Automatic classification and multi-dimensional query methods, systems, media, and equipment for spare parts procurement

Abstract

This invention relates to a method, system, medium, and equipment for automatic classification and multi-dimensional querying of spare parts procurement, including: constructing a big data platform based on multi-source data; dividing spare parts procurement progress nodes according to the spare parts procurement workflow and sub-workflows; acquiring procurement application and line number data of the target type from the big data platform, and classifying the procurement applications according to the spare parts procurement progress nodes and procurement progress classification strategies to obtain procurement application classification data; statistically analyzing the procurement application classification data and classifying it to the corresponding spare parts procurement progress nodes, and displaying the data records on the corresponding spare parts procurement progress nodes in the visualization process; simultaneously constructing a multi-dimensional fast query path and generating a list of key nodes. This invention automatically captures multi-source data, automatically classifies spare parts procurement progress, and visualizes it, automatically summarizing the lists of each node, automatically providing classification lists for manual intervention, significantly improving procurement efficiency and contract performance.

Description

Technical Field

[0001] This invention relates to the technical field of nuclear power plant operation and maintenance management, and more specifically, to a method, system, medium, and equipment for automatic classification and multi-dimensional query of spare parts procurement. Background Technology

[0002] As an important component of the modern energy system, the safe and reliable operation of nuclear power plants is directly related to national energy supply and social stability. Carrying out maintenance and testing of nuclear power plant equipment in accordance with the outline and defect management requirements is a necessary condition for safe and reliable operation. Sufficient spare parts are also an important necessary condition for the smooth progress of maintenance or testing work.

[0003] Currently, nuclear power plants have installed up to 600,000 pieces of various equipment and over 165,000 spare parts. Tens of thousands of these spare parts are used or procured annually. Spare parts demand management and procurement management processes typically only utilize spare parts codes and purchase requests, lacking tools and systems for directly querying equipment-level and work order-level information on spare parts inventory and procurement progress. These issues lead to low efficiency in spare parts inventory management, procurement management, and maintenance plan preparation at nuclear power plants. Personnel in positions such as equipment management, maintenance plan preparation, planning management, routine maintenance management, and overhaul management have long been unable to directly access the inventory and procurement progress of the spare parts they are interested in and require. They have relied on querying, processing, and analyzing information from spare parts engineers and procurement engineers to obtain progress feedback, severely hindering the efficiency of on-site safety production activities. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, system, medium and equipment for automatic classification and multi-dimensional query of spare parts procurement, in view of the problems existing in the prior art.

[0005] The technical solution adopted by this invention to solve its technical problem is: to construct a method for automatic classification and multi-dimensional query of spare parts procurement, including: Step S11: Obtain multi-source data of nuclear power plant equipment and maintenance work orders, and build a big data platform based on the multi-source data; the multi-source data includes: spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work orders and spare parts demand data, equipment functional location BOM and material BOM data, and spare parts acceptance difference data. Step S12: Based on the spare parts procurement workflow and sub-workflows, divide the spare parts procurement progress nodes; Step S13: Obtain the purchase application and line number data of the target type voucher in the big data platform, and classify the purchase application according to the spare parts purchase progress node and purchase progress classification strategy to obtain purchase application classification data; Step S14: Statistically analyze the procurement application classification data and classify it to the corresponding spare parts procurement progress node, and display the data record on the corresponding spare parts procurement progress node in the visualization process; Step S15: Construct a multi-dimensional fast query approach and generate a list of key nodes to focus on; the list of key nodes to focus on includes: overdue unsplitting of orders, overdue uninquiry of prices, overdue unclarification of prices, overdue undelivered goods, overdue unordered items, and overdue unprocessed discrepancies.

