Cable trench intelligent cover plate control system and method based on multi-dimensional perception

Abstract

This invention discloses a smart cover control system and method for cable trenches based on multi-dimensional perception, belonging to the field of data analysis technology. The invention acquires operational data from various monitoring areas within the cable trench to construct a risk propagation network and identify multi-area propagation paths. It calculates propagation enhancement indices by combining airflow exchange volume, propagation speed, risk concentration changes, and spatial distance, screening cross-area enhancement paths and forming a bridging channel set. It calculates comprehensive propagation weights to construct a weighted risk propagation network. Based on operational data, it calculates path risk flow energy values ​​to identify candidate propagation paths and dominant channels. It establishes constraint relationships based on the cover opening state and dominant channels to evaluate the propagation capacity changes under different cover combinations. Based on the propagation capacity changes and control efficiency coefficients, it determines the optimal control combination and adjusts the cover opening state, thereby achieving a better balance between safety and operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a control system and method for intelligent cable trench covers based on multi-dimensional perception. Background Technology

[0002] Cable trenches typically house power cables, communication cables, and related auxiliary equipment. The space is relatively enclosed, and when abnormal situations such as temperature rise, accumulation of combustible gas, smoke diffusion, or equipment failure occur, the risks can easily spread to the surrounding area along the air flow channels and spatial connection paths within the trench, thereby affecting the safety of cable operation over a wider area. Existing cable trench monitoring technologies mainly use sensors such as temperature, gas, and smoke to achieve status monitoring and anomaly alarms. Most of them focus on risk identification at local monitoring points and lack the ability to analyze the risk propagation process and path inside the cable trench, making it difficult to quantitatively assess the risk diffusion trend. Some cable trenches are equipped with openable and closable protective covers or ventilation isolation structures, but the existing control methods are usually based on preset rules or human experience. They lack quantitative analysis of the relationship between changes in the cover status and the risk propagation path, and cannot optimize control based on the impact of different cover combinations on risk propagation capabilities. Therefore, how to achieve quantitative analysis of risk propagation paths and their diffusion range under the complex spatial connectivity conditions of cable trenches, and reflect the impact of changes in cover plate status on risk propagation, is a technical problem that needs to be solved in this field. Summary of the Invention

[0003] The purpose of this invention is to provide a control system and method for intelligent cable trench covers based on multi-dimensional perception, so as to solve the problems raised in the prior art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-dimensional sensing-based intelligent cable trench cover control method, the method comprising: Step S100: Obtain operational data from multiple monitoring areas within the cable trench, construct a risk propagation network based on the airflow connection between the monitoring areas, and determine the propagation path composed of multiple monitoring areas; Step S200: Based on the airflow exchange volume, propagation speed, risk concentration change and spatial distance of each propagation connection edge in the propagation path, calculate the propagation enhancement index, identify cross-regional propagation enhancement paths, and construct a set of bridging propagation channels; Step S300: Obtain the propagation enhancement index corresponding to each propagation connection edge, combine historical operation data to calculate the propagation frequency evaluation value, calculate the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and construct a weighted risk propagation network; Step S400: Based on the operational data of the monitoring area corresponding to the propagation path, calculate the risk flow energy value of each propagation path, identify candidate risk propagation paths, and determine the set of risk flow energy dominant propagation channels; Step S500: Obtain the opening status of each cover plate, construct a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculate the risk propagation capability change value corresponding to different cover plate state combinations; Step S600: Determine the target control combination based on the risk propagation capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjust the opening state of each cover plate according to the generated target control combination.

[0005] Furthermore, step S100 includes: By deploying temperature sensors, wind speed sensors, and combustible gas concentration sensors in multiple monitoring areas within the cable trench, operational data for each monitoring area is collected. The operational data includes temperature data, airflow propagation speed data, airflow direction data, and combustible gas concentration data. Based on the spatial dimensions and structural node distribution in the cable trench structural drawings, the cable trench is divided into segments according to a preset length threshold. The corner nodes and branch nodes in the cable trench are used as the boundaries of the area division. Each segment is defined as a monitoring area. The preset length threshold is used to limit the maximum spatial span of adjacent monitoring areas in the length direction of the cable trench. Information on ventilation ducts, inspection holes, cover gaps, and connecting channels between monitoring areas is obtained. Based on the information on ventilation ducts, inspection holes, cover gaps, and connecting channels, the airflow connection relationship between monitoring areas is determined. The airflow connection relationship is that there is an airflow path between two monitoring areas formed by at least one of the connecting structures of ventilation ducts, inspection holes, cover gaps, or connecting channels. Each monitoring area is marked as a node in the risk propagation network. When there is an airflow connection between two monitoring areas, a propagation connection edge is established between the corresponding nodes. The direction of the propagation connection edge is determined according to the airflow direction. A set of propagation paths consisting of multiple monitoring areas connected in sequence is formed according to the topological connection relationship between each propagation connection edge. By segmenting the cable trench according to the spatial dimensions, corner nodes, and branch nodes in the cable trench structure drawings, and defining each segment as an independent monitoring area, the monitoring area division can be matched with the actual structural characteristics of the cable trench. This avoids the problem of the area being too large or too small due to the fixed spacing division method, and improves the rationality of the monitoring area division and the accuracy of risk location. Establishing air circulation connections between monitoring areas can accurately reflect the propagation paths of gas and heat inside cable trenches. Compared to building monitoring models solely based on spatial adjacency, this approach can more accurately describe the diffusion patterns of fire smoke, combustible gases, and heat within cable trenches.

[0006] Furthermore, step S200 includes: Traverse each propagation path in the risk propagation path set, and for each propagation connection edge in each propagation path, obtain the airflow exchange volume, propagation speed, risk concentration change, and spatial distance of each propagation connection edge within a preset time window. The calculation process of the airflow exchange volume is to obtain the cross-sectional area of ​​the connection channel corresponding to the airflow connection edge, and obtain the airflow speed corresponding to the propagation connection edge. Multiply the airflow speed by the cross-sectional area of ​​the connection channel to calculate the airflow exchange volume. Statistically analyze the airflow exchange volume, propagation speed, and risk concentration change to obtain the window average value corresponding to each parameter within the time window. The average values ​​of the airflow exchange rate window, the average value of the propagation velocity window, and the average value of the risk concentration change window are normalized and calculated to obtain the airflow exchange evaluation value, the propagation velocity evaluation value, and the concentration change evaluation value. The distance evaluation value D is calculated based on the spatial distance: D = (L1-L) ÷ (L1-L2), where L represents the spatial distance of the propagation connection edge, L1 represents the maximum spatial distance, and L2 represents the minimum spatial distance. The propagation enhancement index is calculated by weighting and summing the airflow exchange evaluation value, propagation speed evaluation value, concentration change evaluation value, and distance evaluation value according to preset weights. Obtain the propagation enhancement index within the historical operating cycle, calculate the mean and standard deviation of the propagation enhancement index, preset the threshold coefficient, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the propagation enhancement threshold. When the propagation enhancement index is greater than the propagation enhancement threshold, mark the corresponding propagation connection edge as a propagation enhancement edge. The proportion of propagation enhancement edges in each propagation path to the total number of propagation connection edges in that propagation path is calculated. When the proportion is greater than a preset proportion threshold, the corresponding propagation path is determined as a cross-regional propagation enhancement path. The corresponding propagation enhancement edges are extracted from each cross-regional propagation enhancement path, and propagation enhancement edges with the same connection nodes and propagation direction are merged to construct a bridging propagation channel set. The propagation enhancement index, obtained through weighted calculation, can comprehensively reflect the common impact on the risk diffusion process compared to single-parameter judgment methods, thereby improving the comprehensiveness and accuracy of propagation capability assessment. By marking propagation connection edges where the propagation enhancement index exceeds the propagation enhancement threshold as propagation enhancement edges, key connection links with significantly enhanced risk transmission capabilities can be quickly identified from a large number of propagation relationships. This provides a basis for discovering rapid risk diffusion pathways and abnormal propagation areas, thereby improving the pertinence of risk monitoring and early warning.

