A tunnel construction safety remote monitoring method and device

Abstract

The present invention discloses a method and device for remote monitoring of tunnel construction safety, which relates to the technical field of tunnel safety monitoring. The method includes: locating multiple safety risk areas; obtaining multiple associated risk data sets mapped to multiple safety risk areas; pre-building a risk quantification model to obtain multiple quantitative risk coefficients; obtaining multiple equipment dynamic deployment strategies; pre-building a risk remote operation and maintenance architecture; mapping and communicating multiple groups of tunnel intelligent control robots that have been deployed to multiple edge risk analysis nodes; and remote monitoring of tunnel construction safety for target monitoring tunnels. The method solves the technical problems of the existing tunnel construction safety monitoring, which is that it is difficult to conduct comprehensive, real-time and accurate monitoring of tunnel construction, which leads to incomplete identification of construction safety risks, inaccurate positioning, low efficiency and lack of pertinence in safety monitoring, and poor safety of tunnel construction, and achieves the technical effect of improving the efficiency of tunnel construction safety monitoring and reducing construction safety risks.

Description

Technical Field

[0001] The present application relates to the technical field related to tunnel safety monitoring, and in particular to a method and device for remote monitoring of tunnel construction safety. Background Art

[0002] In modern urban construction and infrastructure upgrades, tunnel engineering is a key project that connects urban space and alleviates traffic pressure, and its construction safety is particularly important. However, the tunnel construction environment is complex and changeable, involving multiple factors such as geological conditions, groundwater hydrology, construction machinery operation, and personnel work safety. These uncertainties greatly increase the safety risks in the construction process. Traditional tunnel construction safety monitoring methods often rely on on-site inspections and experience judgments. There are problems such as unreasonable layout of monitoring equipment, incomplete coverage, large monitoring blind spots, slow response speed, and incomplete data records. It is difficult to achieve comprehensive, real-time, and accurate monitoring of construction safety. It is impossible to perform data analysis based on real-time monitored on-site environmental parameters, predict potential construction safety risks, and accurately locate safety risk areas in the construction process, which increases the safety risks in the construction process and fails to discover hidden safety hazards in time.

[0003] Therefore, at the current stage, there are technical problems in the relevant technologies for remote monitoring of tunnel construction safety, such as difficulty in conducting comprehensive, real-time and accurate monitoring of tunnel construction, which leads to incomplete identification of construction safety risks, inaccurate positioning, low efficiency and lack of pertinence in safety monitoring, resulting in poor safety of tunnel construction. Summary of the invention

[0004] The present application provides a method and device for remote monitoring of tunnel construction safety, and adopts technical means such as accurate positioning of risk areas, intelligent data collection and analysis, dynamic deployment of monitoring equipment, and construction of a risk remote operation and maintenance architecture to solve the technical problems existing in existing tunnel construction safety monitoring, such as difficulty in conducting comprehensive, real-time, and accurate monitoring of tunnel construction, which leads to incomplete identification of construction safety risks, inaccurate positioning, low efficiency, and lack of pertinence in safety monitoring, resulting in poor safety of tunnel construction. The application achieves the technical effect of improving the efficiency of tunnel construction safety monitoring and reducing construction safety risks.

[0005] The present application provides a method for remote monitoring of tunnel construction safety, the method comprising: interactively obtaining a tunnel construction drawing of a target monitored tunnel, and performing tunnel construction risk analysis based on the tunnel construction drawing to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier; presetting an associated risk indicator set, and using the associated risk indicator set as a data collection constraint to obtain multiple associated risk data sets mapped to the multiple safety risk areas; pre-building a risk quantification model, and synchronizing the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients; calling multiple main safety risk types of the multiple safety risk areas, and according to the A comprehensive analysis of the deployment of monitoring equipment is performed based on multiple main safety risk types and multiple quantitative risk coefficients to obtain multiple dynamic deployment strategies for equipment; a risk remote operation and maintenance architecture is pre-built, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes; multiple groups of tunnel intelligent control robots are dispatched and deployed to the multiple safety risk areas according to the multiple dynamic deployment strategies for equipment, and the multiple groups of tunnel intelligent control robots that have been deployed are mapped and communicated to the multiple edge risk analysis nodes according to the multiple safety risk areas to complete the localization of the risk remote operation and maintenance architecture; and the target monitoring tunnel is remotely monitored for tunnel construction safety based on the risk remote operation and maintenance architecture.

[0006] In a possible implementation, tunnel construction drawings are obtained interactively, and tunnel construction risk analysis is performed based on the tunnel construction drawings to locate multiple safety risk areas, and the following processing is also performed: the target construction boundary is located based on the tunnel construction drawings; relevant geological data is called with the target construction boundary as a constraint to obtain target geological data; the tunnel construction drawings are used as a modeling basis, and a tunnel three-dimensional model of the target monitored tunnel is constructed using BIM technology; grid constraints are preset, and the tunnel three-dimensional model is divided into K local tunnel models based on the grid constraints; tunnel construction risk analysis is performed on the K local tunnel models based on the target geological data, and the K local tunnel models are merged based on the analysis results to obtain the multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier.

[0007] In a possible implementation, a method for remote monitoring of tunnel construction safety also performs the following processing: interacting with historical tunnel accidents to obtain multiple sample risk types, multiple sample tunnel feature sets and multiple sample local geological feature sets of multiple sample local tunnels; aggregating feature indicators of the multiple sample tunnel feature sets to obtain a first feature indicator set, and constructing a first feature acquisition engine based on the first feature indicator set; aggregating feature indicators of the multiple sample local geological feature sets to obtain a second feature indicator set, and constructing a second feature acquisition engine based on the second feature indicator set; integrating the first feature acquisition engine and the second feature acquisition engine to obtain a data feature extraction layer; constructing a tunnel risk identification layer based on a decision tree, and using the multiple sample risk types, multiple sample tunnel feature sets and multiple sample local geological feature sets as training data to optimize the risk identification accuracy of the tunnel risk identification layer; and cascading the data feature extraction layer and the tunnel risk identification layer to obtain a tunnel risk identification model.