[0006] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, step S12, which divides the spare parts procurement progress nodes according to the spare parts procurement operation flow and sub-flows, includes: Based on the spare parts procurement process and sub-processes, the procurement progress corresponding to the procurement application is divided into 10 spare parts procurement progress nodes; The 10 spare parts procurement progress milestones include: Purchase requests are pending approval, pending order splitting, pending price inquiry, purchase clarification, pending ordering, ordered but not delivered, goods not yet delivered, goods arriving but pending inspection, discrepancies pending handling, and all items have been inspected.

[0007] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, in step S15, the multi-dimensional fast query path includes: a work order dimension fast query path; the work order dimension fast query path includes: work order-spare parts code-purchase application mapping relationship; Step S15 includes: Obtain the spare part code of the spare part to be queried; The procurement progress data of the spare part to be queried can be obtained by querying the work order-spare part code-purchase application mapping relationship and the spare part code.

[0008] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, in step S15, the multi-dimensional fast query path includes: a fast query path for equipment functional location; the fast query path for equipment functional location includes: the indirect association relationship between functional location BOM, material BOM, spare parts, and procurement application. Step S15 includes: Retrieve the equipment functional location BOM and material BOM data of the spare part to be queried; Based on the equipment functional location BOM and material BOM data and the indirect relationship between functional location BOM-material BOM-spare parts-purchase application, the procurement progress data of the spare parts to be queried is obtained.

[0009] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, step S15 includes: Obtain the node status data of the spare parts procurement progress node in step S14; Based on the node status data, the node status is identified as procurement clarification data; The overdue unclarified list is generated based on the data where the node status is "procurement clarification".

[0010] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, step S15 includes: Obtain the node status data of the procurement progress node in step S14; Based on the node status data, the node status is identified as data to be ordered; The overdue unordered list is generated based on the data whose node status is pending order.

[0011] In the automatic classification and multi-dimensional query method for spare parts procurement described in this invention, step S15 includes: Obtain the node status data of the procurement progress node in step S14; Based on the node status data, the node status is identified as either data indicating that the goods have not yet arrived or data indicating discrepancies that need to be processed. The overdue undelivered list and the overdue unprocessed discrepancy list are generated based on the node status of the data indicating that the goods have not yet arrived.

[0012] This invention also provides an automatic classification and multi-dimensional query system for spare parts procurement, comprising: The big data construction unit is used to acquire multi-source data of nuclear power plant equipment and maintenance work orders, and to build a big data platform based on the multi-source data. The multi-source data includes: spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work orders and spare parts demand data, equipment functional location BOM and material BOM data, and spare parts acceptance difference data. The procurement progress node division unit is used to divide the spare parts procurement progress nodes according to the spare parts procurement operation process and sub-processes; The procurement application classification unit is used to obtain procurement applications and line number data with the voucher type of the target type from the big data platform, and classify the procurement applications according to the spare parts procurement progress node and procurement progress classification strategy to obtain procurement application classification data. The procurement classification and statistical visualization unit is used to statistically analyze the procurement application classification data and classify it to the corresponding spare parts procurement progress node, and display the data records on the corresponding spare parts procurement progress node in the visualization process; A multi-dimensional query unit is used to construct a multi-dimensional fast query path and to query the procurement progress based on the multi-dimensional fast query path; The procurement requisition list generation unit is used to generate a list of key nodes of concern based on node status data. The list of key nodes of concern includes: overdue order allocation list, overdue inquiry list, overdue clarification list, overdue delivery list, overdue order list, and overdue unresolved discrepancies list.

[0013] The present invention also provides a storage medium storing a computer program adapted for loading by a processor to execute the steps of the automatic classification and multi-dimensional query method for spare parts procurement as described above.

[0014] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the automatic classification and multi-dimensional query method for spare parts procurement as described above by calling the computer program stored in the memory.

