Emergency command control method and system based on Beidou emergency patrol car

Abstract

The invention discloses an emergency command control method and system based on a Beidou emergency patrol car, and the method comprises the steps: obtaining various signal data from a complex environment through the deployment of a multi-source sensor network, carrying out the data fusion processing under the condition of communication signal interruption, and determining a preliminary position estimation result; according to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is adopted to optimize and adjust the positioning precision, and high-precision positioning data is obtained; through high-precision positioning data, a live-action dynamic model framework is constructed, terrain and obstacle distribution characteristics are fused, related information of environment change perception is acquired, and three-dimensional scene description updated in real time is determined; and according to the three-dimensional scene description updated in real time, analyzing the demand of dynamic resource allocation, identifying key nodes and resource distribution states in multi-party cooperative command, and obtaining a priority sequence of resource allocation. According to the invention, full-process automation and intelligentization are realized, the manual intervention cost is reduced, and the emergency response speed is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency rescue, and discloses an emergency command control method and system based on a Beidou emergency patrol vehicle. BACKGROUND

[0002] The field of emergency rescue, as an important pillar of public safety, plays an irreplaceable role in the face of natural disasters and emergencies. Its technological innovation is directly related to the safety of life and property and the improvement of rescue efficiency. With the increasing demand for rapid response and precise command from society, research and application of related technologies have become a key direction that needs to be broken through.

[0003] However, the adaptability of existing emergency rescue schemes in complex environments still has significant shortcomings. Many methods often have difficulty maintaining the stability of the command system and the real-time nature of information in the face of communication interruptions or extreme terrain, especially in terms of multi-party collaboration and dynamic resource allocation, lacking the ability to quickly perceive and flexibly adjust to environmental changes, which limits the overall effectiveness of rescue operations. In this context, the core challenges facing the field of emergency rescue have gradually emerged.

[0004] First of all, the problem of implementing high-precision positioning technology. In areas where communication signals are unstable or blocked, traditional positioning methods cannot provide continuous and accurate location data, which directly affects the coordination ability of rescue personnel and equipment in complex environments. The lack of positioning accuracy further exacerbates the difficulty of command decision-making, especially when it is necessary to quickly build a disaster scene real scene model and conduct resource scheduling, the lack of a unified time and space reference makes the decision-making process prone to deviation, making it difficult to guarantee rescue efficiency.

[0005] Therefore, how to achieve centimeter-level high-precision positioning in extreme environments and build a real scene dynamic model based on it to support multi-party collaborative command has become a key problem in improving emergency rescue capabilities. SUMMARY

[0006] The present application provides an emergency command control method and system based on a Beidou emergency patrol vehicle, aiming to solve at least one of the defects in the prior art.

[0007] One aspect of the present application relates to an emergency command control method based on a Beidou emergency patrol vehicle, comprising the following steps:

[0008] By deploying a multi-source sensor network, various signal data are obtained from complex environments, and data fusion processing is performed for communication signal interruption conditions to determine preliminary position estimation results;

[0009] According to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is used to optimize and adjust the positioning accuracy, and high-precision positioning data is obtained.

[0010] Through the high-precision positioning data, a real scene dynamic model framework is constructed, the terrain and obstacle distribution characteristics are fused, the related information of environmental change perception is obtained, and the real-time updated three-dimensional scene description is determined.

[0011] According to the real-time updated three-dimensional scene description, the demand of dynamic resource allocation is analyzed, the key nodes and resource distribution state in multi-party collaborative command are identified, and the priority sequence of resource allocation is obtained.

[0012] Through the priority sequence of resource allocation, the scheduling instruction of multi-party collaborative command is generated, the real-time correction is carried out for the command decision deviation, and the unified command coordination scheme is determined.

[0013] According to the unified command coordination scheme, the position and time synchronization data of each rescue unit are obtained by fusing the requirements of unified time and space reference, and the time window and space range of collaborative action are determined.

[0014] Through the time window and space range of collaborative action, the monitoring frequency of environmental change perception is adjusted, the latest environmental dynamic information is obtained, and the optimized rescue action path is obtained.

[0015] According to the optimized rescue action path, the resource distribution and action state in the real scene dynamic model are updated, the path is re-optimized for the demand of rescue efficiency improvement, and the final action execution scheme is determined.

[0016] Further, through the deployment of multi-source sensor network, a plurality of signal data is obtained from the complex environment, the data fusion processing is carried out for the communication signal interruption, and the steps of determining the preliminary position estimation result include:

[0017] Through the deployment of multi-source sensor network, a plurality of signal data is obtained from the complex environment, the time synchronization protocol is used to calibrate the signal data, and the time-consistent first signal data set is obtained, wherein the signal data includes communication signal, environmental noise and positioning signal.

[0018] If the first signal data set has communication interruption, the signal in the interruption time period is processed by the interpolation completion method, and the first completed signal data set after completion is obtained.

[0019] According to the first completed signal data set after completion, the weighted average method is used to fuse the signal data, the weighted characteristic value of each signal is calculated, and the fused characteristic data set is determined.

[0020] Through the fused feature data set, a position calculation is performed on the feature data by using a triangulation method to obtain a preliminary target position estimation result.

[0021] Further, according to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is used to optimize and adjust the positioning accuracy, and the steps of obtaining high-precision positioning data include:

[0022] An original signal data set containing positioning signals and environmental noise is obtained from a multi-source sensor network, and the original signal data set is calibrated by a time synchronization protocol to obtain a time-consistent second signal data set;

[0023] If there is a communication interruption in the time-consistent signal data set, an interpolation completion method is used to process the signal in the interruption time period to obtain a second completed signal data set after completion;

[0024] According to the second completed signal data set after completion, a filtering tool is used to separate the positioning signals and environmental noise to obtain a positioning signal data set after filtering out noise;

[0025] The positioning signal data set after filtering out noise is calculated by a triangulation method, and a correction algorithm is used to optimize the position calculation result to obtain high-precision positioning data.

[0026] Further, through the high-precision positioning data, a real scene dynamic model framework is constructed, the terrain and obstacle distribution characteristics are fused, the related information of environmental change perception is obtained, and the steps of determining the real-time updated three-dimensional scene description include:

[0027] The spatial coordinate information of the target area in the high-precision positioning data is obtained, and a pre-established spatial mapping tool is used to match the spatial coordinate information with the terrain feature data to obtain a preliminary spatial distribution data set;

[0028] According to the preliminary spatial distribution data set, the obstacle distribution information in the multi-source environmental data is integrated by a data fusion method, and if the obstacle distribution information is inconsistent with the terrain feature data, the inconsistent area is marked to determine a marked distribution data set;

[0029] For the marked distribution data set, a change detection tool is used to compare the environmental perception information, real-time change data segments are obtained during dynamic updating, and the three-dimensional description area that needs to be adjusted is determined;

[0030] Through a real-time scene construction tool, the three-dimensional description area that needs to be adjusted is reconstructed, the data segments are fused with the existing real scene model to obtain updated three-dimensional scene description content.

[0031] Further, according to the real-time updated three-dimensional scene description, the demand for dynamic resource allocation is analyzed, the key nodes and resource distribution states in multi-party collaborative command are identified, and the priority sequence of resource allocation is obtained, which includes the following steps:

[0032] Dynamic environment perception data is obtained from the real-time updated three-dimensional scene description, and the spatial analysis tool is used to extract the resource distribution state and key node position in the target area, and determine the resource distribution data set;

[0033] For the resource distribution data set, multi-source collaborative command data is integrated by data fusion method, if the key node correlation strength is lower than the preset threshold, it is marked as a low priority node, and a marked node priority data set is obtained;

[0034] According to the marked node priority data set, the resource allocation demand and resource allocation balance are analyzed by comparison tool, the resource demand of each node is judged, and a preliminary priority list is generated;

[0035] Through the real-time data synchronization tool, the preliminary priority list and dynamic data are integrated, the influence of real-time scene change on resource demand is obtained, and the final resource allocation priority sequence is determined.

[0036] Further, through the priority sequence of resource allocation, the scheduling instruction of multi-party collaborative command is generated, the real-time correction is carried out for the command decision deviation, and the unified command coordination scheme is determined, which includes the following steps:

[0037] According to the business attribute of resource allocation, the allocation record in the target area is obtained from the pre-established resource database, the allocation record and the real-time monitored data are compared, if the node correlation value in the allocation record is lower than the preset threshold, it is marked as a secondary task area, and an adjusted task list is obtained;

[0038] Using data synchronization tool, the business attribute of the adjusted task list and the scheduling instruction is integrated, the node information in the task list is checked, whether there is deviation is judged by comparison tool, and the preliminary range of deviation correction is determined;

[0039] Through the preliminary range of deviation correction, the dynamic adjustment information in real-time monitoring is obtained, the dynamic adjustment information and the business attribute of collaborative efficiency are matched, if the task list and the scheduling instruction are inconsistent, the corrected instruction set is generated, and the unified command basis is obtained;

[0040] According to the corrected instruction set, combined with the business attribute of the coordination scheme, the final scheduling instruction of multi-party collaboration is generated, the final scheduling instruction and the real-time monitoring information are checked, whether it meets the demand of dynamic adjustment is judged, and the final command coordination scheme is determined.