[0008] In a possible implementation, tunnel construction risk analysis is performed on the K local tunnel models according to the target geological data, and the K local tunnel models are merged based on the analysis results to obtain the multiple safety risk areas, and the following processing is also performed: the target geological data is divided according to the K local tunnel models to obtain K local geological data; the K local geological data and the K local tunnel models are input into the tunnel risk identification model as input data; the first feature acquisition engine is used in the data feature extraction layer of the tunnel risk identification model to perform feature extraction on the K local tunnel models; Extraction to obtain K first local feature sets, use a second feature acquisition engine to extract features from the K local geological data to obtain K second local feature sets; synchronize the K first local feature sets and the K second local feature sets to the tunnel risk identification model to obtain K local construction risk types; according to the K local construction risk types, traverse and merge the K local tunnel models that are adjacent and have the same local construction risk type to obtain the multiple safety risk areas, and use the local construction risk type of each safety risk area as the main safety risk type for regional identification.

[0009] In a possible implementation, the associated risk indicator set is used as a data acquisition constraint to obtain multiple associated risk data sets mapped to the multiple safety risk areas, and the following processing is also performed: the associated risk indicator set includes ambient humidity, ambient temperature, water pressure, water pressure direction, steel bar stress, anchor tension and vibration frequency.

[0010] In a possible implementation, a risk quantification model is pre-constructed, and the multiple associated risk data sets are synchronized to the risk quantification model to obtain multiple quantitative risk coefficients. The following processing is also performed: a risk quantification function is pre-constructed, and the risk quantification function is as follows:

[0011] ;

[0012] in, To quantify the risk factor, is the ambient humidity, is the ambient temperature, is the water pressure, is the water pressure direction, is the steel bar stress, is the anchor tension, is the anchor tension limit, is the vibration frequency; constructs the risk quantification model based on the risk quantification function; interactively obtains multiple anchor tension limits of the multiple safety risk areas, and synchronizes the multiple anchor tension limits and multiple associated risk data sets to the risk quantification model, and obtains the multiple quantitative risk coefficients through the risk quantification function calculation.

[0013] In a possible implementation, the tunnel construction safety remote monitoring of the target monitored tunnel is performed based on the risk remote operation and maintenance architecture, and the following processing is performed: in the risk remote operation and maintenance architecture, the multiple edge risk analysis nodes receive and analyze in real time the real-time construction monitoring data uploaded by the multiple groups of tunnel intelligent control robots, and locate the tunnel risk position set, wherein the multiple tunnel risk positions in the tunnel risk position set have risk type identifications; the multiple edge risk analysis nodes upload the tunnel risk position set to the risk operation and maintenance scheduling cloud; the risk operation and maintenance scheduling cloud analyzes the tunnel risk position set to generate risk operation and maintenance instructions; the operation and maintenance center performs tunnel construction risk intervention on the target monitored tunnel according to the risk operation and maintenance instructions.

[0014] The present application also provides a remote monitoring device for tunnel construction safety, including: a safety risk area positioning module, which is used to interactively obtain tunnel construction drawings of a target monitored tunnel, and perform tunnel construction risk analysis based on the tunnel construction drawings to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier; an associated risk data set acquisition module, which is used to preset an associated risk indicator set, and use the associated risk indicator set as a data acquisition constraint to acquire multiple associated risk data sets mapped to the multiple safety risk areas; a quantitative risk coefficient acquisition module, which is used to pre-build a risk quantification model, and synchronize the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients; an equipment dynamic deployment strategy acquisition module, which is used to call multiple main safety risk types of the multiple safety risk areas, And a comprehensive analysis of the deployment of monitoring equipment is performed based on the multiple main safety risk types and multiple quantitative risk coefficients to obtain multiple dynamic deployment strategies for equipment; a risk remote operation and maintenance architecture construction module is used to pre-build a risk remote operation and maintenance architecture, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes; a tunnel intelligent control robot deployment module is used to schedule and deploy multiple groups of tunnel intelligent control robots to the multiple safety risk areas according to the multiple equipment dynamic deployment strategies, and map the deployed multiple groups of tunnel intelligent control robots to the multiple edge risk analysis nodes according to the multiple safety risk areas to complete the localization of the risk remote operation and maintenance architecture; a tunnel construction safety remote monitoring module is used to perform tunnel construction safety remote monitoring of the target monitoring tunnel based on the risk remote operation and maintenance architecture.

[0015] Through the method and device for remote monitoring of tunnel construction safety proposed in this application, multiple safety risk areas are located; multiple associated risk data sets mapped to multiple safety risk areas are obtained; risk quantification models are pre-built to obtain multiple quantitative risk coefficients; multiple equipment dynamic deployment strategies are obtained; risk remote operation and maintenance architecture is pre-built; multiple groups of tunnel intelligent control robots that have been deployed are mapped and communicated to multiple edge risk analysis nodes; and tunnel construction safety remote monitoring is performed on the target monitoring tunnel. This solves the technical problem that it is difficult to conduct comprehensive, real-time, and accurate monitoring of tunnel construction in existing tunnel construction safety monitoring, which leads to incomplete identification of construction safety risks, inaccurate positioning, low efficiency of safety monitoring, and lack of pertinence, resulting in poor safety of tunnel construction, and achieves the technical effect of improving the efficiency of tunnel construction safety monitoring and reducing construction safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0017] Figure 1 A schematic diagram of a tunnel construction safety remote monitoring method provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the structure of a tunnel construction safety remote monitoring device provided in an embodiment of the present application.

