Emergency control methods and systems for vehicles in mining accident scenarios

Abstract

This invention discloses an emergency control method and system for vehicles in mining accident scenarios. It predicts the accident area based on a dynamic mining area model and performs location verification to collect actual data. The state of the accident area is defined based on multiple sets of actual data, and prediction and verification are performed for the accident area. The system triggers the vehicle's ejection based on its escape route and surrounding environmental features. The vehicle's driving state is defined based on multiple driving data points. The system triggers the vehicle's self-regulation logic based on the vehicle's driving state, the mining accident morphology of the surrounding environment, and the current view of the dynamic mining area model. This self-regulation logic controls the vehicle's posture during driving, achieving dynamic control of the vehicle's posture and ensuring the vehicle's stability along its escape route. It incorporates multi-dimensional considerations of the vehicle's driving state, the mining accident morphology of the surrounding environment, and the current view of the dynamic mining area model.

Description

Technical Field

[0001] This invention relates to the technical field of emergency control of vehicles, and more particularly to an emergency control method and system for vehicles in mining accident scenarios. Background Technology

[0002] With the development of technology, mining areas have become the main concentration of mining. The unmined parts of the mining area are mined by workers. The mining area changes gradually as the unmined parts are mined. In the existing technology, the corresponding mined parts are recorded in the mining area, and the stability of multiple mined parts is assessed. However, the state of the mining accident area is not fully considered, and the stability of vehicles driving under mining accident conditions in the surrounding environment cannot be guaranteed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an emergency control method and system for vehicles in mining accident scenarios. Based on the three-dimensional space of the mining area and the mining section within the mining area, multiple monitoring zones are divided. Multiple sets of detectors are deployed according to each monitoring zone. Data detected by multiple sets of detectors within the mine is collected, and a dynamic model of the mining area is constructed based on the multiple sets of data and the corresponding locations of the monitoring zones. The mining accident occurrence area is predicted based on the dynamic model of the mining area, and the location of the mining accident occurrence area is re-checked to collect actual data. The state of the mining accident occurrence area is defined based on multiple actual data, and prediction and re-checking are performed for the mining accident occurrence area to ensure the accuracy of the state of the mining accident occurrence area.

[0004] Furthermore, based on the mining accident area, a corresponding mining accident passage is defined, and the mining accident occurrence time of each node in the mining accident passage is predicted based on the mining accident passage, the mining accident area, and the set of mining accident factors corresponding to the state of the mining accident passage and the mining accident area. The escape route of the vehicle is defined based on the mining accident passage, the mining accident occurrence time of each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the vehicle's escape route and the characteristics of the surrounding environment. The driving state of the vehicle is defined based on multiple driving data. The self-regulation logic of the vehicle is triggered based on the driving state of the vehicle, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model. Based on the self-regulation logic of the vehicle, the attitude of the vehicle during the driving process is controlled, realizing dynamic control of the vehicle's attitude during the driving process, ensuring the driving stability of the vehicle along the escape route, and incorporating multi-dimensional considerations of the vehicle's driving state, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model.

[0005] This invention provides an emergency control method for vehicles in mining accident scenarios, applicable to emergency control scenarios for vehicles in mining accident scenarios;

[0006] The emergency control method for the vehicle in a mining accident scenario includes:

[0007] Based on the three-dimensional space of the mining area and the mining section within the mining area, multiple monitoring areas are divided, and multiple sets of detectors are deployed according to each monitoring area.

[0008] Collect mine data detected by multiple sets of detectors, and construct a dynamic model of the mine area based on multiple mine data and the location of the corresponding monitoring area;

[0009] The mining area dynamic model is used to predict the mining accident area and to locate and re-examine the mining accident area in order to collect actual data. The status of the mining accident area is defined based on multiple actual data.

[0010] The corresponding mine disaster passage is defined according to the area where the mine disaster occurs, and the time of mine disaster occurrence at each node in the mine disaster passage is predicted based on the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster area.

[0011] The escape route of the vehicle is defined based on the mine accident passage, the time of the mine accident at each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the escape route and the characteristics of the surrounding environment.

[0012] The vehicle's driving status is defined based on multiple driving data. The vehicle's self-regulation logic is triggered based on the driving status, the mining disaster situation in the surrounding environment, and the current scene of the mining area dynamic model. The vehicle's attitude during driving is controlled based on the self-regulation logic.

[0013] Optionally, the mining area is divided into multiple monitoring zones based on its three-dimensional space and the mining portion within the mining area, and multiple sets of detectors are deployed according to each monitoring zone, including:

[0014] The location of the mining area is collected, and the outer contour of the mining area is defined based on the location detection of the mining area;

[0015] A three-dimensional spatial model of the mining area is constructed based on its outer contour and UAV detection data.

[0016] The three-dimensional space of the associated mining area and the mining section within the mining area are divided into multiple monitoring zones based on the three-dimensional space of the mining area, the mining section within the mining area, and the vehicle driving channels.

[0017] Mark the location of each monitoring area, and construct the corresponding detection space based on the location of each monitoring area and its surrounding space;

[0018] Multiple detection points are defined based on the detection space, and multiple sets of detectors are matched to multiple detection points.

[0019] Optionally, the step of collecting mine data detected by multiple sets of detectors and constructing a dynamic model of the mine area based on multiple mine data and the corresponding monitoring area locations includes:

[0020] Collect data from within the mine detected by multiple sets of detectors;

[0021] Mark the corresponding location for multiple mining data;

[0022] The activity level within a mine is defined based on multiple mine data and their corresponding locations.

[0023] Construct a mining area activity set based on multiple mine activity levels and the corresponding monitoring area locations;

[0024] Activity data of related mining areas, past records of mining areas, and three-dimensional space of mining areas;

[0025] Based on the activity set of the mining area, the past records of the mining area, and the three-dimensional space of the mining area, a dynamic model of the mining area is constructed. At this time, the dynamic model of the mining area controls the dynamic changes of the mining area in real time and performs autonomous optimization in combination with the real-time data of the mining area.

