Electric power material supply management and control system and method thereof

Abstract

The invention discloses an electric power material supply management and control system and a method thereof. The electric power material supply management and control system comprises a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The electric power material supply management and control method comprises the following steps: S1, material demand prediction; s2, intelligent scheduling; s3, performing real-time monitoring; s4, risk assessment and early warning; and S5, emergency response is carried out. The electric power material supply management and control system and the method thereof can improve the material supply efficiency, enhance the decision support capability, make the decision more accurate and efficient, help to optimize resource configuration, improve the overall operation efficiency, improve the transparency and visualization of a supply chain, enhance risk management, guarantee the safe and stable operation of a power grid, improve the emergency response speed, and improve the power supply efficiency. The resource allocation is optimized, the inventory cost is reduced, the fund utilization efficiency is improved, the optimal allocation of resources is realized, and the electric power material requirements of each region can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of power material management, and in particular to a power material supply management and control system and method thereof. Background Art

[0002] There are many problems in the existing power material management system. In the traditional material supply model, material allocation often relies on manual judgment and experience, resulting in low allocation efficiency and the failure of materials to be delivered to the demand point in time. In the complex power material supply environment, decision makers cannot make decisions quickly. In the traditional supply chain management, the information in each link is not transparent, and it is difficult to monitor and track in real time. There are many potential risks in the power material supply process, such as supply interruption and inventory backlog. In emergencies or emergency situations, the traditional material supply model is often difficult to respond in time. Due to inaccurate demand forecasting or unintelligent scheduling algorithms, the traditional material supply model is prone to waste of resources or excessive hoarding. Therefore, it is particularly important to develop a power material supply management and control system that can improve material supply efficiency, enhance decision support capabilities, improve supply chain transparency and visualization, strengthen risk management, improve emergency response speed, and optimize resource allocation. Summary of the invention

[0003] The object of the present invention is to provide a power material supply management and control system and method thereof to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A power material supply management and control system and method thereof, the power material supply management and control system comprising a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform, the master control center is used for overall dispatching and decision-making, the sub-control center is used for the allocation and management of materials in the region, the material warehouse is used for storing and issuing materials, the mobile control terminal is used for on-site operations and real-time communication, and the data processing platform is used for data processing and analysis;

[0006] The power material supply management and control method comprises the following steps:

[0007] S1. Material demand forecast: Based on historical data and real-time information, data analysis algorithms are used to predict the demand for power materials in various regions in the future;

[0008] S2. Intelligent scheduling: Automatically generate material allocation plans based on forecast results and warehouse inventory to ensure that materials are delivered to demand points in a timely manner;

[0009] S3. Real-time monitoring: Use IoT technology to monitor the inventory status of material warehouses, material transportation status, and on-site operations in real time to ensure the continuity and safety of material supply;

[0010] S4. Risk assessment and early warning: Build a risk monitoring model to conduct risk assessment on each link in the supply chain. Once a potential risk is found, the early warning mechanism will be triggered immediately and pushed to relevant personnel for processing;

[0011] S5. Emergency response: In the event of an unexpected incident or emergency, the emergency response plan will be quickly activated and emergency materials will be automatically deployed to ensure the safe and stable operation of the power grid.

[0012] Preferably, the master control center also includes a decision support module, which can generate scheduling decisions based on data analysis results provided by the data processing platform. The decision support module is integrated with an artificial intelligence algorithm for automatically adjusting scheduling strategies based on historical data and real-time feedback information. The decision support module has a friendly user interface and supports manual input or adjustment of scheduling instructions.

[0013] Preferably, the mobile control terminal also has remote monitoring and remote control functions, which can be used for operators to monitor and remotely control material warehouses or transport vehicles in real time when they are away from the site. The mobile control terminal also integrates a GPS positioning module and electronic map service, which can display the location information of material warehouses, transport vehicles and on-site workers in real time. The mobile control terminal supports voice command recognition and gesture operation.

[0014] Preferably, the data processing platform also has a data visualization function for converting complex supply chain data into intuitive charts and reports. The data visualization function of the data processing platform also supports custom dashboards and interactive analysis interfaces, allowing managers to flexibly configure data display methods according to their own needs, and also supports data export and sharing functions.

