Material digital supply chain test method

Abstract

The invention provides a material digital supply chain test method, and relates to the technical field of power grid information management, and the method comprises the steps: obtaining collected supply chain data; detecting link state change of each link to obtain a detection result based on the supply chain data and a non-linear relationship of each preset link of the supply chain; judging whether an abnormal event occurs or not according to a detection result, and if the abnormal event is detected, generating an adjustment strategy through a preset rule and real-time calculation according to the abnormal event; simulating a normal state and an abnormal state of each link of the power grid construction project in a simulation environment to obtain influence parameters; and optimizing the adjustment strategy based on the influence parameter, simulating the optimized adjustment strategy and monitoring the execution condition, and evaluating the influence of the optimized adjustment strategy on the supply chain according to the execution condition and a preset evaluation index. The method can capture the real-time change of the state of each link of the power grid construction project, and formulates an adjustment strategy according to the abnormal event and carries out verification.

Description

Technical Field

[0001] This application relates to the technical field of power grid information management, and specifically relates to a method for testing a digital supply chain of materials. Background Art

[0002] In power grid construction projects, the material supply chain involves multiple preset links such as material production, transportation, and equipment installation. There are highly non-linear relationships and dynamic changes among these links. Traditional supply chain production testing methods often rely on static data and stable assumptions, making it difficult to comprehensively capture the real-time changes in the status of each link, and thus unable to promptly detect and respond to abnormal events such as production delays, transportation failures, and equipment failures.

[0003] Existing methods mostly rely on statistical analysis of historical data and fixed rules, ignoring the multi-variable and dynamic interaction characteristics caused by the large scale, wide geographical distribution, and numerous stakeholders in the process of power grid project construction. Therefore, when abnormal events occur, it is difficult to make timely and accurate adjustments to key links such as production plans, inventory strategies, and transportation scheduling. This not only affects the overall operation efficiency of the supply chain but may also lead to interruptions in material supply, thus affecting the progress and safe operation of the project. Therefore, a method is needed to capture the real-time changes in the status of each link in the power grid construction project, formulate adjustment strategies based on abnormal events, and verify them. Summary of the Invention

[0004] This application provides a method for testing a digital supply chain of materials, which can capture the real-time changes in the status of each link in the power grid construction project, formulate adjustment strategies based on abnormal events, and verify them.

[0005] In a first aspect of this application, a method for testing a digital supply chain of materials is provided. The method includes:

[0006] Obtain the collected supply chain data based on the data collection devices deployed in multiple preset links of the power grid construction project;

[0007] Detect the changes in the status of each preset link of the supply chain based on the supply chain data and the non-linear relationships of each preset link of the supply chain, and obtain a detection result;

[0008] Judge whether an abnormal event has occurred according to the detection result. If an abnormal event is detected, generate an adjustment strategy through preset rules and real-time calculation according to the abnormal event to adjust the production plan, inventory strategy, and transportation scheduling of each preset link;

[0009] Simulate the normal state and abnormal state of each link of the power grid construction project in a simulation environment to obtain influence parameters;

[0010] Optimize the adjustment strategy based on the impact parameters, simulate the optimized adjustment strategy, and monitor the execution of the optimized adjustment strategy. Evaluate the impact of the optimized adjustment strategy on the supply chain according to the execution and preset evaluation metrics.

[0011] In some embodiments, simulating the normal state and abnormal state of each link of the power grid construction project in a simulation environment to obtain impact parameters specifically includes:

[0012] In a simulation platform, establish link models including multiple dimensions of power equipment, communication systems, logistics transportation, and inventory management;

[0013] Install sensors in the link models to simulate real-time collection of physical parameters and business data; wherein, the physical parameters include current, voltage, temperature, humidity, and pressure, and the business data includes production progress, inventory level, and transportation status;

[0014] Run each link model on the simulation platform according to the physical parameters to simulate the normal state of the power grid construction project and obtain reference data;

[0015] According to the design and operation specifications of the power grid construction project, set the normal operation parameters of each preset link, and the normal operation parameters include equipment operation load, production progress, and inventory level;

[0016] Simulate the abnormal state of the abnormal event by adjusting the normal operation parameters to obtain abnormal parameters;

[0017] Evaluate the impact parameters of the abnormal event on the power grid construction project based on the reference data and the abnormal parameters.

[0018] In some embodiments, the optimizing the adjustment strategy based on the impact parameters, simulating the optimized adjustment strategy, and monitoring the execution of the optimized adjustment strategy includes:

[0019] Optimize the adjustment strategy based on the impact parameters to obtain the optimized adjustment strategy;

[0020] Integrate the optimized adjustment strategy into the simulation platform and configure strategy parameters according to the optimized adjustment strategy; wherein, the strategy parameters include production speed, inventory level, and transportation frequency;

[0021] Deploy the optimized adjustment strategy based on the strategy parameters and collect the updated operation parameters of each preset link in real time through data collection devices; wherein, the updated operation parameters include equipment operation load, production progress, and inventory level.

[0022] In some embodiments, evaluating the impact of the optimized adjustment strategy on the supply chain according to the execution situation and preset evaluation metrics includes:

[0023] Determining preset evaluation metrics according to the goals and requirements of the power grid construction project; wherein, the preset evaluation metrics include production efficiency, inventory turnover rate, transportation timeliness, and cost control;

[0024] Determining updated operation parameters according to the execution situation, performing statistical analysis on the updated operation parameters, and obtaining a statistical result;

[0025] Quantitatively calculating the impact values of the optimized adjustment strategy on each of the preset links according to the preset evaluation metrics and the statistical result.

