Method and device for arranging sealed cavity gas monitoring sensor, and electronic device

Abstract

The present disclosure provides a closed chamber gas monitoring sensor arrangement method and device and electronic equipment, and relates to the technical field of sensor arrangement. By adopting the method of acquiring the space structure of the closed chamber and the airflow environment parameters to construct a three-dimensional model, combining target gas diffusion simulation and path algorithm calculation to generate a time matrix representing the space-time characteristics of gas diffusion, and dividing the risk monitoring partition according to the gas concentration distribution, combining effective monitoring matrix and coverage calculation to determine the sensor arrangement scheme, the problems of monitoring blind area and unreasonable sensor arrangement caused by lack of scientific simulation and quantitative calculation support and relying only on experience in the prior art can be solved, and the technical effects of accurately determining the layout position, optimizing the number of sensors, eliminating the monitoring blind area and realizing the full coverage and efficient and accurate monitoring of the closed chamber gas can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of sensor placement technology, and in particular to a method and apparatus for arranging gas monitoring sensors in a sealed cavity, as well as electronic equipment. Background Technology

[0002] Gas monitoring in enclosed chambers of hydropower stations is a core aspect of operational safety and is widely used in scenarios such as water intake shafts and underground powerhouses. With the development of intelligent operation and maintenance technologies, related technologies, through the collaborative operation of spatial surveying, fluid simulation, and sensor networks, have constructed a monitoring system encompassing data acquisition and risk warning. Specifically, this system covers key aspects such as environmental parameter acquisition, diffusion pattern analysis, and monitoring node deployment, aiming to achieve comprehensive perception of flammable and hazardous gases.

[0003] However, existing sensor deployment methods rely directly on experience or uniform placement strategies without incorporating computational fluid dynamics simulations and quantification algorithms for scientific planning. This results in an inability to accurately identify areas prone to gas accumulation, easily creating monitoring blind spots or causing equipment redundancy. Furthermore, traditional solutions often neglect sensor compatibility under high humidity and corrosion conditions and seamless integration with existing systems, leading to long-term decreased operational accuracy and data transmission bottlenecks, severely impacting the safety protection effectiveness of confined spaces in hydropower stations. Summary of the Invention

[0004] This disclosure provides a method and apparatus for arranging gas monitoring sensors in a sealed cavity, as well as electronic equipment. Its main purpose is to at least partially solve one of the technical problems in related technologies.

[0005] According to a first aspect of this disclosure, a method for arranging gas monitoring sensors in a sealed cavity is provided, comprising:

[0006] Obtain the spatial structural parameters and airflow environment parameters of the sealed cavern, and use them to construct a three-dimensional model of the cavern; Based on the three-dimensional model of the cavern, the diffusion of the target gas is simulated, and the gas diffusion time between monitoring nodes is calculated by combining the path algorithm to generate a time matrix characterizing the spatiotemporal characteristics of gas diffusion. Based on the gas concentration distribution results obtained from simulation, monitoring zones with different risk levels are divided, and an effective monitoring matrix is ​​constructed by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined by coverage calculation.

[0007] According to a second aspect of this disclosure, a gas monitoring sensor arrangement device for a sealed cavity is provided, comprising: The acquisition unit is used to acquire the spatial structural parameters and airflow environment parameters of the sealed cavern, and to construct a three-dimensional model of the cavern based on these parameters. The simulation unit is used to simulate the diffusion of target gas based on a three-dimensional model of the cavern, and to calculate the gas diffusion time between monitoring nodes by combining the path algorithm, thereby generating a time matrix characterizing the spatiotemporal characteristics of gas diffusion. The determination unit is used to divide the monitoring zones into different risk levels based on the gas concentration distribution results obtained from the simulation, and to construct an effective monitoring matrix by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined through coverage calculation.

[0008] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0009] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0010] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0011] The gas monitoring sensor arrangement method, device, and electronic equipment disclosed herein for sealed caverns utilize a three-dimensional model constructed by acquiring spatial structure and airflow environment parameters of the sealed cavern. This model is then combined with target gas diffusion simulation and path algorithm calculations to generate a time matrix characterizing the spatiotemporal properties of gas diffusion. Furthermore, risk monitoring zones are divided based on gas concentration distribution, and a sensor arrangement scheme is determined by combining an effective monitoring matrix and coverage calculations. Therefore, this method can solve the problems of monitoring blind spots and unreasonable sensor arrangement caused by the lack of scientific simulation and quantitative calculation support and reliance on experience-based point placement in existing technologies. It achieves the technical effects of accurately delineating the placement location, optimizing the number of sensors, eliminating monitoring blind spots, and realizing efficient and accurate full-coverage monitoring of gases in sealed caverns.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A schematic flowchart illustrating a method for arranging gas monitoring sensors in a sealed cavity, as provided in an embodiment of this disclosure; Figure 2 A schematic diagram of a gas monitoring sensor arrangement device for a sealed cavity provided in an embodiment of this disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0014] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0015] The embodiments disclosed herein, at every technical stage of the data lifecycle, including but not limited to data collection, transmission, storage, computation, use, disclosure, and destruction, are fundamentally based on strict adherence to and embedding of current laws, regulations, and regulatory requirements in their system architecture, protocols, and process controls. At the design level, the solution ensures, through systematic rules and strategies, that all processing activities automatically adhere to the principles of legality, legitimacy, necessity, and good faith, and technically implements core rules such as clear purpose, minimum necessity, transparency, and security.

