A Method and System for Optimizing Joint Logistics Equipment Testing Based on Digital Twin Modeling

Abstract

This invention provides a method and system for testing and optimizing joint logistics equipment based on digital twin modeling, belonging to the field of digital twin technology. The method includes: collecting multi-source heterogeneous data of the target joint logistics equipment; performing simulation modeling to generate a target twin model; conducting usage correlation analysis to obtain multiple sets of correlated twin models; obtaining a set of operating environment conditions; conducting simulated operation and collecting operating performance datasets for performance deviation analysis to locate the first set of performance bottlenecks; conducting collaborative operation tests on the target twin model and multiple sets of correlated twin models, and identifying performance deviations based on the operating process data to locate the second set of performance bottlenecks; and optimizing the equipment design of the target joint logistics equipment based on the bottleneck integration analysis results. This invention solves the technical problems of existing technologies that rely on limited experimental data, and where test data is often limited to specific environments and lacks comprehensive data coverage across multiple dimensions and scenarios, resulting in poor testing efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and more specifically to a method and system for optimizing joint logistics equipment testing based on digital twin modeling. Background Technology

[0002] Joint logistics equipment refers to logistical support equipment used to support and ensure missions such as natural disasters, accidents, and medical emergencies. This type of equipment is diverse and plays a crucial role in ensuring the smooth execution of these missions. However, traditional methods for testing and optimizing joint logistics equipment largely rely on limited experimental data, which is often confined to specific environments or conditions, lacking comprehensive data coverage across multiple dimensions and scenarios. This prevents the effective integration of data from different equipment scenarios, thus limiting the representativeness and accuracy of test results. Furthermore, since equipment faces various complex extreme environments in actual operation, such as high temperature, high humidity, and low pressure, traditional testing methods struggle to fully simulate these conditions in real-world environments. Even when testing under extreme conditions is possible, the cost is extremely high, and the methods lack repeatability and flexibility, resulting in low testing efficiency. Summary of the Invention

[0003] This application provides a method and system for optimizing joint logistics equipment testing based on digital twin modeling. It aims to solve the technical problem that most existing methods for optimizing joint logistics equipment testing rely on limited experimental data, and the test data is often limited to specific environments and lacks comprehensive data coverage in multiple dimensions and scenarios, resulting in poor testing efficiency and accuracy.

[0004] The first aspect disclosed in this application provides a method for testing and optimizing joint logistics equipment based on digital twin modeling. The method includes: collecting multi-source heterogeneous data of the target joint logistics equipment; performing simulation modeling of the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model; performing usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlated twin models corresponding to various usage scenarios; interactively obtaining a set of operating environment conditions for the target joint logistics equipment; simulating the target twin model in a virtual environment using the set of operating environment conditions as constraints, and performing performance deviation analysis on the operating performance dataset collected during the simulation to locate a first set of performance bottlenecks in the target twin model; performing collaborative operation testing on the target twin model and multiple sets of correlated twin models using the set of operating environment conditions as constraints, and identifying performance deviations based on the operation process data to locate a second set of performance bottlenecks in the target twin model; integrating and analyzing the first and second sets of performance bottlenecks, and optimizing the equipment design of the target joint logistics equipment based on the bottleneck integration analysis results.

[0005] The second aspect of this application discloses a joint logistics equipment testing optimization system based on digital twin modeling. The system is used in the aforementioned joint logistics equipment testing optimization method based on digital twin modeling. The system includes: a simulation modeling module, used to collect target multi-source heterogeneous data of the target joint logistics equipment and perform simulation modeling on the target joint logistics equipment based on the target multi-source heterogeneous data to generate a target twin model; a usage correlation analysis module, used to perform usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlated twin models corresponding to various usage scenarios; an operating environment acquisition module, used to interactively obtain the operating environment condition set of the target joint logistics equipment; and a performance deviation analysis module, whereby the performance deviation... The analysis module is used to simulate the target twin model in a virtual environment under the constraints of the set of operating environment conditions, and to perform performance deviation analysis on the operating performance dataset collected during the simulation to locate the first set of performance bottlenecks in the target twin model; the collaborative operation testing module is used to perform collaborative operation testing on the target twin model and multiple sets of associated twin models under the constraints of the set of operating environment conditions, and to identify performance deviations based on the operating process data to locate the second set of performance bottlenecks in the target twin model; the equipment design optimization module is used to integrate the analysis of the first set of performance bottlenecks and the second set of performance bottlenecks, and to optimize the equipment design of the target joint logistics equipment based on the results of the bottleneck integration analysis.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By collecting multi-source heterogeneous data on the target joint logistics equipment, including physical characteristic data, operational behavior data, and environmental condition data, and generating accurate twin models based on this data, the physical characteristics and dynamic behavior of the equipment can be simulated. This high-precision modeling provides a foundation for subsequent simulation, testing, and optimization. Through correlation analysis, multiple sets of correlated twin models corresponding to actual mission scenarios are constructed, enabling the equipment to adapt to diverse and complex application scenarios, such as natural disaster relief and traffic accident handling. This allows for testing and optimization of equipment performance under various usage scenarios, giving it broader application adaptability. Furthermore, by acquiring the set of operating environment conditions for the target joint logistics equipment, including standard operating environments and multiple extreme operating environments, and then using this set of operating environment conditions as constraints, the twin model is further refined in a virtual environment. Simulated operation allows for the collection of detailed operational performance data under both standard and extreme operating environments. Based on this data, performance deviation analysis can be performed, accurately pinpointing equipment performance bottlenecks and providing direction for subsequent optimization. Similarly, using a set of operating environment conditions as constraints, collaborative operation testing of the target twin model and multiple related twin models can not only optimize the performance of individual equipment but also identify performance bottlenecks in collaborative operation, improving the overall collaborative efficiency of the equipment. By integrating the first and second sets of performance bottlenecks, a comprehensive set of performance bottlenecks is obtained, resolving the main bottlenecks in independent and collaborative operation of the equipment. This promotes the optimization and upgrading of the overall equipment design, ultimately resulting in an optimized design scheme that exhibits high stability and reliability under various operating environments.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 A schematic diagram of the testing optimization method for joint logistics equipment based on digital twin modeling provided in this application embodiment;

[0010] Figure 2 A schematic diagram of the structure of a joint logistics equipment testing optimization system based on digital twin modeling, provided for an embodiment of this application.

