110 results about "Decision cycle" patented technology

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention discloses a rice field irrigation drainage strategy dynamic optimization method and system. The method comprises the steps of collecting environmental parameters of a target rice field in a current decision period; determining a to-be-optimized target of the target rice field, and constructing a multi-target optimization model in combination with the environmental parameters; performing dynamic planning solution on the multi-objective optimization model by adopting an intelligent algorithm to obtain a real-time optimized irrigation drainage strategy of the current decision period; implementing a real-time optimized irrigation drainage strategy of the current decision period; monitoring whether the final period of the rice growth period is reached when the current decision-making period is ended or not: otherwise, acquiring actual environment parameters when the decision-making period is ended, correcting the state vector at the moment, updating the initial state vector of the next decision-making period of the target rice field through the transfer function, and repeating the solving and implementation process of the irrigation and drainage strategy optimized in real time; if yes, the circulation is ended, and the optimal irrigation drainage strategy in the whole growth period of the target rice field is obtained.

The invention discloses a heterogeneous unmanned cluster collaborative hunting method, device and equipment and a storage medium, and relates to the technical field of agent automatic control, and the method comprises the steps: determining a heterogeneous unmanned cluster, environment information and constraint conditions of a multi-agent collaborative hunting system, and constructing a physical model of the multi-agent collaborative hunting system; at the beginning of each decision cycle, modeling a task allocation problem of multiple agents into a'maximum flow-minimum cost 'graph theory problem, constructing a task allocation directed graph, determining an individual capture target corresponding to each agent, and based on a PPO framework, taking the maximum return of the corresponding individual capture target completed by each agent as an individual target, and performing task allocation on each agent; and with the maximum return of all captured targets completed by the heterogeneous unmanned cluster as a global target, an intelligent agent is guided to learn an optimal hunting strategy. According to the method, the problem of obvious insufficiency in the aspect of processing heterogeneous capability constraints in the prior art is solved.

The invention discloses a digital bond risk assessment method and system based on a block chain and privacy calculation, electronic equipment and a storage medium, and the method achieves the innovation of digital bond risk assessment, and has the core effect of enhancing the data security and privacy protection in the assessment process. And meanwhile, the integrity and non-tampering property of the data are ensured. According to the method, the homomorphic encryption technology is utilized, the encryption state of the data in the processing process can be ensured even if calculation is carried out at the cloud, and data leakage and unauthorized access are effectively prevented. Besides, the evaluation system provided by the invention greatly improves the transparency and credibility of evaluation, so that investors can more accurately grasp the risk condition of the digital bond, thereby promoting the healthy development of the digital financial market and the efficient configuration of capital. Through the intelligent evaluation process, the evaluation efficiency is remarkably improved, the decision-making period is shortened, and the capacity of quickly responding to market changes is provided for issuers and investors.

The invention provides a rice irrigation online learning forecasting method and system, the method is realized according to a pre-constructed physical mechanism-neural network hybrid model based on physical mechanism model prediction and neural network error correction fusion, and the method comprises the following steps: S1, obtaining real-time environment data of a current decision period of a target rice field; s2, on the basis of the real-time environment data of the current decision period, forecasting a paddy field water layer depth predicted value of the next decision period through a physical mechanism-neural network hybrid model, and generating an irrigation drainage forecast in combination with a crop irrigation drainage mode; s3, real-time environment data of the target rice field in the next decision period after irrigation drainage forecast is executed are obtained, training samples are constructed and put into an experience playback pool, and the physical mechanism-neural network hybrid model executes online learning based on the experience playback pool; and S4, when the next decision cycle starts, returning to S2 until a preset stop condition is met.

The invention discloses an electric vehicle charging station recommendation method based on multi-agent reinforcement learning and user preference, and the method comprises the following steps: S1.1, constructing a dynamic intelligent electric vehicle charging station recommendation (EVCSR) frame based on a Markov decision process (MDP), determining a decision period, a state space, an action set, a reward function and a state-action transition probability of the frame, according to the electric vehicle charging station recommendation method based on multi-agent reinforcement learning and user preference, through an integrated framework, the problems that a traditional method is insufficient in individual demand, weak in multi-vehicle cooperation and low in high-dimensional state decision-making efficiency are effectively solved, the accuracy, the real-time performance and the user experience of electric vehicle charging station recommendation under the urban scale are remarkably improved, and the method is suitable for popularization and application. And support can be provided for charging facility optimization and power grid dispatching.

