Power grid material procurement processing method and device based on intelligent model and electronic equipment

Abstract

Embodiments of the present application provide a kind of power grid material purchase processing method, device and electronic equipment based on intelligent model.The method comprises: obtaining historical material consumption, and obtaining power grid project information, wherein historical material consumption characterizes the power grid material consumed by historical power grid construction, based on the preset intelligent prediction model, historical material consumption and power grid project information are extracted and handled, and material characteristics are obtained, and based on the preset intelligent prediction model, material characteristics are predicted, and the power grid material demand in the preset time window is obtained;Wherein, power grid material demand represents the quantity of power grid material required by power grid construction;According to power grid material demand, determine the material procurement information in the preset time window;And based on material procurement information, generate procurement instruction, and send procurement instruction to the terminal of user.The method is used to improve the accuracy of power grid material procurement, to save the effect of cost expenditure.

Description

Technical Field

[0001] This application relates to the field of power grid procurement technology, and in particular to a power grid material procurement processing method, apparatus, and electronic equipment based on an intelligent model. Background Technology

[0002] With the continuous rise in electricity demand, the importance of the power grid, as the central nervous system for energy transmission and distribution, in its construction, upgrading, and maintenance is becoming increasingly prominent. Furthermore, the procurement of power grid materials, as a key support for power grid construction and operation, is of paramount importance.

[0003] The current procurement of power grid materials mainly relies on human experience to predict the material demand of projects. This method is greatly affected by subjective factors and is difficult to accurately match the actual project needs, which leads to problems such as overspending in procurement.

[0004] Therefore, there is an urgent need for a solution that can accurately predict the power grid material requirements of a project in order to improve the accuracy of power grid material procurement and thus save costs. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for power grid material procurement based on an intelligent model, which aims to improve the accuracy of power grid material procurement and thus save costs.

[0006] In a first aspect, embodiments of this application provide a power grid material procurement processing method based on an intelligent model, including:

[0007] Historical material consumption is obtained, and power grid project information is obtained, wherein the historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid.

[0008] Based on a preset intelligent prediction model, feature extraction processing is performed on the historical material consumption and the power grid project information to obtain material characteristics. Then, based on the preset intelligent prediction model, the material characteristics are predicted to obtain the power grid material demand within a preset time window. The power grid material demand represents the quantity of power grid materials required for power grid construction.

[0009] Based on the power grid material demand, determine the material procurement information within the preset time window; and generate a procurement instruction based on the material procurement information, and send the procurement instruction to the user's terminal.

[0010] In one possible implementation, the historical material consumption and the power grid project information are processed based on a preset intelligent prediction model to obtain material characteristics, including:

[0011] The historical material consumption data is processed to obtain the processed historical material consumption data.

[0012] Time series feature processing is performed on the processed historical material consumption to obtain the changing trend features of historical material consumption.

[0013] The material characteristics are obtained based on the historical trends in material consumption and the power grid project information.

[0014] In one possible implementation, determining the material procurement information within the preset time window based on the power grid material demand includes:

[0015] Obtain supplier information for the power grid materials represented by the power grid project information;

[0016] Based on the supplier information and the power grid material demand, a material procurement cost sequence is generated; wherein, the material procurement cost sequence includes the supplier's procurement cost value;

[0017] The material procurement cost sequence is processed using a constraint method that minimizes the cost value to obtain the final procurement cost value; based on the final procurement cost value, the material procurement information within the preset time window is determined.

[0018] In one possible implementation, a material procurement cost sequence is generated based on the supplier information and the power grid material demand, including:

[0019] Based on the supplier information, the supplier's evaluation information is determined, wherein the evaluation information represents the supplier's rating;

[0020] Based on the supplier's evaluation information and the power grid's material demand, a material procurement cost sequence is generated.

[0021] In one possible implementation, the procurement cost value in the material procurement cost sequence is:

[0022]

[0023] in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier; The inventory cost of the j-th power grid material is represented.

[0024] In one possible implementation, determining the supplier's evaluation information based on the supplier's supplier information includes:

[0025] The supplier's evaluation information is determined based on the supplier's supplier information and preset weighting coefficients; wherein the supplier information includes one or more of the following: the supplier's on-time delivery rate, material quality qualification rate, and price volatility.

[0026] In one possible implementation, the rating represented by the supplier's evaluation information is W = a × 40% + b × 30% + c × 30%;

[0027] Where a represents the supplier's on-time delivery rate; b represents the supplier's material quality qualification rate; and c represents the supplier's price volatility.

[0028] In one possible implementation, the method further includes:

[0029] If the rating represented by the supplier's evaluation information is less than or equal to the first threshold and greater than or equal to the second threshold, a level one alarm message is issued to the supplier.

[0030] If the rating represented by the supplier's evaluation information is less than the second threshold and greater than or equal to the third threshold, a level two alarm message is issued to the supplier.

[0031] If the rating represented by the supplier's evaluation information is less than the third threshold, a level three alarm message will be issued to the supplier.

[0032] In one possible implementation, obtaining supplier information for the power grid materials represented by the power grid project information includes:

[0033] The supplier corresponding to each type of power grid material is determined from the preset database, and the supplier information of the determined supplier is retrieved from the preset database.

[0034] The preset database includes supplier information for different power grid materials.