[0015] Preferably, the electric power material supply control system also includes a supplier management platform for managing supplier qualification review, performance evaluation, contract management and supply progress tracking. The supplier management platform includes a supplier evaluation function, which conducts a comprehensive evaluation based on the supplier's historical supply record, product quality, delivery punctuality, price competitiveness and after-sales service quality to form a supplier performance scoring system. It also has an automatic reminder function. When the supplier's performance score is lower than a preset threshold, an early warning is automatically issued to the procurement department. The supplier management function also supports online bidding, contract management and payment process automation.

[0016] Preferably, the electric power material supply management and control system supports multi-source data fusion, can integrate information from different data sources, and form a unified and comprehensive supply chain view through data cleaning, conversion and integration. The multi-source data fusion function adopts advanced data integration technology and algorithms to effectively integrate data from different data sources, different formats and different frequencies. The electric power material supply management and control system supports two modes: real-time data stream processing and batch data processing. During the data fusion process, the electric power material supply management and control system also has data cleaning and deduplication functions to eliminate noise and redundant information in the data. The fused data is provided to each functional module of the system through a unified data model and interface.

[0017] Preferably, the electric power material supply management and control system supports multi-platform access from mobile and PC terminals. Users can choose suitable terminal devices according to their own needs to perform system operations and view data. The multi-platform access function is achieved by adopting responsive design technology and a cross-platform development framework. It can automatically identify the type of terminal device and operating system accessed by the user, and adjust the interface layout and interaction method to adapt to the screen size and operating habits of different devices. It also supports access methods of multiple browsers and mobile application clients.

[0018] Preferably, the electric power material supply management and control system is highly scalable and can support the rapid integration and deployment of new functional modules, data sources or user needs. The high scalability is reflected in multiple aspects such as system architecture design, functional module division and data storage solutions. It adopts microservice architecture and modular design ideas to split the system into multiple independent services and components. Each service and component has independent business logic and data storage capabilities. It also supports dynamic expansion and load balancing technology, and can automatically adjust resource allocation and the number of service instances according to system load conditions.

[0019] Preferably, in S1, the data analysis algorithm adopts a machine learning algorithm, which continuously learns and optimizes the accuracy of material demand forecasting by building a forecasting model, and uses the following formula to build and evaluate the forecasting model:

[0020] Prediction model building:

[0021] X represents the characteristic vector that affects material demand, and Y represents the future material demand. The prediction model f can be expressed as:

[0022]

[0023] in, is the predicted material demand, θ is the model parameter, which is optimized by learning the data set;

[0024] Loss function:

[0025] In order to evaluate the accuracy of the model prediction, a loss function L is used to quantify the predicted value. The difference between the actual value Y and the loss function is the mean square error:

[0026]

[0027] Where n is the sample size;

[0028] Optimization algorithm:

[0029] Use the optimization algorithm to minimize the loss function L(θ) to find the optimal model parameter θ ＊ :

[0030]

[0031] Machine learning algorithms combined with:

[0032] The machine learning algorithm is combined with time series analysis and association rule mining techniques to learn and optimize the model f.

[0033] Preferably, in S3, the real-time monitoring also includes monitoring of material quality and safety monitoring during material transportation. The temperature and humidity environmental parameters of the materials are detected by sensors. The driving status of the vehicle, driver behavior and road safety are monitored in real time by cameras and sensors installed on the transport vehicles. It also has an automatic alarm function, and once an abnormal situation is found, relevant personnel are notified immediately for processing.

[0034] Preferably, in S4, the risk assessment and early warning also includes an interface with a third-party service provider and the ability to self-learn and optimize, so as to call on external resources or expertise to assess specific risks or formulate response measures when necessary, and can automatically adjust the risk monitoring model and early warning thresholds based on historical risk events and response effects. It also supports risk simulation and plan rehearsal functions to help managers formulate effective response measures before risks occur.

[0035] Preferably, in S5, the emergency response plan includes multiple levels of response strategies and has an intelligent decision support function for automatically selecting a corresponding response level according to the severity of the emergency and triggering corresponding emergency measures, while providing detailed emergency operation guidelines and resource allocation plans.

