A device acceptance method, apparatus, device, medium and product

Abstract

The application discloses a kind of equipment acceptance method, device, equipment, medium and product.The method comprises: if detecting procurement equipment acceptance task, according to procurement contract file, determine the equipment reference information corresponding to procurement equipment, and according to procurement acceptance file, determine the actual procurement information corresponding to acceptance equipment;According to equipment reference information and actual procurement information, equipment matching is carried out, if matching is successful, then field-by-field checking is carried out, to determine the field checking result corresponding to acceptance equipment;Determine the confidence of the field checking result corresponding to each acceptance equipment, and according to the actual procurement information corresponding to each acceptance equipment, field checking result and confidence, generate equipment acceptance report.The technical scheme of the present application, by first completing equipment main body matching, then through field-by-field checking to lock deviation item, while introducing confidence quantification mechanism to give the basis of credibility for verification result, finally generate comprehensive equipment acceptance report, realize equipment acceptance whole process automation.

Description

Technical Field

[0001] This invention relates to the field of big data, and in particular to a method, apparatus, equipment, medium and product for equipment acceptance. Background Technology

[0002] In equipment procurement and acceptance management, the core task is to verify the equipment configuration requirements in the procurement contract against the equipment demand information submitted by the acceptance personnel, item by item, to confirm whether the procured goods match the actual delivered and accepted goods. Traditional manual verification methods are inefficient and highly subjective. Existing automated solutions, when faced with the complex matching relationship between equipment procurement contracts and demand tables, ignore the differentiated importance of different fields in the verification process, resulting in many shortcomings.

[0003] Therefore, there is an urgent need for an equipment acceptance solution with deep semantic understanding, dynamic programming, and self-verification capabilities. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for equipment acceptance. By first completing the matching of the main equipment, then locking the deviation items through field-by-field verification, and introducing a confidence quantification mechanism to give the verification results a basis for credibility, a comprehensive equipment acceptance report is finally generated, thereby realizing the full automation of the equipment acceptance process.

[0005] According to one aspect of the present invention, a method for equipment acceptance is provided, comprising: If a procurement equipment acceptance task is detected, the equipment baseline information corresponding to the procurement equipment is determined according to the procurement contract documents, and the actual procurement information corresponding to the accepted equipment is determined according to the procurement acceptance documents. Equipment is matched based on the equipment baseline information and the actual procurement information. If the match is successful, a field-by-field check is performed to determine the field check results corresponding to the accepted equipment. Determine the confidence level of the field verification results for each accepted device, and generate an equipment acceptance report based on the actual procurement information, field verification results, and confidence level for each accepted device.

[0006] According to another aspect of the present invention, an equipment acceptance apparatus is provided, comprising: The determination module is used to determine the equipment baseline information corresponding to the purchased equipment based on the purchase contract documents and the actual purchase information corresponding to the accepted equipment based on the purchase acceptance documents if a purchase equipment acceptance task is detected. The matching module is used to match equipment based on equipment baseline information and actual procurement information. If the match is successful, it performs field-by-field verification to determine the field verification results corresponding to the accepted equipment. The generation module is used to determine the confidence level of the field verification results for each accepted device, and generate an equipment acceptance report based on the actual procurement information, field verification results, and confidence level of each accepted device.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the device acceptance method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the device acceptance method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program that, when executed by a processor, implements the device acceptance method of any embodiment of the present invention.

[0010] The technical solution of this invention, when detecting a procurement equipment acceptance task, determines the equipment baseline information corresponding to the procurement equipment based on the procurement contract documents, and determines the actual procurement information corresponding to the acceptance equipment based on the procurement acceptance documents; equipment matching is performed based on the equipment baseline information and the actual procurement information; if the matching is successful, field-by-field verification is performed to determine the field verification results corresponding to the acceptance equipment; the confidence level of the field verification results corresponding to each acceptance equipment is determined, and an equipment acceptance report is generated based on the actual procurement information, field verification results, and confidence level of each acceptance equipment. By automatically associating procurement contract documents to extract equipment baseline information and extracting actual procurement information from procurement acceptance documents, replacing the traditional manual document sifting mode, the main equipment matching is completed first, and then deviation items are locked through field-by-field verification. At the same time, a confidence quantification mechanism is introduced to give the verification results a credible basis, and finally the data is automatically integrated to generate a standardized equipment acceptance report. This not only significantly reduces the amount of repetitive manual work and shortens the acceptance cycle, but also avoids the omissions and subjective errors of manual verification, realizing full automation of the equipment acceptance process and making the results traceable and auditable.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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 This is a flowchart of an equipment acceptance method provided by an embodiment of the present invention; Figure 2 This is a structural block diagram of an equipment acceptance device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the invention described herein can be practiced in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant laws and regulations.