[0016] For any data collection, processing, or other activities involved in the embodiments of this disclosure, corresponding verification, tracking, and constraint mechanisms are implemented at the system level to ensure that their execution has a clear legal basis or contractual foundation, and to automatically trigger and record the corresponding notification process. The processing purpose of related data is bound to its specific use at the metadata layer, and is strictly limited through the system's embedded flow strategy and access control model, thereby ensuring that data is accessed and used only within the scope necessary to achieve the initial collection purpose and as determined by technical criteria. The system has a multi-layered authorization management and compliance audit mechanism to ensure that related data will not be used for any other purpose without separate legal permission or valid separate consent from the information subject. This solution natively supports and protects the information subject's various legal rights to their data in its technical implementation, and provides standardized interfaces and automated processes to achieve efficient exercise of these rights.

[0017] The following description, with reference to the accompanying drawings, describes a method and apparatus for arranging gas monitoring sensors in a sealed cavity, as well as electronic equipment, according to embodiments of the present disclosure.

[0018] Figure 1 This is a schematic flowchart illustrating a method for arranging gas monitoring sensors in a sealed cavity, as provided in an embodiment of this disclosure.

[0019] like Figure 1 As shown, the method includes the following steps: Step 101: Obtain the spatial structure parameters and airflow environment parameters of the sealed cavern, and use them to construct a three-dimensional model of the cavern.

[0020] In the embodiments of this disclosure, the system uses a data acquisition terminal to collect on-site data from the target enclosed cavern, obtaining its spatial structure parameters and airflow environment parameters. These two types of parameters serve as the core input data for constructing a three-dimensional model of the cavern. The data acquisition terminal transmits the collected raw parameters to a three-dimensional modeling processor. The processor cleans, denoises, and normalizes the raw parameters, removing abnormal data that deviates from the normal range. Based on the processed spatial structure parameters and airflow environment parameters, the processor completes the construction of the three-dimensional model of the cavern through preset three-dimensional modeling logic. The constructed three-dimensional model can completely map the spatial structure and basic airflow environment characteristics of the enclosed cavern. For example, the data acquisition terminal can use a three-dimensional laser scanning device to collect spatial structure parameters such as the three-dimensional dimensions, corners, and ventilation opening distribution of the cavern, and use wind speed and airflow acquisition devices to obtain airflow environment parameters such as airflow velocity and airflow in different areas of the cavern. The three-dimensional modeling processor generates a three-dimensional geometric model consistent with the actual cavern based on these parameters.

[0021] By accurately obtaining the basic parameters of the sealed cavity and constructing a three-dimensional model through step 101, a real and complete basic data support is provided for subsequent gas diffusion simulation and monitoring node calculation, avoiding model deviation problems caused by missing or distorted basic data.

[0022] Step 102: Based on the three-dimensional model of the cavern, perform diffusion simulation on the target gas, and combine the path algorithm to calculate the gas diffusion time between monitoring nodes, generating a time matrix characterizing the spatiotemporal characteristics of gas diffusion.

[0023] In the embodiments of this disclosure, the simulation computing processor uses the three-dimensional cavern model constructed in step 101 as the basic simulation carrier, loads preset gas physical property parameters and cavern boundary conditions, and performs diffusion simulation on the target gas to obtain simulation data such as the diffusion trajectory and concentration field distribution of the target gas in the sealed cavern space. The path calculation module delineates the distribution of monitoring nodes based on the three-dimensional cavern model, calls a preset path algorithm to traverse all monitoring nodes, and calculates the gas diffusion time between any two monitoring nodes one by one, combining the gas diffusion rate and node spatial distance obtained from the simulation. The path calculation module integrates the diffusion time of all node pairs in an orderly manner according to the node number to generate a time matrix that can intuitively characterize the spatiotemporal characteristics of gas diffusion. This matrix provides quantitative data support for subsequent sensor deployment calculations. For example, the simulation computing processor can use fluid dynamics simulation logic to complete the target gas diffusion simulation, and the path algorithm can be the shortest path algorithm. The diffusion time calculation is completed based on the actual spatial distance between nodes and the gas diffusion speed, and finally a two-dimensional time matrix matching the number of monitoring nodes is generated.