[0011] Figure labeling: Simulation modeling module 10, correlation analysis module 20, runtime environment acquisition module 30, performance deviation analysis module 40, collaborative operation testing module 50, equipment design optimization module 60. Detailed Implementation

[0012] This application provides a method and system for optimizing joint logistics equipment testing based on digital twin modeling. This solves the technical problem that most existing methods for optimizing joint logistics equipment testing rely on limited experimental data, and the test data is often limited to specific environments and lacks comprehensive data coverage across multiple dimensions and scenarios, resulting in poor testing efficiency and accuracy.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] Example 1, as Figure 1 As shown in the embodiments of this application, a method for optimizing joint logistics equipment testing based on digital twin modeling is provided, the method comprising:

[0015] Collect multi-source heterogeneous data of the target joint logistics equipment, and perform simulation modeling of the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model.

[0016] During the testing and optimization of joint logistics equipment, various data types are involved, including physical parameters, operational behaviors, and environmental conditions. Corresponding sensors are deployed on the target joint logistics equipment to collect multi-source heterogeneous data of the equipment in real time, including physical characteristic data, such as the equipment's temperature, pressure, vibration, speed, and displacement; operational behavior data, which records the equipment's operational behaviors, such as operating steps, usage modes, and control logic; and environmental condition data, which records the external environmental conditions during the equipment's operation, such as temperature, humidity, and pressure.

[0017] Based on the design information of the target joint logistics equipment, the associated component models are called. Since the joint logistics equipment is obtained through standardized production, the component models can be called and assembled to generate the model. Based on the calling results, the target simulation model is assembled and constructed. The collected multi-source heterogeneous data of the target and the target simulation model are fused to obtain the target twin model. This model can accurately simulate the operating status and behavior of the equipment in a virtual environment, supporting subsequent testing and optimization.

[0018] Furthermore, the method further includes collecting multi-source heterogeneous data on the target joint logistics equipment, and performing simulation modeling on the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model.

[0019] The target joint logistics equipment is configured with real-time monitoring facilities to obtain a multi-dimensional monitoring array, which includes a physical characteristic monitoring array, an operational behavior monitoring array, and an environmental condition monitoring array. A pre-configured operation monitoring window and operation condition timing sequence are used. During the operation of the target joint logistics equipment with the operation monitoring window as the time constraint and the operation condition timing sequence as the control constraint, the multi-dimensional monitoring array is used to collect real-time data from the target joint logistics equipment to obtain multi-source heterogeneous data of the target. Based on the design information of the target joint logistics equipment, related component models are called, and a target simulation model is assembled based on the calling results. The multi-source heterogeneous data of the target and the target simulation model are fused to obtain a target twin model.

[0020] Real-time monitoring facility configuration refers to the real-time monitoring of target joint logistics equipment through the deployment of sensors, monitoring equipment, and data acquisition systems. This ensures the acquisition of comprehensive real-time data during equipment operation, including the equipment's physical state, operational behavior, and environmental conditions. Based on the equipment's structure and monitoring requirements, sensors and monitoring equipment are rationally deployed to obtain a multi-dimensional monitoring array. This array includes physical characteristic monitoring, operational behavior monitoring, and environmental condition monitoring. The physical characteristic monitoring array monitors the physical state and performance indicators of the joint logistics equipment, and its monitoring equipment includes temperature sensors, pressure sensors, accelerometers, vibration monitors, and power meters. The operational behavior monitoring array monitors the equipment's operation process and the behavior of operators, collecting data on equipment usage, control inputs, and action feedback. Its monitoring equipment includes operation log recorders, motion sensors (such as position sensors and rotary encoders), and status monitoring instruments. The environmental condition monitoring array monitors the external conditions of the equipment's environment, which directly affect its performance. Its monitoring equipment includes temperature and humidity sensors, barometers, anemometers, GPS, cameras, and lidar.

[0021] The operation monitoring window refers to the time interval within a set period for centralized monitoring and data collection of target joint logistics equipment. The operation monitoring window can be set for a specific operational phase of the equipment or for a time period under specific operational conditions (such as high temperature and high humidity environments). The operation monitoring window plays a role in time control, ensuring that monitoring and data collection are completed within a reasonable time range, and that the collected data can cover the critical periods of equipment operation.

[0022] Operating condition timing refers to the scheduling and control rules for equipment operation under different operating conditions. This ensures sufficient data can be collected under various environmental and operational conditions. The timing includes multiple operating conditions and the duration of each condition. For example, the equipment may operate for 5 hours in a high-temperature environment or 3 hours in a high-humidity environment, so that the monitoring system can capture the equipment's performance in different environments. The purpose of pre-configuration is to ensure that the multi-dimensional monitoring array can collect sufficient data under different operating conditions by rationally planning the monitoring window and operating condition timing to meet the requirements of twin modeling.

[0023] Within the monitoring window, the target joint operations equipment is operated with the timing of the operating conditions as the control constraint. During this process, various types of data of the equipment are collected in real time through a preset multi-dimensional monitoring array to obtain physical characteristic data, operational behavior data, and environmental condition data. The collected data are then integrated to obtain multi-source heterogeneous data of the target.

[0024] The design information of the target joint logistics equipment refers to a detailed description of the equipment's structure, function, and component composition. This information comes from the equipment's CAD drawings, engineering documents, or specifications provided by the manufacturer. Based on the design information, key components of the equipment are identified. For example, key components of lifting equipment include the boom, hydraulic cylinders, power system, and control panel. Each component of the equipment has a corresponding digital model, which has been pre-built and stored in a model library. Since joint logistics equipment is produced in a standardized manner, the structure, function, and components of each piece of equipment are uniform during the production process. Therefore, the corresponding standardized component models can be called based on the equipment's design information, avoiding the need for modeling each component from scratch. Once the component models are called, these components are assembled into a complete simulation model. For example, according to the design drawings, the geometric models of each component are pieced together to obtain the target simulation model.

[0025] Based on the target's multi-source heterogeneous data, the parameters of the target simulation model are adjusted so that the model can reflect the equipment's operating status under specific conditions, ultimately forming a target twin model. This model can not only reflect the equipment's static structure but also simulate the equipment's dynamic behavior under various operating conditions.

[0026] A usage correlation analysis was performed on the target joint logistics equipment to obtain multiple sets of correlation twin models corresponding to various usage scenarios.