[0026] Optionally, the step of predicting the mining accident area based on the dynamic model of the mining area, and conducting a location verification of the mining accident area to collect actual data, and defining the state of the mining accident area based on multiple actual data, includes:

[0027] Freeze-frame dynamic model of the mining area;

[0028] Collect the activity set of the mining area, and define multiple abnormal activity levels based on the filtering of the activity set of the mining area;

[0029] Multiple mining accident areas are predicted based on multiple abnormal activity levels, real-time messages from the mining area, and the response locations of the mining area dynamic model.

[0030] The locations of multiple mining accident sites were collected, and the locations of these sites were re-examined to collect actual data on the multiple mining accident sites.

[0031] In each area where a mining accident occurred, corresponding mining accident characteristics were constructed based on multiple actual data and the corresponding spatial data.

[0032] The state of a mining accident area is defined based on the characteristics of the accident, its corresponding location, and the change in the activity level of the area where the accident occurred.

[0033] Optionally, the step of defining a corresponding mine disaster passage based on the mine disaster occurrence area, and predicting the mine disaster occurrence time of each node in the mine disaster passage based on the mine disaster passage and the set of mine disaster factors corresponding to the state of the mine disaster occurrence area, includes:

[0034] The location of the mining accident site was determined and the internal outline of the site was collected.

[0035] The internal outline of the area where the mine accident occurred, the vehicle access routes, and the location of the workers;

[0036] The corresponding mine accident passage is defined based on the internal outline of the area where the mine accident occurred, the vehicle driving passage, and the location of the workers.

[0037] Targeted detection was conducted along the mine disaster passage, and multiple mine disaster factors were collected during the targeted detection.

[0038] Based on multiple mining accident factors, a set of mining accident factors is constructed, which is the set of mining accident factors corresponding to the state of the associated mining accident passage and the mining accident occurrence area.

[0039] Predict the time of occurrence of a mine accident at each node in the mine accident channel based on the set of mine accident factors corresponding to the state of the mine accident channel and the area where the mine accident occurred.

[0040] Optionally, the step of defining the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the travel passage, and triggering the vehicle's ejection based on the vehicle's escape route and surrounding environmental features, includes:

[0041] The time of the mine disaster at each node is frozen in time. At this time, the time of the mine disaster at each node, the current location of the vehicle, and the driving route are associated with the mine disaster passage.

[0042] The first escape range is defined based on the mine accident passage, the current location of the vehicle, and the route of travel;

[0043] The second escape range is defined based on the time of the mine accident at each node, the current location of the vehicle, and the route of travel.

[0044] The vehicle's escape route is defined based on the second escape range, the second escape range, and the vehicle's current location.

[0045] Optionally, the step of defining the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current location, and the travel passage, and triggering the vehicle's ejection based on the escape route and surrounding environmental features, further includes:

[0046] The system triggers environmental detection along the vehicle's escape route and collects corresponding surrounding environmental features.

[0047] The ejection point is defined based on the vehicle's escape route and the characteristics of the surrounding environment;

[0048] The vehicle is launched into position based on the launch node. At this time, the launch power of the vehicle is defined according to the launch node, the number of passengers on the vehicle, and the launch space in the mine tunnel.

[0049] Optionally, the step of defining the vehicle's driving state based on multiple driving data, triggering the vehicle's self-regulation logic based on the vehicle's driving state, the mining disaster situation in the vehicle's surrounding environment, and the current image of the mining area dynamic model, and controlling the vehicle's attitude during driving based on the vehicle's self-regulation logic includes:

[0050] Real-time monitoring of multiple driving data points of the vehicle during the escape process;

[0051] The vehicle's driving status is defined based on multiple driving data points and the number of passengers in the vehicle.

[0052] Collect obstacle data during vehicle operation, and trigger primary vehicle control based on obstacle data and the relative distance between the vehicle and obstacles;

[0053] In the initial control of the vehicle, the vehicle effectively avoids obstacles based on the obstacle features formed by obstacle data, and ensures the driving stability of the vehicle during the avoidance process.

[0054] Optionally, the step of defining the vehicle's driving state based on multiple driving data, triggering the vehicle's self-regulation logic based on the vehicle's driving state, the mining disaster situation in the vehicle's surrounding environment, and the current image of the mining area dynamic model, and controlling the vehicle's attitude during driving based on the vehicle's self-regulation logic, further includes:

[0055] The vehicle's self-regulation logic is triggered based on the vehicle's driving status, the mining disaster situation in the vehicle's surrounding environment, and the current screen of the mining area dynamic model. At this time, the first regulation parameter is defined based on the vehicle's driving status and the mining disaster situation in the vehicle's surrounding environment, and the second regulation parameter is defined based on the vehicle's driving status and the current screen of the mining area dynamic model. The vehicle's self-regulation logic is defined based on the first regulation parameter, the second regulation parameter, and the logic matching table.

[0056] The vehicle's attitude during driving is controlled based on the vehicle's self-regulation logic.

[0057] In addition, this invention also provides an emergency control system for vehicles in mining accident scenarios, the emergency control system for vehicles in mining accident scenarios including:

[0058] The detector module is used to divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining section within the mining area, and to deploy multiple sets of detectors according to each monitoring zone.

[0059] The mine dynamic module is used to collect mine data detected by multiple sets of detectors and to build a mine dynamic model based on multiple mine data and the location of the corresponding monitoring area.

[0060] The status module is used to predict the mining accident area based on the dynamic model of the mining area, and to locate and verify the mining accident area in order to collect actual data and define the status of the mining accident area based on multiple actual data.

[0061] The prediction module is used to define the corresponding mine disaster channels according to the mine disaster occurrence area, and predict the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster occurrence area.

[0062] The ejection module is used to define the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the driving passage, and to trigger the vehicle's ejection into position based on the vehicle's escape route and the characteristics of the surrounding environment.