[0026] In some embodiments, generating an adjustment strategy according to the abnormal event through preset rules and real-time calculation includes:

[0027] Establishing a rule library corresponding to different abnormal events based on historical experience data; wherein, the rule library includes adjusting the production plan, optimizing the inventory strategy, and rescheduling transportation;

[0028] Determining the adjusted production plan corresponding to the abnormal event according to the rule library;

[0029] Determining the corresponding optimized inventory strategy in the rule library according to the demand change and inventory level of the abnormal event;

[0030] If it is determined that there is a transportation delay or demand change in the abnormal event, recalculate and plan the transportation route and schedule.

[0031] In some embodiments, detecting changes in the state of each preset link of the supply chain based on the supply chain data and the non-linear relationship between each preset link of the supply chain, and obtaining a detection result includes:

[0032] Establishing a non-linear relationship model between each of the preset links using a machine learning algorithm;

[0033] Training the non-linear relationship model using historical data, and regularly updating the non-linear relationship model to obtain a trained non-linear relationship model;

[0034] Monitoring the supply chain data according to the trained non-linear relationship model, and identifying changes in the state of each of the preset links.

[0035] In some embodiments, the method further includes:

[0036] Predicting the current link state through the trained non-linear relationship model to obtain a predicted value;

[0037] Obtain the observed value of the current link status collected in real time;

[0038] Calculate the error index between the predicted value and the observed value; wherein, the error index includes one of an absolute difference value, a relative difference value, a root mean square error value or an average absolute error value;

[0039] Judge whether the error index is greater than or equal to a preset threshold;

[0040] If it is determined that the error index is greater than or equal to the preset threshold, it is determined that an abnormal event exists in the current link status.

[0041] In a second aspect of the present application, a material digital supply chain testing system is provided, including:

[0042] An acquisition module, configured to acquire the collected supply chain data based on data acquisition devices deployed in multiple preset links of the power grid construction project;

[0043] A processing module, configured to detect the change of the link status of each preset link of the supply chain based on the supply chain data and the non-linear relationship of each preset link of the supply chain, and obtain a detection result;

[0044] The processing module is further configured to judge whether an abnormal event occurs according to the detection result. If an abnormal event is detected, an adjustment strategy is generated through preset rules and real-time calculation according to the abnormal event to adjust the production plan, inventory strategy and transportation scheduling of each preset link;

[0045] The processing module is further configured to simulate the normal state and abnormal state of each link of the power grid construction project in a simulation environment to obtain influence parameters;

[0046] An output module, configured to optimize the adjustment strategy based on the influence parameters, simulate the optimized adjustment strategy and monitor the execution situation of the optimized adjustment strategy, and evaluate the influence of the optimized adjustment strategy on the supply chain according to the execution situation and preset evaluation indicators.

[0047] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method described in any one of the above.

[0048] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0049] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0050] 1. In the present application, data acquisition devices are deployed at each key link of the power grid construction project to obtain supply chain data in real time, and based on the non-linear relationship model, the state changes of each link are monitored, so that abnormal events can be discovered in time. Through preset rules and real-time calculations, the production plan, inventory strategy, and transportation scheduling are adjusted for abnormal events to generate adjustment strategies. Then, the engineering links are reproduced in the simulation environment to verify the effect of the adjustment strategy and evaluate its impact on the system according to the evaluation indicators. This process enables the dynamic capture of state changes, rapid response to abnormal events, and ensures the effectiveness of the adjustment strategy through verification.

[0051] 2. By establishing a multi-dimensional link model in the simulation platform and combining it with real-time data acquisition devices, the normal and abnormal states of the power grid construction project can be accurately simulated. By adjusting the operating parameters and simulating abnormal events, the specific impact of the abnormality on the power grid construction can be evaluated, and the interference of external environment changes on the project can be predicted. This not only provides simulation verification for coping with abnormal events but also helps to optimize the supply chain and project management strategies, improves the response speed and decision-making accuracy, and effectively ensures the smooth progress of the power grid construction process.

[0052] 3. Integrating the adjustment strategy into the simulation and monitoring the implementation of the strategy through real-time data acquisition and evaluation indicators can effectively quantify the impact of the adjustment strategy on each link of the power grid construction. By setting evaluation indicators such as production efficiency, inventory turnover rate, transportation timeliness, and cost control, the adjustment strategy can be analyzed and optimized in real time to ensure the coordination and efficiency of each link.

[0053] 4. By establishing a rule library for coping with different abnormal events and combining real-time calculations with historical data, effective adjustment strategies can be quickly generated. By adjusting the production plan according to the nature of the abnormal event, optimizing the inventory strategy, and rescheduling transportation, the sudden situations in the power grid construction process can be flexibly handled, ensuring the smooth operation of each link, reducing the supply chain interruption caused by abnormal events, ensuring that the power grid construction project can be promoted according to the plan, and improving the overall operation efficiency.

