Purchase order full-process management and control method and equipment for power transmission and transformation equipment manufacturing, and medium

Abstract

The invention belongs to the technical field of purchase management and data processing of power transmission and transformation equipment manufacturing, and particularly relates to a power transmission and transformation equipment manufacturing-oriented purchase order full-process management and control method, equipment and a medium, and the method responds to a login request, and distributes permissions according to matching of a supplier unique identifier and an order manufacturer name. And creating an order containing specific parameters of power transmission and transformation, and transmitting the order to the cloud after verification. The edge end collects acceptance data, carries out edge processing, and transmits key data to the cloud end. The cloud integrates data and calculates indexes, qualified data block chains are stored according to project partitions, and hash chain type association cannot be tampered. And according to material category standard verification, three-level abnormity marking and graded feedback are carried out. And providing query, statistical evaluation and check report generation. According to the invention, edge processing is combined with cloud computing, so that the data processing efficiency and real-time performance are improved. A dynamic quality checking and grading early warning mechanism effectively recognizes and quickly responds to abnormity, and the quality risk is reduced. And the supply chain collaboration level and the overall management efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of procurement management and data processing technology in power transmission and transformation equipment manufacturing, specifically involving a method, equipment, and medium for full-process control of procurement orders in power transmission and transformation equipment manufacturing. Background Technology

[0002] Power transmission and transformation equipment includes transformers, GIS (gas-insulated metal-enclosed switchgear), circuit breakers, etc. Power transmission and transformation equipment has a complex structure and many parts, and has certain technical requirements for electrical performance, insulation level and mechanical strength.

[0003] In related technologies, power transmission and transformation equipment materials have unique technical parameters. When entering these parameters into the procurement system, mismatches between item numbers and names, and discrepancies between technical parameters and applicable equipment types, are common. Furthermore, the lack of logical timing checks for planned and usage dates can lead to conflicting delivery information, resulting in unreasonable procurement cycles that do not meet the production plan requirements for power transmission and transformation equipment, thus impacting production progress.

[0004] Related technologies store collected and processed material data in the cloud. However, due to poor matching between the data and on-site data, matching errors and time-series discrepancies easily occur, leading to distorted calculations of procurement cycle deviation rates. Key quantitative indicators cannot accurately reflect the actual procurement situation, multiple project data are stored together, query and traceability efficiency is low, and the reliability of procurement data is low. Moreover, the quality inspection of the procurement system does not establish differentiated standards for different material categories such as transformer components, circuit breaker components, and cables in power transmission and transformation equipment, resulting in distorted verification results and affecting production progress and product quality. Summary of the Invention

[0005] This invention provides a method for full-process management of procurement orders for power transmission and transformation equipment manufacturing. It constructs a closed-loop process from access control, order creation, acceptance data collection, data processing, quality inspection to query and evaluation, thereby improving the accuracy, security and efficiency of procurement order management for power transmission and transformation equipment manufacturing and supporting project-level data traceability.

[0006] The methods include: S1: Verify the identity of the login requester through the user interaction module, assign full order management permissions to the administrator based on the project dimension, and assign the supplier permission to only access the order data bound to their identifier; S2: Respond to the order creation instruction to obtain basic information of power transmission and transformation materials, and after completing data consistency and logic verification, transmit the information to the cloud processing module; S3: Use the edge terminal control module to collect the acceptance images and quantities of materials, associate the data with the storage location code, and then transmit it to the cloud processing module after preprocessing; S4: The cloud processing module integrates order information with on-site collected data, calculates the procurement cycle deviation rate and generates core statistical indicators, and stores the hash values ​​of qualified data in a chain on the blockchain to achieve project-level traceability. S5: Verify cloud data according to preset material category standards, classify identified anomalies by parameters, quantity, and delivery date, and trigger corresponding level of audible and visual alarms and feedback. S6: Receives multi-dimensional query conditions through the user interaction module, asynchronously loads and displays order status tracking results, and calculates the impact of procurement delays on the process based on production plan bottlenecks.

[0007] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for full-process control of procurement orders for power transmission and transformation equipment manufacturing.

[0008] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of a method for full-process control of procurement orders for power transmission and transformation equipment manufacturing are implemented.

[0009] As can be seen from the above technical solutions, the present invention has the following advantages: This invention provides a method for full-process management of procurement orders in the power transmission and transformation equipment manufacturing industry. Upon receiving login requests from administrators or suppliers, the method performs dual identity verification through a user interaction module. Based on the unique identifier assigned during supplier registration and the manufacturer name in the procurement order, the method automatically matches the supplier with permissions to access only the order data corresponding to the bound identifier. Administrators are granted full procurement order management permissions categorized by power transmission and transformation equipment manufacturing project dimensions, ensuring the security of power transmission and transformation procurement data. Procurement orders are created through a partitioned input interface, with input fields supporting automatic auto-completion. A preset material item number-name mapping library is invoked to verify the consistency of the entered item number and name in real time. A cloud-edge-device collaborative architecture is adopted, with the edge terminal control module integrating RFID readers, weight sensors, and visual recognition devices to achieve multi-dimensional data collection. The collected data undergoes edge processing, standardizing timestamps, normalizing units of measurement, and eliminating redundant and duplicate data, improving on-site management efficiency.

[0010] This invention uses material identifiers and project numbers as dual association keys, combined with a time-series alignment algorithm, to achieve precise data integration. Differential weights are assigned according to the category of power transmission and transformation materials, the procurement cycle deviation rate is calculated, and cloud computing distributed nodes are invoked to calculate core quantity indicators by project-based task allocation. Qualified data is transmitted to a blockchain storage module, stored in project-partitioned storage, and the hash values ​​of data from each stage are linked in a chain to achieve rapid project-level traceability.

