Resource optimization scheduling method and system based on physical ID driving

Abstract

The invention relates to the technical field of resource scheduling, in particular to a resource optimization scheduling method and system based on physical ID driving. The method comprises the following steps: performing power plant position data acquisition on a target city to obtain a power plant position coordinate set; building a power plant distribution map for the target city based on the power plant position coordinate set to obtain an urban power plant distribution map; carrying out material warehouse layout analysis on the target city based on the urban power plant distribution map to obtain a material warehouse distribution map; performing standby material demand analysis on the power plant to obtain a warehouse inventory material list; and according to the material warehouse distribution map and the warehouse inventory material list, carrying out physical ID coding on the inventory materials to obtain a global resource RFID mapping table. According to the invention, the intelligence and automation of material management are enhanced, the management complexity is reduced, and the allocation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling, and in particular to a resource optimization scheduling method and system driven by physical ID. Background Art

[0002] In the field of resource scheduling, especially for the material management and scheduling of key infrastructure such as power systems, traditional scheduling methods often face many challenges. First, the existing resource scheduling system lacks precision in material demand analysis, and it is difficult to accurately predict the types of power plant accidents and their material consumption patterns, which leads to insufficient response speed and accuracy in material allocation when faced with emergencies. Secondly, the layout of material warehouses often does not fully take into account the complexity and dynamic changes of urban transportation networks, which limits the efficiency of material transportation in emergency situations and prevents them from reaching the required locations quickly.

[0003] Furthermore, the existing resource scheduling system has defects in material coding and identification, and lacks a unified physical ID coding mechanism, which not only increases the complexity of material management, but also affects the efficiency and accuracy of material allocation. In addition, the path planning in the material allocation process often does not make full use of modern information technology, such as RFID technology, to achieve real-time tracking and intelligent scheduling of materials, which limits the intelligence level and automation of the scheduling system. In addition, when faced with large-scale, multi-regional material allocation, the existing resource scheduling system often lacks effective strategies and tools to achieve global optimization of resources. This results in the material allocation plan not being economical or efficient enough in actual operation, and failing to maximize resource utilization efficiency and reduce operating costs. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a resource optimization scheduling method and system driven by physical ID to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a resource optimization scheduling method based on physical ID drive includes the following steps:

[0006] Step S1: Collect power plant location data for the target city to obtain a power plant location coordinate set; construct a power plant distribution map for the target city based on the power plant location coordinate set to obtain a city power plant distribution map; analyze the material warehouse layout for the target city based on the city power plant distribution map to obtain a material warehouse distribution map;

[0007] Step S2: Analyze the demand for spare materials of the power plant and obtain a list of warehouse inventory materials; encode the physical ID of the inventory materials according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table;

[0008] Step S3: Collect material demand work orders for the target power plant to obtain material demand work orders for the power plant; identify the logistics mode of the target power plant to obtain material acquisition method data;

[0009] Step S4: Based on the urban power plant distribution map, the material warehouse distribution map and the global resource RFID mapping table and according to the power plant material demand work order, materials are allocated to the target power plant to obtain a material coordinated allocation plan;

[0010] Step S5: According to the coordinated material allocation plan, a dispatching path is planned for the target power plant to obtain a material dispatching path map; and material dispatching operations are performed on the target power plant according to the material acquisition method data and the material dispatching path map.

[0011] The present invention significantly improves the speed and accuracy of rapid response to emergency material needs by accurately collecting power plant location data and constructing a distribution map. At the same time, through in-depth analysis of the urban transportation network, the geographical location of the material warehouse is optimized to ensure that the materials can be efficiently and quickly transported to the required location. In addition, by utilizing physical ID coding and RFID technology, this solution enhances the intelligence and automation of material management, reduces management complexity, and improves deployment efficiency. Through the strategy of global optimization, this solution not only improves resource utilization efficiency, but also reduces operating costs, achieving dual optimization of economy and efficiency. Through the implementation of intelligent path planning, combined with real-time traffic data, the timeliness and reliability of material transportation are ensured. This solution also demonstrates a high degree of flexibility and adaptability, can meet the material scheduling needs under different logistics modes, and can be easily adjusted and expanded according to new data and needs.

[0012] Preferably, the present invention further provides a resource optimization scheduling system driven by a physical ID, which is used to execute the resource optimization scheduling method driven by a physical ID as described above, and the resource optimization scheduling system driven by a physical ID comprises:

[0013] The warehouse layout module is used to collect the power plant location data of the target city to obtain the power plant location coordinate set; construct the power plant distribution map of the target city based on the power plant location coordinate set to obtain the city power plant distribution map; analyze the material warehouse layout of the target city based on the city power plant distribution map to obtain the material warehouse distribution map;

[0014] The physical coding module is used to analyze the demand for spare materials in the power plant and obtain a list of warehouse inventory materials; the inventory materials are physically ID-coded according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table;

[0015] The demand identification module is used to collect material demand work orders for the target power plant and obtain the material demand work orders for the power plant; identify the logistics mode of the target power plant and obtain the material acquisition method data;

[0016] The material allocation module is used to allocate materials to the target power plant based on the urban power plant distribution map, material warehouse distribution map and global resource RFID mapping table and according to the power plant material demand work order to obtain a coordinated material allocation plan;

[0017] The scheduling planning module is used to plan the scheduling path for the target power plant according to the material coordination allocation plan to obtain the material scheduling path map; and perform material scheduling operations on the target power plant according to the material acquisition method data and the material scheduling path map.

[0018] In the present invention, by collecting precise location data and constructing a distribution map of the target city power plant, data support is provided for the reasonable layout of the material warehouse, ensuring the realization of rapid response and efficient material allocation. The physical coding module in the system significantly improves the accuracy and tracking efficiency of material management through demand analysis and the creation of RFID mapping tables. The intelligent automatic collection and logistics pattern recognition of the demand identification module provide accurate input information for material allocation, enhancing the adaptability and flexibility of the system. The material allocation module uses comprehensive data resources to formulate a collaborative allocation plan and optimize the resource allocation and scheduling process. The intelligent path planning of the scheduling planning module further ensures the economy and efficiency of material transportation. The automated operation of the entire system reduces human errors and improves operating efficiency and accuracy. The design of the system not only improves the overall performance of resource scheduling, but also reduces operating costs and enhances reliability and stability through its automation, intelligence and collaboration characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1 A schematic flow chart of the steps of a resource optimization scheduling method driven by a physical ID according to an embodiment is shown.

[0021] Figure 2 A detailed flow chart of step S2 of an embodiment is shown.

[0022] Figure 3 A detailed flow chart of step S3 of an embodiment is shown. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a resource optimization scheduling method based on physical ID drive, comprising the following steps:

[0027] Step S1: Collect power plant location data for the target city to obtain a power plant location coordinate set; construct a power plant distribution map for the target city based on the power plant location coordinate set to obtain a city power plant distribution map; analyze the material warehouse layout for the target city based on the city power plant distribution map to obtain a material warehouse distribution map;

[0028] Step S2: Analyze the demand for spare materials of the power plant and obtain a list of warehouse inventory materials; encode the physical ID of the inventory materials according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table;

[0029] Step S3: Collect material demand work orders for the target power plant to obtain material demand work orders for the power plant; identify the logistics mode of the target power plant to obtain material acquisition method data;

[0030] Step S4: Based on the urban power plant distribution map, the material warehouse distribution map and the global resource RFID mapping table and according to the power plant material demand work order, materials are allocated to the target power plant to obtain a material coordinated allocation plan;

[0031] Step S5: According to the coordinated material allocation plan, a dispatching path is planned for the target power plant to obtain a material dispatching path map; and material dispatching operations are performed on the target power plant according to the material acquisition method data and the material dispatching path map.

