A method for optimizing equipment configuration

Abstract

The present invention belongs to the field of resource optimization configuration and decision support technology, and discloses a method for optimizing equipment configuration, including obtaining multi-source heterogeneous data such as mission requirement information, available equipment resource information, and environmental situation information, constructing a standardized configuration basic data set, constructing a mission requirement dynamic prediction model, an equipment capability assessment model, and an environmental impact analysis model, defining multi-dimensional configuration constraints and optimization objective functions; performing iterative optimization of equipment configuration schemes, generating an initial optimized configuration scheme, triggering a dynamic reconfiguration mechanism based on real-time feedback information and environmental changes during mission execution, and adaptively adjusting and online optimizing the optimized configuration scheme. The present invention realizes the intelligent, dynamic, and refined configuration of equipment, significantly improves configuration efficiency, resource utilization, and mission success rate, and effectively addresses configuration challenges in complex dynamic environments.

Description

Technical Field

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

[0002] The rational allocation of equipment and materials is crucial for ensuring the smooth execution of various missions, particularly complex ones like emergency response, military operations, and large-scale engineering projects. The timeliness, accuracy, and cost-effectiveness of such allocations directly impact mission efficiency, cost control, and ultimately success or failure. Traditional equipment and materials allocation methods often rely on static plans, standardized configuration checklists, and the judgment of decision-makers.

[0003] However, with the increasing complexity of mission environments and the increasing dynamism of mission requirements, traditional configuration methods have exposed many drawbacks:

[0004] 1) Insufficient information processing capabilities: Traditional methods struggle to effectively process massive, heterogeneous, and dynamically changing data from multiple aspects, including mission requirements, resource status, and environmental conditions. Incomplete, inaccurate, or delayed information often leads to a disconnect between configuration plans and actual conditions.

[0005] 2) Static and Lagging: Configuration models based on pre-planned plans or fixed checklists lack flexibility and are unable to adapt to changing requirements, unexpected resource depletion, or sudden changes in environmental conditions during the mission. Configuration adjustments often lag behind actual needs, hindering mission progress.

[0006] 3) Experience dependence and subjectivity: Over-reliance on the decision maker’s personal experience and intuition makes the configuration decision-making process lack scientific basis and quantitative standards, prone to subjective bias, difficult to ensure the stability and optimality of the configuration results, and difficult to pass on and replicate experience.

[0007] 4) Lack of optimization capabilities: Traditional methods usually adopt a single objective or simple multi-objective trade-off, which makes it difficult to systematically optimize among multiple conflicting objectives such as cost, time, efficiency, and risk. It is impossible to obtain a globally optimal or near-optimal configuration solution, resulting in waste of resources or low task efficiency.

[0008] 5) Lack of dynamic feedback and closed-loop control: Traditional configuration processes are often one-time decisions, lacking real-time monitoring of the configuration plan execution process and a dynamic adjustment mechanism based on feedback. This makes it impossible to form closed-loop control and difficult to deal with uncertainties during the execution process.

[0009] Especially in scenarios such as emergency rescue and modern military operations where time windows are tight, environmental uncertainty is high, and resource constraints are strict, the above problems are particularly prominent and may lead to serious consequences such as rescue delays, inadequate support, cost overruns, and even mission failure.

[0010] Therefore, there is an urgent need for a new equipment configuration optimization method that can integrate multi-source information, has intelligent analysis and optimization capabilities, can 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

[0011] In order to overcome the problems of staticness, subjectivity, insufficient information processing capability, lack of optimization capability and lack of dynamic adaptability in the equipment configuration methods in the prior art, the present invention aims to provide an equipment configuration optimization method and system, aiming to realize the intelligent, dynamic, refined and optimized equipment configuration.

[0012] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for optimizing equipment configuration, comprising the following steps:

[0013] Step S1: Acquire multi-source heterogeneous configuration data.