[0011] This invention's standard value setting module presets dynamic quality standards according to the categories of power transmission and transformation materials; the data quality inspection module calls these standards to perform multi-dimensional parallel verification of cloud data. After identifying abnormal data, it is classified and marked in three levels: parameter exceeding limits, quantity shortage, and delivery delay. According to the level of abnormality, the abnormal information is pushed to the corresponding role module and triggers the corresponding level of audible and visual alarms. This allows administrators, suppliers, and production planning units to quickly obtain targeted abnormal information and take timely countermeasures to reduce the interference of abnormalities on production progress and product quality. The user interaction module sets up exclusive filtering dimensions for the power transmission and transformation industry, and the purchase order management module adopts asynchronous loading technology to realize multi-dimensional order query, support project summary and order detail viewing, and the statistics unit generates color-coded statistical cards to intuitively display the order status and facilitate quick information location. Attached Figure Description

[0012] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating the entire process of procurement order management for power transmission and transformation equipment manufacturing; Figure 2 A system diagram illustrating the method for achieving full-process control of procurement orders; Figure 3 A schematic diagram of a system implementation method for achieving full-process control of procurement orders; Figure 4 This is a schematic diagram of an electronic device. Detailed Implementation

[0014] The following describes in detail the method for managing the entire procurement order process for power transmission and transformation equipment manufacturing, as described in this application. Specific details, such as particular system structures and technologies, are presented for illustrative purposes rather than limiting, to facilitate a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0015] It should be understood that, when used in this specification, terms include indicating the presence of a described feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms include, encompass, have, and variations thereof mean including but not limited to, unless otherwise specifically emphasized.

[0016] The statements such as "one embodiment" or "some embodiments" described in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the statements such as "in one embodiment," "in some embodiments," "in other embodiments," and "in still other embodiments" in this application do not necessarily refer to the same embodiment, but rather mean one or more, but not all, embodiments, unless otherwise specifically emphasized.

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 The diagram shows a flowchart of a method for managing the entire procurement order process for power transmission and transformation equipment manufacturing, as described in a specific embodiment. The method includes: S1: Receive login requests from administrators or suppliers and verify their identity through the user interaction module 502; based on the automatic matching relationship between the unique identifier assigned during supplier registration and the manufacturer name in the purchase order, assign the supplier permission to access only the order data corresponding to the bound identifier; the administrator assigns full purchase order management permissions divided by the dimension of power transmission and transformation equipment manufacturing projects to achieve permission isolation and data security control.

[0019] In some embodiments, when a user initiates a login request, the user interaction module 502 needs to input an account, password, and dynamic verification code. The identity verification unit calls a preset identity information database to perform two-way verification on the input information.

[0020] Furthermore, the system compares administrator / supplier role identifiers. For suppliers, the system extracts the unique identifier assigned during registration and performs a fuzzy match with the vendor names pre-stored in the purchase order. If the match is successful, the permission allocation unit automatically generates a temporary access token. The token only includes access permissions for the order data corresponding to the supplier's bound identifier and prohibits access to data related to other vendors and projects.

[0021] For administrators, the permission allocation unit divides the permission scope according to the power transmission and transformation equipment manufacturing project number. Administrators can only manage the purchase orders of the projects they are responsible for throughout the entire process. The permissions of different project administrators are isolated from each other, and the permission allocation records are synchronously written to the blockchain storage module to form an immutable permission allocation log.

[0022] S2: Create a material purchase order for power transmission and transformation equipment manufacturing, and obtain basic order information including material number, name, purchase order number, purchaser, manufacturer name, purchase date, planned date, usage date, purchase cycle, delivery status, and power transmission and transformation-specific parameters; call the preset material number-name mapping library to verify consistency, verify parameter range and logical timing, and then transmit it to the cloud processing module 501.

[0023] In some embodiments, when creating a purchase order, basic fields such as material item number, material name, purchase order number, purchaser, and manufacturer name are entered sequentially, as well as power transmission and transformation-specific parameters such as voltage level, insulation level, and applicable equipment type based on transformer, circuit breaker, and switchgear.

[0024] After the basic order information is configured, range verification is performed on the power transmission and transformation-specific parameters. Logical timing verification is also performed on the planned date, usage date, and procurement cycle. The judgment is made by determining whether the planned date is less than the usage date or whether the procurement cycle is equal to the planned date minus the procurement date. Order information that passes the verification is transmitted to the cloud processing module 501. Orders that fail the verification are returned to the administrator interaction module, with specific errors marked and prompts for correction, ensuring the stability and security of order information transmission.

[0025] S3: Material acceptance data is collected through the edge terminal control module of the cloud-edge-device collaborative architecture. The edge terminal control module integrates RFID readers, weight sensors, and visual recognition devices to obtain material identification, actual quantity, and images of appearance defects. The collected data is synchronously associated with the material storage location code.

[0026] The data undergoes edge processing, standardizing timestamps and units of measurement and removing redundant duplicates. The data is then transmitted to the cloud processing module 501 and uploaded to the central control module for display in real time.

[0027] As one implementation of the present invention, step S3 specifically includes the following: S31: Establish a connection between the lower controller, relay switch module, and digital acquisition board of the side terminal control module and the RFID reader, weight sensor, and visual recognition device of the material acceptance station, and assign a unique power transmission and transformation equipment material storage location code to each material acceptance station and corresponding storage area.

[0028] In some embodiments, the analog input port of the digital acquisition board of the edge terminal control module is connected to the signal output terminal of the weight sensor, the digital IO port is connected to the control terminal of the RFID reader / writer and the visual recognition device, and the relay switch module is controlled by the lower controller to realize the power supply on / off control of the sensor.

[0029] Furthermore, unique location codes are created for the transformer storage area, circuit breaker storage area, and cable storage area of ​​power transmission and transformation equipment manufacturing. The location codes adopt a three-segment coding rule of project number-storage area type-workstation number, and the location codes are bound to the addresses of the corresponding workstations for storage.

[0030] S32: Drives the RFID reader to read the electronic tag of the material through the side terminal control module, drives the weight sensor to collect the actual weight data of the material, drives the vision recognition device to collect the appearance image data of the material, collects the material entry trigger signal, and transmits various raw data to the data buffer of the lower controller.

