geological conditions, and dense surrounding buildings, makes the subway construction project have a high risk. In addition, during the underground excavation construction of the subway, it faces a variety of construction disasters. The occurrence of construction disasters can easily pose a serious threat to construction safety. Therefore, establishing an effective early warning alarm system to timely monitor and warn of abnormal conditions in the construction environment is of great significance for preventing and reducing the occurrence of construction disasters.

[0003] For example, Chinese patent publication number: CN117912212A is a geotechnical engineering monitoring data analysis and early warning system, which includes: a perception layer, a network transmission layer, a support layer and an application layer; the perception layer is used to collect status information of the geotechnical engineering; the network transmission layer is used to output the real-time collected geotechnical engineering status information to the support layer.

[0004] In the existing technology, the underground construction environment is complex, including various factors such as groundwater level, geological conditions, and the influence of surrounding buildings. Although the monitoring items such as wall top horizontal displacement, deep horizontal displacement, and groundwater level can be used to warn of safety hazards in construction, the underground excavation construction is limited by the interference of tunnel excavation and support structure stress changes, which will affect the accuracy and timeliness of the early warning alarm. Therefore, a safety early warning alarm system based on the subway excavation construction environment is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a safety early warning alarm system based on subway dark excavation construction environment to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A safety early warning alarm system based on a subway underground excavation construction environment includes a safety early warning platform, wherein the safety early warning platform is communicatively connected with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module and a visual report module, wherein electrical signals are connected between the modules;

[0008] The data acquisition module is used to collect the original data of the construction status of the construction site, including the horizontal displacement of the wall top, the horizontal displacement of the deep layer, the groundwater level, the ground stress and the stress of the supporting structure. The data is collected through various sensors and data acquisition instruments installed at the subway dark excavation construction site, and the operation data of the tunnel boring equipment is obtained and integrated to obtain the construction data sequence table, so as to ensure the accuracy and real-time nature of the data and provide a reliable data basis for subsequent analysis and early warning;

[0009] The data transmission module is used to achieve extensive coverage of underground construction areas through wireless sensor networks, transmit raw data to the safety warning platform in real time, improve the real-time, flexibility and reliability of data collection, and reduce the construction environment's dependence on wiring;

[0010] The data analysis module, based on machine learning technology, performs data mining, feature extraction and status analysis on the collected construction status data and operation data of the tunnel boring equipment, identifies potential safety hazards and abnormal patterns, and improves the prediction accuracy of the early warning system;

[0011] The early warning analysis module, based on the extracted features and analysis results of the data analysis module and combined with the status information of the tunnel boring equipment, constructs a safety early warning model, obtains the early warning response coefficient, sets the corresponding safety early warning level, and performs early warning analysis to ensure the accuracy and timeliness of the early warning alarm and reduce false alarms and missed alarms;

[0012] The alarm notification module activates the corresponding emergency response mechanism according to the safety warning level determined by the early warning analysis module, and sends alarm information to relevant personnel, quickly notifies relevant personnel through multiple channels to ensure the timeliness and coverage of information transmission, and activates the emergency plan at the same time to reduce accident losses;

[0013] The visualization report module is used to present monitoring data, analysis results, and early warning alarm information in a visual form, generate various reports, and provide an intuitive and clear graphical interface to facilitate managers and technicians to quickly understand the safety status of the construction environment.

[0014] A further improvement of the technical solution of the present invention is that in the data acquisition module, the process of obtaining the construction data sequence table is as follows:

[0015] At the subway tunnel construction site, various types of sensors are arranged, including displacement sensors, water level sensors, and stress sensors, to monitor key construction parameters. Displacement sensors are used to monitor the horizontal displacement of the wall top and the deep horizontal displacement, water level sensors are used to monitor the groundwater level, and stress sensors are used to monitor the ground stress and the stress of the supporting structure. Data acquisition instruments are also installed to receive signals from the sensors for preliminary processing.

[0016] Monitor the operating status of tunnel boring equipment, including tunneling speed, operating hours, equipment temperature, equipment failures and vibrations;

[0017] The original data of the construction status and the operation data of the tunnel boring equipment are obtained through sensors and data acquisition instruments. The collection cycle of equal duration is set to collect related data, and the data is classified to obtain a construction data sequence list.