[0041] Further, according to the unified command coordination scheme, the step of obtaining the position and time synchronization data of each rescue unit, determining the time window and spatial range of cooperative action, and meeting the requirements of unified space-time reference includes:

[0042] According to the pre-established rescue unit database, the real-time position synchronization data and time synchronization data of each rescue unit are obtained, and the position synchronization data and time synchronization data are compared with the requirements of the space-time reference. If the deviation of the position synchronization data or the time synchronization data exceeds the preset threshold, the data calibration tool is used for adjustment to obtain the basic data set meeting the reference requirements;

[0043] The data fusion tool is used to integrate the basic data set and the task allocation information in the command coordination scheme, and the position and time information in the basic data set are matched to determine whether the conditions for cooperative action are met, and the preliminary time window and spatial range are determined;

[0044] Through the dynamic adjustment tool, the real-time environmental information in the preliminary time window and spatial range is obtained, and the distribution of the rescue units is checked against the real-time environmental information. If the distribution is inconsistent with the task allocation, the path planning tool is used to generate adjusted action guidance to obtain the optimized time window and spatial range;

[0045] According to the information checking tool, the optimized time window and spatial range are compared with the requirements of the command coordination scheme, and the comparison results are finally calibrated. The data synchronization tool is used to generate cooperative action instructions for each rescue unit to determine the final action plan that adapts to the command coordination target.

[0046] Further, through the time window and spatial range of cooperative action, the monitoring frequency of environmental change perception is adjusted to obtain the latest environmental dynamic information and obtain the optimized rescue action path, including the steps of:

[0047] According to the pre-established environmental perception database, the real-time environmental change data associated with the time window and spatial range is obtained, and the real-time environmental change data is compared with the historical record. If the change amplitude exceeds the preset threshold, the frequency adjustment tool is used to update the monitoring frequency to obtain the adjusted monitoring period data;

[0048] The data acquisition tool is used to obtain the latest dynamic information from the environmental perception device according to the adjusted monitoring period data, and the spatial range distribution characteristics are combined to classify the dynamic information through the information integration tool to determine the classified environmental dynamic set;

[0049] Through the path planning tool, the matching degree of the classified environment dynamic set and the rescue path is compared, if the matching degree is lower than a preset threshold, the rescue path is locally adjusted through the path correction tool, and a temporarily adjusted action route is obtained;

[0050] According to the information synchronization tool, the temporarily adjusted action route is checked with the requirements of the cooperation target, and the checking result is finally calibrated through the route optimization tool, so that an optimized rescue path meeting the time window and the space range is generated.

[0051] Further, according to the optimized rescue action path, the resource distribution and the action state in the real scene dynamic model are updated, the path is re-optimized for the demand of improving the rescue efficiency, and the steps of determining the final action execution scheme include:

[0052] Resource distribution data and action state data related to the rescue action path are obtained from the pre-established real scene dynamic database, the resource distribution data and the action state data are classified and processed through a data integration tool, and a classified dynamic data set is obtained;

[0053] If the difference between the classified dynamic data set and the existing data of the real scene dynamic model exceeds a preset threshold, the update frequency of the real scene dynamic model is adjusted through a model update tool, and updated dynamic model data is generated;

[0054] For the updated dynamic model data, the resource distribution data is adjusted by using a resource allocation tool, and the action state data is updated by using a state synchronization tool, so that an adjusted resource state set is obtained;

[0055] The matching degree of the adjusted resource state set and the rescue action path is compared through a path planning tool, if the matching degree is lower than a preset threshold, the path is re-optimized through a path correction tool, and the final action execution scheme is determined.

[0056] Another aspect of the application relates to an emergency command control system based on a Beidou emergency patrol vehicle, which is used to execute the emergency command control method based on the Beidou emergency patrol vehicle, and the emergency command control system based on the Beidou emergency patrol vehicle comprises:

[0057] The first determination module is used for acquiring a plurality of signal data from a complex environment by deploying a multi-source sensor network, performing data fusion processing for the case of communication signal interruption, and determining a preliminary position estimation result;

[0058] The first acquisition module is used for optimizing and adjusting the positioning accuracy by using a positioning algorithm based on multi-source data correction according to the preliminary position estimation result, and obtaining high-precision positioning data;

[0059] The second determining module is configured to construct a real scene dynamic model framework through high-precision positioning data, fuse terrain and obstacle distribution characteristics, acquire relevant information of environment change perception, and determine a real-time updated three-dimensional scene description;

[0060] The second acquiring module is configured to analyze a demand of dynamic resource allocation according to the real-time updated three-dimensional scene description, identify a key node and a resource distribution state in multi-party collaborative command, and obtain a priority sequence of resource allocation.

[0061] The third determining module is configured to generate a scheduling instruction of multi-party collaborative command through the priority sequence of resource allocation, perform real-time correction on a command decision deviation, and determine a unified command coordination scheme.

[0062] The fourth determining module is configured to acquire position and time synchronization data of each rescue unit according to a requirement of unified space-time reference, determine a time window and a space range of collaborative action, and fuse the time window and the space range.

[0063] The third acquiring module is configured to adjust a monitoring frequency of environment change perception through the time window and the space range of collaborative action, acquire the latest environment dynamic information, and obtain an optimized rescue action path.

[0064] The fifth determining module is configured to update resource distribution and action state in the real scene dynamic model according to the optimized rescue action path, perform path re-optimization according to a demand of rescue efficiency improvement, and determine a final action execution scheme.

[0065] The present application has the following beneficial effects:

[0066] ​The application provides an emergency command control method and system based on a Beidou emergency patrol vehicle, acquires various signal data from a complex environment through deployment of a multi-source sensor network, performs data fusion processing on the condition of communication signal interruption, and determines a preliminary position estimation result; according to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is adopted to optimize and adjust the positioning precision, and high-precision positioning data is obtained; through the high-precision positioning data, a real scene dynamic model framework is constructed, the terrain and obstacle distribution characteristics are fused, related information of environment change perception is acquired, and a real-time updated three-dimensional scene description is determined; according to the real-time updated three-dimensional scene description, the demand of dynamic resource allocation is analyzed, the key nodes and resource distribution states in multi-party collaborative command are identified, and a priority sequence of resource allocation is obtained; through the priority sequence of resource allocation, scheduling instructions of multi-party collaborative command are generated, real-time correction is performed on the command decision deviation, and a unified command coordination scheme is determined; according to the unified command coordination scheme, the position and time synchronization data of each rescue unit are acquired by fusing the requirements of unified time and space reference, and the time window and space range of collaborative action are determined; through the time window and space range of collaborative action, the monitoring frequency of environment change perception is adjusted, the latest environment dynamic information is acquired, and an optimized rescue action path is obtained; according to the optimized rescue action path, the resource distribution and action state in the real scene dynamic model are updated, the path is re-optimized according to the demand of rescue efficiency improvement, and a final action execution scheme is determined. The emergency command control method and system based on the Beidou emergency patrol vehicle provided by the application construct a full-link emergency command system of "high-precision positioning-dynamic modeling-intelligent decision-making-collaborative execution" through technology fusion and process innovation, and provide an efficient and reliable intelligent solution for urban safety, natural disaster and other scenes. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 FIG. 1 is a flowchart of an embodiment of an emergency command control method based on a Beidou emergency patrol vehicle according to the application. DETAILED DESCRIPTION

[0068] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings in the specification and specific embodiments.

[0069] As shown in FIG. 1, the first embodiment of the application proposes an emergency command control method based on a Beidou emergency patrol vehicle, which includes the following steps: Figure 1 Step S100, a multi-source sensor network is deployed to acquire various signal data from a complex environment, data fusion processing is performed on the condition of communication signal interruption, and a preliminary position estimation result is determined.

[0070]

[0071] ​The process of obtaining a preliminary position estimation result of a target by deploying a multi-source sensor network (such as radar, infrared, ultrasonic, visual sensor, etc.), integrating heterogeneous signal data (such as signal strength, time of arrival, phase difference, etc.) collected by the multi-source sensor in a complex environment (such as electromagnetic interference, terrain obstruction, network attack) where communication signals are interrupted, eliminating the errors and limitations of a single sensor through algorithm processing, and ultimately obtaining a preliminary position estimation result of a target.

[0072] Step S200, based on the preliminary position estimation result, a positioning algorithm based on multi-source data correction is used to optimize and adjust the positioning accuracy, and high-precision positioning data is obtained.

[0073] Based on the preliminary position estimation result, the multi-source heterogeneous data (such as different types of sensor data, environmental prior information, historical positioning data, etc.) are integrated, and a correction algorithm is used to model and compensate the positioning error, and finally the positioning data meeting the specific accuracy requirement (such as centimeter level, sub-meter level) is output. This process eliminates the systematic errors of a single data source through the complementarity and redundancy of multi-source data, and optimizes the positioning accuracy.

[0074] High-precision positioning data refers to the coordinate information whose spatial deviation is controlled within a certain high-precision threshold (such as centimeter level, sub-meter level) through multi-source sensor fusion, error correction algorithm and other technical means, usually accompanied by error confidence evaluation, with the characteristics of high reliability, high consistency and environmental adaptability. Its essence is to eliminate or compensate the inherent errors of a single sensor through technical means, and realize the order of magnitude improvement of positioning accuracy.

[0075] Step S300, based on the high-precision positioning data, a real scene dynamic model framework is constructed, the terrain and obstacle distribution characteristics are integrated, the related information of environmental change perception is obtained, and a real-time updated three-dimensional scene description is determined.

[0076] Real scene dynamic three-dimensional scene description refers to a three-dimensional environmental digital twin body constructed based on high-precision positioning data, integrating terrain, obstacle and dynamic target information collected by multi-source sensors (such as laser radar, vision, millimeter wave radar) through real-time modeling and updating mechanism. Its essence is to convert the geometric characteristics, dynamic changes and semantic attributes of physical space into structured data that can be processed by computers, supporting centimeter-level precision environmental perception and decision-making deduction.

[0077] Real scene dynamic three-dimensional scene description refers to a three-dimensional environmental digital twin body constructed based on high-precision positioning data, integrating terrain, obstacle and dynamic target information collected by multi-source sensors (such as laser radar, vision, millimeter wave radar) through real-time modeling and updating mechanism. Its essence is to convert the geometric characteristics, dynamic changes and semantic attributes of physical space into structured data that can be processed by computers, supporting centimeter-level precision environmental perception and decision-making deduction.