[0007] Furthermore, step S300 includes: Obtain the propagation enhancement index corresponding to each propagation connection edge in the risk propagation network. Based on historical operation data, count the number of times each propagation connection edge participates in risk propagation within a preset historical time window, and normalize the propagation number to obtain the propagation frequency evaluation value. The propagation enhancement index and the propagation frequency evaluation value are weighted and summed according to the preset weight coefficients to obtain the comprehensive propagation weight of the propagation connection edge. The comprehensive propagation weight is assigned to the corresponding propagation connection edge to form a weighted risk propagation network. This approach comprehensively evaluates the propagation connection edges from two dimensions: real-time propagation capability and historical propagation activity. This avoids relying solely on monitoring data from a single moment, thereby improving the comprehensiveness and reliability of risk propagation analysis results. By calculating the propagation frequency evaluation value, a unified evaluation scale is established for the historical propagation activity of different propagation connection edges. This reduces the impact of statistical dimension differences on the analysis results and enhances the rationality and comparability of subsequent weight calculations.

[0008] Furthermore, step S400 includes: Obtain the sequence of path nodes corresponding to each propagation path in the risk propagation network and the associated monitoring area operation data. For each propagation path, extract the temperature difference between adjacent monitoring areas on that propagation path to obtain the temperature gradient value. Obtain the center coordinates of the monitoring areas in adjacent monitoring areas. For any adjacent monitoring area i and monitoring area j, their center coordinates are (xi, yi) and (xj, yj) respectively. Obtain the propagation speed data and airflow direction data of the airflow corresponding to monitoring area i. Determine the velocity component vx in the X direction and the velocity component vy in the Y direction based on the propagation speed data and airflow direction data. Construct the airflow direction vector (vx, vy) and use the two-parameter arctangent function to calculate the direction angle F=atan2(vy, vx). Based on the center coordinates of the two monitoring areas, a connecting direction vector (xj-xi, yj-yi) is constructed from monitoring area i to monitoring area j, and the direction angle E=atan2(yj-yi, xj-xi) of the connecting direction vector is calculated using the two-parameter arctangent function. Calculate the absolute difference between the direction angle of the airflow direction vector and the direction angle of the connected direction vector to obtain the direction deviation angle B. Calculate the direction consistency evaluation value according to the following formula: ; Where A represents the directional consistency evaluation value; Extract airflow velocity data from the monitoring area corresponding to the propagation path, calculate the average airflow velocity, and extract combustible gas concentration data from the monitoring area corresponding to the propagation path, calculate the average concentration. The temperature gradient value, directional consistency evaluation value, average airflow velocity value, and average concentration value are normalized to obtain normalized temperature gradient value, normalized directional consistency value, normalized airflow velocity value, and normalized concentration value. The risk flow energy value for each propagation path is calculated by weighting and summing the normalized values ​​according to preset weighting coefficients. Obtain the risk energy value within the historical operating cycle, calculate the mean and standard deviation of the risk energy value, preset the threshold coefficient of the risk energy value, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the risk energy threshold, and mark the corresponding propagation path as a candidate risk propagation path when the risk energy value is greater than the risk energy threshold. The risk energy values ​​of all candidate risk propagation paths are sorted in descending order. The candidate risk propagation paths with the highest sorting position are selected as the dominant risk energy propagation channels. The dominant risk energy propagation channels are then summarized to obtain a set of dominant risk energy propagation channels. It can assess the propagation path from two aspects: the risk formation conditions and the propagation driving force. Compared with the method of analyzing only the network topology relationship, it can more accurately reflect the propagation potential of the risk in the actual environment and improve the authenticity and reliability of the propagation path identification results. When the airflow direction is highly consistent with the propagation path direction, a high directional consistency evaluation value can be obtained, thereby accurately identifying the propagation path enhanced by airflow transport and improving the scientificity and objectivity of propagation direction determination. By calculating the average airflow velocity and average combustible gas concentration in the monitoring area corresponding to the propagation path, the transport capacity of the risk medium and the enrichment degree of the risk source can be reflected respectively. Airflow velocity can characterize the risk propagation efficiency, and combustible gas concentration can characterize the risk accumulation level. Combined with the ability to comprehensively reflect the risk diffusion conditions of the propagation path, the ability to identify high-risk propagation channels can be improved.

[0009] Furthermore, step S500 includes: The opening status of each cover plate in the cable trench is obtained. The cover plate has only two working conditions: open and closed. When the cover plate is in the open state, the equivalent effective opening area is calculated based on the length and width of the ventilation opening formed after the cover plate is opened, and the equivalent effective opening area is used as the air circulation area. When the cover is closed, the equivalent ventilation area of ​​the gap is calculated based on the gap width and gap length of the gap at the boundary of the cover, and the equivalent ventilation area of ​​the gap is used as the air circulation area. Taking the assumed working condition where all covers are closed as the baseline state, the minimum value of the equivalent ventilation flow area of ​​the gaps corresponding to each cover through which the propagation path passes is selected as the baseline passage capacity value of the corresponding propagation connection edge. The cover plates that the propagation path passes through for each propagation connection edge are counted, and the minimum value of the air circulation area corresponding to each cover plate passed through by the propagation path is selected as the actual passage capacity value of the propagation connection edge in the current state. The difference between the actual capacity value and the baseline capacity value is calculated to obtain the capacity change of each propagation connection edge; Based on the bridging propagation channel set and the risk flow-dominated propagation channel set, propagation connection edges that belong to both sets are extracted as core constraint edges, and propagation connection edges that belong to only one set are extracted as secondary constraint edges. Based on the preset constraint coefficients, the core constraint edges and secondary constraint edges are assigned constraint weights respectively. The constraint weights and the change in the passage capacity of the corresponding propagation connection edges are multiplied to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The cover plates are sorted in descending order according to their constraint contribution values. The cover plates ranked in the top K positions are selected to form a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are then traversed. For each combination of cover plate states, the comprehensive propagation weight of the propagation connection edge is updated according to the opening state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated, and the comprehensive propagation weight of each propagation path is accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current combination of cover plate states. The difference between the risk propagation capability value corresponding to the current cover plate state combination and the risk propagation capability value in the initial state is calculated to obtain the risk propagation capability change value of the corresponding cover plate state combination. Using the gap flow area under the closed state of the cover plate as the benchmark passage capacity value, the passage capacity assessment of the propagation connection edge can truly reflect the impact of the open and closed state of the cover plate on the air flow conditions. Compared with the method of fixed passage capacity parameters, it can more accurately characterize the propagation capacity of the risk medium under different ventilation conditions and improve the authenticity and adaptability of the risk propagation modeling results. By integrating the analysis of cover opening and closing status, air circulation capacity, network bridging characteristics, and risk flow propagation characteristics, it is possible not only to identify key control locations in the risk propagation network, but also to evaluate the actual suppression effect of different control strategies. This achieves an integrated analysis process from "key cover identification" to "optimal control scheme generation," which helps to reduce the propagation range and speed of risk factors such as combustible gases, flue gas, and heat in cable trenches, thereby improving the inherent safety level and risk prevention and control capabilities of the cable trench operating environment.