[0035] In one possible implementation, the method further includes:

[0036] Receive logistics tracking information of the power grid materials; wherein the logistics tracking information includes sensor information and reading information; wherein the sensor information is information of the power grid materials collected by the intelligent sensing terminal set on the power grid materials, and the reading information is information of the power grid materials read by the RFID tag set on the power grid materials;

[0037] Store the logistics tracking information.

[0038] Secondly, embodiments of this application provide a power grid material procurement processing device based on an intelligent model, comprising:

[0039] The acquisition module is used to acquire historical material consumption and power grid project information, wherein the historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid.

[0040] The processing module is used to perform feature extraction processing on the historical material consumption and the power grid project information based on a preset intelligent prediction model to obtain material features, and to predict the power grid material demand within a preset time window based on the material features; wherein, the power grid material demand represents the quantity of power grid materials required for power grid construction.

[0041] The determination module is used to determine the material procurement information within the preset time window based on the power grid material demand; and to generate a procurement instruction based on the material procurement information and send the procurement instruction to the user's terminal.

[0042] In one possible implementation, the processing module includes:

[0043] The historical material consumption data is processed to obtain the processed historical material consumption data.

[0044] Time series feature processing is performed on the processed historical material consumption to obtain the changing trend features of historical material consumption.

[0045] The material characteristics are obtained based on the historical trends in material consumption and the power grid project information.

[0046] In one possible implementation, the determining module includes:

[0047] The acquisition submodule is used to acquire supplier information of the suppliers corresponding to the power grid materials represented by the power grid project information;

[0048] The generation module is used to generate a material procurement cost sequence based on the supplier information and the power grid material demand; wherein, the material procurement cost sequence includes the supplier's procurement cost value;

[0049] The processing submodule is used to process the material procurement cost sequence using a constraint method with the minimum cost value as the constraint condition to obtain the final procurement cost value; and to determine the material procurement information within the preset time window based on the final procurement cost value.

[0050] In one possible implementation, the generation module includes:

[0051] The determination submodule is used to determine the evaluation information of the supplier based on the supplier information of the supplier, wherein the evaluation information represents the supplier's rating;

[0052] The generation submodule is used to generate a material procurement cost sequence based on the supplier's evaluation information and the power grid's material demand.

[0053] In one possible implementation, the procurement cost value in the material procurement cost sequence is:

[0054]

[0055] in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier; The inventory cost of the j-th power grid material is represented.

[0056] In one possible implementation, determining the submodule includes:

[0057] The supplier's evaluation information is determined based on the supplier's supplier information and preset weighting coefficients; wherein the supplier information includes one or more of the following: the supplier's on-time delivery rate, material quality qualification rate, and price volatility.

[0058] In one possible implementation, the rating represented by the supplier's evaluation information is W = a × 40% + b × 30% + c × 30%;

[0059] Where a represents the supplier's on-time delivery rate; b represents the supplier's material quality qualification rate; and c represents the supplier's price volatility.

[0060] In one possible implementation, the device further includes:

[0061] If the rating represented by the supplier's evaluation information is less than or equal to the first threshold and greater than or equal to the second threshold, a level one alarm message is issued to the supplier.

[0062] If the rating represented by the supplier's evaluation information is less than the second threshold and greater than or equal to the third threshold, a level two alarm message is issued to the supplier.

[0063] If the rating represented by the supplier's evaluation information is less than the third threshold, a level three alarm message will be issued to the supplier.

[0064] In one possible implementation, acquiring the submodule includes:

[0065] The supplier corresponding to each type of power grid material is determined from the preset database, and the supplier information of the determined supplier is retrieved from the preset database.

[0066] The preset database includes supplier information for different power grid materials.

[0067] In one possible implementation, the device further includes:

[0068] Receive logistics tracking information of the power grid materials; wherein the logistics tracking information includes sensor information and reading information; wherein the sensor information is information of the power grid materials collected by the intelligent sensing terminal set on the power grid materials, and the reading information is information of the power grid materials read by the RFID tag set on the power grid materials;

[0069] Store the logistics tracking information.

[0070] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0071] The memory stores computer-executed instructions;

[0072] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0073] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0074] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0075] This application provides a method, apparatus, and electronic device for power grid material procurement based on an intelligent model. By inputting historical material consumption data and current power grid project information into a preset intelligent prediction model, the model uses a feature extraction algorithm to process the historical material consumption and power grid project information, extracting key features reflecting the patterns of material demand. Based on these features, a prediction algorithm is used to accurately predict the power grid material demand within a preset time window. Finally, based on the predicted power grid material demand, the system automatically generates detailed material procurement information, including the type, quantity, and time of the procured materials. Based on this information, a procurement instruction is generated and sent directly to the user's terminal device, achieving automation and intelligence in the procurement process. This effectively solves the problem of material backlog or shortage caused by inaccurate predictions in traditional power grid material procurement, significantly improving the timeliness and accuracy of material procurement, reducing procurement costs, and enhancing the overall efficiency and quality of power grid construction. Attached Figure Description

[0076] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0077] Figure 1 A flowchart illustrating a power grid material procurement processing method based on an intelligent model, provided in this application embodiment. Figure 1 ;

[0078] Figure 2 A flowchart illustrating a power grid material procurement processing method based on an intelligent model, provided in this application embodiment. Figure 2 ;

[0079] Figure 3 A schematic diagram of the structure of a power grid material procurement processing device based on an intelligent model provided in this application embodiment. Figure 1 ;

[0080] Figure 4 A schematic diagram of the structure of a power grid material procurement processing device based on an intelligent model provided in this application embodiment. Figure 2 ;

[0081] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0082] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0083] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0084] With rapid socio-economic development, accelerated industrialization, urbanization, and improved living standards, electricity is increasingly used in various fields. The construction of power grids requires a large amount of materials, such as transformers, power cables, transmission towers, and switchgear. The quality and timeliness of these materials directly affect the progress and quality of power grid construction.

