Equipment configuration optimization method

Abstract

The invention belongs to the technical field of resource optimization configuration and decision support, and discloses an equipment configuration optimization method, which comprises the following steps of: acquiring multi-source heterogeneous data such as task demand information, available equipment resource information and environment situation information, constructing a standardized configuration basic data set, and establishing a standardized configuration basic data set; constructing a task demand dynamic prediction model, an equipment capability evaluation model and an environmental impact analysis model, and defining a multi-dimensional configuration constraint condition and an optimization objective function; the method comprises the following steps: performing iterative optimization on an equipment configuration scheme, generating an initial optimal configuration scheme, triggering a dynamic reconfiguration mechanism according to real-time feedback information and environment change in a task execution process, and performing adaptive adjustment and online optimization on the optimal configuration scheme. According to the invention, the intelligent, dynamic and refined configuration of the equipment is realized, the configuration efficiency, the resource utilization rate and the task success rate are obviously improved, and the configuration challenge in a complex dynamic environment is effectively dealt with.

Description

Technical Field

[0001] The present invention belongs to the technical field of resource optimal allocation and intelligent decision-making. More specifically, it relates to an equipment and material allocation optimization method and system based on multi-source information fusion, intelligent modeling, and multi-objective optimization, and is particularly applicable to application scenarios that require efficient and accurate equipment and material planning and allocation in complex and dynamic environments, such as emergency rescue, military operation support, large-scale engineering project management, logistics distribution, etc. Background Art

[0002] The reasonable allocation of equipment and materials is a key link to ensure the smooth implementation of various tasks, especially complex tasks such as emergency response, military operations, and large-scale engineering projects. The timeliness, accuracy, and economy of its allocation directly affect the execution efficiency, cost control, and even the ultimate success or failure of the task. Traditional equipment and material allocation methods often rely on static plans, standardized allocation lists, and the experience and judgment of decision-makers.

[0003] However, with the increasing complexity of the task environment and the growing dynamic nature of task requirements, traditional allocation methods have exposed many drawbacks: 1) Insufficient information processing ability: Traditional methods are difficult to effectively process the massive, heterogeneous, and dynamically changing data from multiple aspects such as task requirements, resource status, and environmental situation. The incompleteness, inaccuracy, or lag of information often leads to the disconnection between the allocation plan and the actual situation.

[0004] 2) Staticness and lag: The allocation mode based on plans or fixed lists lacks flexibility and is difficult to adapt to changes in requirements, unexpected losses of resources, or sudden changes in environmental conditions during the task process. Allocation adjustments often lag behind actual needs, affecting the task progress.

[0005] 3) Experience dependence and subjectivity: Excessive dependence on the personal experience and intuition of decision-makers makes the allocation decision-making process lack scientific basis and quantitative standards, prone to subjective biases, and it is difficult to guarantee the stability and optimality of the allocation results. Moreover, experience is difficult to inherit and replicate.

[0006] 4) Lack of optimization ability: Traditional methods usually adopt single-objective or simple multi-objective trade-offs and are difficult to systematically optimize among multiple conflicting objectives such as cost, time, efficiency, and risk, and cannot obtain a globally optimal or near-optimal allocation plan, resulting in resource waste or low task efficiency.

[0007] 5) Lack of dynamic feedback and closed-loop control: Traditional allocation processes are often one-time decisions, lacking real-time monitoring of the execution process of the allocation plan and a dynamic adjustment mechanism based on feedback, unable to form a closed-loop control, and difficult to cope with uncertainties during the execution process.

[0008] Especially in scenarios such as emergency rescue and modern military operations where time windows are tight, environmental uncertainties are high, and resource constraints are strict, the above problems are particularly prominent, which may lead to serious consequences such as rescue delays, inadequate support, cost overruns, and even mission failures.

[0009] Therefore, there is an urgent need for a new method for optimizing the configuration of equipment and materials that can integrate multi-source information, possess intelligent analysis and optimization capabilities, adapt to dynamic environments, and achieve closed-loop control, so as to improve the scientificity, timeliness, and optimization level of configuration decisions. Summary of the Invention

[0010] In order to overcome the problems of staticity, subjectivity, insufficient information processing ability, lack of optimization ability, and lack of dynamic adaptability existing in the existing methods for configuring equipment and materials, the present invention aims to provide a method and system for optimizing the configuration of equipment and materials, aiming to achieve the intelligence, dynamization, refinement, and optimization of the configuration of equipment and materials.