The invention relates to the technical field of intelligent decision making, and discloses an intelligent decision making method and system based on a computing network integrated industrial PLC, and the method comprises the steps: carrying out the time slice distribution of control parameters of water supply equipment, and dividing the decision period of the PLC, and obtaining an initial control parameter queue; determining occurrence probability distribution of a normal operation state, an efficient control state and a decision conflict state to obtain a water supply parameter conflict probability table; redistributing the parameter control time resource to obtain a water supply parameter optimization control time sequence; converting the parameter groups which still have conflicts into mutually independent control variables, and respectively executing optimization calculation to obtain a multi-parameter parallel control instruction of the water supply equipment; and carrying out dynamic resource scheduling on the multi-parameter parallel control instruction of the water supply equipment to generate an energy-saving control scheme of the water supply equipment. The method effectively optimizes the dynamic distribution of control resources, remarkably shortens the response time of control parameters, and improves the overall throughput of the system.

The invention relates to a ground penetrating radar parameter optimization and general survey method for highway subgrade condition detection. The method comprises four core modules: one is ground penetrating radar parameter optimization configuration, and automatic parameter correction during medium change is realized through static basic configuration and dynamic adaptive adjustment in combination with real-time sensing and PID (Proportion Integration Differentiation) control; the second method is rapid general survey execution, traffic flow, pavement vision and historical disease data are fused, a route is optimized through a Dijkstra algorithm, and abnormity is judged through multi-feature fusion; thirdly, imaging processing is optimized, multi-layer medium correction time delay imaging and complex disease classification imaging are provided, and the deep disease recognition rate is increased; and fourthly, maintenance decision linkage is realized, the disease level and priority are quantified, the maintenance scheme is automatically output, and the decision period is shortened. The method solves the problems of poor dynamic adaptation, difficulty in complex disease identification and disjunction in decision making in the prior art, has been verified in multiple sections, and is suitable for highway subgrade disease full-chain monitoring and maintenance.

The invention relates to the technical field of Internet of Things data processing, in particular to a multi-modal data real-time processing method for an Internet of Things gateway, and the method comprises the steps: obtaining multi-source operation data, and packaging the multi-source operation data into a standard operation data set; obtaining high-bandwidth modal data in the standard operation data set; when the maximum link delay time is greater than a preset delay abnormal threshold value, obtaining a version effective fingerprint of the target model and a local model fingerprint; when a model version abnormity diagnosis result is obtained, calculating information redundancy; obtaining correction delay time, and comparing the correction delay time with a preset normal reference delay interval; and when the correction delay time is greater than or equal to the upper limit value of the normal reference delay interval, adjusting the current decision period. By monitoring delay in real time, diagnosing consistency of model versions, eliminating redundant information and executing link adjustment, accurate processing of high-bandwidth modal data is realized, and real-time performance and reliability of Internet of Things gateway data processing are improved.

The invention provides a building industry finance and tax compliance digital evidence chain integrated platform which is deployed based on a cloud computing architecture and comprises a distributed server cluster, a network communication module and a database storage system. By constructing the integrated digital platform, fundamental efficiency improvement and risk management and control reinforcement of finance and taxation management in the building industry are realized, the platform enables a data acquisition process to be highly automatic, manual repeated entry work is greatly eliminated, financial personnel are liberated from tedious account checking, and the financial personnel are greatly improved. The compliance check module based on a rule engine and artificial intelligence can perform real-time and comprehensive automatic analysis on all transaction data, significantly improves the identification accuracy and timeliness of potential compliance risks, realizes instant alarm, converts passive risk response into active risk response, and improves the risk handling efficiency. Compared with a traditional manual mode, the data processing speed is improved in order of magnitude, the monthly checkout and report generation time is greatly shortened, the enterprise decision cycle is remarkably accelerated, and the overall operation efficiency is improved.