[0019] Explanation of the accompanying drawings: safety risk area positioning module 10, associated risk data set acquisition module 20, quantitative risk coefficient acquisition module 30, equipment dynamic deployment strategy acquisition module 40, risk remote operation and maintenance architecture construction module 50, tunnel intelligent control robot deployment module 60, tunnel construction safety remote monitoring module 70. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0023] The present application embodiment provides a tunnel construction safety remote monitoring method, such as Figure 1 As shown, the method includes:

[0024] Step S100, interactively obtain the tunnel construction drawings of the target monitored tunnel, and perform tunnel construction risk analysis based on the tunnel construction drawings to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier. In the tunnel construction project, the construction drawings of the target tunnel are obtained through interaction and the tunnel construction risk analysis is carried out. Specifically, the tunnel construction drawings usually contain detailed design information of the tunnel, such as structural layout, construction process requirements, etc. First, the tunnel construction drawings are analyzed to analyze the overall structure, support method, drainage system, etc. of the tunnel to determine the potential structural risks, and at the same time obtain the relevant information of the construction process requirements marked in the construction drawings. Then, based on the analysis of the construction drawings, the tunnel construction risk analysis is carried out, which mainly includes technical risks (such as great construction difficulty, high technical requirements, complex process, etc.), safety risks (such as landslides, water inrush and other geological disasters, fire, explosion, etc.), and environmental risks (such as the impact of construction on the surrounding environment, including underground pipelines, buildings, traffic, etc.). Then, the main safety risk type identification is marked for each safety risk area to ensure that the main risk type of the area is clearly and accurately reflected. For example, the landslide risk area indicates that there is a high landslide risk in the area, which may be due to unstable geological conditions and broken rock formations; the water inrush risk area indicates that there is a water inrush risk in the area, which may be due to abundant groundwater and weak aquiclude.

[0025] In a possible implementation, step S100 further includes step S110, locating the target construction boundary according to the tunnel construction drawings. The target construction boundary is located according to the tunnel construction drawings, and the design size, shape, location and other information of the tunnel are determined, and then the specific location and scope of the tunnel construction are determined. Step S120, relevant geological data are called with the target construction boundary as a constraint to obtain target geological data. According to the target construction boundary, the geological survey report, geological profile, geophysical data, etc. in the area are called, and the geological data are analyzed to understand the geological structure, rock layer distribution, groundwater conditions, etc. of the tunnel crossing area, identify possible geological risks such as faults, weak strata, groundwater, etc., understand the geological conditions of the construction area, and provide geological basis for tunnel construction. Step S130, the tunnel construction drawings are used as the modeling basis, and the BIM technology is used to construct a tunnel three-dimensional model of the target monitoring tunnel. Through BIM technology and BIM software (such as Bentley's CNCCBIM OpenRoads), a tunnel three-dimensional model is created according to the tunnel construction drawings, including detailed information such as the geometry, size, structure, etc. of the tunnel, which is convenient for construction simulation and risk analysis.

[0026] Step S140, preset grid constraints, and divide the tunnel three-dimensional model into K local tunnel models according to the grid constraints. Preset grid constraints in the tunnel three-dimensional model, determine the standards for dividing local tunnel models (such as length, width, height, etc.), and divide the tunnel three-dimensional model into K local tunnel models according to the grid constraints, that is, divide it into smaller areas, so as to perform more detailed risk analysis. Step S150, perform tunnel construction risk analysis on the K local tunnel models according to the target geological data, and merge the K local tunnel models based on the analysis results to obtain the multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier. Risk analysis is conducted on each local tunnel model, taking into account factors such as geological conditions, construction methods, and construction environment, to identify safety risks in each local tunnel model, such as landslides and water inrush. Based on the risk analysis results, local tunnel models with similar risk types are merged into a safety risk area to identify safety risk areas in tunnel construction, determine the main risk type in each area, and assign a main safety risk type identifier to each safety risk area so that targeted risk management and control can be carried out during construction.

[0027] In a possible implementation, step S100 also includes step S144, interacting with historical tunnel accidents to obtain multiple sample risk types of multiple sample local tunnels, multiple sample tunnel feature sets, and multiple sample local geological feature sets. An in-depth investigation and analysis of tunnel accidents that have occurred in history, which may include fires, traffic accidents, landslides, water inrush, rock bursts, gas explosions and other types, is conducted to obtain multiple sample risk types of multiple sample local tunnels, multiple sample tunnel feature sets and multiple sample local geological feature sets. Among them, the multiple sample risk types may include engineering technical risks (such as improper construction methods, failure of support structures, etc.), management risks (such as incomplete rules and regulations, inadequate supervision, etc.), natural risks (such as earthquakes, floods, geological disasters, etc.) and man-made risks (such as operational errors, deliberate destruction, etc.); tunnel characteristics describe tunnel geometry, structure, construction conditions and other characteristics. The feature sets of multiple sample tunnels may include tunnel length, width, height, burial depth, construction methods, support structures, drainage systems, ventilation systems, etc.; the geological feature set describes the geological conditions of the tunnel area. The geological feature sets of multiple sample local areas include geological structure, rock type, rock strength, groundwater conditions, seismic activity, etc.

[0028] Step S145, feature index aggregation is performed on the plurality of sample tunnel feature sets to obtain a first feature index set, and a first feature acquisition engine is constructed based on the first feature index set. Feature indexes refer to various metrics or attributes describing tunnel features, such as tunnel length, width, burial depth, construction method (such as drilling and blasting method, shield method, etc.), support structure type (such as initial support, secondary lining, etc.), rock strength, etc., common or key feature indexes are extracted from the plurality of sample tunnel feature sets, and they are combined into a set (i.e., the first feature index set), and then, based on the first feature index set, the acquisition rules for each feature index are defined, and then the first feature acquisition engine is obtained, which is used to automatically extract feature data corresponding to the first feature index set from a new tunnel project.

[0029] Step S146, perform feature index aggregation on the multiple sample local geological feature sets to obtain a second feature index set, and construct a second feature acquisition engine based on the second feature index set. Similarly, common or key geological feature indexes are extracted from multiple sample local geological feature sets, and they are combined into a new set, namely, a second feature index set. A second feature acquisition engine is constructed based on the second feature index set, which can automatically extract geological feature data corresponding to the second feature index set from the geological environment of the area where the new tunnel project is located. Step S147, integrate the first feature acquisition engine and the second feature acquisition engine to obtain a data feature extraction layer. The first feature acquisition engine and the second feature acquisition engine are integrated together to form a data feature extraction layer, which is responsible for extracting key feature data for risk identification from tunnel projects and geological environments.