[0063] The attitude module is used to define the vehicle's driving state based on multiple driving data. It triggers the vehicle's self-regulation logic based on the vehicle's driving state, the mining disaster state of the vehicle's surrounding environment, and the current screen of the mining area dynamic model. Based on the vehicle's self-regulation logic, it controls the vehicle's attitude during driving.

[0064] In this embodiment of the invention, the method is used to divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining portion within the mining area. Multiple sets of detectors are deployed in each monitoring zone. Mining data detected by multiple sets of detectors are collected, and a dynamic model of the mining area is constructed based on the multiple mining data and the corresponding monitoring zone locations. The mining area dynamic model is used to predict the mining accident occurrence area, and the mining accident occurrence area is located and re-checked to collect actual data. The state of the mining accident occurrence area is defined based on multiple actual data, and prediction and re-checking are performed for the mining accident occurrence area to ensure the accuracy of the state of the mining accident occurrence area.

[0065] Furthermore, based on the mining accident area, a corresponding mining accident passage is defined, and the mining accident occurrence time of each node in the mining accident passage is predicted based on the mining accident passage, the mining accident area, and the set of mining accident factors corresponding to the state of the mining accident passage and the mining accident area. The escape route of the vehicle is defined based on the mining accident passage, the mining accident occurrence time of each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the vehicle's escape route and the characteristics of the surrounding environment. The driving state of the vehicle is defined based on multiple driving data. The self-regulation logic of the vehicle is triggered based on the driving state of the vehicle, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model. Based on the self-regulation logic of the vehicle, the attitude of the vehicle during the driving process is controlled, realizing dynamic control of the vehicle's attitude during the driving process, ensuring the driving stability of the vehicle along the escape route, and incorporating multi-dimensional considerations of the vehicle's driving state, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart illustrating the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0068] Figure 2 This is a flowchart illustrating step S11 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0069] Figure 3 This is a flowchart illustrating step S12 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0070] Figure 4 This is a flowchart illustrating step S13 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0071] Figure 5 This is a flowchart illustrating step S14 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0072] Figure 6 This is a flowchart illustrating step S15 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0073] Figure 7 This is a flowchart illustrating step S16 of the emergency control method for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0074] Figure 8 This is a schematic diagram of the structural composition of the emergency control system for a vehicle in a mining accident scenario according to an embodiment of the present invention;

[0075] Figure 9 This is a hardware diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0076] 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.

[0077] Please see Figures 1 to 9An emergency control method for vehicles in mining accident scenarios, applied to emergency control scenarios for vehicles in mining accident scenarios; the emergency control method for vehicles in mining accident scenarios includes:

[0078] Step S11: Divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining section within the mining area, and deploy multiple sets of detectors according to each monitoring zone;

[0079] Step S12: Collect mine data detected by multiple sets of detectors, and construct a dynamic model of the mine area based on multiple mine data and the location of the corresponding monitoring area;

[0080] Step S13: Predict the mining accident area based on the dynamic model of the mining area, and conduct a location review of the mining accident area to collect actual data, and define the status of the mining accident area based on multiple actual data.

[0081] Step S14: Define the corresponding mine disaster channel according to the mine disaster area, and predict the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster area;

[0082] Step S15: Define the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current location, and the driving passage, and trigger the vehicle's ejection to its designated position based on the vehicle's escape route and the characteristics of the surrounding environment.

[0083] Step S16: Define the vehicle's driving state based on multiple driving data. Trigger the vehicle's self-regulation logic based on the vehicle's driving state, the mining disaster state of the vehicle's surrounding environment, and the current screen of the mining area dynamic model. Control the vehicle's posture during driving based on the vehicle's self-regulation logic.

[0084] In this embodiment of the invention, the method is used to divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining portion within the mining area. Multiple sets of detectors are deployed in each monitoring zone. Mining data detected by multiple sets of detectors are collected, and a dynamic model of the mining area is constructed based on the multiple mining data and the corresponding monitoring zone locations. The mining area dynamic model is used to predict the mining accident occurrence area, and the mining accident occurrence area is located and re-checked to collect actual data. The state of the mining accident occurrence area is defined based on multiple actual data, and prediction and re-checking are performed for the mining accident occurrence area to ensure the accuracy of the state of the mining accident occurrence area.

[0085] Furthermore, based on the mining accident area, a corresponding mining accident passage is defined, and the mining accident occurrence time of each node in the mining accident passage is predicted based on the mining accident passage, the mining accident area, and the set of mining accident factors corresponding to the state of the mining accident passage and the mining accident area. The escape route of the vehicle is defined based on the mining accident passage, the mining accident occurrence time of each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the vehicle's escape route and the characteristics of the surrounding environment. The driving state of the vehicle is defined based on multiple driving data. The self-regulation logic of the vehicle is triggered based on the driving state of the vehicle, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model. Based on the self-regulation logic of the vehicle, the attitude of the vehicle during the driving process is controlled, realizing dynamic control of the vehicle's attitude during the driving process, ensuring the driving stability of the vehicle along the escape route, and incorporating multi-dimensional considerations of the vehicle's driving state, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model.

[0086] refer to Figure 2 In step S11, multiple monitoring areas are divided based on the three-dimensional space of the mining area and the mining part within the mining area, and multiple sets of detectors are arranged according to each monitoring area.

[0087] In the specific implementation of this invention, the specific steps can be as follows:

[0088] S111: Collect the location of the mining area and define the outer contour of the mining area based on the location detection of the mining area;

[0089] S112: Construct a three-dimensional space of the mining area based on its outer contour and UAV detection data;

[0090] S113: The three-dimensional space of the associated mining area and the mining section within the mining area are divided into multiple monitoring zones based on the three-dimensional space of the mining area, the mining section within the mining area, and the vehicle driving channels.

[0091] S114: Mark the location of each monitoring area, and construct the corresponding detection space based on the location of each monitoring area and the corresponding surrounding space;

[0092] S115: Define multiple detection points according to the detection space, and match multiple sets of detectors to multiple detection points.