[0010] Furthermore, step S600 includes: Obtain the risk propagation capability change value for each cover plate state combination, collect the number of cover plates that need to be controlled in the cover plate state combination, calculate the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, obtain the control benefit coefficient corresponding to each cover plate state combination, sort all cover plate state combinations according to the control benefit coefficient corresponding to each cover plate state combination, and select the cover plate state combination with the largest control benefit coefficient as the target control combination. The cover plate corresponding to the target control combination is determined as the minimum control set, and the opening state control scheme of the corresponding cover plate is generated. The opening state of each cover plate is adjusted according to the cover plate opening state control scheme. By calculating the ratio of the change in risk transmission capacity to the number of cover plates that need to be controlled, a control benefit coefficient is constructed, which enables a quantitative evaluation of the comprehensive effectiveness of different cover plate control schemes. Compared with methods that only focus on risk suppression effects, this approach can simultaneously consider control benefits and implementation costs, thereby improving the rationality of control strategy selection.

[0011] To better implement the above methods, a smart cable trench cover control system based on multi-dimensional perception is also proposed. The system includes a propagation path module, a bridging propagation channel set module, a weighted risk propagation network module, a risk flow energy-dominant propagation channel set module, a risk propagation capability change value module, and a control module. Propagation Path Module: Acquires operational data from multiple monitoring areas within the cable trench, constructs a risk propagation network based on the airflow connection between the monitoring areas, and determines the propagation path composed of multiple monitoring areas; Bridged Propagation Channel Set Module: Based on the airflow exchange volume, propagation speed, risk concentration change and spatial distance of each propagation connection edge in the propagation path, calculate the propagation enhancement index, identify cross-regional propagation enhancement paths, and construct a bridged propagation channel set; Weighted risk propagation network module: Obtain the propagation enhancement index corresponding to each propagation connection edge, combine historical operation data to calculate the propagation frequency evaluation value, calculate the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and construct a weighted risk propagation network; Risk flow energy dominant propagation channel set module: Based on the operational data of the monitoring area corresponding to the propagation path, calculate the risk flow energy value of each propagation path, identify candidate risk propagation paths, and determine the risk flow energy dominant propagation channel set; Risk propagation capability change value module: Obtain the opening status of each cover plate, construct a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculate the risk propagation capability change value corresponding to different cover plate state combinations; Control module: Determines the target control combination based on the risk propagation capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjusts the opening state of each cover plate according to the generated target control combination.

[0012] Furthermore, the risk transmission capacity change value module includes a unit for calculating the change in traffic capacity and a unit for calculating the risk transmission capacity change value: Unit for calculating the change in passage capacity: Obtain the opening state of each cover plate in the cable trench. The cover plate has only two working conditions: open and closed. When the cover plate is in the open state, the equivalent effective opening area is calculated based on the length and width of the ventilation opening formed after the cover plate is opened, and the equivalent effective opening area is used as the air circulation area. When the cover plate is in the closed state, the equivalent ventilation circulation area of ​​the gap is calculated based on the gap width and gap length of the gap at the cover plate boundary, and the equivalent ventilation circulation area of ​​the gap is used as the air circulation area. Taking the assumed working condition that all covers are in the closed state as the baseline state, the minimum value of the equivalent ventilation circulation area of ​​the gap corresponding to each cover plate through which the propagation path passes is selected as the baseline passage capacity value of the corresponding propagation connection edge. The cover plates through which the propagation path passes for each propagation connection edge are counted, and the minimum value of the air circulation area corresponding to each cover plate through which the propagation path passes is selected as the actual passage capacity value of the propagation connection edge in the current state. The difference between the actual passage capacity value and the baseline passage capacity value is calculated to obtain the change in passage capacity of each propagation connection edge. The unit for calculating the change in risk propagation capability is as follows: Based on the set of bridging propagation channels and the set of risk flow-dominant propagation channels, propagation connection edges belonging to both sets are extracted as core constraint edges, and propagation connection edges belonging to only one set are extracted as secondary constraint edges. Constraint weights are assigned to the core and secondary constraint edges according to preset constraint coefficients. The constraint weights are multiplied by the corresponding change in the passage capability of the propagation connection edge to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The cover plates are then sorted in descending order based on their constraint contribution values, and the sorting is performed accordingly. The first K cover plates constitute a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are traversed. For each cover plate state combination, the comprehensive propagation weight of the propagation connection edge is updated according to the open state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated and accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current cover plate state combination. The difference between the risk propagation capability value corresponding to the current cover plate state combination and the risk propagation capability value in the initial state is calculated to obtain the risk propagation capability change value of the corresponding cover plate state combination.

[0013] Furthermore, the control module includes a unit for calculating control efficiency coefficients and a unit for determining the control scheme for the on-state: The control benefit coefficient calculation unit obtains the risk propagation capability change value for each cover plate state combination, collects the number of cover plates that need to be controlled in the cover plate state combination, calculates the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, and obtains the control benefit coefficient corresponding to each cover plate state combination. Unit for determining the opening state control scheme: Based on the control benefit coefficient corresponding to each cover plate state combination, sort all cover plate state combinations, select the cover plate state combination with the largest control benefit coefficient as the target control combination, determine the cover plate corresponding to the target control combination as the minimum control set, generate the opening state control scheme for the corresponding cover plate, and adjust the opening state of each cover plate according to the opening state control scheme.

[0014] Compared with the prior art, the beneficial effects of the present invention are: by combining the cable trench structure and connectivity to construct a risk propagation network, risk identification no longer relies on a single indicator, but forms a multi-dimensional coupled analysis system that is closer to the real working conditions, thereby improving the accuracy and completeness of risk modeling; By introducing a multi-layered evaluation mechanism, including propagation enhancement indicators, propagation frequency assessment, and risk flow energy values, the propagation process of risks between different monitoring areas can be modeled in layers. This can identify cross-regional propagation enhancement paths and key bridging channels, thereby improving the ability to identify potential risk diffusion paths. When constructing a weighted risk propagation network, real-time propagation enhancement indicators are integrated with historical propagation frequencies, so that the model reflects both current state changes and long-term operating patterns, thereby reducing misjudgments caused by random fluctuations and improving system stability and adaptability. By constructing risk flow energy values ​​and combining them with statistical thresholds to screen candidate paths and dominant propagation channels, key paths that play a dominant role in risk diffusion can be effectively identified, making control decisions more focused on core risk sources and propagation links. By introducing the joint constraint relationship between the bridging propagation channel and the dominant propagation channel, and combining the risk propagation capability change and control benefit coefficient of the cover plate state combination, the optimal control combination can be selected from multiple control schemes to achieve the maximum risk suppression effect with the least cover plate adjustment, thereby reducing control costs and the probability of misoperation. By using a comprehensive evaluation mechanism of "change in risk propagation capability - control benefit coefficient", the control strategy not only focuses on the risk reduction effect, but also takes into account the control cost, thereby achieving a better balance between safety and operational efficiency and improving the safety and intelligence level of cable trench operation. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the intelligent cable trench cover control system based on multi-dimensional perception according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides a technical solution: a control method for intelligent cable trench covers based on multi-dimensional perception, the method comprising: Step S100: Obtain operational data from multiple monitoring areas within the cable trench, construct a risk propagation network based on the airflow connection between the monitoring areas, and determine the propagation path composed of multiple monitoring areas; Step S100 includes: By deploying temperature sensors, wind speed sensors, and combustible gas concentration sensors in multiple monitoring areas within the cable trench, operational data for each monitoring area is collected. The operational data includes temperature data, airflow propagation speed data, airflow direction data, and combustible gas concentration data. Based on the spatial dimensions and structural node distribution in the cable trench structural drawings, the cable trench is divided into segments according to a preset length threshold. The corner nodes and branch nodes in the cable trench are used as the boundaries of the area division. Each segment is defined as a monitoring area. The preset length threshold is used to limit the maximum spatial span of adjacent monitoring areas in the length direction of the cable trench. Information on ventilation ducts, inspection holes, cover gaps, and connecting channels between monitoring areas is obtained. Based on the information on ventilation ducts, inspection holes, cover gaps, and connecting channels, the airflow connection relationship between monitoring areas is determined. The airflow connection relationship is that there is an airflow path between two monitoring areas formed by at least one of the connecting structures of ventilation ducts, inspection holes, cover gaps, or connecting channels. Each monitoring area is marked as a node in the risk propagation network. When there is an airflow connection between two monitoring areas, a propagation connection edge is established between the corresponding nodes. The direction of the propagation connection edge is determined according to the airflow direction. A set of propagation paths consisting of multiple monitoring areas connected in sequence is formed according to the topological connection relationship between each propagation connection edge. For example, taking a 220kV substation cable trench as an example, the total length of the cable trench is 180m, including 2 corner nodes and 1 branch node. The trench width is 1.8m and the height is 2.0m. The preset length threshold is set to 30m. Based on the location of the corner nodes and branch nodes, the cable trench is divided into 6 monitoring areas, which are denoted as Area A, Area B, Area C, Area D, Area E and Area F respectively. The length of Area A is 25m, the length of Area B is 30m, the length of Area C is 20m, the length of Area D is 35m, the length of Area E is 40m, and the length of Area F is 30m. Temperature sensors, wind speed and direction sensors, and combustible gas concentration sensors are deployed in each monitoring area to acquire operational data in real time. At a certain moment, the monitoring data for each area are as follows: Area A: temperature 28.4℃, airflow velocity 0.32m / s, airflow direction towards Area B, combustible gas concentration 3ppm; Area B: temperature 31.8℃, airflow velocity 0.41m / s, airflow direction towards Area C, combustible gas concentration 7ppm; Area C: temperature 34.6℃, airflow velocity 0.48m / s, airflow direction towards Area D, combustible gas concentration 18ppm; Area D: temperature 36.2℃, airflow velocity 0.52m / s, airflow direction towards Area E, combustible gas concentration 25ppm; Area E: temperature 33.7℃, airflow velocity 0.44m / s, airflow direction towards Area F, combustible gas concentration 12ppm; Area F: temperature 30.5℃, airflow velocity 0.28m / s, combustible gas concentration 6ppm. Based on the cable trench structure drawings, the connectivity between areas is further determined. Area A and Area B are connected by a ventilation duct, Area B and Area C are connected by an inspection hole, Area C and Area D are connected by an air exchange channel formed by the gap in the cover plate, Area D and Area E are connected by a branch of the trench, and Area E and Area F are connected by a ventilation hole. Thus, the air circulation connection relationships are determined to be A→B, B→C, C→D, D→E, and E→F, respectively.