[0030] Step S148, construct a tunnel risk identification layer based on a decision tree, and use the multiple sample risk types, multiple sample tunnel feature sets, and multiple sample local geological feature sets as training data to optimize the risk identification accuracy of the tunnel risk identification layer. Use a decision tree algorithm to construct a tunnel risk identification model, learn the mapping relationship between features and risk types through training data, wherein the model training data is composed of multiple sample risk types, multiple sample tunnel feature sets, and multiple sample local geological feature sets, and optimize the identification accuracy of the tunnel risk identification layer by adjusting the parameters of the decision tree (such as tree depth, splitting criteria, etc.) and the training process (such as cross-validation, regularization, etc.). Step S149, cascade the data feature extraction layer and the tunnel risk identification layer to obtain a tunnel risk identification model. The data feature extraction layer and the tunnel risk identification layer are cascaded to form a complete tunnel risk identification model, which can automatically extract feature data from tunnel projects and geological environments, and perform risk identification based on the decision tree algorithm.

[0031] In a possible implementation, step S150 further includes step S151, dividing the target geological data according to the K local tunnel models to obtain K local geological data. According to the spatial position and range of the K local tunnel models, the target geological data is divided into K local geological data accordingly, each local geological data corresponds to a local tunnel model, and reflects the geological characteristics of the area where the model is located. Step S152, inputting the K local geological data and the K local tunnel models as input data into the tunnel risk identification model. The K local geological data and the K local tunnel models are sent as input data to the tunnel risk identification model, and the tunnel risk identification model can process geological and tunnel model data and output construction risk information. Step S153, in the data feature extraction layer of the tunnel risk identification model, the first feature acquisition engine is used to extract features from the K local tunnel models to obtain K first local feature sets, and the second feature acquisition engine is used to extract features from the K local geological data to obtain K second local feature sets. The first feature acquisition engine is used to extract key features from K local tunnel models to form K first local feature sets, including tunnel geometric parameters, construction methods, support structures, etc. The second feature acquisition engine is used to extract key geological features from K local geological data to form K second local feature sets, including rock type, rock strength, groundwater conditions, seismic activity, etc.

[0032] Step S154, synchronizing the K first local feature sets and the K second local feature sets to the tunnel risk identification model to obtain K local construction risk types. The K first local feature sets and the K second local feature sets are used as input data and input into the tunnel risk identification model. By performing complex calculations and analyses on the input feature sets, the construction risk corresponding to each local tunnel model is evaluated, including the calculation and weight allocation of multiple risk indicators, as well as the classification of risk levels, and then K local construction risk types are output. These risk types are a refined summary of the construction risks of each local tunnel model, which may include landslide risk, water inrush risk, rock burst risk, gas explosion risk, etc. Step S155, according to the K local construction risk types, the K local tunnel models that are adjacent and have the same local construction risk type are traversed and merged to obtain the multiple safety risk areas, and the local construction risk type of each safety risk area is used as the main safety risk type for regional identification. K local tunnel models are traversed, the adjacent relationships between them are checked, and adjacent local tunnel models with the same local construction risk type are identified. The identified adjacent local tunnel models with the same risk type are merged into a larger safety risk area, that is, based on the similarity of risk types, tunnel sections with similar risk characteristics are divided into the same risk area, and finally multiple safety risk areas are formed. Each area contains a group of adjacent local tunnel models with the same risk type. Each safety risk area is geographically complete, that is, all tunnel sections in the area are connected, and the construction risk types they face are consistent. For each safety risk area, its main (or most common) local construction risk type is selected as the main safety risk type of the area, and then the main safety risk type is used to identify the safety risk area.

[0033] Step S200, preset an associated risk indicator set, and use the associated risk indicator set as a data collection constraint to obtain multiple associated risk data sets mapped to the multiple safety risk areas. The associated risk indicator set includes environmental humidity, environmental temperature, water pressure, water pressure direction, steel bar stress, anchor tension and vibration frequency. According to the characteristics of tunnel construction and the results of safety risk analysis, a set of associated risk indicators is preset, including ambient humidity, ambient temperature, water pressure, water pressure direction, steel bar stress, anchor tension and vibration frequency. These indicators can comprehensively reflect the safety risk status during tunnel construction. Specifically, ambient humidity reflects the humidity status of the tunnel construction environment. High humidity may lead to problems such as accelerated concrete coagulation and decreased material performance, increasing construction difficulty and safety hazards. Ambient temperature affects the thermal expansion and contraction of materials, the work efficiency and safety of construction personnel. Too high or too low temperature may have an adverse effect on the strength and durability of materials such as concrete and steel bars. Water pressure is an important safety risk factor in tunnel construction areas with abundant groundwater. Excessive water pressure may lead to problems such as rupture and leakage of tunnel lining, and even cause water inrush accidents. The direction of water pressure helps to judge the trend of groundwater flow and the areas that may be affected. Steel bar stress directly reflects the stability and safety of the support structure. Monitoring steel bar stress can timely discover potential structural damage risks. Anchors are Monitoring the tension of anchor bolts is a commonly used reinforcement measure in tunnel support, which can evaluate the reinforcement effect and ensure the stability and safety of the support structure. The vibration generated during tunnel construction may affect the surrounding environment, such as causing structural damage to adjacent buildings or damage to underground pipelines. Monitoring the vibration frequency helps to evaluate the impact of construction activities on the surrounding environment. Taking these associated risk indicators as constraints for data collection means that during the data collection process, it is ensured that the collected data can accurately reflect the changes in these indicators, including the selection and layout of data collection equipment, the setting of data collection frequency, and the control of data quality. According to the data collection constraints, various data in the tunnel construction process are collected in real time by installing sensors, monitoring equipment, etc., thereby forming an associated risk data set mapped to multiple safety risk areas. Each data set contains data records of key indicators such as ambient humidity, ambient temperature, water pressure, water pressure direction, steel bar stress, anchor bolt tension and vibration frequency in a specific safety risk area within a certain period of time.

[0034] Step S300, pre-constructing a risk quantification model, and synchronizing the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients. Taking the safety risk of the tunnel construction process as the target, a risk quantification model is constructed based on regression analysis, logistic regression, decision tree or neural network. Considering the complexity and uncertainty of the tunnel construction risk, the overall framework of the model is designed, including input layer, output layer, etc., and key parameters (such as weights, bias terms, activation functions, etc.) and variables (input variable associated risk indicators and output variable quantitative risk coefficients) in the model are determined. Then, the historical associated risk data set of the target tunnel is obtained, pre-processed and divided into a training set and a validation set, the model is trained using the training set data, the parameters of the model are adjusted by an optimization algorithm, and the parameters of the model are optimized according to the results of the validation set, such as adjusting the learning rate, batch size, number of iterations, etc. The training process and the tuning process are iterated repeatedly until the model performance reaches a satisfactory level, so that the model can obtain the corresponding quantitative risk coefficient according to the associated risk data, and then the multiple associated risk data sets are synchronized to the risk quantification model to obtain the corresponding quantitative risk coefficient, reflecting the risk level and development trend of different safety risk areas.