[0014] Comprehensively collect all types of information related to equipment and material configuration, including clear mission requirements (such as mission type, objectives, location, time requirements, scale, and expected level of effectiveness), detailed information on available equipment and material resources (covering equipment model specifications, technical parameters, current status, remaining lifespan, location distribution, available quantity, as well as the category, inventory, expiration date, storage requirements, performance indicators, and even the qualifications, number, and status of operators), and real-time environmental situation information (such as the mission area's geography, road traffic conditions, meteorological and hydrological conditions, electromagnetic environment, social support conditions, and possible natural disaster risks, man-made threats, or interference). Ensure the comprehensiveness, real-time nature, and multi-dimensionality of the data.

[0015] Step S2: Perform data fusion processing and spatiotemporal alignment calibration.

[0016] Preprocessing of collected multi-source, heterogeneous data involves data cleaning (removing noise and outliers), data standardization (unifying formats and dimensions), filling in missing values ​​(using interpolation, regression, or machine learning methods), data fusion (integrating data from different sensors or information sources, such as using techniques like Kalman filtering and Bayesian inference), and spatiotemporal alignment (aligning data from different sources in time and space based on timestamps and geographic coordinates). Ultimately, a standardized, structured, and spatially consistent configuration base dataset is constructed, providing a high-quality data foundation for subsequent modeling and analysis.

[0017] Step S3: Build a multi-dimensional intelligent analysis model.

[0018] Based on the basic configuration data set, a series of interrelated intelligent analysis models are constructed using machine learning, deep learning, mechanism modeling, simulation and other technical means to deeply understand the configuration issues. These models should at least include:

[0019] (1) Dynamic prediction model for mission requirements: Analyze historical mission data, current situation information, and the inherent correlations between missions to predict the demand, demand type, and probability distribution of various equipment and materials in different regions over a period of time. This can be achieved by combining time series models (such as ARIMA, Prophet, and LSTM) with spatial analysis methods.

[0020] (2) Equipment Capability Assessment Model: This model evaluates the comprehensive operational performance, sustained operational capability, and failure probability of a single piece of equipment or a combination of equipment in a specific mission scenario based on the equipment's technical parameters, current status, reliability data, operator skills, and environmental factors. This model can be constructed using performance network models, Agent-Based Modeling and Simulation (ABMS), or data-based regression models (e.g., SVR, Random Forest).

[0021] (3) Environmental Impact Analysis Model: Quantitatively analyze the specific impacts of geographical, meteorological, electromagnetic, and risk-related environmental factors on equipment performance (e.g., communication distance, detection range), availability (e.g., deployment restrictions), transportation efficiency (e.g., travel time), and mission execution itself (e.g., safety risks). This can be achieved by combining GIS spatial analysis, physical models (e.g., propagation models, hydrological models), and expert knowledge bases.

[0022] Step S4: Define the configuration constraint set and the multi-objective optimization function.

[0023] Based on mission requirements, resource constraints, and operational procedures, the constraints that must be adhered to during equipment deployment are clearly defined, and a multi-objective optimization function is constructed to evaluate the merits of deployment options. Constraints may include: mission completion deadlines, total cost budget caps, available quantity limits for various equipment types, vehicle weight / volume limits, equipment compatibility / interoperability requirements, deployment site environmental adaptability requirements, safety regulations, and risk tolerance. A multi-objective optimization function aims to simultaneously pursue multiple, potentially conflicting objectives, such as minimizing total deployment costs (procurement, transportation, maintenance, manpower, etc.), minimizing mission completion time or response time, maximizing overall mission effectiveness (such as coverage, processing capacity, and success rate), and minimizing risks during mission execution (such as casualties, equipment loss, and environmental damage). Appropriate methods (such as weighted summation, goal programming, and Pareto frontier thinking) are required to formalize these objectives.

[0024] Step S5: Perform iterative optimization and generation of configuration solutions.