[0036] Preferably, in S5, the emergency response also includes integration with the power dispatching system and joint drills and emergency drills, which can simulate the material supply and grid restoration process in power grid failures or emergencies, and also support the evaluation and analysis of emergency drill results.

[0037] Preferably, the power material supply management and control method also includes a quality traceability step, by assigning a unique identifier to each power material and recording its circulation information throughout the supply chain process, by connecting with the supplier's production management system, obtaining data on key links of raw material procurement, production and processing, and quality inspection, and forming a complete material production history.

[0038] Preferably, the power material supply management and control method also includes a knowledge base construction step, which builds a comprehensive knowledge base by collecting, organizing and analyzing historical cases, best practices, industry standards and regulatory requirements knowledge resources. The knowledge base construction step includes multiple links of knowledge collection, organization, classification, storage and retrieval. Knowledge resources are collected through crawler technology, manual entry and partner sharing. The collected knowledge is stored in the knowledge base after being organized and classified, and is provided to users through search engines and intelligent recommendation algorithms. It also supports the continuous updating and expansion functions of the knowledge base.

[0039] Preferably, the power material supply management and control method also includes a continuous optimization step, by regularly collecting user feedback, system performance data and market change information, the system function, performance, user experience aspects are continuously optimized and improved, the continuous optimization step includes regular collection of user feedback, system performance monitoring, data analysis and decision support multiple links, collect user feedback through user surveys, online comments and customer service hotlines, understand user satisfaction with the system and improvement suggestions, and also has a performance monitoring function, which can monitor the system's response time, throughput, error rate and other key indicators in real time, and generate performance reports for management personnel to refer to, based on user feedback and performance monitoring data, through data analysis technology to explore potential problems and improvement points, and generate optimization suggestions for decision makers to refer to, decision makers according to the optimization suggestions to develop specific optimization plans and implement them, so as to improve system performance and user experience;

[0040] The improvement of system performance and user satisfaction is calculated by weighted sum method, and the calculation formula is:

[0041] O=α·S+β·P+γ·C

[0042] Among them: O is the system optimization index, which indicates the degree of overall system optimization; S is user satisfaction, which can be collected and quantified through user surveys, online comments, etc.; P is system performance improvement, which can be calculated by comparing key performance indicators before and after optimization; C is market adaptability, which can be evaluated by analyzing the system's response speed and effect to changes in market demand, technological trends, etc.; α, β, γ are weight coefficients, which are used to indicate the importance of each factor in system optimization and can be adjusted according to actual conditions; the data of these indicators can be collected regularly and calculated using the above formula to quantify the effect of system optimization, and the optimization strategy can be adjusted according to the results.

[0043] Preferably, the power material supply management and control method also includes a data security and privacy protection step, which includes data encryption, access control, audit tracking and compliance checking through technical means such as data encryption, access control, audit tracking and compliance checking. Advanced encryption algorithms are used to encrypt, store and transmit sensitive data. At the same time, it also has a strict access control mechanism to limit user access rights to data through identity authentication and permission management.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The power material supply management and control system and method thereof can improve the efficiency of material supply, significantly improve the accuracy and timeliness of material supply, reduce the cost and risk caused by material shortage or surplus, enhance decision-making support capabilities, and be able to carry out overall scheduling and decision-making more scientifically, making decisions more accurate and efficient, helping to optimize resource allocation, improve overall operational efficiency, improve supply chain transparency and visualization, realize full supply chain visualization, help to timely discover problems and respond quickly, and also enhance the transparency and traceability of the supply chain, improve management efficiency, strengthen risk management, enable managers to take measures in advance to prevent risks, help reduce the risk of supply chain disruptions, ensure the safe and stable operation of the power grid, and improve the emergency response speed. It is of great significance to ensure the safe and stable operation of the power grid under extreme conditions, optimize resource allocation, reasonably allocate material resources, avoid resource waste and excessive hoarding, help reduce inventory costs, improve capital utilization efficiency, achieve optimal resource allocation, improve customer satisfaction, and better meet the power material needs of various regions, thereby improving customer satisfaction and trust, which is conducive to establishing a good corporate image and market reputation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The system framework of the power material supply control system provided by the present invention Figure 1 ;