[0093] In the embodiments of this application, the location of the smart device is collected in the indoor space where the smart device is located, and the location of the smart device is introduced. Based on the detection of the surrounding environment of the smart device's location, multiple surrounding environment features are defined. At this time, multiple surrounding environment parameters of the smart device's location are defined for the surrounding environment detection, so as to define the corresponding surrounding environment features according to the multiple surrounding environment parameters and the corresponding orientation, thereby clarifying the multiple surrounding environment features. Optionally, the multiple surrounding environment features include work surface features, obstacle features, spatial features, etc.

[0094] Furthermore, by associating the location of the smart device with multiple surrounding environmental features, scene detection is triggered based on the location of the smart device and multiple surrounding environmental features, thereby introducing multi-dimensional control over the location of the smart device and multiple surrounding environmental features, and thus realizing scene detection.

[0095] At this point, the system dynamically monitors the smart device and collects multiple task signals from the smart device in real time. Further control is then exercised over these signals to correlate them with the device's current environment and task signals. This multi-dimensional control of the smart device's environment and task signals enables matching between the environment and corresponding task signals. Furthermore, based on the environment and task signals, the system defines the smart device's task information in the current environment, ensuring the accuracy of this information and achieving comprehensive control over the smart device's task information in the current environment.

[0096] refer to Figure 3 In step S12, mine data detected by multiple sets of detectors are collected, and a dynamic model of the mine area is constructed based on multiple mine data and the location of the corresponding monitoring area.

[0097] In the specific implementation of this invention, the specific steps can be as follows:

[0098] S121: Collect mine data detected by multiple sets of detectors;

[0099] S122: Mark the corresponding orientation for multiple mine data;

[0100] S123: Define the corresponding mine activity level based on multiple mine data and corresponding location;

[0101] S124: Construct a mining area activity set based on multiple mine activity levels and the corresponding monitoring area locations;

[0102] S125: Activity set of associated mining areas, past records of mining areas, and three-dimensional space of mining areas;

[0103] S126: Based on the activity set of the mining area, the past records of the mining area, and the three-dimensional space of the mining area, a dynamic model of the mining area is constructed. At this time, the dynamic model of the mining area controls the dynamic changes of the mining area in real time and performs autonomous optimization in combination with the real-time data of the mining area.

[0104] In the embodiments of this application, mine data detected by multiple sets of detectors are collected, and multiple mine data are introduced to mark the corresponding directions of multiple mine data. Multi-dimensional control is carried out on multiple mine data and their corresponding directions, thereby defining the corresponding mine activity level based on multiple mine data and their corresponding directions, ensuring the accuracy of mine activity level. At the same time, real-time monitoring of mine activity level is carried out in the mining area.

[0105] At this point, an activity set of the mining area is constructed based on the activity levels of multiple mines and the corresponding locations of the monitoring areas. This activity set of the mining area is then introduced to achieve overall control over the activity set of the mining area.

[0106] Furthermore, by associating the activity set, past records, and three-dimensional space of the mining area, a multi-dimensional control is achieved. Based on this, a dynamic model of the mining area is constructed. This dynamic model monitors the dynamic changes of the mining area in real time and performs autonomous optimization based on real-time data. By dynamically controlling the mining area through the dynamic model, the overall impact of the activity set, past records, and three-dimensional space of the mining area can be fully considered, ensuring multi-dimensional control of these factors.

[0107] refer to Figure 4 In step S13, the mining accident area is predicted based on the dynamic model of the mining area, and the mining accident area is located and re-examined to collect actual data. The state of the mining accident area is defined based on multiple actual data.

[0108] In the specific implementation of this invention, the specific steps can be as follows:

[0109] S131: Freeze-frame dynamic model of the mining area;

[0110] S132: Collect the activity set of the mining area, and define multiple abnormal activity levels based on the filtering of the activity set of the mining area;

[0111] S133: Predict multiple mining accident areas based on multiple abnormal activity levels, real-time messages from the mining area, and the response location of the mining area dynamic model;

[0112] S134: Collect the locations of multiple mining accident areas and conduct location verification based on the locations of multiple mining accident areas to collect actual data of multiple mining accident areas;

[0113] S135: In each mining accident area, construct corresponding mining accident characteristics based on multiple actual data and the corresponding space;

[0114] S136: Define the state of the mining accident area based on the characteristics of the mining accident, the corresponding location, and the change in the activity level of the mining accident area.

[0115] In the embodiments of this application, multiple monitoring areas are divided based on the three-dimensional space of the mining area and the mining portion within the mining area. Multiple sets of detectors are deployed according to each monitoring area. Mining data detected by multiple sets of detectors are collected, and a dynamic model of the mining area is constructed based on the multiple mining data and the corresponding monitoring area locations. The mining area dynamic model is used to predict the mining accident occurrence area, and the mining accident occurrence area is located and re-examined to collect actual data. The state of the mining accident occurrence area is defined based on multiple actual data, and prediction and re-examination are performed for the mining accident occurrence area to ensure the accuracy of the state of the mining accident occurrence area.

[0116] At this point, the dynamic model of the mining area is frozen to facilitate further analysis of the dynamic model. Simultaneously, the activity set of the mining area is collected, and multiple abnormal activity levels are defined based on the filtering of the activity set. This achieves the abnormal filtering of the activity set of the mining area, ensures the accuracy of multiple abnormal activity levels, and enables precise control of multiple abnormal activity levels.

[0117] Furthermore, multiple mining accident occurrence areas are predicted based on multiple abnormal activity levels, real-time messages from the mining area, and the response locations of the mining area dynamic model. By introducing multiple abnormal activity levels, real-time messages from the mining area, and the response locations of the mining area dynamic model, multi-dimensional control of multiple abnormal activity levels, real-time messages from the mining area, and the response locations of the mining area dynamic model is achieved, ensuring the accuracy of multiple mining accident occurrence areas.

[0118] Therefore, the locations of multiple mining accident areas were collected, and the locations of multiple mining accident areas were re-examined to collect actual data on multiple mining accident areas.