[0032] The present invention significantly improves the speed and accuracy of rapid response to emergency material needs by accurately collecting power plant location data and constructing a distribution map. At the same time, through in-depth analysis of the urban transportation network, the geographical location of the material warehouse is optimized to ensure that the materials can be efficiently and quickly transported to the required location. In addition, by utilizing physical ID coding and RFID technology, this solution enhances the intelligence and automation of material management, reduces management complexity, and improves deployment efficiency. Through the strategy of global optimization, this solution not only improves resource utilization efficiency, but also reduces operating costs, achieving dual optimization of economy and efficiency. Through the implementation of intelligent path planning, combined with real-time traffic data, the timeliness and reliability of material transportation are ensured. This solution also demonstrates a high degree of flexibility and adaptability, can meet the material scheduling needs under different logistics modes, and can be easily adjusted and expanded according to new data and needs.

[0033] In this embodiment, the power plant location data collection work is first started, and the precise location of all power plants in the target city is recorded using high-precision GPS equipment and geographic information system (GIS) software to generate a power plant location coordinate set. Subsequently, based on these coordinate data, a power plant distribution map is constructed in GIS to create a city power plant distribution map, which clearly shows the geographical layout of each power plant. Next, the urban power plant distribution map is used to analyze the layout of material warehouses, considering the distribution of power plants, transportation networks and emergency response time factors, determine the optimal location of material warehouses, and form a material warehouse distribution map. At the same time, the spare material demand analysis is performed for each power plant, and the required spare materials are predicted and listed based on historical accident data and material consumption patterns to obtain a warehouse inventory material list. According to the material warehouse distribution map and the warehouse inventory material list, each inventory material is physically ID-coded, and a unique identifier is assigned to each material item using RFID technology, and the global resource RFID mapping table is updated in GIS to achieve accurate positioning and tracking of materials. Subsequently, the material demand work order is collected for the target power plant, and a power plant material demand work order is formed through investigation and data integration to clarify the material demand details of the power plant. At the same time, by analyzing the material usage pattern and historical records of the target power plant, the material acquisition method is identified and determined, and the material acquisition method data is obtained. Using the urban power plant distribution map, material warehouse distribution map and global resource RFID mapping table, combined with the power plant material demand work order, a material coordination allocation plan is formulated to ensure that the materials can be quickly and accurately allocated to the target power plant. According to the plan, the optimal material dispatching path is planned in GIS, and a material dispatching path map is generated to provide clear navigation for material transportation. Finally, combined with the material acquisition method data and the material dispatching path map, the material dispatching operation is performed in the intelligent logistics management system, the real-time status of the materials is monitored, and the materials are ensured to arrive at the target power plant on time according to the predetermined plan, completing the entire material optimization dispatching process.

[0034] Preferably, step S1 comprises the following steps:

[0035] Step S11: collecting power plant location data for the target city to obtain a power plant location coordinate set;

[0036] Specifically, a global positioning system (GPS) device can be used to determine the precise latitude and longitude coordinates of each power plant, thereby obtaining a set of power plant location coordinates. These devices are usually equipped with built-in map and navigation software, which can provide real-time geolocation services.

[0037] Step S12: Collect data on the disabled power plants in the target city to obtain data on the disabled power plants;

[0038] Specifically, the history and documents of decommissioned power plants can be obtained from power companies, public archives or online databases. In addition, these documents can be analyzed to extract key information such as the name of the power plant, the date of decommissioning, and the reason, and finally the decommissioned power plant data can be obtained.

[0039] Step S13: geographically locate the idle power plant according to the idle power plant data to obtain a location coordinate set of the idle power plant;

[0040] Specifically, the approximate location of the decommissioned power plant can be determined by analyzing the addresses, place names or other geographic reference information mentioned in the data of the decommissioned power plant. Then, use the address matching function of the GIS software to convert these addresses into specific geographic coordinates. If the address information of some decommissioned power plants is incomplete or unclear, it can be assisted in positioning by combining historical maps, satellite images and other geographic data. In the positioning process, geocoding services such as Google Maps API or Bing Maps API can also be used. After completing the geolocation, the obtained coordinate data is integrated into the coordinate set of the decommissioned power plant location.

[0041] Step S14: screening the power plant location coordinate set according to the deactivated power plant location coordinate set to obtain the active power plant location coordinate set;

[0042] Specifically, GIS software can be used to load the decommissioned power plant location coordinate set and the power plant location coordinate set into the same working environment. The two data sets are compared through the software's overlay analysis tool to identify coordinate points that exist in both sets. These points represent decommissioned power plants. Subsequently, these duplicate coordinate points are excluded using the selection tool, and a new layer is created to display only the remaining active power plant locations. Finally, the active power plant location coordinate set is obtained.

[0043] Step S15: constructing a power plant distribution map for the target city based on the active power plant location coordinate set to obtain a city power plant distribution map;

[0044] Specifically, GIS software can be used to build a city power plant distribution map. First, open the GIS software and create a new map project. Next, import the active power plant location coordinate set as a data source into this new project. Use the layer management function of the GIS software to create a new point layer for active power plants. In the layer property settings, define the style of each point, such as the size, color, and shape of the point, to distinguish different types of power plants. In addition, you can also use graduated symbols or color gradients to enhance the visualization of the map based on the attributes of the power plant, such as scale or power generation.

[0045] Step S16: Analyze the material warehouse layout of the target city based on the urban power plant distribution map to obtain a material warehouse distribution map.

[0046] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0047] The present invention ensures the real-time and accuracy of the power plant distribution map through accurate power plant data collection and effective data screening, greatly improving the pertinence and practicality of resource scheduling. By constructing a city power plant distribution map based on the active power plant coordinate set, it not only enhances the practicality of the map, but also provides accurate geographic information support for the reasonable layout of material warehouses. Through further warehouse layout analysis and optimization, the matching of the geographical distribution of warehouses and power plants is ensured, effectively improving the efficiency and response speed of material allocation.

[0048] Preferably, step S16 comprises the following steps:

[0049] Step S161: assigning identifiers to the urban power plant distribution map to obtain a power plant ID mapping table, and marking the urban power plant distribution map according to the power plant ID mapping table to obtain a marked urban power plant distribution map;

[0050] Specifically, you can start GIS software, such as ArcGIS or QGIS, and load the urban power plant distribution map into the working interface. Use the attribute table function of the GIS software to create a unique identifier, namely the power plant ID, for each power plant point on the map. This ID will be used as a keyword in the mapping table and associated with other attribute information of each power plant. Next, use the annotation tool of the GIS software to add corresponding text annotations to each power plant point on the map according to the power plant ID mapping table. These annotations include not only the ID of the power plant, but also the name and operating status information. In this way, you can get an identified urban power plant distribution map with detailed identification information.

[0051] Step S162: Collect multimodal traffic data for the target city to obtain a city multimodal traffic data set, wherein the multimodal traffic specifically includes urban roads, railways and waterways;

[0052] Specifically, various data collection tools and methods, such as satellite remote sensing, drone aerial photography, and vehicle-mounted GPS recorders, can be used to obtain geographic information on urban roads, railways, and waterways. Existing geospatial data resources can also be used, such as the transportation network data provided by OpenStreetMap. Through these data sources, a comprehensive dataset containing all modes of transportation in the city can be constructed, and ultimately a multimodal urban transportation dataset can be obtained.

[0053] Step S163: performing multi-level transportation network fusion on the target city according to the urban multimodal transportation data set to obtain an urban comprehensive transportation path network;

[0054] Specifically, GIS software can be used to analyze and process urban multimodal transportation datasets. First, import the data layers of different transportation modes into the GIS project, including road, railway, and waterway layers. Then, use the network analysis tools of GIS software, such as ArcGIS's Network Analyst or QGIS's Graph Builder, to build a comprehensive transportation path network for the city. Define the connection relationships and traffic rules in the transportation network to ensure smooth conversion between different transportation modes. For example, you can set the connection points between roads, as well as the transportation capacity of railways and waterways. In this way, a multi-level, multi-modal urban comprehensive transportation path network is obtained.