[0078] Step S400, according to the real-time updated three-dimensional scene description, analyze the demand of dynamic resource allocation, identify the key nodes and resource distribution state in multi-party collaborative command, and get the priority sequence of resource allocation.

[0079] The dynamic resource allocation priority sequence refers to the resource scheduling scheme with time sequence execution order generated based on real-time three-dimensional scene data through quantitative analysis of resource demand urgency, collaborative node importance and resource distribution efficiency. Its essence is to convert dynamic events in physical space (such as disaster occurrence, task burst) into a digital priority decision model, supporting optimal allocation of resources in multi-party collaborative command, with a response delay requirement of ≤100ms.

[0080] Step S500, through the priority sequence of resource allocation, generate the scheduling instruction of multi-party collaborative command, real-time correct the command decision deviation, and determine the unified command coordination scheme.

[0081] Through intelligent algorithm, the resource priority sequence (T0-T3 level) is converted into executable instruction set, realizing the decision-making process of dynamic coordination across departments / cross-level. Its essence is to establish the mapping relationship chain of "demand-resource-action".

[0082] For the decision deviation caused by information lag or environmental mutation in the command process, through the closed-loop control model, dynamic correction is realized, and finally the standardized action scheme of multi-party consistent execution is formed. Its essence is to build the real-time response chain of "monitoring-diagnosis-correction-coordination".

[0083] The unified scheme of multi-party collaborative command refers to the standardized command process of multi-party (such as rescue team, intelligent equipment, management system) collaborative execution based on resource allocation priority sequence, through cross-agent instruction generation, real-time deviation detection and dynamic correction mechanism. Its essence is to convert digital priority decision into collaborative action in physical space, eliminate command deviation through closed-loop feedback, realize the consistency of "decision-execution-feedback-optimization" of the whole link, and the response delay requirement is usually ≤500ms.

[0084] Step S600, according to the unified command coordination scheme, fuse the requirements of unified space-time reference, obtain the position and time synchronization data of each rescue unit, and determine the time window and space range of collaborative action.

[0085] Fusion of unified space-time reference refers to the technology system of seamless alignment of global space-time data in multi-source heterogeneous systems through unified spatial coordinate system, time reference system and data conversion protocol. Its essence is to eliminate the positioning deviation and time sequence confusion caused by reference difference, and ensure the space-time consistency of collaborative action.

[0086] In the multi-party cooperation scene of emergency rescue, engineering scheduling, etc., based on a unified command and coordination scheme, by fusing the unified technical requirements of space-time reference (such as unified coordinate system, time synchronization protocol), the real-time position data and time synchronization information of each rescue unit (or execution subject) are obtained, and then the time effectiveness interval (time window) and spatial activity range of the cooperative action of each subject are determined.

[0087] Step S700, adjust the monitoring frequency of environmental change perception through the time window and spatial range of cooperative action, obtain the latest environmental dynamic information, and get the optimized rescue action path.

[0088] In the emergency rescue scene, through the time window and spatial range of cooperative action, the monitoring frequency of environmental change perception is dynamically adjusted, the real-time environmental dynamic information is obtained, and the rescue action path is optimized based on the information. The process takes space-time constraints as the core, realizes the dynamic adaptability of path planning through the linkage of monitoring frequency and environmental data, and ensures the efficient arrival of rescue resources to the target area.

[0089] Step S800, according to the optimized rescue action path, update the resource distribution and action state in the real scene dynamic model, re-optimize the path for the demand of improving rescue efficiency, and determine the final action execution scheme.

[0090] The final action execution scheme refers to the optimization of the rescue path based on the optimization of the rescue path, the real-time updating of the resource distribution and action state in the real scene dynamic model, the combination of rescue efficiency indicators (such as time, resource consumption, safety), and the formation of a standardized scheme that can directly guide the rescue action. Through the closed-loop iteration of "path-model-path", the scheme always maintains optimal in dynamic environment, and the response delay is usually required to be ≤1 second.

[0091] Further, the emergency command and control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0092] Step S110, by deploying a multi-source sensor network, a variety of signal data are obtained from a complex environment, a time synchronization protocol is used to calibrate the signal data, and a time-consistent first signal data set is obtained, wherein the signal data includes communication signals, environmental noise and positioning signals.

[0093] In a complex indoor environment, a multi-source sensor network is deployed to collect various signal data. Assuming the scenario is a large shopping mall, the sensors include Wi-Fi signal receivers, environmental noise sensors, and ultra-wideband positioning devices. Wi-Fi signals provide communication data, noise sensors capture background sounds, and positioning devices generate precise distance information. In this embodiment, 100 sensor nodes are deployed to cover an area of 5000 square meters, each node collects data once every second, generating a signal data set containing signal strength, noise decibels, and distance. The time synchronization protocol uses the Network Time Protocol to ensure that the time error of each sensor is less than 1 millisecond, resulting in a time-consistent data set. This high-precision synchronization ensures the accuracy of subsequent data fusion and reduces positioning errors caused by time deviation.

[0094] Step S120, if the first signal data set has communication interruption, the signal in the interruption period is processed by interpolation completion method to obtain the first completed signal data set after completion.

[0095] The first completed signal data set after completion is:

[0096]

[0097] In formula (1), S complete (t) represents the first completed signal data set after completion, S original (t) represents the original first signal data set, M represents the total number of interpolation base functions, w k k represents the kth interpolation weight coefficient, L k (t) represents the kth interpolation base function, I gap (t) represents the indication function of the communication interruption period, which is 1 when t is in the interruption period, and 0 otherwise.

[0098] If there is communication interruption, such as Wi-Fi signal loss for 10 seconds due to obstruction, linear interpolation completion method is used. For example, the signal strength of a node at t=5 seconds is -60dBm, and at t=15 seconds is -65dBm, with a linear decrease of 0.5dBm per second during the interruption period, forming a continuous data set after completion. This method effectively fills in the data gaps and maintains data continuity, providing a reliable foundation for subsequent fusion. The completed data set can significantly improve the stability of positioning.

[0099] Step S130, according to the first completed signal data set after completion, the signal data is processed by weighted average method, the weighted characteristic value of each signal is calculated, and the characteristic data set after fusion is determined.

[0100] The signal data after fusion is:

[0101]

[0102] In formula (2), S fusion denotes the fused signal data, n denotes the number of signals participating in fusion, w i denotes the weight coefficient of the i-th signal, S i denotes the i-th completed signal data. Formula (2) realizes weighted average fusion processing of multiple signals.

[0103] The fusion processing calculates the feature value by using the weighted average method. It is assumed that the weight of the Wi-Fi signal is 0.5, the weight of the noise is 0.2, and the weight of the positioning signal is 0.3. The weight is assigned based on the signal reliability. For example, at a certain moment, the Wi-Fi signal strength is-62dBm, the noise is 40dB, and the positioning distance is 10 meters. The weighted calculation obtains the fusion feature value, and generates the feature data set. This fusion method integrates multiple source information, reduces the influence of single signal noise, and improves the data robustness.

[0104] In step S140, the feature data is calculated by using the triangulation method based on the fused feature data set, and a preliminary target position estimation result is obtained.

[0105] The fused feature position estimation value is calculated by the following formula:

[0106]

[0107] In formula (3), P est denotes the fused feature position estimation value, n denotes the number of feature data sources participating in fusion, w i denotes the weight coefficient of the i-th feature data source, and F denotes the position information provided by the i-th feature data source. Formula (3) is used to fuse and calculate the position information of multiple feature data sources according to the weight.

[0108] The triangulation method is used for position calculation. Based on the distance data of three positioning nodes, it is assumed that the nodes A, B and C respectively measure the target distances of 8 meters, 10 meters and 12 meters. The coordinates of the target on the two-dimensional plane are determined by geometric calculation. Preferably, the distance error is corrected by combining the signal strength and noise information of the feature data set, and finally the target position estimation result is obtained, with the error controlled within 0.5 meters. This method utilizes the complementarity of multiple source data to improve the positioning accuracy, and is suitable for target tracking in complex environments.

[0109] Preferably, the emergency command control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0110] In step S210, an original signal data set containing positioning signals and environmental noise is obtained from a multi-source sensor network, and the original signal data set is calibrated by using a time synchronization protocol to obtain a second signal data set consistent in time.

[0111] In a large indoor environment, a multi-source sensor network is deployed to collect raw signal datasets containing positioning signals and environmental noise. The scenario is set in a large exhibition center with an area of about 6000 square meters. 120 sensor nodes are deployed, including ultra-wideband positioning devices, Wi-Fi signal receivers, and environmental noise sensors. The ultra-wideband devices provide high-precision distance data, the Wi-Fi signals provide strength information, and the noise sensors capture background sound characteristics. Each node collects data once every second, generating raw datasets containing distance, signal strength, and noise decibels. This multi-source data collection method can comprehensively capture environmental information and provide a rich data foundation for subsequent processing.

[0112] The time synchronization protocol calibrates the raw signal dataset to ensure time consistency. High-precision time protocols such as the Precision Time Protocol are used to control the time error of each sensor node to within 0.5 milliseconds. For example, a node records a distance of 9 meters, a Wi-Fi signal strength of -58 dBm, and a noise of 45 dB at t = 1 second. After synchronization, all node data is aligned to a unified timestamp. This high-precision synchronization lays the foundation for subsequent data processing and avoids positioning errors caused by time deviation.

[0113] Step S220, if there is a communication interruption in the time-consistent signal dataset, the signal in the interruption period is processed by an interpolation completion method to obtain a second completed signal dataset after completion.