The invention discloses a multi-robot path coordination system based on reinforcement learning, and the system comprises a state collection module which is used for collecting the operation state and environment perception information of each robot, and generating an original data set; the state modeling module is used for constructing local state representation for each robot in each decision period; the opponent perception module is used for executing opponent learning perception training and outputting opponent parameter estimation vectors; the improved LOLA module is used for generating a final estimation result on the basis of self-strategy updating of the traditional LOLA; the strategy updating module is used for correcting the updating direction of the reinforcement learning strategy of the local machine and generating a feasible action set; the arbitration decision module is used for executing arbitration solution in combination with the candidate path action distribution to generate a scheduling result; and the execution feedback module is used for issuing the control action to a robot executor for execution, generating a path coordination result and storing the path coordination result. According to the invention, path coordination of multiple robots is realized.

The invention relates to a self-adaptive agent decision framework training method with dynamic environment perception capability, which comprises the following steps of: 1, collecting multi-modal sensor data in parallel, and carrying out hardware-level preprocessing on the collected data; step 2, based on the data preprocessed in the step 1, performing dynamic security boundary driven real-time feature extraction to obtain security boundary coordinates; 3, based on the safety boundary coordinates obtained in the step 2, generating an execution action strategy in a decision period; and 4, based on the action strategy generated in the step 3, constructing a feedback closed loop of the distributed execution framework, and realizing real-time closed loop optimization. According to the method, the core requirements of scenes such as mobile robot navigation and intelligent industrial control on autonomous decision-making safety and real-time performance are met.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and device fusing lane dynamic evaluation and safety indexes and a medium. Real-time collision risks are calculated by collecting dynamic data of a vehicle and surrounding vehicles, multi-lane traffic flow data are collected under triggering of specific requirements, and characteristics such as density, speed and flow are extracted; and a lane basic passage score is calculated by fusing the flow and the large vehicle influence, and correction is carried out by combining the lane changing intention of surrounding vehicles to obtain a dynamic lane evaluation result. And constructing a multi-objective decision function based on the safety index and the lane evaluation result, and finally generating and selecting a trajectory with the minimum comprehensive cost value in a decision period for execution. According to the invention, collaborative evaluation of the vehicle close-range safety risk and the lane passing potential is realized, and the lane evaluation can reflect the traffic flow interaction influence. The driving efficiency and the lane adaptability are optimized, and the decision-making ability of the automatic driving system in the traffic environment is improved.

The invention provides a method for opening a section emergency lane under large traffic flow, which comprises the following steps: when the traffic flow density of a target section reaches a first saturation threshold value, starting an elastic lane dynamic decision engine, matching real-time traffic data with a starting criterion to generate an initial control decision, broadcasting the initial control decision, and triggering a first observation window; based on the section occupancy rate, generating a resource configuration scheme containing a starting gradient, issuing an intelligence mark updating instruction and triggering a second window; reconstructing an optimization strategy according to the blocking coefficient and setting a decision period, and triggering a third window after generating a dynamic configuration command broadcast; generating a final control instruction according to the millimeter wave radar response data and triggering a fourth window; and integrating the traffic efficiency indexes, determining an optimal elastic matching scheme through multi-cycle iteration, reconstructing a strategy, and returning to a strategy reconfiguration point to finally serve as an execution basis. According to the invention, the road network traffic efficiency under large traffic flow can be improved, and regional cooperative control and optimal resource configuration are ensured.

The invention provides a hierarchical man-machine cooperation multi-target dynamic job shop scheduling method, and relates to the field of man-machine cooperation dynamic scheduling. On the basis of a hierarchical collaborative reinforcement learning framework, a high-dimensional global scheduling problem is decomposed into a plurality of low-dimensional sub-problems by dynamically partitioning a job set and allocating robot resources through a high-layer agent in a decision-making period, and the sub-problems are processed by the low-layer agent in parallel; two-tuple actions of a process selection rule and a processing mode selection rule of the process selection rule are quickly generated for the responsible stations in each time step and are verified, and the state space dimension needing to be processed by the reinforcement learning agent is remarkably compressed. Besides, a proposed target sharing type collaborative mechanism ensures that global multi-target weight parameters calculated by a high-level agent guide local decision of a low-level agent in real time, the deviation between a local optimization target and a global target is effectively reduced, the problem of multi-agent target splitting is solved, and the multi-target collaborative optimization capability is improved.