[0025] Utilizing the intelligent analysis model constructed in step S3 and the constraints and objectives defined in step S4, advanced multi-objective intelligent optimization algorithms (such as NSGA-II / III, MOPSO, and multi-objective reinforcement learning) are employed to search the space of configuration options. During the optimization process, candidate configuration options are evaluated and screened through a combination of multi-scenario simulations (simulating environmental changes, resource depletion, and emergencies under varying probabilities) and configuration risk assessments (evaluating the potential risks of different options under various scenarios). Through iterative calculations, a set or single initial optimization configuration is ultimately found that satisfies all constraints and achieves the optimal (or Pareto optimal) state of the multi-objective optimization function. This configuration should specify the precise model, quantity, source, deployment location, deployment timeline, transportation method, and necessary staffing of the required equipment.

[0026] Step S6: Implement dynamic monitoring and adaptive adjustment of the configuration scheme.

[0027] After the optimized configuration plan is adopted and implemented, a real-time monitoring mechanism is established to continuously obtain the latest information on the actual progress of the mission, the real-time status of configured equipment (location, fuel consumption, fault information, etc.), dynamic changes in available resources (additions, losses, redeployments, etc.), and environmental conditions. Trigger conditions for dynamic adjustments are set (such as significant changes in key requirements, failures of core equipment, sudden increases in environmental risks, or significant deviations between actual performance and expectations). Once these trigger conditions are met, the system automatically initiates the reconfiguration and online optimization process, potentially requiring a rapid return to step S5 (or a simplified version thereof) for recalculation to generate the adjusted optimized configuration plan and issue execution instructions. This creates an optimization process with dynamic feedback and closed-loop control, ensuring that the configuration plan consistently adapts to changing realities until the mission is complete.

[0028] The present invention also provides an equipment 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 target definition unit, an optimization and simulation unit, and a dynamic monitoring and adjustment unit.

[0029] These units can be software modules, hardware modules or a combination of software and hardware modules. They are connected and exchange data through internal data buses, networks or other communication methods, and work together to complete the intelligent and dynamic configuration optimization tasks of equipment.

[0030] The technical effects and advantages of the equipment configuration optimization method of the present invention are as follows:

[0031] First, it improves the intelligence level of configuration decisions. By introducing intelligent technologies such as machine learning and deep learning for demand forecasting, capacity assessment, and environmental analysis, it overcomes the traditional methods' reliance on experience and subjectivity, and improves the scientific nature of decision-making.

[0032] Second, the dynamic adaptability of the configuration plan has been enhanced, and a real-time monitoring and dynamic adjustment mechanism has been established, enabling the configuration plan to quickly respond and adaptively optimize according to task progress, resource changes, and environmental changes, thereby improving the ability to cope with uncertainty.

[0033] Third, it achieves refinement and optimization of configuration optimization. By adopting a multi-objective optimization algorithm, it can make systematic trade-offs among multiple dimensions (cost, time, efficiency, risk, etc.) to find the globally optimal or near-optimal configuration solution, thereby improving resource utilization efficiency and task execution results.

[0034] Fourth, it improves the efficiency and accuracy of information processing, and can effectively integrate and process massive dynamic data from multiple sources and heterogeneity, providing comprehensive, accurate and timely information support for configuration decisions.

[0035] 5. It reduces the labor and time costs of the configuration process. The automated and intelligent process reduces the need for manual intervention, shortens the decision-making cycle, and improves the overall efficiency of the configuration work. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0037] Figure 1 A flowchart of an equipment configuration optimization method provided in an embodiment of the present invention.

[0038] Figure 2 A functional block diagram of an equipment configuration optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] Example 1

[0041] This embodiment provides an equipment configuration optimization method for large-scale disaster emergency rescue scenarios, such as Figure 1 As shown, this method aims to deploy rescue forces and supplies quickly, efficiently and scientifically.

[0042] Step S1: Obtain multi-source heterogeneous configuration data

[0043] In this embodiment, after a disaster occurs, the system first obtains information through multiple channels:

[0044] Mission requirements: Receive rescue orders from higher-level command, specifying the disaster type (e.g., earthquake, flood, fire), the affected area (geographic coordinates, impacted region), primary rescue objectives (search and rescue, medical care, resettlement, infrastructure repair), the expected rescue time window, and preliminary estimates of the affected population and losses. Furthermore, obtain more detailed mission requirements through drone aerial photography, satellite remote sensing, and on-site advance team reports.