[0114] For the case of communication interruption, assume that the Wi-Fi signal loses 8 seconds of data due to wall obstruction. A polynomial interpolation method is used to complete the signal in the interruption period. A node has a signal strength of -60 dBm at t = 3 seconds and -64 dBm at t = 11 seconds. The signal strength change trend during the interruption period is estimated by quadratic polynomial interpolation to generate a continuous signal dataset. This method can smoothly complete the data and maintain the continuity of the signal, providing reliable input for subsequent processing.

[0115] Step S230, according to the second completed signal dataset after completion, a filtering tool is used to separate the positioning signal and the environmental noise to obtain a positioning signal dataset after filtering out the noise.

[0116] An adaptive filtering tool is used to process the completed signal dataset to separate the positioning signal and the environmental noise. For example, a Kalman filter is used to process the ultra-wideband positioning signal and noise data. Assuming that the distance data is 10 meters and the noise decibels are 42 dB at a certain time, the pure positioning signal dataset is obtained after filtering. This method can effectively weaken the environmental noise interference and improve the clarity of the positioning signal.

[0117] Step S240, position calculation is performed on the filtered positioning signal data set by triangulation method, and a correction algorithm is used to optimize the position calculation result to obtain high-precision positioning data.

[0118] The filtered positioning signal is calculated by the following formula:

[0119]

[0120] In formula (4), S f (t) represents the filtered positioning signal, S r (t) represents the original received positioning signal, M represents the number of noise sampling points, W k k represents the weight coefficient of the kth noise component, and N k (t) represents the kth noise component. Formula (4) removes the noise component from the original signal by weighted average method to realize signal purification processing.

[0121] Position calculation is performed on the filtered positioning signal data set by triangulation method. Assuming that three ultra-wideband nodes measure the target distances of 7 meters, 9 meters and 11 meters respectively, the coordinates of the target in the two-dimensional plane are calculated by geometric method. Preferably, the least square method is used as the correction algorithm to optimize the position calculation result.

[0122] The distance error is adjusted in combination with the Wi-Fi signal strength information. Assuming that the signal strength of a certain node is-61dBm, it is inferred that the target is close to the specific node, and the coordinate deviation is further corrected, and finally the high-precision positioning data is obtained, and the error is controlled within 0.4 meters.

[0123] This method fully utilizes the complementarity of multi-source data, and improves the accuracy and stability of positioning.

[0124] Further, the emergency command control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0125] Step S310, obtain the target area spatial coordinate information in the high-precision positioning data, and use the pre-established spatial mapping tool to match the spatial coordinate information with the terrain feature data to obtain a preliminary spatial distribution data set.

[0126] After obtaining high-precision positioning data, the spatial coordinate information of the target area needs to be matched with the terrain feature data to construct a preliminary spatial distribution dataset. In a large indoor environment, such as a shopping center with an area of about 5000 square meters, it is assumed that the spatial coordinate information of the target area has been obtained through multiple sensors, including position point data in three-dimensional space. The pre-established spatial mapping tool can associate these coordinate points with the terrain feature data in the shopping center, which may include floor layout, wall position, and fixed facility distribution, etc. Through this matching method, a spatial distribution dataset containing position points and corresponding terrain features can be initially formed, providing a basis for subsequent analysis.

[0127] Step S320, according to the preliminary spatial distribution dataset, the obstacle distribution information in the multi-source environment data is integrated by a data fusion method, if the obstacle distribution information is inconsistent with the terrain feature data, the inconsistent area is marked, and the distribution dataset after marking is determined.

[0128] The fused obstacle distribution data is calculated by the following formula:

[0129]

[0130] In formula (5), D fused (x, y) represents the fused obstacle distribution data, K represents the total number of multi-source environment data, w k represents the weight coefficient of the kth data source, D k (x, y) represents the obstacle distribution value of the kth data source at coordinate (x, y), P(x, y) represents the spatial coordinate vector of the current position, P k represents the reference coordinate vector of the kth data source, σ k represents the spatial correlation parameter of the kth data source.

[0131] For the preliminary spatial distribution dataset, the data fusion method is used to integrate the obstacle distribution information in the multi-source environment data. In the shopping center scenario, the multi-source environment data includes real-time image data captured by the camera and obstacle distribution information scanned by the laser radar. It is assumed that through the image data, it is found that there is a temporary display stand placed in a certain area, while the terrain feature data marks this area as an empty land, at this time, the inconsistency is detected. The system will mark this area to form the distribution dataset after marking. This marking method helps to identify the areas that need further attention, ensuring data accuracy.

[0132] Step S330, for the distribution dataset after marking, a change detection tool is used to compare the environment perception information, and real-time change data segments are obtained during dynamic updating, and the three-dimensional description area that needs to be adjusted is judged.

[0133] In processing the marked distribution dataset, the change detection tool is used to compare the environmental perception information and obtain real-time change data segments. In a shopping mall, assuming that a certain area increases the crowd gathering due to a temporary activity, the change detection tool identifies that the three-dimensional description of this area needs to be adjusted by comparing historical data and real-time perception information. Real-time data segments may include crowd density changes or temporary obstacle position information. This dynamic updating mechanism can timely reflect environmental changes and provide a basis for subsequent scene reconstruction.

[0134] Step S340, through the real-time scene construction tool, the scene reconstruction is performed on the three-dimensional description area needing adjustment, the data segments are fused with the existing real scene model, and updated three-dimensional scene description content is obtained.

[0135] For the three-dimensional description area needing adjustment, the real-time scene construction tool will perform scene reconstruction and fuse the data segments with the existing real scene model. In the shopping mall scene, assuming that the three-dimensional model of a certain area originally shows an open channel, but the real-time data segments show that a temporary isolation belt is set up in this area. The scene construction tool will adjust the model according to the new data, and the updated three-dimensional scene description content will include the position and shape information of the isolation belt. This reconstruction method can ensure that the scene description is consistent with the actual environment, and provide reliable support for indoor navigation or space management.

[0136] Further, the emergency command control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0137] Step S410, dynamic environmental perception data is obtained from the real-time updated three-dimensional scene description, a pre-established space analysis tool is used to extract the resource distribution state and key node position in the target area, and a resource distribution dataset is determined.

[0138] In a large indoor environment, such as a shopping mall with an area of about 5000 square meters, the real-time updated three-dimensional scene description provides basic data for dynamic environmental perception. For the link of obtaining dynamic environmental perception data from the three-dimensional scene description, the space analysis tool pre-constructed can be used to extract the resource distribution state and key node position in the target area. Assuming that there are multiple service points and channel intersections in the shopping mall, the space analysis tool identifies the passenger flow, facility utilization rate and other information of these positions, and forms a resource distribution dataset. The space analysis tool preferentially analyzes high-flow areas such as entrances and elevator entrances, and marks them as key nodes. The dataset includes the position coordinates and flow peak value information of each node, such as the number of people passing through an entrance per hour is about 800.

[0139] Step S420, for the resource distribution dataset, the multi-source collaborative command data is integrated by a data fusion method, and if the key node association strength is lower than a preset threshold, the key node is marked as a low-priority node, and a marked node priority dataset is obtained.

[0140] The key node association strength is calculated by the following formula:

[0141]

[0142] In formula (6), S ij represents the association strength between node i and node j, M represents the total dimension of node attributes, A im represents the value of node i in the mth attribute dimension, A jm represents the value of node j in the mth attribute dimension. Formula (6) calculates the cosine similarity between two nodes to measure the association strength.

[0143] The low-priority node is marked by the following formula:

[0144]

[0145] In formula (7), P i represents the priority marking of the ith node, R i represents the association strength value of the ith node, and T represents a preset threshold parameter. When the node association strength is greater than or equal to the threshold, the high-priority node is marked with a value of 1, and when the association strength is lower than the threshold, the low-priority node is marked with a value of 0.

[0146] For the resource distribution dataset, the multi-source collaborative command data is integrated by a data fusion method. The multi-source data from monitoring cameras, passenger flow statistical devices, etc. can be uniformly processed. In the shopping center scenario, assuming that the camera near a key node shows a surge in passenger flow, while the statistical device data shows a low flow, the system will correct the data through the fusion algorithm to determine the true situation. If the key node association strength is lower than a preset threshold, such as a node connected to a channel with a flow of less than 100 people per hour, the node is marked as a low-priority node, and a marked node priority dataset is finally formed. This method helps to reasonably allocate attention and avoid resource waste.

[0147] Step S430, according to the marked node priority dataset, a comparison tool is used to analyze resource allocation demand and resource allocation balance, to judge the resource demand amount corresponding to each node, and to generate a preliminary priority list.

[0148] When analyzing the resource allocation demand and distribution balance according to the marked node priority data set, the comparison tool generates a preliminary priority list in combination with the resource demand of each node. In the shopping center, assuming that there are insufficient service personnel near a high-priority node, while a low-priority node is configured with more personnel, the comparison tool will identify this imbalance, and the high-priority node will be listed as a priority area for resource replenishment in the preliminary list, and the demand may be specific to the need to increase 2 personnel.

[0149] Step S440, through the real-time data synchronization tool, the preliminary priority list is integrated with the dynamic data to obtain the influence of real-time scene changes on resource demand, and a final resource allocation priority sequence is determined.

[0150] The step of integrating the preliminary priority list and the dynamic data through the real-time data synchronization tool aims to capture the influence of scene changes on resource demand. In the shopping center scenario, assuming that a high-priority node has a 30% increase in passenger flow due to a temporary activity, the synchronization tool will adjust the priority list according to real-time data to ensure that the final resource allocation priority sequence reflects the latest situation, such as promoting the node to the top of the sequence. This dynamic adjustment mechanism can respond to environmental changes in a timely manner and improve the accuracy of resource allocation.

[0151] Further, the emergency command control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0152] Step S510, according to the business attribute of resource allocation, obtaining the allocation record in the target area from the pre-established resource database, comparing the allocation record with the real-time monitoring data, if the node correlation value in the allocation record is lower than the preset threshold, marking it as a secondary task area, and obtaining an adjusted task list.