[0047] Figure 2The system framework of the power material supply control system provided by the present invention Figure 2 ;

[0048] Figure 3 The present invention provides a method for controlling the supply of electric power materials. Figure 1 ;

[0049] Figure 4 The present invention provides a method for controlling the supply of electric power materials. Figure 2 . DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1-4 The present invention provides a power material supply management and control system and method thereof. The power material supply management and control system includes a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The master control center is used for overall scheduling and decision-making, the sub-control center is used for the allocation and management of materials in the region, the material warehouse is used for storing and issuing materials, the mobile control terminal is used for on-site operations and real-time communication, and the data processing platform is used for data processing and analysis;

[0053] The power material supply control method includes the following steps:

[0054] S1. Material demand forecast: Based on historical data and real-time information, data analysis algorithms are used to predict the demand for power materials in various regions in the future;

[0055] S2. Intelligent scheduling: Automatically generate material allocation plans (including allocation quantity, allocation time, and allocation path, etc.) based on forecast results and warehouse inventory status to ensure that materials are delivered to demand points in a timely manner;

[0056] S3. Real-time monitoring: Use IoT technology to monitor the inventory status of material warehouses, material transportation status, and on-site operations in real time to ensure the continuity and safety of material supply;

[0057] S4. Risk assessment and early warning: Build a risk monitoring model to conduct risk assessment on each link in the supply chain. Once a potential risk is found, the early warning mechanism will be triggered immediately and pushed to relevant personnel for processing;

[0058] S5. Emergency response: In the event of an unexpected incident or emergency, the emergency response plan will be quickly activated and emergency materials will be automatically deployed to ensure the safe and stable operation of the power grid.

[0059] The power material supply management and control system and method can improve the efficiency of material supply: through the intelligent dispatching system, combined with the material demand forecast and warehouse inventory status, the optimal material allocation plan is automatically generated, including the allocation quantity, time and path, etc., which significantly improves the accuracy and timeliness of material supply and reduces the cost and risk caused by material shortage or surplus; enhances decision-making support capabilities: based on the large amount of real-time and historical data provided by the data processing platform, the general control center can make overall dispatch and decision-making more scientifically, and adopts a data-driven decision-making method to make decisions more accurate and efficient, which helps to optimize resource allocation and improve overall operational efficiency; improves the transparency and visualization of the supply chain: through the Internet of Things technology, the inventory status of the material warehouse, the material transportation status and the on-site operation status are monitored in real time, and the whole supply chain is visualized, which not only helps to find problems in time and respond quickly, but also enhances the transparency and traceability of the supply chain and improves management efficiency; strengthens risk management: builds The risk monitoring model can comprehensively assess the potential risks in the supply chain and trigger the early warning mechanism in time, so that managers can take measures in advance to prevent risks. Through active risk management, it helps to reduce the risk of supply chain disruptions and ensure the safe and stable operation of the power grid; improve emergency response speed: in the event of an emergency or emergency, the system can quickly initiate an emergency response plan and automatically allocate emergency materials, greatly shortening the response time, which is of great significance for ensuring the safe and stable operation of the power grid under extreme conditions; optimize resource allocation: through accurate demand forecasting and intelligent scheduling algorithms, the system can reasonably allocate material resources to avoid resource waste and excessive hoarding, help reduce inventory costs, improve capital utilization efficiency, and achieve optimal resource allocation; improve customer satisfaction: because the supply of materials is more timely and accurate, it can better meet the power material needs of various regions, thereby improving customer satisfaction and trust, and is conducive to establishing a good corporate image and market reputation.