[0015] The automatic classification and multi-dimensional query method, system, medium, and equipment for spare parts procurement of this invention have the following beneficial effects: They include: acquiring multi-source data on nuclear power plant equipment and maintenance work orders, and constructing a big data platform based on this multi-source data; dividing spare parts procurement progress nodes according to the spare parts procurement operation process and sub-processes; acquiring procurement applications and line number data of the target type from the big data platform, and classifying the procurement applications according to the spare parts procurement progress nodes and procurement progress classification strategies to obtain procurement application classification data; statistically analyzing the procurement application classification data and classifying it to the corresponding spare parts procurement progress nodes, and displaying the data records on the corresponding spare parts procurement progress nodes in the visualization process; simultaneously constructing a multi-dimensional fast query path and generating a list of key nodes. This invention automatically captures multi-source data, automatically classifies spare parts procurement progress, and visualizes it, automatically summarizing the lists of each node, automatically providing classification lists for manual intervention, significantly improving procurement efficiency and contract performance. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart illustrating an embodiment of the automatic classification and multi-dimensional query method for spare parts procurement provided by the present invention. Figure 2 This is a visual flowchart of the procurement progress nodes provided by the present invention; Figure 3 This is a logical block diagram of the automatic classification and multi-dimensional query system for spare parts procurement provided by the present invention; Figure 4 This is a diagram of the interface of the spare parts procurement process visualization application system provided by the present invention; Figure 5This is a schematic diagram of monitoring indicators automatically generated by the spare parts procurement process visualization application system provided by the present invention. Detailed Implementation

[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] To address the problems existing in the procurement of spare parts for nuclear power equipment and maintenance work orders, this invention provides a method for automatic classification and multi-dimensional query of spare parts procurement.

[0019] refer to Figure 1 In a preferred embodiment, the automatic classification and multi-dimensional query method for spare parts procurement includes steps S11 to S14.

[0020] Step S11: Obtain multi-source data on nuclear power plant equipment and maintenance work orders, and build a big data platform based on the multi-source data. The multi-source data includes: spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work orders and required spare parts data, equipment functional location BOM and material BOM data, and spare parts acceptance discrepancy data. BOM (Bill of Material) refers to the bill of materials.

[0021] Specifically, spare parts technical information includes spare parts code, spare parts description, purchasing group, manufacturer code, manufacturer name, etc.; spare parts purchase request data includes purchase request number, line number, spare parts code, quantity requested, requester, request date, demand tracking number, processing status, approval mark, deletion mark, purchase order, etc.; spare parts purchase progress data includes purchase request approval date, line number (i.e., purchase order line number), quantity ordered, expected delivery date, order date, delivery quantity, quantity registered upon arrival, quantity accepted, etc.; spare parts purchase clarification data includes clarification form number, order, etc. Spare parts data includes: part number, spare part code, spare part description, clarification content, processing progress, etc.; spare parts inventory and demand data includes inventory quantity, reserved quantity, reorder point, etc.; maintenance work order and spare parts demand data includes work order number, work order description, spare part code, demand quantity, demand date, planned start date, etc.; equipment functional location BOM and material BOM data include functional location code and corresponding main component spare part code and component spare part code and quantity, etc.; spare parts acceptance discrepancy data includes discrepancy order number, order number, order line number, spare part code, spare part description, discrepancy description, discrepancy generation date, etc.

[0022] In this embodiment of the invention, the above-mentioned multi-source data can be directly obtained from the enterprise management solution software system (hereinafter referred to as the SAP system), and after obtaining the multi-source data, it can be organized to obtain a big data platform.

[0023] Step S12: Based on the spare parts procurement operation process and sub-processes, divide the spare parts procurement progress nodes.

[0024] In some embodiments, step S12, dividing the spare parts procurement progress nodes according to the spare parts procurement operation process and sub-processes, includes: dividing the procurement progress corresponding to the procurement application into 10 spare parts procurement progress nodes according to the spare parts procurement operation process and sub-processes. Preferably, the 10 spare parts procurement progress nodes include: procurement application pending approval, pending order allocation, pending price inquiry, procurement clarification, pending order, ordered but not delivered, goods not yet delivered, goods delivered but pending acceptance, discrepancies pending processing, and all accepted. The various spare parts procurement progress nodes are shown in Table 1.