[0035] In a possible implementation, step S300 further includes step S310, pre-building a risk quantification function, where the risk quantification function is as follows:

[0036] ;

[0037] in, To quantify the risk factor, is the ambient humidity, is the ambient temperature, is the water pressure, is the water pressure direction, is the steel bar stress, is the anchor tension, is the anchor tension limit, is the vibration frequency. Specifically, the anchor tension limit refers to the maximum value of the anchor tension. The larger the value of the risk quantification function is, the higher the risk level is.

[0038] Step S320, construct and obtain the risk quantification model based on the risk quantification function. Using the defined risk quantification function, combined with the specific characteristics and requirements of the tunnel project, a risk quantification model is constructed. This model can receive input data (such as anchor tension limit, geological conditions, construction parameters, etc.) and output a quantitative risk coefficient. Step S330, interactively obtain multiple anchor tension limits of the multiple safety risk areas, and synchronize the multiple anchor tension limits and multiple associated risk data sets to the risk quantification model, and calculate the multiple quantitative risk coefficients through the risk quantification function. Obtain the anchor tension limit corresponding to each safety risk area, sort out the anchor tension limits of the multiple safety risk areas obtained interactively, ensure the accuracy and completeness of the data, and synchronize the sorted anchor tension limit and multiple associated risk data sets (such as geological survey data, construction monitoring data, historical risk event data, etc.) to the risk quantification model. After receiving these data, the risk quantification model uses the built-in risk quantification function to calculate and evaluate the potential risk level of each safety risk area. After the calculation is completed, multiple quantitative risk coefficients are output, representing the risk level and level of different safety risk areas.

[0039] Step S400, calling multiple main security risk types of the multiple security risk areas, and performing a comprehensive analysis of the deployment of monitoring equipment according to the multiple main security risk types and multiple quantitative risk coefficients to obtain multiple dynamic deployment strategies for equipment. In the tunnel construction safety monitoring system, multiple main safety risk types of multiple safety risk areas are called, and a comprehensive analysis of the deployment of monitoring equipment (such as tunnel intelligent control robots) is performed based on these main safety risk types and multiple quantitative risk coefficients. Specifically, multiple main safety risk types of multiple safety risk areas are obtained, and multiple connected risk thresholds are set for each main safety risk type, wherein the risk threshold is determined based on historical data and is used to plan different risk levels. For each risk threshold, a corresponding tunnel intelligent control robot deployment density is mapped. The deployment density indicates the number of robots that should be deployed per unit area or unit length under a specific risk level. The risk threshold of each main safety risk type and the corresponding deployment density are integrated into a table, namely, a risk threshold-deployment density table. Then, the quantitative risk coefficient of each safety risk area is mapped to the risk threshold-deployment density table of the corresponding main safety risk type. By traversing the table, the risk threshold matching the quantitative risk coefficient is found, and the tunnel intelligent control robot deployment density corresponding to the threshold is directly obtained. According to the results obtained by mapping and traversing the table, the tunnel intelligent control robot deployment density of each safety risk area is determined, which constitutes the core content of the dynamic deployment strategy of the monitoring equipment. According to the actual situation of the construction site and the real-time feedback of the monitoring data, the deployment strategy is optimized and adjusted as necessary. For example, the deployment density of tunnel intelligent control robots is increased in areas where the risk level suddenly increases; the deployment density is appropriately reduced in areas where the risk level tends to be stable, etc. The dynamic deployment strategy of the equipment is implemented in accordance with the formulation, and the working status and monitoring data of the robot are monitored in real time. Among them, the tunnel intelligent control robot consists of a lower power layer and an upper equipment layer. The equipment carried can be flexibly configured according to the actual needs of the site. The lower power layer adopts a crawler walking platform, which has the advantages of good stability, strong passability, and large carrying capacity. Adaptable to various complex terrains, the operating system adopts wireless remote control technology, which is convenient and flexible for remote operation. The upper equipment layer adopts an embedded layout, including video monitoring, one-button alarm, emergency call (broadcast) and other modules, which can maximize the protection of equipment. It combines multiple advantages such as flexible mobility, easy operation, safety and reliability, durability, explosion-proof and dustproof. It can provide accurate information for emergency management in a timely manner when encountering dangerous situations, allowing safety personnel to quickly make correct instructions, making up for the shortcomings of existing technologies that cannot continuously monitor the face, replacing the heavy safety control tasks in the past, and solving the problems of fragmented and discontinuous safety management.

[0040] Step S500, pre-build a risk remote operation and maintenance architecture, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes. Pre-building a risk remote operation and maintenance architecture refers to designing and building a comprehensive architecture for remote monitoring and management of various construction safety risks, aiming to improve the ability to identify and respond to risks, and more efficiently manage and handle various potential risks through remote operation and maintenance. Specifically, the risk remote operation and maintenance architecture is conventional risk identification and risk operation and maintenance, and its core function is to identify and handle risks in a timely manner. Conventional risk identification includes monitoring various possible sources of risk, obtaining real-time data through sensors and data acquisition equipment, and analyzing these data to identify potential risks. Risk operation and maintenance refers to taking appropriate actions to control and eliminate risks after risk identification to ensure the safe and stable operation of equipment. The edge risk analysis nodes in the architecture can identify tunnel risk types based on the real-time risk data of the acquired area. Specifically, it refers to setting up multiple edge computing nodes in the architecture, which are located close to The location of the data source is responsible for real-time analysis and processing of risk data collected in the area. Through edge computing, it can quickly analyze at a location close to the data source and identify various types of risks that may exist in the tunnel, such as structural damage, leakage, rising water levels, etc. Edge computing nodes can reduce data transmission delays and improve the real-time and accuracy of risk identification; the risk operation and maintenance scheduling cloud in the architecture allocates risk tasks according to the work status and risk elimination skills of the operation and maintenance personnel, and can comprehensively monitor and manage the work status of the operation and maintenance personnel, including their workload, skill level and availability. Based on this information, the risk operation and maintenance scheduling cloud can intelligently allocate risk handling tasks and assign tasks to the most suitable operation and maintenance personnel, so as to achieve accurate risk identification and efficient risk operation and maintenance, so as to ensure that risks can be eliminated quickly and effectively, thereby improving operation and maintenance efficiency and improving the efficiency and effectiveness of risk handling.