[0031] In some embodiments, a read command is sent to an RFID reader, which then collects the unique identification data of the material's UHF electronic tag. The weight sensor employs a strain gauge weighing module, converting the material's analog weight signal into a digital signal and transmitting it to a digital acquisition board. The visual recognition device uses an industrial area scan camera to acquire images of the material's appearance, which are then processed by an edge preprocessing unit for grayscale conversion and noise reduction before outputting the appearance image data. The warehousing trigger signal is generated by a photoelectric switch at the storage station; the rising edge of the signal triggers synchronous acquisition by all sensors, and all raw data is stored according to the acquisition sequence.

[0032] S33: Add a reference timestamp to the original collected data, and normalize the measurement units of weight and quantity data according to the material measurement standard library of power transmission and transformation equipment to generate standardized collected data.

[0033] In some embodiments, a reference time is obtained, and a standard timestamp is appended to each frame of acquired data. The measurement units for power transmission and transformation equipment materials are normalized; the input is the original measurement unit Min, and the output is the standard measurement unit Mout. The conversion formula is Mout = K. Min and K are conversion factors for measurement. Weight units are uniformly converted to kilograms, quantity units are uniformly converted to pieces, and length units are uniformly converted to meters. The conversion factor K is preset according to the national measurement standard.

[0034] S34: Perform duplicate frame identification, invalid data filtering, and key feature extraction on standardized collected data, retaining key data groups such as material identifiers, valid weight values, appearance defect feature values, storage location codes, and standard timestamps.

[0035] In some embodiments, key feature extraction retains five types of features: material identifier, standard weight, defect feature value, location code, and standard timestamp.

[0036] S35: Transmit the filtered key data set to the cloud processing module 501, and push the key data set to the cloud-edge-device collaborative architecture for visualization.

[0037] S4: The cloud processing module 501 receives order data and edge field data, integrates the planned date and usage date of related orders to calculate the procurement cycle deviation rate, which is the percentage difference between the actual cycle and the planned cycle; and uses cloud computing to generate core indicators such as total procurement quantity, received quantity, warehousing quantity, undelivered quantity, and cycle deviation rate.

[0038] Qualified data is transmitted to the blockchain data storage module and stored in partitions according to the power transmission and transformation equipment manufacturing project. The hash values ​​of the order basic information, acceptance data, warehousing data, and quality inspection results are linked in a chain. The entire process hash value is stored in the database 503 to ensure that the data is tamper-proof and project-level traceable.

[0039] S5: Verify cloud data according to the preset quality standards for power transmission and transformation materials such as transformer components, circuit breaker components, and cables, including parameter ranges, quantity accuracy, and delivery time thresholds; identify abnormal data and classify them into three levels: parameter over-limit, quantity shortage, and delivery delay; feed back the first-level parameter over-limit anomaly to the administrator interaction module and trigger an audible and visual alarm, feed back the second-level quantity shortage anomaly to the supplier interaction module and the administrator module, and feed back the third-level delivery delay anomaly to the production planning unit of the central control module and trigger the corresponding level alarm prompt.

[0040] S6: Filtering conditions can be set through the user interaction module 502, and multi-dimensional queries can be achieved using asynchronous loading technology. Query dimensions include voltage level of power transmission and transformation projects, applicable equipment type for materials, and purchase order status. Results display supports viewing by project summary or single order details. Administrators can view the status tracking of all orders, and suppliers can view the status flow of orders with bound identifiers.

[0041] The statistical unit generates statistical cards, displaying quantity indicators and status distributions with color codes corresponding to normal, urgent material shortages, and delivery delays. Combined with bottleneck processes in the production plan, it calculates the impact of procurement delays on process waiting time.

[0042] In some embodiments, the user interaction module 502 has a built-in multi-dimensional filtering interface, which allows administrators and suppliers to set filtering conditions according to their own permissions. The filtering dimensions include the voltage level of the power transmission and transformation project, the type of equipment applicable to the material, the status of the purchase order, and the supplier name. The filtering conditions support multi-dimensional combinations and use a combination of drop-down boxes and input boxes to facilitate quick settings by users.

[0043] Furthermore, asynchronous loading technology is adopted, and a pagination loading mechanism is set up. After the user sets the filtering conditions, the system only loads the data of the current page, and automatically loads the next page when scrolling the page, avoiding the interface lag caused by loading a large amount of data at once.

[0044] Furthermore, the query results are displayed in a table format, with the table divided into sections based on project number, material information, delivery status, and acceptance status. It supports viewing summaries by project or details of a single order. Clicking on the order details allows you to view the full status flow tracking record of that order.

[0045] Furthermore, administrators can view the full order status stream for all power transmission and transformation projects, while suppliers can only view the order status stream corresponding to their own bound identifier. Access control remains consistent with S1.

[0046] Furthermore, visual statistical cards are generated. These cards are color-coded as follows: green for normal orders, red for urgent material shortages, and blue for delayed delivery. Green cards correspond to orders with all indicators meeting the requirements; red cards correspond to orders with outstanding quantities exceeding production needs and nearing their delivery dates; and blue cards correspond to orders with a procurement cycle deviation rate greater than 15%. The cards display the quantity, percentage, and key quantity indicators of each color of order in real time. Combined with the delivery status of procurement orders and material arrival progress, the impact of procurement delays on bottleneck processes is calculated using the process waiting time impact value Δt = (actual arrival time - planned arrival time) × k. k is the process dependency coefficient; the higher the dependency of the bottleneck process, the larger the k value, ranging from 0.8 to 1.0. The calculation results are simultaneously pushed to the administrator interaction module and the production planning unit of the central control module.

[0047] S7: Export purchase order data, supporting export of the current page or all records, with embedded data traceability QR codes within the file; generate quality inspection reports based on verification results, including details of abnormal data, inspection pass rate, and rectification suggestions based on a historical database of similar abnormal solutions; the report is visually displayed and supports export and printing; when the user clicks to log out, the system clears the login status, saves the user's personalized query conditions, and redirects to the login page.