[0055] Step S164: Identify traffic congestion hotspot paths in the target city to obtain a traffic congestion hotspot path data set, and remove traffic congestion hotspot paths from the city's comprehensive transportation path network based on the traffic congestion hotspot path data set to obtain an optimized city's comprehensive transportation path network;

[0056] Specifically, real-time traffic flow data of the target city can be obtained through traffic monitoring cameras, vehicle-mounted GPS tracking systems, mobile application data or other intelligent transportation systems. These data are imported into GIS software and correlated with the city's comprehensive transportation path network dataset. Using the heat map generation function of GIS software, areas with high traffic flow and frequent congestion are identified to form a traffic congestion hotspot path dataset. Subsequently, the network analysis tools of GIS software, such as ArcGIS's Network Analyst tool, are used to evaluate the comprehensive transportation path network, identify and mark the congestion hotspot paths. Then, the editing function of GIS software is used to remove these congestion hotspot paths from the comprehensive transportation network, thereby obtaining an optimized city comprehensive transportation path network.

[0057] Step S165: collecting the geographical locations of candidate warehouse locations in the target city to obtain a set of candidate warehouse location coordinates, and collecting the number of warehouses built in the target city to obtain warehouse building quantity data;

[0058] Specifically, geographic information of candidate warehouse locations in the target city can be collected manually. This includes a detailed review of urban planning documents, land use records, and geographic feature data to identify areas suitable for building warehouses, such as undeveloped land, abandoned industrial areas, or around logistics centers. Using GIS software, manually mark these candidate locations on the map interface, and accurately record the geographic coordinates of each location through the coordinate capture function of the software to create a warehouse candidate location coordinate set. In addition, it is necessary to evaluate the number of warehouses required, which is based on data analysis of demographics, material demand forecasts, and existing warehouse capacity, which are manually entered into the GIS database. Use GIS analysis tools, such as buffer and proximity analysis, to predict the demand for warehouses in each region. Finally, combined with urban development plans and material strategies, determine and record the number of warehouses recommended for each location to form warehouse construction quantity data.

[0059] Step S166: selecting warehouse construction sites for the target city based on the distribution map of power plants in the marked city and the optimized city comprehensive transportation path network and according to the warehouse candidate site coordinate set and warehouse construction quantity data, and obtaining the warehouse construction site coordinate set;

[0060] Specifically, the final warehouse construction location can be determined by combining the distribution map of power plants in the city, the optimization of the city's comprehensive transportation path network, the warehouse candidate location coordinate set and the warehouse construction quantity data. Load all relevant data layers in the GIS software and use spatial analysis tools such as overlay analysis and proximity analysis to evaluate the geographical advantages and transportation connections of each candidate location. Other factors such as land cost, accessibility and environmental impact should also be considered. By integrating this information, the most suitable warehouse construction location is selected and the coordinates of these locations are recorded to form a warehouse construction location coordinate set.

[0061] Step S167: Visualize the warehouse construction location coordinate set to obtain a material warehouse distribution map.

[0062] Specifically, you can use the visualization tools of GIS software to display the warehouse location coordinate set. First, create a new layer in the GIS software specifically for displaying warehouse locations. Then, use each coordinate point in the warehouse location coordinate set as a feature in the layer, and use different symbols or colors to distinguish different warehouse locations. You can also add other relevant information as attributes of the feature, such as the size and capacity of the warehouse or the power plant it serves. Using the mapping function of GIS software, design and generate a material warehouse distribution map that clearly shows the location and key attribute information of each warehouse.

[0063] Through identification allocation and labeling, the present invention enables users to intuitively and quickly identify and locate power plants. At the same time, through the comprehensive collection and integration of multimodal traffic data, a variety of path options are provided for material transportation, ensuring the comprehensiveness of traffic network analysis. Measures to optimize traffic routes reduce material transportation delays and improve the timeliness of deployment. Through precise warehouse site selection, the distribution of power plants, traffic networks and warehouse construction requirements are taken into account, ensuring the strategic geographical location of warehouses to maximize the efficiency of material deployment. In addition, the visualized material warehouse distribution map promotes the optimization of resource allocation, reduces logistics costs, and improves operational efficiency.

[0064] Preferably, step S2 comprises the following steps:

[0065] Step S21: Obtain a distribution map of power plants in the marked city;

[0066] Specifically, a map of power plants that have been marked can be obtained from relevant departments or databases. This map shows the location of each power plant in the city in detail and assigns a unique identifier to each power plant.

[0067] Step S22: collecting historical single specific accident data of the power plant according to the distribution map of the power plant in the marked city to obtain a historical single specific accident data set;

[0068] Specifically, the power company's internal database, safety records or accident report documents can be accessed. Cooperation with the power company is required to obtain electronic copies or scanned copies of this data. Once this data is obtained, use data sorting tools such as Microsoft Excel or professional data analysis software to extract and sort the detailed information of each accident, including but not limited to the type of accident, time of occurrence, and scope of impact. This information is then entered into a unified database or data set to form a historical single specific accident data set.

[0069] Step S23: collecting historical single material consumption records of power plants according to the distribution map of power plants in the marked cities, and obtaining a historical single material consumption data set;

[0070] Specifically, you can use database query tools or cooperate with the material management department to ensure that comprehensive and accurate material consumption data is collected. These data will be organized into spreadsheets or database tables, recording the use of various materials in each accident, including but not limited to material type, quantity, and purpose information. Ensure that these records can correspond to the accident data collected in step S22 in terms of time and other key attributes, and finally obtain a historical single material consumption data set.

[0071] Step S24: Associating the historical single specific accident data set with the historical single material consumption data set to obtain an accident-material consumption associated data set;

[0072] Specifically, data association technology, such as the JOIN operation of the SQL database, or the VLOOKUP function in the data analysis software, can be used to match the historical single accident data set with the historical single material consumption data set. In this way, a comprehensive data set can be created, which not only contains the detailed information of the accident, but also associates the corresponding material consumption, and finally obtains the accident-material consumption association data set.

[0073] Step S25: Based on the historical single specific accident data set and the accident-material consumption associated data set, the power plant's spare material demand analysis is performed to obtain a warehouse inventory material list;

[0074] Specifically, please refer to the sub-steps of step S25 for the detailed implementation process of this embodiment.

[0075] Step S26: Perform physical ID encoding on the inventory materials according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table.

[0076] Specifically, please refer to the sub-steps of step S26 for the detailed implementation process of this embodiment.

[0077] The present invention significantly enhances the ability to analyze historical data by collecting and analyzing historical accident and material consumption data of power plants, and clarifies the correlation between accidents and material consumption. Through accurate material demand forecasting, combined with the physical ID coding of inventory materials, not only the efficiency and response speed of material management are improved, but also resource allocation is optimized, and the risk of material backlog and shortage is reduced. The adaptability and flexibility of this solution are improved, and it can be dynamically adjusted according to real-time data to ensure that material supply is consistent with demand. Automated data analysis and material coding reduce manual operations and improve the level of intelligence. In addition, accurate material demand forecasting and efficient material management provide a solid foundation for emergency response, ensuring that materials can be deployed quickly and effectively in emergency situations.

[0078] Preferably, step S25 comprises the following steps:

[0079] Step S251: classify the accident types of the historical single specific accident data set to obtain power plant accident type classification data;

[0080] Specifically, data analysis software, such as Python with the Pandas library or R language, can be used to conduct a detailed review of the historical single accident data set. By defining different accident type classification standards, the accident records can be classified and marked. For example, the accident type can be distinguished according to the cause of the accident, the scope of impact, or the degree of urgency. These classification information are added as new fields to the accident data set to form the power plant accident type classification data.

[0081] Step S252: performing frequency distribution statistics of accident types of the power plant according to the historical single specific accident data set and the power plant accident type classification data to obtain a frequency distribution diagram of the power plant accident types;

[0082] Specifically, statistical analysis methods such as frequency counting and distribution analysis can be used based on the historical single accident data set and the power plant accident type classification data to determine the number of occurrences of each accident type and calculate their relative frequencies. These data are organized into tables, and chart generation tools such as bar charts or pie charts are used to create a frequency distribution chart of power plant accident types. This chart clearly shows the frequency of occurrence of different accident types.

[0083] Step S253: Based on the accident-material consumption association data set and the power plant accident type frequency distribution diagram, the material consumption pattern of the power plant accident is identified to obtain a power plant material consumption pattern feature vector set;

[0084] Specifically, data mining techniques can be used to identify material consumption patterns by combining the accident-material consumption association data set and the frequency distribution diagram of power plant accident types. First, data preprocessing is performed in the data analysis software, including data cleaning and normalization steps. Then, pattern recognition methods, such as cluster analysis or principal component analysis, are used to explore the material consumption patterns under different accident types, thereby obtaining a set of feature vectors of power plant material consumption patterns. These feature vectors contain material consumption patterns associated with specific accident types.