[0041] Step S600, scheduling and deploying multiple groups of tunnel intelligent control robots to the multiple security risk areas according to the multiple equipment dynamic deployment strategies, and mapping and communicating the deployed multiple groups of tunnel intelligent control robots to the multiple edge risk analysis nodes according to the multiple security risk areas to complete the localization of the risk remote operation and maintenance architecture. According to the formulated equipment dynamic deployment strategy, the number, type and specific deployment location of tunnel intelligent control robots to be deployed in each safety risk area are clarified. In the safety risk area, the tunnel intelligent control robots are deployed according to the locations specified by the strategy. The location of the edge risk analysis node corresponding to each safety risk area is determined. These nodes are usually deployed at a location close to the safety risk area to receive and analyze data from the tunnel intelligent control robot in real time. A unified communication protocol is formulated, including data transmission format, communication frequency, error handling mechanism, etc., to ensure smooth communication between the tunnel intelligent control robot and the edge risk analysis node. The deployed tunnel intelligent control robot is mapped to the corresponding edge risk analysis node through wired or wireless communication. Then, multiple edge risk analysis nodes are integrated with the risk operation and maintenance scheduling cloud, including configuring the cloud scheduling strategy, receiving data from the node and performing global analysis, etc. According to the actual situation of each safety risk area and the operating status of the tunnel intelligent control robot, the risk remote operation and maintenance architecture is locally optimized, for example, adjusting the data processing capability of the node, optimizing the stability of the communication network, etc.

[0042] Step S700, based on the risk remote operation and maintenance architecture, the target monitoring tunnel is remotely monitored for tunnel construction safety. Advanced information technology means such as the Internet of Things, cloud computing, and big data are used to achieve real-time monitoring, early warning, analysis, and emergency response of safety risks during tunnel construction through the remote operation and maintenance architecture. Specifically, through the tunnel intelligent control robot and various sensors deployed in the tunnel, various data of the construction site are collected in real time. The collected data are transmitted to the edge risk analysis node through the communication network for real-time analysis, and the data are quickly processed to identify possible safety risks. According to the edge analysis results, the safety risks existing in the construction process are timely identified, and an early warning signal is issued, which is transmitted to the on-site construction personnel and the remote operation and maintenance center through the risk operation and maintenance scheduling cloud to ensure that relevant personnel can take measures in time. The risk operation and maintenance scheduling cloud intelligently allocates risk handling tasks based on real-time risk information and the work status of the operation and maintenance personnel to improve the safety, efficiency, and management level of tunnel construction.

[0043] In a possible implementation, step S700 further includes step S710, in which, in the risk remote operation and maintenance architecture, the multiple edge risk analysis nodes receive and analyze the real-time construction monitoring data uploaded by the multiple groups of tunnel intelligent control robots in real time, and locate the tunnel risk position set, wherein the multiple tunnel risk positions in the tunnel risk position set have risk type identification. The tunnel intelligent control robot is an automated monitoring device deployed at the tunnel construction site, which can collect various data during the construction process in real time and upload them to multiple edge risk analysis nodes. After receiving the data, the edge risk analysis node uses the built-in risk analysis algorithm to analyze and process the monitoring data to identify and locate multiple potential risk positions in the tunnel, each of which has a risk type identification. Step S720, the multiple edge risk analysis nodes upload the tunnel risk position set to the risk operation and maintenance scheduling cloud. The edge risk analysis node uploads the located tunnel risk position set to the risk operation and maintenance scheduling cloud for higher-level analysis and decision-making.

[0044] Step S730, the risk operation and maintenance scheduling cloud analyzes the tunnel risk location set to generate risk operation and maintenance instructions. After receiving the tunnel risk location set from the edge node, the risk operation and maintenance scheduling cloud uses more powerful computing resources and a more comprehensive database for further analysis, such as combining historical data to more accurately assess tunnel risks, and generates corresponding risk operation and maintenance instructions, which may include emergency evacuation, reinforcement measures, adjustment of construction plans, etc., aiming to minimize the risks in tunnel construction. Step S740, the operation and maintenance center performs tunnel construction risk intervention on the target monitored tunnel according to the risk operation and maintenance instructions. After receiving the instructions from the risk operation and maintenance scheduling cloud, the operation and maintenance center will immediately organize relevant personnel and equipment to perform tunnel construction risk intervention on the target monitored tunnel, which may include on-site investigation, emergency maintenance, adjustment of construction plans, enhanced monitoring and other measures to ensure that all instructions are executed in a timely and effective manner to ensure the safety and smooth progress of tunnel construction.

[0045] In the above, refer to Figure 1 A tunnel construction safety remote monitoring method according to an embodiment of the present invention is described in detail. Figure 2 A tunnel construction safety remote monitoring device according to an embodiment of the present invention is described.

[0046] A remote monitoring device for tunnel construction safety according to an embodiment of the present invention is used to solve the technical problem that it is difficult to conduct comprehensive, real-time and accurate monitoring of tunnel construction in the existing tunnel construction safety monitoring, which leads to incomplete identification of construction safety risks, inaccurate positioning, low efficiency and insufficient pertinence of safety monitoring, and poor safety of tunnel construction, thereby achieving the technical effect of improving the efficiency of tunnel construction safety monitoring and reducing construction safety risks. A remote monitoring device for tunnel construction safety includes: a safety risk area positioning module 10, an associated risk data set acquisition module 20, a quantitative risk coefficient acquisition module 30, an equipment dynamic deployment strategy acquisition module 40, a risk remote operation and maintenance architecture construction module 50, a tunnel intelligent control robot deployment module 60, and a tunnel construction safety remote monitoring module 70.