[0048] In some embodiments, convenient output of procurement data, quality traceability, and user operation are achieved through export and traceability QR codes, enabling convenient use of order data and full-process traceability. Targeted quality inspection reports are generated based on quality inspection results and historical cases. Optimized user logout processes ensure account security, preserve personalized settings, enhance user experience, and improve the overall control of procurement orders.

[0049] In one embodiment of the present invention, such as Figure 2 and Figure 3 As shown, based on step S4, the cloud processing module 501 receives order data and edge-end field data, integrates the associated order plan date and usage date to calculate the procurement cycle deviation rate; and uses cloud computing to generate core indicators such as total procurement quantity, received quantity, warehousing quantity, undelivered quantity, and cycle deviation rate. The specific steps include: S411: The cloud processing module 501 establishes a table linking order data (including planned date, usage date, material number, and planned quantity) with edge field data (including acceptance completion date, actual quantity, and storage location code) using the power transmission and transformation equipment manufacturing project number as the primary index key and the purchase order number as the secondary index key. It predefines logical associations between consistent project numbers, matching material numbers, and planned and actual quantities, storing the indexes through a distributed hash table.

[0050] S412: Let the actual period Tact be the time difference between the acceptance completion date and the planned date, and the planned period Tplan be the time difference between the usage date and the planned date; define the abnormal event factor λ, λ=1.2 when acceptance is delayed, λ=0.8 when acceptance is completed ahead of schedule, and λ=1 when there is no abnormality. Calculate the deviation rate according to the formula η=(Tact-Tplan) / Tplan×λ×100%. When Tact<0, η takes a negative value to indicate early completion.

[0051] An exception flag is triggered when |Tact-Tplan| > 0.5Tplan.

[0052] In some embodiments, the deviation rate calculation is a date conversion, such as Tact=JAcceptance-JPlan, Tplan=JUse-JPlan. The delay reason code in the edge acceptance data, such as D1=Equipment Failure, D2=Logistics Delay, is matched with λ. When D1 / D2, λ=1.2; when there is no code, λ=1; and when ahead of schedule, λ=0.8. In η, when Tact<0, it is completed ahead of schedule, and η=(Tact-Tplan) / Tplan×0.8×100%.

[0053] The procurement cycle of power transmission and transformation materials here is greatly affected by the acceptance process. The abnormal factor λ is used to quantify external interference and avoid misjudgment due to simple time difference.

[0054] S413: Assign weights w based on the categories of power transmission and transformation materials, including transformer components, circuit breaker components, and cables, and calculate the total procurement quantity. Quantity received Quantity of goods received Unsubmitted quantity The periodic deviation rate is the average of the deviation rates for each category. .

[0055] In some embodiments, material categories are grouped, and the weighted configuration layers w1-w3 are invoked. The category mapping table pre-stores rules such as transformer components=TR-, circuit breaker components=CB-, and cables=CA- to classify material numbers. During calculation, Qorder / Qreceipt / Qstorage are taken from the edge acceptance data and weighted by w to obtain Qtotal / Qrec / Qin.

[0056] This approach considers the significant differences in the impact of different materials on production during power transmission and transformation, with weights w reflecting these differences in importance. Distributed aggregation and parallel processing of multi-project data improve computational efficiency. S414: Input the historical deviation rate sequence ηhist and the index error e, and update the weights using gradient descent. The learning rate α = 0.01, and calibration is performed every 10 calculations to make ηavg approach the target value; the calibration parameters are stored in the cloud configuration library and isolated by project number.

[0057] S414: Input the historical deviation rate sequence ηhist and the index error e, and update the weights using gradient descent: The learning rate α = 0.01, and calibration is performed every 10 calculations to make ηavg approach the target value; the calibration parameters are stored in the cloud configuration library and isolated by project number.

[0058] In some embodiments, based on the changing importance of materials at different stages of a power transmission and transformation project, the gradient descent method automatically optimizes the weights through historical deviations, making ηavg approach the ideal period. Dynamic weights replace fixed weights, adapting to the phased needs of the project, and gradient descent converges quickly.

[0059] S415: Set up a verification unit to calculate the hash value H for Qtotal, Qrec, Qin, Qudetel, and ηavg, and compare it with the hash value Hedge during edge data preprocessing; if |H-Hedge|>5%, mark the data as abnormal and trigger recalculation. After the verification passes, output the indicator set to the blockchain data storage module.

[0060] In some embodiments, hash comparison verifies data integrity. Based on the degree of difference in distance quantization, a 5% threshold is used to balance false positives and false negatives, improving the reliability of the results.

[0061] In one embodiment of the present invention, step S4 involves storing data by power transmission and transformation equipment manufacturing project partitions, and linking the hash values ​​of order basic information, acceptance data, warehousing data, and quality inspection results in a chain. Database 503 is used to store the hash values ​​of the entire process, ensuring data immutability and project-level traceability. Specifically, this includes the following steps: S421: Generate a three-level dynamic partition identifier based on the power transmission and transformation equipment manufacturing project number, project voltage level, and equipment type; by defining partition hierarchy rules and creating a physical storage directory, each partition pre-stores data including project start and end time, responsible person, bill of materials version number, and a list of associated purchase order numbers.

[0062] In some embodiments, power transmission and transformation projects involve the parallel construction of multiple substations. Generally, three-level partitioning is used to achieve physical isolation based on project, voltage level, and equipment type. Encoding rules are configured to adapt to different project specifications. The HDFS directory structure corresponds one-to-one with partition identifiers, supporting quick location of physical storage locations by identifier.

[0063] S422: The hash generator calculates hashes for four types of data: order basic information, acceptance data, warehousing data, and quality inspection results, respectively, based on project context, data type, and time sequence identifier. Let the project ID be PID, the data type code be DT, the time sequence identifier be TS, the data content be D, and the predecessor hash be Hpre. Then the hash value H = SHA-256(PID||DT||TS||D||Hpre), where || represents byte stream concatenation.