[0085] Step S254: performing a multi-factor correlation analysis of material consumption on the power plant material consumption pattern feature vector set based on the accident-material consumption association data set to obtain a set of influencing factors of power plant material consumption;

[0086] Specifically, key information in the accident-material consumption association data set can be extracted, which records the connection between historical accident types and material consumption. Using statistical analysis software, such as SPSS, the characteristic vector set of power plant material consumption patterns is imported, which contains a detailed quantitative description of various material consumption patterns. Through the software, descriptive statistical analysis is performed to obtain basic statistical indicators of the data set, such as mean and standard deviation. Next, multi-factor correlation analysis is performed to calculate correlation coefficients, such as Pearson correlation coefficients, to evaluate the linear relationship between different material consumption characteristics. Further regression analysis is used to establish a prediction model to analyze the impact of accident severity, power plant operating conditions, and material availability on material consumption. By setting material consumption as the dependent variable and other factors as independent variables, the statistical significance and influence coefficient of each factor are evaluated. Finally, based on the results of regression analysis, the key factors that have a significant impact on material consumption are identified, and these factors are organized into a set of influencing factors for power plant material consumption.

[0087] Step S255: Perform statistics on all historical consumed materials data for the accident-material consumption association data set to obtain a consumed materials detailed data database;

[0088] Specifically, the accident-material consumption association dataset can be imported into a database management system such as MySQL or MongoDB. Using SQL query or NoSQL query language, a thorough statistical analysis of the historical records is performed to summarize the consumption of various materials in different accidents. Create a query to count the total consumption of each material, the frequency of consumption, and the correlation with other materials. The statistical results will be organized into a detailed consumption material information database, which contains detailed information about the materials, such as name, consumption, consumption time, and accident type.

[0089] Step S256: constructing a material demand forecasting model for the power plant based on the accident-material consumption association data set and the material consumption pattern feature vector set and the material consumption influencing factor set of the power plant to obtain a standby material demand forecasting model;

[0090] Specifically, SPSS or R language can be used to perform exploratory data analysis on the accident-material consumption association data set, and the distribution characteristics and potential trends of the data can be identified by drawing box plots, histograms, and scatter plots. Next, the scikit-learn library of Python is used to preprocess the data set, including data cleaning, standardization, normalization, and feature engineering, to ensure data quality and prepare a material consumption pattern feature vector set and a material consumption influencing factor set for model input. Then, a suitable prediction algorithm is selected, such as ARIMA for time series analysis, or linear regression, ridge regression, and random forest and gradient boosting machine machine learning algorithms are applied to build a model. The model is trained using the training set by dividing the data set into a training set and a test set. After the model training is completed, the prediction performance of the model is evaluated with the test set to ensure its effectiveness on unknown data. The satisfactory model results are adopted as the standby material demand forecasting model.

[0091] Step S257: forecasting the demand for spare materials of the power plant by using the spare material demand forecasting model according to the frequency distribution diagram of the power plant accident types, and obtaining the forecast data of the demand for spare materials of the power plant;

[0092] Specifically, the historical frequency of accident types and related material consumption patterns can be input into the reserve material demand forecasting model, which will calculate the amount of materials required when different accident types occur. The forecast results are organized into power plant reserve material demand forecasting data, which reflects the amount of material reserves required for different accident types within a specific time.

[0093] Step S258: replenish the power plant's spare material demand forecast data according to the consumable material detailed information database to obtain a warehouse inventory material list.

[0094] Specifically, database management tools or spreadsheet software can be used to compare and analyze the power plant's standby material demand forecast data with the actual material consumption records. According to the current inventory, procurement cycle, and validity period constraints of the materials, the power plant's standby material demand forecast data is adjusted to ensure that the predicted material demand meets the actual demand while avoiding excessive reserves. Finally, the adjusted standby material demand forecast data is combined with the detailed information of the materials to form a complete warehouse inventory material list, which will guide warehouse managers in the procurement, storage, and allocation of materials.

[0095] The present invention lays a clear foundation for accident analysis through accurate classification and identification of historical accident data. In-depth analysis of accident frequency and pattern reveals the intrinsic connection between accidents and material demand, while multi-factor correlation analysis further deepens the understanding of factors affecting material consumption. Comprehensive statistics of historical material consumption data provide rich data support for building a scientific forecasting model, making the forecast of standby material demand more accurate and ensuring the foresight and adequacy of material preparation. Optimized material replenishment strategies make inventory management more efficient, improve emergency response capabilities, and reduce delays caused by material shortages. At the same time, accurate forecasting and inventory optimization effectively reduce operating costs, and data-driven decision support makes the decision-making process more scientific and reasonable. The intelligence and automation level of the entire solution are significantly improved, reducing the need for manual operation and providing solid technical support for the efficiency and accuracy of resource scheduling.

[0096] Preferably, step S26 comprises the following steps:

[0097] Step S261: Performing warehouse network topology analysis on the material warehouse distribution map to obtain a warehouse network feature matrix;

[0098] Specifically, you can use professional network analysis software, such as ArcGIS Network Analyst or Ucinet, to conduct an in-depth network topology analysis of the material warehouse distribution map. First, import the warehouse distribution map into the analysis software and define the connection relationship between warehouses, such as transportation routes and network nodes. Through the software algorithm, identify the central nodes, edge nodes, and potential bottlenecks in the network to obtain the topological structure of the warehouse network. Subsequently, export the results of the network analysis to form a warehouse network feature matrix, which contains the key characteristic indicators of the warehouse, including connectivity, centrality, and betweenness.

[0099] Step S262: performing material usage frequency statistics on the power plant based on the accident-material consumption association data set to obtain material usage frequency distribution data;

[0100] Specifically, database query and statistical analysis tools, such as SQL Server Reporting Services or Python's Pandas library, can be used to conduct in-depth analysis of the accident-material consumption correlation data set. First, write an SQL query or use a data processing script to extract the usage records of various materials in a specific time period. Then, summarize and perform frequency statistics on these records to obtain the number of times and frequency of use of each material. Through visualization tools such as Tableau or Matplotlib, the statistical results are displayed as charts, such as bar charts or pie charts, to intuitively reflect the distribution of material usage frequency, and finally obtain the material usage frequency distribution data.

[0101] Step S263: collecting storage conditions of the warehouses according to the material warehouse distribution map to obtain a warehouse storage condition data set;

[0102] Specifically, the storage condition information of the warehouse can be obtained through on-site investigation and data collection. It is necessary to visit each warehouse, use temperature and humidity recorders and light meters to measure the storage environment of the warehouse, and record the layout, shelf system, and safety facilities of the warehouse. These data are organized into spreadsheets or database tables to form a warehouse storage condition data set.

[0103] Step S264: re-order the warehouse inventory material list by material priority according to the material usage frequency distribution data to obtain an optimized warehouse inventory material list;

[0104] Specifically, a spreadsheet software such as Microsoft Excel can be used to assign a priority score to each material based on the frequency of use and consumption of the material revealed in the material use frequency distribution data. The calculation method of the priority score can be adjusted according to the urgency and importance of the material to ensure that key materials are given priority. Then, the material list is sorted according to the priority score to form an optimized warehouse inventory material list.

[0105] Step S265: formulating a material allocation strategy for optimizing the warehouse inventory material list based on the material warehouse distribution map according to the warehouse network feature matrix and the warehouse storage condition data set to obtain a material-warehouse initial matching strategy;

[0106] Specifically, the material warehouse distribution map, warehouse network feature matrix, and warehouse storage condition dataset can be integrated into the GIS software. Using the spatial analysis function of the GIS software, the geographical location, network connectivity, and storage conditions of each warehouse can be evaluated to determine its suitability for different materials. Using multi-criteria decision analysis methods, such as weighted overlay analysis, the characteristics of the warehouses are matched with the material demand to develop a preliminary material-warehouse matching strategy. This strategy takes into account the priority and demand of the materials and the capacity and accessibility of the warehouses to ensure the effective allocation of materials. The analysis results are organized into an initial material-warehouse matching strategy.