[0047] The safety risk area positioning module 10 is used to interactively obtain the tunnel construction drawings of the target monitored tunnel, and perform tunnel construction risk analysis according to the tunnel construction drawings to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier;

[0048] An associated risk data set acquisition module 20, configured to preset an associated risk indicator set, and use the associated risk indicator set as a data acquisition constraint to acquire a plurality of associated risk data sets mapped to the plurality of security risk areas;

[0049] A quantitative risk coefficient obtaining module 30 is used to pre-build a risk quantification model and synchronize the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients;

[0050] The equipment dynamic deployment strategy acquisition module 40 is used to call multiple main security risk types of the multiple security risk areas, and perform a comprehensive analysis of the monitoring equipment deployment according to the multiple main security risk types and multiple quantitative risk coefficients to obtain multiple equipment dynamic deployment strategies;

[0051] A risk remote operation and maintenance architecture building module 50 is used to pre-build a risk remote operation and maintenance architecture, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes;

[0052] The tunnel intelligent control robot deployment module 60 is used to schedule and deploy multiple groups of tunnel intelligent control robots to the multiple security risk areas according to the multiple equipment dynamic deployment strategies, and map and communicate the multiple groups of tunnel intelligent control robots that have been deployed to the multiple edge risk analysis nodes according to the multiple security risk areas to complete the localization of the risk remote operation and maintenance architecture;

[0053] The tunnel construction safety remote monitoring module 70 is used to perform tunnel construction safety remote monitoring on the target monitoring tunnel based on the risk remote operation and maintenance architecture.

[0054] The specific configuration of the safety risk area positioning module 10 will be described in detail below. The safety risk area positioning module 10 may further include: locating the target construction boundary according to the tunnel construction drawings; calling relevant geological data with the target construction boundary as a constraint to obtain target geological data; using the tunnel construction drawings as a modeling basis, and using BIM technology to construct a tunnel three-dimensional model of the target monitoring tunnel; presetting grid constraints, and dividing the tunnel three-dimensional model into K local tunnel models according to the grid constraints; performing tunnel construction risk analysis on the K local tunnel models according to the target geological data, and merging the K local tunnel models based on the analysis results to obtain the multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier.

[0055] The specific configuration of the safety risk area positioning module 10 will be described in detail below. The safety risk area positioning module 10 further includes: traversing the multiple temperature monitoring data sets and the multiple trend analysis starting points to perform difference calculations, and obtaining multiple temperature deviation value sets according to the calculation results; calculating the variance of the multiple temperature deviation value sets to obtain multiple data deviation trend degrees.

[0056] The specific configuration of the safety risk area positioning module 10 will be described in detail below. The safety risk area positioning module 10 may further include: extracting multiple abnormal temperature detectors corresponding to the multiple abnormal temperature monitoring data sets; matching the positions of the multiple abnormal temperature detectors with the temperature detector layout array to obtain multiple abnormal areas.

[0057] The following will describe in detail the specific configuration of the associated risk data set acquisition module 20. The associated risk data set acquisition module 20 may further include: the associated risk indicator set includes environmental humidity, environmental temperature, water pressure, water pressure direction, steel bar stress, anchor tension and vibration frequency.

[0058] The specific configuration of the quantitative risk coefficient obtaining module 30 will be described in detail below. The quantitative risk coefficient obtaining module 30 may further include: pre-building a risk quantification function, the risk quantification function is as follows:

[0059] ;

[0060] in, To quantify the risk factor, is the ambient humidity, is the ambient temperature, is the water pressure, is the water pressure direction, is the steel bar stress, is the anchor tension, is the anchor tension limit, is the vibration frequency; constructs the risk quantification model based on the risk quantification function; interactively obtains multiple anchor tension limits of the multiple safety risk areas, and synchronizes the multiple anchor tension limits and multiple associated risk data sets to the risk quantification model, and obtains the multiple quantitative risk coefficients through the risk quantification function calculation.

[0061] The specific configuration of the tunnel construction safety remote monitoring module 70 will be described in detail below. The tunnel construction safety remote monitoring module 70 further includes: in the risk remote operation and maintenance architecture, the multiple edge risk analysis nodes receive and analyze the real-time construction monitoring data uploaded by the multiple groups of tunnel intelligent control robots in real time, and locate the tunnel risk position set, wherein the multiple tunnel risk positions in the tunnel risk position set have risk type identifications; the multiple edge risk analysis nodes upload the tunnel risk position set to the risk operation and maintenance scheduling cloud; the risk operation and maintenance scheduling cloud analyzes the tunnel risk position set to generate risk operation and maintenance instructions; the operation and maintenance center performs tunnel construction risk intervention on the target monitored tunnel according to the risk operation and maintenance instructions.