[0064] For multiple batches of data of the same type in the same project, append hash suffixes according to batch number to generate hierarchical hash sets (HO,HB1,HI1,HQ1,HB2...).

[0065] In some embodiments, the project ID, data type code, time sequence identifier, and data content are extracted. The hash is calculated class-wise using the compound formula H=SHA-256(PID||DT||TS||D||Hpre): order data Hpre=0, acceptance data Hpre=HO, warehousing data Hpre=HB, and quality inspection data Hpre=HI. For multiple batches of data of the same type within the same project, B1 and B2 are appended according to the batch number to ensure hash uniqueness. For power transmission and transformation materials with multiple batches, context hashing is used to avoid hash collisions between different projects for the same data. A hierarchical calculation forms a hash tree with the order as the root and subsequent stages linked sequentially; tampering with any stage will cause a chain reaction of hash changes in subsequent stages. Batch suffixes distinguish multiple data of the same type, adapting to phased acceptance scenarios.

[0066] S423: The main chain is constructed according to the order creation, acceptance completion, warehousing registration, and quality inspection judgment. Each data block contains a predecessor pointer pointing to the hash value of the previous stage, a successor pointer pointing to the hash value of the subsequent stage, and a project partition ID.

[0067] For cross-stage related data, add a horizontal association pointer containing the hash value of the related data and the association type code RT; then store the association relationship, define the node as the hash value and the edge as the pointer type to form a chain structure.

[0068] In some embodiments, a submodule, a horizontal association submodule, and a graph storage adapter are constructed. Predecessor / successor pointers are added to each data block according to the order, acceptance, warehousing, and quality inspection processes, forming a unidirectional main chain. The horizontal association submodule identifies cross-stage associations, such as associating acceptance defects with applicable equipment type parameters of orders, generating association pointers containing related data hashes and RT codes: RT=1 for table parameter associations and RT=2 for table quantity associations. The graph storage adapter uses hash values ​​as nodes and pointers as edges, with edge attributes including pointer type and project partition ID, and writes them to the database (503 error code), supporting Cypher query language for retrieving association paths.

[0069] S424: The hash chain is stored in shards according to the project partition ID, with 3 replicas for each shard, and synchronously written using the Raft protocol; an inverted index table is constructed with the material item number as the first-level key, the project partition ID as the second-level key, and the purchase order number as the third-level key, to store the storage node address of each hash value in database 503.

[0070] During retrieval, the entry hash is located by index, and the data of all stages is traversed along the chain pointer to output a traceability report with project context.

[0071] In some embodiments, power transmission and transformation projects involve large amounts of data, and consistent hashing sharding is used to balance the load. An inverted index enables fast retrieval. Chained traversal reconstructs the entire data process, and verifying hash continuity ensures no tampering.

[0072] As an alternative to step S4 described above, the following is a possible embodiment of the present invention, which is not intended to be limiting. Step S4 further includes the following specific implementation: S431. Extract the characteristics of procurement needs and construct a structured data model.

[0073] Extract technical terms related to power transmission and transformation materials from documents, and then map these terms to a standardized model using a feature mapping middleware. The urgency of orders is calculated using the following weighted eigenvector sorting algorithm.

[0074] in, For feature f j The standardized function for the value of T; deadline For the remaining delivery period factor, an exponential decay function is used to handle the time constraint; P priority These are manually assigned priority labels; α, β, and γ are balance coefficients. α = 0.5, β = 0.3, and γ = 0.2.

[0075] In some embodiments, a demand parsing engine, a feature mapping middleware, and a multidimensional data repository are defined. The demand parsing engine is fine-tuned using a BERT pre-trained model to identify entities such as transformers and circuit breakers, and extracts relationships such as rated voltage -110kV. The feature mapping middleware associates the Dtech domain parameters with a technical parameter dictionary table through foreign keys. link The domain uses the Neo4j graph to link suppliers / historical orders.

[0076] S432: Build a digital twin mirror of the purchase order process and synchronize it in real time.

[0077] It adopts a cloud-native microservice architecture with a physical entity layer, a data access layer, a core service layer, and a user interaction layer, and accesses real-time data from supplier production lines and logistics vehicles via the MQTT protocol; Define the following consistency index between virtual and real states:

[0078] The verification service is triggered when Ct < 0.95; the data is cleaned using Apache Flink streaming computation, and metadata such as order status and geographic coordinates are encapsulated in JSON-API, supporting dynamic updates of the twin and command issuance.

[0079] In some embodiments, in the microservice architecture, the physical entity layer includes supplier PLCs and logistics GPS terminals. The data access layer uses an EMQX IoT gateway, with MQTT QoS=1 to ensure reliable transmission. The core service layer is split into independent services for order management (SpringBoot), logistics tracking (Node.js), and quality monitoring (Go), with a unified RESTful interface provided by the API gateway (Kong). The digital twin image is rendered using Unity3D, and the virtual-physical synchronization module includes an event bus (RabbitMQ). Physical state changes publish OrderStatusUpdated events, and the virtual model is updated after the twin service subscribes to them.

[0080] S433: Optimize dynamic procurement order scheduling with multiple constraints.

[0081] Establish an NP-hard scheduling model, decision variables For example, order i starts production at time t on supplier j's production line k.

[0082] Define the objective function as

[0083] Among them, C total For total cost, T delay For total delay, U var Let ω1 = 0.4, ω2 = 0.3, and ω3 = 0.3 represent the capacity fluctuation variance. Constraints include time windows, capacity, quality, and process conditions. A Deep Q-Network (DQN) is used for solving the problem. The state space S contains order status, capacity load, and inventory. The action space A represents order-supplier time slot allocation. The reward function is defined as follows: Dynamically optimize the agent through MDP training.