[0054] 5. By using machine learning algorithms to establish a non-linear relationship model and training and evaluating it in combination with historical data, accurate monitoring of the states of each link of the power grid construction supply chain is achieved. The model can dynamically capture the complex non-linear relationships between each link, adapt to environmental changes, and continuously optimize the monitoring accuracy. By regularly updating the model and real-time monitoring, the state changes of each link can be identified in time, so as to early warn of abnormal events, improve the flexibility and response ability of supply chain management, and ensure the efficient and safe operation of the power grid construction project. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic flowchart of a method for testing a digital supply chain of materials disclosed in an embodiment of the present application;

[0056] Figure 2 It is a schematic diagram of modules of a system for testing a digital supply chain of materials disclosed in an embodiment of the present application;

[0057] Figure 3 It is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0058] Explanation of reference numerals: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed implementation manners

[0059] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0060] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0061] In the description of the embodiments of the present application, the meaning of the term "a plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0062] The material supply chain in power grid construction projects involves multiple links and exhibits high nonlinearity and dynamic changes. Traditional methods rely on static data and historical analysis, making it difficult to capture the changes in each link in real time and unable to promptly respond to abnormal events such as production delays and transportation failures. Due to the large scale of the project, wide geographical distribution, and numerous stakeholders, existing methods have failed to effectively consider the characteristics of multivariable dynamic interactions, resulting in the inability to quickly adjust key links, thus affecting the supply chain efficiency, project progress, and safe operation. Therefore, there is an urgent need for a method that can monitor the status of each link in real time, respond to abnormal events, and formulate adjustment strategies.

[0063] This embodiment discloses a testing method for a digital material supply chain, referring to Figure 1 , and includes the following steps S110 - S150:

[0064] S110, based on the data acquisition devices deployed in multiple preset links of the power grid construction project, obtain the collected supply chain data.

[0065] The testing method for a digital material supply chain disclosed in the embodiments of this application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers). It can also be a background server running a testing method for a digital material supply chain. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0066] In the power grid construction project, based on the data acquisition devices deployed in multiple preset links, it is first necessary to install sensors, monitoring devices, or data acquisition terminals in multiple preset key links. The preset links include, but are not limited to, material production, transportation, equipment installation, etc. These sensors, monitoring devices, or data acquisition terminals can collect supply chain - related data in real time, such as production progress, inventory levels, transportation status, equipment operation conditions, etc. Through wireless networks or other data transmission methods, the collected data is transmitted to a central system (such as an electronic device) for analysis and processing. The collected data not only includes basic physical parameters such as temperature, humidity, location, load, etc., but may also involve information such as communication records between links, operation plans, and progress reports. In this embodiment, through the collection of these real - time data, the operation status of each link can be accurately grasped, providing data support for subsequent detection of status changes and response to abnormal events.

[0067] S120, based on the supply chain data and the nonlinear relationships of each preset link in the supply chain, detect the changes in the link status of each preset link in the supply chain to obtain a detection result.

[0068] In a possible implementation manner, based on the supply chain data and the non-linear relationships of various preset links in the supply chain, the state changes of each of the preset links in the supply chain are detected to obtain a detection result, including: establishing a non-linear relationship model between each of the preset links by using a machine learning algorithm; training the non-linear relationship model with historical data, and regularly updating the non-linear relationship model to obtain a trained non-linear relationship model; monitoring the supply chain data according to the trained non-linear relationship model to identify the state changes of each of the preset links.

[0069] Specifically, first, relevant data between different preset links need to be collected, including data on production, transportation, inventory, and other links. By selecting appropriate machine learning algorithms, such as neural networks, support vector machines, or decision trees, a complex non-linear relationship model between each link is established. This non-linear relationship model can describe the interactions and dynamic changes between each link, thus providing a basis for subsequent state change detection.

[0070] Next, a non-linear relationship model between each of the preset links is established by using a machine learning algorithm; the non-linear relationship model is trained with historical data, and the accuracy and generalization ability of the non-linear relationship model are evaluated by methods such as cross-validation, and the non-linear relationship model is regularly updated to adapt to the changes in the supply chain environment. Specifically, the constructed non-linear relationship model is trained with the collected historical data. The non-linear relationship model is evaluated by methods such as cross-validation to verify its accuracy and generalization ability, ensuring that the non-linear relationship model can effectively predict and describe the relationships between each link under different circumstances. As time goes by and the environment changes, the non-linear relationship model needs to be regularly updated to ensure that it can continuously adapt to the new supply chain conditions and actual situations, and finally a trained non-linear relationship model is obtained.

[0071] Exemplarily, the constructed non-linear relationship model (the trained non-linear relationship model) is used to monitor the supply chain data to identify the state changes of each of the preset links. Once the non-linear relationship model is established and verified, it can be applied to the real-time monitoring of supply chain data. The trained non-linear relationship model will perform real-time analysis on the data of each of the preset links to identify the state changes of the links, such as production schedule delays, inventory fluctuations, transportation anomalies, etc. By monitoring these changes to obtain the detection result, potential risks and abnormal events can be captured in a timely manner, thus providing a basis for subsequent adjustment strategies.

[0072] S130, determine whether an abnormal event has occurred according to the detection result. If an abnormal event is detected, then according to the abnormal event, an adjustment strategy is generated through preset rules and real-time calculation to adjust the production plan, inventory strategy, and transportation scheduling of each of the preset links.

[0073] Specifically, according to the detection results of the changes in the state of multiple preset links, if it is determined that an abnormal event is detected, an adjustment strategy is generated according to the abnormal event through preset rules and real-time calculation, and is used to adjust the production plan, inventory strategy, and transportation scheduling of each preset link.