[0107] Step S266: Perform dynamic material allocation efficiency simulation on the material-warehouse initial matching strategy to obtain material allocation efficiency simulation data, and iteratively optimize the material-warehouse initial matching strategy based on the material allocation efficiency simulation data to obtain the material-warehouse optimal matching strategy;

[0108] Specifically, supply chain simulation software, such as AnyLogic or Arena, can be used to dynamically simulate the initial material-warehouse matching strategy. Set simulation parameters in the software, including but not limited to changes in material demand, storage and transportation capacity of warehouses, to reflect the dynamic conditions in actual operations. Through simulation, collect data on material allocation efficiency, such as material arrival time, inventory level, and transportation cost. According to the simulation results, adjust the material allocation and warehouse selection in the matching strategy, and perform iterative optimization until the optimal material-warehouse matching strategy is obtained.

[0109] Step S267: construct an RFID encoder for the warehouse and inventory materials to obtain an intelligent RFID code generator;

[0110] Specifically, you can select appropriate RFID tags and readers to ensure that they are compatible with the materials and storage environment of the inventory materials. Then, use RFID encoding software, such as Zebra RFID Locator, to assign a unique RFID code to each material item and generate a corresponding RFID tag. Deploy RFID readers in the warehouse to track the inflow and outflow of materials in real time. In this way, an intelligent RFID code generator is established.

[0111] Step S268: According to the material-warehouse optimal matching strategy, the intelligent RFID code generator is used to model the inventory materials as digital twins to obtain a material digital twin library, and the RFID tags are intelligently allocated and activated for the materials according to the material digital twin library and the intelligent RFID code generator to obtain an RFID tag-bound material library;

[0112] Specifically, the material digital twin modeling can be started in the ERP system or supply chain management software based on the material-warehouse optimal matching strategy. Each inventory material is assigned a digital twin, which records detailed information including RFID code, material attributes, size, weight and storage location. Subsequently, a unique code is assigned using an intelligent RFID code generator and registered in the system to ensure that each RFID tag is accurately associated with the digital twin of the material, thereby achieving accurate tracking and management of the material. RFID readers are deployed in the warehouse to read the material RFID tags in real time, update the storage status and location, and keep the digital twin synchronized with the physical material. The activation of the RFID tag further ensures real-time interaction between the tag and the digital twin of the material. This coherent process ultimately establishes an RFID tag-bound material library.

[0113] Step S269: Generate a global resource RFID mapping table for the RFID tag-bound material library to obtain a global resource RFID mapping table.

[0114] Specifically, a global resource RFID mapping table can be created based on the material library bound to the RFID tag using database management tools such as Microsoft Access or SQLite. The RFID code, material description, storage location and other key information of each material item are entered into the database to form a global resource RFID mapping table. This mapping table allows for quick query of the current status and location of any material, improving the transparency and response speed of material allocation.

[0115] The present invention optimizes the understanding of the connection relationship between warehouses through the topological analysis of the warehouse network, forms a warehouse network feature matrix, and provides a network perspective for material distribution. Secondly, through accurate material use frequency statistics based on accident-material consumption data, the urgency and importance of materials are revealed, supporting the effective sorting of material priorities. Through standardized storage condition data collection, materials are ensured to be stored in a suitable environment, extending the validity period and reducing losses. The dynamically optimized inventory material list is more in line with actual needs, improving the efficiency and response speed of material allocation. Through intelligent material distribution strategies, combined with warehouse network characteristics and storage conditions, through simulation and iterative optimization, the optimal material-warehouse matching solution is formed. Through the integrated application of RFID technology and the global visualization tool of material management, the automation level of material tracking and management is improved, and the monitoring and control capabilities are enhanced. These measures jointly improve the emergency response speed, reduce operating costs, and improve the utilization efficiency of resources through accurate material demand forecasting and reasonable material allocation. Finally, data-driven decision support improves the scientific nature of decision-making, reduces the risk of subjective judgment, and ensures the efficiency and accuracy of resource scheduling.

[0116] Preferably, step S3 comprises the following steps:

[0117] Step S31: collecting emergency event data of the target power plant in real time to obtain emergency event data of the power plant;

[0118] Specifically, an emergency event data input interface can be embedded in the central monitoring system of the target power plant. This interface is designed for staff in emergency situations to quickly record and report key information. The interface has an intuitive user design, including predefined drop-down menus and text input boxes, to ensure that operators can enter key data in a predetermined format, such as event type, occurrence time, affected area, and measures taken. The event type is selected through a drop-down menu, such as fire, equipment failure, or natural disaster, and the system automatically fills in the current timestamp to record the occurrence time, and finally obtains the power plant emergency event data.

[0119] Step S32: If the power plant emergency event data is power plant accident type data, a spare material demand prediction model is obtained, and replacement material prediction is performed on the target power plant using the spare material demand prediction model according to the power plant accident type data to obtain power plant replacement material data;

[0120] Specifically, if the power plant emergency event data is power plant accident type data, the previously developed spare material demand forecasting model is used to forecast material demand. The accident type data, such as the severity of the accident and the affected equipment information, is input into the data analysis software, and then the forecasting model is run. The software will predict the type and quantity of replacement materials required based on the input parameters and historical data. The forecast results are organized into power plant replacement material data.

[0121] Step S33: integrating the power plant replacement material data to obtain a power plant material demand work order;

[0122] Specifically, database management tools or spreadsheet software can be used to organize the data of power plant replacement materials into a unified material demand work order. Material demands can also be sorted and optimized to ensure priority supply of key materials and check the availability and delivery time of materials.

[0123] Step S34: If the power plant emergency event data is an emergency material demand work order, the emergency material demand work order is directly used as the power plant material demand work order;

[0124] Specifically, if the power plant emergency event data is an emergency material demand work order, the emergency material demand work order is directly used as the power plant material demand work order.

[0125] Step S35: collecting the material acquisition and transportation methods of the target power plant to obtain material acquisition and transportation method data, and performing logistics mode recognition on the material acquisition and transportation method data to obtain material acquisition method data.

[0126] Specifically, the target power plant's material acquisition and transportation methods can be collected through the data collection page. Configure a user-friendly data input interface and integrate it into the power plant's material management department or supply chain system, so that staff can manually enter the transportation method through a drop-down menu and text box when the materials arrive, such as selecting "self-collection" or entering "home delivery". Then use data analysis tools, such as Excel or specialized logistics analysis software, to review and classify the collected data to identify and classify the material acquisition methods. The identification results are organized into material acquisition method data.

[0127] The present invention ensures rapid response and timely handling of power plant emergencies by real-time collection of power plant emergency event data. By utilizing the standby material demand forecasting model, accurate forecasting of power plant accident type data is achieved, enhancing the foresight and accuracy of material allocation. Efficient material demand integration simplifies the management process and improves allocation efficiency, while the flexible demand processing mechanism speeds up the response to emergency material demand. Through the optimization and identification of logistics modes, material allocation can select the most appropriate mode of transportation, effectively reducing costs and improving transportation efficiency.

[0128] Preferably, step S4 comprises the following steps:

[0129] Step S41: spatially registering and fusing the urban power plant distribution map and the material warehouse distribution map to obtain a power plant-warehouse location spatial map;

[0130] Specifically, GIS software, such as ArcGIS or QGIS, can be used to import the urban power plant distribution map and the material warehouse distribution map into the same workspace. The spatial registration tool of the GIS software can be used to ensure that the two maps are aligned in the same geographic coordinate system to eliminate spatial deviation. Then, using the layer fusion function of the GIS software, the distribution layers of the power plant and material warehouse are merged into a comprehensive power plant-warehouse location spatial map, which shows the relative position and spatial relationship between the power plant and the material warehouse.