[0084] In some embodiments, the model includes a mathematical optimization layer and a DQN solution layer. The state space S contains N order states: 0 pending / 1 scheduled / 2 completed. There are M supplier capacity loads and K types of material inventory. The action space A is a triplet of order i-supplier j-time slot t, which is executed after feasibility verification.

[0085] In the reward function, ΔC cost=New scheduling cost - original cost, IMeetingDeadline=1, delivery deadline met, otherwise 0, Load variance =Supplier load variance. Training uses experience replay, and the target network is updated periodically. The MDP modeling here incorporates equipment failure / raw material delays into state transitions, and the DQN agent learns the optimal scheduling method through trial and error. Multi-objective optimization balances cost / time / capacity. DQN solves NP-hard problems to obtain near-optimal solutions and responds to order changes in real time.

[0086] S434: Assess supplier dynamic risk based on AHP-Bayesian network.

[0087] Construct an AHP-BN fusion model, with the AHP layer using a judgment matrix. Determine the risk weights W (CR<0.1) for quality / cost / delivery / technology / environment as the prior probabilities of the root node of the Bayesian network.

[0088] Define a Bayesian layer (BN) based on raw material prices and equipment failure rates, with risk factors based on production capacity and quality assurance capabilities, and leaf nodes based on comprehensive risk levels. Utilize historical data and expert scoring. The Bayesian formula for real-time evidence collection (E) is:

[0089] Update the posterior probability; trigger an alert if the threshold is exceeded.

[0090] In some embodiments, the AHP layer uses Yaahp software to construct a 5-level indicator judgment matrix, calculates the weights W, and then performs a test of CR=CI / RI<0.1. The BN layer uses the GenNIe tool to construct the topology. The root node's prior probability = P(Risk)·Wi, where Wi is the AHP weight. The intermediate node's CPT is such that P(delivery risk = high | equipment failure rate = high, logistics delay = yes) = 0.85 (historical data statistics). The leaf nodes' comprehensive risk level is low / medium / high. During dynamic updates, evidence E includes supplier production reports and third-party quality inspection data, and the posterior probability is iteratively updated using Bayesian formulas.

[0091] S435: Predicting procurement demand based on LSTM-Attention.

[0092] A hybrid LSTM-Attention network with 128 neurons and Dropout=0.5 was constructed, based on time features: quarterly / infrastructure peaks; policy features: power grid investment / UHV planned mileage; and historical features: lagged terms / moving average terms. PCA dimensionality reduction was used as the input multi-source features. The Adam optimizer was used for training, with the training / validation sets divided according to time sequence, and evaluation metrics were defined. , Output the predicted value and the confidence level of the Bootstrap sampling prediction interval.

[0093] In some embodiments, an LSTM-Attention hybrid network is used to predict future procurement demand. This embodiment takes historical order data from S411 and real-time twin data from S412 as inputs, outputting a demand forecast that provides future input for demand feature extraction in S411, forming a closed loop. Further, S411 provides standardized data for all steps; S412 configures the data into a digital twin; S413 optimizes scheduling using dynamic data; S414 assesses risk using scheduling data; and S415 predicts demand using historical and real-time data, feeding back into the input of S411, thus achieving full-process control of power transmission and transformation procurement orders.

[0094] In one embodiment of the present invention, based on step S5, the following is a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S5 specifically includes the following steps: S51: The cloud processing module reads historical quality data for various types of materials, including transformer components, circuit breaker components, cables, etc.

[0095] For each material category, extract the key parameter set P={p1,p2,...,pn}, such as voltage level, insulation level, quantity accuracy, delivery time threshold, etc.

[0096] For each parameter, select the most recent N batches of qualified data and calculate its mean μ and standard deviation σ.

[0097] Define the sliding window size N to be dynamically adjusted according to the material arrival frequency. For example, N is 30 for transformer parts and 100 for cables.

[0098] Furthermore, the urgency factor E of the current production plan is obtained. E is calculated by comprehensively considering factors such as the waiting time of bottleneck processes and the urgency of project delivery.

[0099] Based on the above data, a dynamic threshold range is generated: lower limit L = μ - k1·σ - α·E, upper limit U = μ + k2·σ + β·E, where k1 and k2 are confidence coefficients, preset according to the criticality of materials. Optionally, transformer components are set to 2.5, circuit breaker components to 2.0, and cables to 1.5. α and β are adjustment coefficients, corresponding to the emergency impact weights of the lower and upper limits, respectively, such as α = 0.1 and β = 0.2.

[0100] Furthermore, a material criticality weight vector W=[wtrans,wbreaker,wcable] is constructed. This vector is dynamically calculated using the entropy weight method based on the proportion of historical quality accident losses and production downtime, and is updated quarterly. The dynamic threshold parameters and weight vectors for each material category are stored in the database for real-time retrieval by the verification process.

[0101] In this way, by combining the urgency of the production plan, the threshold can respond to the urgency of the production line, thus improving the sensitivity and accuracy of the verification.

[0102] S52: Obtain the actual data vector X=[x1,x2,...,xm] of the material to be verified, including measured parameter values, actual quantity received, and actual delivery date.

[0103] Based on the category of the material, extract the corresponding upper and lower bounds (Li, Ui) of the threshold and the keyness weight vector W from the dynamic threshold library generated in step S51.

[0104] For each dimension of data, calculate the relative deviation.

[0105] If the parameter value xi belongs to the over-limit type (such as voltage level), then the relative deviation This refers to the ratio of the portion exceeding the allowed range.

[0106] xi is the actual detected value of the i-th parameter, U i L represents the upper limit of the allowed value for the i-th parameter. i U is the allowed lower limit value of the i-th parameter. i L i The allowed range width.

[0107] If the data is quantitative, calculate the shortage rate. Where Qplan is the planned quantity and Qactual is the actual quantity accepted.

[0108] If the data is delivery date, calculate the delay rate. T actual T represents the actual date of receipt. plan For the planned date, T cycle This refers to the procurement cycle.

[0109] Combine the deviations of all dimensions into a deviation vector. .