[0024] Step 102 combines gas diffusion simulation with path algorithms to transform the spatiotemporal characteristics of gas diffusion into standardized time matrix data, laying a core data foundation for the subsequent quantitative calculation of sensor deployment schemes.

[0025] Step 103: Based on the gas concentration distribution results obtained from the simulation, monitoring zones with different risk levels are divided, and an effective monitoring matrix is ​​constructed by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined by coverage calculation.

[0026] In the embodiments of this disclosure, the partitioning module retrieves the gas concentration distribution simulation results output in step 102, divides the entire enclosed cavern into monitoring zones of different risk levels according to preset concentration judgment rules, and outputs standardized partition spatial data. The matrix construction module calls the aforementioned time matrix and the system's preset effective monitoring time threshold to match and determine the gas diffusion time of each monitoring node pair with the threshold, and constructs an effective monitoring matrix to characterize the effective monitoring relationship between nodes based on the determination results. The coverage calculation unit uses the effective monitoring matrix as the core basis, combines the risk level and spatial characteristics of each monitoring zone to perform coverage calculation, and determines key parameters such as the deployment location and number of sensors in each monitoring zone through quantitative calculation, ultimately forming a sensor layout scheme adapted to the characteristics of the cavern. For example, the partitioning module can divide the risk monitoring zones into high, medium, and low risk zones according to the gas concentration value range, the matrix construction module marks the node pairs whose diffusion time does not exceed the effective monitoring time threshold as effective monitoring status, generates a binary effective monitoring matrix, and the coverage calculation unit completes the quantitative determination of the sensor layout scheme based on the full coverage deployment logic.

[0027] Step 103 combines risk zoning, effective monitoring matrix and coverage calculation to support the determination of sensor deployment scheme with quantitative data, avoiding monitoring blind spots and resource redundancy caused by experience-based point placement, and improving the scientificity and adaptability of sensor deployment.

[0028] The gas monitoring sensor deployment method for sealed caverns disclosed herein constructs a three-dimensional model by acquiring spatial structure and airflow environment parameters of the sealed cavern. It then combines target gas diffusion simulation and path algorithm calculation to generate a time matrix characterizing the spatiotemporal properties of gas diffusion. Based on the gas concentration distribution, risk monitoring zones are divided, and the sensor deployment scheme is determined by combining the effective monitoring matrix and coverage calculation. Therefore, it can solve the problems of monitoring blind spots and unreasonable sensor deployment caused by the lack of scientific simulation and quantitative calculation support and reliance on experience-based point placement in existing technologies. This method achieves the technical effects of accurately delineating deployment locations, optimizing the number of sensors, eliminating monitoring blind spots, and realizing efficient and accurate full-coverage monitoring of gases in sealed caverns.

[0029] In the embodiments involved in this application, there are various feasible specific implementation methods. To clearly and completely illustrate the technical solutions of this disclosure, the implementation methods listed below are merely exemplary and do not constitute a limitation on the scope of protection of this disclosure. That is, in addition to the implementation methods described below, other implementation methods that can be obtained by those skilled in the art based on the technical content disclosed in this disclosure through reasonable logical analysis, reasoning, or limited experimentation should also be covered within the scope of protection of this disclosure. The following specifically describes some exemplary implementation methods: As a specific implementation of this disclosure, based on the basic scheme, the spatial structure parameters and airflow environment parameters of the sealed cavern are obtained, and a three-dimensional model of the cavern is constructed accordingly. Further, the following steps are taken: a three-dimensional scanning device is used to survey the sealed cavern, obtaining its three-dimensional geometric dimensions, corner orientations, ventilation outlet distribution coordinates, and the location and range of areas prone to microbial growth. An airflow parameter acquisition device is used to acquire multiple sets of wind speed and airflow data in multiple areas within the cavern. The acquired three-dimensional data of the cavern is used to model the cavern, generating a three-dimensional model of the cavern. The acquired wind speed and airflow data are then numerically processed to remove outliers that deviate from the average value beyond a preset range, thereby determining the representative wind speed and airflow parameters for each area, forming a set of survey parameters for subsequent calculations.

[0030] Specifically, in one optional refined implementation, the steps for acquiring the spatial structural parameters and airflow environment parameters of the enclosed cavern, as well as the construction of the 3D model, are further defined. This implementation uses specialized surveying equipment to collect data in modules. One module relies on 3D scanning equipment to map the geometric features and key locations of the cavern space, while the other module uses airflow parameter acquisition equipment to collect airflow data from multiple regions and in multiple batches. In the data processing stage, the 3D model of the cavern is first reconstructed based on the 3D mapping data, and then outlier filtering and normalization are performed on the raw airflow data to ultimately form a standardized set of surveying parameters.