[0027] A correlated twin model is a collaborative digital twin model of multiple pieces of equipment, capable of simulating their joint operation in specific scenarios. Specifically, it first collects service records of the target joint logistics equipment, including dispatch records of different equipment in past rescue and accident handling operations. Based on different tasks, the usage scenarios of the joint logistics equipment are categorized, such as natural disaster scenarios, accident handling scenarios, and medical emergencies. For example, natural disaster scenarios include earthquakes and floods, and corresponding usage scenarios include building collapses and road blockages. In this case, joint logistics equipment includes heavy lifting equipment, support equipment, and drones. The dispatch records of joint logistics equipment in different scenarios are aggregated. For example, in a building collapse rescue scenario, heavy lifting equipment and support equipment may have been used multiple times. By aggregating these historical dispatch records, the collaborative role of these equipment in specific tasks can be determined. Based on the aggregated equipment models, the corresponding digital twin models are invoked. Through a simulation platform, the digital twin models of multiple pieces of equipment in the same scenario are assembled together to form a complete correlated twin model capable of simulating that scenario.

[0028] Furthermore, the method further includes performing usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlation twin models corresponding to various usage scenarios, and the method also includes:

[0029] The system interacts with the service records of the target joint logistics equipment to obtain historical application data; it then performs time-series segmentation on the historical application data to obtain multiple joint logistics equipment scheduling records, each of which includes application scenario information and a combination of joint logistics equipment; it aggregates the multiple joint logistics equipment scheduling records based on application scenario types to obtain multiple sets of joint logistics equipment scheduling records corresponding to the various usage scenarios; it presets an association frequency threshold, and after aggregating the multiple sets of joint logistics equipment scheduling records, it uses the association frequency threshold to filter the aggregation results to obtain multiple sets of associated joint logistics equipment; and it calls twin models of the multiple sets of associated joint logistics equipment based on equipment models to obtain the multiple sets of associated twin models.

[0030] Service records are usage records of target joint logistics equipment in different mission scenarios. These records include the equipment's mission execution status, usage frequency, fault records, maintenance logs, etc., and are usually stored in the database of the equipment management system. By interacting with the equipment management system, the service records of the target joint logistics equipment can be obtained, and historical application data can be extracted from them.

[0031] Time-series splitting involves dividing the acquired historical application data into segments according to time sequence, dividing each segment into individual task execution cycles. By segmenting the data according to the task timeline, a joint logistics equipment scheduling record is generated for each task execution period. Each joint logistics equipment scheduling record includes application scenario information and joint logistics equipment combinations. The application scenario information refers to the specific task scenario in which the equipment is used, such as natural disaster relief, accident scene handling, and emergency medical assistance. The joint logistics equipment combinations refer to the equipment combinations used simultaneously in each application scenario, including the type, function, and configuration of each type of equipment.

[0032] Based on the scenario type in the dispatch records, such as earthquake rescue and traffic accident handling, the equipment combinations used in different tasks are aggregated. For example, in earthquake rescue, if drones and lifting equipment are dispatched at the same time, these equipment combinations can be aggregated together. Through aggregation, multiple sets of scenario-based joint logistics equipment dispatch records are obtained. Each set of records corresponds to an application scenario and records the equipment combinations used.

[0033] The association frequency threshold is a preset threshold for the frequency of equipment appearing simultaneously in a specific scenario. By setting a threshold, equipment combinations that frequently work together in a specific scenario can be selected. For example, setting the association frequency threshold to 60% means that only equipment combinations that appear more than 60% of the time in a certain scenario will be selected as associated joint support equipment.

[0034] By analyzing historical application data, the frequency of each equipment combination in specific scenarios was statistically analyzed. Based on frequency thresholds, high-frequency associated joint support equipment in specific scenarios was identified, and multiple sets of associated joint support equipment were obtained based on the filtering results. High-frequency association indicates that equipment combinations work together multiple times in the same scenario, indicating that these equipment have high collaborative efficiency and necessity in that scenario; low-frequency association indicates that equipment combinations work together occasionally in the scenario, with low association, and may not be necessary to model and simulate as standard combinations.

[0035] Based on the selected sets of related joint operations equipment, their specific models are matched, and the corresponding twin models are called from the twin model library to obtain the sets of related twin models.

[0036] The set of operating environment conditions for the target joint logistics equipment is obtained interactively.

[0037] Furthermore, the set of operating environment conditions includes a standard operating environment and multiple extreme operating environments.

[0038] Operating environment conditions refer to various external factors that affect the performance of joint logistics equipment in actual operation, including climate conditions, geographical location, physical obstacles, etc., during equipment operation. The performance of the equipment under these environmental conditions, such as work efficiency, performance, and stability, is directly affected by these external conditions, including standard operating environment and multiple extreme operating environments.

[0039] The standard operating environment is the typical operating environment assumed during equipment design. It usually represents the operating conditions under ideal or normal conditions, such as normal temperature, normal humidity, low altitude, and no serious physical obstacles. Equipment tends to perform most stably, with a low failure rate and performance parameters closest to the design standards under these conditions. Extreme operating environments refer to relatively harsh conditions. Equipment may face challenges such as performance degradation and increased failure rate under these conditions. Examples include high temperature, high humidity, low temperature, low pressure (high altitude), and extreme weather (such as storms, rainstorms, and snowstorms). Under extreme conditions, the performance of equipment may deteriorate significantly. For example, the engine may overheat in a high-temperature environment, electronic equipment may malfunction due to a high-humidity environment, and mechanical structures may operate unstablely in a high-altitude, low-pressure environment.

[0040] Using the set of operating environment conditions as constraints, the target twin model is simulated in a virtual environment, and the performance deviation analysis is performed on the operating performance dataset collected during the simulation to locate the first set of performance bottlenecks in the target twin model.

[0041] The set of operating environment conditions, including the standard operating environment and multiple extreme operating environments, is used as the input of the simulation conditions to constrain the simulation process in the virtual environment, so that the simulation can realistically reflect the performance of the equipment in the real environment. During the simulation operation, the operating performance data of the equipment, including key performance parameters, status parameters, environmental response data, etc., are collected in real time, and the collected data are integrated to obtain the operating performance dataset.

[0042] Performance deviation refers to the degree to which the performance indicators of equipment deviate from the design standards during actual operation. By comparing the operating performance data in the simulated environment with the performance parameters under standard operating conditions, we can analyze which performance indicators deviate from the design expectations. For example, heavy lifting equipment can lift 5 tons of objects under standard conditions, but can only lift 3.5 tons under extreme high temperatures, indicating that its performance has decreased significantly under high temperatures. Multiple performance deviations are classified into a set of performance shortcomings. For example, for heavy lifting equipment, components such as the hydraulic system and engine cooling system perform poorly under high-temperature conditions, forming a set of performance shortcomings under high-temperature conditions. The performance shortcomings set is then located in the target twin model.