[0085] Current power grid material procurement plans rely heavily on manual experience to predict future project needs. This method is highly subjective and struggles to accurately match actual project requirements. Manual forecasting is susceptible to interference from historical data biases, market fluctuations, project changes, and other factors, leading to inaccurate material quantity estimates. This information distortion not only causes inventory buildup or shortages but can also trigger a chain reaction of problems, including procurement cost overruns and project delays.

[0086] Therefore, the power grid material procurement processing method, device, and electronic equipment based on intelligent models provided in this application can solve the above-mentioned problems.

[0087] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0088] Figure 1 A flowchart illustrating a power grid material procurement processing method based on an intelligent model, provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0089] S101. Obtain historical material consumption and power grid project information. The historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid.

[0090] For example, historical material consumption and power grid project information can be obtained through channels such as the power grid material management system and project archives. The power grid material management system records detailed information on the procurement, requisition, and consumption of materials for each power grid construction project, including material name, specifications, consumption quantity, usage time, and project application. Project archives, including but not limited to project feasibility study reports, design documents, and construction records, are reviewed to extract information related to material consumption. Project archives reflect the actual use of materials in projects and are crucial for accurately collecting material consumption data. Finally, historical material consumption and power grid project information are obtained and integrated from different sources. For example, the basic project information in the power grid material management system can be linked with the historical material consumption data in the project archives to form a complete power grid project information table. This table may include fields such as project name, construction location, construction scale, material name, specifications, and material consumption.

[0091] S102. Based on the preset intelligent prediction model, the historical material consumption and power grid project information are processed to obtain material characteristics. Based on the preset intelligent prediction model, the material characteristics are predicted to obtain the power grid material demand within the preset time window. Among them, the power grid material demand represents the quantity of power grid materials required for power grid construction.

[0092] For example, the preset intelligent prediction model can be, but is not limited to, an Autoregressive Integrated Moving Average (ARIMA) model, random forest, or Long Short-Term Memory (LSTM) network. The acquired historical material consumption data and power grid project information are merged to form a complete dataset. The merged data is then cleaned and preprocessed, including removing duplicate data, correcting erroneous data, and filling in missing data. Simultaneously, the data is normalized or standardized to improve its processing capabilities. Features are extracted from the time dimension, such as year, quarter, month, and week. These features help the model capture seasonal and periodic changes in the time series. For example, for power grid construction materials, the demand for certain materials may increase significantly in summer or winter.

[0093] The processed dataset is divided into training and test sets. This division can typically be done chronologically; for example, historical data can be used as the training set, and recent data as the test set. The training set is used to train a pre-defined intelligent prediction model. During training, model parameters are adjusted to optimize performance. For example, for neural network models, adjusting parameters such as the learning rate and number of iterations can improve training effectiveness. The test set is used to evaluate the trained model, calculating performance metrics such as mean squared error (MSE), mean absolute error (MAE), and accuracy. Based on the evaluation results, model parameters are further adjusted, or alternative models are selected.

[0094] The extracted feature data is input into the trained intelligent prediction model. This feature data includes project information at the current time, historical material consumption, and known information within a preset time window (such as planned project information). Based on the input feature data, the model outputs the power grid material demand within the preset time window. For example, it can predict the demand for a certain material within the next month, three months, or one year.

[0095] S103. Based on the power grid's material demand, determine the material procurement information within a preset time window; and generate a procurement instruction based on the material procurement information, sending the procurement instruction to the user's terminal.

[0096] For example, by combining the planned cycle and construction schedule of the power grid construction project with the company's financial budget cycle, a suitable time window, such as monthly, quarterly, or semi-annual, can be determined. For instance, if the power grid construction project proceeds quarterly, a quarter can be set as the preset time window. Based on the type and specifications of the materials, suppliers with the corresponding qualifications and production capabilities are selected from the company's supplier database. Considering factors such as supplier reputation, product quality, and service ratings, a preliminary evaluation of suppliers is conducted to determine a list of candidate suppliers. Quotations from candidate suppliers are collected and organized to establish a quotation database for subsequent analysis and comparison.

[0097] Calculate the procurement cost for each material based on supplier quotations and purchase quantities. Procurement costs include material price, transportation costs, and other expenses. Select the optimal supplier based on factors such as procurement cost, supplier reputation, and supply capacity. Develop detailed procurement instructions based on material requirements and the selected supplier. Procurement instructions should include, but are not limited to, the following: material requirements, supplier information, procurement cost, delivery information, quality standards, packaging requirements, and transportation requirements. Send the procurement instructions to the user's terminal.