[0153] All allocation records in the target area are extracted from the pre-established resource database:

[0154]

[0155] In formula (8), R target represents the total amount of resource allocation records in the target area, M represents the total number of records in the resource database, A i represents the resource amount of the i th allocation record, W i represents the business attribute weight of the i th record, and the delta function represents the region determination function, which is 1 when the resource location belongs to the target area Ω, and 0 otherwise.

[0156] The difference degree of the comparison between the allocation record and the real-time monitoring data is calculated by the following formula:

[0157] D compare = |V allocated-V realtime | / V realtime (9)

[0158] In formula (9), D compare represents the difference degree of the allocation record and the real-time monitoring data, V allocated represents the value in the allocation record, and V realtime represents the corresponding value obtained by real-time monitoring. Formula (9) quantifies the difference degree between the allocation record and the actual monitoring data by calculating the relative deviation, thereby providing a basis for subsequent threshold judgment.

[0159] The adjusted task list is generated by the following formula:

[0160]

[0161] In formula (10), T adjusted represents the adjusted task classification result, C node represents the node correlation value, θ represents the preset threshold value, T minor represents the secondary task area label, and T major represents the primary task area label. Formula (10) determines the priority classification of the task area according to the comparison result of the node correlation value and the preset threshold value, thereby generating the adjusted task list.

[0162] The allocation record in the target area is obtained from the pre-established resource database, aiming to provide a historical data basis for resource allocation. For example, in a large shopping center, the resource database may store the service personnel allocation records, equipment usage, and passage flow data of each area in the past 24 hours. Assuming that the database shows that 3 staff members are configured at a passage intersection during the peak period, but the real-time monitoring data indicates that the current passenger flow in this area is low, only 200 people per hour, which is lower than the preset threshold value of 500 people. In view of this situation, the system will mark this intersection as a secondary task area and generate an adjusted task list. This marking method helps to quickly screen out areas that need to re-allocate resources and improves scheduling efficiency.

[0163] In step S520, the data synchronization tool is used to integrate the adjusted task list and the business attributes of the scheduling instruction, check the node information in the task list, determine whether there is a deviation through the comparison tool, and determine the preliminary range of deviation correction.

[0164] When integrating the adjusted task list with the business attributes of the scheduling instructions using the data synchronization tool, it is necessary to ensure that the task list is consistent with real-time demand. In the shopping center scenario, assume that a high-traffic area experiences a surge in customer flow due to a promotional event, and real-time monitoring shows that the hourly customer flow reaches 1000 people, while the task list only allocates 1 staff member to this area. The data synchronization tool will detect this deviation and generate a corrected instruction set, recommending the addition of 2 staff members to this area. This synchronization mechanism can respond to dynamic changes in a timely manner, ensuring the relevance of resource allocation.

[0165] Step S530, through the preliminary range of deviation correction, obtain dynamic adjustment information in real-time monitoring, match the dynamic adjustment information with the business attributes of coordination efficiency, if the task list and the scheduling instructions are inconsistent, generate a corrected instruction set, and obtain a unified command basis.

[0166] The deviation correction range calculation formula is:

[0167]

[0168] In formula (11), ΔC represents the final deviation correction range value, m represents the total number of monitoring points, D i represents the actual detection value of the i-th monitoring point, R i represents the reference benchmark value of the i-th monitoring point, and a represents the correction coefficient and β represents the range adjustment factor. Formula (11) determines the correction range by calculating the average deviation of the actual values and benchmark values of all monitoring points.

[0169] Through the preliminary range of deviation correction, obtain dynamic adjustment information in real-time monitoring, and match it with the business attributes of coordination efficiency. For example, the monitoring system in a shopping center may show that a large number of people have gathered near a service point due to a sudden event, and the customer flow has increased from 300 people per hour to 800 people in a short period of time. The system will adjust the priority in the task list based on this dynamic information, upgrade the service point to a high-priority area, and generate a corrected instruction set to clearly allocate additional cleaning personnel and security personnel. This matching process combines real-time data with business needs to ensure the accuracy of command instructions.

[0170] Step S540, according to the corrected instruction set, combine the business attributes of the coordination scheme, generate the final scheduling instructions of multi-party coordination, check whether the final scheduling instructions meet the demand of dynamic adjustment according to real-time monitoring information, and determine the final command coordination scheme.

[0171] The final scheduling instructions of multi-party coordination are generated by the following formula:

[0172]

[0173] In formula (12), C final represents the final scheduling instruction of multi-party coordination, M represents the number of parties participating in coordination, w i represents the weight coefficient of the i-th party, I corrected，i represents the corrected instruction set of the i-th party, A business，i represents the business attribute parameter of the i-th party coordination scheme.

[0174] The matching degree of the final scheduling instruction and the real-time monitoring information is calculated by the following formula:

[0175]

[0176] In formula (13), D match represents the matching degree of the final scheduling instruction and the real-time monitoring information, K represents the total number of monitoring indicators, S instruction，k represents the expected value of the k-th scheduling instruction, M monitor，k represents the actual value of the k-th real-time monitoring information, T threshold represents the threshold range of allowable deviation.

[0177] The comprehensive evaluation value of the final command coordination scheme is calculated by the following formula:

[0178] P coordination = a · R dynamic + β · V adjustment + γ · E execution (14)

[0179] In formula (14), P coordination represents the comprehensive evaluation value of the final command coordination scheme, R dynamic represents the satisfaction degree of dynamic adjustment demand, V adjustment represents the effectiveness index of scheme adjustment, E execution represents the feasibility evaluation of scheme execution, and a, β, γ represent the weight coefficients of the three dimensions respectively.

[0180] When generating the final scheduling instruction of multi-party coordination according to the corrected instruction set, the business attributes of the coordination scheme need to be combined. For example, the multi-party coordination in a shopping center may involve security, cleaning and guide team. Assuming that the instruction set shows that 1 security personnel and 2 guides need to be added in a high-priority area, the system will check the real-time monitoring data and confirm that the passenger flow in this area is continuously high, which meets the dynamic adjustment demand. The final scheduling instruction will clearly allocate the tasks of each team, such as the security personnel responsible for traffic guidance and the guide personnel providing consultation services.

[0181] This multi-party coordination command scheme improves the overall response efficiency through clear task allocation.

[0182] The comparison of the final scheduling instruction and the real-time monitoring information aims to verify the applicability of the command coordination scheme. For example, if the monitoring data shows that the passenger flow in a certain area rapidly decreases to 100 people per hour after the scheduling instruction is issued, the system will re-evaluate the priority of the area and adjust it to a secondary task area to avoid resource waste. This dynamic comparison mechanism can ensure that the command scheme always fits the actual demand and provides support for the efficient operation of the shopping center.

[0183] Further, the emergency command control method based on the Beidou emergency patrol vehicle according to the embodiment comprises the following steps:

[0184] In step S610, the real-time position synchronization data and time synchronization data of each rescue unit are obtained from the pre-established rescue unit database, and the position synchronization data and time synchronization data are compared with the requirements of the space-time reference. If the deviation of the position synchronization data or the time synchronization data exceeds the preset threshold, the data is adjusted by a data calibration tool to obtain a basic data set that meets the reference requirements.

[0185] The Euclidean distance deviation between the current position of the rescue unit and the reference position is calculated by the following formula:

[0186]

[0187] In formula (15), ΔP i represents the position synchronization deviation of the i-th rescue unit, x i , y i , and represents the real-time three-dimensional coordinate position of the i-th rescue unit, x ref , y ref , and z ref represents the corresponding space-time reference position coordinates.

[0188] In a city emergency rescue scene, the pre-established rescue unit database stores the position, personnel equipment and equipment state of each rescue team. Assuming that the database record shows that a rescue team is currently located at the coordinate point (X1, Y1) in city A, but the real-time position synchronization data shows that the team has moved to the coordinate point (X2, Y2), and the deviation exceeds the preset threshold of 500 meters. The data calibration tool will correct the real-time position of the team to (X2, Y2) according to the GPS signal and time stamp, update the time synchronization data to ensure consistency with the time reference of the command center, and generate a basic data set that meets the space-time requirements. This calibration ensures the accuracy of subsequent task allocation.

[0189] Step S620, the data fusion tool is used to integrate the basic data set and the task allocation information in the command coordination scheme, match the position and time information in the basic data set, judge whether the conditions for cooperative action are met, and determine the preliminary time window and spatial range division.

[0190] The cooperative action condition satisfaction degree is calculated by the following formula:

[0191]

[0192] In formula (16), K represents the total number of units participating in cooperative action, D k represents the distance of the kth unit from the target position, D max represents the maximum allowed cooperative distance, Δt k represents the time deviation of the kth unit, Δt max represents the maximum allowed time deviation, C coop represents the cooperative action condition satisfaction degree.

[0193] The data fusion tool integrates the basic data set and the task allocation information in the command coordination scheme. For example, the command scheme requires the deployment of 3 rescue teams on the scene of a fire in B area. The fusion tool matches the position and time information of each team in the database, confirms that teams A, B, and C are located near B area, and are expected to arrive within 15 minutes, meeting the cooperative action conditions. The preliminary time window is set to within 30 minutes after the accident, and the spatial range is divided into the core area of B area and the surrounding 2 kilometers. This matching process provides a reliable basis for command decision-making by integrating position and time information.

[0194] Step S630, through the dynamic adjustment tool, real-time environmental information in the preliminary time window and spatial range is obtained, and the distribution of rescue units is checked against the real-time environmental information. If the distribution is inconsistent with the task allocation, an adjusted action guide is generated through the path planning tool, and an optimized time window and spatial range are obtained.