[0074] In a possible implementation manner, the method further includes: predicting the current link state through the trained non-linear relationship model to obtain a predicted value; acquiring an observed value of the current link state collected in real time; calculating an error index between the predicted value and the observed value; wherein, the error index includes one of an absolute difference value, a relative difference value, a root mean square error value, or an average absolute error value; determining whether the error index is greater than or equal to a preset threshold; if it is determined that the error index is greater than or equal to the preset threshold, it is determined that an abnormal event exists in the current link state.

[0075] Specifically, before determining that an abnormal event is detected according to the detection results of the changes in the state of multiple preset links, it further includes: predicting the current link state through the trained non-linear relationship model to obtain a predicted value; acquiring an observed value of the current link state collected in real time. Exemplarily, calculate the absolute difference between the predicted value and the observed value, and determine whether the absolute difference is greater than or equal to a preset first threshold. If it is determined that the absolute difference is greater than or equal to the preset first threshold, it is determined that an abnormal event exists in the current link state. Exemplarily, calculate the relative difference value between the predicted value and the observed value, and determine whether the relative difference value is greater than or equal to a preset second threshold. If it is determined that the relative difference value is greater than or equal to the preset second threshold, it is determined that an abnormal event exists in the current link state. Exemplarily, calculate the root mean square error value between the predicted value and the observed value, and determine whether the root mean square error value is greater than or equal to a preset third threshold. If it is determined that the root mean square error value is greater than or equal to the preset third threshold, it is determined that an abnormal event exists in the current link state. Exemplarily, calculate the average absolute error value between the predicted value and the observed value, and determine whether the average absolute error value is greater than or equal to a preset fourth threshold. If it is determined that the average absolute error value is greater than or equal to the preset fourth threshold, it is determined that an abnormal event exists in the current link state.

[0076] Specifically, using the previously established non-linear relationship model (the trained non-linear relationship model), the ideal state or expected performance of the current process is predicted based on the real-time collected data. For example, in the production process, the trained non-linear relationship model will predict the current production progress or the operating state of the equipment according to the historical data and various relevant factors. This predicted value serves as the benchmark for subsequent determination of whether there are abnormalities. Real-time data acquisition devices deployed at each preset process are used to obtain the actual observed values of the current process. These observed values may involve data such as production progress, equipment operating state, transportation conditions, etc., which reflect the real situation of each process and are used for comparison with the predicted values. According to the obtained predicted values and observed values, comparisons are made through different error calculation methods. The absolute difference between the two, i.e., |predicted value - observed value|, the relative difference value, i.e., |(predicted value - observed value) / predicted value|, the root mean square error (RMSE), or the mean absolute error (MAE) can be calculated. These error metrics help to evaluate the deviation between the predicted value and the actual situation.

[0077] Specifically, according to different error metrics, comparisons are made with the preset thresholds. If any of the absolute difference, relative difference value, root mean square error, or mean absolute error exceeds the preset threshold, it means that the state of the current process deviates significantly from the expectation and there may be an abnormality. Each threshold corresponds to a different tolerance range and can be set according to the characteristics of the process. If a certain error metric exceeds its corresponding threshold, it is determined that an abnormal event exists in the current process. At this time, anomaly detection is triggered, relevant information is recorded, and a basis is provided for formulating subsequent adjustment strategies. In this embodiment, this process ensures a rapid response to changes in the process state and timely identification of potential supply chain problems.

[0078] For example, assume that in a power grid construction project, the material supply of a certain production process depends on the normal operation of the equipment. Through prediction by the non-linear relationship model, the equipment should operate at an efficiency of 95% at the current time point. The real-time data acquisition device reports that the current operating efficiency of the equipment is 90%. After calculation, the absolute difference between the predicted value and the observed value is 5%. Assume that the preset first threshold is 3%, that is, the allowable error range is 3%. Since the absolute difference (5%) is greater than the preset first threshold (3%), it is recognized that there is an abnormality in the equipment, and then the alarm mechanism is triggered, the abnormality is recorded, and subsequent adjustment strategies are initiated, such as optimizing the production plan, dispatching standby equipment, or adjusting the transportation route, to cope with the impact of the abnormal operation of the equipment.

[0079] In a possible implementation manner, according to the abnormal event, an adjustment strategy is generated through preset rules and real-time calculation, including: establishing a rule base corresponding to different abnormal events based on historical experience data; wherein, the rule base includes adjusting the production plan, optimizing the inventory strategy, and rescheduling transportation; determining the adjusted production plan corresponding to the abnormal event according to the rule base; determining the corresponding inventory optimization strategy in the rule base according to the demand change and inventory level of the abnormal event; if it is determined that there is a transportation delay or demand change in the abnormal event, recalculate and plan the transportation route and schedule.

[0080] Specifically, based on historical data and experience (historical experience data), a rule base for dealing with different abnormal events is established. The rule base includes adjusting the production plan, optimizing the inventory strategy, and rescheduling transportation; determining the corresponding adjusted production plan in the rule base according to the nature of the abnormal event; determining the corresponding inventory optimization strategy in the rule base according to the demand change and inventory level; if it is determined that there is a transportation delay or demand change, recalculate and plan the transportation route and schedule.