The invention discloses a nuclear power plant distributed control system and control method, and relates to the technical field of nuclear power plant digital control, and the method comprises the steps: a global optimization layer generates a cross-unit general control strategy library based on a transfer learning algorithm according to the decision period of the global optimization layer; when the coordination control layer operates by using the transferable control strategy corresponding to the unit equipment where each two-loop subsystem is located, the corresponding two-loop subsystem control weight is dynamically allocated based on the multi-agent attention network according to the decision period of the coordination control layer; when the execution control layer operates by using the dynamically distributed two-loop subsystem control weight, the first-loop temperature, the first-loop pressure and the coolant flow are obtained according to the decision period of the control layer, a safety constraint matrix is calculated through a safety manifold projection module, and a quadratic programming problem with a relaxation factor is solved; and the corrected control weight of the two-loop subsystem is obtained and converted into a control instruction to be issued to a DCS field control station.

The invention discloses an Internet of Vehicles cooperative unloading method based on hybrid action deep reinforcement learning, and relates to the technical field of Internet of Vehicles and mobile edge computing. In order to solve the technical problem of joint optimization of unloading target selection and resource allocation, the problem is modeled as a Markov decision process, and a mixed action soft actor-commentator (HA-SAC) algorithm is provided for solving; the core is that a multi-head strategy network outputs discrete actions and continuous actions at the same time in a decision period. A centralized training and decentralized execution architecture is adopted, and the value network receiving the global state guides the intelligent agent which only depends on the local state to make decisions to learn; compared with the prior art, the method has the advantages that the precision loss caused by action space discretization is avoided, the task completion time delay is obviously reduced, and the robustness and the adaptive decision-making capability of the system in a highly dynamic network environment are enhanced.

The invention discloses a self-adaptive process scheduling method and system based on real-time state perception, and the method comprises the steps: firstly carrying out the low-overhead real-time monitoring of a system operation state through an extended Berkley data packet filter technology, and collecting multi-dimensional original data; secondly, identifying a current system load mode by using a pre-trained gradient boosting decision tree model, and performing stable prediction on a load mode of a next decision period in combination with a historical window mechanism based on time weighted voting so as to smooth instantaneous fluctuation; then, querying a'load mode-target scheduler 'mapping table which is constructed offline in advance according to a prediction result to determine a target scheduling strategy; and finally, dynamically and safely switching to a target scheduling strategy through the extensible scheduler class framework, and realizing self-adaptive adjustment of system scheduling. According to the method, macroscopic strategy selection and microcosmic scheduling decision are decoupled, high real-time inference overhead is avoided, safety and stability of the system are guaranteed by utilizing a kernel native mechanism, and the system operation efficiency under diversified loads is remarkably improved while low performance overhead, high safety and easy deployment are kept.

The invention discloses an ultra-dense heterogeneous wireless network access control method fusing prediction and reinforcement learning, and the method comprises the steps: simulating a movement track of a user through employing an SLAW model, and carrying out the normalization of the movement track; inputting the normalized moving track into the LSTM model, and outputting a predicted coordinate of the next moment; calculating the received signal strength of the user to the current access network and all other candidate networks under the predicted coordinates, and judging whether to trigger a network switching mechanism in advance according to the received signal strength; for a network switching decision problem, constructing a Markov decision model by defining a state space, an action space and a reward function; when a network switching mechanism needs to be triggered in advance or a preset decision period is reached, solving the Markov decision model by using a DDQN algorithm, selecting a target network from the candidate networks, and switching to the target network; according to the invention, the response speed and accuracy of network switching can be effectively improved, so that the user experience and the network performance are improved.

The invention relates to an integrated energy system low-carbon scheduling method and system based on carbon capture and demand response, electronic equipment and a storage medium, and aims to adjust scheduling actions in time according to continuously changing state information to adapt to various working conditions. In each decision-making period, through circulation of strategy network generation action, execution action, reward calculation and parameter optimization, the system always runs towards a low-carbon, economical and efficient direction, can dynamically adapt to environment changes (such as wind-solar power generation fluctuation, power grid load demand change, user behavior response and the like), continuously optimizes scheduling strategies of subsystems, and improves the scheduling efficiency. The utilization rate of wind and light power generation is effectively improved, the wind and light abandoning phenomenon is reduced, the power generation and carbon capture efficiency of a thermal power generating unit is optimized, the utilization of redundant wind and light power by P2G equipment is maximized, meanwhile, the peak load is reduced through user demand response, and low-carbon efficient operation of a comprehensive energy system is achieved on the whole.