[0031] In some specific embodiments, the 3D scanning equipment uses a 3D laser scanner. The spatial structural parameters obtained from the survey include the 3D geometric dimensions of the sealed cavern, the orientation of corners, the coordinates of the ventilation openings, and the specific location and coverage of areas prone to microbial growth. The airflow parameter acquisition equipment is deployed with collection points along different areas of the cavern, collecting multiple sets of wind speed and airflow data at each point. When processing the wind speed and airflow data, the average value of all the original data at a single point is first calculated. Values ​​deviating from the average value by more than ±10% are judged as outliers and directly removed. The average value of the remaining valid data is used as the representative wind speed and representative airflow parameters for that area. Finally, the spatial structural parameters, representative wind speed parameters, and representative airflow parameters are integrated to form a survey parameter set.

[0032] In other equivalent implementations, three-dimensional photogrammetry equipment can be used to replace three-dimensional laser scanners to complete spatial surveys, and the deviation range for outlier removal can be adjusted adaptively according to the actual working conditions of the cavern.

[0033] By conducting precise surveys using different equipment and eliminating outliers, interference data was effectively removed, improving the accuracy of the cavern's 3D model and survey parameters, and providing reliable basic data support for subsequent gas diffusion simulation.

[0034] As a specific implementation of this disclosure, based on the basic scheme, an airflow parameter acquisition device is used to acquire multiple sets of wind speed data and air volume data in multiple areas of the cavern. It is further defined as follows: multiple acquisition points are arranged at preset intervals along the extension direction of the cavern, and multiple sets of wind speed data and multiple sets of air volume data are repeatedly collected at each acquisition point. The arithmetic mean of the multiple sets of wind speed data corresponding to a single acquisition point is taken as the representative wind speed of the acquisition point area, and the arithmetic mean of the multiple sets of air volume data corresponding to a single acquisition point is taken as the representative air volume of the acquisition point area.

[0035] Specifically, in one optional refined implementation, the rules for the layout of collection points, the data collection method, and the calculation logic for representative parameters are further defined. This implementation standardizes the layout of collection points, repeatedly collects multiple sets of raw data, and then uses the arithmetic mean method to determine the representative parameters of a single point, ensuring that the airflow parameters can truly reflect the actual airflow state of the corresponding area.

[0036] In some specific embodiments, multiple airflow collection points are evenly distributed at preset intervals along the extension direction of the sealed chamber. At each collection point, multiple sets of wind speed data and multiple sets of air volume data are repeatedly collected using airflow parameter acquisition equipment to obtain the raw airflow dataset for that point. For a single collection point, the arithmetic mean of all wind speed data corresponding to that collection point is calculated, and this average value is used as the representative wind speed for the area where the collection point is located. Simultaneously, the arithmetic mean of all air volume data corresponding to that collection point is calculated, and this average value is used as the representative air volume for the area where the collection point is located.

[0037] In other equivalent implementations, the preset data collection interval can be adjusted adaptively according to the size and structural complexity of the cavern space, and the number of data sets to be repeatedly collected can be flexibly set according to the on-site working conditions.

[0038] By setting up collection points at fixed intervals and using the arithmetic mean method to process repeated data collection, the random error of a single collection is effectively reduced, and the stability and characterization accuracy of airflow parameters in a single region are significantly improved.

[0039] As a specific implementation of this disclosure, based on the basic scheme, a diffusion simulation of the target gas is performed using a three-dimensional model of the cavern. The gas diffusion time between monitoring nodes is calculated using a path algorithm, generating a time matrix characterizing the spatiotemporal properties of gas diffusion. Further, the simulation is defined as follows: based on the three-dimensional model of the cavern and a set of survey parameters, a gas diffusion simulation is performed using a mixed gas containing the target gas as the simulation medium. The simulation process follows the gas diffusion control equation, and the ventilation opening is set as the pressure outlet boundary, and the cavern wall as the no-slip boundary. After the simulation is completed, multiple monitoring nodes are deployed at preset intervals based on the three-dimensional model of the cavern. The shortest path distance between any two monitoring nodes is calculated using a shortest path algorithm, and a path distance matrix between nodes is established. Then, the gas diffusion time between any two monitoring nodes is calculated using representative wind speeds of each region, and a time matrix is ​​established.

[0040] Specifically, in one optional refined implementation, the simulation medium, governing equations, boundary conditions, monitoring node layout, path algorithm, and time matrix construction process are further defined. This implementation uses survey parameters as the basic input, conducts numerical simulations following the physical laws of gas diffusion, and combines the shortest path algorithm with measured representative wind speeds to quantify diffusion time, ultimately generating a time matrix that closely matches the actual airflow environment of the cavern.