[0062] A tunnel construction safety remote monitoring device provided by an embodiment of the present invention can execute a tunnel construction safety remote monitoring method provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0063] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0064] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A tunnel construction safety remote monitoring method, characterized in that: The method comprises: Interactively obtain a tunnel construction drawing of a target monitored tunnel, and perform tunnel construction risk analysis based on the tunnel construction drawing to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier; Presetting an associated risk indicator set, and using the associated risk indicator set as a data collection constraint to obtain a plurality of associated risk data sets mapped to the plurality of security risk areas; Pre-constructing a risk quantification model, and synchronizing the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients; Calling multiple main security risk types of the multiple security risk areas, and performing a comprehensive analysis of the deployment of monitoring equipment according to the multiple main security risk types and multiple quantitative risk coefficients to obtain multiple dynamic deployment strategies for equipment; Pre-build a risk remote operation and maintenance architecture, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes; According to the multiple dynamic deployment strategies of the multiple devices, multiple groups of tunnel intelligent control robots are deployed to the multiple security risk areas, and the multiple groups of tunnel intelligent control robots that have been deployed are mapped and communicated to the multiple edge risk analysis nodes according to the multiple security risk areas, so as to complete the localization of the risk remote operation and maintenance architecture; Based on the risk remote operation and maintenance architecture, remote monitoring of tunnel construction safety of the target monitoring tunnel is performed; Interactively obtaining tunnel construction drawings, and performing tunnel construction risk analysis according to the tunnel construction drawings to locate multiple safety risk areas, the method further includes: Locating the target construction boundary according to the tunnel construction drawing; Relevant geological data are called based on the target construction boundary as a constraint to obtain target geological data; The tunnel construction drawings are used as a modeling basis, and BIM technology is used to construct a three-dimensional model of the target monitoring tunnel; Presetting a grid constraint, and dividing the three-dimensional tunnel model into K local tunnel models according to the grid constraint; Performing tunnel construction risk analysis on the K local tunnel models according to the target geological data, and merging the K local tunnel models based on the analysis results to obtain the multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier; Interactive historical tunnel accidents, obtain multiple sample risk types of multiple sample local tunnels, multiple sample tunnel feature sets and multiple sample local geological feature sets; Aggregating feature indicators of the plurality of sample tunnel feature sets to obtain a first feature indicator set, and constructing a first feature acquisition engine based on the first feature indicator set; Aggregating feature indicators of the plurality of sample local geological feature sets to obtain a second feature indicator set, and constructing a second feature acquisition engine based on the second feature indicator set; Integrate the first feature acquisition engine and the second feature acquisition engine to obtain a data feature extraction layer; Constructing a tunnel risk identification layer based on a decision tree, and optimizing the risk identification accuracy of the tunnel risk identification layer by using the multiple sample risk types, multiple sample tunnel feature sets, and multiple sample local geological feature sets as training data; Cascading the data feature extraction layer and the tunnel risk identification layer to obtain a tunnel risk identification model; Performing tunnel construction risk analysis on the K local tunnel models according to the target geological data, and merging the K local tunnel models based on the analysis results to obtain the multiple safety risk areas, the method further comprising: Divide the target geological data according to the K local tunnel models to obtain K local geological data; Inputting the K local geological data and K local tunnel models as input data into the tunnel risk identification model; In the data feature extraction layer of the tunnel risk identification model, the first feature acquisition engine is used to perform feature extraction on the K local tunnel models to obtain K first local feature sets, and the second feature acquisition engine is used to perform feature extraction on the K local geological data to obtain K second local feature sets; Synchronizing the K first local feature sets and the K second local feature sets to the tunnel risk identification model to obtain K local construction risk types; According to the K local construction risk types, the K local tunnel models that are adjacent and have the same local construction risk type are traversed and merged to obtain the multiple safety risk areas, and the local construction risk type of each safety risk area is used as the main safety risk type for area identification.

2. A tunnel construction safety remote monitoring method as claimed in claim 1, characterized in that: The set of associated risk indicators includes ambient humidity, ambient temperature, water pressure, water pressure direction, steel bar stress, anchor tension and vibration frequency.

3. A tunnel construction safety remote monitoring method as claimed in claim 2, characterized in that: Pre-constructing a risk quantification model, and synchronizing the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients, the method further comprising: A risk quantification function is pre-built, and the risk quantification function is as follows: ； in, To quantify the risk factor, is the ambient humidity, is the ambient temperature, is the water pressure, is the water pressure direction, is the steel bar stress, is the anchor tension, is the anchor tension limit, is the vibration frequency; Constructing and obtaining the risk quantification model based on the risk quantification function; Interactively obtain multiple anchor tension limit values ​​of the multiple safety risk areas, and synchronize the multiple anchor tension limit values ​​and multiple associated risk data sets to the risk quantification model, and obtain the multiple quantitative risk coefficients through the risk quantification function calculation.

4. A tunnel construction safety remote monitoring method as claimed in claim 1, characterized in that: Based on the risk remote operation and maintenance architecture, remote monitoring of tunnel construction safety is performed on the target monitoring tunnel, and the method further includes: In the risk remote operation and maintenance architecture, the multiple edge risk analysis nodes receive and analyze the real-time construction monitoring data uploaded by the multiple groups of tunnel intelligent control robots in real time, and locate the tunnel risk position set, wherein the multiple tunnel risk positions in the tunnel risk position set have risk type identifiers; The plurality of edge risk analysis nodes upload the tunnel risk location set to the risk operation and maintenance scheduling cloud; The risk operation and maintenance scheduling cloud analyzes the tunnel risk location set to generate a risk operation and maintenance instruction; The operation and maintenance center performs tunnel construction risk intervention on the target monitored tunnel according to the risk operation and maintenance instruction.

5. A tunnel construction safety remote monitoring device, characterized in that: The device is used to implement a tunnel construction safety remote monitoring method according to any one of claims 1 to 4, and the device comprises: A safety risk area positioning module, used to interactively obtain a tunnel construction drawing of a target monitored tunnel, and perform tunnel construction risk analysis based on the tunnel construction drawing to locate multiple safety risk areas, wherein each safety risk area has a main safety risk type identifier; An associated risk data set acquisition module, used for presetting an associated risk indicator set, and using the associated risk indicator set as a data acquisition constraint to acquire a plurality of associated risk data sets mapped to the plurality of security risk areas; A quantitative risk coefficient acquisition module is used to pre-build a risk quantification model and synchronize the multiple associated risk data sets to the risk quantification model to obtain multiple quantitative risk coefficients; A device dynamic deployment strategy acquisition module is used to call multiple main security risk types of the multiple security risk areas, and perform a comprehensive analysis of monitoring device deployment according to the multiple main security risk types and multiple quantitative risk coefficients to obtain multiple device dynamic deployment strategies; A risk remote operation and maintenance architecture building module, used to pre-build a risk remote operation and maintenance architecture, wherein the risk remote operation and maintenance architecture includes a risk operation and maintenance scheduling cloud and multiple edge risk analysis nodes; A tunnel intelligent control robot deployment module is used to schedule and deploy multiple groups of tunnel intelligent control robots to the multiple security risk areas according to the multiple equipment dynamic deployment strategies, and map and communicate the multiple groups of tunnel intelligent control robots that have been deployed to the multiple edge risk analysis nodes according to the multiple security risk areas to complete the localization of the risk remote operation and maintenance architecture; The tunnel construction safety remote monitoring module is used to perform tunnel construction safety remote monitoring on the target monitoring tunnel based on the risk remote operation and maintenance architecture.

Images