[0025] Table 1 Step S13: Obtain the purchase application and line number data of the target type voucher from the big data platform, and classify the purchase application according to the spare parts purchase progress node and purchase progress classification strategy to obtain purchase application classification data.

[0026] Optionally, in this embodiment of the invention, the target type is "DM" or "DR". Specifically, in this step, purchase requests and line number data with voucher types of "DM" or "DR" are read from the big data platform, and the purchase requests are automatically classified according to the purchase progress classification strategy to obtain purchase request classification data. The purchase progress classification strategy is shown in Table 2.

[0027] Table 2 Step S14: Statistically analyze the procurement application classification data and classify it to the corresponding spare parts procurement progress node, and display the data record on the corresponding spare parts procurement progress node in the visualization process.

[0028] Specifically, after classifying purchase requisitions in step S13, the system automatically counts the number of purchase requisitions and line numbers assigned to the corresponding 10 nodes, and displays the data records on the corresponding nodes of the visualization process, as shown below. Figure 2 As shown. Among them, Figure 2 The document does not show all the acceptance milestones.

[0029] This invention automatically categorizes procurement requests and dynamically displays the data and node status on the corresponding nodes of a visualized process. This significantly improves the efficiency of business management for key nodes affecting supply by categorizing procurement requests by node status. It also enables a visualized display of the procurement progress of equipment and work order spare parts, providing multiple positions such as spare parts engineers, procurement engineers, preparation engineers, equipment management engineers, and planning engineers with the ability to query, process, and analyze work data, effectively improving the efficiency of on-site safe production activities.

[0030] Furthermore, after automatically classifying purchase requests, this invention also provides a multi-dimensional query function. Specifically, such as... Figure 1 As shown, after step S14, step S15 is also included. Step S15 primarily constructs a multi-dimensional rapid query method and generates a list of key focus nodes. This list includes: overdue unallocated orders, overdue uninquired prices, overdue unclarified statements, overdue undelivered goods, overdue unordered items, and overdue unprocessed discrepancies. By constructing this multi-dimensional rapid query method, multi-dimensional queries such as work order-based queries or equipment function / location queries can be achieved.

[0031] Specifically, the multi-dimensional rapid query methods include: a work order-based rapid query method and an equipment functional location-based rapid query method. The work order-based rapid query method includes the mapping relationship between work order, spare parts code, and purchase requisition; the equipment functional location-based rapid query method includes the indirect association between functional location BOM, material BOM, spare parts, and purchase requisition. Step S15 specifically includes: obtaining the spare parts code of the spare parts to be queried; querying based on the work order-spare parts code-purchase requisition mapping relationship and the spare parts code to obtain the procurement progress data of the spare parts required for the maintenance work order.

[0032] In some embodiments, step S15 includes: obtaining the equipment functional location BOM and material BOM data of the spare part to be queried; querying the equipment functional location BOM and material BOM data and the indirect correlation between the functional location BOM-material BOM-spare parts-purchase application to obtain the procurement progress data of the spare part to be queried.

[0033] Specifically, after automatically classifying and identifying the procurement progress of purchase requests in steps S11-S14, a work order-spare part code-purchase request mapping relationship can be constructed to obtain a quick query method at the work order level. Specifically, using ZWIBK (a ZWIBK work order is a transaction code in SAP system used to check the availability of material requirements for a work order; entering a work order allows querying and exporting the availability check status of spare parts used in the maintenance work order, such as "shortage, confirmed quantity less than required quantity," "confirmed material requirement quantity equal to required quantity," etc.) spare part codes and required quantities of the spare parts required for the work order, a work order-spare part code-purchase request mapping relationship is constructed. By constructing this work order-spare part code-purchase request mapping relationship, the procurement progress of 10 nodes for the required spare parts can be directly queried by work order number. In practical applications, when the work order number of the required spare part is entered, the availability of the spare parts required by the equipment maintenance work order and the procurement progress of related spare parts can be quickly queried based on the work order-spare part code-purchase request mapping relationship.