[0110] Define a fuzzy comprehensive evaluation method and construct a membership function μ(d) to describe the degree of deviation as slight, moderate, or severe. For example, a trapezoidal membership function can be used.

[0111]

[0112]

[0113] Using the membership function described above, the deviation vector D is transformed into a fuzzy evaluation matrix R. Matrix R has a dimension of m×3, where m is the number of indicators and 3 is the number of evaluation levels.

[0114] r ij represents the membership degree of the i-th deviation index to the j-th evaluation level, where j = 1: slight, j = 2: medium, j = 3: severe. The sum of the elements in each row should theoretically be close to 1.

[0115] S53: Obtain the corresponding weight wclass in the criticality weight vector W from step S51 according to the material category. Based on the comprehensive deviation degree vector D and the fuzzy evaluation matrix R, calculate the comprehensive anomaly index A.

[0116] Specifically, a method combining weighted average and fuzzy synthesis is adopted: calculate the weighted deviation degree of each dimension Dweighted = ∑wi * di, and then define the category amplification factor γclass to obtain the basic anomaly index Abase = Dweighted * γclass.

[0117] Using the principle of maximum membership degree of the fuzzy matrix R, determine the main level of the deviation degree and quantify it as a numerical value Lfuzzy. For example, slight = 0.2, medium = 0.5, severe = 0.9.

[0118] Finally, the comprehensive anomaly index A = (Abase + Lfuzzy) / 2. Set three thresholds: Alow = 0.3, Amid = 0.6, Ahigh = 0.85. Divide the anomaly levels according to the A value: If A > Ahigh, it is determined as a first-level anomaly, corresponding to parameter overlimit or serious quality problems, and marked in red.

[0119] If Amid < A ≤ Ahigh, it is determined as a second-level anomaly, corresponding to quantity shortage, and marked in orange.

[0120] If Alow < A ≤ Amid, it is determined as a third-level anomaly, corresponding to delivery delay, and marked in yellow.

[0121] If A ≤ Alow, it is regarded as normal and marked in green.

[0122] For materials with high criticality, even if the A value is slightly low, if wclass > 0.8 and dq > 0.1, it is directly upgraded to a second-level anomaly to achieve rule supplementation based on expert experience. The classification results, together with the deviation details, A value, etc., are output.

[0123] It can be seen that step S53 organically integrates the material criticality, deviation degree, and fuzzy level, obtains a continuous comprehensive anomaly index through weighted and fuzzy quantification, and then divides it into three levels of anomalies according to the preset thresholds. The category amplification factor reflects the importance differences of different materials in the power transmission and transformation equipment, enabling small deviations of high-risk materials to be effectively captured.

[0124] S54: Encapsulate the classification results output in step S53 into standardized anomaly data labels. The anomaly data labels include the following fields: Material Unique Identifier (UID), Order Number (PO), Anomaly Level, Anomaly Type, Comprehensive Anomaly Index (A), Deviation Details, Suggested Processing Priority, Timestamp, and Data Source Module Identifier.

[0125] Based on the anomaly level and type in the tag, the tags are distributed to different target modules: Level 1 anomaly, corresponding to parameter exceeding limit tag: routed to the administrator interaction module, and simultaneously triggers the red audible and visual alarm in the audible and visual alarm module.

[0126] Level 2 anomaly, corresponding to a quantity shortage label: routed to the supplier interaction module and the administrator interaction module, triggering an orange audible and visual alarm.

[0127] Level 3 anomaly, corresponding to the delivery delay label: routed to the production planning unit of the central control module, triggering a yellow audible and visual alarm for production scheduling reference.

[0128] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] like Figure 4 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, a communication module 104, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of a method for managing the entire process of procurement orders for power transmission and transformation equipment manufacturing.

[0130] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0131] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0132] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0133] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0134] The communication module 104 transmits radio signals to and / or receives radio signals from at least one of a base station, an external terminal, and a server. Such radio signals may include voice call signals, video call signals, or various types of data sent and / or received according to text and / or multimedia messages.

[0135] The present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the method for full-process control of procurement orders for power transmission and transformation equipment manufacturing.

[0136] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0137] The storage medium stores a program product capable of implementing the methods described above in this specification. In some possible implementations, various aspects of this disclosure can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the exemplary methods section of this specification according to various exemplary embodiments of this disclosure.

[0138] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for full-process management and control of procurement orders for power transmission and transformation equipment manufacturing, characterized in that the method... include: S1: Verify the identity of the login requester through the user interaction module, assign full order management permissions to the administrator based on the project dimension, and assign the supplier permission to only access the order data bound to the identifier; S2: Respond to the order creation instruction to obtain basic information of power transmission and transformation materials, and after completing data consistency and logic verification, transmit the information to the cloud processing module; S3: Use the edge terminal control module to collect the acceptance images and quantities of materials, associate the data with the storage location code, and then transmit it to the cloud processing module after preprocessing; S4: The cloud processing module integrates order information with on-site collected data, calculates the procurement cycle deviation rate and generates core statistical indicators, and stores the hash values ​​of qualified data in a chain on the blockchain to achieve project-level traceability. S5: Verify cloud data according to preset material category standards, classify identified anomalies by parameters, quantity, and delivery date, and trigger corresponding level of audible and visual alarms and feedback. S6: Receives multi-dimensional query conditions through the user interaction module, asynchronously loads and displays order status tracking results, and calculates the impact of procurement delays on the process based on production plan bottlenecks.

2. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 1, characterized in that, Step S3 specifically includes the following: S31: Establish a connection between the lower controller, relay switch module, and digital acquisition board of the side terminal control module and the RFID reader, weight sensor, and visual recognition device of the material acceptance station, and assign a unique power transmission and transformation equipment material storage area code to each material acceptance station and corresponding storage area. S32: Drives the RFID reader to read the electronic tag of the material through the edge terminal control module, drives the weight sensor to collect the actual weight data of the material, drives the vision recognition device to collect the appearance image data of the material, collects the material entry trigger signal, and transmits all kinds of raw data to the data buffer of the lower controller. S33: Call the timestamp calibration unit to add a unified UTC reference timestamp to the original collected data, and normalize the measurement units of weight and quantity data according to the material measurement standard library of power transmission and transformation equipment to generate standardized collected data; S34: Perform duplicate frame identification, invalid data filtering and key feature extraction on standardized collected data, and retain key data groups such as material identification, valid weight value, appearance defect feature value, storage location code and standard timestamp; S35: Transmit the filtered key data sets to the cloud processing module, and push the key data sets to the cloud-edge-device collaborative architecture for visualization.

3. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 1, characterized in that, In step S4, the cloud processing module integrates order information with on-site collected data, calculates the procurement cycle deviation rate, and generates core statistical indicators, specifically including the following steps: S411: The cloud processing module establishes a table linking order data and item number using the power transmission and transformation equipment manufacturing project number as the primary index key and the purchase order number as the secondary index key; S412: Let the actual period Tact be the time difference between the acceptance completion date and the planned date, and the planned period Tplan be the time difference between the usage date and the planned date; define the abnormal event factor λ, and calculate the deviation rate according to the formula η=(Tact-Tplan) / Tplan×λ×100%; S413: Assign weights w based on the categories of power transmission and transformation materials, including transformer components, circuit breaker components, and cables, and calculate the total procurement quantity. Quantity received Quantity of goods received Unsubmitted quantity The periodic deviation rate is the average of the deviation rates for each category. ; S414: Input the historical deviation rate sequence ηhist and the index error e, and update the weights using gradient descent: The learning rate α = 0.01 is used, and the algorithm is calibrated after every 10 calculations to make ηavg approach the target value. Calibration parameters are stored in a cloud configuration library and isolated by project number; S415: Set up a verification unit to calculate the hash value H for Qtotal, Qrec, Qin, Qudetel, and ηavg, and compare it with the hash value Hedge during edge data preprocessing; if |H-Hedge|>5%, mark the data as abnormal and trigger recalculation. After the verification passes, output the indicator set to the blockchain data storage module.

4. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 3, characterized in that, In step S4, the hash values ​​of qualified data are stored in a chain on the blockchain to achieve project-level traceability. This specifically includes the following steps: S421: Generate project partition identifiers based on project number, voltage level, and equipment type of power transmission and transformation equipment manufacturing projects, and create corresponding physical storage directories to pre-store project start and end times, responsible persons, bill of materials version numbers, and a list of associated purchase order numbers; S422: Use a hash generator to calculate hash values ​​from basic order information, acceptance data, warehousing data, and quality inspection results, based on project context, data type, and time sequence identifier, to generate a hierarchical hash set; S423: Construct a main chain containing predecessor and successor hash pointers in the order of order creation, acceptance completion, warehousing registration and quality inspection judgment, and add horizontal association pointers for cross-stage related data to form a chain graph structure; S424: The hash chain is segmented and stored according to the project partition identifier, and an inverted index table is constructed with the material number, project partition identifier and purchase order number as keys. The entry hash is located by index and traversed along the chain pointer to realize data traceability.

5. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 3, characterized in that, Step S4 also includes: Step S431: Extract technical terms for power transmission and transformation materials from the document, and map the features to the standardized model using the feature mapping middleware. ; using a weighted eigenvector sorting algorithm Calculate order urgency; in, For feature f j The standardized function for the value of T; deadline P is the remaining delivery time factor. priority Priority labels; α, β, γ are balance coefficients; S432: Build a digital twin mirror of the procurement order process and synchronize it in real time; S433: Optimize dynamic procurement order scheduling with multiple constraints; S434: Assessing vendor dynamic risk based on AHP-Bayesian networks; S435: Predicting procurement demand based on LSTM-Attention.

6. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 1, characterized in that, S5 specifically includes the following steps: S51: Generate corresponding parameter threshold ranges and criticality weight vectors based on historical quality data of each type of material and the urgency of the current production plan; S52: Obtain the actual data of the current material, calculate the relative deviation of each dimension based on the threshold range and weight vector, and construct a fuzzy evaluation matrix through the membership function; S53: Calculate the comprehensive anomaly index based on the weight vector and fuzzy evaluation matrix, and compare it with the preset threshold to classify anomalies into level one, level two or level three. S54: Encapsulate the anomaly level and details into standardized tags, distribute the tags to the corresponding interactive modules according to the level, and trigger the corresponding level of audible and visual alarm.

7. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 6, characterized in that, Specifically, the methods include the following: Obtain the actual data vector X=[x1,x2,...,xm] of the material to be verified; Based on the category to which the material belongs, extract the corresponding threshold upper and lower bounds (Li, Ui) and the keyness weight vector W; For each dimension of data, calculate its relative deviation: If the parameter value xi belongs to the out-of-limit type, then the relative deviation ; xi is the actual detected value of the i-th parameter, U i L represents the upper limit of the allowed value for the i-th parameter. i U is the allowed lower limit value of the i-th parameter. i L i The allowed range width; If the data is quantitative, calculate the shortage rate. Where Qplan is the planned quantity and Qactual is the actual quantity accepted; If the data is delivery date, calculate the delay rate. T actual T represents the actual date of receipt. plan For the planned date, T cycle For procurement cycle; Combine the deviations of all dimensions into a deviation vector. ; Define a fuzzy comprehensive evaluation method and construct a membership function μ(d) to describe the degree of deviation as slight, moderate, or severe. Using the membership function, the deviation vector D is transformed into a fuzzy evaluation matrix R.

8. The method for full-process control of procurement orders for power transmission and transformation equipment manufacturing according to claim 1, characterized in that, S6 and later versions also include: exporting purchase order data, supporting export of the current page or all records, embedding data traceability QR codes within the exported file; and generating quality inspection reports based on verification results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the procurement order full-process management method for power transmission and transformation equipment manufacturing as described in any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the procurement order full-process management method for power transmission and transformation equipment manufacturing as described in any one of claims 1 to 8.

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