[0018] The above six monitoring areas are respectively regarded as nodes in the risk propagation network. The air circulation relationship is used as the propagation connection edge, and the propagation direction of the edge is determined according to the airflow direction to form a directed risk propagation network. The propagation connection edges A→B, B→C, C→D, D→E and E→F ​​are formed between nodes A, B, C, D, E and F. According to the topological relationship between each propagation connection edge, the propagation path P1 can be obtained as A→B→C→D→E→F. Since area B has an air exchange relationship with area E through the auxiliary ventilation hole, the cross-area propagation path P2 is formed as B→E→F.

[0019] Step S200: Based on the airflow exchange volume, propagation speed, risk concentration change and spatial distance of each propagation connection edge in the propagation path, calculate the propagation enhancement index, identify cross-regional propagation enhancement paths, and construct a set of bridging propagation channels; Step S200 includes: Traverse each propagation path in the risk propagation path set, and for each propagation connection edge in each propagation path, obtain the airflow exchange volume, propagation speed, risk concentration change, and spatial distance of each propagation connection edge within a preset time window. The calculation process of the airflow exchange volume is to obtain the cross-sectional area of ​​the connection channel corresponding to the airflow connection edge, and obtain the airflow speed corresponding to the propagation connection edge. Multiply the airflow speed by the cross-sectional area of ​​the connection channel to calculate the airflow exchange volume. Statistically analyze the airflow exchange volume, propagation speed, and risk concentration change to obtain the window average value corresponding to each parameter within the time window. The average values ​​of the airflow exchange rate window, the average value of the propagation velocity window, and the average value of the risk concentration change window are normalized and calculated to obtain the airflow exchange evaluation value, the propagation velocity evaluation value, and the concentration change evaluation value. The distance evaluation value D is calculated based on the spatial distance: D = (L1-L) ÷ (L1-L2), where L represents the spatial distance of the propagation connection edge, L1 represents the maximum spatial distance, and L2 represents the minimum spatial distance. The propagation enhancement index is calculated by weighting and summing the airflow exchange evaluation value, propagation speed evaluation value, concentration change evaluation value, and distance evaluation value according to preset weights. Obtain the propagation enhancement index within the historical operating cycle, calculate the mean and standard deviation of the propagation enhancement index, preset the threshold coefficient, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the propagation enhancement threshold. When the propagation enhancement index is greater than the propagation enhancement threshold, mark the corresponding propagation connection edge as a propagation enhancement edge. The proportion of propagation enhancement edges in each propagation path to the total number of propagation connection edges in that propagation path is calculated. When the proportion is greater than a preset proportion threshold, the corresponding propagation path is determined as a cross-regional propagation enhancement path. The corresponding propagation enhancement edges are extracted from each cross-regional propagation enhancement path, and propagation enhancement edges with the same connection nodes and propagation direction are merged to construct a bridging propagation channel set. For example, the preset time window is set to 30 minutes, data is collected every 5 minutes, the average airflow exchange rate between sides A and B is 85 m³ / h, the average propagation speed is 0.32 m / s, the average risk concentration change is 4 ppm, and the spatial distance is 25 m. The average airflow exchange rate window for the B→C side is 110 m³ / h, the average propagation velocity window is 0.41 m / s, the average risk concentration change window is 11 ppm, and the spatial distance is 30 m. The average airflow exchange rate window for the C→D side is 138 m³ / h, the average propagation velocity window is 0.48 m / s, the average risk concentration change window is 7 ppm, and the spatial distance is 20 m. The average airflow exchange rate window for the D→E side is 152 m³ / h, the average propagation velocity window is 0.52 m / s, the average risk concentration change window is 13 ppm, and the spatial distance is 35 m. The average airflow exchange rate window for the E→F side is 96 m³ / h, the average propagation velocity window is 0.44 m / s, the average risk concentration change window is 6 ppm, and the spatial distance is 40 m. The average airflow exchange rate window for the B→E side is 165 m³ / h, the average propagation velocity window is 0.61 m / s, the average risk concentration change window is 15 ppm, and the spatial distance is 45 m. The weights for airflow exchange evaluation are set to 0.35, propagation speed evaluation to 0.30, concentration change evaluation to 0.25, and distance evaluation to 0.10. The propagation enhancement index for the B→E side is 0.35×0.944+0.30×0.820+0.25×0.750+0.10×0.167=0.780. The propagation enhancement indices for each propagation connection edge were calculated using the same method: A→B edge = 0.193; B→C edge = 0.498; C→D edge = 0.589; D→E edge = 0.681; E→F edge = 0.370; B→E edge = 0.780. By retrieving historical operating cycle data for the most recent 12 months, a total of 180 sets of propagation enhancement index samples were obtained. After statistical calculation, the mean value of the propagation enhancement index was 0.420 and the standard deviation was 0.160. The threshold coefficient was set to 1.5, so the propagation enhancement threshold was 0.420 + 1.5 × 0.160 = 0.660. The propagation enhancement index of edge C→D is 0.589, which is less than 0.660, so it is not a propagation enhancement edge; the propagation enhancement index of edge D→E is 0.681, which is greater than 0.660, so it is marked as a propagation enhancement edge; the propagation enhancement index of edge B→E is 0.780, which is greater than 0.660, so it is marked as a propagation enhancement edge. Therefore, two propagation enhancement edges were finally identified, namely D→E and B→E. For propagation path P1, it contains 5 propagation connection edges, of which 1 is a propagation enhancement edge, accounting for 1 / 5 = 20%; the threshold for the proportion of propagation enhancement edges is set to 15%. Since 20% is greater than 15%, propagation path P1 is determined to be a cross-regional propagation enhancement path. For propagation path P2, it contains 2 propagation connection edges, of which 1 is a propagation enhancement edge, accounting for 1 / 2 = 50%; since 50% is greater than 15%, propagation path P2 is determined to be a cross-regional propagation enhancement path.