[0018] A further improvement of the technical solution of the present invention is that: in the data transmission module, the process of transmitting the original data to the security warning platform in real time is:

[0019] Install wireless communication base stations in the underground construction area to form a wireless sensor network to ensure wireless signal coverage of the entire construction area, and integrate the data acquisition instrument with the wireless sensor network so that each data collection point is wirelessly connected to the wireless sensor network;

[0020] Encapsulate the raw data collected by the sensor into data packets and encrypt the data packets to ensure the security of data transmission and prevent unauthorized access;

[0021] The data packets are transmitted to the nearest communication gateway through the wireless sensor network and integrated and transmitted to the server of the security early warning platform;

[0022] The security warning platform unpacks and decrypts the data packets, restores the original data format, verifies the received data, checks the data integrity and accuracy, and stores the data in the data warehouse of the security warning platform.

[0023] A further improvement of the technical solution of the present invention is that in the data analysis module, the process of identifying potential safety hazards and abnormal patterns is as follows:

[0024] Extract construction status data and tunnel boring equipment operation data, and perform preprocessing operations. Remove duplicate, erroneous, and missing data through data cleaning to ensure data quality. Convert data from different sources and formats into a unified format to facilitate subsequent processing. Normalize the data to ensure that all features are on the same scale.

[0025] The pre-processed construction status data is analyzed to extract characteristic indicators of associated safety hazards and abnormal patterns. Combined with the associated data of construction status characteristic indicators, a construction abnormality prediction model is constructed to obtain the construction abnormality index and clarify the correlation between the construction status and potential safety hazards and abnormal patterns. The construction status characteristic indicators are the horizontal displacement of the top of the wall, the horizontal displacement of the deep layer, the groundwater level, the ground stress and the stress of the support structure.

[0026] The pre-processed tunnel boring equipment operation data is analyzed to extract characteristic indicators of associated safety hazards and abnormal patterns. Combined with the associated data of characteristic indicators of tunnel boring equipment operation status, an operation abnormality prediction model is constructed to obtain the equipment abnormality index and clarify the correlation between the operation status of tunnel boring equipment and potential safety hazards and abnormal patterns. The characteristic indicators of tunnel boring equipment operation status are tunneling speed, operation time, equipment temperature, equipment failure and vibration.

[0027] Analyze trends in construction status data and tunnel boring equipment operating data to identify anomalies and risks in the underground construction environment.

[0028] A further improvement of the technical solution of the present invention is that the calculation formula of the construction abnormality index is: ;

[0029] in, is the construction abnormality index, For the The measured ground stress values ​​at each monitoring point are is the average value of the ground stress, For the The structural stress measurement value of each monitoring point, is the average value of the structural stress, and are the number of monitoring points for ground stress and structural stress, is the change in groundwater level, is the preset groundwater level change threshold, is the base of natural logarithms, It shows that when the groundwater level change exceeds the set threshold, the abnormal index increases exponentially, emphasizing the importance of water level change to construction safety;

[0030] The calculation formula of the equipment abnormality index is: ;

[0031] in, is the device abnormality index, For the The observed excavation speed, is the average excavation speed, For the The running time of each observation, is the average running time, For the The device temperature observed, is the average temperature of the device, , and Represent the excavation speed, operation time and equipment temperature observation times respectively. For the The observed equipment vibration value, and Represent the maximum and minimum values ​​of vibration respectively. is the reference value of the equipment vibration value, It reflects the ratio of the variation range of equipment vibration to the vibration threshold. When the vibration variation range exceeds the preset threshold, the exponential function will increase sharply, which emphasizes the impact of equipment vibration on the equipment abnormality index.