[0081] Specifically, first, based on past project experience and historical data analysis, a rule base is constructed, covering response strategies for various abnormal events, such as equipment failures, transportation delays, raw material shortages, etc. Each type of abnormal event will have a corresponding set of emergency handling rules, involving how to adjust the production plan, how to optimize the inventory strategy, and how to reschedule transportation. These rules are obtained through historical analysis, aiming to ensure that adjustments can be made quickly when an abnormality occurs, minimizing the impact on the entire supply chain.

[0082] Specifically, once an abnormal event is identified, the corresponding adjustment strategy will be searched for in the rule base according to the type of the abnormality. If the abnormal event is related to the production plan, such as a production halt caused by equipment failure, the rule for adjusting the production plan will be selected, which may include changing the production schedule, reallocating production tasks, or temporarily increasing shifts, etc. When the abnormal event is related to demand changes or inventory problems, such as a sudden increase in demand or insufficient inventory, the corresponding inventory optimization strategy will be selected from the rule base in combination with real-time inventory data and demand forecasts. This may include adjusting the safety inventory level, re-planning the material procurement plan, or optimizing inventory allocation, etc., to ensure that critical materials are in place in a timely manner and avoid production interruptions. If the abnormal event involves transportation delays or demand changes, the transportation route and scheduling plan will be recalculated. For example, if there is a transportation delay, the transportation route will be re-planned according to the real-time traffic conditions and the urgency of the demand, and the transportation mode may be adjusted, such as using air transportation instead of land transportation or changing the transportation time window. At the same time, the transportation frequency and the amount of materials will be adjusted according to the demand change to ensure the smooth connection of all links in the supply chain.

[0083] S140. Simulate the normal and abnormal states of each link of the grid construction project in a simulation environment to obtain influence parameters.

[0084] In a possible implementation manner, simulating the normal and abnormal states of each link of the grid construction project in a simulation environment to obtain influence parameters specifically includes: in a simulation platform, establishing a link model including multiple dimensions such as power equipment, communication systems, logistics transportation, and inventory management; installing sensors in the link model to simulate real-time collection of physical parameters and business data; wherein, the physical parameters include current, voltage, temperature, humidity, and pressure, and the business data includes production progress, inventory level, and transportation status; running each link model on the simulation platform according to the physical parameters to simulate the normal state of the grid construction project to obtain reference data; setting the normal operation parameters of each preset link according to the design and operation specifications of the grid construction project, and the normal operation parameters include equipment operation load, production progress, and inventory level; simulating the abnormal state of the abnormal event by adjusting the normal operation parameters to obtain abnormal parameters; evaluating the influence parameters of the abnormal event on the grid construction project based on the reference data and the abnormal parameters.

[0085] Specifically, reproduce each link of the grid construction project in a simulation environment, simulate the deployment of data collection equipment, simulate normal and abnormal states, and simulate the manufacturing of abnormal events: in a simulation platform, establish a link model including multiple dimensions such as power equipment, communication systems, logistics transportation, and inventory management; install sensors in key equipment and links to collect physical parameters and business data in real time; run each link model on the simulation platform according to the physical parameters to simulate the normal state of the grid construction project and obtain reference data; set the normal operation parameters of each preset link according to the design and operation specifications of the grid construction project, and the normal operation parameters include equipment operation load, production progress, and inventory level; according to the abnormal event, adjust the normal operation parameters to obtain abnormal parameters; calculate the influence of the abnormal event on the grid construction project based on the abnormal parameters, adjust the reference data, and simulate the influence of the external environment on the grid construction project to obtain influence parameters.

[0086] Specifically, on the simulation platform, a comprehensive power grid construction project model is built, covering multiple aspects such as power equipment, communication systems, logistics transportation, and inventory management. The behaviors of each aspect are defined through mathematical models or computer simulations to ensure that the dynamic interactions and non-linear relationships among various aspects can be accurately reproduced. Through multi-dimensional modeling, the simulation platform can simulate the key factors in the entire power grid construction process, ensuring that the simulated environment is close to the actual operation situation. Sensors and monitoring devices are installed on various key aspects and equipment of the power grid construction to collect real-time physical parameters such as current, voltage, temperature, humidity, pressure, and business data such as production progress, inventory level, and transportation status. These physical parameters and business data will be transmitted to the simulation platform for real-time analysis to ensure that they can dynamically reflect the actual operation of each aspect and provide accurate data support for subsequent state change detection and anomaly response.

[0087] Specifically, in the simulation platform, the physical parameters collected by the sensors are input into the corresponding aspect models, and each aspect of the power grid construction project is run to simulate the behavior of the supply chain system under normal conditions. This process will generate benchmark data, which serves as the standard values for the normal operation of the power grid construction. These benchmark data will be used for subsequent comparative analysis to judge the changes in the supply chain system when abnormal events occur. Based on the design and operation specifications of the power grid construction project, the normal operation parameters of each aspect are set. For example, the operating load of equipment, the production schedule, and the reasonable inventory level. Through these normal operation parameters, the operation effect of the supply chain system under normal conditions can be ensured and a reference for simulating the normal state can be provided.

[0088] Specifically, once an abnormal event is identified, the abnormal state can be simulated by adjusting the operation parameters. These abnormal parameters reflect the changes in the power grid construction caused by external factors or emergencies. For example, equipment failure may lead to a decrease in load, and transportation delays may cause the inventory level to not meet the standard. According to the adjusted abnormal parameters, calculate the actual impact of the abnormal event on the power grid construction. For example, equipment failure may lead to a delay in the production schedule, and transportation delays may cause a shortage of materials. By comparing the parameters in the abnormal state with the benchmark data, simulate the impact of the external environment on the power grid construction, evaluate the duration, scope of influence, and possible consequences of the delay or problem, and obtain the impact parameters. This helps to formulate subsequent adjustment strategies and emergency plans to mitigate the impact of abnormal events.