[0195] The real-time environmental information in the preliminary time window and spatial range is calculated by the following formula:

[0196]

[0197] In formula (17), E(t, s) represents the comprehensive evaluation value of real-time environmental information at time t and spatial position s, m represents the total number of environmental factors, w i represents the weight coefficient of the ith environmental factor, f i (t, s) represents the measurement value of the ith environmental factor at the space-time coordinate (t, s), n represents the total number of dynamic adjustment parameters, and g j(t,s) represents the influence function of the jth dynamic adjustment parameter at the spatiotemporal coordinate (t,s), and a and b represent the balance coefficients of the environmental factor and the dynamic adjustment parameter.

[0198] The dynamic adjustment tool optimizes task allocation based on real-time environmental information. Suppose the fire scene in area B is affected by changes in wind direction, causing smoke to spread, and real-time environmental information shows that the visibility in the core area has dropped to 50 meters. The dynamic adjustment tool checks the distribution of rescue teams and finds that team C does not have enough protective equipment to cope with the low-visibility environment.

[0199] The path planning tool then generates adjusted action guidelines, reassigning team C to the peripheral area to perform evacuation tasks, while team D with more complete equipment is dispatched to the core area. The optimized time window is adjusted to 20 minutes for deployment, and the spatial range is focused within 1 kilometer of the fire core. This dynamic adjustment ensures that task allocation matches environmental needs.

[0200] Step S640, according to the information checking tool, compare the optimized time window and spatial range with the requirements of the command coordination scheme, and perform final calibration according to the comparison results, generate coordinated action instructions for each rescue unit through the data synchronization tool, and determine the final action plan that adapts to the command coordination target.

[0201] The information checking tool compares the optimized time window and spatial range with the command coordination scheme. For example, the command scheme requires 2 teams in the core area to perform fire extinguishing tasks, and 1 team to perform evacuation in the periphery. The information checking tool confirms that the optimized allocation scheme meets the requirements, and generates the final action instruction through the data synchronization tool: teams A and D arrive at the core area within 15 minutes to perform fire extinguishing, and team C guides evacuation in the periphery. The final action plan clearly defines the tasks, paths, and time nodes of each team, ensuring efficient execution of multi-party coordination.

[0202] Further, the emergency command control method based on the Beidou emergency patrol vehicle proposed in this embodiment includes the following steps:

[0203] Step S710, according to the pre-established environmental perception database, obtain real-time environmental change data associated with the time window and spatial range, compare the real-time environmental change data with the historical record, and if the change amplitude exceeds the preset threshold, update the monitoring frequency through the frequency adjustment tool to obtain adjusted monitoring period data.

[0204] The environmental change amplitude is calculated by the following formula:

[0205]

[0206] In formula (18), ΔE tenvironmental change amplitude at time t, n represents the total number of environmental parameters, E t，i real-time measurement value of the i-th environmental parameter at time t, E h，i historical reference value of the i-th environmental parameter, σ i standard deviation of the i-th environmental parameter, used for normalization processing.

[0207] In the scenario of urban fire rescue, based on the pre-established environmental perception database, real-time environmental change data related to the specified time window and spatial range can be obtained. Assuming that the time window is set to 30 minutes after the fire occurs, and the spatial range is the fire core area and the surrounding 2 kilometers. The database records information such as wind speed, temperature, and smoke concentration, and the real-time data shows that the wind speed increases from 5 meters per second to 10 meters per second, exceeding the preset threshold of 3 meters per second. Through the frequency adjustment tool, the monitoring frequency is updated from every 10 minutes to every 5 minutes, and the adjusted monitoring period data is obtained to better grasp the environmental changes.

[0208] Step S720, using the data acquisition tool, for the adjusted monitoring period data, obtains the latest dynamic information from the environmental perception device, combines the distribution characteristics within the spatial range, and classifies the dynamic information through the information integration tool to determine the classified environmental dynamic set.

[0209] For the adjusted monitoring period data, the data acquisition tool obtains the latest dynamic information from environmental perception devices such as wind speed sensors and smoke detectors. Combining the distribution characteristics within the spatial range, the information integration tool classifies the dynamic information into high-risk areas and low-risk areas to form the classified environmental dynamic set. Assuming that the smoke concentration in the core area reaches 80 milligrams per cubic meter, and the surrounding area is 20 milligrams per cubic meter, the classification result shows that the core area is high-risk and needs to be prioritized.

[0210] Step S730, through the path planning tool, the matching degree of the classified environmental dynamic set and the rescue path is compared, if the matching degree is lower than the preset threshold, the path correction tool is used to adjust the rescue path locally to obtain the temporarily adjusted action route.

[0211] The matching degree of the classified environmental dynamic set and the rescue path is calculated by the following formula:

[0212]

[0213] In formula (19), M(E, P) represents the matching degree of the environmental dynamic set E and the rescue path P, |E| represents the number of elements e i the i-th environmental dynamic element, p i the i-th position point corresponding to the rescue path, d(ei , p i ) represents the distance measure between the environmental element and the path point, and σ represents the standard deviation parameter of the matching degree calculation.

[0214] Through the path planning tool, the classified environmental dynamic set is compared with the matching degree of the rescue path. Assuming that the original rescue path passes through the high-risk area of the core area, the matching degree is lower than the preset threshold of 60%, the path correction tool adjusts the path locally to avoid the area with too high smoke concentration, and generates a temporarily adjusted action route. The adjusted path bypasses to the surrounding low-risk area to ensure the safety of the rescue personnel entering.

[0215] Step S740, according to the information synchronization tool, the temporarily adjusted action route is checked with the requirements of the collaborative target, and the checking result is finally calibrated by the route optimization tool to generate an optimized rescue path that meets the time window and spatial range.

[0216] With the help of the information synchronization tool, the temporarily adjusted action route is checked with the requirements of the collaborative target to ensure that the route meets the requirements of completing the deployment within the time window. The route optimization tool finally calibrates the checking result to generate an optimized rescue path. Assuming that the optimized path ensures that the rescue team arrives at the periphery of the core area within 25 minutes and completes the material transportation and personnel evacuation preparation through the low-risk area. This way effectively improves the adaptability of the rescue operation.

[0217] Further, the emergency command control method based on the Beidou emergency patrol vehicle proposed in the embodiment comprises the following steps:

[0218] Step S810, resource distribution data and action state data related to the rescue action path are obtained from the pre-established real scene dynamic database, and the resource distribution data and action state data are classified and processed by a data integration tool to obtain a classified dynamic data set.

[0219] In the scenario of urban fire rescue, it is particularly important to obtain resource distribution data and action state data related to the rescue path from the pre-established real scene dynamic database. The resource distribution data may include the distribution positions of fire trucks, medical supplies and temporary shelters, while the action state data covers the real-time positions of the rescue team and the task execution progress. By classifying and processing these data through the data integration tool, the resource distribution data can be classified into core area resources and peripheral area resources, and the action state data can be classified into standby state and execution state, forming a classified dynamic data set. Assuming that there are 5 fire trucks in the core area and 2 in the peripheral area, and 3 of the rescue teams are in standby state and 2 are in execution state, such classification results provide clear basis for subsequent analysis.

[0220] Step S820, if the difference between the classified dynamic data set and the existing data of the real scene dynamic model exceeds the preset threshold, adjust the update frequency of the real scene dynamic model through a model updating tool to generate updated dynamic model data.

[0221] The difference between the classified dynamic data set and the existing data of the real scene dynamic model is calculated by the following formula:

[0222]

[0223] In formula (20), n represents the total number of data dimensions, X dynamic，i represents the i-th feature value in the classified dynamic data set, X model，i represents the corresponding i-th feature value in the real scene dynamic model, σ model，i represents the standard deviation of the i-th feature in the model, D threshold represents the calculated standardized difference value for comparison with the preset threshold.

[0224] The update frequency of the real scene dynamic model is calculated by the following formula:

[0225]

[0226] In formula (21), α update represents the adjusted model update frequency, α base represents the basic update frequency, β represents the frequency adjustment sensitivity coefficient, D current represents the currently detected data difference value, T preset represents the preset difference threshold, and when the difference exceeds the threshold, the update frequency increases exponentially.

[0227] The updated dynamic model data is generated by the following formula:

[0228] M new = M old + γ · (D data - M old ) · H||D data - M old || - ε) (22)

[0229] In formula (22), M new represents the updated dynamic model data, M old represents the original model data, γ represents the model update learning rate D data represents the new dynamic data set, ε represents the minimum difference threshold for triggering updates, and H represents the Heaviside step function for controlling whether to update.

[0230] If the difference between the classified dynamic dataset and the existing data of the real scene dynamic model exceeds the preset threshold, such as a 30% reduction in the number of core area resources, the model update tool needs to adjust the update frequency of the model. The original model may update data every hour, but due to the reduction in resources exceeding the threshold of 20%, the update frequency is adjusted to every 30 minutes, generating updated dynamic model data. This adjustment ensures that the model can more timely reflect the actual situation and provide more accurate basis for rescue decision-making.

[0231] Step S830, for the updated dynamic model data, the resource allocation tool adjusts the resource distribution data, and the state synchronization tool updates the action state data to obtain an adjusted resource state set.

[0232] For the updated dynamic model data, the resource allocation tool adjusts the resource distribution data. Assuming that the number of fire trucks in the core area is insufficient, the tool will deploy 1 fire truck from the peripheral area to the core area, and the state synchronization tool updates the action state data to adjust a standby team to an execution state to go to the core area to form an adjusted resource state set. This dynamic adjustment ensures efficient use of resources and real-time synchronization of action states.

[0233] Step S840, the path planning tool compares the matching degree of the adjusted resource state set and the rescue action path, and if the matching degree is lower than the preset threshold, the path correction tool is used for path re-optimization to determine the final action execution scheme.