The invention provides a ship navigation decision-making method fusing a large language model and a lightweight neural network, and belongs to the technical field of ship intelligent navigation and decision-making optimization. The method comprises the following steps: constructing a time sequence feature vector and a structured text in a predefined format according to ship multi-source data; outputting a real-time control instruction through a lightweight neural network based on the time sequence feature vector; determining a global control instruction through a large language model based on the structured text; according to a preset risk level, dynamically distributing weight coefficients of the instant control instruction and the global control instruction, and generating a fusion control instruction through weighted fusion; an instant control instruction and a fusion control instruction are sent according to a time sequence in each decision period, and a ship execution mechanism completes navigation control according to the instructions. Through the steps, the contradiction between large language model reasoning delay and fixed-period instruction output of the ship control system is solved, and periodic stable generation and safe execution of decision instructions are ensured.

The invention discloses an electric vehicle charging station cooperative scheduling and dynamic regulation and control method based on a space-time demand gradient. The method comprises the following steps: step 1) obtaining space-time characteristics of a charging station; 2) based on historical operation characteristics, classifying the charging stations according to the utilization rate; 3) based on the time-space characteristics of the charging stations, predicting the future short-term coincidence rate of each type of charging stations to obtain the future short-term coincidence rate of the charging stations; if the future short-term coincidence rate of the charging station is greater than a preset threshold value, entering a step 4), otherwise, returning to the step 1) after T time; 4) calculating a space transfer identifier; 5) calculating the total demand transfer amount of each charging station by using a double-pressure module, and 6) solving the MPC-based multi-target rolling optimization model in a rolling manner in each decision period, and generating and executing an electricity price adjustment strategy and a vehicle guiding strategy. The time-space equilibrium distribution of the charging coincidence rate is realized, the congestion is effectively relieved, and the comprehensive benefit of the system is improved.

The invention provides a method for realizing an intelligent non-player character in an interactive game, which relates to the field of data processing and comprises the following steps of: packaging a decision intention into an immutable action package in a predefined decision period and placing the action package in a time domain trusteeship queue to realize time decoupling of a decision and a runtime load; the world state projection is divided into an opaque decision phase and a reconciliation phase, the decision phase only exposes the event category projection and the historical abstract, and the reconciliation phase reflows detailed trigger details in an independent window for posteriori maintenance but does not backtrack submitted actions; submission follows an atomization criterion and the submitted abstract does not contain time information; during resource contention, accumulating in response to a debt, and repaying through display shaping on the premise of not exposing a time sequence so as to keep the semantic consistency of main games; the posterior processing updates the action banks and event mappings offline based on the backflow details to improve subsequent decisions.

The invention discloses a traffic signal control method, an electronic device and a system, and the control method comprises the steps: obtaining historical traffic data of a target intersection, the historical traffic data comprising a plurality of historical traffic sequence elements, and any historical traffic sequence element comprising a return, a state and a signal control period; obtaining the state of the target decision period by adopting the thunder equipment, and generating traffic sequence elements to be decided based on the state; generating a to-be-decided sequence based on the historical traffic sequence elements and the to-be-decided traffic sequence elements; inputting the to-be-decided sequence into a preset off-line reinforcement learning model, and performing reasoning by the off-line reinforcement learning model based on the return, the state and the credit control period in the historical traffic sequence element to obtain a credit control period reasoning value of the to-be-decided traffic sequence element; and carrying out signal lamp duration distribution of different phases based on the signal control period reasoning value. According to the method provided by the invention, the historical information can be fully utilized, so that the method can better adapt to the fluctuation of the traffic flow, and the vehicle congestion is effectively reduced.

The present application relates to the field of intelligent agricultural planting management technology, in particular to a method for intelligent agricultural planting water and fertilizer integration decision optimization based on big data, comprising: obtaining multi-source heterogeneous agricultural big data of a target planting area, including soil moisture, meteorological observation, crop growth image and historical water and fertilizer records; separating the associated soil, meteorological and crop phenotype feature fields through space-time fusion and feature extraction; and constructing a dynamic water and fertilizer state space model. The real-time observation data and the model prediction data are assimilated, the hidden state variables are iteratively corrected to obtain high-confidence farmland state data, the model is run forward to simulate the evolution trajectory under different water and fertilizer strategies in multiple decision cycles, and the optimal water and fertilizer decision scheme is obtained through optimization solution. The method realizes the correlation modeling of multi-source agricultural data and the dynamic calibration of farmland state, completes the water and fertilizer decision optimization through multi-cycle trajectory simulation optimization, and adapts to the dynamic water and fertilizer regulation and control requirements of farmland.