[0011] To achieve the above object, the present invention provides the following technical solution: A method for optimizing the configuration of equipment and materials, comprising the following steps: Step S1: Obtain multi-source heterogeneous configuration data.

[0012] Comprehensively collect various types of information related to the configuration of equipment and materials, including clear task requirement information (such as task type, target, location, time requirement, scale, expected achieved effect level, etc.), detailed available equipment and material resource information (covering the model specifications, technical parameters, current status, remaining life, location distribution, available quantity of equipment, as well as the categories, inventory, validity period, storage requirements, performance indicators of related materials, and even including the qualifications, quantity, and status of operators), and real-time environmental situation information (such as the geographical landform, road traffic conditions, meteorological and hydrological conditions, electromagnetic environment, social reliance conditions of the task area, as well as possible natural disaster risks, human threats, or interferences, etc.). Ensure the comprehensiveness, timeliness, and multi-dimensionality of the data.

[0013] Step S2: Perform data fusion processing and spatio-temporal alignment and calibration.

[0014] Preprocess the collected multi-source heterogeneous data, including data cleaning (removing noise and outliers), data standardization (unifying the format and dimension), missing value filling (using interpolation, regression, or machine learning methods), data fusion (integrating data from different sensors or information sources, such as using Kalman filtering, Bayesian inference, etc. technologies), and spatio-temporal alignment (unifying different sources of data in time and space based on timestamps and geographical coordinates). Finally, construct a standardized, structured, and spatio-temporally consistent configuration basic data set to provide a high-quality data basis for subsequent modeling and analysis.

[0015] Step S3: Build a multi-dimensional intelligent analysis model.

[0016] Based on the configured basic data set, use technical means such as machine learning, deep learning, mechanism modeling, and simulation to build a series of interrelated intelligent analysis models for in-depth understanding of configuration problems. These models should at least include: (1) Task requirement dynamic prediction model: Analyze historical task data, current situation information, and task internal associations to predict the demand quantity, demand type, and their probability distributions of various equipment and materials in different regions within a certain period in the future. It can be achieved by combining time series models (such as ARIMA, Prophet, LSTM) with spatial analysis methods.

[0017] (2) Equipment and material capability evaluation model: Based on the technical parameters, current status, reliability data, operator skills, and environmental factors of equipment and materials, evaluate the comprehensive operation efficiency, continuous working ability, failure probability, etc. of a single piece of equipment or equipment combination in a specific task scenario. It can be built using an effectiveness network model, Agent-Based Modeling and Simulation (ABMS), or a data-based regression model (such as SVR, Random Forest).

[0018] (3) Environmental impact analysis model: Quantitatively analyze the specific impacts of environmental factors such as geography, meteorology, electromagnetics, and risks on equipment performance (such as communication distance, detection range), availability (such as deployment restrictions), transportation efficiency (such as travel time), and task execution itself (such as safety risks). It can be achieved by combining GIS spatial analysis, physical models (such as propagation models, hydrological models), and expert knowledge bases.

[0019] Step S4: Define the configuration constraint condition set and multi-objective optimization function.

[0020] According to the task requirements, resource constraints, and operating procedures, clarify the various constraints that must be adhered to during the equipment and material configuration process, and construct a multi-objective optimization function for evaluating the quality of the configuration plan. The set of constraints can include: task completion time limit, upper limit of total cost budget, available quantity limits of various types of equipment and materials, load / volume limits of transportation tools, compatibility / interoperability requirements between equipment, environmental adaptability requirements of the deployment location, safety specification requirements, risk tolerance level, etc. The multi-objective optimization function aims to pursue multiple potentially conflicting goals simultaneously, such as: minimizing the total configuration cost (procurement, transportation, maintenance, manpower, etc.), minimizing the task completion time or response time, maximizing the overall task effectiveness (such as coverage, processing capacity, success rate), minimizing the risks during task execution (such as personnel casualty risk, equipment loss risk, environmental damage risk), etc. Appropriate methods (such as weighted summation, goal programming, Pareto front idea) need to be used to formalize these goals.