[0034] Furthermore, this invention can also construct a rapid query method for equipment functional locations. Specifically, it establishes an indirect correlation between equipment functional location BOM and material BOM data and functional location-purchase request data based on equipment functional location BOM and material BOM data and purchase request-spare part code data. This allows for direct querying by equipment functional location and, following step S14, visualizes the specific procurement progress of required spare parts at 10 nodes at the corresponding location, providing a query list and report export function. By constructing a query method based on equipment functional location, when equipment functional location data is input, the procurement progress of related spare parts for that equipment can be quickly queried based on the indirect correspondence between equipment functional location BOM and material BOM data and functional location-purchase request data.

[0035] In some embodiments, step S15 includes: acquiring node status data of the spare parts procurement progress nodes in step S14; identifying data with a procurement clarification status based on the node status data; and generating an overdue unclarified list based on the data with a procurement clarification status. Alternatively, acquiring node status data of the procurement progress nodes in step S14; identifying data with a pending order status based on the node status data; and generating an overdue unordered list based on the data with a pending order status (this overdue unordered list is a list of unordered items over 90 days old). Alternatively, acquiring node status data of the procurement progress nodes in step S14; identifying data with a shipment not yet delivered and data with discrepancies pending processing based on the node status data; and generating an overdue undelivered list based on the data with a shipment not yet delivered (wherein, the overdue undelivered list).

[0036] Specifically, after the automatic classification and visualization process is completed in step S14, an overdue unclarified list can be generated. Specifically, data with the node status of "Procurement Clarification" in the spare parts procurement progress node is extracted. For items in the procurement clarification ledger of the PISR system (Procurement Execution and Supplier Relationship Management System) whose status is "Yes" (excluding "Closed"), the number of items is counted by procurement application + line number, and the list (i.e., the overdue unclarified list) is automatically generated.

[0037] List of unordered items over 90 days: Take the data of the previously identified spare parts procurement progress nodes with the node status of "pending order", take the procurement applications + line number list with the operating system [current date] - [procurement application approval date] ≥ 90 days, count the number of items, and generate a list of unordered items over 90 days.

[0038] Overdue delivery: Take the data of the previously identified spare parts procurement progress nodes with the node status of "transportation not yet received", and take the data of the operating system [current date] - [expected delivery period] > 0 days. Count the number of items by purchase application + line number, and automatically generate a list of items that have not been delivered (i.e., the overdue delivery list).

[0039] Overdue delivery: Take the data of the previously identified spare parts procurement progress nodes with the node status of "Difference pending processing", and take the number of days that are greater than the number of days in the operating system [current date] - [124 voucher date] by order number + spare parts code. Then, automatically count the number of items by purchase application + line number and automatically summarize and generate a difference pending processing list (i.e., overdue unprocessed difference list).

[0040] refer to Figure 3 The present invention also provides an automatic classification and multi-dimensional query system for spare parts procurement.

[0041] Specifically, such as Figure 3 As shown, this spare parts procurement automatic classification and multi-dimensional query system includes: Big data construction unit 401 is used to acquire multi-source data of nuclear power plant equipment and maintenance work orders, and to build a big data platform based on the multi-source data. The multi-source data includes: spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work orders and spare parts demand data, equipment functional location BOM and material BOM data, and spare parts acceptance difference data.

[0042] The procurement progress node division unit 402 is used to divide the spare parts procurement progress nodes according to the spare parts procurement operation process and sub-processes.

[0043] The procurement application classification unit 403 is used to obtain procurement applications and line number data with the voucher type as the target type from the big data platform, and classify the procurement applications according to the spare parts procurement progress node and procurement progress classification strategy to obtain procurement application classification data.