[0032] A further improvement of the technical solution of the present invention is that in the early warning analysis module, the process of obtaining the early warning response coefficient is:

[0033] Establish a construction safety hazard database to obtain historical risk types during subway tunnel construction. Historical risk data includes risk type, occurrence time, impact scope, treatment measures, etc. Historical risk types include but are not limited to groundwater level changes, support structure instability, ground subsidence, tunnel collapse, etc.;

[0034] For the historical risk types obtained, identify the specific patterns and precursor characteristics of various risk types, analyze the correlation between each risk type and the construction status and status information of tunnel boring equipment, establish a mapping relationship, and extract related data from the construction data sequence table;

[0035] Based on the historical risk types in the subway tunnel construction process, the safety warning levels are set according to the severity of the risk types, namely the first level safety warning level, the second level safety warning level and the third level safety warning level. The safety warning levels increase in severity from level one to level three.

[0036] Based on the historical risk type correlation data obtained, a safety early warning model is constructed. By combining the construction abnormality index and equipment abnormality index, the early warning response coefficient is obtained to analyze the safety risks of the subway underground excavation construction process.

[0037] Based on the warning response coefficient and the set safety warning level, determine the warning alarm threshold corresponding to each safety warning level;

[0038] The early warning response coefficient is calculated based on the real-time data stream, and the safety warning level corresponding to the safety risk of subway underground excavation construction is evaluated.

[0039] A further improvement of the technical solution of the present invention is that the calculation formula of the early warning response coefficient is: ;

[0040] in, is the warning response coefficient, For the The construction anomaly index of the observation, Representation and The relevant weight coefficients reflect the importance of different construction abnormality indicators. For the The device anomaly index of the observation, Representation and The relevant weight coefficient reflects the importance of abnormal indicators of different devices. and are the observation times of construction and equipment abnormality index, is the maximum value among all construction abnormality indices. is the benchmark value of the construction abnormality index. is the maximum value among all device abnormality indexes. is the average value of the equipment abnormality index, is the standard deviation of the equipment anomaly index.

[0041] A further improvement of the technical solution of the present invention is that: multiple safety warning levels correspond to multiple warning alarm thresholds, wherein the warning alarm thresholds include an upper threshold and a lower threshold;

[0042] The multiple safety warning levels and the multiple warning alarm thresholds satisfy the following relationship:

[0043] Level 1 safety warning level ; The lowest risk, indicating that the current construction environment and equipment status are within the normal range or have only slight risks. Monitor the status, conduct regular inspections, and make small-scale adjustments or optimizations;

[0044] Second level safety warning level ; Medium risk, indicating that there are some risks in the construction environment or equipment status, which requires attention and certain preventive measures, such as increasing the monitoring frequency, reviewing the construction plan and equipment maintenance records, implementing preventive maintenance, and preparing emergency response plans;

[0045] Three-level safety warning level ; The highest risk, indicating that there is a serious risk in the construction environment or equipment status, and immediate action is required to immediately review the site situation, and it may be necessary to suspend construction or equipment operation, conduct on-site inspections and risk assessments, and activate emergency plans;

[0046] in, is the warning response coefficient, is the lower threshold corresponding to the second-level safety warning level and the upper threshold corresponding to the first-level safety warning level. It is the lower threshold corresponding to the third-level safety warning level and the upper threshold corresponding to the second-level safety warning level.

[0047] A further improvement of the technical solution of the present invention is that in the alarm notification module, the process of sending the alarm information is:

[0048] Receive the security warning level determined by the warning analysis module, trigger the corresponding alarm notification process according to the security warning level, and immediately generate warning information matching the security warning level;

[0049] Generate detailed alarm information based on the received warning information, including warning level, time, location, possible impact, recommended actions, etc., and automatically activate the corresponding emergency response mechanism to ensure rapid response;

[0050] Send alarm information to relevant personnel through multiple channels to ensure the timeliness and coverage of information transmission. Alarm information is sent through SMS, email, phone, and system message push;

[0051] When sending the alarm information, the alarm history is recorded, including the recipient, sending time, sending content, and sending status, to facilitate the subsequent tracing and evaluation of the alarm event. The relevant personnel who receive the alarm information respond and handle it according to the requirements of the emergency plan, and feedback the processing results to the system. The emergency plan includes measures such as evacuating personnel, reinforcing structures, and closing dangerous areas to reduce accident losses and ensure personnel safety.