[0119] Furthermore, in each mining accident area, corresponding mining accident characteristics are constructed based on multiple actual data and corresponding spatial data. The state of the mining accident area is defined based on the mining accident characteristics, corresponding location, and activity change of the mining accident area. This approach integrates the overall consideration of mining accident characteristics, corresponding location, and activity change of the mining accident area, achieving multi-dimensional control of these factors. This enables prediction and review of mining accident areas, ensuring the accuracy of the state of the mining accident area.

[0120] refer to Figure 5 S14: Define the corresponding mine disaster channel according to the mine disaster area, and predict the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster area.

[0121] In the specific implementation of this invention, the specific steps can be as follows:

[0122] S141: Locate and detect the area where the mine accident occurred, and collect the internal outline of the area where the mine accident occurred;

[0123] S142: The internal outline of the area where the mine accident occurred, the vehicle access routes, and the location of the workers;

[0124] S143: Define the corresponding mine accident passage based on the internal outline of the area where the mine accident occurred, the vehicle passageway, and the location of the workers;

[0125] S144: Conduct fixed-point detection along the mine disaster passage and collect multiple mine disaster factors during the fixed-point detection;

[0126] S145: Constructing mining accident factors based on multiple mining accident factors, where the set of mining accident factors corresponds to the state of the associated mining accident passage and the mining accident occurrence area;

[0127] S146: Predict the time of occurrence of a mine disaster at each node in the mine disaster passage based on the set of mine disaster factors corresponding to the state of the mine disaster passage and the area where the mine disaster occurs.

[0128] In the embodiments of this application, the state of the mining accident area is defined according to the characteristics of the mining accident, the corresponding location, and the change in the activity level of the mining accident area. The characteristics of the mining accident, the corresponding location, and the change in the activity level of the mining accident area are introduced to conduct multi-dimensional control over the characteristics of the mining accident, the corresponding location, and the change in the activity level of the mining accident area.

[0129] Simultaneously, the system links the internal outline of the mining accident area, vehicle access routes, and the location of workers to facilitate comprehensive control of these factors. This allows for the definition of corresponding mining accident routes based on these elements, ensuring their accuracy and enabling multi-dimensional control over the internal outline of the mining accident area, vehicle access routes, and the location of workers.

[0130] Therefore, fixed-point detection is carried out along the mine disaster passage, and multiple mine disaster factors are collected during the fixed-point detection. Based on the multiple mine disaster factors, a set of mine disaster factors is constructed. At this time, the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster occurrence area is associated. The time of mine disaster occurrence at each node in the mine disaster passage is predicted according to the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster occurrence area. This approach takes into account the overall consideration of the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster occurrence area, ensuring multi-dimensional control of the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster occurrence area, and realizing accurate prediction of the time of mine disaster occurrence at each node in the mine disaster passage.

[0131] refer to Figure 6 S15: Define the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current location, and the driving passage, and trigger the vehicle's ejection to its designated position based on the vehicle's escape route and the characteristics of the surrounding environment.

[0132] In the specific implementation of this invention, the specific steps can be as follows:

[0133] S151: Freeze the mining accident passage and the time of the mining accident at each node. At this time, associate the mining accident passage, the time of the mining accident at each node, the current position of the vehicle, and the driving passage.

[0134] S152: Define the first escape range based on the mine accident passage, the current location of the vehicle, and the travel route;

[0135] S153: Define the second escape range based on the time of the mine accident at each node, the current location of the vehicle, and the travel route;

[0136] S154: Define the vehicle's escape route based on the second escape range, the second escape range, and the vehicle's current position;

[0137] S155: Define the vehicle's escape route based on the second escape range, the second escape range, and the vehicle's current position;

[0138] S156: Define ejection nodes based on the vehicle's escape route and surrounding environmental characteristics; trigger the vehicle's ejection to its designated position according to the ejection nodes. At this time, define the vehicle's ejection power based on the ejection nodes, the number of passengers on the vehicle, and the ejection space in the mine disaster passage.

[0139] In the embodiments of this application, the mining accident passage and the time of the mining accident at each node are fixed. At this time, the mining accident passage, the time of the mining accident at each node, the current position of the vehicle, and the driving channel are associated to achieve overall control of the mining accident passage, the time of the mining accident at each node, the current position of the vehicle, and the driving channel.

[0140] At this point, the first escape range is defined based on the mine disaster passage, the current location of the vehicle, and the driving route. Multi-dimensional control is carried out based on the mine disaster passage, the time of the mine disaster at each node, the current location of the vehicle, and the driving route. The first escape range is introduced to achieve preliminary control of the vehicle's escape route.

[0141] Furthermore, a second escape range is defined based on the time of the mine accident at each node, the current location of the vehicle, and the travel route. Multi-dimensional control is achieved based on the time of the mine accident at each node, the current location of the vehicle, and the travel route, and the introduction of the second escape range enables the final control of the vehicle's escape route. Therefore, defining the vehicle's escape route based on the second escape range and the vehicle's current location ensures multiple authentications of the vehicle's escape route and improves the accuracy of the vehicle's escape route.

[0142] Therefore, environmental detection is triggered along the vehicle's escape route, and corresponding surrounding environmental features are collected to introduce these features and ensure further control over the vehicle's escape route. At the same time, ejection nodes are defined based on the vehicle's escape route and surrounding environmental features. The vehicle is ejected to its designated position based on the ejection nodes. At this point, the ejection power of the vehicle is defined based on the ejection nodes, the number of passengers on the vehicle, and the ejection space in the mine tunnel.

[0143] refer to Figure 7 S16: Define the vehicle's driving state based on multiple driving data, trigger the vehicle's self-regulation logic based on the vehicle's driving state, the mining disaster state of the vehicle's surrounding environment, and the current screen of the mining area dynamic model, and control the vehicle's posture during driving based on the vehicle's self-regulation logic.