[0131] Step S42: collecting geographic location data of the target power plant to obtain the geographic location coordinates of the target power plant, and marking the power plant-warehouse location space map according to the geographic location coordinates of the target power plant to obtain a marked power plant-warehouse location space map;

[0132] Specifically, you can use the location service of GPS equipment or GIS software to collect data on the precise location of the target power plant and record the geographic coordinates of the target power plant. Enter these coordinate data into the previously created power plant-warehouse location space map. Use the annotation tool of GIS software to add text labels or symbols to the target location on the map to clearly mark the specific location of the target power plant. In this way, you will get a labeled power plant-warehouse location space map that includes the geographic location annotation of the target power plant.

[0133] Step S43: Based on the global resource RFID mapping table, a single warehouse material satisfaction judgment is performed on the power plant material demand work order to obtain material demand satisfaction status data;

[0134] Specifically, the existing global resource RFID mapping table can be used, which records the storage location and status information of materials. In the information system, such as ERP or supply chain management software, the detailed information of the power plant material demand work order is input, and the system queries according to the RFID mapping table to determine whether the required materials can be found in a single warehouse. Use query and report generation tools to extract the results of material satisfaction judgment from the system to form material demand satisfaction data. These data clearly indicate which material demands can be fully met in a single warehouse.

[0135] Step S44: If the material demand satisfaction data shows that the materials required by the power plant material demand work order can be obtained in the same warehouse, a warehouse search is performed on the global resource RFID mapping table according to the power plant material demand work order to obtain a preferred warehouse set for material allocation;

[0136] Specifically, if the material demand satisfaction data shows that all materials required by the power plant can be obtained in the same warehouse, this warehouse will be marked as the preferred warehouse. Create a warehouse search task in the information system, enter the specific content of the material demand work order, and automatically identify and recommend warehouses that meet all material needs based on the data in the RFID mapping table to form a preferred warehouse set for material allocation.

[0137] Step S45: If only one warehouse in the preferred warehouse set for material allocation meets the conditions, a material coordinated allocation plan is formulated for the target power plant based on the preferred warehouse set for material allocation and the material demand work order of the power plant to obtain a material coordinated allocation plan; otherwise, the preferred warehouse set for material allocation is screened for the nearest warehouse based on the marked power plant-warehouse location space diagram to obtain the nearest preferred warehouse for material allocation, and a material coordinated allocation plan is formulated for the target power plant based on the material demand work order of the power plant based on the nearest preferred warehouse for material allocation to obtain a material coordinated allocation plan;

[0138] Specifically, if only one warehouse in the preferred warehouse set for material allocation meets the conditions. If so, this warehouse will be directly used as the preferred location for material allocation. Using the supply chain management software, enter the detailed information of the power plant material demand work order. The software automatically matches the material inventory in the warehouse according to the demand and generates a material coordination allocation plan. The plan details the list and quantity information of the required materials. If there are multiple warehouses in the preferred warehouse set, use the network analysis tool of the GIS software to calculate the warehouse closest to the target power plant based on the marked power plant-warehouse location spatial map, and select it as the nearest preferred warehouse for material allocation. Then, formulate a material coordination allocation plan based on the material inventory of this nearest warehouse.

[0139] Step S46: If the material demand satisfaction data shows that the materials required by the power plant material demand work order cannot be obtained in the same warehouse, the target factory is screened for warehouses based on the power plant material demand work order based on the marked power plant-warehouse location space map and the global resource RFID mapping table to obtain a candidate list of material allocation warehouses;

[0140] Specifically, if the materials needed by the power plant cannot be fully obtained in a single warehouse, GIS software is used to screen warehouses for the target power plant, combining the labeled power plant-warehouse location spatial map and the global resource RFID mapping table. The screening conditions are defined in the GIS software, such as the availability of materials, the capacity and location of the warehouse. The software automatically screens out a list of warehouses that can meet the material needs based on these conditions. The screened warehouses are evaluated, taking into account the diversity of materials and the geographical location of the warehouses to form a candidate list of warehouses for material allocation.

[0141] Step S47: Combining the candidate list of material allocation warehouses obtained according to the material demand work order of the power plant to obtain a coordinated material allocation plan.

[0142] Specifically, supply chain management software can be used to develop a coordinated material allocation plan based on the power plant material demand work order and the candidate list of material allocation warehouses. Enter the specific requirements of the work order in the software, and the software automatically selects the most suitable warehouse from the candidate list for material allocation. Considering the transportation route, cost, and time factors of the materials, the software provides a variety of allocation combination plans based on these factors. Evaluate the pros and cons of each plan and select the best allocation plan to ensure that the materials can be delivered to the target power plant at the lowest cost and fastest speed. Finally, a coordinated material allocation plan is obtained.

[0143] The present invention creates a comprehensive spatial view through precise spatial data integration, providing an accurate geographic information basis for material allocation. The refined management of geographic location enhances the accurate identification of the power plant location and optimizes the material allocation process. Through efficient material satisfaction judgment, rapid decision-making using RFID mapping tables improves decision-making efficiency. The optimized material allocation plan simplifies the process and ensures that a coordinated allocation plan can be quickly formulated when materials are available in a single warehouse. In the face of dispersed storage of materials, the nearest warehouse is flexibly selected to formulate the optimal allocation plan, thereby improving resource allocation efficiency. The enhanced adaptability of material allocation allows the system to flexibly combine multiple warehouse resources to cope with complex needs. In addition, the optimized plan reduces unnecessary material transportation, effectively reduces logistics costs, improves the overall utilization efficiency of resources, and ensures that the system can operate efficiently, economically, and intelligently under various circumstances.

[0144] Preferably, step S5 comprises the following steps:

[0145] Step S51: obtaining a marked power plant-warehouse location space map, and searching for potential passable paths for the target power plant based on the marked power plant-warehouse location space map and the material coordinated deployment plan to obtain a set of potential passable paths between the power plant and the warehouse;

[0146] Specifically, the marked power plant-warehouse location spatial map can be retrieved from the GIS system to ensure that the specific locations of the target power plant and warehouse are clearly displayed on the map. Then, using the network analysis function of the GIS software, set the starting point to the power plant and the end point to the warehouse to search for all potential paths. GIS software provides a variety of path search algorithms, such as the shortest path and the fastest path. Select the appropriate algorithm for search based on the actual situation. After the search is completed, all potential paths are visualized on the map, and a set of potential paths from power plant to warehouse is generated.

[0147] Step S52: collecting the current vehicle flow of the paths in the power plant-warehouse potential passage path set to obtain a real-time traffic data set of the transportation path;

[0148] Specifically, the current traffic flow of the power plant-warehouse potential traffic path can be obtained through the traffic monitoring system of the urban traffic management department, or the traffic flow information can be collected by using vehicle-mounted GPS equipment and mobile application technology. The collected traffic flow data is classified and sorted according to different time periods and paths to form a real-time traffic data set for the transportation path.

[0149] Step S53: traffic congestion identification and feasible path screening are performed on the power plant-warehouse potential passage path set according to the real-time traffic data set of the transportation path to obtain the power plant-warehouse feasible passage path set;

[0150] Specifically, the real-time traffic data set of the transportation path can be loaded into the GIS software, and the congested sections can be identified by setting the traffic flow threshold. The GIS software provides a variety of analysis tools, such as hot spot analysis and density analysis. Then, according to the congestion situation, the path screening function of the GIS software is used to exclude those congested paths that are not suitable for passage, and finally a feasible path set from the power plant to the warehouse is obtained. This set contains all the paths that are suitable for use under the current traffic conditions.

[0151] Step S54: Visualize the feasible path set from the power plant to the warehouse to obtain a material dispatch path diagram;

[0152] Specifically, GIS software can be used to visualize the feasible path set from power plant to warehouse. Appropriate symbols and colors are selected in GIS software to mark each feasible path to distinguish different route options. By adjusting visual elements such as transparency and line width, a material scheduling path map is finally obtained.

[0153] Step S55: If the material acquisition method data indicates that the self-collection method is adopted, the material coordination allocation plan and the material dispatching path map are sent to the material management department of the target power plant or the designated material receiving personnel to perform material dispatching operations;

[0154] Specifically, if the material acquisition method data indicates that the self-collection method is adopted, the previously formulated material coordination allocation plan and the generated material dispatch path map can be organized into a complete dispatch plan document. This document is sent to the material management department or designated material receiving personnel of the target power plant via email, internal corporate communication system or logistics management platform. Ensure that the receiving personnel can understand the content of the dispatch plan, including the type, quantity, expected arrival time and recommended transportation route of the materials.