[0052] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0053] The present invention provides a safety early warning alarm system based on the subway underground excavation construction environment. By monitoring the status information of key parts and performing real-time data processing and analysis, it can timely capture subtle changes that are prone to safety hazards and quickly discover potential safety hazards. Once the monitoring data exceeds the preset alarm threshold, the system will immediately trigger the alarm mechanism and send the alarm information to relevant personnel in a variety of ways such as text messages, emails or system notifications, greatly shortening the risk response time, providing management personnel with decision-making time, and effectively reducing the risk of safety accidents.

[0054] The present invention provides a safety early warning alarm system based on the subway underground excavation construction environment. By integrating sensor technology, wireless communication technology, data analysis and machine learning algorithms, it realizes real-time monitoring and intelligent analysis of the construction environment, automatically collects and processes a large amount of construction data, and promptly identifies abnormal patterns and hidden dangers that may affect construction safety. Through intelligent data analysis, it provides more accurate and timely safety warnings, reduces misjudgments and missed judgments caused by human factors, and significantly improves the intelligence level of construction safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a module diagram of the present invention;

[0057] Figure 2 Flowchart for identifying potential safety hazards and abnormal patterns for the present invention;

[0058] Figure 3 This is a flow chart for obtaining the early warning response coefficient of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a safety early warning alarm system based on the subway dark excavation construction environment, including a safety early warning platform, the safety early warning platform is communicatively connected with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module and a visual report module, wherein the modules are connected by electrical signals;

[0061] The data acquisition module is used to collect the original data of the construction status of the construction site, including the horizontal displacement of the top of the wall, the horizontal displacement of the deep layer, the groundwater level, the ground stress and the stress of the supporting structure. The data is collected by installing various sensors and data acquisition instruments at the subway dark excavation construction site, and the operation data of the tunnel boring equipment is obtained. The construction data sequence table is integrated to ensure the accuracy and real-time nature of the data, and provide a reliable data basis for subsequent analysis and early warning. At the subway dark excavation construction site, various types of sensors are arranged, including displacement sensors, water level sensors, and stress sensors, to monitor key construction parameters. Among them, the displacement sensor is used to monitor the horizontal displacement of the top of the wall and the horizontal displacement of the deep layer, the water level sensor is used to monitor the groundwater level, and the stress sensor is used to monitor the ground stress and the stress of the supporting structure. The data acquisition instrument is installed to receive signals from the sensor for preliminary processing, monitor the operation status of the tunnel boring equipment, including the excavation speed, operation time, equipment temperature, equipment failure and vibration, obtain the original data of the construction status and the operation data of the tunnel boring equipment through the sensors and data acquisition instruments, set an equal-length collection cycle to collect related data, and classify them to obtain a construction data sequence table;

[0062] The data transmission module is used to achieve extensive coverage of underground construction areas through wireless sensor networks, transmit raw data to the safety warning platform in real time, improve the real-time, flexibility and reliability of data collection, reduce the construction environment's dependence on wiring, install wireless communication base stations in the underground construction area to form a wireless sensor network, ensure wireless signal coverage of the entire construction area, and integrate the data acquisition instrument with the wireless sensor network so that each data collection point is wirelessly connected to the wireless sensor network. The raw data collected by the sensor is encapsulated into data packets, and the data packets are encrypted to ensure security during data transmission and prevent unauthorized access. The data packets are transmitted to the nearest communication gateway through the wireless sensor network, and integrated and transmitted to the server of the safety warning platform. The data packets are decapsulated and decrypted on the safety warning platform to restore the original data format, and the received data is verified to check the data integrity and accuracy, and the data is stored in the data warehouse of the safety warning platform;