[0060] Among them, the above-mentioned general control center also includes a decision support module, which can generate scheduling decisions based on the data analysis results provided by the data processing platform to optimize the efficiency and accuracy of material allocation. The decision support module is integrated with an artificial intelligence algorithm, which is used to automatically adjust the scheduling strategy according to historical data and real-time feedback information to respond to emergencies and market changes, and ensure the efficiency and flexibility of material supply. The decision support module has a friendly user interface and supports manual input or adjustment of scheduling instructions to adapt to special needs or emergencies; the above-mentioned mobile control terminal also has remote monitoring and remote control functions, which is used for operators to monitor and remotely control material warehouses or transport vehicles in real time when they are away from the site. The mobile control terminal is also integrated with GPS positioning The location module and electronic map service can display the location information of material warehouses, transport vehicles and on-site workers in real time, realize accurate scheduling and rapid response, and the mobile control terminal supports voice command recognition and gesture operation to improve the convenience and efficiency of on-site operations; the above-mentioned data processing platform also has data visualization function, which is used to convert complex supply chain data into intuitive charts and reports, so that managers can quickly understand the supply chain status and make decisions. The data visualization function of the data processing platform also supports custom dashboards and interactive analysis interfaces, allowing managers to flexibly configure data display methods according to their own needs and deeply explore the value behind the data. It also supports data export and sharing functions to facilitate data exchange and collaboration with other departments or partners.

[0061] Secondly, the above-mentioned power material supply control system also includes a supplier management platform, which is used to manage the supplier's qualification review, performance evaluation, contract management and supply progress tracking to ensure that suppliers can provide power materials on time, according to quality and quantity, and improve the reliability and stability of the supply chain. The supplier management platform includes a supplier evaluation function, which is based on the supplier's historical supply record, product quality, delivery punctuality, price competitiveness and after-sales service quality. The comprehensive evaluation is based on multiple dimensions to form a supplier performance scoring system. It also has an automatic reminder function. When the supplier's performance score is lower than the preset threshold, it automatically sends an early warning to the procurement department so that timely measures can be taken to improve the supplier's performance or find alternative suppliers. The supplier management function also supports online bidding, contract management and payment process automation to improve procurement efficiency and transparency.

[0062] At the same time, the power material supply control system supports multi-source data fusion, and can integrate information from different data sources (such as sensors, ERP systems, CRM systems, etc.). Through data cleaning, conversion and integration, a unified and comprehensive supply chain view is formed to provide more accurate and comprehensive data support for decision-making. The multi-source data fusion function uses advanced data integration technology and algorithms to effectively integrate data from different data sources, different formats and different frequencies. The power material supply control system supports two modes: real-time data stream processing and batch data processing to ensure the timeliness and accuracy of the data. During the data fusion process, the power material supply control system also has data cleaning and deduplication functions to eliminate noise and redundant information in the data. The fused data is provided to each functional module of the system through a unified data model and interface, providing comprehensive and accurate data support for decision-making; the power material supply control system supports multi-platform access on mobile and PC terminals. Users can choose suitable terminal devices for system operation and data viewing according to their own needs, thereby improving the flexibility and convenience of the system. The multi-platform access function is It adopts responsive design technology and cross-platform development framework, and can automatically identify the type of terminal device (such as mobile phone, tablet, computer, etc.) and operating system (such as iOS, Android, Windows, etc.) accessed by users, and adjust the interface layout and interaction method to adapt to the screen size and operation habits of different devices. It also supports multiple browsers and mobile application client access methods to ensure that users can access the system and obtain the required information through any device at any time and any place; the power material supply management and control system is highly scalable and can support the rapid integration and deployment of new functional modules, data sources or user needs, reducing the cost and risk of system upgrades and maintenance. The high scalability is reflected in the system architecture design, functional module division and data storage solution. It adopts microservice architecture and modular design ideas to split the system into multiple independent services and components. Each service and component has independent business logic and data storage capabilities. It also supports dynamic expansion and load balancing technology, and can automatically adjust resource allocation and the number of service instances according to system load conditions to ensure system stability and efficiency.

[0063] In S1 of the above-mentioned power material supply control method, the data analysis algorithm adopts a machine learning algorithm, which continuously learns and optimizes the accuracy of material demand forecasting by building a forecasting model. The following formula is used to build and evaluate the forecasting model:

[0064] Prediction model building:

[0065] X represents the characteristic vector that affects material demand (such as historical demand, weather conditions, economic activity index, etc.), and Y represents future material demand. The forecast model f can be expressed as:

[0066]

[0067] in, is the predicted material demand, θ is the model parameter, which is optimized by learning the data set;

[0068] Loss function:

[0069] In order to evaluate the accuracy of the model prediction, a loss function L is used to quantify the predicted value. The difference between the actual value Y and the loss function is the mean square error:

[0070]

[0071] Where n is the sample size;

[0072] Optimization algorithm:

[0073] Use an optimization algorithm (such as gradient descent) to minimize the loss function L(θ) to find the optimal model parameters θ * :

[0074]

[0075] Machine learning algorithms combined with:

[0076] Machine learning algorithms (such as linear regression, decision trees, random forests, neural networks, etc.) are combined with time series analysis and association rule mining technology to learn and optimize model f and improve the accuracy of material demand forecasts. In addition, the algorithm can automatically identify and eliminate abnormal data to further ensure the stability and reliability of the forecast results.