[0020] The propagation enhancement edges D→E and B→E are extracted from the cross-regional propagation enhancement paths P1 and P2. The bridging propagation channel TC1 is B→E, which represents the cross-regional rapid propagation channel formed by the auxiliary ventilation hole; the bridging propagation channel TC2 is D→E, which represents the enhanced propagation channel driven by the airflow in the main channel. Thus, the bridging propagation channel set TC={TC1, TC2} is obtained.

[0021] Step S300: Obtain the propagation enhancement index corresponding to each propagation connection edge, combine historical operation data to calculate the propagation frequency evaluation value, calculate the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and construct a weighted risk propagation network; Step S300 includes: Obtain the propagation enhancement index corresponding to each propagation connection edge in the risk propagation network. Based on historical operation data, count the number of times each propagation connection edge participates in risk propagation within a preset historical time window, and normalize the propagation number to obtain the propagation frequency evaluation value. The propagation enhancement index and the propagation frequency evaluation value are weighted and summed according to the preset weight coefficients to obtain the comprehensive propagation weight of the propagation connection edge. The comprehensive propagation weight is assigned to the corresponding propagation connection edge to form a weighted risk propagation network. For example, retrieve the historical operating data of the cable trench for the past 12 months, set the historical time window to 12 months, and take each occurrence of abnormal diffusion of combustible gas concentration, abnormal temperature transmission, or airflow-driven risk migration event as a valid risk propagation event. Count the number of times each propagation connection edge participates in risk propagation. The number of times A→B participates in risk propagation is 18 times, B→C participates in risk propagation is 42 times, C→D participates in risk propagation is 57 times, D→E participates in risk propagation is 76 times, E→F participates in risk propagation is 35 times, and B→E participates in risk propagation is 88 times. The calculated propagation frequency evaluation value for the A→B side is 18÷88=0.205; The propagation frequency evaluation value of the B→C side is 42÷88=0.477; The propagation frequency evaluation value of the C→D edge is 57÷88=0.648; The propagation frequency evaluation value of the D→E edge is 76÷88=0.864; The propagation frequency evaluation value of the E→F edge is 35÷88=0.398; The propagation frequency evaluation value of the B→E edge is 88÷88=1.000; The weighting coefficient for the propagation enhancement index is set to 0.65, and the weighting coefficient for the propagation frequency evaluation value is set to 0.35. The comprehensive propagation weighting value of the bridging propagation channel B→E is calculated to be 0.65×0.780+0.35×1.000=0.857. The overall propagation weight of the A→B edge is 0.65×0.193+0.35×0.205=0.197; The overall propagation weight of the B→C edge is 0.65×0.498+0.35×0.477=0.491; The overall propagation weight of the C→D edge is 0.65×0.589+0.35×0.648=0.610; The overall propagation weight of the D→E edge is 0.65×0.681+0.35×0.864=0.745; The overall propagation weight of the E→F edge is 0.65×0.370+0.35×0.398=0.380; The aforementioned comprehensive dissemination weights are assigned to the corresponding dissemination connection edges to construct a weighted risk dissemination network.

[0022] Step S400: Based on the operational data of the monitoring area corresponding to the propagation path, calculate the risk flow energy value of each propagation path, identify candidate risk propagation paths, and determine the set of risk flow energy dominant propagation channels; Step S400 includes: Obtain the sequence of path nodes corresponding to each propagation path in the risk propagation network and the associated monitoring area operation data. For each propagation path, extract the temperature difference between adjacent monitoring areas on that propagation path to obtain the temperature gradient value. Obtain the center coordinates of the monitoring areas in adjacent monitoring areas. For any adjacent monitoring area i and monitoring area j, their center coordinates are (xi, yi) and (xj, yj) respectively. Obtain the propagation speed data and airflow direction data of the airflow corresponding to monitoring area i. Determine the velocity component vx in the X direction and the velocity component vy in the Y direction based on the propagation speed data and airflow direction data. Construct the airflow direction vector (vx, vy) and use the two-parameter arctangent function to calculate the direction angle F=atan2(vy, vx). Based on the center coordinates of the two monitoring areas, a connecting direction vector (xj-xi, yj-yi) is constructed from monitoring area i to monitoring area j, and the direction angle E=atan2(yj-yi, xj-xi) of the connecting direction vector is calculated using the two-parameter arctangent function. Calculate the absolute difference between the direction angle of the airflow direction vector and the direction angle of the connected direction vector to obtain the direction deviation angle B. Calculate the direction consistency evaluation value according to the following formula: ; Where A represents the directional consistency evaluation value; Extract airflow velocity data from the monitoring area corresponding to the propagation path, calculate the average airflow velocity, and extract combustible gas concentration data from the monitoring area corresponding to the propagation path, calculate the average concentration. The temperature gradient value, directional consistency evaluation value, average airflow velocity value, and average concentration value are normalized to obtain normalized temperature gradient value, normalized directional consistency value, normalized airflow velocity value, and normalized concentration value. The risk flow energy value for each propagation path is calculated by weighting and summing the normalized values ​​according to preset weighting coefficients. Obtain the risk energy value within the historical operating cycle, calculate the mean and standard deviation of the risk energy value, preset the threshold coefficient of the risk energy value, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the risk energy threshold, and mark the corresponding propagation path as a candidate risk propagation path when the risk energy value is greater than the risk energy threshold. The risk energy values ​​of all candidate risk propagation paths are sorted in descending order. The candidate risk propagation paths with the highest sorting position are selected as the dominant risk energy propagation channels. The dominant risk energy propagation channels are then summarized to obtain a set of dominant risk energy propagation channels. For example, based on the cable trench structure drawings, the center coordinates of each monitoring area are determined. The center coordinates of area A are (12.5, 0), area B is (40, 0), area C is (65, 0), area D is (90, 20), area E is (125, 20), and area F is (160, 20). The airflow propagation velocity and direction data for each monitoring area are obtained. Specifically, the airflow velocity in area A is 0.32 m / s, and the airflow direction is along the positive X direction of the cable trench; the airflow velocity in area B is 0.41 m / s, and the airflow direction is along the positive X direction; the airflow velocity in area C is 0.48 m / s, and the angle between the airflow direction and the positive X direction is 38.7°; the airflow velocity in area D is 0.52 m / s, and the airflow direction is along the positive X direction; and the airflow velocity in area E is 0.44 m / s, and the airflow direction is along the positive X direction. Based on the airflow propagation speed and direction angle, the velocity components are calculated as follows: For region A, the corresponding velocity components are vx = 0.32 m / s and vy = 0 m / s, so the airflow direction vector is (0.32, 0), and the direction angle F1 = atan2(0, 0.32) = 0°; for region B, the corresponding velocity components are vx = 0.41 m / s and vy = 0 m / s, so the direction angle F2 = 0°; for region C, the corresponding velocity component is vx = 0.48 × cos38°. 0.7° = 0.375 m / s, vy = 0.48 × sin38.7° = 0.300 m / s, then the direction angle F3 = atan2(0.300, 0.375) = 38.7°; the velocity components corresponding to region D are vx = 0.52 m / s, vy = 0 m / s, then the direction angle F4 = 0°; the velocity components corresponding to region E are vx = 0.44 m / s, vy = 0 m / s, then the direction angle F5 = 0°; The connection direction vector and its direction angle are calculated based on the center coordinates of adjacent monitoring areas. The connection direction vector from area A to area B is (40-12.5, 0-0) = (27.5, 0), and the corresponding direction angle E1 = atan2(0, 27.5) = 0°; the connection direction vector from area B to area C is (25, 0), and the corresponding direction angle E2 = 0°; the connection direction vector from area C to area D is (25, 20), and the corresponding direction angle E3 = atan2(20, 25) = 38.7°; the connection direction vector from area D to area E is (35, 0), and the corresponding direction angle E4 = 0°; the connection direction vector from area E to area F is (35, 0), and the corresponding direction angle E5 = 0°.