[0044] The procurement classification and statistical visualization unit 404 is used to statistically analyze procurement application classification data and classify it to the corresponding spare parts procurement progress node, and then display the data records on the corresponding spare parts procurement progress node in the visualization process.

[0045] The multi-dimensional query unit 405 is used to construct a multi-dimensional fast query path and perform procurement progress query based on the multi-dimensional fast query path.

[0046] The procurement requisition list generation unit 406 is used to generate a list of key nodes of concern based on node status data; the list of key nodes of concern includes: overdue unallocated orders, overdue uninquired prices, overdue unclarified information, overdue undelivered goods, overdue unordered items, and overdue unresolved discrepancies.

[0047] Specifically, the specific coordination process between the various units in the automatic classification and multi-dimensional query system for spare parts procurement can be referred to the above-mentioned automatic classification and multi-dimensional query method for spare parts procurement, and will not be repeated here.

[0048] This invention automatically retrieves maintenance work orders, equipment functional location BOMs, material BOMs, and purchase requisition data from the SAP system and imports them into a big data platform. It then automatically categorizes and calculates the progress of spare parts procurement and displays it visually at specific nodes corresponding to 10 procurement progress stages. It automatically summarizes the lists for each node and automatically identifies procurement requisition lists such as overdue orders, overdue requests for quotations, overdue clarifications, overdue deliveries, overdue orders, and overdue discrepancies. This allows for proactive, categorized lists to be automatically prioritized for manual intervention in procurement, improving procurement efficiency and contract fulfillment. Simultaneously, it constructs a two-dimensional rapid query path: Work order dimension: through the mapping relationship of "work order - spare parts code - purchase requisition," users can query the procurement progress of corresponding spare parts by entering the work order number. Equipment functional location dimension: through the indirect association of "functional location BOM - material BOM - spare parts code - purchase requisition," users can query the procurement progress of related spare parts by entering the functional location code. The interface of the spare parts procurement process visualization application system is shown below. Figure 4 As shown, the monitoring indicators automatically generated by the spare parts procurement process visualization application system are as follows: Figure 5As shown. Additionally, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the spare parts procurement automatic classification and multi-dimensional query method as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined in the methods of the embodiments of the present invention. The electronic device of the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it can be a server.

[0049] Furthermore, one type of storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the spare parts procurement automatic classification and multi-dimensional query method described above. Specifically, it should be noted that the storage medium described above in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can 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 of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0050] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0051] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0052] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0053] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0054] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for spare parts procurement automatic classification and multi-dimensional query, characterized in that, The method comprises the following steps: Step S11, obtaining multi-source data of nuclear power plant equipment and maintenance work orders, and constructing a big data platform based on the multi-source data; The multi-source data comprises spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work order and demand spare parts data, equipment function location BOM and material BOM data, and spare parts acceptance difference data; Step S12, dividing spare parts procurement progress nodes according to spare parts procurement operation processes and sub-processes; Step S13, obtaining procurement application and line number data with a target type of credentials in the big data platform, and classifying the procurement application according to the spare parts procurement progress nodes and procurement progress classification strategies to obtain procurement application classification data; Step S14, classifying and classifying the procurement application classification data to the corresponding spare parts procurement progress nodes, and displaying the data records to the visual process corresponding spare parts procurement progress nodes; Step S15, constructing a multi-dimensional quick query approach, and generating a list of key attention nodes; the list of key attention nodes comprises a list of overdue unassigned orders, a list of overdue unquoted orders, a list of overdue unclarified orders, a list of overdue unshipped orders, a list of overdue unprocessed differences, and a list of overdue unprocessed differences.