The invention provides a military plotting-based auxiliary tactical calculation and decision-making system, and belongs to the technical field of computers. Comprising a data processing and acquisition module, a plotting system development module, a tactical calculation model module, a decision support system module, a communication and network technology module, a man-machine interaction module, a security and encryption technology module and a continuous improvement and optimization module. The system provided by the invention improves the decision-making efficiency, greatly improves the decision-making efficiency of a commander through automatic and intelligent data processing, tactical analysis and decision-making suggestion, shortens the decision-making period, carries out data analysis and tactical prediction by using an advanced artificial intelligence technology, can provide a more accurate and reliable decision-making basis, and improves the decision-making efficiency of the commander. Dynamic analysis and adjustment can be carried out according to real-time battlefield data, it is ensured that a commander can make a decision according to the latest situation, and the real-time performance and accuracy of the decision are improved.

The invention relates to the technical field of oil and gas field exploration and development, in particular to a while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method for a helium-containing natural gas reservoir. According to the technical scheme, the while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method for the helium-containing natural gas reservoir comprises the working process of the while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method; according to the invention, a high-precision hydrogen helium detection mass spectrometer and logging equipment are integrated, through mechanical installation optimization, automatic calibration and pre-drilling simulation test, the detection precision and data timeliness are significantly improved, a helium concentration vertical profile can be obtained in real time, lithologic changes and helium enrichment horizon can be dynamically identified, and the reliability of the system is improved. An instant basis is provided for well drilling parameter adjustment and gas testing interval selection, and the decision-making period is effectively shortened.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system. The method comprises the following steps: collecting multi-source financial data, and carrying out standardized integration on the financial data; performing quality detection and cleaning on the data, and constructing a standardized data set; constructing an AI model, inputting the standardized data set into the AI model, and performing optimization training; inputting to-be-processed fact financial business data into the model, analyzing applicant information, performing transaction risk monitoring, and outputting a result; constructing a dual verification mechanism, and verifying the output processing result; a data storage library of a distributed architecture is constructed, a processing result passing verification is received, and financial service data traceability is established; the system comprises a data processing module, an AI model construction module, an analysis module, a verification module and a storage module. Through the mode, the time and manpower of the business processing and decision period can be greatly shortened, and the cost is effectively controlled.

The invention discloses a national defense mobilization digital intelligence platform system and method, and relates to the technical field of national defense mobilization digital intelligence. Comprising a data acquisition and preprocessing module, a federated learning data governance module, a block chain evidence storage and intelligent contract module, a mobilization demand analysis module, a potential resource modeling module, a generative AI plan generation module, a digital twinborn drill and evaluation module, a task allocation and execution monitoring module and a system management and maintenance module. According to the method, military data security collaboration is realized through federal learning and the block chain, model training is completed on the premise of not sharing original data, and resource matching efficiency is improved while data privacy is guaranteed; the generative AI automatically generates a plan and quantitatively evaluates the drilling effect through digital twinning, traditional manual deduction is replaced, the decision-making period is shortened, and the method adapts to a dynamic mobilization scene; the digital twinborn drilling is combined with an evaluation formula, the training effect is converted from qualitative description to quantitative calculation, the training short board is accurately positioned, and the training efficiency is improved.

The application discloses a kind of edge computing detection mine car performance method, device, equipment, medium and program, the method includes: receiving performance detection request by cloud platform and extracting the frame number of target mine car;Determine performance detection template according to frame number and return to mobile terminal for template analysis;Receive the performance test instruction generated based on template analysis result returned by mobile terminal, and forward to vehicle terminal for edge computing;Receive the edge computing result returned by vehicle terminal, including the highest speed peak value of mine car and the time used for acceleration in specified speed interval, generate performance detection report according to edge computing result and performance detection template and synchronize to mobile terminal.The technical scheme of the embodiment of the application adopts cloud, mobile terminal, vehicle terminal three-level architecture to realize task cooperation to reduce delay, supports mobile terminal to receive report containing deviation value highlight in real time to shorten decision-making cycle, and simultaneously improves the efficiency and scalability of mine car performance detection through edge computing.