[0041] In some specific embodiments, the simulation is based on a three-dimensional model of the cavern and a set of survey parameters. An air-gas mixture containing the target gas is used as the simulation medium, and the simulation process strictly follows the gas diffusion control equation:

[0042] Where ρ is the gas density, c is the gas concentration, t is the diffusion time, u is the airflow velocity, D is the gas diffusion coefficient, and S is the gas generation source term. Boundary conditions are configured as follows: the vent is set as a pressure outlet, the pressure is taken as standard atmospheric pressure, and the cavern wall is set as a no-slip boundary. After the simulation, monitoring nodes are deployed at preset intervals based on the three-dimensional model of the cavern. The shortest path algorithm is used to calculate the shortest path distance between any two nodes, and a path distance matrix is ​​constructed. Then, using the formula: The time required to calculate gas diffusion between nodes is as follows: The shortest path distance between nodes, path weight Take the actual straight-line distance between nodes (unit: m), and then use the formula Calculate the gas diffusion time between nodes and establish the time matrix of adjacent nodes. .

[0043] In other equivalent implementations, compatible fluid dynamics simulation tools can be used, and the shortest path algorithm can be replaced by an equivalent path optimization method.

[0044] By strictly following the gas diffusion control equations in simulation and combining measured wind speeds to quantify diffusion time, the time matrix accurately maps the spatiotemporal patterns of gas diffusion, providing a high-precision quantitative basis for the subsequent construction of an effective monitoring matrix.

[0045] As a specific implementation of this disclosure, based on the basic scheme, the shortest path algorithm is used to calculate the shortest path distance between any two monitoring nodes, further defined as follows: the distance label of the starting monitoring node is set to zero, and the distance labels of the remaining monitoring nodes are set to infinity; all unmarked monitoring nodes are traversed, and the node with the smallest distance label is selected as the intermediate node and marked; the unmarked nodes adjacent to the intermediate node are examined one by one, and if the path distance to the adjacent node via the intermediate node is less than the current distance label of the adjacent node, the distance label of the adjacent node is updated; the above traversal and update process is repeated until all monitoring nodes are marked, thereby obtaining the shortest path distance between any two monitoring nodes.

[0046] Specifically, in one optional refined implementation, the node initialization, label selection, distance update, and iteration termination logic of the algorithm are further defined, and the shortest path distance between nodes is accurately solved through a standardized iterative process.

[0047] In some specific embodiments, the shortest path calculation is performed as follows: First, the distance labels of the monitoring nodes are initialized, the distance label of the starting monitoring node is set to 0, and the distance labels of all other monitoring nodes are uniformly set to infinity; then, all unmarked monitoring nodes are traversed, the node with the smallest distance label is selected as the intermediate node and marked; then, the unmarked nodes adjacent to the intermediate node are checked one by one, the path distance to the adjacent node via the intermediate node is calculated, and if the path distance is less than the current distance label of the adjacent node, its distance label is updated with the value; the traversal selection, marking, and distance update operations are repeated until all monitoring nodes are marked, and finally the shortest path distance between any two monitoring nodes is obtained.

[0048] In other equivalent implementations, equivalent path optimization algorithms can be used to replace the execution to achieve the same shortest path calculation effect.

[0049] By using a fixed label initialization, iterative update, and full node marking process, the accuracy and uniqueness of the shortest path distance calculation are ensured, providing stable and reliable basic data for subsequent diffusion time-consuming calculations.

[0050] As a specific implementation of this disclosure, based on the basic scheme, monitoring zones of different risk levels are divided according to the gas concentration distribution results obtained from simulation. An effective monitoring matrix is ​​constructed by combining the time matrix and the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined through coverage calculation. It is further defined as follows: based on the gas concentration distribution cloud map obtained from simulation, the internal space of the cavern is divided into a high-risk zone with concentration values ​​in the first threshold range, a medium-risk zone with concentration values ​​in the second threshold range, and a low-risk zone with concentration values ​​in the third threshold range. The effective monitoring time threshold is determined according to the spatial scale of the cavern, and the time matrix is ​​transformed into an effective monitoring matrix. When the gas diffusion time between any two nodes is less than or equal to the effective monitoring time threshold, the corresponding matrix element is set as a valid identifier; otherwise, it is set as an invalid identifier. Based on the principle of minimum full coverage, the minimum number of sensors required in each risk zone is calculated according to the effective monitoring matrix, and the monitoring coverage is ensured to meet the preset standard to generate a sensor layout scheme.

[0051] Specifically, in one optional refined implementation, the concentration threshold division rules for risk zones, the spatial scale adaptation logic for effective monitoring time thresholds, the assignment method for effective monitoring matrix elements, and the sensor coverage calculation principles are further defined. Through hierarchical quantification and optimal placement calculation, a scientific and compliant sensor deployment scheme is formed.