2. The method of claim 1, wherein, In step S12, according to the spare parts procurement operation process and sub-process, the spare parts procurement progress node is divided into: According to the spare parts procurement operation process and sub-process, the procurement application corresponding to the procurement progress is divided into 10 spare parts procurement progress nodes; The 10 spare parts procurement progress nodes comprise: Purchase application pending approval, pending order, pending quotation, procurement clarification, pending order, ordered but not shipped, transportation not arrived, arrived for acceptance, difference to be handled, and all acceptance.

3. The method of claim 1, wherein the method further comprises: In step S15, the multi-dimensional quick query approach comprises a work order dimension quick query approach; the work order dimension quick query approach comprises a work order-spare parts code-purchase application mapping relationship; Step S15 comprises: Obtain the spare parts code of the spare parts to be queried; According to the work order-spare parts code-purchase application mapping relationship and the spare parts code, the procurement progress data of the spare parts to be queried is obtained.

4. The method of claim 1, wherein the method further comprises: In step S15, the multi-dimensional quick query approach comprises a device function location quick query approach; the device function location quick query approach comprises an indirect association relationship between function location BOM-material BOM-spare parts-purchase application; Step S15 comprises: Obtain the device function location BOM and material BOM data of the spare parts to be queried; According to the device function location BOM and material BOM data and the indirect association relationship between function location BOM-material BOM-spare parts-purchase application, the procurement progress data of the spare parts to be queried is obtained.

5. The method of claim 1, wherein the method further comprises: Step S15 comprises: Obtain the node state data of the spare parts procurement progress node in step S14; According to the node state data, the data with the node state of procurement clarification is identified; Based on the data with the node state of procurement clarification, the overdue unclarified list is generated.

6. The method of claim 1, wherein the method further comprises: Step S15 comprises: Obtaining node state data of the procurement progress node in step S14; According to the node state data, identifying data of which the node state is to be ordered; Based on the data of which the node state is to be ordered, generating the overdue non-ordering list.

7. The method of claim 1, wherein the method further comprises: The step S15 further comprises: Obtaining node state data of the procurement progress node in step S14; According to the node state data, identifying data of which the node state is in transit and data of which the difference is to be processed; Based on the data of which the node state is in transit, generating the overdue non-delivery list and the overdue non-processed difference list.

8. A spare parts procurement automatic classification and multi-dimensional query system, characterized in that, Comprise: A big data construction unit for obtaining multi-source data of nuclear power plant equipment and maintenance work orders, and constructing a big data platform based on the multi-source data; The multi-source data comprises: spare parts technical information, spare parts procurement application data, spare parts procurement progress data, spare parts procurement clarification data, spare parts inventory and demand data, maintenance work order and demand spare parts data, equipment function position BOM and material BOM data, and spare parts acceptance difference data; A procurement progress node division unit for dividing spare parts procurement progress nodes according to spare parts procurement operation processes and sub-processes; A procurement application classification unit for obtaining procurement applications and line number data of which the voucher type is the target type in the big data platform, and classifying the procurement applications according to the spare parts procurement progress nodes and procurement progress classification strategies to obtain procurement application classification data; A procurement classification statistical visualization unit for performing statistics on the procurement application classification data and classifying them to corresponding spare parts procurement progress nodes, and displaying data records to the visualization process corresponding spare parts procurement progress nodes; A multi-dimensional query unit for constructing a multi-dimensional quick query approach, and performing procurement progress query according to the multi-dimensional quick query approach; A procurement application list generation unit for generating key attention node lists based on node state data; the key attention node lists comprise: overdue non-division list, overdue non-quotation list, overdue non-clarification list, overdue non-delivery list, overdue non-ordering list, and overdue non-processed difference list.

9. A storage medium, characterized by The storage medium stores a computer program, and the computer program is suitable for being loaded by the processor to execute the steps of the spare parts procurement automatic classification and multi-dimensional query method according to any one of claims 1 to 7.

10. An electronic device, comprising: Comprise a memory and a processor, the memory stores a computer program, and the processor executes the steps of the spare parts procurement automatic classification and multi-dimensional query method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

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