[0023] Step S500: Obtain the opening status of each cover plate, construct a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculate the risk propagation capability change value corresponding to different cover plate state combinations; Step S500 includes: The opening status of each cover plate in the cable trench is obtained. The cover plate has only two working conditions: open and closed. When the cover plate is in the open state, the equivalent effective opening area is calculated based on the length and width of the ventilation opening formed after the cover plate is opened, and the equivalent effective opening area is used as the air circulation area. When the cover is closed, the equivalent ventilation area of ​​the gap is calculated based on the gap width and gap length of the gap at the boundary of the cover, and the equivalent ventilation area of ​​the gap is used as the air circulation area. Taking the assumed working condition where all covers are closed as the baseline state, the minimum value of the equivalent ventilation flow area of ​​the gaps corresponding to each cover through which the propagation path passes is selected as the baseline passage capacity value of the corresponding propagation connection edge. The cover plates that the propagation path passes through for each propagation connection edge are counted, and the minimum value of the air circulation area corresponding to each cover plate passed through by the propagation path is selected as the actual passage capacity value of the propagation connection edge in the current state. The difference between the actual capacity value and the baseline capacity value is calculated to obtain the capacity change of each propagation connection edge; Based on the bridging propagation channel set and the risk flow-dominated propagation channel set, propagation connection edges that belong to both sets are extracted as core constraint edges, and propagation connection edges that belong to only one set are extracted as secondary constraint edges. Based on the preset constraint coefficients, the core constraint edges and secondary constraint edges are assigned constraint weights respectively. The constraint weights and the change in the passage capacity of the corresponding propagation connection edges are multiplied to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The cover plates are sorted in descending order according to their constraint contribution values. The cover plates ranked in the top K positions are selected to form a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are then traversed. For each combination of cover plate states, the comprehensive propagation weight of the propagation connection edge is updated according to the opening state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated, and the comprehensive propagation weight of each propagation path is accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current combination of cover plate states. The risk propagation capability value corresponding to the current cover plate state combination is calculated by comparing it with the risk propagation capability value in the initial state, thus obtaining the change value of the risk propagation capability for the corresponding cover plate state combination.

[0024] Step S600: Determine the target control combination based on the risk transmission capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjust the opening state of each cover plate according to the generated target control combination; Step S600 includes: Obtain the risk propagation capability change value for each cover plate state combination, collect the number of cover plates that need to be controlled in the cover plate state combination, calculate the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, obtain the control benefit coefficient corresponding to each cover plate state combination, sort all cover plate state combinations according to the control benefit coefficient corresponding to each cover plate state combination, and select the cover plate state combination with the largest control benefit coefficient as the target control combination. The cover plate corresponding to the target control combination is determined as the minimum control set, and the opening state control scheme of the corresponding cover plate is generated. The opening state of each cover plate is adjusted according to the cover plate opening state control scheme.

[0025] To better implement the above methods, a smart cable trench cover control system based on multi-dimensional perception is also proposed. The system includes a propagation path module, a bridging propagation channel set module, a weighted risk propagation network module, a risk flow energy-dominant propagation channel set module, a risk propagation capability change value module, and a control module. Propagation Path Module: Acquires operational data from multiple monitoring areas within the cable trench, constructs a risk propagation network based on the airflow connection between the monitoring areas, and determines the propagation path composed of multiple monitoring areas; Bridged Propagation Channel Set Module: Based on the airflow exchange volume, propagation speed, risk concentration change and spatial distance of each propagation connection edge in the propagation path, calculate the propagation enhancement index, identify cross-regional propagation enhancement paths, and construct a bridged propagation channel set; Weighted risk propagation network module: Obtain the propagation enhancement index corresponding to each propagation connection edge, combine historical operation data to calculate the propagation frequency evaluation value, calculate the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and construct a weighted risk propagation network; Risk flow energy dominant propagation channel set module: Based on the operational data of the monitoring area corresponding to the propagation path, calculate the risk flow energy value of each propagation path, identify candidate risk propagation paths, and determine the risk flow energy dominant propagation channel set; Risk propagation capability change value module: Obtain the opening status of each cover plate, construct a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculate the risk propagation capability change value corresponding to different cover plate state combinations; The risk transmission capacity change module includes a unit for calculating the change in traffic capacity and a unit for calculating the change in risk transmission capacity. Unit for calculating the change in passage capacity: Obtain the opening state of each cover plate in the cable trench. The cover plate has only two working conditions: open and closed. When the cover plate is in the open state, the equivalent effective opening area is calculated based on the length and width of the ventilation opening formed after the cover plate is opened, and the equivalent effective opening area is used as the air circulation area. When the cover plate is in the closed state, the equivalent ventilation circulation area of ​​the gap is calculated based on the gap width and gap length of the gap at the cover plate boundary, and the equivalent ventilation circulation area of ​​the gap is used as the air circulation area. Taking the assumed working condition that all covers are in the closed state as the baseline state, the minimum value of the equivalent ventilation circulation area of ​​the gap corresponding to each cover plate through which the propagation path passes is selected as the baseline passage capacity value of the corresponding propagation connection edge. The cover plates through which the propagation path passes for each propagation connection edge are counted, and the minimum value of the air circulation area corresponding to each cover plate through which the propagation path passes is selected as the actual passage capacity value of the propagation connection edge in the current state. The difference between the actual passage capacity value and the baseline passage capacity value is calculated to obtain the change in passage capacity of each propagation connection edge. The unit for calculating the change in risk propagation capability is as follows: Based on the set of bridging propagation channels and the set of risk flow-dominant propagation channels, propagation connection edges belonging to both sets are extracted as core constraint edges, and propagation connection edges belonging to only one set are extracted as secondary constraint edges. Constraint weights are assigned to the core and secondary constraint edges according to preset constraint coefficients. The constraint weights are multiplied by the corresponding change in the passage capability of the propagation connection edge to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The cover plates are then sorted in descending order based on their constraint contribution values, and the sorting is performed accordingly. The first K cover plates constitute a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are traversed. For each cover plate state combination, the comprehensive propagation weight of the propagation connection edge is updated according to the open state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated and accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current cover plate state combination. The difference between the risk propagation capability value corresponding to the current cover plate state combination and the risk propagation capability value in the initial state is calculated to obtain the risk propagation capability change value of the corresponding cover plate state combination.

[0026] Control module: Determines the target control combination based on the risk propagation capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjusts the opening state of each cover plate according to the generated target control combination; The control module includes a unit for calculating control efficiency coefficients and a unit for determining the control scheme for the on-state. The control benefit coefficient calculation unit obtains the risk propagation capability change value for each cover plate state combination, collects the number of cover plates that need to be controlled in the cover plate state combination, calculates the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, and obtains the control benefit coefficient corresponding to each cover plate state combination. Unit for determining the opening state control scheme: Based on the control benefit coefficient corresponding to each cover plate state combination, sort all cover plate state combinations, select the cover plate state combination with the largest control benefit coefficient as the target control combination, determine the cover plate corresponding to the target control combination as the minimum control set, generate the opening state control scheme for the corresponding cover plate, and adjust the opening state of each cover plate according to the opening state control scheme.