[0021] Step S5: Conduct iterative optimization and generation of the configuration plan.

[0022] Comprehensively utilize the intelligent analysis model constructed in Step S3 and the constraints and goals defined in Step S4, and adopt advanced multi-objective intelligent optimization algorithms (such as NSGA-II / III, MOPSO, multi-objective reinforcement learning, etc.) to search the configuration plan space. During the optimization process, combine multi-scenario simulation and deduction (simulating environmental changes, resource depletion, emergencies, etc. under different probabilities) and configuration risk assessment (evaluating the potential risks of different plans in various scenarios) to evaluate and screen the candidate configuration plans. Through iterative calculations, finally find a set or one initial optimized configuration plan that reaches the optimal (or Pareto optimal) state of the multi-objective optimization function while satisfying all the constraints. This plan should specifically list the exact models, quantities, sources, configuration locations, configuration time nodes, transportation methods, and necessary personnel allocations of the required equipment and materials. The plan should specifically list the exact models, quantities, sources, configuration locations, configuration time nodes, transportation methods, and necessary personnel allocations of the required equipment and materials.

[0023] Step S6: Implement dynamic monitoring and adaptive adjustment of the configuration plan.

[0024] After the optimized configuration plan is adopted and starts to be executed, a real-time monitoring mechanism is established to continuously obtain the latest information on the actual progress of the task, the real-time status of the configured equipment and materials (location, fuel consumption, fault information, etc.), the dynamic changes of available resources (new addition, loss, allocation, etc.), and the environmental situation. Set the trigger conditions for dynamic adjustment (such as major changes in key requirements, failures of core equipment, sudden increase in environmental risks, excessive deviation between actual effectiveness and expectations, etc.). Once the trigger conditions are met, the system automatically starts the reconfiguration and online optimization program, and may need to quickly return to step S5 (or its simplified version) for recalculation, generate an adjusted optimized configuration plan, and issue an execution instruction, thus forming a dynamic feedback and closed-loop control optimization process to ensure that the configuration plan can always adapt to the changing actual situation until the task ends.

[0025] The present invention also provides an equipment and material configuration optimization system, which is designed to execute the above method. The system includes: a data acquisition unit, a data processing unit, an intelligent modeling unit, a constraint and objective definition unit, an optimization and simulation unit, and a dynamic monitoring and adjustment unit.

[0026] These units can be software modules, hardware modules, or modules combining software and hardware. They are connected and interact with data through an internal data bus, network, or other communication methods to jointly complete the intelligent and dynamic configuration optimization task of equipment and materials.

[0027] The technical effects and advantages of an equipment and material configuration optimization method of the present invention: No. 1. It improves the intelligent level of configuration decision-making. By introducing intelligent technologies such as machine learning and deep learning for demand prediction, capability assessment, and environmental analysis, it overcomes the dependence on experience and subjectivity of traditional methods and improves the scientificity of decision-making.

[0028] No. 2. It enhances the dynamic adaptability of the configuration plan. A real-time monitoring and dynamic adjustment mechanism is established, enabling the configuration plan to quickly respond and adaptively optimize according to task progress, resource changes, and environmental changes, and improving the ability to cope with uncertainties.

[0029] No. 3. It realizes the refinement and optimization of configuration optimization. By adopting a multi-objective optimization algorithm, it can systematically balance among multiple dimensions (cost, time, effectiveness, risk, etc.) to find a globally optimal or near-optimal configuration plan, and improve the resource utilization efficiency and task execution effect.

[0030] No. 4. It improves the efficiency and accuracy of information processing. It can effectively integrate and process a large amount of dynamic data of multiple sources and heterogeneous types, providing comprehensive, accurate, and timely information support for configuration decision-making.

[0031] 5. Reduced the labor cost and time cost of the configuration process. The automated and intelligent processes reduce the need for manual intervention, shorten the decision-making cycle, and improve the overall efficiency of the configuration work. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings, where: Figure 1 FIG. is a schematic flowchart of an equipment and material configuration optimization method provided by an embodiment of the present invention.

[0033] Figure 2 FIG. is a schematic functional block diagram of an equipment and material configuration optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0035] Embodiment 1 This embodiment provides an equipment and material configuration optimization method applied to large-scale disaster emergency rescue scenarios. As Figure 1 shown, the purpose of this method is to quickly, efficiently, and scientifically configure rescue forces and materials.