[0043] Furthermore, using the aforementioned set of operating environment conditions as constraints, the target twin model is simulated and run in a virtual environment. Performance deviation analysis is performed on the performance dataset collected during the simulation to locate the first set of performance bottlenecks in the target twin model. The method further includes:

[0044] A performance evaluation system is preset, and performance analysis and optimization mapping are performed on the performance evaluation system to obtain performance improvement areas. The performance evaluation system includes M performance indicators, and the performance improvement areas include M key performance components mapped to the M performance indicators. The target twin model is simulated using the standard operating environment as the simulation environment constraint, and its operational performance is collected using the performance evaluation system as the constraint to obtain standard operating performance information. The standard operating performance information includes M standard operating parameters. Based on environmental fusion, multiple extreme operating environments are combined and enumerated to obtain K multimodal operating environments. The target twin model is simulated in virtual environments under multiple extreme operating environments and K multimodal operating environments. The performance of the target twin model is collected under the constraints of the performance evaluation system, resulting in W extreme performance information sets. Each of the W extreme performance information sets includes M extreme operating parameters, where W is a positive integer greater than K. The standard performance information and the W extreme performance information sets are stored in a table to form the performance dataset. The performance improvement domain is fitted to the performance dataset to match the first performance bottleneck set, and the first performance bottleneck set is located and identified in the target twin model.

[0045] A pre-defined performance evaluation system is a framework for assessing the operational performance of target joint logistics equipment under different operating conditions. The system covers M performance indicators of the equipment, where M is a positive integer. Each performance indicator corresponds to a key performance component of the equipment. By evaluating these indicators, the overall performance of the equipment can be comprehensively measured.

[0046] Based on the twin model, each performance index is analyzed to determine the equipment's performance under various environmental and operational conditions. For example, the heat dissipation efficiency of the hydraulic system of heavy lifting equipment in high-temperature environments or the power consumption of a drone's battery during long-term operation are analyzed. The analysis results of each performance index are mapped to possible improvement schemes. For example, if the hydraulic system's efficiency decreases at high temperatures, the heat dissipation system can be optimized or more efficient hydraulic oil can be used. Based on the mapping results, areas for performance improvement are obtained. These areas are directly mapped to M key performance components, each of which is related to a certain performance index. Improving these components can significantly enhance the overall performance of the equipment.

[0047] The target twin model is run under standard operating conditions to simulate various operations and functions of the equipment. During the simulation, M performance indicators of the performance evaluation system are monitored to obtain the corresponding M standard operating parameters, which constitute the standard operating performance information.

[0048] Environmental compatibility refers to whether multiple extreme operating environments can coexist. For example, high temperature and high humidity can coexist, but high temperature and extreme cold cannot coexist because they are mutually exclusive. Based on environmental compatibility, we enumerate the possible combinations of multiple extreme operating environments to generate K multimodal operating environments, where K is the number of these combined environments and K is a positive integer.

[0049] A virtual operating environment is constructed within the target twin model. This includes individual simulations of each extreme operating environment to determine the equipment's performance under each condition, as well as simulations of combined multimodal operating environments to determine the equipment's performance under multiple simultaneous extreme conditions. The target twin model is run in these extreme environments to simulate the equipment's operational performance. During the simulation, M performance indicators from the performance evaluation system are monitored. Each simulation collects M corresponding computational operating parameters, forming the extreme operating performance information set for that simulation. Through multiple simulations of multiple extreme operating environments and K multimodal operating environments, W extreme operating performance information sets are ultimately generated, where W is a positive integer greater than K.

[0050] The table is similar to a spreadsheet, with each row representing a simulation and each column representing a performance parameter. It stores the standard operating performance information collected under standard operating conditions, as well as W extreme operating performance information sets obtained under extreme operating conditions, in a table format to form a complete operating performance dataset.

[0051] The standard operating performance information and W sets of extreme operating performance information are compared and analyzed. By analyzing each performance index, its deviation in extreme environments is identified. The parts with large deviations are matched with performance improvement areas to identify specific performance components that need improvement. For example, if the hydraulic system is found to perform poorly in high temperature and high humidity environments, the hydraulic system is identified as a performance bottleneck. Through the fitting process, the key performance components with poor performance are identified and formed into the first performance bottleneck set, which includes N key performance components.

[0052] Each performance bottleneck is marked in the target twin model to visually demonstrate the equipment's performance in specific environments. For example, in the twin model's visualization interface, the hydraulic system is marked in red to indicate that it has problems in high-temperature environments.

[0053] Furthermore, the method further includes fitting the performance improvement domain to the runtime performance dataset that hits the first performance bottleneck set:

[0054] In the performance dataset, the M standard operating parameters are used to traverse and compare the W extreme performance information sets to obtain W sets of hit deviation performance indicators; the W sets of hit deviation performance indicators are de-hierarchized, and the frequency of deviation performance indicators is statistically analyzed to obtain the M deviation recurrence frequencies of the M performance indicators; a preset component optimization frequency threshold is used to traverse the M deviation recurrence frequencies to obtain N performance indicators whose deviation recurrence frequencies exceed the component optimization frequency threshold, where N is a positive integer less than or equal to M; based on the N performance indicators, the first performance bottleneck set is obtained in the performance improvement domain, wherein the first performance bottleneck set includes N key performance components.

[0055] For each of the M standard operating parameters and W extreme operating performance information sets, a comparison is made one by one, such as by directly comparing the values. The numerical deviation between the extreme operating performance information and the standard operating parameters under each indicator is calculated, and finally W sets of hit deviation performance indicators are obtained.

[0056] After initial comparison, multiple sets of deviation performance indicators are obtained. These indicators often come from different levels of equipment or systems. Hierarchical classification means that these deviations are classified according to system level, such as from component level to system level. By removing the hierarchical processing, the deviations at each level are treated uniformly, ensuring that each deviation performance indicator can be evaluated independently. This helps to avoid overly complex hierarchical interference and simplify the analysis. For example, the hydraulic pressure problem at the component level is treated separately from the overall energy consumption problem at the system level to avoid confusion.

[0057] After de-hierarchical processing, each performance index is statistically analyzed to obtain the number of times it deviates in the W extreme operating performance information sets. For example, if the pressure of the hydraulic system deviates in 60% of the W tests, then its deviation recurrence frequency is 60%. Similarly, this process is repeated for M performance indexes to obtain M deviation recurrence frequencies.