[0077] In S3, real-time monitoring also includes monitoring of material quality and safety monitoring during material transportation. Sensors are used to detect the temperature and humidity environmental parameters of materials to ensure that the quality of materials is not damaged during storage and transportation. Cameras and sensors installed on transport vehicles monitor the vehicle's driving status, driver behavior and road safety in real time to ensure the safety of materials during transportation. It also has an automatic alarm function to immediately notify relevant personnel to handle any abnormal situation.

[0078] In S4, risk assessment and early warning also include interfaces with third-party service providers and the ability to self-learn and optimize, so that external resources or expertise can be called upon when necessary to assess specific risks or develop countermeasures. It can automatically adjust risk monitoring models and early warning thresholds based on historical risk events and response effects, thereby improving the accuracy of risk assessment and the timeliness of early warning. It also supports risk simulation and plan rehearsal functions to help managers develop effective countermeasures before risks occur.

[0079] In S5, the emergency response plan includes multiple levels of response strategies and intelligent decision-making support functions, which are used to automatically select the corresponding response level according to the severity of the emergency and trigger the corresponding emergency measures. It can automatically evaluate and select the most appropriate response level and measure combination according to the nature, scope of impact and urgency of the emergency, and provide detailed emergency operation guidelines and resource allocation plans to ensure that the emergency response is rapid, orderly and efficient; emergency response also includes integration with the power dispatching system and joint drills and emergency drills to ensure that in the event of power grid failure or emergency, power supply and grid restoration work can be carried out in coordination to improve the overall emergency response efficiency. It can simulate the material supply and grid restoration process in the event of power grid failure or emergency, and improve the coordination ability and emergency response speed among departments through joint drills. It also supports the evaluation and analysis of emergency drill results to help managers continuously optimize emergency plans and response processes.

[0080] Secondly, the power material supply control method also includes a quality tracing step, a knowledge base construction step, a continuous optimization step, and a data security and privacy protection step;

[0081] The specific steps of quality traceability are as follows: by assigning a unique identifier (such as a QR code, RFID tag, etc.) to each piece of power material and recording its circulation information throughout the supply chain, the quality of the materials can be traced throughout the process. Once a problem is found, it can be quickly located and handled. By connecting with the supplier's production management system, data on key links such as raw material procurement, production and processing, and quality inspection can be obtained to form a complete material production history. During the use of materials, once a quality problem is found, it can be quickly traced back to the specific production batch, production line, or even the operator, providing strong support for problem analysis and accountability; by collecting, organizing and analyzing historical cases, best practices, and Practice, industry standards and regulatory requirements knowledge resources, build a comprehensive knowledge base, provide managers with decision support, problem diagnosis and solution reference, the knowledge base construction steps include knowledge collection, organization, classification, storage and retrieval, collect knowledge resources (including technical documents, operation manuals, case analysis, industry standards and regulatory requirements) through crawler technology, manual entry and partner sharing, the collected knowledge is stored in the knowledge base after being organized and classified, and provided to users through search engines and intelligent recommendation algorithms, and also supports the continuous update and expansion of the knowledge base to adapt to the ever-changing business needs and market environment.

[0082] The specific steps of continuous optimization are: by regularly collecting user feedback, system performance data and market change information, the system's functions, performance and user experience are continuously optimized and improved to adapt to the ever-changing business needs and market environment. The continuous optimization steps include regular collection of user feedback, system performance monitoring, data analysis and decision support. User feedback is collected through user surveys, online comments and customer service hotlines to understand user satisfaction with the system and improvement suggestions. It also has performance monitoring functions, which can monitor the system's response time, throughput, error rate and other key indicators in real time, and generate performance reports for management personnel to refer to. Based on user feedback and performance monitoring data, data analysis technology is used to explore potential problems and improvement points, and optimization suggestions are generated for decision makers to refer to. Decision makers formulate specific optimization plans based on the optimization suggestions and implement them to improve system performance and user experience.