[0036] Step S1: Obtain multi-source heterogeneous configuration data In this embodiment, after the disaster occurs, the system first obtains information through multiple channels: Task requirement information: Receive rescue instructions issued by the superior command department, clarify the type of disaster (such as earthquake, flood, fire), the affected area (geographical coordinates, affected area), the main rescue objectives (search and rescue, medical treatment, resettlement, infrastructure repair), the expected rescue time window, the preliminary estimated affected population and loss situation. At the same time, obtain more detailed task requirement details through methods such as drone aerial photography, satellite remote sensing, and reports from on-site advance teams.

[0037] Available equipment and resource information: Connect to the national, provincial, and municipal emergency resource databases to obtain the quantity, location, professional capabilities, and personnel composition of available rescue teams (such as fire, medical, engineering, volunteers, etc.) in the vicinity; obtain the models, quantities, current technical status (in good condition / awaiting repair / in use), storage locations, and performance parameters of various rescue equipment (such as large excavators, life detectors, inflatable boats, water pumps, emergency communication vehicles, drones); obtain the types, inventory levels, specifications, shelf lives, and storage locations of rescue supplies (such as tents, food, medicine, drinking water, fuel). At the same time, update the dynamic availability of resources in real-time (such as whether they have been dispatched).

[0038] Environmental situation information: Access the weather forecast system to obtain the weather conditions (rainfall, wind speed, temperature) for the next few days; call the GIS system to obtain the detailed topography, road network (especially those that may be interrupted), bridge and tunnel conditions, and river and lake water level information of the disaster area; obtain the social support conditions around the disaster area (such as available airports, stations, docks, hospitals, schools, shelters); assess the risks of secondary disasters (such as aftershocks, landslides, barrier lakes, epidemics).

[0039] Step S2: Data fusion processing and spatio-temporal alignment and calibration Process the collected information: Data cleaning and standardization: Identify and process errors, conflicts, and outliers in the data. Unify the formats, units, and coding standards of various types of data. For example, uniformly convert location information from different sources to WGS84 coordinates.

[0040] Missing value filling: For missing key information, use a prediction model based on historical data or correlation to fill in the blanks, such as using a regression model to predict the initial consumption of supplies.

[0041] Data fusion: Integrate information from different sources to improve the accuracy and integrity of the information. For example, fuse satellite images and drone images to more accurately assess road damage.

[0042] Spatio-temporal alignment: Attach accurate timestamps and geographical tags to all data to ensure the comparability of data in terms of time and space.

[0043] After processing, form a standardized and structured emergency configuration basic database that includes task requirements, resource status, and environmental conditions.

[0044] Step S3: Build a multi-dimensional intelligent analysis model Based on the data in the database, train and build the following models: Task Requirement Dynamic Prediction Model: Use LSTM (Long Short-Term Memory Network) to analyze the evolution law of rescue requirements for historical similar disasters, and combine the current disaster situation assessment and population distribution data to predict the demand for food, medicine, tents, and search and rescue forces in different regions within the next 24 hours and 48 hours.

[0045] Equipment and Equipment Capacity Evaluation Model: Establish a capacity-based evaluation framework. For example, to evaluate the fire-fighting capacity of a fire brigade, it is necessary to consider its personnel number, skill level, water tank / foam vehicle load, high-pressure water gun range, distance from the current location to the fire site, road traffic capacity, and the expected fire site environment (wind direction, combustible type). Use simulation or expert systems to evaluate the collaborative operation efficiency of different equipment combinations.

[0046] Environmental Impact Analysis Model: Establish a road traffic capacity model to predict vehicle travel time and accessibility based on terrain, road grade, damage reports, and weather (rain and snow). Establish a meteorological impact model to evaluate the impact of rainfall on flood levels, landslide risks, and the operation windows of aircraft (unmanned aerial vehicles, helicopters).

[0047] Step S4: Define the configuration constraint set and multi-objective optimization function Constraints: Time Constraint: The first batch of rescue forces need to arrive at the designated area within 2 hours; complete the core search and rescue tasks within the 72-hour golden rescue period.