[0089] S150, optimize the adjustment strategy based on the impact parameters, simulate the optimized adjustment strategy and monitor the execution of the optimized adjustment strategy, and evaluate the impact of the optimized adjustment strategy on the supply chain according to the execution and preset evaluation indicators.

[0090] In a possible implementation, the adjustment strategy is optimized based on the impact parameters, the optimized adjustment strategy is simulated, and the execution of the optimized adjustment strategy is monitored, including: optimizing the adjustment strategy based on the impact parameters to obtain the optimized adjustment strategy; integrating the optimized adjustment strategy into a simulation platform, and configuring strategy parameters according to the optimized adjustment strategy; wherein, the strategy parameters include production speed, inventory level, and transportation frequency; deploying the optimized adjustment strategy based on the strategy parameters, and real-time collecting updated operation parameters of each of the preset links through a data collection device; wherein, the updated operation parameters include equipment operation load, production progress, and inventory level.

[0091] Specifically, the optimized adjustment strategy is deployed in the power grid system simulation, and the execution of the optimized adjustment strategy is monitored: integrating the optimized adjustment strategy into the power grid system simulation, and configuring strategy parameters according to the optimized adjustment strategy, where the strategy parameters include but are not limited to production speed, inventory level, and transportation frequency; through a data collection device, real-time collecting the updated operation parameters of each preset link after deploying the optimized adjustment strategy, and the updated operation parameters include but are not limited to equipment operation load, production progress, and inventory level.

[0092] Exemplarily, a pre-formulated adjustment strategy, such as production plan adjustment, inventory optimization, or transportation rescheduling, is integrated into the simulation platform of the power grid system. By configuring strategy parameters, the adjustment measures for each link are determined. For example, according to abnormal events, the production speed is adjusted to speed up or slow down the production progress, a reasonable inventory level is set, the inventory quantity is increased or decreased, or the transportation frequency is changed, etc. These parameters will directly affect the operation of each link and simulate the behavior of the supply chain after adjustment. After the adjustment strategy is deployed, a data collection device is used to real-time monitor the updated operation parameters of each link, such as equipment operation load, production progress, and inventory level. These data can reflect the actual effect of the optimized adjustment strategy and help verify whether the strategy improves the operation of the supply chain system as expected. For example, whether the equipment load in the production link returns to normal, whether the inventory level reaches the predetermined target, and whether the transportation frequency meets the demand, etc. The real-time collected data provides a basis for subsequent strategy optimization and adjustment.

[0093] In a possible implementation, the impact of the optimized adjustment strategy on the supply chain is evaluated according to the execution situation and preset evaluation indicators, including: determining preset evaluation indicators according to the objectives and requirements of the power grid construction project; wherein, the preset evaluation indicators include production efficiency, inventory turnover rate, transportation timeliness, and cost control; determining updated operation parameters according to the execution situation, performing statistical analysis on the updated operation parameters to obtain a statistical result; and quantitatively calculating the impact value of the optimized adjustment strategy on each of the preset links according to the preset evaluation indicators and the statistical result.

[0094] Specifically, evaluate the impact of the optimized adjustment strategy on the supply chain according to the preset evaluation indicators: Before implementing the optimized adjustment strategy, a series of evaluation indicators (preset evaluation indicators) need to be determined according to the overall goals and specific requirements of the power grid construction project. These preset evaluation indicators usually cover production efficiency (such as the completion speed and quality of production tasks), inventory turnover rate (i.e., the turnover frequency of inventory materials), transportation timeliness (i.e., the ability to complete transportation tasks on time), and cost control (i.e., the control effect of costs during the adjustment process). These preset evaluation indicators can help measure whether the adjustment strategy has achieved the expected effect and provide a comprehensive evaluation standard.

[0095] Specifically, after implementing the optimized adjustment strategy, it is necessary to collect and analyze the updated operation parameters of each link, such as equipment operation load, production progress, inventory level, etc. Through statistical analysis methods such as mean, standard deviation, and trend analysis, these data are quantitatively calculated, and the impact values of the optimized adjustment strategy on each link are calculated according to the requirements of the preset evaluation indicators. For example, by calculating the improvement rate of production efficiency, the change in inventory turnover rate, the improvement degree of transportation timeliness, and the effect of cost control, evaluate the actual impact of the optimized adjustment strategy on power grid construction and provide data support for subsequent strategy optimization.

[0096] This embodiment provides a method for testing a digital supply chain of materials, which can capture the real-time changes in the status of each link of the power grid construction project, formulate an adjustment strategy according to abnormal events and verify it.

[0097] This embodiment also discloses a testing system for a digital supply chain of materials. Refer to Figure 2 , the testing system for the digital supply chain of materials includes an acquisition module 201, a processing module 202, and an output module 203, where:

[0098] The acquisition module 201 is used to obtain the collected supply chain data based on the data acquisition devices deployed in multiple preset links of the power grid construction project.

[0099] The processing module 202 is used to detect the change in the link status of each preset link of the supply chain based on the supply chain data and the non-linear relationship of each preset link of the supply chain, and obtain a detection result.