[0052] In some specific embodiments, based on the simulated gas concentration distribution cloud map, the cavern space is divided into three monitoring zones according to preset concentration threshold ranges: areas with gas concentrations within the first threshold range are high-risk zones, areas within the second threshold range are medium-risk zones, and areas within the third threshold range are low-risk zones. The specific value of the effective monitoring time threshold is determined based on the actual spatial scale of the sealed cavern, and the time matrix is ​​transformed element-by-element into an effective monitoring matrix. The gas diffusion time between any two monitoring nodes is judged; if the time is less than or equal to the effective monitoring time threshold, the corresponding matrix element is marked as valid; otherwise, it is marked as invalid. Based on the principle of minimum full coverage deployment, using the effective monitoring matrix as the core calculation basis, the minimum number of sensors to be deployed in each risk zone is calculated, while ensuring that the overall monitoring coverage meets preset standards, ultimately generating a complete sensor deployment scheme.

[0053] In other equivalent implementations, the concentration threshold range, effective monitoring time threshold, and coverage preset standard can be adaptively adjusted according to the actual working conditions of the cavern.

[0054] By using concentration threshold grading and zoning, spatial scale adaptation thresholds, and minimum full coverage calculations, the monitoring intensity requirements of different risk areas are accurately matched, achieving the optimal configuration of the number of sensors while meeting coverage standards.

[0055] As a specific implementation of this disclosure, a sensor arrangement scheme is generated based on the basic scheme, further defined as follows: In high-risk areas, sensors are set up according to a first preset installation density and installed at a preset first distance from potential gas release sources, fixed at a preset first installation tilt angle, with the sampling port facing the direction in which gas tends to accumulate; in medium-risk areas, sensors are set up according to a second preset installation density and installed at a preset second distance from the ground; in low-risk areas, sensors are set up according to a third preset installation density and installed at a preset third distance from the ground; in areas where the airflow fluctuation amplitude exceeds a preset threshold, redundant sensors are added to ensure that the overall monitoring coverage meets the preset standards.

[0056] Specifically, in one optional refined implementation, the sensor deployment parameters, installation requirements, and redundancy compensation strategies for high, medium, and low-risk areas, as well as areas with abnormal airflow, are further defined. By zoning-differentiated deployment and redundancy reinforcement, the unity of monitoring coverage and deployment rationality is achieved.

[0057] In some specific embodiments, sensors are deployed at a first preset installation density in high-risk areas, with the sensors installed at a preset first distance from potential gas release sources and fixed at a preset first installation angle, with the sampling ports facing the direction where gas tends to accumulate. Sensors are deployed at a second preset installation density in medium-risk areas, with the sensors installed at a preset second distance from the ground. Sensors are deployed at a third preset installation density in low-risk areas, with the sensors installed at a preset third distance from the ground. For areas where the airflow fluctuation amplitude inside the cave exceeds a preset threshold, redundant sensors are added on top of the conventional deployment. The redundant nodes enhance the monitoring coverage capability, ensuring that the overall monitoring coverage meets the preset standards.

[0058] Monitoring area division: Based on the gas concentration cloud map obtained from CFD simulation, the cavern is divided into three types of areas: high-risk monitoring area (gas concentration peak area, microbial dense area, concentration ≥50ppm), medium-risk monitoring area (middle area of ​​gas diffusion path, concentration 10-50ppm), and low-risk monitoring area (well ventilated area, concentration <10ppm). The division results are marked on the three-dimensional model of the cavern.

[0059] 2. Effective monitoring matrix construction: Setting the effective monitoring time. The range of values ​​is determined based on the size of the cavern: when the cavern length is ≤100m, When the length of the cavern is >100m, ; the adjacent node time matrix Transform into an effective monitoring matrix Matrix elements are arranged according to the formula definition.

[0060] 3. Sensor Quantity and Location Calculation: Based on the minimum full coverage deployment method and the principle of minimal edge dominating set, the calculation is performed using the formula... Calculate the minimum number of sensors required for each area; simultaneously calculate the branch coverage C = (number of effectively monitored branches / total number of branches in the cavern) × 100%, ensuring coverage. Finalized layout parameters: High-risk area: Place one sensor every 5-8 meters, with the installation position 0.5m±0.1m away from the gas source, the installation angle 45°±5°, and the sampling tube facing the direction in which the gas is likely to accumulate; Medium-risk area: Place one sensor every 10-15 meters, at a height of 2.5m ± 0.2m above the ground; Low-risk areas: Place one sensor every 15-20 meters, with an installation height of 2-3 meters above the ground; In areas with unstable airflow (wind speed fluctuations ≥ 0.5 m / s): Add redundant sensors, the number of which is determined by calculation based on the effective monitoring matrix, to ensure coverage of no less than 95%.

[0061] In other equivalent implementations, the preset installation density, installation distance, and installation angle of each area can be adjusted according to the characteristics of the cavern space, gas type, and on-site working conditions.