[0058] The preset component optimization frequency threshold is designed to filter out performance indicators that need to be focused on and optimized. If the frequency of deviation of a certain performance indicator exceeds this threshold, it indicates that the indicator exhibits more frequent problems under extreme operating conditions and therefore needs to be optimized. The threshold can be set based on historical data or experience, for example, it can be set to 50% or 60%.

[0059] The M frequency of deviations obtained from the statistics are iterated one by one. The frequency of each performance indicator is compared with the preset component optimization frequency threshold. If the frequency of deviation of a certain performance indicator is less than the preset threshold, the deviation of the performance indicator is considered not serious and does not need to be optimized first. Otherwise, if it is higher than the preset threshold, the performance indicator is marked as a target that needs to be optimized. After filtering, N performance indicators are obtained, where N is a positive integer less than or equal to M. The N performance indicators represent the performance indicators that show obvious deviations under multiple extreme operating environments.

[0060] Based on the selected N performance indicators, components related to these performance indicators in the performance improvement field are called up, and N key performance components that need to be optimized are identified. For example, if the pressure performance of the hydraulic system is not good, components related to the hydraulic system, such as hydraulic cylinders and hydraulic pumps, are called up in the performance improvement field. The resulting N key performance components form the first set of performance bottlenecks.

[0061] Using the set of operating environment conditions as constraints, the target twin model and multiple sets of associated twin models are subjected to collaborative operation tests, and performance deviations are identified based on the operation process data, thereby locating a second set of performance bottlenecks in the target twin model.

[0062] The simulation takes a set of operating environment conditions, including standard operating environment and multiple extreme operating environment, as input to the simulation conditions. The target twin model and the associated twin model are tested in a virtual environment to simulate the scenario of multiple equipment working together in actual work. For example, in the rescue mission of building collapse, the lifting equipment works simultaneously with the drone and demolition tools. The simulation simulates their mutual influence and cooperation. The collaborative operation test can combine the mutual influence between different equipment. For example, the drone provides real-time monitoring data to the lifting equipment, and the demolition tools need to pause the lifting operation at certain stages to carry out demolition work. Through the simulation platform, these complex workflows and their performance under different environmental conditions are simulated.

[0063] During collaborative simulation, real-time performance data of each piece of equipment is collected. By comparing the expected performance data under standard operating conditions with the actual performance data under extreme operating conditions, performance deviations are identified. By analyzing the performance of each piece of equipment in collaborative operation, it can be determined whether the performance deviation is a problem of the individual piece of equipment or a problem of synergistic effect when the equipment operates together. For example, a demolition tool may perform normally under high temperature conditions, but when working with other equipment, the operating efficiency will decrease due to material stress. Based on the analysis results, a second set of performance bottlenecks is obtained. Compared with the first set of performance bottlenecks, the second set of performance bottlenecks focuses more on the performance of equipment when operating in collaboration with other equipment.

[0064] Furthermore, using the aforementioned set of operating environment conditions as constraints, the method performs collaborative operation testing on the target twin model and multiple sets of associated twin models, identifies performance deviations based on the operating process data, and locates a second set of performance bottlenecks in the target twin model. The method also includes:

[0065] Using the multiple extreme operating environments and K multimodal operating environments as environmental simulation constraints, the target twin model and the first set of associated twin models are subjected to collaborative operation testing. The performance evaluation system is used as a constraint to collect the operational performance data of the target twin model, obtaining V extreme operational performance information sets. Here, the first set of associated twin models is any one of the multiple sets of associated twin models, and V is a positive integer greater than K. The standard operational performance information and the V extreme operational performance information sets are compared and analyzed, and performance bottlenecks are identified using the performance improvement domain to obtain a first subset of associated performance bottlenecks. Similarly, using the set of operating environment conditions as constraints, the target twin model and the multiple sets of associated twin models are subjected to collaborative operation testing, and performance deviations are identified based on the operational process data to obtain multiple subsets of associated performance bottlenecks. The multiple subsets of associated performance bottlenecks are aggregated to obtain a second set of performance bottlenecks.

[0066] The target twin model is a twin model of the target joint logistics equipment, while the associated twin model is a twin model of other equipment that works in conjunction with the target joint logistics equipment. One set of associated twin models is randomly selected from multiple sets as the analysis object, which is the first set of associated twin models.

[0067] The target twin model and the first set of associated twin models were subjected to collaborative operation tests. Collaborative operation tests simulate the performance of multiple equipment working together in extreme or multimodal environments. For example, in a rescue mission, lifting equipment (target twin model) and drones (associated twin model) work together. Using a simulation platform, the target twin model and the associated twin model are placed in the same extreme or multimodal operating environment to simulate their collaborative operation process.

[0068] During the collaborative operation test, the process is the same as obtaining W extreme performance information sets. The target twin model is also subjected to performance evaluation system constraints to collect V extreme performance information sets, where V is a positive integer greater than K.

[0069] The system uses a table to store standard operating performance information and V sets of extreme operating performance information. It then uses performance improvement domains to identify performance bottlenecks and obtain a first associated performance bottleneck subset. The process of obtaining the first associated performance bottleneck subset is the same as that of obtaining the first performance bottleneck set, and will not be described in detail here for the sake of brevity.

[0070] By repeating this process, multiple sets of associated twin models are traversed, and each is subjected to multiple collaborative running tests with the target twin model. The above steps are repeated to obtain multiple subsets of associated performance bottlenecks.

[0071] The obtained subsets of related performance bottlenecks are integrated to form a complete second performance bottleneck set. Compared with the first performance bottleneck set, the second performance bottleneck set focuses more on the collaborative work between devices, rather than just the performance of the devices themselves.

[0072] The first and second performance bottleneck sets are integrated and analyzed, and the equipment design of the target joint logistics equipment is optimized based on the results of the bottleneck integration analysis.

[0073] Integrated analysis involves identifying all performance shortcomings of the equipment during independent and collaborative operation. Based on the operating parameter ranges of the key performance components corresponding to these shortcomings, the target twin model is iteratively optimized and improved in a virtual environment. This includes adjusting the operating parameters of components, such as voltage, current, and pressure, to optimize their performance until standard operating parameters are achieved. At this point, the improved target equipment information is output, thereby optimizing the equipment design of the target joint logistics equipment.

[0074] Furthermore, the method further includes integrating and analyzing the first set of performance shortcomings and the second set of performance shortcomings, and optimizing the equipment design of the target joint logistics equipment based on the results of the shortcomings integration analysis.