[0155] Step S56: If the material acquisition method data indicates that the door-to-door method is adopted, the material dispatch operation is performed through the preset intelligent logistics management system according to the material coordinated allocation plan and the material dispatch path map.

[0156] Specifically, if the data on the material acquisition method indicates that the door-to-door method is adopted, the preset intelligent logistics management system can be used to perform the material dispatch operation. In the system, enter the detailed information of the material coordination allocation plan, including the source, destination, quantity and scheduled delivery time of the materials. The intelligent logistics management system automatically plans the transportation tasks and assigns transportation tools and drivers based on this information and the material dispatch route map. Monitor the system's dispatch process to ensure that all tasks are carried out according to plan. The system also provides a real-time tracking function, allowing operators and power plants to understand the transportation status of materials in real time. After the materials are delivered, the operator confirms the receipt through the system and updates the inventory and logistics records to complete the entire material dispatch operation.

[0157] The present invention can identify and provide a variety of potential passages by integrating the geographical location information of power plants and warehouses, thus providing a guarantee for the diversity and selectivity of material transportation. The timeliness of path planning is ensured by the collection and application of real-time traffic data, while the system's traffic congestion identification function effectively avoids delays and ensures that materials arrive on time as planned. The visualization of the material dispatching path further simplifies the understanding and execution process and improves the execution efficiency. The flexibility of this solution is reflected in its ability to adapt to different ways of obtaining materials, whether it is self-collection or door-to-door delivery, and can quickly respond and provide the required material coordination deployment plan and dispatching path map. The integration of the intelligent logistics management system automates the material dispatching operations of door-to-door delivery, reduces manual operations, and improves the accuracy and efficiency of operations. In addition, optimized path planning and traffic congestion avoidance reduce logistics costs and improve resource utilization efficiency. Fast path search and real-time data collection improve the overall response speed of the system and ensure the timeliness and reliability of material dispatching.

[0158] Preferably, the present invention further provides a resource optimization scheduling system driven by a physical ID, which is used to execute the resource optimization scheduling method driven by a physical ID as described above, and the resource optimization scheduling system driven by a physical ID comprises:

[0159] The warehouse layout module is used to collect the power plant location data of the target city to obtain the power plant location coordinate set; construct the power plant distribution map of the target city based on the power plant location coordinate set to obtain the city power plant distribution map; analyze the material warehouse layout of the target city based on the city power plant distribution map to obtain the material warehouse distribution map;

[0160] The physical coding module is used to analyze the demand for spare materials in the power plant and obtain a list of warehouse inventory materials; the inventory materials are physically ID-coded according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table;

[0161] The demand identification module is used to collect material demand work orders for the target power plant and obtain the material demand work orders for the power plant; identify the logistics mode of the target power plant and obtain the material acquisition method data;

[0162] The material allocation module is used to allocate materials to the target power plant based on the urban power plant distribution map, material warehouse distribution map and global resource RFID mapping table and according to the power plant material demand work order to obtain a coordinated material allocation plan;

[0163] The scheduling planning module is used to plan the scheduling path for the target power plant according to the material coordination allocation plan to obtain the material scheduling path map; and perform material scheduling operations on the target power plant according to the material acquisition method data and the material scheduling path map.

[0164] In the present invention, by collecting precise location data and constructing a distribution map of the target city power plant, data support is provided for the reasonable layout of the material warehouse, ensuring the realization of rapid response and efficient material allocation. The physical coding module in the system significantly improves the accuracy and tracking efficiency of material management through demand analysis and the creation of RFID mapping tables. The intelligent automatic collection and logistics pattern recognition of the demand identification module provide accurate input information for material allocation, enhancing the adaptability and flexibility of the system. The material allocation module uses comprehensive data resources to formulate a collaborative allocation plan and optimize the resource allocation and scheduling process. The intelligent path planning of the scheduling planning module further ensures the economy and efficiency of material transportation. The automated operation of the entire system reduces human errors and improves operating efficiency and accuracy. The design of the system not only improves the overall performance of resource scheduling, but also reduces operating costs and enhances reliability and stability through its automation, intelligence and collaboration characteristics.

[0165] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0166] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A resource optimization scheduling method based on physical ID drive, characterized in that: The following steps are involved: Step S1: Collect power plant location data for the target city to obtain a power plant location coordinate set; construct a power plant distribution map for the target city based on the power plant location coordinate set to obtain a city power plant distribution map; analyze the material warehouse layout for the target city based on the city power plant distribution map to obtain a material warehouse distribution map; Step S2: Analyze the demand for spare materials of the power plant and obtain a list of warehouse inventory materials; According to the material warehouse distribution map and the warehouse inventory material list, the inventory materials are physically ID-encoded to obtain the global resource RFID mapping table; Step S3: Collect material demand work orders for the target power plant to obtain the material demand work orders for the power plant; Identify the logistics pattern of the target power plant and obtain data on material acquisition methods; Step S4: Based on the urban power plant distribution map, the material warehouse distribution map and the global resource RFID mapping table and according to the power plant material demand work order, materials are allocated to the target power plant to obtain a material coordinated allocation plan; Step S5: According to the coordinated material allocation plan, a dispatching path is planned for the target power plant to obtain a material dispatching path map; and material dispatching operations are performed on the target power plant according to the material acquisition method data and the material dispatching path map.

2. The resource optimization scheduling method based on physical ID drive according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting power plant location data for the target city to obtain a power plant location coordinate set; Step S12: Collect data on the disabled power plants in the target city to obtain data on the disabled power plants; Step S13: geographically locate the idle power plant according to the idle power plant data to obtain a location coordinate set of the idle power plant; Step S14: screening the power plant location coordinate set according to the deactivated power plant location coordinate set to obtain the active power plant location coordinate set; Step S15: constructing a power plant distribution map for the target city based on the active power plant location coordinate set to obtain a city power plant distribution map; Step S16: Analyze the material warehouse layout of the target city based on the urban power plant distribution map to obtain a material warehouse distribution map.

3. The resource optimization scheduling method based on physical ID drive according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: assigning identifiers to the urban power plant distribution map to obtain a power plant ID mapping table, and marking the urban power plant distribution map according to the power plant ID mapping table to obtain a marked urban power plant distribution map; Step S162: Collect multimodal traffic data for the target city to obtain a city multimodal traffic data set, wherein the multimodal traffic specifically includes urban roads, railways and waterways; Step S163: performing multi-level transportation network fusion on the target city according to the urban multimodal transportation data set to obtain an urban comprehensive transportation path network; Step S164: Identify traffic congestion hotspot paths in the target city to obtain a traffic congestion hotspot path data set, and remove traffic congestion hotspot paths from the city's comprehensive transportation path network based on the traffic congestion hotspot path data set to obtain an optimized city's comprehensive transportation path network; Step S165: collecting the geographical locations of candidate warehouse locations in the target city to obtain a set of candidate warehouse location coordinates, and collecting the number of warehouses built in the target city to obtain warehouse building quantity data; Step S166: selecting warehouse construction sites for the target city based on the distribution map of power plants in the marked city and the optimized city comprehensive transportation path network and according to the warehouse candidate site coordinate set and warehouse construction quantity data, and obtaining the warehouse construction site coordinate set; Step S167: Visualize the warehouse construction location coordinate set to obtain a material warehouse distribution map.

4. The resource optimization scheduling method based on physical ID drive according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtain a distribution map of power plants in the marked city; Step S22: collecting historical single specific accident data of the power plant according to the distribution map of the power plant in the marked city to obtain a historical single specific accident data set; Step S23: collecting historical single material consumption records of power plants according to the distribution map of power plants in the marked cities, and obtaining a historical single material consumption data set; Step S24: Associating the historical single specific accident data set with the historical single material consumption data set to obtain an accident-material consumption associated data set; Step S25: Based on the historical single specific accident data set and the accident-material consumption associated data set, the power plant's spare material demand analysis is performed to obtain a warehouse inventory material list; Step S26: Perform physical ID encoding on the inventory materials according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table.