[0063] The data analysis module, based on machine learning technology, performs data mining, feature extraction and status analysis on the collected construction status data and tunnel boring equipment operation data, identifies potential safety hazards and abnormal patterns, improves the prediction accuracy of the early warning system, extracts construction status data and tunnel boring equipment operation data, and performs preprocessing operations. Through data cleaning, duplicate, erroneous and missing data are removed to ensure data quality. Through data conversion, data from different sources and formats are converted into a unified format for subsequent processing. By normalizing the data, it is ensured that all features are on the same scale. The preprocessed construction status data is analyzed to extract characteristic indicators related to safety hazards and abnormal patterns. Combined with the associated data of construction status characteristic indicators, a construction anomaly prediction model is constructed to obtain construction anomaly prediction results. Constant index, clarify the correlation between construction status and potential safety hazards and abnormal patterns, among which the characteristic indicators of construction status are wall top horizontal displacement, deep horizontal displacement, groundwater level, ground stress and support structure stress. Analyze the pre-processed tunnel boring equipment operation data, extract the characteristic indicators of related safety hazards and abnormal patterns, and build an operation abnormality prediction model based on the correlation data of the characteristic indicators of tunnel boring equipment operation status to obtain the equipment abnormality index, clarify the correlation between the operation status of tunnel boring equipment and potential safety hazards and abnormal patterns, among which the characteristic indicators of tunnel boring equipment operation status are excavation speed, operation time, equipment temperature, equipment failure and vibration. Analyze the trends of construction status data and tunnel boring equipment operation data to identify abnormalities and risks in the underground construction environment.

[0064] Furthermore, the calculation formula of the construction abnormality index is: ;

[0065] in, is the construction abnormality index, For the The measured ground stress values ​​at each monitoring point are is the average value of the ground stress, For the The structural stress measurement value of each monitoring point, is the average value of the structural stress, and are the number of monitoring points for ground stress and structural stress, is the change in groundwater level, is the preset groundwater level change threshold, is the base of natural logarithms, It shows that when the groundwater level change exceeds the set threshold, the abnormal index increases exponentially, emphasizing the importance of water level change to construction safety. It should be noted that when all monitored construction status characteristic indicators are close to their respective average values, the index should be small, indicating that the construction status is normal. As any one or more characteristic indicators significantly deviate from their respective average values, the index value will increase, indicating an increase in potential safety hazards.

[0066] The calculation formula of the equipment abnormality index is: ;

[0067] in, is the device abnormality index, For the The observed excavation speed, is the average excavation speed, For the The running time of each observation, is the average running time, For the The device temperature observed, is the average temperature of the device, , and Represent the excavation speed, operation time and equipment temperature observation times respectively. For the The observed equipment vibration value, and Represent the maximum and minimum values ​​of vibration respectively. is the reference value of the equipment vibration value, It reflects the ratio of the variation range of equipment vibration to the vibration threshold. When the vibration variation range exceeds the predetermined threshold, the exponential function will increase sharply, which emphasizes the impact of equipment vibration on the equipment abnormality index. It should be noted that when all equipment operation status characteristic indicators are close to their respective average values ​​and the equipment vibration variation range is small, the index value should be small, indicating that the equipment operation status is normal. As any one or more characteristic indicators significantly deviate from their respective average values, or the equipment vibration variation range increases significantly, the index value will increase, indicating that the potential equipment safety hazards increase.

[0068] The early warning analysis module builds a safety early warning model based on the extracted features and analysis results of the data analysis module and the status information of the tunnel boring equipment, obtains the early warning response coefficient, sets the corresponding safety early warning level, and conducts early warning analysis to ensure the accuracy and timeliness of early warning alarms and reduce false alarms and missed alarms;

[0069] The alarm notification module activates the corresponding emergency response mechanism according to the safety warning level determined by the early warning analysis module, and sends alarm information to relevant personnel, quickly notifying relevant personnel through multiple channels to ensure the timeliness and coverage of information transmission, and at the same time activates the emergency plan to reduce accident losses;

[0070] The visual report module is used to display monitoring data, analysis results, and early warning alarm information in a visual form, generate various reports, and provide an intuitive and clear graphical interface to facilitate managers and technicians to quickly understand the safety status of the construction environment.