[0048] Cost Constraint: The total budget does not exceed X million yuan.

[0049] Resource Constraint: The available large excavators do not exceed Y units; the first-aid medicine inventory is Z units.

[0050] Transportation Constraint: The maximum single-load of a helicopter is W tons; road interruptions result in the need for air or water transportation in some areas.

[0051] Compatibility Constraint: A certain type of communication equipment needs to be compatible with the command vehicle.

[0052] Safety Constraint: Rescue personnel need to be equipped with specific protective equipment when entering dangerous areas; night operations need to meet the minimum lighting conditions.

[0053] Multi-objective Optimization Function: Objective 1: Minimize the total response time (the time from receiving the order to the arrival of the first batch of forces at the core area).

[0054] Objective 2: Maximize the number of rescued people (or the affected area covered).

[0055] Objective 3: Minimize the total configuration cost (including transportation, material consumption, personnel costs, etc.).

[0056] Objective 4: Minimize the risks brought by secondary disasters or the rescue operations themselves (such as the risk of casualties among rescue personnel).

[0057] Step S5: Conduct iterative optimization and generation of the configuration plan Optimization algorithm: The NSGA-II algorithm is adopted. The chromosome encoding of the algorithm includes which rescue teams, equipment, and supplies to select, as well as their quantities, departure times, transportation routes, and deployment locations.

[0058] Simulation and deduction: For each generated candidate configuration plan, conduct Monte Carlo simulation, simulate 100 possible scenarios (such as further road interruptions, sudden weather changes, finding a large number of trapped people, etc.), and evaluate the average response time, rescue effect, cost, and risk of the plan under these scenarios.

[0059] Risk assessment: Combine the simulation results and the environmental impact model, conduct FMEA analysis on each plan, identify potential failure modes (such as transportation delays, equipment failures, material shortages), evaluate their occurrence probabilities and consequence severities, and calculate the comprehensive risk score.

[0060] Plan generation: The NSGA-II algorithm continuously iterates and evolves based on the simulation evaluation and risk scores, and finally generates a set of Pareto optimal solution sets. The commander can select the most appropriate plan from the solution set as the initial optimized configuration plan according to the current focus (priority time? Priority effect? Priority cost?). The plan may include: the 2nd detachment of the Fire Brigade in City A (including personnel and vehicles), 1 team of the Medical Rescue Team in Province B, 5 Type C life detectors, 10 Type D inflatable boats, E tons of food, F boxes of medicine... They depart from the base at times T1 and T2 respectively, and arrive at disaster areas Z1 and Z2 via routes P1 and P2.

[0061] Step S6: Implement dynamic monitoring and adaptive adjustment of the configuration plan Real-time monitoring: Track the positions of rescue teams and vehicles through the Beidou / GPS positioning system; monitor the status of equipment (fuel quantity, power quantity, working duration) through Internet of Things sensors; obtain the task progress and newly discovered needs through on-site reports and real-time drone videos; receive the latest meteorological warnings and geological disaster warnings.

[0062] Trigger conditions: It is monitored that a new serious landslide has occurred on a key transportation road, and the expected interruption time exceeds 6 hours. A medical team reports that the medicine is about to run out, far exceeding the expected consumption speed. The meteorological station issues a heavy rainfall warning, which may trigger a new round of floods. A large gathering point of trapped people is found on-site, and additional search and rescue forces need to be dispatched immediately.

[0063] Reconfiguration and Online Optimization: When the trigger condition is met, the system automatically initiates a rapid re-optimization process. For example, after a road is interrupted, the system re-plans the transportation routes of the affected teams (which may be changed to detours or air transportation), evaluates the time and cost changes, and may dispatch supplementary forces from the backup resources. If additional search and rescue forces are needed, the system will run the optimization algorithm again based on the current available resources, locations, and capabilities, and give suggestions for reinforcement plans. The updated plan is sent to the relevant units for implementation in real time. This process continues until the main objectives of the rescue mission are completed.

[0064] Embodiment 2 This embodiment provides an equipment and material configuration optimization system, as Figure 2 shown. This system aims to implement the method described in Embodiment 1. This system can be deployed on the emergency command center server or the cloud computing platform. The system includes the following functional units / modules: Data Acquisition Unit (201): Responsible for connecting to various information sources through interfaces, such as emergency resource database interfaces, meteorological information interfaces, GIS platform interfaces, on-site information reporting system interfaces, sensor network interfaces (such as Beidou / GPS, equipment status sensors). Perform the data collection task of step S1.