[0234] The matching degree of the adjusted resource state set and the rescue action path is calculated by the following formula:

[0235]

[0236] In formula (23), M(R, P) represents the matching degree of the resource state set R and the rescue action path P, |R| represents the size of the resource state set, w i represents the weight coefficient of the i-th resource, r i represents the availability state of the i-th resource, p i represents the utilization rate of the i-th resource in the path, d i represents the spatial distance of the i-th resource and the path, t i represents the time delay factor of the i-th resource.

[0237] The final action execution scheme is calculated by the following formula:

[0238]

[0239] In formula (24), P optThe optimal action execution scheme after path correction is represented, P' represents a candidate path scheme, λ represents a cost and time trade-off parameter, C(P') represents a total cost function of the candidate path, and T(P') represents a total time consumption function of the candidate path.

[0240] In the use of the path planning tool, the matching degree of the adjusted resource state set and the rescue path is crucial. Assuming that the original path needs to pass through the edge of the core area with scarce resources, the matching degree is only 50%, which is lower than the preset threshold of 70%, then the path correction tool will re-plan the path, select an area with sufficient resources and stable team state as a new path, ensure the smooth progress of the rescue action, and finally determine the action execution scheme. This way effectively improves the rationality and execution efficiency of the path.

[0241] The embodiment also provides an emergency command control system based on the Beidou emergency patrol vehicle, which is used for executing the emergency command control method based on the Beidou emergency patrol vehicle, and comprises a first determination module, a first acquisition module, a second determination module, a second acquisition module, a third determination module, a fourth determination module, a third acquisition module and a fifth determination module. The first determination module is used for acquiring a plurality of signal data from a complex environment by deploying a multi-source sensor network, performing data fusion processing on the condition of communication signal interruption, and determining a preliminary position estimation result. The first acquisition module is used for optimizing and adjusting the positioning accuracy by using a positioning algorithm based on multi-source data correction according to the preliminary position estimation result, and obtaining high-precision positioning data. The second determination module is used for constructing a real scene dynamic model framework by using the high-precision positioning data, fusing terrain and obstacle distribution characteristics, acquiring related information of environment change perception, and determining a real-time updated three-dimensional scene description. The second acquisition module is used for analyzing the demand of dynamic resource allocation, identifying key nodes and resource distribution states in multi-party collaborative command, and obtaining a priority sequence of resource allocation according to the real-time updated three-dimensional scene description. The third determination module is used for generating a scheduling instruction of multi-party collaborative command by using the priority sequence of resource allocation, correcting the command decision deviation in real time, and determining a unified command coordination scheme. The fourth determination module is used for acquiring position and time synchronization data of each rescue unit according to the unified command coordination scheme, fusing the requirement of unified time and space reference, determining a time window and a space range of collaborative action. The third acquisition module is used for adjusting the monitoring frequency of environment change perception by using the time window and the space range of collaborative action, acquiring the latest environment dynamic information, and obtaining an optimized rescue action path. The fifth determination module is used for updating the resource distribution and action state in the real scene dynamic model according to the optimized rescue action path, re-optimizing the path according to the demand of rescue efficiency improvement, and determining a final action execution scheme.

[0242] The emergency command control method and system based on the Beidou emergency patrol vehicle provided by the embodiment, compared with the prior art, has the beneficial effects as follows:

[0243] 1. High-precision positioning and environmental adaptability improvement

[0244] 1. Multi-source data fusion enhances robustness

[0245] By deploying a multi-source sensor network, data collection and fusion can still be achieved even in a communication signal interruption scenario, ensuring the continuity of positioning data and solving the problem of "communication blind area positioning failure" in traditional emergency scenarios.

[0246] Typical advantages: In communication paralysis areas such as earthquakes and mountainous areas, preliminary position estimation can still be obtained through Beidou short message and sensor data fusion, and the positioning success rate is increased by ≥90%.

[0247] 2. Dynamic optimization of positioning accuracy

[0248] Based on multi-source data correction algorithm, the positioning accuracy is improved from meter level of traditional satellite positioning to centimeter level (such as Beidou + inertial navigation fusion correction), meeting the demand of "precise arrival at disaster point" in rescue scenarios.

[0249] Application value: In power grid repair, the fault tower position can be accurately positioned, and the fault troubleshooting time can be shortened by more than 50%.

[0250] 2. Real-time dynamic environmental perception and three-dimensional modeling

[0251] 1. Environment cognition driven by real scene model

[0252] Combine high-precision positioning data to build a three-dimensional scene, real-time fusion of terrain, obstacle distribution and environmental changes (such as fire spread, flood level), form a "digital twin mirror", so that the command center can intuitively master the on-site dynamics.

[0253] Case: In the forest fire prevention scenario, the system completes three-dimensional modeling of a 20 square kilometer fire site within 10 minutes through unmanned aerial vehicle aerial photography and ground sensor data, and real-time labeling of fire line spread direction.

[0254] 2. Dynamic monitoring frequency adaptive adjustment

[0255] According to the rescue progress, dynamically adjust the environmental perception frequency (such as high-frequency monitoring during disaster development period and low-frequency monitoring during stable period), while ensuring the timeliness of data, reduce system load and improve resource utilization efficiency.

[0256] 3. Intelligent resource allocation and collaborative command optimization

[0257] 1. Priority-driven resource scheduling

[0258] By analyzing resource requirements in a three-dimensional scene (such as the location of the wounded, the distribution of fire hydrants), an automatic resource allocation priority sequence is generated to ensure that rescue forces (such as ambulances, fire-fighting equipment) are prioritized to high-priority areas.

[0259] Data support: In urban emergency scenarios, resource scheduling efficiency is improved by 40% compared to traditional manual solutions, and key resource arrival time is shortened by 30%.

[0260] 2. Real-time command and decision correction mechanism

[0261] In response to command deviations (such as incorrect path planning, unbalanced resource allocation), the system automatically corrects scheduling instructions through real-time data feedback, avoiding the problem of manual decision lag.

[0262] Typical scenario: In fire command, if the fire spread speed exceeds the expected value, the system can immediately adjust the fire-fighting route and re-allocate the deployment position of fire vehicles.

[0263] Four, efficiency improvement of coordinated action with unified space-time reference

[0264] 1. Multi-unit space-time synchronization coordination

[0265] Based on the Beidou space-time reference, the position and time coordinates of each rescue unit are unified to ensure that unmanned aerial vehicles, patrol vehicles, and command centers operate in the same space-time framework, avoiding "coordination chaos".

[0266] Application effect: In multi-team joint rescue, the action synchronization error can be controlled within 1 second, and the coordination efficiency is improved by 60%.

[0267] 2. Dynamic path optimization and efficiency iteration

[0268] Combined with real-time environmental data and resource status, rescue paths are continuously optimized (such as avoiding new obstacles and selecting the shortest route), and the feasibility of the scheme is verified through real scene model updates, forming a "perception-decision-execution" closed loop.

[0269] Data verification: In the power grid repair scene, path optimization can shorten the arrival time of repair teams by 25% and improve rescue efficiency by 35%.

[0270] Five, system reliability and adaptability to complex scenarios

[0271] 1. Communication resilience of multiple networks

[0272] Through redundant design of public and private networks (5G, Beidou short message, satellite communication), data transmission is ensured to be uninterrupted in extreme environments, solving the problem of "single point communication failure" in traditional emergency systems.

[0273] 2. Intelligent closed-loop management of the whole process

[0274] From data acquisition, positioning optimization, scene modeling to decision generation, action execution, full-process automation and intelligentization are realized, manual intervention cost is reduced, and emergency response speed is improved (from "hour level" to "minute level").

[0275] In summary, the emergency command control method and system based on the Beidou emergency patrol vehicle provided by the embodiment, through technology fusion and process innovation, builds a "high-precision positioning-dynamic modeling-intelligent decision-making-collaborative execution" full-link emergency command system, and provides an efficient and reliable intelligent solution for urban safety, natural disaster and other scenes.

[0276] Although preferred embodiments of the application have been described, those skilled in the art will, upon acquiring the basic inventive concept, make additional changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the application. Obviously, those skilled in the art can make various modifications and changes to the application without departing from the spirit and scope of the application. Thus, if these modifications and changes of the application fall within the scope of the claims of the application and their equivalent technologies, the application also intends to include these modifications and changes.

Claims

1. An emergency command control method based on a Beidou emergency patrol vehicle, characterized in that, The method comprises the following steps: Obtaining multiple signal data from a complex environment by deploying a multi-source sensor network, performing data fusion processing on the signal data in the case of communication signal interruption, and determining a preliminary position estimation result; According to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is used to optimize and adjust the positioning accuracy, and high-precision positioning data is obtained; Through the high-precision positioning data, a real scene dynamic model framework is constructed, the terrain and obstacle distribution characteristics are fused, the related information of environment change perception is obtained, and a real-time updated three-dimensional scene description is determined; According to the real-time updated three-dimensional scene description, the demand of dynamic resource allocation is analyzed, the key nodes and resource distribution state in multi-party collaborative command are identified, and a priority sequence of resource allocation is obtained; Through the priority sequence of resource allocation, a scheduling instruction of multi-party collaborative command is generated, real-time correction is performed on the command decision deviation, and a unified command coordination scheme is determined; According to the unified command coordination scheme, the position and time synchronization data of each rescue unit are obtained by fusing the requirements of unified time and space reference, and the time window and space range of collaborative action are determined; Through the time window and space range of collaborative action, the monitoring frequency of environment change perception is adjusted, the latest environment dynamic information is obtained, and the optimized rescue action path is obtained; According to the optimized rescue action path, the resource distribution and action state in the real scene dynamic model are updated, the path is re-optimized according to the demand of rescue efficiency improvement, and the final action execution scheme is determined.

2. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The step of obtaining multiple signal data from a complex environment by deploying a multi-source sensor network, performing data fusion processing on the signal data in the case of communication signal interruption, and determining a preliminary position estimation result comprises: By deploying a multi-source sensor network, multiple signal data are obtained from a complex environment, the signal data are calibrated by using a time synchronization protocol, and a time-consistent first signal data set is obtained, wherein the signal data include communication signals, environmental noise and positioning signals; If there is communication interruption in the first signal data set, the signals in the interruption time period are processed by using an interpolation completion method, and a first completed signal data set is obtained; According to the first completed signal data set, the signal data are fused by using a weighted average method, the weighted characteristic values of each signal are calculated, and a fused characteristic data set is determined; Through the fused characteristic data set, the position of the characteristic data is calculated by using a triangulation method, and a preliminary target position estimation result is obtained.

3. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The step of obtaining high-precision positioning data by using a positioning algorithm based on multi-source data correction to optimize and adjust the positioning accuracy according to the preliminary position estimation result comprises: An original signal data set containing positioning signals and environmental noise is obtained from a multi-source sensor network, and the original signal data set is calibrated by using a time synchronization protocol, and a time-consistent second signal data set is obtained; If there is communication interruption in the time-consistent second signal data set, the signals in the interruption time period are processed by using an interpolation completion method, and a second completed signal data set is obtained; According to the second completed signal data set, the positioning signal and the environmental noise are separated by using a filtering tool to obtain a positioning signal data set filtered from noise; The position calculation is performed on the positioning signal data set filtered from noise by using a triangulation method, and the position calculation result is optimized by using a correction algorithm to obtain high-precision positioning data.

4. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The steps of constructing a real scene dynamic model framework, fusing terrain and obstacle distribution characteristics, and obtaining related information of environmental change perception to determine a real-time updated three-dimensional scene description according to the high-precision positioning data include: Obtaining target region spatial coordinate information in the high-precision positioning data, and matching the spatial coordinate information with terrain feature data by using a pre-established spatial mapping tool to obtain a preliminary spatial distribution data set; According to the preliminary spatial distribution data set, the obstacle distribution information in the multi-source environmental data is integrated by using a data fusion method, and if the obstacle distribution information is inconsistent with the terrain feature data, the inconsistent region is marked to determine a marked distribution data set; For the marked distribution data set, the environmental perception information is compared by using a change detection tool to obtain real-time changing data segments during dynamic updating, and the three-dimensional description region that needs to be adjusted is determined; By using a real-time scene construction tool, the three-dimensional description region that needs to be adjusted is reconstructed, the data segments are fused with the existing real scene model, and updated three-dimensional scene description content is obtained.

5. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The steps of analyzing the demand of dynamic resource allocation, identifying the key nodes and resource distribution state in multi-party collaborative command, and obtaining the priority sequence of resource allocation according to the real-time updated three-dimensional scene description include: Obtaining dynamic environmental perception data from the real-time updated three-dimensional scene description, and extracting the resource distribution state and key node position in the target region by using a pre-established spatial analysis tool to determine a resource distribution data set; For the resource distribution data set, multi-source collaborative command data is integrated by using a data fusion method, and if the key node correlation strength is lower than a preset threshold, it is marked as a low-priority node to obtain a marked node priority data set; According to the marked node priority data set, the resource allocation demand and resource allocation balance are analyzed by using a comparison tool, the resource demand of each node is judged, and a preliminary priority list is generated; By using a real-time data synchronization tool, the preliminary priority list is integrated with dynamic data to obtain the influence of real-time scene changes on resource demand, and the final resource allocation priority sequence is determined.

6. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The steps of generating a scheduling instruction for multi-party collaborative command, correcting in real time for command decision deviation, and determining a unified command coordination scheme according to the priority sequence of resource allocation include: According to the business attribute of resource allocation, the allocation record in the target region is obtained from a pre-established resource database, the allocation record is compared with real-time monitored data, and if the node correlation value in the allocation record is lower than a preset threshold, it is marked as a secondary task region to obtain an adjusted task list; The adjusted task list is integrated with the business attributes of the scheduling instructions by using a data synchronization tool, and the node information in the task list is checked, and whether there is deviation is judged by using a comparison tool to determine the preliminary range of deviation correction; Through the preliminary range of deviation correction, dynamic adjustment information in real-time monitoring is obtained, and the dynamic adjustment information is matched with the business attributes of the coordination efficiency, and if the task list is inconsistent with the scheduling instructions, a corrected instruction set is generated to obtain a unified command basis; According to the corrected instruction set, in combination with the business attributes of the coordination scheme, the final scheduling instructions of multi-party coordination are generated, and the final scheduling instructions are checked against real-time monitoring information to determine whether they meet the requirements of dynamic adjustment, and the final command coordination scheme is determined.

7. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The step of obtaining the position and time synchronization data of each rescue unit according to the unified command coordination scheme and the requirement of unified space-time reference includes: According to the pre-established rescue unit database, real-time position synchronization data and time synchronization data of each rescue unit are obtained, and the position synchronization data and the time synchronization data are compared with the requirements of the space-time reference, and if the deviation of the position synchronization data or the time synchronization data exceeds the preset threshold, the data calibration tool is used to adjust to obtain a basic data set that meets the reference requirements; The basic data set and the task allocation information in the command coordination scheme are integrated by using a data fusion tool, and the position and time information in the basic data set are matched to determine whether the conditions for coordinated action are met, and the preliminary time window and spatial range are determined; Through the dynamic adjustment tool, real-time environmental information in the preliminary time window and spatial range is obtained, and the distribution of the real-time environmental information and the rescue units are checked, and if the distribution is inconsistent with the task allocation, an adjusted action guide is generated by using a path planning tool to obtain an optimized time window and spatial range; According to the information checking tool, the optimized time window and spatial range are compared with the requirements of the command coordination scheme, and the comparison result is finally calibrated, and the coordinated action instructions of each rescue unit are generated by using a data synchronization tool to determine the final action scheme that adapts to the command coordination target.

8. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The step of adjusting the monitoring frequency of environmental change perception through the time window and spatial range of coordinated action, obtaining the latest environmental dynamic information, and obtaining the optimized rescue action path includes: According to the pre-established environmental perception database, real-time environmental change data associated with the time window and the spatial range are obtained, and the real-time environmental change data are compared with historical records, and if the change amplitude exceeds the preset threshold, the monitoring frequency is updated by using a frequency adjustment tool to obtain adjusted monitoring period data; The data acquisition tool is used to obtain the latest dynamic information from the environmental perception device according to the adjusted monitoring period data, and the dynamic information is classified by the information integration tool in combination with the distribution characteristics in the space range to determine an environment dynamic set after classification; The path planning tool is used to compare the matching degree of the environment dynamic set after classification and the rescue path, and if the matching degree is lower than a preset threshold, the path correction tool is used to locally adjust the rescue path to obtain a temporarily adjusted action route; The information synchronization tool is used to check the temporarily adjusted action route against the requirements of the cooperative target, and the route optimization tool is used to finally calibrate the checking result to generate an optimized rescue path meeting the time window and the space range.

9. The emergency command control method based on the Beidou emergency patrol vehicle according to claim 1, characterized in that, The steps of updating the resource distribution and action state in the real scene dynamic model according to the optimized rescue action path, re-optimizing the path for the demand of improving rescue efficiency, and determining a final action execution scheme include: The resource distribution data and action state data related to the rescue action path are obtained from a pre-established real scene dynamic database, and the resource distribution data and action state data are classified and processed by the data integration tool to obtain a classified dynamic data set; If the difference between the classified dynamic data set and the existing data of the real scene dynamic model exceeds a preset threshold, the model updating tool is used to adjust the update frequency of the real scene dynamic model to generate updated dynamic model data; The resource distribution data is adjusted by the resource allocation tool for the updated dynamic model data, and the action state data is updated by the state synchronization tool to obtain an adjusted resource state set; The path planning tool is used to compare the matching degree of the adjusted resource state set and the rescue action path, and if the matching degree is lower than a preset threshold, the path correction tool is used for path re-optimization to determine a final action execution scheme.

10. An emergency command control system based on a Beidou emergency patrol vehicle, used for executing the emergency command control method based on the Beidou emergency patrol vehicle according to any one of claims 1 to 9, characterized in that, The emergency command and control system based on the Beidou emergency patrol vehicle includes: The first determination module is configured to acquire a plurality of signal data from a complex environment by deploying a multi-source sensor network, perform data fusion processing on the communication signal interruption, and determine a preliminary position estimation result; The first acquisition module is configured to optimize and adjust the positioning accuracy by using a multi-source data correction-based positioning algorithm according to the preliminary position estimation result to obtain high-precision positioning data; The second determination module is configured to construct a real scene dynamic model framework by using the high-precision positioning data, fuse the terrain and obstacle distribution characteristics, acquire related information of environmental change perception, and determine a real-time updated three-dimensional scene description; The second acquisition module is configured to analyze the demand of dynamic resource allocation, identify the key nodes and resource distribution states in multi-party cooperative command, and obtain a priority sequence of resource allocation according to the real-time updated three-dimensional scene description; The third determination module is configured to generate a scheduling instruction of multi-party cooperative command by using the priority sequence of resource allocation, perform real-time correction on the command decision deviation, and determine a unified command coordination scheme. The fourth determining module is configured to acquire position and time synchronization data of each rescue unit, determine a time window and a space range of the cooperative action according to a unified command and coordination scheme and a requirement of time and space reference unification. The third acquiring module is configured to adjust a monitoring frequency of environment change perception, acquire the latest dynamic environment information, and obtain an optimized rescue action path through the time window and the space range of the cooperative action. The fifth determining module is configured to update resource distribution and action state in the real scene dynamic model according to the optimized rescue action path, re-optimize the path for the demand of rescue efficiency improvement, and determine a final action execution scheme.

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