[0098] This application provides a power grid material procurement processing method based on an intelligent model. By inputting historical material consumption data and current power grid project information into a preset intelligent prediction model, the model uses a feature extraction algorithm to process the historical material consumption and power grid project information, extracting key features reflecting the patterns of material demand. Based on these features, a prediction algorithm is used to accurately predict the power grid material demand within a preset time window. Finally, based on the predicted power grid material demand, the system automatically generates detailed material procurement information, including the type, quantity, and time of the procured materials. Based on this information, a procurement instruction is generated and sent directly to the user's terminal device, achieving automation and intelligence in the procurement process. This effectively solves the problem of material backlog or shortage caused by inaccurate predictions in traditional power grid material procurement, significantly improving the timeliness and accuracy of material procurement, reducing procurement costs, and enhancing the overall efficiency and quality of power grid construction.

[0099] Figure 2 A flowchart illustrating a power grid material procurement processing method based on an intelligent model, provided in this application embodiment. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, a method for power grid material procurement based on an intelligent model is described in detail. The method includes:

[0100] S201. Obtain historical material consumption and power grid project information.

[0101] For example, this step can refer to step S101 above, and will not be repeated here.

[0102] S202. Based on the preset intelligent prediction model, the historical material consumption and power grid project information are processed to obtain material characteristics.

[0103] For example, this step can refer to step S102 above, and will not be repeated here.

[0104] In one example, historical material consumption data is processed to obtain processed historical material consumption; time series feature processing is performed on the processed historical material consumption to obtain the trend features of historical material consumption; based on the trend features of historical material consumption and power grid project information, material characteristics are obtained.

[0105] For example, historical material consumption data is first cleaned and preprocessed, including removing duplicate data, correcting erroneous data, and filling in missing data. Simultaneously, the data is normalized or standardized. For missing values, methods such as mean imputation, median imputation, and top / bottom value imputation can be used; for outliers, judgment and correction are made according to business rules. For example, consumption significantly exceeding the normal range may be an entry error and needs verification and adjustment; for duplicate values, deduplication is performed. Data from different sources and in different formats are uniformly formatted to ensure data consistency and standardization. For example, the date format is unified to "YYYY-MM-DD", and the material names and specifications are standardized for subsequent analysis. To eliminate dimensional differences between different material consumption quantities, the cleaned data is standardized. Common standardization methods include Z-score standardization, which converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. Normalization scales the data to the [0,1] interval. Commonly used normalization methods include Min-Max normalization, which involves subtracting the minimum value from the data and then dividing by the difference between the maximum and minimum values.

[0106] For example, time series decomposition is performed on the processed historical material consumption to obtain the trend characteristics of historical material consumption. Available methods for time series decomposition include trend decomposition, seasonal decomposition, and periodic decomposition. Trend decomposition uses methods such as moving averages, exponential smoothing, and trend line fitting to decompose the processed historical material consumption time series and extract the long-term trend characteristics of material consumption. For example, using a linear regression model to fit the monthly consumption series yields a linear growth trend in material consumption. Seasonal decomposition uses seasonal decomposition algorithms (such as STL decomposition) to decompose the time series into three parts: trend, seasonality, and random fluctuation, analyzing the seasonal variation patterns of material consumption. For example, analyzing the consumption differences of a certain type of material in different seasons determines whether there are obvious seasonal peaks and troughs. Periodic decomposition uses methods such as Fourier transform and wavelet transform to decompose time series with periodic fluctuations, determining the period length and periodic characteristics of material consumption. For example, analyzing whether the consumption of certain materials in power grid construction projects exhibits annual periodic fluctuations. For example, historical trends in material consumption can be integrated with power grid project information to construct comprehensive material characteristics. For instance, monthly consumption trends can be combined with project construction cycles, projected budgets, and other characteristics to obtain material consumption characteristics at different project stages.

[0107] S203. Obtain supplier information for the power grid materials represented by the power grid project information; generate a material procurement cost sequence based on the supplier information and the power grid material demand; wherein the material procurement cost sequence includes the supplier's procurement cost value; process the material procurement cost sequence using a constraint method with the minimum cost value as the constraint condition to obtain the final procurement cost value; determine the material procurement information within a preset time window based on the final procurement cost value.

[0108] For example, for each type of material, the procurement cost of a single supplier is calculated based on the supplier's quotation and the demand for the power grid material. The calculation formula is: Procurement Cost = Unit Price of Material × Demand Quantity + Transportation Costs + Other Additional Costs. Considering all possible suppliers, the procurement cost of each supplier is calculated, forming a material procurement cost sequence. Each element in the sequence represents the procurement cost value of a supplier. When calculating the procurement cost, in addition to considering the material quotation, supplier ratings (such as quality delivery rate, service rating, etc.) should also be considered. Supplier ratings can be incorporated into the cost calculation using a weighted average method to reflect the overall service quality of the supplier. For example, weighting coefficients can be set so that suppliers with high quality delivery rates have an advantage in cost calculation.

[0109] For example, minimizing material procurement costs is used as a constraint, i.e., selecting the supplier combination with the lowest procurement cost. Besides cost minimization, other constraints can be considered, such as supplier delivery capability, quality assurance, and service rating. These constraints can be quantified by setting thresholds or priorities. Based on the cost minimization constraint, the material procurement cost sequence is sorted, and the supplier with the lowest cost is selected. Based on the final procurement cost value, material procurement information within a preset time window is determined.

[0110] In one example, the supplier corresponding to each type of power grid material is determined from a preset database, and the supplier information of the determined supplier is retrieved from the preset database; wherein, the preset database includes supplier information of suppliers corresponding to different power grid materials.