[0045] Available equipment and resource information: Connect to national, provincial, and municipal emergency resource databases to obtain the number, location, professional capabilities, and personnel composition of available rescue teams (firefighting, medical, engineering, volunteer, etc.) in the surrounding area; obtain the model, quantity, current technical status (intact / needing repair / in use), storage location, and performance parameters of various types of rescue equipment (such as large excavators, life detectors, assault boats, water pumps, emergency communication vehicles, and drones); and obtain the type, inventory level, specifications, shelf life, and storage location of rescue supplies (such as tents, food, medicine, drinking water, and fuel). Simultaneously, the dynamic availability of resources (such as whether they have been deployed) is updated in real time.

[0046] Environmental situation information: Access the meteorological forecast system to obtain weather conditions (rainfall, wind speed, temperature) for the next few days; call the GIS system to obtain detailed topography and landforms of the disaster area, road networks (especially those that may be interrupted), bridge and tunnel conditions, and river and lake water level information; obtain social support conditions around the disaster area (such as available airports, stations, docks, hospitals, schools, and shelters); and assess the risks of secondary disasters (such as aftershocks, landslides, barrier lakes, and epidemics).

[0047] Step S2: Data fusion processing and spatiotemporal alignment calibration

[0048] Processing of collected information:

[0049] Data cleaning and standardization: Identify and address errors, conflicts, and outliers in the data. Standardize the formats, units, and encoding standards for various data types. For example, convert location information from different sources to WGS84 coordinates.

[0050] Missing value filling: For missing key information, use prediction models based on historical data or correlation to fill in the gaps, such as using regression models to predict initial material consumption.

[0051] Data fusion: Integrating information from different sources to improve accuracy and completeness. For example, fusing satellite and drone imagery can provide a more accurate assessment of road damage.

[0052] Spatiotemporal alignment: All data are accurately time-stamped and geo-tagged to ensure data comparability in time and space.

[0053] After processing, a standardized and structured emergency configuration basic database including task requirements, resource status, and environmental conditions is formed.

[0054] Step S3: Build a multi-dimensional intelligent analysis model

[0055] Based on the data in the database, train and build the following models:

[0056] Dynamic prediction model for mission requirements: Utilizes LSTM (Long Short-Term Memory) to analyze the evolution of rescue demand during similar historical disasters. Combined with current disaster assessments and population distribution data, it predicts the demand for food, medicine, tents, and search and rescue forces in different regions over the next 24 and 48 hours.

[0057] Equipment Capability Assessment Model: Establish a capability-based assessment framework. For example, evaluating a firefighting team's firefighting capabilities requires considering personnel numbers, skill levels, water tank / foam truck capacity, high-pressure water cannon range, distance from the current location to the fire, road accessibility, and expected fire environment (wind direction, combustible material type). Use simulation or expert systems to assess the collaborative effectiveness of different equipment combinations.

[0058] Environmental Impact Analysis Model: Build a road capacity model to predict vehicle travel times and accessibility based on topography, road grade, damage reports, and weather (rain and snow). Build a meteorological impact model to assess the impact of rainfall on flood levels, landslide risks, and aircraft (drones, helicopters) operating windows.

[0059] Step S4: Define the configuration constraint set and multi-objective optimization function

[0060] Constraints:

[0061] Time constraints: The first rescue force must arrive at the designated area within 2 hours; the core search and rescue mission must be completed within the 72-hour golden rescue period.

[0062] Cost constraint: The total budget does not exceed X million yuan.

[0063] Resource constraints: No more than Y large excavators are available; the inventory of emergency medicines is Z units.

[0064] Transportation constraints: The maximum payload of a single helicopter is W tons; road disruptions mean that some areas can only be transported by air or water.

[0065] Compatibility constraint: A certain type of communication equipment must be compatible with the command vehicle.