[0144] In the specific implementation of this invention, the specific steps can be as follows:

[0145] S161: Real-time monitoring of multiple driving data points of the vehicle during the escape process;

[0146] S162: Define the vehicle's driving status based on multiple driving data and the number of passengers in the vehicle;

[0147] S163: Collect obstacle data during vehicle operation, and trigger primary vehicle control based on obstacle data and the relative distance between the vehicle and obstacles;

[0148] S164: In the initial control of the vehicle, the vehicle effectively avoids obstacles based on the obstacle features formed by obstacle data, and ensures the driving stability of the vehicle during the avoidance process.

[0149] S165: Trigger the vehicle's self-regulation logic based on the vehicle's driving status, the mining disaster state of the vehicle's surrounding environment, and the current screen of the mining area dynamic model. At this time, define the first regulation parameter based on the vehicle's driving status and the mining disaster state of the vehicle's surrounding environment, define the second regulation parameter based on the vehicle's driving status and the current screen of the mining area dynamic model, and define the vehicle's self-regulation logic based on the first regulation parameter, the second regulation parameter, and the logic matching table.

[0150] S166: Vehicle-based self-regulation logic controls the vehicle's attitude during driving.

[0151] In the specific implementation of this invention, a corresponding mine disaster passage is defined according to the mine disaster occurrence area, and the mine disaster occurrence time of each node in the mine disaster passage is predicted based on the mine disaster passage, the mine disaster occurrence area and the corresponding set of mine disaster factors. The escape route of the vehicle is defined according to the mine disaster passage, the mine disaster occurrence time of each node, the current position of the vehicle and the driving passage, and the vehicle's ejection is triggered based on the vehicle's escape route and the surrounding environmental features. The driving state of the vehicle is defined according to multiple driving data, and the self-regulation logic of the vehicle is triggered according to the driving state of the vehicle, the mine disaster form of the surrounding environment of the vehicle and the current picture of the mine area dynamic model. The self-regulation logic of the vehicle controls the attitude of the vehicle during the driving process, realizing the dynamic control of the vehicle's attitude during the driving process, ensuring the driving stability of the vehicle along the vehicle's escape route, and incorporating multi-dimensional considerations of the vehicle's driving state, the mine disaster form of the surrounding environment of the vehicle and the current picture of the mine area dynamic model.

[0152] At this time, multiple driving data points of the vehicle during the escape process are monitored in real time. Multiple driving data points are introduced to facilitate overall control of multiple driving data points. The driving status of the vehicle is defined based on multiple driving data points and the number of people on board, realizing multi-dimensional control of multiple driving data points and the number of people on board, and ensuring the accuracy of the vehicle's driving status.

[0153] Furthermore, obstacle data is collected during vehicle operation. Based on this obstacle data and the relative distance between the vehicle and the obstacle, primary vehicle control is triggered to facilitate this initial control. This process fully considers obstacle data and the relative distance between the vehicle and the obstacle, reducing the impact of obstacles on the vehicle. Therefore, in the primary vehicle control, the vehicle effectively avoids obstacles based on the obstacle characteristics formed by the obstacle data, ensuring vehicle stability during the avoidance process.

[0154] Furthermore, the vehicle's self-regulation logic is triggered based on its driving status, the mine disaster situation in its surrounding environment, and the current view of the mine dynamic model. At this time, a first regulation parameter is defined based on the vehicle's driving status and the mine disaster situation in its surrounding environment, and a second regulation parameter is defined based on the vehicle's driving status and the current view of the mine dynamic model. The introduction of the first and second regulation parameters enables multi-dimensional control of the first and second regulation parameters, so as to define the vehicle's self-regulation logic based on the first and second regulation parameters and the logic matching table. Based on the vehicle's self-regulation logic, the vehicle's attitude during driving is controlled, realizing dynamic control of the vehicle's attitude during driving, ensuring the stability of the vehicle along the escape route, and taking into account the multi-dimensional considerations of the vehicle's driving status, the mine disaster situation in its surrounding environment, and the current view of the mine dynamic model.

[0155] In this embodiment of the invention, the method is used to divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining portion within the mining area. Multiple sets of detectors are deployed in each monitoring zone. Mining data detected by multiple sets of detectors are collected, and a dynamic model of the mining area is constructed based on the multiple mining data and the corresponding monitoring zone locations. The mining area dynamic model is used to predict the mining accident occurrence area, and the mining accident occurrence area is located and re-checked to collect actual data. The state of the mining accident occurrence area is defined based on multiple actual data, and prediction and re-checking are performed for the mining accident occurrence area to ensure the accuracy of the state of the mining accident occurrence area.

[0156] Furthermore, based on the mining accident area, a corresponding mining accident passage is defined, and the mining accident occurrence time of each node in the mining accident passage is predicted based on the mining accident passage, the mining accident area, and the set of mining accident factors corresponding to the state of the mining accident passage and the mining accident area. The escape route of the vehicle is defined based on the mining accident passage, the mining accident occurrence time of each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the vehicle's escape route and the characteristics of the surrounding environment. The driving state of the vehicle is defined based on multiple driving data. The self-regulation logic of the vehicle is triggered based on the driving state of the vehicle, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model. Based on the self-regulation logic of the vehicle, the attitude of the vehicle during the driving process is controlled, realizing dynamic control of the vehicle's attitude during the driving process, ensuring the driving stability of the vehicle along the escape route, and incorporating multi-dimensional considerations of the vehicle's driving state, the mining accident form of the surrounding environment, and the current picture of the mining area dynamic model.

[0157] Please see Figure 8 , Figure 8 This is a schematic diagram of the structural composition of the emergency control system for vehicles in a mining accident scenario according to an embodiment of the present invention.

[0158] like Figure 8 As shown, an emergency control system for vehicles in mining accident scenarios includes:

[0159] Detector module 21 is used to divide the mining area into multiple monitoring areas based on the three-dimensional space of the mining area and the mining part within the mining area, and to deploy multiple sets of detectors according to each monitoring area.

[0160] The mine dynamic module 22 is used to collect mine data detected by multiple sets of detectors and to build a mine dynamic model based on multiple mine data and the location of the corresponding monitoring area.