[0071] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the early warning analysis module, the process of obtaining the early warning response coefficient is:

[0072] Establish a construction safety hazard database, obtain historical risk types in the subway dark excavation construction process, where the historical risk data includes risk type, occurrence time, impact scope, treatment measures, etc. Historical risk types include but are not limited to groundwater level changes, support structure instability, ground subsidence, tunnel collapse, etc. For the historical risk types obtained, identify the specific patterns and precursor characteristics of various risk types, and analyze the correlation between each risk type and the construction status and the status information of the tunnel excavation equipment, establish a mapping relationship, extract related data from the construction data sequence table, and set safety warning levels based on the historical risk types in the subway dark excavation construction process according to the severity of the risk type, which are the first level safety warning level, the second level safety warning level and the third level safety warning level, respectively. Wherein, the safety warning level increases in severity from the first level to the third level. According to the associated data of the historical risk types obtained, a safety warning model is constructed, and the warning response coefficient is obtained by combining the construction abnormality index and the equipment abnormality index. The safety risk of the subway dark excavation construction process is analyzed. Based on the warning response coefficient and the set safety warning level, the warning alarm threshold corresponding to each safety warning level is determined, the warning response coefficient is calculated according to the real-time data stream, and the safety warning level corresponding to the safety risk of the subway dark excavation construction is evaluated;

[0073] Furthermore, the calculation formula of the early warning response coefficient is: ;

[0074] in, is the warning response coefficient, For the The construction anomaly index of the observation, Representation and The relevant weight coefficients reflect the importance of different construction abnormality indicators. For the The device anomaly index of the observation, Representation and The relevant weight coefficient reflects the importance of abnormal indicators of different devices. and are the observation times of construction and equipment abnormality index, is the maximum value among all construction abnormality indices. is the benchmark value of the construction abnormality index. is the maximum value among all device abnormality indexes. is the average value of the equipment abnormality index, is the standard deviation of the equipment abnormality index, It is a weighted average value, which combines the influence of construction and equipment abnormality index. The root form and maximum function are used to represent the impact of the most serious situation in the construction abnormality index. It is an exponential function, which reflects the degree of deviation between the most serious situation and the average value in the equipment abnormality index. It should be noted that the early warning response coefficient As the construction and equipment abnormality index increases, or When the value of exceeds a certain threshold, will increase significantly, indicating a high-risk state;

[0075] Furthermore, multiple safety warning levels correspond to multiple warning alarm thresholds, wherein the warning alarm thresholds include an upper threshold and a lower threshold;

[0076] Multiple security warning levels and multiple warning alarm thresholds satisfy the following relationship:

[0077] Level 1 safety warning level ; The lowest risk, indicating that the current construction environment and equipment status are within the normal range or have only slight risks. Monitor the status, conduct regular inspections, and make small-scale adjustments or optimizations;

[0078] Second level safety warning level ; Medium risk, indicating that there are some risks in the construction environment or equipment status, which requires attention and certain preventive measures, such as increasing the monitoring frequency, reviewing the construction plan and equipment maintenance records, implementing preventive maintenance, and preparing emergency response plans;

[0079] Three-level safety warning level ; The highest risk, indicating that there is a serious risk in the construction environment or equipment status, and immediate action is required to immediately review the site situation, and it may be necessary to suspend construction or equipment operation, conduct on-site inspections and risk assessments, and activate emergency plans;

[0080] in, is the warning response coefficient, is the lower threshold corresponding to the second-level safety warning level and the upper threshold corresponding to the first-level safety warning level. The lower threshold corresponding to the third-level safety warning level and the upper threshold corresponding to the second-level safety warning level;

[0081] In the alarm notification module, the process of sending alarm information is:

[0082] Receive the safety warning level determined by the warning analysis module, and trigger the corresponding alarm notification process according to the safety warning level, immediately generate warning information matching the safety warning level, generate detailed alarm information according to the received warning information, including warning level, time, location, possible impact, recommended actions, etc., and automatically activate the corresponding emergency response mechanism to ensure rapid response, send alarm information to relevant personnel through multiple channels to ensure the timeliness and coverage of information transmission, and send alarm information through SMS, email, phone, system message push and other methods. While sending the alarm information, record the alarm history, including recipient, sending time, sending content, and sending status, to facilitate subsequent tracing and evaluation of the alarm event, and the relevant personnel who receive the alarm information respond and handle it according to the requirements of the emergency plan, and feedback the processing results to the system. The emergency plan includes measures such as evacuating personnel, reinforcing structures, and closing dangerous areas to reduce accident losses and ensure personnel safety.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A safety early warning alarm system based on subway underground excavation construction environment, including a safety early warning platform, characterized in that: The safety warning platform is connected in communication with a data acquisition module, a data transmission module, a data analysis module, a warning analysis module, an alarm notification module and a visual report module, wherein the modules are connected by electrical signals; The data acquisition module is used to collect the original data of the construction status of the construction site, including the horizontal displacement of the wall top, the horizontal displacement of the deep layer, the groundwater level, the ground stress and the stress of the supporting structure, and obtain the operation data of the tunnel boring equipment, and integrate them to obtain the construction data sequence table; The data transmission module is used to achieve extensive coverage of underground construction areas through a wireless sensor network and transmit raw data to the safety warning platform in real time; The data analysis module performs data mining, feature extraction and status analysis on the collected construction status data and operation data of the tunnel boring equipment based on machine learning technology to identify potential safety hazards and abnormal patterns. In the data analysis module, the process of identifying potential safety hazards and abnormal patterns is as follows: Extract construction status data and tunnel boring equipment operation data and perform pre-processing operations; The pre-processed construction status data is analyzed to extract characteristic indicators of associated safety hazards and abnormal patterns. Combined with the associated data of construction status characteristic indicators, a construction abnormality prediction model is constructed to obtain the construction abnormality index and clarify the correlation between the construction status and potential safety hazards and abnormal patterns. The construction status characteristic indicators are the horizontal displacement of the top of the wall, the horizontal displacement of the deep layer, the groundwater level, the ground stress and the stress of the support structure. The pre-processed tunnel boring equipment operation data is analyzed to extract characteristic indicators of associated safety hazards and abnormal patterns. Combined with the associated data of characteristic indicators of tunnel boring equipment operation status, an operation abnormality prediction model is constructed to obtain the equipment abnormality index and clarify the correlation between the operation status of tunnel boring equipment and potential safety hazards and abnormal patterns. The characteristic indicators of tunnel boring equipment operation status are tunneling speed, operation time, equipment temperature, equipment failure and vibration. Analyze the trends of construction status data and tunnel boring equipment operation data to identify anomalies and risks in the underground construction environment; The calculation formula of the construction abnormality index is: Among them, I C is the construction abnormality index, S i is the measured value of the ground stress at the ith monitoring point, μS is the average ground stress, and D j is the measured value of structural stress at the jth monitoring point, μD is the average value of structural stress, n and m are the number of monitoring points for ground stress and structural stress respectively, ΔH is the change of groundwater level, H th is the preset threshold of groundwater level change, and e is the base of the natural logarithm; The calculation formula of the equipment abnormality index is: Among them, I E is the equipment abnormality index, V k is the k-th observed tunneling speed, μV is the average tunneling speed, T l is the running time of the lth observation, μT is the average running time, X t is the equipment temperature observed at the tth time, μX is the average value of the equipment temperature, p, q and r represent the excavation speed, operation time and number of equipment temperature observations respectively, and F i is the vibration value of the equipment observed at the ith time, max(F i ) and min(F i ) represent the maximum and minimum values ​​of vibration respectively, and θF is the reference value of the equipment vibration value; The early warning analysis module, based on the extracted features and analysis results of the data analysis module and combined with the status information of the tunnel boring equipment, constructs a safety early warning model, obtains the early warning response coefficient, sets the corresponding safety early warning level, and performs early warning analysis. In the early warning analysis module, the early warning response coefficient is obtained as follows: Establish a construction safety hazard database to obtain historical risk types during subway tunnel construction; For the historical risk types obtained, identify the specific patterns and precursor characteristics of various risk types, analyze the correlation between each risk type and the construction status and status information of tunnel boring equipment, establish a mapping relationship, and extract related data from the construction data sequence table; Based on the historical risk types in the subway tunnel construction process, the safety warning levels are set according to the severity of the risk types, namely the first level safety warning level, the second level safety warning level and the third level safety warning level. The safety warning levels increase in severity from level one to level three. Based on the historical risk type correlation data obtained, a safety early warning model is constructed. By combining the construction abnormality index and equipment abnormality index, the early warning response coefficient is obtained to analyze the safety risks of the subway underground excavation construction process. Based on the warning response coefficient and the set safety warning level, determine the warning alarm threshold corresponding to each safety warning level; Calculate the early warning response coefficient based on real-time data streams and evaluate the safety warning level corresponding to the safety risk of subway tunnel construction; The calculation formula of the early warning response coefficient is: Among them, R is the early warning response coefficient, I C , i is the construction anomaly index of the ith observation, w i Indicates that C , i The relevant weight coefficient, I E , j is the equipment abnormality index observed at the jth time, w j Indicates that E , j The relevant weight coefficients, v and b are the number of observations of construction and equipment abnormality index, respectively, max(I C ) is the maximum value of all construction abnormality indexes, λC is the reference value of the construction abnormality index, max(I E ) is the maximum value of all equipment abnormality indexes, μE is the average value of the equipment abnormality index, and σE is the standard deviation of the equipment abnormality index; The alarm notification module activates the corresponding emergency response mechanism according to the safety warning level determined by the early warning analysis module, sends alarm information to relevant personnel, and activates the emergency plan at the same time; The visualization report module is used to present monitoring data, analysis results, and early warning alarm information in a visual form and generate various reports.