[0075] The first and second performance bottleneck sets are integrated based on component repetition to obtain a comprehensive performance bottleneck set. Multiple operating parameter value ranges for several key performance components within the comprehensive performance bottleneck set are interactively obtained. Using these multiple operating parameter value ranges as performance tuning constraints, the set of operating environment conditions as environmental constraints, and the M standard operating parameters as qualification constraints, the target twin model is iteratively optimized and improved in a virtual environment to obtain target equipment improvement information. This target equipment improvement information includes multiple improved operating parameters for the multiple key performance components. The equipment design of the target joint logistics equipment is optimized based on the target equipment improvement information.

[0076] By comparing the components in the first and second performance bottleneck sets, duplicate components that appear in both sets are identified. These components exhibit insufficient performance in both independent and collaborative operation of the equipment and should be the focus of optimization. By integrating the duplicate components, a unified comprehensive performance bottleneck set is obtained.

[0077] By interacting with the actual operating data or design documents of the equipment, the range of operating parameters of each key component in the overall performance bottleneck is obtained. The range of values ​​represents the performance range of the equipment under different operating environments, such as the range of parameters such as temperature, pressure, and power consumption.

[0078] Multiple operating parameter value ranges serve as performance tuning constraints. These ranges provide the acceptable performance range for each component under different conditions, ensuring that the equipment parameters remain within normal limits during optimization and improvement. A set of operating environment conditions serves as environmental constraints, ensuring that the optimized equipment can meet these complex environmental conditions. M standard operating parameters serve as compliance constraints, defining the ideal performance of the equipment under normal operating conditions, ensuring that the optimized equipment meets the design requirements under standard operating conditions.

[0079] Through virtual simulation using a digital twin model, the behavior of equipment under various operating environments is simulated. Based on calibration constraints, environmental constraints, and compliance constraints, the operating parameters of the equipment are continuously iteratively optimized. Specifically, within the set range of operating parameter values, key components are optimized, and the optimized performance data is collected and compared with standard operating parameters and performance requirements under extreme environments to identify any remaining performance gaps or deficiencies. Based on the feedback results, the operating parameters of each key performance component are adjusted. In particular, for components that perform poorly under extreme environments, through multiple simulations and feedback adjustments, all key performance components of the equipment are gradually optimized until all components meet the compliance constraints and performance calibration constraints under different environments. At this point, multiple improved operating parameters for multiple key performance components are output, obtaining the target equipment improvement information.

[0080] The target equipment improvement information includes multiple improved working parameters for several key performance components. These parameters provide the basis for the equipment's optimal performance under standard and extreme environments. Through these improved parameters, the design optimization of the target joint logistics equipment is guided, so that the equipment can meet these performance requirements in actual use.

[0081] In summary, the joint logistics equipment testing optimization method based on digital twin modeling provided in this application has the following technical effects:

[0082] By collecting multi-source heterogeneous data on the target joint logistics equipment, including physical characteristic data, operational behavior data, and environmental condition data, and generating accurate twin models based on this data, the physical characteristics and dynamic behavior of the equipment can be simulated. This high-precision modeling provides a foundation for subsequent simulation, testing, and optimization. Through correlation analysis, multiple sets of correlated twin models corresponding to actual mission scenarios are constructed, enabling the equipment to adapt to diverse and complex application scenarios, such as natural disaster relief and traffic accident handling. This allows for testing and optimization of equipment performance under various usage scenarios, giving it broader application adaptability. Furthermore, by acquiring the set of operating environment conditions for the target joint logistics equipment, including standard operating environments and multiple extreme operating environments, and then using this set of operating environment conditions as constraints, the twin model is further refined in a virtual environment. Simulated operation allows for the collection of detailed operational performance data under both standard and extreme operating environments. Based on this data, performance deviation analysis can be performed, accurately pinpointing equipment performance bottlenecks and providing direction for subsequent optimization. Similarly, using a set of operating environment conditions as constraints, collaborative operation testing of the target twin model and multiple related twin models can not only optimize the performance of individual equipment but also identify performance bottlenecks in collaborative operation, improving the overall collaborative efficiency of the equipment. By integrating the first and second sets of performance bottlenecks, a comprehensive set of performance bottlenecks is obtained, resolving the main bottlenecks in independent and collaborative operation of the equipment. This promotes the optimization and upgrading of the overall equipment design, ultimately resulting in an optimized design scheme that exhibits high stability and reliability under various operating environments.

[0083] Example 2, based on the same inventive concept as the joint logistics equipment testing optimization method based on digital twin modeling in the previous examples, such as... Figure 2 As shown in the embodiment of this application, a joint logistics equipment testing optimization system based on digital twin modeling is provided. The system includes:

[0084] The system includes: a simulation modeling module 10, which collects multi-source heterogeneous data of the target joint logistics equipment and performs simulation modeling on the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model; a usage correlation analysis module 20, which performs usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlation twin models corresponding to various usage scenarios; a runtime environment acquisition module 30, which interactively obtains the runtime environment condition set of the target joint logistics equipment; and a performance deviation analysis module 40, which uses the runtime environment condition set as constraints to perform performance deviation analysis on the target equipment in a virtual environment. The twin model is simulated and run, and performance deviation analysis is performed on the operational performance dataset collected during the simulation to locate the first set of performance shortcomings in the target twin model; the collaborative operation test module 50 is used to conduct collaborative operation tests on the target twin model and multiple sets of associated twin models with the set of operational environment conditions as constraints, and to identify performance deviations based on the operational process data to locate the second set of performance shortcomings in the target twin model; the equipment design optimization module 60 is used to integrate and analyze the first set of performance shortcomings and the second set of performance shortcomings, and to optimize the equipment design of the target joint logistics equipment based on the shortcomings integration analysis results.

[0085] Furthermore, the system also includes a target twin model acquisition module to perform the following steps:

[0086] The target joint logistics equipment is configured with real-time monitoring facilities to obtain a multi-dimensional monitoring array, which includes a physical characteristic monitoring array, an operational behavior monitoring array, and an environmental condition monitoring array. A pre-configured operation monitoring window and operation condition timing sequence are used. During the operation of the target joint logistics equipment with the operation monitoring window as the time constraint and the operation condition timing sequence as the control constraint, the multi-dimensional monitoring array is used to collect real-time data from the target joint logistics equipment to obtain multi-source heterogeneous data of the target. Based on the design information of the target joint logistics equipment, related component models are called, and a target simulation model is assembled based on the calling results. The multi-source heterogeneous data of the target and the target simulation model are fused to obtain a target twin model.