[0161] The status module 23 is used to predict the mining accident area based on the dynamic model of the mining area, and to locate and re-examine the mining accident area in order to collect actual data and define the status of the mining accident area based on multiple actual data.

[0162] The prediction module 24 is used to define the corresponding mine disaster channel according to the mine disaster occurrence area, and predict the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster occurrence area.

[0163] The ejection module 25 is used to define the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the driving passage, and to trigger the vehicle's ejection into position based on the vehicle's escape route and the characteristics of the surrounding environment.

[0164] The attitude module 26 is used to define the vehicle's driving state based on multiple driving data. Based on the vehicle's driving state, the mining disaster state of the vehicle's surrounding environment, and the current screen of the mining area dynamic model, the vehicle's self-regulation logic is triggered, and the vehicle's attitude is controlled based on the vehicle's self-regulation logic.

[0165] Please see Figure 9 See below for reference. Figure 9 To describe an electronic intelligent device 40 according to this embodiment of the present invention. Figure 9 The electronic smart device 40 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0166] like Figure 9 As shown, the electronic intelligent device 40 is manifested in the form of a general-purpose computing intelligent device. The components of the electronic intelligent device 40 may include, but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including storage unit 42 and processing unit 41).

[0167] The storage unit stores program code, which can be executed by the processing unit 41 to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of the present invention.

[0168] Storage unit 42 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 421 and / or cache memory 422, and may further include a read-only memory (ROM) 423.

[0169] Storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0170] Bus 43 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0171] The electronic intelligent device 40 can also communicate with one or more external intelligent devices (e.g., keyboards, pointing intelligent devices, Bluetooth intelligent devices, etc.), and with one or more intelligent devices that enable users to interact with the electronic intelligent device 40, and / or with any intelligent device (e.g., routers, modems, etc.) that enables the electronic intelligent device 40 to communicate with one or more other computing intelligent devices. This communication can be performed through the input / output (I / O) interface 44. Furthermore, the electronic intelligent device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through the network adapter 45. Figure 9 As shown, network adapter 45 communicates with other modules of electronic intelligent device 40 via bus 43. It should be understood that, although... Figure 9 As not shown, other hardware and / or software modules can be used in conjunction with the electronic intelligent device 40, including but not limited to: microcode, intelligent device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0172] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing intelligent device (such as a personal computer, server, terminal device, or network intelligent device, etc.) to execute the method according to the embodiments of this disclosure.

[0173] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. Furthermore, it stores computer program instructions, which, when executed by a computer, cause the computer to perform the methods described above.

[0174] Furthermore, the emergency control method and system for vehicles in mining accident scenarios provided by the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An emergency control method for vehicles in a mining accident scenario, characterized in that, It is applied to emergency control scenarios for vehicles in mining accidents. The emergency control method for the vehicle in a mining accident scenario includes: Based on the three-dimensional space of the mining area and the mining section within the mining area, multiple monitoring areas are divided, and multiple sets of detectors are deployed according to each monitoring area. Collect mine data detected by multiple sets of detectors, and construct a dynamic model of the mine area based on multiple mine data and the location of the corresponding monitoring area; The mining area dynamic model is used to predict the mining accident area and to locate and re-examine the mining accident area in order to collect actual data. The status of the mining accident area is defined based on multiple actual data. The corresponding mine disaster passage is defined according to the area where the mine disaster occurs, and the time of mine disaster occurrence at each node in the mine disaster passage is predicted based on the set of mine disaster factors corresponding to the state of the mine disaster passage and the mine disaster area. The escape route of the vehicle is defined based on the mine accident passage, the time of the mine accident at each node, the current position of the vehicle, and the driving passage. The vehicle's ejection is triggered based on the escape route and the characteristics of the surrounding environment. The vehicle's driving state is defined based on multiple driving data points. The vehicle's self-regulation logic is triggered based on its driving state, the mine disaster morphology of its surrounding environment, and the current view of the mine dynamic model. This self-regulation logic controls the vehicle's posture during driving, including: real-time monitoring of multiple driving data points during the escape process; defining the vehicle's driving state based on these data points and the number of passengers on board; collecting obstacle data during the vehicle's journey and triggering primary control based on this data and the relative distance between the vehicle and obstacles; in this primary control, the vehicle effectively avoids obstacles based on the obstacle features formed by the obstacle data, ensuring driving stability during the avoidance process; triggering the self-regulation logic based on the vehicle's driving state, the mine disaster morphology of its surrounding environment, and the current view of the mine dynamic model; defining a first control parameter based on the vehicle's driving state and the mine disaster morphology of its surrounding environment; defining a second control parameter based on the vehicle's driving state and the current view of the mine dynamic model; defining the vehicle's self-regulation logic based on the first and second control parameters and a logic matching table; and controlling the vehicle's posture during driving based on this self-regulation logic.

2. The emergency control method for vehicles in mining accident scenarios according to claim 1, characterized in that, The mining area is divided into multiple monitoring zones based on its three-dimensional space and the mining portion within the mining area. Multiple sets of detectors are deployed in each monitoring zone, including: The location of the mining area is collected, and the outer contour of the mining area is defined based on the location detection of the mining area; A three-dimensional spatial model of the mining area is constructed based on its outer contour and UAV detection data. The three-dimensional space of the associated mining area and the mining section within the mining area are divided into multiple monitoring zones based on the three-dimensional space of the mining area, the mining section within the mining area, and the vehicle driving channels. Mark the location of each monitoring area, and construct the corresponding detection space based on the location of each monitoring area and its surrounding space; Multiple detection points are defined based on the detection space, and multiple sets of detectors are matched to multiple detection points.