[0065] Data Processing Unit (202): Built-in data cleaning, standardization, and fusion algorithm libraries (such as rule-based cleaning, unit conversion, Kalman filtering, spatio-temporal registration algorithms). Perform the data preprocessing and dataset construction tasks of step S2.

[0066] Intelligent Modeling Unit (203): Contains multiple pre-trained or online learnable model libraries, such as LSTM demand prediction models, ABMS-based capability evaluation simulation engines, and GIS and physical rule-based environmental impact analysis modules. Perform the model construction and invocation tasks of step S3.

[0067] Constraint and Objective Definition Unit (204): Provides a user interface that allows the commander to input or select constraint conditions (such as time, budget) and optimization objectives and their weights according to the specific task. Convert these inputs into formal mathematical expressions. Perform step S4.

[0068] Optimization and Simulation Unit (205): The core computing unit, built-in multi-objective optimization algorithm engine (such as implemented by NSGA-II) and simulation engine (such as discrete event simulator). Receive the model output, constraints, and objectives, perform the iterative optimization, simulation deduction, and risk assessment of step S5, and generate a configuration plan.

[0069] Dynamic Monitoring and Adjustment Unit (206): Receives the latest information transmitted by the Data Acquisition Unit in real time, compares it with the expected state generated by the Optimization and Simulation Unit, and determines whether the adjustment condition is triggered. Once triggered, it calls the Optimization and Simulation Unit to perform reconfiguration calculations and issues the updated plan through the output interface (such as the Task Management System, Scheduling Instruction System). Execute step S6.

[0070] Human-Machine Interaction and Visualization Unit (not explicitly marked in Figure 2 but implicitly exists): Provides a graphical interface to display real-time data, model analysis results, optimization plans, simulation processes, risk assessment reports, task execution status, etc., facilitating the commander to understand the situation, make decisions, and select the final plan from the Pareto solution set.

[0071] These units are connected through an internal high-speed data bus or network, share data storage (such as the Configuration Basic Database, Model Library, Plan Library), and work together to complete the entire equipment and material configuration optimization process.

[0072] By constructing a method and system integrating data acquisition, intelligent modeling, multi-objective optimization, and dynamic adjustment, the present invention can significantly improve the scientificity, timeliness, adaptability, and optimization level of equipment and material configuration. Compared with traditional methods, the present invention can better handle configuration problems in complex dynamic environments, effectively improve the task success rate and resource utilization efficiency, and is particularly applicable to key fields such as emergency rescue and military operation support.

[0073] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0074] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing equipment configuration, characterized in that: The following steps are involved: Step S1: Acquire mission requirement information, available equipment and material resource information, and environmental situation information as multi-source heterogeneous configuration data; the mission requirement information includes mission objectives, execution time windows, geographical scope, and expected effect levels; the available equipment and material resource information includes equipment models, quantities, technical status, performance parameters, location distribution, and equipment types, inventory, specifications, and storage conditions; the environmental situation information includes geographic information, meteorological and hydrological information, electromagnetic environment information, and potential risk factors; Step S2: performing data fusion processing and spatiotemporal alignment calibration on the multi-source heterogeneous configuration data, filtering out noise and redundant information, filling in missing values, and constructing a standardized and structured configuration basic data set; Step S3: Based on the configuration basic data set, a multi-dimensional intelligent analysis model is constructed, and the multi-dimensional intelligent analysis model at least includes: a task demand dynamic prediction model, which is used to predict the demand and type of equipment at different time nodes and spatial regions; an equipment capability evaluation model, which is used to evaluate the comprehensive operational efficiency and reliability of different equipment combinations in a specific environment; an environmental impact analysis model, which is used to analyze the degree and mode of influence of environmental factors on equipment performance, transportation deployment and task execution; Step S4: defining a set of constraints and a multi-objective optimization function for equipment configuration; the set of constraints at least includes time constraints, cost budget constraints, resource quantity constraints, transportation capacity constraints, equipment compatibility constraints, and safety risk threshold constraints; the multi-objective optimization function aims to simultaneously optimize the total cost of the configuration, the task completion time, the overall combat effectiveness or support capability, and the task execution risk; Step S5: Based on the multi-dimensional intelligent analysis model and constraint condition set, a multi-objective intelligent optimization algorithm is used to perform iterative optimization calculations on equipment configuration schemes, and combined with multi-scenario simulation deduction and configuration risk assessment results, an initial optimization configuration scheme that meets the constraints and has the best comprehensive performance is screened and generated; the optimization configuration scheme specifies the model, quantity, configuration location and timing arrangement of the required equipment; Step S6: During the execution of the equipment configuration plan, task progress feedback information, resource status change information, and environmental situation update information are obtained in real time; when the real-time information triggers the preset dynamic adjustment threshold, the dynamic reconfiguration and online optimization process of the configuration plan is started, and the process returns to step S5 for recalculation to generate an updated optimized configuration plan until the task is completed.