[0100] The processing module 202 is further used to judge whether an abnormal event occurs according to the detection result. If an abnormal event is detected, an adjustment strategy is generated according to the abnormal event through preset rules and real-time calculation to adjust the production plan, inventory strategy, and transportation scheduling of each preset link.

[0101] The processing module 202 is further configured to simulate the normal state and abnormal state of each link of the power grid construction project in a simulation environment to obtain impact parameters.

[0102] The output module 203 is configured to optimize the adjustment strategy based on the impact parameters, simulate the optimized adjustment strategy, monitor the execution of the optimized adjustment strategy, and evaluate the impact of the optimized adjustment strategy on the supply chain according to the execution and preset evaluation indicators.

[0103] In a possible implementation manner, the processing module 202 is specifically configured to establish a link model including multiple dimensions of power equipment, communication systems, logistics transportation, and inventory management in a simulation platform; install sensors in the link model to simulate real-time collection of physical parameters and business data; where the physical parameters include current, voltage, temperature, humidity, and pressure, and the business data includes production progress, inventory level, and transportation status; run each link model on the simulation platform according to the physical parameters to simulate the normal state of the power grid construction project and obtain reference data; set the normal operation parameters of each preset link according to the design and operation specifications of the power grid construction project, where the normal operation parameters include equipment operation load, production progress, and inventory level; simulate the abnormal state of the abnormal event by adjusting the normal operation parameters to obtain abnormal parameters; evaluate the impact parameters of the abnormal event on the power grid construction project based on the reference data and the abnormal parameters.

[0104] In a possible implementation manner, the output module 203 is specifically configured to optimize the adjustment strategy based on the impact parameters to obtain an optimized adjustment strategy; integrate the optimized adjustment strategy into the simulation platform and configure strategy parameters according to the optimized adjustment strategy; where the strategy parameters include production speed, inventory level, and transportation frequency; deploy the optimized adjustment strategy based on the strategy parameters and collect the updated operation parameters of each preset link in real time through data collection devices; where the updated operation parameters include equipment operation load, production progress, and inventory level.

[0105] In a possible implementation manner, the output module 203 is specifically configured to determine preset evaluation indicators according to the objectives and requirements of the power grid construction project; where the preset evaluation indicators include production efficiency, inventory turnover rate, transportation timeliness, and cost control; determine the updated operation parameters according to the execution, perform statistical analysis on the updated operation parameters to obtain statistical results; and quantitatively calculate the impact value of the optimized adjustment strategy on each preset link according to the preset evaluation indicators and the statistical results.

[0106] In a possible implementation, the processing module 202 is specifically configured to establish a rule base corresponding to different abnormal events based on historical experience data; wherein, the rule base includes adjusting the production plan, optimizing the inventory strategy, and rescheduling transportation; determining the adjusted production plan corresponding to the abnormal event according to the rule base; determining the corresponding optimized inventory strategy in the rule base according to the demand change and inventory level of the abnormal event; if it is determined that there is a transportation delay or demand change in the abnormal event, recalculate and plan the transportation route and schedule.

[0107] In a possible implementation, the processing module 202 is specifically configured to establish a non-linear relationship model between the preset links by using a machine learning algorithm; train the non-linear relationship model with historical data, and regularly update the non-linear relationship model to obtain a trained non-linear relationship model; monitor the supply chain data according to the trained non-linear relationship model, and identify the state changes of each preset link.

[0108] In a possible implementation, the material digital supply chain test system further includes a comparison module, and the comparison module is configured to predict the current link state through the trained non-linear relationship model to obtain a predicted value; obtain the observed value of the current link state collected in real time; calculate the error index between the predicted value and the observed value; wherein, the error index includes one of an absolute difference value, a relative difference value, a root mean square error value, or an average absolute error value; determine whether the error index is greater than or equal to a preset threshold; if it is determined that the error index is greater than or equal to the preset threshold, it is determined that there is an abnormal event in the current link state.

[0109] This embodiment provides a material digital supply chain test system, which can capture the real-time changes in the states of all links of the power grid construction project, formulate adjustment strategies according to abnormal events, and verify them.

[0110] It should be noted that: when the material digital supply chain test system provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0111] This embodiment also discloses an electronic device, referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0112] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0113] Among them, the user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0114] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0115] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect all parts within the entire server. By running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, it executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately through a single chip.

[0116] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface 303 module, and an application program for a material digital supply chain testing method.

[0117] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; while the processor 301 can be used to call the application program for a material digital supply chain testing method stored in the memory 305. When executed by one or more processors 301, the electronic device executes the method as described in one or more of the above embodiments.

[0118] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0119] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] In several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some service interfaces. The indirect couplings or communication connections of devices or units can be in electrical or other forms.

[0121] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, in each embodiment of this application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of this application. And the aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0124] This application also discloses a computer-readable storage medium storing instructions. When executed by one or more processors 301, it enables the electronic device to execute the method as described in one or more of the above embodiments.