[0111] For example, the pre-set database includes supplier information for different power grid materials. During supplier information retrieval, the first step is to identify the supplier corresponding to each type of power grid material from the pre-set database. Specifically, based on the power grid material classification system, such as transformers, switchgear, and cables, we perform a matching query in the pre-set database one by one. By setting relevant search criteria, such as material name and specifications, the system can locate the list of suppliers associated with each type of power grid material. After identifying the suppliers corresponding to each type of power grid material, detailed supplier information for these suppliers is retrieved from the pre-set database. The supplier information section in the pre-set database includes multiple dimensions such as basic supplier information, business status, product quality, service capabilities, and historical pricing.

[0112] In one example, supplier evaluation information is determined based on the supplier's supplier information, where the evaluation information represents the supplier's rating; and a material procurement cost sequence is generated based on the supplier's evaluation information and the power grid's material demand.

[0113] For example, a multi-dimensional indicator system can be constructed around supplier evaluation. For instance, the quality dimension collects data such as product qualification rate and quality inspection records; the delivery dimension collects data such as on-time delivery rate and delivery cycle fluctuations; the service dimension collects data such as service response time and problem-solving efficiency; and the price dimension collects data such as quotations for different batches of materials and price adjustment frequency. This data can be obtained through direct communication with suppliers, reviewing historical purchase orders, and customer feedback records. Based on the characteristics and actual needs of power grid material procurement, appropriate methods can be used to determine the weight of each evaluation indicator. For example, an expert scoring method can be used, inviting procurement experts and technical personnel to score the importance of each indicator based on their experience, and then calculating the weight of each indicator; alternatively, the analytic hierarchy process (AHP) can be used to construct a hierarchical model and calculate the weight of each indicator through a comparison judgment matrix.

[0114] Based on the supplier quotations and the power grid's material requirements, the procurement cost is calculated. The formula is: Procurement Cost = Unit Price of Material × Quantity Required + Transportation Costs + Other Additional Costs. According to the supplier order in the supplier evaluation information table, the quotation information and power grid material requirements of each supplier are substituted into the procurement cost calculation method to calculate the procurement cost for each supplier. The calculated procurement costs of each supplier are then arranged in the order of the supplier evaluation information table to form a material procurement cost sequence. Each element in the sequence includes the supplier name and the corresponding procurement cost value.

[0115] In one example, the procurement cost value in the material procurement cost series is:

[0116]

[0117] in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier; The inventory cost of the j-th power grid material is represented.

[0118] For example, the procurement cost value in the material procurement cost series is:

[0119]

[0120] in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier; The inventory cost of the j-th power grid material is represented.

[0121] In one example, the supplier's evaluation information is determined based on the supplier's supplier information and preset weighting coefficients; wherein, the supplier information includes one or more of the following: the supplier's on-time delivery rate, material quality qualification rate, and price volatility.

[0122] For example, a supplier's on-time delivery rate is the percentage of orders for a specific type of goods delivered on time out of the total number of orders. The formula is: On-time Delivery Rate = (Number of Orders Delivered on Time / Total Number of Orders) × 100%. The material quality pass rate is the percentage of qualified materials delivered by the supplier out of the total quantity of materials. The formula is: Material Quality Pass Rate = (Quantity of Qualified Materials / Total Quantity of Materials) × 100%. The price volatility is the percentage of the difference between the maximum and minimum prices quoted by the supplier for a specific type of goods, relative to the average price quoted. The formula is: Price Volatility = (Maximum Price - Minimum Price) / Average Price × 100%.

[0123] For example, weights are assigned to each evaluation indicator based on the company's needs and strategies. For instance: On-time delivery rate: 40%; Material quality pass rate: 30%; Price volatility: 30%. Based on the weights and standardized indicator values, a score is calculated for each supplier. The calculation formula is: Supplier Score = 40% × On-time Delivery Rate + 30% × Material Quality Pass Rate + 30% × Price Volatility.

[0124] Assume that supplier A has the following performance indicators for supplying material B: on-time delivery rate of 95%; quality qualification rate of 90%; and price fluctuation rate of 10%. Then supplier A's score for supplying material B is 0.92. The calculation process is as follows: Supplier A's score = 0.4 × 0.95 + 0.3 × 0.9 + 0.3 × 0.9 = 0.92.

[0125] In one example, the supplier's evaluation information represents a score of W = a × 40% + b × 30% + c × 30%; where a represents the supplier's on-time delivery rate; b represents the supplier's quality pass rate; and c represents the supplier's price volatility.

[0126] For example, the rating represented by the supplier's evaluation information is = 40% × on-time delivery rate + 30% × material quality qualification rate + 30% × price volatility.

[0127] In one example, if the rating represented by the supplier's evaluation information is less than or equal to the first threshold and greater than or equal to the second threshold, a level one alarm message is issued to the supplier.

[0128] If the rating represented by the supplier's evaluation information is less than the second threshold and greater than or equal to the third threshold, a level 2 alarm message will be issued to the supplier.

[0129] If the rating indicated by the supplier's evaluation information is less than the third threshold, a level three alarm message will be issued to the supplier.