[0087] Furthermore, the system also includes multiple sets of associated twin model acquisition modules to perform the following operational steps:

[0088] The system interacts with the service records of the target joint logistics equipment to obtain historical application data; it then performs time-series segmentation on the historical application data to obtain multiple joint logistics equipment scheduling records, each of which includes application scenario information and a combination of joint logistics equipment; it aggregates the multiple joint logistics equipment scheduling records based on application scenario types to obtain multiple sets of joint logistics equipment scheduling records corresponding to the various usage scenarios; it presets an association frequency threshold, and after aggregating the multiple sets of joint logistics equipment scheduling records, it uses the association frequency threshold to filter the aggregation results to obtain multiple sets of associated joint logistics equipment; and it calls twin models of the multiple sets of associated joint logistics equipment based on equipment models to obtain the multiple sets of associated twin models.

[0089] Furthermore, the set of operating environment conditions includes a standard operating environment and multiple extreme operating environments.

[0090] Furthermore, the system also includes a positioning identification module to perform the following operational steps:

[0091] A performance evaluation system is preset, and performance analysis and optimization mapping are performed on the performance evaluation system to obtain performance improvement areas. The performance evaluation system includes M performance indicators, and the performance improvement areas include M key performance components mapped to the M performance indicators. The target twin model is simulated using the standard operating environment as the simulation environment constraint, and its operational performance is collected using the performance evaluation system as the constraint to obtain standard operating performance information. The standard operating performance information includes M standard operating parameters. Based on environmental fusion, multiple extreme operating environments are combined and enumerated to obtain K multimodal operating environments. The target twin model is simulated in virtual environments under multiple extreme operating environments and K multimodal operating environments. The performance of the target twin model is collected under the constraints of the performance evaluation system, resulting in W extreme performance information sets. Each of the W extreme performance information sets includes M extreme operating parameters, where W is a positive integer greater than K. The standard performance information and the W extreme performance information sets are stored in a table to form the performance dataset. The performance improvement domain is fitted to the performance dataset to match the first performance bottleneck set, and the first performance bottleneck set is located and identified in the target twin model.

[0092] Furthermore, the system also includes a first performance bottleneck set acquisition module to perform the following operation steps:

[0093] In the performance dataset, the M standard operating parameters are used to traverse and compare the W extreme performance information sets to obtain W sets of hit deviation performance indicators; the W sets of hit deviation performance indicators are de-hierarchized, and the frequency of deviation performance indicators is statistically analyzed to obtain the M deviation recurrence frequencies of the M performance indicators; a preset component optimization frequency threshold is used to traverse the M deviation recurrence frequencies to obtain N performance indicators whose deviation recurrence frequencies exceed the component optimization frequency threshold, where N is a positive integer less than or equal to M; based on the N performance indicators, the first performance bottleneck set is obtained in the performance improvement domain, wherein the first performance bottleneck set includes N key performance components.

[0094] Furthermore, the system also includes a second performance bottleneck set acquisition module to perform the following steps:

[0095] Using the multiple extreme operating environments and K multimodal operating environments as environmental simulation constraints, the target twin model and the first set of associated twin models are subjected to collaborative operation testing. The performance evaluation system is used as a constraint to collect the operational performance data of the target twin model, obtaining V extreme operational performance information sets. Here, the first set of associated twin models is any one of the multiple sets of associated twin models, and V is a positive integer greater than K. The standard operational performance information and the V extreme operational performance information sets are compared and analyzed, and performance bottlenecks are identified using the performance improvement domain to obtain a first subset of associated performance bottlenecks. Similarly, using the set of operating environment conditions as constraints, the target twin model and the multiple sets of associated twin models are subjected to collaborative operation testing, and performance deviations are identified based on the operational process data to obtain multiple subsets of associated performance bottlenecks. The multiple subsets of associated performance bottlenecks are aggregated to obtain a second set of performance bottlenecks.

[0096] Furthermore, the system also includes a design optimization module to perform the following steps:

[0097] The first and second performance bottleneck sets are integrated based on component repetition to obtain a comprehensive performance bottleneck set. Multiple operating parameter value ranges for several key performance components within the comprehensive performance bottleneck set are interactively obtained. Using these multiple operating parameter value ranges as performance tuning constraints, the set of operating environment conditions as environmental constraints, and the M standard operating parameters as qualification constraints, the target twin model is iteratively optimized and improved in a virtual environment to obtain target equipment improvement information. This target equipment improvement information includes multiple improved operating parameters for the multiple key performance components. The equipment design of the target joint logistics equipment is optimized based on the target equipment improvement information.

[0098] Through the foregoing detailed description of the method for optimizing joint logistics equipment testing based on digital twin modeling, those skilled in the art can clearly understand the system for optimizing joint logistics equipment testing based on digital twin modeling in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0099] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing joint logistics equipment testing based on digital twin modeling, characterized in that, The method includes: Collect multi-source heterogeneous data of the target joint logistics equipment, and perform simulation modeling of the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model; The target joint logistics equipment was subjected to usage correlation analysis to obtain multiple sets of correlation twin models corresponding to various usage scenarios; The set of operating environment conditions for the target joint logistics equipment is obtained interactively; Using the set of operating environment conditions as constraints, the target twin model is simulated in a virtual environment, and the performance deviation is analyzed by collecting the operating performance dataset during the simulation to locate the first set of performance bottlenecks in the target twin model. Using the set of operating environment conditions as constraints, the target twin model and multiple sets of associated twin models are subjected to collaborative operation tests, and performance deviations are identified based on the operation process data, and a second set of performance bottlenecks is located in the target twin model; The first and second performance bottleneck sets are integrated and analyzed, and the equipment design of the target joint logistics equipment is optimized based on the results of the bottleneck integration analysis.

2. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 1, characterized in that, The method further includes: collecting multi-source heterogeneous data on target joint logistics equipment, and performing simulation modeling on the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model; The target joint logistics equipment is configured with real-time monitoring facilities to obtain a multi-dimensional monitoring array, wherein the multi-dimensional monitoring array includes a physical characteristic monitoring array, an operational behavior monitoring array, and an environmental condition monitoring array. Pre-configure the running monitoring window and running condition timing; During the operation of the target joint logistics equipment with the operation monitoring window as the time constraint and the operation condition timing as the control constraint, the multi-dimensional monitoring array is used to collect real-time data of the target joint logistics equipment to obtain the target multi-source heterogeneous data. Based on the design information of the target joint logistics equipment, the associated component models are called up, and the target simulation model is assembled and constructed based on the calling results; The target multi-source heterogeneous data and the target simulation model are fused to obtain a target twin model.

3. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 1, characterized in that, The method further includes performing usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlation twin models corresponding to various usage scenarios. Interact with the service records of the target joint logistics equipment to obtain historical application data; The historical application data is split into time series to obtain multiple joint logistics equipment scheduling records. Each joint logistics equipment scheduling record includes application scenario information and joint logistics equipment combination. Based on the application scenario type, the multiple joint logistics equipment scheduling records are aggregated to obtain multiple sets of joint logistics equipment scheduling records corresponding to the multiple usage scenarios. A preset association frequency threshold is set, and after aggregating the scheduling records of the multiple sets of joint logistics equipment, the aggregation results are filtered using the association frequency threshold to obtain multiple sets of associated joint logistics equipment. Based on the equipment model, the twin models of the multiple sets of related joint operations equipment are called to obtain the multiple sets of related twin models.

4. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 3, characterized in that, The set of operating environment conditions includes a standard operating environment and multiple extreme operating environments.

5. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 4, characterized in that, Using the aforementioned set of operating environment conditions as constraints, the target twin model is simulated in a virtual environment, and performance deviation analysis is performed on the operating performance dataset collected during the simulation to locate the first set of performance bottlenecks in the target twin model. The method further includes: A performance evaluation system is preset, and the performance evaluation system is subjected to performance analysis and optimization mapping to obtain performance improvement areas. The performance evaluation system includes M performance indicators, and the performance improvement areas include M key performance components mapped to the M performance indicators. The target twin model is simulated using the standard operating environment as the simulation environment constraint, and the target twin model's operational performance is collected using the performance evaluation system as the constraint to obtain standard operational performance information, wherein the standard operational performance information includes M standard operational parameters; Based on environmental fusion, the multiple extreme operating environments are combined and enumerated to obtain K multimodal operating environments; The target twin model is simulated in a virtual environment based on the multiple extreme operating environments and K multimodal operating environments. The target twin model is subjected to performance data collection based on the performance evaluation system to obtain W extreme operating performance information sets. Each of the W extreme operating performance information sets includes M extreme operating parameters, where W is a positive integer greater than K. The standard operating performance information and W extreme operating performance information sets are stored in a table to form the operating performance dataset. The performance improvement domain is fitted to the runtime performance dataset to hit the first performance bottleneck set, and the first performance bottleneck set is located and identified in the target twin model.

6. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 5, characterized in that, The method further includes fitting the performance improvement domain to the runtime performance dataset to hit the first performance bottleneck set: In the aforementioned operational performance dataset, the M standard operational parameters are used to traverse and compare the W extreme operational performance information sets to obtain W sets of hit deviation performance indicators. The hit deviation performance indicators of the W groups are de-hierarchized, and the frequency of deviation performance indicators is statistically analyzed on the processing results to obtain the M deviation recurrence frequencies of the M performance indicators. A preset component optimization frequency threshold is used, and the M deviation recurrence frequencies are traversed using the component optimization frequency threshold to obtain N performance indicators where the deviation recurrence frequency exceeds the component optimization frequency threshold, where N is a positive integer less than or equal to M; The first set of performance bottlenecks is obtained by calling upon the N performance indicators in the performance improvement field, wherein the first set of performance bottlenecks includes N key performance components.

7. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 5, characterized in that, Using the set of operating environment conditions as constraints, the method performs collaborative operation testing on the target twin model and multiple sets of associated twin models, identifies performance deviations based on the operating process data, and locates a second set of performance bottlenecks in the target twin model. The method further includes: Using the multiple extreme operating environments and K multimodal operating environments as environmental simulation constraints, the target twin model and the first set of associated twin models are subjected to collaborative operation tests. The performance evaluation system is used as a constraint to collect the operational performance of the target twin model, resulting in V extreme operational performance information sets. Here, the first set of associated twin models is any one of the multiple sets of associated twin models, and V is a positive integer greater than K. By comparing and analyzing the standard operating performance information and V extreme operating performance information sets, and using the performance improvement areas to identify performance bottlenecks, a first associated performance bottleneck subset is obtained. Similarly, using the set of operating environment conditions as constraints, the target twin model and multiple sets of associated twin models are subjected to collaborative operation tests, and performance deviations are identified based on the operation process data to obtain multiple subsets of associated performance bottlenecks. By aggregating the multiple related performance bottleneck subsets, the second performance bottleneck set is obtained.

8. The method for optimizing joint logistics equipment testing based on digital twin modeling as described in claim 5, characterized in that, The method further includes: integrating and analyzing the first set of performance shortcomings and the second set of performance shortcomings, and optimizing the equipment design of the target joint logistics equipment based on the results of the shortcomings integration analysis; Based on component repetition, the first and second performance bottleneck sets are integrated to obtain a comprehensive performance bottleneck set. Interactively obtain the value ranges of multiple operating parameters of multiple key performance components in the overall performance bottleneck set; Using the range of values ​​for the multiple working parameters as performance tuning constraints, the set of operating environment conditions as environmental constraints, and the M standard operating parameters as qualification constraints, the target twin model is iteratively optimized and improved in a virtual environment to obtain target equipment improvement information, wherein the target equipment improvement information includes multiple improved working parameters of the multiple key performance components. Based on the target equipment improvement information, optimize the equipment design of the target joint logistics equipment.

9. A joint logistics equipment testing and optimization system based on digital twin modeling, characterized in that, The system is used to implement the joint logistics equipment testing optimization method based on digital twin modeling as described in any one of claims 1-8, the system comprising: The simulation modeling module is used to collect multi-source heterogeneous data of the target joint logistics equipment, and to perform simulation modeling of the target joint logistics equipment based on the multi-source heterogeneous data to generate a target twin model. The correlation analysis module is used to perform a usage correlation analysis on the target joint logistics equipment to obtain multiple sets of correlation twin models corresponding to various usage scenarios. The operating environment acquisition module is used to interactively obtain the set of operating environment conditions for the target joint logistics equipment. The performance deviation analysis module is used to simulate the target twin model in a virtual environment with the set of operating environment conditions as constraints, and to perform performance deviation analysis on the operating performance dataset collected during the simulation to locate the first set of performance bottlenecks in the target twin model. The collaborative operation testing module is used to perform collaborative operation testing on the target twin model and multiple sets of associated twin models under the constraints of the operation environment condition set, and to identify performance deviations based on the operation process data, and to locate the second set of performance bottlenecks in the target twin model. The equipment design optimization module is used to integrate and analyze the first set of performance shortcomings and the second set of performance shortcomings, and to optimize the equipment design of the target joint logistics equipment based on the results of the shortcomings integration analysis.

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