[0062] By setting sensor deployment parameters differently for different regions and adding redundancy in areas of airflow fluctuation, the system can accurately adapt to the monitoring characteristics of different regions, effectively eliminate local monitoring blind spots, and stably ensure that the overall monitoring coverage meets the standards.

[0063] As a specific embodiment of this disclosure, in addition to the basic scheme, it also includes: acquiring monitoring and verification data collected by a sensor verification network temporarily constructed based on the sensor layout scheme, and correcting the sensor layout scheme based on the monitoring and verification data to obtain the optimal sensor layout scheme verified by actual measurement.

[0064] Specifically, in one optional refined implementation, a test verification and scheme correction step is added. On-site data is collected by temporarily setting up a verification network, and the initial layout scheme is iteratively optimized to form the optimal scheme that adapts to the actual on-site working conditions.

[0065] In some specific embodiments, according to the generated initial sensor layout scheme, a temporary sensor verification network is built and debugged in a sealed cavity; the verification network is started to carry out on-site testing, and monitoring and verification data such as concentration response, coverage effectiveness, and data stability of each sensor are collected; the monitoring and verification data are compared with the simulation expected indicators and preset coverage standards item by item to locate problems such as insufficient monitoring coverage, response lag, and local redundancy in the initial scheme; based on the comparison results, the sensor layout position, quantity, and installation parameters are modified accordingly, and the verification and modification process is repeated until all monitoring and verification data meet the preset requirements, and finally the optimal sensor layout scheme verified by actual testing is obtained.

[0066] In other equivalent implementation methods, segmented verification, sampling verification, etc., can be used to replace the full-domain temporary network verification, and the verification and comparison indicators can be adaptively adjusted according to the actual monitoring needs of the cavern.

[0067] Through on-site testing and iterative correction, the discrepancy between simulation calculations and actual working conditions is effectively bridged, significantly improving the on-site adaptability and long-term monitoring reliability of the sensor deployment scheme.

[0068] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0069] Corresponding to the above-described method for arranging gas monitoring sensors in a sealed cavity, this disclosure also proposes a device for arranging gas monitoring sensors in a sealed cavity. Since the device embodiments of this disclosure correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0070] Figure 2 This is a schematic diagram of the structure of a gas monitoring sensor arrangement device for a sealed cavity provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: Acquisition unit 21 is used to acquire the spatial structure parameters and airflow environment parameters of the sealed cavern, and to construct a three-dimensional model of the cavern based on these parameters; Simulation unit 22 is used to simulate the diffusion of target gas based on the three-dimensional model of the cavern, and to calculate the gas diffusion time between monitoring nodes by combining the path algorithm, thereby generating a time matrix characterizing the spatiotemporal characteristics of gas diffusion. Unit 23 is used to divide monitoring zones of different risk levels based on the gas concentration distribution results obtained from simulation, and to construct an effective monitoring matrix by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined by coverage calculation.

[0071] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0072] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0073] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0074] like Figure 3As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0075] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the method for arranging gas monitoring sensors in a sealed cavity. For example, in some embodiments, the method for arranging gas monitoring sensors in a sealed cavity can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned method for arranging gas monitoring sensors in a sealed cavity by any other suitable means (e.g., by means of firmware).

[0077] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0082] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0083] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0084] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0085] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0086] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0087] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-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 disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for arranging gas monitoring sensors in a sealed cavern, characterized in that, include: Obtain the spatial structural parameters and airflow environment parameters of the sealed cavern, and use them to construct a three-dimensional model of the cavern; Based on the three-dimensional model of the cavern, the diffusion of the target gas is simulated, and the gas diffusion time between monitoring nodes is calculated by combining the path algorithm to generate a time matrix characterizing the spatiotemporal characteristics of gas diffusion. Based on the gas concentration distribution results obtained from simulation, monitoring zones with different risk levels are divided, and an effective monitoring matrix is ​​constructed by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined by coverage calculation.

2. The method according to claim 1, characterized in that, The process of obtaining the spatial structural parameters and airflow environment parameters of the sealed cavern, and constructing a three-dimensional model of the cavern based on these parameters, includes: A three-dimensional scanning device was used to survey the sealed cavern to obtain the three-dimensional geometric dimensions, corner orientations, ventilation outlet distribution coordinates, and the location and range of areas where microorganisms are prone to grow. In addition, an airflow parameter acquisition device was used to obtain multiple sets of wind speed and air volume data in multiple areas within the cavern. The acquired three-dimensional data of the cavern are modeled to generate the three-dimensional model of the cavern. The acquired wind speed and air volume data are numerically processed to remove outliers that deviate from the average value by more than a preset range, so as to determine the representative wind speed parameters and representative air volume parameters of each area, forming a set of survey parameters for subsequent calculations.