[0083] The improvement of system performance and user satisfaction is calculated by weighted sum method, and the calculation formula is:

[0084] O=α·S+β·P+γ·C

[0085] Among them: O is the system optimization index, which indicates the degree of overall system optimization; S is user satisfaction, which can be collected and quantified through user surveys, online comments, etc. (such as expressed in percentage or rating); P is system performance improvement, which can be calculated by comparing key performance indicators before and after optimization (such as response time, throughput, error rate, etc.); C is market adaptability, which can be evaluated by analyzing the system's response speed and effect to changes in market demand, technological trends, etc.; α, β, γ are weight coefficients, which are used to indicate the importance of each factor in system optimization and can be adjusted according to actual conditions; the data of these indicators can be collected regularly and calculated using the above calculation formula to quantify the effect of system optimization and adjust the optimization strategy according to the results.

[0086] The specific steps for data security and privacy protection are: through technical means such as data encryption, access control, audit tracking, etc., to ensure the security, integrity and privacy of supply chain data, prevent data leakage and illegal access. The steps for data security and privacy protection include data encryption, access control, audit tracking and compliance checks. Advanced encryption algorithms are used to encrypt storage and transmission of sensitive data to ensure the security of data during storage and transmission. At the same time, it also has a strict access control mechanism to limit user access rights to data through identity authentication and permission management.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power material supply management and control system and method, characterized in that: The power material supply management and control system includes a main control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The main control center is used for overall scheduling and decision-making, the sub-control center is used for the allocation and management of materials in the region, the material warehouse is used for storing and issuing materials, the mobile control terminal is used for on-site operations and real-time communication, and the data processing platform is used for data processing and analysis; The power material supply management and control method comprises the following steps: S1. Material demand forecast: Based on historical data and real-time information, data analysis algorithms are used to predict the demand for power materials in various regions in the future; S2. Intelligent scheduling: Automatically generate material allocation plans based on forecast results and warehouse inventory to ensure that materials are delivered to demand points in a timely manner; S3. Real-time monitoring: Use IoT technology to monitor the inventory status of material warehouses, material transportation status, and on-site operations in real time to ensure the continuity and safety of material supply; S4. Risk assessment and early warning: Build a risk monitoring model to conduct risk assessment on each link in the supply chain. Once a potential risk is found, the early warning mechanism will be triggered immediately and pushed to relevant personnel for processing; S5. Emergency response: In the event of an unexpected incident or emergency, the emergency response plan will be quickly activated and emergency materials will be automatically deployed to ensure the safe and stable operation of the power grid.

2. The power material supply management and control system and method according to claim 1, characterized in that: The master control center also includes a decision support module, which can generate scheduling decisions based on the data analysis results provided by the data processing platform. The decision support module is integrated with an artificial intelligence algorithm for automatically adjusting the scheduling strategy based on historical data and real-time feedback information. The decision support module has a friendly user interface and supports manual input or adjustment of scheduling instructions.

3. The power material supply management and control system and method according to claim 1, characterized in that: The data processing platform also has a data visualization function for converting complex supply chain data into intuitive charts and reports. The data visualization function of the data processing platform also supports custom dashboards and interactive analysis interfaces, allowing managers to flexibly configure data display methods according to their own needs, and also supports data export and sharing functions.

4. The power material supply management and control system and method according to claim 1, characterized in that: The power material supply control system also includes a supplier management platform for managing supplier qualification review, performance evaluation, contract management and supply progress tracking. The supplier management platform includes a supplier evaluation function, which conducts a comprehensive evaluation based on the supplier's historical supply record, product quality, delivery punctuality, price competitiveness and after-sales service quality to form a supplier performance scoring system. It also has an automatic reminder function. When the supplier's performance score is lower than the preset threshold, an early warning is automatically issued to the procurement department. The supplier management function also supports online bidding, contract management and payment process automation.