[0130] For example, the first threshold (T1) represents a high scoring threshold, indicating that the supplier's performance is good but still requires attention. The second threshold (T2) represents a medium scoring threshold, indicating that the supplier's performance is average and a Level 1 alarm needs to be issued. The third threshold (T3) represents a low scoring threshold, indicating that the supplier's performance is poor and a Level 2 alarm needs to be issued. Furthermore, T1, T2, and T3 must satisfy the condition T1 > T2 > T3. When the supplier's score (W), T2 ≤ W ≤ T1, a Level 1 alarm is issued to the supplier. The Level 1 alarm may be a warning letter requiring the supplier to submit an improvement plan. When the supplier's score (W), T3 ≤ W ≤ T2, a Level 2 alarm is issued to the supplier. The Level 2 alarm may be reducing order volume or increasing the frequency of material sampling inspections. When the supplier's score (W), W ≤ T3, a Level 3 alarm is issued to the supplier. The Level 3 alarm may be suspending new orders and initiating a rectification review.

[0131] In one possible implementation, logistics tracking information of power grid materials is received; wherein the logistics tracking information includes sensor information and reading information; wherein the sensor information is information of the power grid materials collected by intelligent sensing terminals installed on the power grid materials, and the reading information is information of the power grid materials read by RFID tags installed on the power grid materials; and the logistics tracking information is stored.

[0132] For example, the purchasing department develops a procurement plan based on needs, issues purchase orders to suppliers, and specifies key information such as the specifications, quantity, and delivery time of the materials. The purchase order requires suppliers to provide relevant data interfaces or file format specifications that include logistics tracking information when the materials are shipped.

[0133] Sensor information is collected by intelligent sensing terminals installed on power grid materials. This information may include: real-time location of the materials (via GPS or other positioning technologies); environmental information during transportation (such as temperature, humidity, vibration, etc.); and status information of the materials (such as whether the materials have been opened or are intact). Information reading involves attaching RFID tags to the power grid materials, with each tag storing basic information about the materials (such as material number, name, and specifications). During logistics and transportation, RFID readers read the tag information in real time. The information read by the RFID readers is integrated with other relevant information during logistics and transportation (such as time, location, and operators) to form complete logistics tracking data. This integrated data is transmitted to the logistics tracking system via wired or wireless means. The logistics tracking system receives and processes the RFID reading information. It then associates the reading information with sensor information to form a complete logistics tracking record for the power grid materials.

[0134] The received sensor information and read data are stored in the logistics tracking system's database according to a pre-defined database table structure. This ensures data integrity and consistency, preventing data loss or corruption. A unique identifier is generated for each logistics tracking record to facilitate subsequent querying and management. The logistics tracking system's database is backed up regularly to ensure data security. A combination of full and incremental backups can be used to improve backup efficiency and recovery speed. A data recovery mechanism is established to promptly recover data in the event of database failure or data loss, ensuring the normal operation of the logistics tracking business.

[0135] Optionally, this invention also provides inventory management functions after receiving power grid materials. Inventory management encompasses multiple aspects, including inventory query, early warning, allocation, and inventory counting, to ensure the efficiency and accuracy of material management. First, the inventory query function allows users to query inventory status in real time by inputting information such as material number, factory code, and storage location. Users can query details of material inbound and outbound transactions, inventory early warnings, inventory counting, batch management, and quality inspection. These functions provide users with comprehensive inventory information, helping them better understand inventory dynamics. In the inventory query, inbound and outbound details record detailed information for each material inbound and outbound transaction, including date, quantity, and responsible person. These records provide important data support for inventory management, facilitating traceability and auditing. Outbound management includes outbound application, picking operations, and outbound registration, ensuring the standardization and accuracy of the outbound process. Strict management of these steps helps avoid errors and delays, improving logistics efficiency. Batch management is an important component of inventory management. It includes tracking the inbound and outbound status of each batch of materials, shelf life, and other detailed information. Through batch management, the system can ensure adherence to the first-in, first-out (FIFO) principle, thereby reducing the risk of material expiration. This is particularly important for materials requiring strict shelf-life control, effectively reducing waste and improving material utilization efficiency. Furthermore, inventory management includes functions such as inventory calculation and analysis, early warning settings (e.g., insufficient or excessive inventory alerts), material allocation, and periodic inventory checks. The calculation and analysis function helps managers understand the overall inventory status and trends, while early warning settings promptly alert managers to take action when inventory levels are abnormal. The material allocation function allows for flexible allocation of materials between different warehouses or departments to meet diverse needs. Periodic inventory checks ensure the accuracy of inventory records, promptly identifying and correcting inventory discrepancies. Through the comprehensive application of these functions, this invention enables comprehensive management and optimization of power grid material inventory, improving the efficiency and accuracy of inventory management, reducing operating costs, and ensuring timely supply of materials.

[0136] This application provides a power grid material procurement processing method based on an intelligent model. The method involves preprocessing historical material consumption data, such as cleaning and filling in missing values, to obtain processed historical material consumption data. Time series analysis is then used to extract features from this processed historical material consumption data, capturing its trend characteristics over time, such as seasonal fluctuations and periodic patterns. Combined with the material demand types and specifications specified in the power grid project information, the system integrates the changing trend characteristics of historical material consumption with the project demand information to generate material characteristics reflecting the material demand. Simultaneously, the system collects detailed information on suppliers corresponding to the power grid materials represented by the power grid project information, including supplier qualifications, historical supply records, and pricing strategies, forming a supplier information database. Based on supplier information and the power grid material demand within a preset time window, the system simulates material procurement cost sequences under different procurement schemes, where each procurement cost value corresponds to a supplier's quotation. Furthermore, a constrained optimization method aimed at minimizing the total procurement cost is employed to screen and adjust the material procurement cost sequence, considering multiple dimensions such as supplier delivery time and quality stability, to obtain the optimal final procurement cost value while ensuring the reliability of material supply. Based on the final procurement cost and material demand, the system determines the optimal material procurement information within a preset time window, including procurement quantity, procurement time, and supplier selection. This effectively solves problems such as high procurement costs and unreasonable supplier selection caused by a single decision-making basis in traditional power grid material procurement, significantly reducing the overall cost of power grid construction, while optimizing supplier resources and improving the overall efficiency of the supply chain.