[0125] The foregoing are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the disclosure of the practical truth, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A material digital supply chain testing method, characterized in that: The method comprises: Based on the data collection equipment deployed in multiple preset links of the power grid construction project, the collected supply chain data is obtained; Based on the supply chain data and the nonlinear relationship between each preset link of the supply chain, detecting the link state change of each preset link of the supply chain to obtain the detection result; Determine whether an abnormal event occurs according to the detection result. If an abnormal event is detected, generate an adjustment strategy according to the abnormal event through preset rules and real-time calculation to adjust the production plan, inventory strategy and transportation scheduling of each preset link; Simulating normal and abnormal states of various links of the power grid construction project in a simulation environment to obtain influencing parameters; The adjustment strategy is optimized based on the influencing parameters, the optimized adjustment strategy is simulated and the execution of the optimized adjustment strategy is monitored, and the impact of the optimized adjustment strategy on the supply chain is evaluated according to the execution status and preset evaluation indicators.

2. The method according to claim 1, characterized in that The normal state and abnormal state of each link of the power grid construction project are simulated in the simulation environment to obtain the influencing parameters, which specifically include: In the simulation platform, a multi-dimensional link model including power equipment, communication system, logistics and transportation, and inventory management is established; Installing sensors in the link model to simulate real-time collection of physical parameters and business data; wherein the physical parameters include current, voltage, temperature, humidity and pressure, and the business data includes production progress, inventory level and transportation status; Running each of the link models on the simulation platform according to the physical parameters to simulate the normal state of the power grid construction project and obtain benchmark data; According to the design and operation specifications of the power grid construction project, the normal operating parameters of each of the preset links are set, and the normal operating parameters include equipment operating load, production progress and inventory level; Simulating the abnormal state of the abnormal event by adjusting the normal operating parameters to obtain abnormal parameters; The impact parameters of the abnormal event on the power grid construction project are evaluated based on the baseline data and the abnormal parameters.

3. The method according to claim 1, characterized in that The optimizing the adjustment strategy based on the influencing parameter, simulating the optimized adjustment strategy and monitoring the execution of the optimized adjustment strategy include: Optimizing the adjustment strategy based on the influencing parameters to obtain an optimized adjustment strategy; Integrate the optimized adjustment strategy into the simulation platform, and configure strategy parameters according to the optimized adjustment strategy; wherein the strategy parameters include production speed, inventory level, and transportation frequency; The optimized adjustment strategy is deployed based on the strategy parameters, and the updated operating parameters of each of the preset links are collected in real time through data collection equipment; wherein the updated operating parameters include equipment operating load, production progress and inventory level.

4. The method according to claim 3, characterized in that The evaluating the impact of the optimized adjustment strategy on the supply chain according to the execution status and preset evaluation indicators includes: Determine preset evaluation indicators according to the goals and requirements of the power grid construction project; wherein the preset evaluation indicators include production efficiency, inventory turnover rate, transportation timeliness and cost control; Determine updated operation parameters according to the execution status, and perform statistical analysis on the updated operation parameters to obtain statistical results; The impact value of the optimized adjustment strategy on each of the preset links is quantitatively calculated based on the preset evaluation index and the statistical results.

5. The method according to claim 1, characterized in that The step of generating an adjustment strategy according to the abnormal event through preset rules and real-time calculation includes: Establish a rule base corresponding to different abnormal events based on historical experience data; wherein the rule base includes adjusting production plans, optimizing inventory strategies and rescheduling transportation; Determining an adjusted production plan corresponding to the abnormal event according to the rule base; Determine the corresponding optimized inventory strategy in the rule base according to the demand change and inventory level of the abnormal event; If it is determined that the abnormal event involves transportation delays or changes in demand, the transportation route and scheduling are recalculated.

6. The method according to claim 1, characterized in that Based on the supply chain data and the nonlinear relationship between the preset links of the supply chain, detecting the link state changes of the preset links of the supply chain to obtain the detection results includes: Using a machine learning algorithm to establish a nonlinear relationship model between each of the preset links; Using historical data to train the nonlinear relationship model, regularly updating the nonlinear relationship model, and obtaining a trained nonlinear relationship model; The supply chain data is monitored according to the trained nonlinear relationship model to identify the status changes of each of the preset links.

7. The method according to claim 6, characterized in that The method further comprises: Predicting the current link state through the trained nonlinear relationship model to obtain a predicted value; Obtain the observation value of the current link status collected in real time; Calculating an error index between the predicted value and the observed value; wherein the error index includes one of an absolute difference, a relative difference, a root mean square error, or a mean absolute error; Determining whether the error index is greater than or equal to a preset threshold; If it is determined that the error index is greater than or equal to the preset threshold, it is determined that an abnormal event exists in the current link state.

8. A material digital supply chain testing system, characterized in that: include: An acquisition module, for acquiring collected supply chain data based on data acquisition devices deployed in multiple preset links of the power grid construction project; A processing module, used to detect the link state change of each preset link of the supply chain based on the supply chain data and the nonlinear relationship of each preset link of the supply chain to obtain a detection result; The processing module is further used to determine whether an abnormal event occurs according to the detection result. If an abnormal event is detected, an adjustment strategy is generated according to the abnormal event through preset rules and real-time calculation to adjust the production plan, inventory strategy and transportation scheduling of each preset link; The processing module is also used to simulate the normal state and abnormal state of each link of the power grid construction project in a simulation environment to obtain influencing parameters; An output module is used to optimize the adjustment strategy based on the influencing parameters, simulate the optimized adjustment strategy and monitor the execution of the optimized adjustment strategy, and evaluate the impact of the optimized adjustment strategy on the supply chain according to the execution status and preset evaluation indicators.

9. An electronic device, characterized in that: The electronic device comprises a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, the communication bus is used to realize connection and communication between components in the electronic device, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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