3. The emergency control method for vehicles in mining accident scenarios according to claim 2, characterized in that, The process of collecting mine data detected by multiple sets of detectors and constructing a dynamic model of the mine area based on the multiple mine data and the corresponding monitoring area locations includes: Collect data from within the mine detected by multiple sets of detectors; Mark the corresponding orientation for multiple mining data; The activity level within a mine is defined based on multiple mine data and their corresponding locations. Construct a mining area activity set based on multiple mine activity levels and the corresponding monitoring area locations; Activity data of related mining areas, past records of mining areas, and three-dimensional space of mining areas; Based on the activity set of the mining area, the past records of the mining area, and the three-dimensional space of the mining area, a dynamic model of the mining area is constructed. At this time, the dynamic model of the mining area controls the dynamic changes of the mining area in real time and performs autonomous optimization in combination with the real-time data of the mining area.

4. The emergency control method for vehicles in mining accident scenarios according to claim 3, characterized in that, The method involves predicting the mining accident area based on a dynamic mining area model, locating and verifying the mining accident area to collect actual data, and defining the state of the mining accident area based on multiple actual data points, including: Freeze-frame dynamic model of the mining area; Collect the activity set of the mining area, and define multiple abnormal activity levels based on the filtering of the activity set of the mining area; Multiple mining accident areas are predicted based on multiple abnormal activity levels, real-time messages from the mining area, and the response locations of the mining area dynamic model. The locations of multiple mining accident sites were collected, and the locations of these sites were re-examined to collect actual data on the multiple mining accident sites. In each area where a mining accident occurred, corresponding mining accident characteristics were constructed based on multiple actual data and the corresponding spatial data. The state of a mining accident area is defined based on the characteristics of the accident, its corresponding location, and the change in the activity level of the area where the accident occurred.

5. The emergency control method for vehicles in mining accident scenarios according to claim 4, characterized in that, The process of defining corresponding mine disaster channels based on the mine disaster occurrence area, and predicting the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster occurrence area, includes: The location of the mining accident site was determined and the internal outline of the site was collected. The internal outline of the area where the mine accident occurred, the vehicle access routes, and the location of the workers; The corresponding mine accident passage is defined based on the internal outline of the area where the mine accident occurred, the vehicle driving passage, and the location of the workers. Targeted detection was conducted along the mine disaster passage, and multiple mine disaster factors were collected during the targeted detection. Based on multiple mining accident factors, a set of mining accident factors is constructed, which is the set of mining accident factors corresponding to the state of the associated mining accident passage and the mining accident occurrence area. Predict the time of occurrence of a mine accident at each node in the mine accident channel based on the set of mine accident factors corresponding to the state of the mine accident channel and the area where the mine accident occurred.

6. The emergency control method for vehicles in mining accident scenarios according to claim 5, characterized in that, The process of defining the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the travel route, and triggering the vehicle's ejection based on the escape route and surrounding environmental features, includes: The time of the mine disaster at each node is frozen in time. At this time, the time of the mine disaster at each node, the current location of the vehicle, and the driving route are associated with the mine disaster passage. The first escape range is defined based on the mine accident passage, the current location of the vehicle, and the route of travel; The second escape range is defined based on the time of the mine accident at each node, the current location of the vehicle, and the route of travel. The vehicle's escape route is defined based on the second escape range, the second escape range, and the vehicle's current location.

7. The emergency control method for vehicles in mining accident scenarios according to claim 6, characterized in that, The method of defining the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the travel passage, and triggering the vehicle's ejection based on the vehicle's escape route and surrounding environmental features, also includes: The system triggers environmental detection along the vehicle's escape route and collects corresponding surrounding environmental features. Ejection nodes are defined based on the vehicle's escape route and surrounding environmental characteristics; the vehicle is ejected upon arrival at the ejection node, and the ejection power of the vehicle is defined based on the ejection node, the number of passengers on the vehicle, and the ejection space in the mine disaster passage.

8. An emergency control system for vehicles in mining accident scenarios, characterized in that, The emergency control system for vehicles in mining accident scenarios is applied to the emergency control method for vehicles in mining accident scenarios as described in any one of claims 1-7, wherein the emergency control system for vehicles in mining accident scenarios includes: The detector module is used to divide the mining area into multiple monitoring zones based on the three-dimensional space of the mining area and the mining section within the mining area, and to deploy multiple sets of detectors according to each monitoring zone. The mine dynamic module is used to collect mine data detected by multiple sets of detectors and to build a mine dynamic model based on multiple mine data and the location of the corresponding monitoring area. The status module is used to predict the mining accident area based on the dynamic model of the mining area, and to locate and verify the mining accident area in order to collect actual data and define the status of the mining accident area based on multiple actual data. The prediction module is used to define the corresponding mine disaster channels according to the mine disaster occurrence area, and predict the mine disaster occurrence time of each node in the mine disaster channel based on the mine disaster channel and the set of mine disaster factors corresponding to the state of the mine disaster occurrence area. The ejection module is used to define the vehicle's escape route based on the mine accident passage, the time of the mine accident at each node, the vehicle's current position, and the driving passage, and to trigger the vehicle's ejection into position based on the vehicle's escape route and the characteristics of the surrounding environment. The attitude module defines the vehicle's driving state based on multiple driving data points. It triggers the vehicle's self-regulation logic based on the driving state, the surrounding mine disaster situation, and the current view of the mine dynamic model. This self-regulation logic controls the vehicle's attitude during driving, including: real-time monitoring of multiple driving data points during the escape process; defining the vehicle's driving state based on multiple driving data points and the number of passengers on board; collecting obstacle data during the vehicle's journey; and triggering primary vehicle control based on obstacle data and the relative distance between the vehicle and obstacles. In this primary control, the vehicle... The obstacle features formed by obstacle data are used to effectively avoid obstacles and ensure the vehicle's driving stability during the avoidance process. The vehicle's self-regulation logic is triggered based on the vehicle's driving status, the mining disaster pattern of the surrounding environment, and the current screen of the mining area dynamic model. At this time, the first regulation parameter is defined based on the vehicle's driving status and the mining disaster pattern of the surrounding environment, and the second regulation parameter is defined based on the vehicle's driving status and the current screen of the mining area dynamic model. The vehicle's self-regulation logic is defined based on the first regulation parameter, the second regulation parameter, and the logic matching table. The vehicle's attitude during driving is controlled based on the vehicle's self-regulation logic.

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