[0137] Figure 3 A schematic diagram of the structure of a power grid material procurement processing device based on an intelligent model provided in this application embodiment. Figure 1 ,like Figure 3 As shown, the power grid material procurement processing device 30 based on an intelligent model provided in this embodiment includes:

[0138] The acquisition module 301 is used to acquire historical material consumption and power grid project information. The historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid.

[0139] The processing module 302 is used to perform feature extraction processing on historical material consumption and power grid project information based on a preset intelligent prediction model to obtain material characteristics, and to predict the power grid material demand within a preset time window based on the material characteristics based on the preset intelligent prediction model; wherein, the power grid material demand represents the quantity of power grid materials required for power grid construction.

[0140] The determination module 303 is used to determine the material procurement information within a preset time window based on the power grid's material demand; and to generate a procurement instruction based on the material procurement information and send the procurement instruction to the user's terminal.

[0141] This embodiment provides a power grid material procurement processing device based on an intelligent model, which can execute the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0142] Figure 4 A schematic diagram of the structure of a power grid material procurement processing device based on an intelligent model provided in this application embodiment. Figure 2 ,like Figure 4 As shown, the power grid material procurement processing device 40 based on an intelligent model provided in this embodiment includes:

[0143] The acquisition module 401 is used to acquire historical material consumption and power grid project information. The historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid.

[0144] The processing module 402 is used to perform feature extraction processing on historical material consumption and power grid project information based on a preset intelligent prediction model to obtain material characteristics, and to predict the power grid material demand within a preset time window based on the material characteristics based on the preset intelligent prediction model; wherein, the power grid material demand represents the quantity of power grid materials required for power grid construction.

[0145] The determination module 403 is used to determine the material procurement information within a preset time window based on the power grid's material demand; and to generate a procurement instruction based on the material procurement information and send the procurement instruction to the user's terminal.

[0146] In one possible implementation, the processing module 402 includes:

[0147] The historical material consumption data is processed to obtain the processed historical material consumption data.

[0148] Time series feature processing is performed on the processed historical material consumption to obtain the changing trend features of historical material consumption.

[0149] Based on the historical trends in material consumption and power grid project information, material characteristics are obtained.

[0150] In one possible implementation, the determining module 403 includes:

[0151] The acquisition submodule 4031 is used to acquire supplier information of the suppliers corresponding to the power grid materials represented by the power grid project information;

[0152] The generation module 4032 is used to generate a material procurement cost sequence based on supplier information and power grid material demand; wherein, the material procurement cost sequence includes the supplier's procurement cost value;

[0153] The processing submodule 4033 is used to process the material procurement cost sequence using a constraint method with the minimum cost value as the constraint condition to obtain the final procurement cost value; and to determine the material procurement information within a preset time window based on the final procurement cost value.

[0154] In one possible implementation, the generation module 4032 includes:

[0155] The determination submodule 40321 is used to determine the supplier's evaluation information based on the supplier's supplier information, wherein the evaluation information represents the supplier's rating;

[0156] The generation submodule 40322 is used to generate a material procurement cost sequence based on supplier evaluation information and power grid material demand.

[0157] In one possible implementation, the procurement cost value in the material procurement cost sequence is:

[0158]

[0159] in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier; The inventory cost of the j-th power grid material is represented.

[0160] In one possible implementation, determining submodule 40321 includes:

[0161] The supplier evaluation information is determined based on the supplier information and preset weighting coefficients. The supplier information includes one or more of the following: the supplier's on-time delivery rate, material quality qualification rate, and price volatility.

[0162] In one possible implementation, the rating represented by the supplier's evaluation information is W = a × 40% + b × 30% + c × 30%;

[0163] Where a represents the supplier's on-time delivery rate; b represents the supplier's material quality qualification rate; and c represents the supplier's price volatility.

[0164] In one possible implementation, the device 40 further includes:

[0165] If the rating represented by the supplier's evaluation information is less than or equal to the first threshold and greater than or equal to the second threshold, a level one alarm message will be issued to the supplier.

[0166] If the rating represented by the supplier's evaluation information is less than the second threshold and greater than or equal to the third threshold, a level 2 alarm message will be issued to the supplier.

[0167] If the rating indicated by the supplier's evaluation information is less than the third threshold, a level three alarm message will be issued to the supplier.

[0168] In one possible implementation, the acquisition submodule 4031 includes:

[0169] The supplier corresponding to each type of power grid material is determined from the preset database, and the supplier information of the determined supplier is retrieved from the preset database.

[0170] The pre-set database includes supplier information for different power grid materials.