[0066] Safety constraints: Rescuers must be equipped with specific protective equipment when entering dangerous areas; night operations must meet minimum lighting conditions.

[0067] Multi-objective optimization function:

[0068] Goal 1: Minimize total response time (the time from receiving the command to the first forces reaching the core area).

[0069] Goal 2: Maximize the number of people rescued (or the disaster-affected area covered).

[0070] Goal 3: Minimize total configuration costs (including transportation, material consumption, personnel costs, etc.).

[0071] Objective 4: Minimize the risks posed by secondary disasters or the rescue operation itself (such as the risk of casualties among rescue workers).

[0072] Step S5: Iterative optimization and generation of configuration solutions

[0073] Optimization algorithm: NSGA-II algorithm is used. The chromosome encoding of the algorithm includes the selection of rescue teams, equipment, supplies, as well as their quantity, departure time, transportation route and deployment location.

[0074] Simulation: For each generated candidate configuration plan, Monte Carlo simulation is performed to simulate 100 possible scenarios (such as further road interruption, sudden weather changes, and finding a large number of trapped people). The average response time, rescue effectiveness, cost, and risk of the plan under these scenarios are evaluated.

[0075] Risk Assessment: Combining simulation results with environmental impact models, we conduct FMEA analysis on each scenario to identify potential failure modes (such as transportation delays, equipment failures, and material shortages), assess their probability of occurrence and severity of consequences, and calculate a comprehensive risk score.

[0076] Solution Generation: The NSGA-II algorithm iterates and evolves based on simulation evaluation and risk scoring, ultimately generating a set of Pareto-optimal solutions. Commanders can select the most appropriate solution from this set of solutions as the initial optimization plan based on their current priorities (prioritizing time, effectiveness, or cost). This solution might include: two detachments of the City A Fire Brigade (including personnel and vehicles), one medical rescue team from Province B, five Type C life detectors, ten Type D assault boats, E tons of food, and F boxes of medicine, departing from the base at times T1 and T2, respectively, and arriving at points Z1 and Z2 in the disaster area via routes P1 and P2.

[0077] Step S6: Implement dynamic monitoring and adaptive adjustment of configuration schemes

[0078] Real-time monitoring: Track the location of rescue teams and vehicles through the Beidou / GPS positioning system; monitor equipment status (fuel level, battery level, working hours) through IoT sensors; obtain mission progress and newly discovered needs through on-site reports and real-time drone video; and receive the latest weather and geological disaster warnings.

[0079] Triggering conditions: A new, severe landslide has been detected on a key transport route, with an expected disruption of more than six hours. A medical team reports that medicines are running low, exceeding their expected rate of consumption. The Meteorological Observatory issues a heavy rainfall warning, potentially triggering further flooding. A large, trapped crowd has been detected, requiring immediate deployment of search and rescue resources.

[0080] Reconfiguration and Online Optimization: When trigger conditions are met, the system automatically initiates a rapid reoptimization process. For example, if a road is disrupted, the system replans the transport routes of the affected teams (possibly using detours or air transport), assesses changes in time and cost, and may deploy additional forces from backup resources. If additional search and rescue forces are needed, the system reruns the optimization algorithm based on currently available resources, location, and capabilities, recommending reinforcement options. The updated plan is then distributed to the relevant units for implementation in real time. This process continues until the primary objectives of the rescue mission are achieved.

[0081] Example 2

[0082] This embodiment provides an equipment configuration optimization system, such as Figure 2 As shown, the system is intended to implement the method described in Example 1. The system can be deployed on an emergency command center server or a cloud computing platform. The system includes the following functional units / modules:

[0083] Data acquisition unit (201): responsible for connecting with various information sources through interfaces, such as emergency resource database interface, meteorological information interface, GIS platform interface, on-site information reporting system interface, sensor network interface (such as Beidou / GPS, equipment status sensor), and executing the data acquisition task of step S1.

[0084] Data processing unit (202): Built-in data cleaning, standardization, and fusion algorithm library (such as rule-based cleaning, unit conversion, Kalman filtering, and spatiotemporal registration algorithm). Executes the data preprocessing and dataset construction tasks of step S2.