5. The power material supply management and control system and method according to claim 1, characterized in that: In S1, the data analysis algorithm adopts a machine learning algorithm, which continuously learns and optimizes the accuracy of material demand forecasting by building a forecasting model. The following formula is used to build and evaluate the forecasting model: Prediction model building: X represents the characteristic vector that affects material demand, and Y represents the future material demand. The prediction model f can be expressed as: in, is the predicted material demand, θ is the model parameter, which is optimized by learning the data set; Loss function: In order to evaluate the accuracy of the model prediction, a loss function L is used to quantify the predicted value. The difference between the actual value Y and the loss function is the mean square error: Where n is the sample size; Optimization algorithm: Use the optimization algorithm to minimize the loss function L(θ) to find the optimal model parameter θ · : Machine learning algorithms combined with: The machine learning algorithm is combined with time series analysis and association rule mining techniques to learn and optimize the model f.

6. The power material supply management and control system and method according to claim 1, characterized in that: In S3, the real-time monitoring also includes monitoring of material quality and safety monitoring during material transportation. The temperature and humidity environmental parameters of the materials are detected by sensors. The driving status of the vehicle, driver behavior and road safety are monitored in real time through cameras and sensors installed on the transport vehicles. It also has an automatic alarm function, and once an abnormal situation is found, relevant personnel will be notified immediately for processing.

7. The power material supply management and control system and method according to claim 1, characterized in that: In S5, the emergency response plan includes multiple levels of response strategies and has intelligent decision support functions, which are used to automatically select the corresponding response level according to the severity of the emergency and trigger corresponding emergency measures, while providing detailed emergency operation guidelines and resource allocation plans.

8. The power material supply management and control system and method according to claim 1, characterized in that: The power material supply control method also includes a quality traceability step, which assigns a unique identifier to each power material and records its circulation information throughout the supply chain process. By connecting with the supplier's production management system, data on key links such as raw material procurement, production and processing, and quality inspection are obtained to form a complete material production history.

9. The power material supply management and control system and method according to claim 1, characterized in that: The power material supply management and control method also includes a knowledge base construction step, which builds a comprehensive knowledge base by collecting, organizing and analyzing historical cases, best practices, industry standards and regulatory requirements. The knowledge base construction step includes multiple links of knowledge collection, organization, classification, storage and retrieval. Knowledge resources are collected through crawler technology, manual entry and partner sharing. The collected knowledge is stored in the knowledge base after being organized and classified, and is provided to users through search engines and intelligent recommendation algorithms. It also supports the continuous updating and expansion functions of the knowledge base.

10. The power material supply management and control system and method according to claim 1, characterized in that: The power material supply management and control method also includes a continuous optimization step, which continuously optimizes and improves the system's functions, performance, and user experience by regularly collecting user feedback, system performance data, and market change information. The continuous optimization step includes multiple links such as regular collection of user feedback, system performance monitoring, data analysis, and decision support. User feedback is collected through user surveys, online comments, and customer service hotlines to understand user satisfaction with the system and improvement suggestions. It also has a performance monitoring function that can monitor multiple key indicators of the system, such as response time, throughput, and error rate, in real time, and generate performance reports for reference by managers. Based on user feedback and performance monitoring data, potential problems and improvement points are discovered through data analysis technology, and optimization suggestions are generated for reference by decision makers. Decision makers formulate specific optimization plans based on the optimization suggestions and implement them to improve system performance and user experience. The improvement of system performance and user satisfaction is calculated by weighted sum method, and the calculation formula is: O=α·S+β·P+γ·C Among them: O is the system optimization index, which indicates the degree of overall system optimization; S is user satisfaction, which can be collected and quantified through user surveys, online comments, etc.; P is system performance improvement, which can be calculated by comparing key performance indicators before and after optimization; C is market adaptability, which can be evaluated by analyzing the system's response speed and effect to changes in market demand, technological trends, etc.; α, β, γ are weight coefficients, which are used to indicate the importance of each factor in system optimization and can be adjusted according to actual conditions; the data of these indicators can be collected regularly and calculated using the above calculation formula to quantify the effect of system optimization and adjust the optimization strategy according to the results.

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