3. The method according to claim 2, characterized in that, The airflow parameter acquisition device acquires multiple sets of wind speed and air volume data in multiple areas within the cavern, including: Multiple collection points are set up at preset intervals along the extension direction of the cavern. Multiple sets of wind speed data and multiple sets of air volume data are repeatedly collected at each collection point. The arithmetic mean of the multiple sets of wind speed data corresponding to a single collection point is taken as the representative wind speed of the collection point area, and the arithmetic mean of the multiple sets of air volume data corresponding to a single collection point is taken as the representative air volume of the collection point area.

4. The method according to claim 1, characterized in that, Based on the three-dimensional model of the cavern, diffusion simulation of the target gas is performed, and the gas diffusion time between monitoring nodes is calculated using a path algorithm to generate a time matrix characterizing the spatiotemporal properties of gas diffusion, including: Based on the three-dimensional model of the cavern and the set of survey parameters, a gas diffusion simulation is performed using a mixed gas containing the target gas as the simulation medium. The simulation process follows the gas diffusion control equation, and the ventilation opening is set as the pressure outlet boundary and the cavern wall is set as the non-slip boundary. After the simulation is completed, multiple monitoring nodes are set up at preset intervals based on the three-dimensional model of the cavern. The shortest path distance between any two monitoring nodes is calculated using the shortest path algorithm, and a path distance matrix between nodes is established based on this. Then, the gas diffusion time between any two monitoring nodes is calculated by combining the representative wind speed of each region, and the time matrix is ​​established.

5. The method according to claim 4, characterized in that, The calculation of the shortest path distance between any two monitoring nodes using the shortest path algorithm includes: Set the distance label of the starting monitoring node to zero, and set the distance labels of the remaining monitoring nodes to infinity; Iterate through all unmarked monitoring nodes, select the node with the smallest distance from the label as the intermediate node and mark it; Examine each unmarked node adjacent to the intermediate node. If the path distance to the adjacent node via the intermediate node is less than the current distance label of the adjacent node, then update the distance label of the adjacent node. Repeat the above traversal and update process until all monitoring nodes are marked, thereby obtaining the shortest path distance between any two monitoring nodes.

6. The method according to claim 1, characterized in that, The process involves dividing monitoring zones into different risk levels based on the gas concentration distribution results obtained from simulation, constructing an effective monitoring matrix by combining the time matrix with a preset effective monitoring time threshold, and determining the sensor layout scheme within each monitoring zone through coverage calculation, including: Based on the gas concentration distribution cloud map obtained from the simulation, the internal space of the cavern is divided into a high-risk area with a concentration value in the first threshold range, a medium-risk area with a concentration value in the second threshold range, and a low-risk area with a concentration value in the third threshold range. The effective monitoring time threshold is determined based on the spatial scale of the cavern, and the time matrix is ​​transformed into an effective monitoring matrix. When the gas diffusion time between any two nodes is less than or equal to the effective monitoring time threshold, the corresponding matrix element is set as an effective identifier; otherwise, it is set as an invalid identifier. Based on the principle of minimum full coverage, the minimum number of sensors required in each risk area is calculated according to the effective monitoring matrix, and the monitoring coverage is ensured to meet the preset standard to generate the sensor layout scheme.

7. The method according to claim 6, characterized in that, The generation of the sensor arrangement scheme includes: In the high-risk area, sensors are set up according to a first preset installation density and installed at a preset first distance from the potential gas release source, fixed at a preset first installation tilt angle, with the sampling port facing the direction in which gas tends to accumulate. In the medium-risk area, sensors are installed at a second preset installation density and at a preset second distance from the ground. In the low-risk area, sensors are installed at a third preset installation density and at a third preset distance from the ground. In areas where airflow fluctuations exceed a preset threshold, redundant sensors are added to ensure that the overall monitoring coverage meets the preset standard.

8. The method according to claim 1, characterized in that, Also includes: The monitoring and verification data collected by the sensor verification network temporarily constructed based on the sensor layout scheme are obtained, and the sensor layout scheme is corrected according to the monitoring and verification data to obtain the optimal sensor layout scheme verified by actual measurement.

9. A device for arranging gas monitoring sensors in a sealed cavern, characterized in that, include: The acquisition unit is used to acquire the spatial structural parameters and airflow environment parameters of the sealed cavern, and to construct a three-dimensional model of the cavern based on these parameters. The simulation unit is used to perform diffusion simulation of the target gas based on the three-dimensional model of the cavern, and to calculate the gas diffusion time between monitoring nodes by combining the path algorithm, thereby generating a time matrix characterizing the spatiotemporal characteristics of gas diffusion. The determination unit is used to divide the monitoring zones into different risk levels based on the gas concentration distribution results obtained from the simulation, and to construct an effective monitoring matrix by combining the time matrix with the preset effective monitoring time threshold. The sensor layout scheme in each monitoring zone is determined by coverage calculation.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

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