2. The equipment configuration optimization method according to claim 1, characterized in that: In step S1, the available equipment and material resource information also includes the skill level, quantity and available status information of the operators, and the environmental situation information also includes enemy threat information or potential interference source information.

3. The equipment configuration optimization method according to claim 1, characterized in that: In step S2, the data fusion processing adopts Kalman filtering, Bayesian inference or multi-sensor information fusion algorithm; the spatiotemporal alignment calibration adopts a method based on timestamp alignment and geographic coordinate conversion.

4. The equipment configuration optimization method according to claim 1, characterized in that: In step S3, the task demand dynamic prediction model adopts a hybrid prediction model based on time series analysis (such as ARIMA, LSTM) and spatial interpolation (such as Kriging); the equipment capability assessment model adopts Agent-based modeling and simulation (ABMS), performance network model or machine learning regression model based on historical data (such as support vector regression, random forest); the environmental impact analysis model adopts spatial analysis based on geographic information system (GIS), mechanism model (such as electromagnetic propagation model, meteorological impact model) or expert system rule base.

5. The equipment configuration optimization method according to claim 3, characterized in that: The task demand dynamic prediction model also combines semantic understanding of task types with association rule mining to improve the prediction accuracy of sudden and associated demands.

6. The equipment configuration optimization method according to claim 1, characterized in that: In step S4, the multi-objective optimization function is constructed using a weighted sum method, an ε-constraint method or a target programming method; the quantification of the mission execution risk involves a comprehensive evaluation model of factors such as equipment failure probability, environmental hazard level, and transportation delay probability.

7. The equipment configuration optimization method according to claim 1, characterized in that: In step S5, the multi-objective intelligent optimization algorithm is selected from an improved non-dominated sorting genetic algorithm (NSGA-II or NSGA-III), a multi-objective particle swarm optimization algorithm (MOPSO), a simulated annealing algorithm or an optimization strategy based on reinforcement learning.

8. The equipment configuration optimization method according to claim 1, characterized in that: In step S5, the multi-scenario simulation deduction adopts discrete event simulation or Monte Carlo simulation method to simulate the execution effect of configuration schemes under different environmental conditions, resource loss and emergencies; the configuration risk assessment adopts failure mode and effect analysis (FMEA), event tree analysis or Bayesian network to conduct a comprehensive assessment of risk probability and consequence severity.

9. The equipment configuration optimization method according to claim 1, characterized in that: In step S6, the dynamic adjustment threshold includes the change in task requirements exceeding a preset percentage, failure of key equipment, sharp deterioration of environmental conditions, or a significant deviation of the monitored configuration performance from the expected value; the online optimization process gives priority to adjusting redundant resources or alternative resources in the configuration scheme to minimize the disturbance caused by the adjustment.

10. A system for optimizing equipment configuration according to any one of claims 1 to 9, characterized in that: The system comprises: A data acquisition unit, configured to execute step S1 of claim 1; A data processing unit, configured to execute step S2 of claim 1; An intelligent modeling unit, configured to execute step S3 of claim 1; A constraint and target definition unit, configured to execute step S4 of claim 1; An optimization and simulation unit, configured to execute step S5 of claim 1; A dynamic monitoring and adjustment unit, configured to execute step S6 of claim 1; The units described above perform data exchange and collaborative work via an internal bus or network.

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