[0085] Intelligent Modeling Unit (203): Contains multiple pre-trained or online learning model libraries, such as LSTM demand forecasting model, ABMS-based capability assessment simulation engine, and GIS and physical rule-based environmental impact analysis module. Executes the model building and calling tasks of step S3.

[0086] Constraint and Goal Definition Unit (204): Provides a user interface that allows the commander to input or select constraints (e.g., time, budget) and optimization goals and their weights based on specific tasks. These inputs are converted into formal mathematical expressions. Execute step S4.

[0087] Optimization and Simulation Unit (205): The core computing unit, with a built-in multi-objective optimization algorithm engine (such as NSGA-II implementation) and a simulation engine (such as a discrete event simulator). It receives model output, constraints, and objectives, performs the iterative optimization, simulation deduction, and risk assessment in step S5, and generates a configuration solution.

[0088] The dynamic monitoring and adjustment unit (206) receives the latest information from 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, the optimization and simulation unit is called to perform reconfiguration calculations and the updated solution is issued through the output interface (such as the task management system or the scheduling instruction system). Step S6 is executed.

[0089] Human-computer interaction and visualization unit (not in Figure 2 (Explicitly marked in the document, but implicitly present): Provides a graphical interface to display real-time data, model analysis results, optimization plans, simulation processes, risk assessment reports, mission execution status, etc., to facilitate commanders to understand the situation, make decisions, and select the final plan from the Pareto solution set.

[0090] These units are connected through an internal high-speed data bus or network, share data storage (such as configuration basic database, model library, solution library), and work together to complete the entire equipment configuration optimization process.

[0091] By integrating data acquisition, intelligent modeling, multi-objective optimization, and dynamic adjustment, this invention significantly improves the scientific nature, timeliness, adaptability, and optimization of equipment configuration. Compared to traditional methods, this invention can better handle configuration issues in complex and dynamic environments, effectively improving mission success rates and resource utilization efficiency. It is particularly suitable for critical areas such as emergency rescue and military operations support.

[0092] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection 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 resource information, and environmental situation information as multi-source heterogeneous configuration data; the mission requirement information includes mission objectives, execution time window, geographical scope, and expected effect level; the available equipment and resource information includes equipment model, quantity, technical status, performance parameters, location distribution, as well as equipment type, 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, wherein the multi-dimensional intelligent analysis model includes at least: a task demand dynamic prediction model for predicting the demand for equipment and types at different time nodes and spatial regions; an equipment capability assessment model for evaluating the comprehensive operational efficiency and reliability of different equipment combinations in a specific environment; and an environmental impact analysis model for analyzing 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 includes at least time constraints, cost budget constraints, resource quantity constraints, transportation capacity constraints, equipment compatibility constraints, and security risk threshold constraints; the multi-objective optimization function aims to simultaneously optimize the total cost of the configuration, mission completion time, overall combat effectiveness or support capability, and mission execution risk; Step S5: Based on the multi-dimensional intelligent analysis model and the set of constraints, a multi-objective intelligent optimization algorithm is used to iteratively optimize the equipment configuration plan. Combined with the results of multi-scenario simulation and configuration risk assessment, an initial optimized configuration plan that meets the constraints and has the best overall performance is screened and generated; the optimized configuration plan specifies the model, quantity, configuration location, and timing 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 the combination of time series analysis and spatial interpolation; the equipment capability assessment model adopts agent-based modeling and simulation (ABMS), performance network model or machine learning regression model based on historical data; the environmental impact analysis model adopts spatial analysis based on geographic information system (GIS), mechanism 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 summation method, an ε-constraint method, or a goal programming method; the quantification of the mission execution risk involves a comprehensive evaluation model of factors including 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, 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 the configuration plan 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 mission 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 plan to minimize the disturbance caused by the adjustment.

10. An equipment configuration optimization system, 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 interact with each other and work in coordination via an internal bus or network.

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