2. According to claim 1, a safety early warning alarm system based on subway underground excavation construction environment is characterized by: In the data acquisition module, the process of obtaining the construction data sequence table is as follows: At the subway tunnel construction site, various types of sensors are arranged, including displacement sensors, water level sensors, and stress sensors, to monitor key construction parameters. Displacement sensors are used to monitor the horizontal displacement of the wall top and the deep horizontal displacement, water level sensors are used to monitor the groundwater level, and stress sensors are used to monitor the ground stress and the stress of the supporting structure. Data acquisition instruments are installed to receive signals from the sensors for preliminary processing. Monitor the operating status of tunnel boring equipment, including tunneling speed, operating hours, equipment temperature, equipment failures and vibrations; The original data of the construction status and the operation data of the tunnel boring equipment are obtained through sensors and data acquisition instruments. The collection cycle of equal duration is set to collect related data, and the data is classified to obtain a construction data sequence list.

3. The safety early warning alarm system based on subway underground excavation construction environment according to claim 2 is characterized by: In the data transmission module, the process of real-time transmission of raw data to the security warning platform is as follows: Install wireless communication base stations in the underground construction area to form a wireless sensor network, and integrate the data acquisition instrument with the wireless sensor network so that each data collection point is wirelessly connected to the wireless sensor network; Encapsulate the raw data collected by the sensor into data packets and encrypt the data packets; The data packets are transmitted to the nearest communication gateway through the wireless sensor network and integrated and transmitted to the server of the security early warning platform; The data packet is decapsulated and decrypted on the security warning platform to restore the original data format, and the received data is verified and stored in the data warehouse of the security warning platform.

4. The subway underground excavation construction environment safety early warning alarm system according to claim 1 is characterized by: A plurality of safety warning levels correspond to a plurality of the warning alarm thresholds, wherein the warning alarm thresholds include an upper threshold and a lower threshold; The multiple safety warning levels and the multiple warning alarm thresholds satisfy the following relationship: Level 1 safety warning level R <R T1 ; Secondary safety warning level R T1 ≤R <R T2 ; Level 3 safety warning level R≥R T2 ; Among them, R is the early warning response coefficient, R T1 is the lower threshold corresponding to the second-level safety warning level and the upper threshold corresponding to the first-level safety warning level, R T2 It is the lower threshold corresponding to the third-level safety warning level and the upper threshold corresponding to the second-level safety warning level.

5. The subway underground excavation construction environment safety early warning alarm system according to claim 4 is characterized by: In the alarm notification module, the process of sending alarm information is as follows: Receive the security warning level determined by the warning analysis module, trigger the corresponding alarm notification process according to the security warning level, and immediately generate warning information matching the security warning level; Generate detailed alarm information based on the received warning information and automatically activate the corresponding emergency response mechanism; Send alarm information to relevant personnel through multiple channels, including SMS, email, phone, and system message push; When sending the alarm information, the alarm history is recorded, including the recipient, sending time, sending content, and sending status. The relevant personnel who receive the alarm information respond and handle it according to the requirements of the emergency plan, and feed back the processing results to the system.

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