[0171] In one possible implementation, the device 40 further includes:

[0172] Receive logistics tracking information of power grid materials; the logistics tracking information includes sensor information and reading information; the sensor information is the information of power grid materials collected by the intelligent sensing terminal set on the power grid materials, and the reading information is the information of power grid materials read by the RFID tag set on the power grid materials.

[0173] Store logistics tracking information.

[0174] This embodiment provides a power grid material procurement processing device based on an intelligent model, which can execute the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0175] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0176] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0177] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0178] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0179] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0180] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0181] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0182] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0183] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0184] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0185] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0186] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0188] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0189] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0190] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A power grid material procurement processing method based on an intelligent model, characterized in that, include: Historical material consumption is obtained, and power grid project information is obtained, wherein the historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid. Based on a preset intelligent prediction model, feature extraction processing is performed on the historical material consumption and the power grid project information to obtain material characteristics. Then, based on the preset intelligent prediction model, the material characteristics are predicted to obtain the power grid material demand within a preset time window. The power grid material demand represents the quantity of power grid materials required for power grid construction. Based on the power grid material demand, determine the material procurement information within the preset time window; and generate a procurement instruction based on the material procurement information, and send the procurement instruction to the user's terminal.

2. The method according to claim 1, characterized in that, Based on a preset intelligent prediction model, feature processing is performed on the historical material consumption and the power grid project information to obtain material characteristics, including: The historical material consumption data is processed to obtain the processed historical material consumption data. Time series feature processing is performed on the processed historical material consumption to obtain the changing trend features of historical material consumption. The material characteristics are obtained based on the historical trends in material consumption and the power grid project information.

3. The method according to claim 1, characterized in that, Based on the power grid's material demand, determine the material procurement information within the preset time window, including: Obtain supplier information for the power grid materials represented by the power grid project information; Based on the supplier information and the power grid material demand, a material procurement cost sequence is generated; wherein, the material procurement cost sequence includes the supplier's procurement cost value; The material procurement cost sequence is processed using a constraint method that minimizes the cost value to obtain the final procurement cost value; based on the final procurement cost value, the material procurement information within the preset time window is determined.

4. The method according to claim 3, characterized in that, Based on the supplier information and the power grid material demand, a material procurement cost sequence is generated, including: Based on the supplier information, the supplier's evaluation information is determined, wherein the evaluation information represents the supplier's rating; Based on the supplier's evaluation information and the power grid's material demand, a material procurement cost sequence is generated.

5. The method according to claim 4, characterized in that, The procurement cost value in the aforementioned material procurement cost sequence is: in, This represents the procurement cost value of the i-th supplier; Represents the total number of power grid materials procured; This represents the total number of suppliers corresponding to the purchased power grid materials; The score representing the supply of the j-th power grid material by the i-th supplier; This represents the purchase price of power grid material j from the i-th supplier; This represents the transportation cost for purchasing power grid material j from the i-th supplier; This represents the quantity of material j purchased from the i-th supplier. The inventory cost of the j-th power grid material is represented.

6. The method according to claim 4, characterized in that, Based on the supplier's supplier information, the supplier's evaluation information is determined, including: The supplier's evaluation information is determined based on the supplier's supplier information and preset weighting coefficients; wherein the supplier information includes one or more of the following: the supplier's on-time delivery rate, material quality qualification rate, and price volatility.

7. The method according to claim 6, characterized in that, The rating represented by the supplier's evaluation information is W = a × 40% + b × 30% + c × 30%; Where a represents the supplier's on-time delivery rate; b represents the supplier's material quality qualification rate; and c represents the supplier's price volatility.

8. The method according to claim 7, characterized in that, The method further includes: If the rating represented by the supplier's evaluation information is less than or equal to the first threshold and greater than or equal to the second threshold, a level one alarm message is issued to the supplier. If the rating represented by the supplier's evaluation information is less than the second threshold and greater than or equal to the third threshold, a level two alarm message is issued to the supplier. If the rating represented by the supplier's evaluation information is less than the third threshold, a level three alarm message will be issued to the supplier.

9. The method according to claim 3, characterized in that, Obtain supplier information for the power grid materials represented by the power grid project information, including: The supplier corresponding to each type of power grid material is determined from the preset database, and the supplier information of the determined supplier is retrieved from the preset database. The preset database includes supplier information for different power grid materials.

10. The method according to any one of claims 1-9, characterized in that, The method further includes: Receive logistics tracking information of the power grid materials; wherein the logistics tracking information includes sensor information and reading information; wherein the sensor information is information of the power grid materials collected by the intelligent sensing terminal set on the power grid materials, and the reading information is information of the power grid materials read by the RFID tag set on the power grid materials; Store the logistics tracking information.

11. A power grid material procurement and processing device based on an intelligent model, characterized in that, include: The acquisition module is used to acquire historical material consumption and power grid project information, wherein the historical material consumption represents the power grid materials consumed in the historical construction of the power grid, and the power grid project information represents the power grid materials required for the construction of the power grid. The processing module is used to perform feature extraction processing on the historical material consumption and the power grid project information based on a preset intelligent prediction model to obtain material features, and to predict the power grid material demand within a preset time window based on the material features; wherein, the power grid material demand represents the quantity of power grid materials required for power grid construction. The determination module is used to determine the material procurement information within the preset time window based on the power grid material demand; and to generate a procurement instruction based on the material procurement information and send the procurement instruction to the user's terminal.

12. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1-10.

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