5. The resource optimization scheduling method based on physical ID drive according to claim 4 is characterized in that: Step S25 includes the following steps: Step S251: classify the accident types of the historical single specific accident data set to obtain power plant accident type classification data; Step S252: performing frequency distribution statistics of accident types of the power plant according to the historical single specific accident data set and the power plant accident type classification data to obtain a frequency distribution diagram of the power plant accident types; Step S253: Based on the accident-material consumption association data set and the power plant accident type frequency distribution diagram, the material consumption pattern of the power plant accident is identified to obtain a power plant material consumption pattern feature vector set; Step S254: performing a multi-factor correlation analysis of material consumption on the power plant material consumption pattern feature vector set based on the accident-material consumption association data set to obtain a set of influencing factors of power plant material consumption; Step S255: Perform statistics on all historical consumed materials data for the accident-material consumption association data set to obtain a consumed materials detailed data database; Step S256: constructing a material demand forecasting model for the power plant based on the accident-material consumption association data set and the material consumption pattern feature vector set and the material consumption influencing factor set of the power plant to obtain a standby material demand forecasting model; Step S257: forecasting the demand for spare materials of the power plant by using the spare material demand forecasting model according to the frequency distribution diagram of the power plant accident types, and obtaining the forecast data of the demand for spare materials of the power plant; Step S258: replenish the power plant's spare material demand forecast data according to the consumable material detailed information database to obtain a warehouse inventory material list.

6. The resource optimization scheduling method based on physical ID drive according to claim 4 is characterized in that: Step S26 includes the following steps: Step S261: Performing warehouse network topology analysis on the material warehouse distribution map to obtain a warehouse network feature matrix; Step S262: performing material usage frequency statistics on the power plant based on the accident-material consumption association data set to obtain material usage frequency distribution data; Step S263: collecting storage conditions of the warehouses according to the material warehouse distribution map to obtain a warehouse storage condition data set; Step S264: re-order the warehouse inventory material list by material priority according to the material usage frequency distribution data to obtain an optimized warehouse inventory material list; Step S265: formulating a material allocation strategy for optimizing the warehouse inventory material list based on the material warehouse distribution map according to the warehouse network feature matrix and the warehouse storage condition data set to obtain a material-warehouse initial matching strategy; Step S266: Perform dynamic material allocation efficiency simulation on the material-warehouse initial matching strategy to obtain material allocation efficiency simulation data, and iteratively optimize the material-warehouse initial matching strategy based on the material allocation efficiency simulation data to obtain the material-warehouse optimal matching strategy; Step S267: construct an RFID encoder for the warehouse and inventory materials to obtain an intelligent RFID code generator; Step S268: According to the material-warehouse optimal matching strategy, the intelligent RFID code generator is used to model the inventory materials as digital twins to obtain a material digital twin library, and the RFID tags are intelligently allocated and activated for the materials according to the material digital twin library and the intelligent RFID code generator to obtain an RFID tag-bound material library; Step S269: Generate a global resource RFID mapping table for the RFID tag-bound material library to obtain a global resource RFID mapping table.

7. The resource optimization scheduling method based on physical ID drive according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting emergency event data of the target power plant in real time to obtain emergency event data of the power plant; Step S32: If the power plant emergency event data is power plant accident type data, a spare material demand prediction model is obtained, and replacement material prediction is performed on the target power plant using the spare material demand prediction model according to the power plant accident type data to obtain power plant replacement material data; Step S33: integrating the power plant replacement material data to obtain a power plant material demand work order; Step S34: If the power plant emergency event data is an emergency material demand work order, the emergency material demand work order is directly used as the power plant material demand work order; Step S35: collecting the material acquisition and transportation methods of the target power plant to obtain material acquisition and transportation method data, and performing logistics mode recognition on the material acquisition and transportation method data to obtain material acquisition method data.

8. The resource optimization scheduling method based on physical ID drive according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: spatially registering and fusing the urban power plant distribution map and the material warehouse distribution map to obtain a power plant-warehouse location spatial map; Step S42: collecting geographic location data of the target power plant to obtain the geographic location coordinates of the target power plant, and marking the power plant-warehouse location space map according to the geographic location coordinates of the target power plant to obtain a marked power plant-warehouse location space map; Step S43: Based on the global resource RFID mapping table, a single warehouse material satisfaction judgment is performed on the power plant material demand work order to obtain material demand satisfaction status data; Step S44: If the material demand satisfaction data shows that the materials required by the power plant material demand work order can be obtained in the same warehouse, a warehouse search is performed on the global resource RFID mapping table according to the power plant material demand work order to obtain a preferred warehouse set for material allocation; Step S45: If only one warehouse in the preferred warehouse set for material allocation meets the conditions, a material coordinated allocation plan is formulated for the target power plant based on the preferred warehouse set for material allocation and the material demand work order of the power plant to obtain a material coordinated allocation plan; otherwise, the preferred warehouse set for material allocation is screened for the nearest warehouse based on the marked power plant-warehouse location space diagram to obtain the nearest preferred warehouse for material allocation, and a material coordinated allocation plan is formulated for the target power plant based on the material demand work order of the power plant based on the nearest preferred warehouse for material allocation to obtain a material coordinated allocation plan; Step S46: If the material demand satisfaction data shows that the materials required by the power plant material demand work order cannot be obtained in the same warehouse, the target factory is screened for warehouses based on the power plant material demand work order based on the marked power plant-warehouse location space map and the global resource RFID mapping table to obtain a candidate list of material allocation warehouses; Step S47: Combining the candidate list of material allocation warehouses obtained according to the material demand work order of the power plant to obtain a coordinated material allocation plan.

9. The resource optimization scheduling method based on physical ID drive according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: obtaining a marked power plant-warehouse location space map, and searching for potential passable paths for the target power plant based on the marked power plant-warehouse location space map and the material coordinated deployment plan to obtain a set of potential passable paths between the power plant and the warehouse; Step S52: collecting the current vehicle flow of the paths in the power plant-warehouse potential passage path set to obtain a real-time traffic data set of the transportation path; Step S53: traffic congestion identification and feasible path screening are performed on the power plant-warehouse potential passage path set according to the real-time traffic data set of the transportation path to obtain the power plant-warehouse feasible passage path set; Step S54: Visualize the feasible path set from the power plant to the warehouse to obtain a material dispatch path diagram; Step S55: If the material acquisition method data indicates that the self-collection method is adopted, the material coordination allocation plan and the material dispatching path map are sent to the material management department of the target power plant or the designated material receiving personnel to perform material dispatching operations; Step S56: If the material acquisition method data indicates that the door-to-door method is adopted, the material dispatch operation is performed through the preset intelligent logistics management system according to the material coordinated allocation plan and the material dispatch path map.

10. A resource optimization scheduling system based on physical ID drive, characterized in that: Used to execute the resource optimization scheduling method based on physical ID drive as claimed in claim 1, the resource optimization scheduling system based on physical ID drive includes: The warehouse layout module is used to collect the power plant location data of the target city to obtain the power plant location coordinate set; construct the power plant distribution map of the target city based on the power plant location coordinate set to obtain the city power plant distribution map; analyze the material warehouse layout of the target city based on the city power plant distribution map to obtain the material warehouse distribution map; The physical coding module is used to analyze the demand for spare materials in the power plant and obtain a list of warehouse inventory materials; the inventory materials are physically ID-coded according to the material warehouse distribution map and the warehouse inventory material list to obtain a global resource RFID mapping table; The demand identification module is used to collect material demand work orders for the target power plant and obtain the material demand work orders for the power plant; identify the logistics mode of the target power plant and obtain the material acquisition method data; The material allocation module is used to allocate materials to the target power plant based on the urban power plant distribution map, material warehouse distribution map and global resource RFID mapping table and according to the power plant material demand work order to obtain a coordinated material allocation plan; The scheduling planning module is used to plan the scheduling path for the target power plant according to the material coordination allocation plan to obtain the material scheduling path map; and perform material scheduling operations on the target power plant according to the material acquisition method data and the material scheduling path map.

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