[0027] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A control method for intelligent cable trench covers based on multi-dimensional perception, characterized in that, The methods include: Step S100: Obtain operational data from multiple monitoring areas within the cable trench, construct a risk propagation network based on the airflow connection between the monitoring areas, and determine the propagation path composed of multiple monitoring areas; Step S200: Based on the airflow exchange volume, propagation speed, risk concentration change and spatial distance of each propagation connection edge in the propagation path, calculate the propagation enhancement index, identify cross-regional propagation enhancement paths, and construct a set of bridging propagation channels; Step S300: Obtain the propagation enhancement index corresponding to each propagation connection edge, combine historical operation data to calculate the propagation frequency evaluation value, calculate the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and construct a weighted risk propagation network; Step S400: Based on the operational data of the monitoring area corresponding to the propagation path, calculate the risk flow energy value of each propagation path, identify candidate risk propagation paths, and determine the set of risk flow energy dominant propagation channels; Step S500: Obtain the opening status of each cover plate, construct a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculate the risk propagation capability change value corresponding to different cover plate state combinations; Step S600: Determine the target control combination based on the risk propagation capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjust the opening state of each cover plate according to the generated target control combination.

2. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S100 includes the following steps: By deploying temperature sensors, wind speed sensors, and combustible gas concentration sensors in multiple monitoring areas within the cable trench, operational data for each monitoring area is collected. The operational data includes temperature data, airflow propagation speed data, airflow direction data, and combustible gas concentration data. Based on the spatial dimensions and structural node distribution in the cable trench structural drawings, the cable trench is divided into segments according to a preset length threshold. The corner nodes and branch nodes in the cable trench are used as the boundaries of the area division. Each segment is defined as a monitoring area. The preset length threshold is used to limit the maximum spatial span of adjacent monitoring areas in the length direction of the cable trench. Information on ventilation ducts, inspection holes, cover gaps, and connecting channels between monitoring areas is obtained. Based on the information on ventilation ducts, inspection holes, cover gaps, and connecting channels, the airflow connection relationship between monitoring areas is determined. The airflow connection relationship is that there is an airflow path between two monitoring areas formed by at least one of the connecting structures of ventilation ducts, inspection holes, cover gaps, or connecting channels. Each monitoring area is marked as a node in the risk propagation network. When there is an airflow connection between two monitoring areas, a propagation connection edge is established between the corresponding nodes. The direction of the propagation connection edge is determined according to the airflow direction. Based on the topological connection relationship between each propagation connection edge, a set of propagation paths is formed by sequentially connecting multiple monitoring areas.

3. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S200 includes the following steps: Traverse each propagation path in the risk propagation path set, and for each propagation connection edge in each propagation path, obtain the airflow exchange volume, propagation speed, risk concentration change, and spatial distance of each propagation connection edge within a preset time window. The calculation process of the airflow exchange volume is to obtain the cross-sectional area of ​​the connection channel corresponding to the airflow connection edge, and obtain the airflow speed corresponding to the propagation connection edge. Multiply the airflow speed by the cross-sectional area of ​​the connection channel to calculate the airflow exchange volume. Statistically analyze the airflow exchange volume, propagation speed, and risk concentration change to obtain the window average value corresponding to each parameter within the time window. The average values ​​of the airflow exchange rate window, the average value of the propagation velocity window, and the average value of the risk concentration change window are normalized and calculated to obtain the airflow exchange evaluation value, the propagation velocity evaluation value, and the concentration change evaluation value. The distance evaluation value D is calculated based on the spatial distance: D = (L1-L) ÷ (L1-L2), where L represents the spatial distance of the propagation connection edge, L1 represents the maximum spatial distance, and L2 represents the minimum spatial distance. The propagation enhancement index is calculated by weighting and summing the airflow exchange evaluation value, propagation speed evaluation value, concentration change evaluation value, and distance evaluation value according to preset weights. Obtain the propagation enhancement index within the historical operating cycle, calculate the mean and standard deviation of the propagation enhancement index, preset the threshold coefficient, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the propagation enhancement threshold. When the propagation enhancement index is greater than the propagation enhancement threshold, mark the corresponding propagation connection edge as a propagation enhancement edge. The proportion of propagation enhancement edges in each propagation path to the total number of propagation connection edges in that propagation path is calculated. When the proportion is greater than a preset threshold, the corresponding propagation path is determined as a cross-regional propagation enhancement path. The corresponding propagation enhancement edges are extracted from each cross-regional propagation enhancement path, and propagation enhancement edges with the same connection nodes and propagation direction are merged to construct a bridging propagation channel set.

4. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S300 includes the following steps: Obtain the propagation enhancement index corresponding to each propagation connection edge in the risk propagation network. Based on historical operation data, count the number of times each propagation connection edge participates in risk propagation within a preset historical time window, and normalize the propagation number to obtain the propagation frequency evaluation value. The propagation enhancement index and the propagation frequency evaluation value are weighted and summed according to the preset weight coefficients to obtain the comprehensive propagation weight of the propagation connection edge. The comprehensive propagation weight is assigned to the corresponding propagation connection edge to form a weighted risk propagation network.

5. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S400 includes the following steps: Obtain the sequence of path nodes corresponding to each propagation path in the risk propagation network and the associated monitoring area operation data. For each propagation path, extract the temperature difference between adjacent monitoring areas on that propagation path to obtain the temperature gradient value. Obtain the center coordinates of the monitoring areas in adjacent monitoring areas. For any adjacent monitoring area i and monitoring area j, their center coordinates are (xi, yi) and (xj, yj) respectively. Obtain the propagation speed data and airflow direction data of the airflow corresponding to monitoring area i. Determine the velocity component vx in the X direction and the velocity component vy in the Y direction based on the propagation speed data and airflow direction data. Construct the airflow direction vector (vx, vy) and use the two-parameter arctangent function to calculate the direction angle F=atan2(vy, vx). Based on the center coordinates of the two monitoring areas, a connecting direction vector (xj-xi, yj-yi) is constructed from monitoring area i to monitoring area j, and the direction angle E=atan2(yj-yi, xj-xi) of the connecting direction vector is calculated using the two-parameter arctangent function. Calculate the absolute difference between the direction angle of the airflow direction vector and the direction angle of the connected direction vector to obtain the direction deviation angle B. Calculate the direction consistency evaluation value according to the following formula: ； Where A represents the directional consistency evaluation value; Extract airflow velocity data from the monitoring area corresponding to the propagation path, calculate the average airflow velocity, and extract combustible gas concentration data from the monitoring area corresponding to the propagation path, calculate the average concentration. The temperature gradient value, directional consistency evaluation value, average airflow velocity value, and average concentration value are normalized to obtain normalized temperature gradient value, normalized directional consistency value, normalized airflow velocity value, and normalized concentration value. The risk flow energy value for each propagation path is calculated by weighting and summing the normalized values ​​according to preset weighting coefficients. Obtain the risk energy value within the historical operating cycle, calculate the mean and standard deviation of the risk energy value, preset the threshold coefficient of the risk energy value, multiply the threshold coefficient by the standard deviation and add it to the mean to calculate the risk energy threshold, and mark the corresponding propagation path as a candidate risk propagation path when the risk energy value is greater than the risk energy threshold. The risk flow energy values ​​of all candidate risk propagation paths are sorted in descending order. The candidate risk propagation paths with the highest sorting position are selected as the dominant risk flow energy propagation channels. The dominant risk flow energy propagation channels are then aggregated to obtain a set of dominant risk flow energy propagation channels.

6. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S500 includes the following steps: The opening status of each cover plate in the cable trench is obtained. The cover plate has only two working conditions: open and closed. When the cover plate is in the open state, the equivalent effective opening area is calculated based on the length and width of the ventilation opening formed after the cover plate is opened, and the equivalent effective opening area is used as the air circulation area. When the cover is closed, the equivalent ventilation area of ​​the gap is calculated based on the gap width and gap length of the gap at the boundary of the cover, and the equivalent ventilation area of ​​the gap is used as the air circulation area. Taking the assumed working condition where all covers are closed as the baseline state, the minimum value of the equivalent ventilation flow area of ​​the gaps corresponding to each cover through which the propagation path passes is selected as the baseline passage capacity value of the corresponding propagation connection edge. The cover plates that the propagation path passes through for each propagation connection edge are counted, and the minimum value of the air circulation area corresponding to each cover plate passed through by the propagation path is selected as the actual passage capacity value of the propagation connection edge in the current state. The difference between the actual capacity value and the baseline capacity value is calculated to obtain the capacity change of each propagation connection edge; Based on the bridging propagation channel set and the risk flow-dominated propagation channel set, propagation connection edges that belong to both sets are extracted as core constraint edges, and propagation connection edges that belong to only one set are extracted as secondary constraint edges. Based on the preset constraint coefficients, the core constraint edges and secondary constraint edges are assigned constraint weights respectively. The constraint weights and the change in the passage capacity of the corresponding propagation connection edges are multiplied to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The cover plates are sorted in descending order according to their constraint contribution values. The cover plates ranked in the top K positions are selected to form a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are then traversed. For each combination of cover plate states, the comprehensive propagation weight of the propagation connection edge is updated according to the opening state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated, and the comprehensive propagation weight of each propagation path is accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current combination of cover plate states. The risk propagation capability value corresponding to the current cover plate state combination is calculated by comparing it with the risk propagation capability value in the initial state, thus obtaining the change value of the risk propagation capability for the corresponding cover plate state combination.

7. The intelligent cable trench cover control method based on multi-dimensional perception according to claim 1, characterized in that, Step S600 includes the following steps: Obtain the risk propagation capability change value for each cover plate state combination, collect the number of cover plates that need to be controlled in the cover plate state combination, calculate the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, obtain the control benefit coefficient corresponding to each cover plate state combination, sort all cover plate state combinations according to the control benefit coefficient corresponding to each cover plate state combination, and select the cover plate state combination with the largest control benefit coefficient as the target control combination. The cover plate corresponding to the target control combination is determined as the minimum control set, and the opening state control scheme of the corresponding cover plate is generated. The opening state of each cover plate is adjusted according to the cover plate opening state control scheme.

8. A multi-dimensional sensing-based intelligent cable trench cover control system, used to implement the multi-dimensional sensing-based intelligent cable trench cover control method according to any one of claims 1-7, characterized in that, The system includes a propagation path module, a bridging propagation channel set module, a weighted risk propagation network module, a risk flow-dominant propagation channel set module, a risk propagation capability change value module, and a control module. The propagation path module acquires operational data from multiple monitoring areas within the cable trench, constructs a risk propagation network based on the airflow connection between the monitoring areas, and determines the propagation path composed of multiple monitoring areas. The bridging propagation channel set module calculates propagation enhancement indices, identifies cross-regional propagation enhancement paths, and constructs a bridging propagation channel set based on the airflow exchange volume, propagation speed, risk concentration change, and spatial distance of each propagation connection edge in the propagation path. The weighted risk propagation network module: obtains the propagation enhancement index corresponding to each propagation connection edge, combines historical operation data to statistically evaluate the propagation frequency, calculates the comprehensive propagation weight of the propagation connection edge based on the propagation enhancement index and the propagation frequency evaluation value, and constructs the weighted risk propagation network; The risk flow energy dominant propagation channel set module: based on the operation data of the monitoring area corresponding to the propagation path, calculates the risk flow energy value of each propagation path, identifies candidate risk propagation paths, and determines the risk flow energy dominant propagation channel set; The risk propagation capability change value module: obtains the opening status of each cover plate, constructs a joint constraint relationship by combining the bridging propagation channel set and the risk flow energy dominant propagation channel set, and calculates the risk propagation capability change value corresponding to different cover plate state combinations; The control module determines the target control combination based on the risk propagation capability change value and control benefit coefficient corresponding to each cover plate state combination, and adjusts the opening state of each cover plate according to the generated target control combination.

9. The intelligent cable trench cover control system based on multi-dimensional perception according to claim 8, characterized in that, The risk transmission capability change value module includes a unit for calculating the change in traffic capacity and a unit for calculating the risk transmission capability change value: The unit for calculating the change in passage capacity is as follows: It acquires the opening status of each cover plate in the cable trench. The cover plate has only two working conditions: open and closed. When the cover plate is open, it calculates the equivalent effective opening area based on the length and width of the ventilation opening formed after the cover plate is opened, and uses this equivalent effective opening area as the airflow area. When the cover plate is closed, it calculates the equivalent ventilation flow area of ​​the gap based on the gap width and gap length of the gap at the cover plate boundary, and uses this equivalent ventilation flow area as the airflow area. Using the assumed working condition that all cover plates are closed as the baseline state, it selects the minimum value among the equivalent ventilation flow areas of the gaps corresponding to each cover plate through which the propagation path passes, as the baseline passage capacity value of the corresponding propagation connection edge. It then counts the cover plates through which the propagation path passes for each propagation connection edge, selects the minimum value among the airflow areas corresponding to each cover plate through which the propagation path passes, as the actual passage capacity value of the propagation connection edge in the current state, and calculates the difference between the actual passage capacity value and the baseline passage capacity value to obtain the change in passage capacity for each propagation connection edge. The unit for calculating the change in risk propagation capability is as follows: Based on the set of bridging propagation channels and the set of risk flow-dominant propagation channels, propagation connection edges belonging to both sets are extracted as core constraint edges, and propagation connection edges belonging to only one set are extracted as secondary constraint edges. Constraint weights are assigned to the core and secondary constraint edges according to preset constraint coefficients. The constraint weights are multiplied by the corresponding change in the passage capability of the propagation connection edge to calculate the edge constraint contribution value of each propagation connection edge. The propagation connection edges associated with each cover plate are counted, and the corresponding edge constraint contribution values ​​are accumulated to obtain the constraint contribution value of each cover plate to the risk propagation network. The constraint contribution values ​​of each cover plate are sorted in descending order, and the top-ranked cover plate is selected. The first K cover plates in the sequence constitute a candidate control cover plate set, where K represents the preset number of cover plates. The open and closed state combinations of each cover plate in the candidate control cover plate set are traversed. For each cover plate state combination, the comprehensive propagation weight of the propagation connection edge is updated according to the open state of the corresponding cover plate. The comprehensive propagation weight of each propagation path in the weighted risk propagation network is calculated and accumulated. The accumulated result is determined as the risk propagation capability value corresponding to the current cover plate state combination. The difference between the risk propagation capability value corresponding to the current cover plate state combination and the risk propagation capability value in the initial state is calculated to obtain the risk propagation capability change value of the corresponding cover plate state combination.

10. The intelligent cable trench cover control system based on multi-dimensional perception according to claim 8, characterized in that, The control module includes a unit for calculating the control efficiency coefficient and a unit for determining the control scheme for the on-state: The control benefit coefficient calculation unit: obtains the risk propagation capability change value of each cover plate state combination, collects the number of cover plates that need to be controlled in the cover plate state combination, calculates the ratio of the risk propagation capability change value to the number of cover plates that need to be controlled, and obtains the control benefit coefficient corresponding to each cover plate state combination. The unit for determining the opening state control scheme: sorts all cover plate state combinations according to the control benefit coefficients corresponding to each cover plate state combination, selects the cover plate state combination with the largest control benefit coefficient as the target control combination, determines the cover plate corresponding to the target control combination as the minimum control set, generates the opening state control scheme for the corresponding cover plate, and adjusts the opening state of each cover plate according to the opening state control scheme.

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