[0016] Example 1 Figure 1This is a flowchart of an equipment acceptance method provided by an embodiment of the present invention. This embodiment is applicable to situations where the equipment body is matched first, then the deviation items are checked and locked field by field to obtain the equipment acceptance verification result. At the same time, a confidence quantification mechanism is introduced to give the verification result a credible basis, and finally a comprehensive equipment acceptance report is generated. This method can be executed by an equipment acceptance device, which can be implemented in hardware and / or software. The equipment acceptance device can be configured in an electronic device, such as an electronic device configured with an enterprise equipment procurement and acceptance management system, such as... Figure 1 As shown, the equipment acceptance method includes: S101. If a procurement equipment acceptance task is detected, the equipment baseline information corresponding to the procurement equipment shall be determined according to the procurement contract documents, and the actual procurement information corresponding to the acceptance equipment shall be determined according to the procurement acceptance documents.

[0017] The equipment procurement acceptance task refers to the task of verifying the actual procurement acceptance documents obtained using the procurement contract documents in order to analyze the acceptance results. The procurement contract documents refer to the contract documents corresponding to the equipment procurement acceptance task, which are used as the basis for acceptance verification. The procurement contract documents are text files, such as PDF (Portable Document Format) or Word documents. The equipment baseline information corresponding to the procured equipment in the procurement contract documents is generally scattered in the contract clauses in the form of free text, which is a typical unstructured data.

[0018] The procurement acceptance document is the acceptance document generated during the actual acceptance process. It contains the equipment to be verified and can be a spreadsheet file, such as a procurement acceptance Excel file exported from the enterprise equipment management information system. Procured equipment refers to the equipment to be procured as recorded in the procurement contract document, while accepted equipment refers to the equipment recorded during the actual acceptance process. Equipment baseline information refers to the relevant attribute information of the procured equipment recorded in the procurement contract document. Actual procurement information refers to the relevant attribute information of the accepted equipment recorded in the procurement acceptance document. Actual procurement information may include structured or semi-structured data such as equipment type, specifications, and quantity.

[0019] For example, equipment acceptance can be achieved through the collaboration of RPA (Robotic Process Automation) technology and AI (Artificial Intelligence) intelligent agents. Specifically, RPA completes file acquisition, task submission, report sending and result archiving, while AI intelligent agents perform contract parsing, Excel equipment record parsing, equipment matching, field verification, confidence calculation and audit report generation. This invention does not limit this.

[0020] Optionally, an RPA (Robotic Process Automation) component can be used to obtain pending procurement equipment acceptance tasks based on preset trigger conditions. Trigger conditions can include at least one of the following: scheduled scanning of procurement acceptance tasks, manual initiation of the verification process, process flow reaching the acceptance verification node, generation of new acceptance tasks by the procurement system, and the appearance of new files to be verified in emails or cloud storage directories. RPA can determine the procurement equipment acceptance tasks currently requiring processing based on the procurement project number, contract number, supplier name, acceptance task number, or procurement requisition number.

[0021] Optionally, after detecting a procurement equipment acceptance task, RPA can be used to automatically log in to the procurement management platform, email platform, or other file storage platform based on the equipment procurement acceptance task information, and download the procurement contract documents and procurement acceptance documents corresponding to the procurement equipment acceptance task.

[0022] Optionally, after the procurement contract documents and procurement acceptance documents are downloaded, RPA can be used to perform basic verification of the documents. This includes checking whether the procurement contract documents and procurement acceptance documents exist, whether the file format is correct, whether the file is empty, and whether the file corresponds to the current procurement task number. This preliminary check will record the file source, download time, and file name. If a file is missing, has an incorrect format, or the task number does not match, the subsequent verification process will be stopped, and an error message will be generated.

[0023] Optionally, RPA can be used to encapsulate the procurement contract documents, procurement acceptance documents, and corresponding task information into an audit task package, and submit it to the AI ​​agent to execute subsequent steps to determine equipment baseline information and actual procurement information, thereby achieving equipment acceptance. The audit task package can include the procurement project number, contract number, supplier name, procurement contract documents, procurement acceptance documents, document download time, document source path, audit report return path, and report recipient information. Through this step, RPA completes the task handover from the business platform to the AI ​​agent's audit process.

[0024] Optionally, the equipment baseline information corresponding to the purchased equipment is determined based on the purchase contract documents, and the actual purchase information corresponding to the accepted equipment is determined based on the purchase acceptance documents. This includes: extracting information from the purchase contract documents to determine the purchased equipment involved in the purchase contract documents and determining the equipment baseline information corresponding to each purchased equipment; and parsing the purchase acceptance documents to determine the accepted equipment involved in the purchase acceptance documents and determining the actual purchase information corresponding to each accepted equipment.

[0025] In the procurement and acceptance document, each row represents a piece of equipment to be accepted, and each column represents the attribute fields of the equipment to be accepted. The attribute fields include equipment name, model, brand, quantity, specifications, technical parameters, delivery requirements, acceptance standards, and remarks.

[0026] Optionally, the header fields of the procurement and acceptance documents can be identified first to determine the acceptance equipment involved in the procurement and acceptance documents. Then, the equipment information corresponding to each acceptance equipment can be read line by line. Each line of equipment record is converted into the actual procurement information of an acceptance equipment. If there are non-standard field names, empty values, merged cells, inconsistent units, abbreviations or aliases in the procurement and acceptance documents, AI intelligent agents can be used to standardize them.

[0027] Optionally, the procurement contract documents can be initially screened based on preset keyword rules such as "equipment name, model, quantity, and unit price". Irrelevant clauses can be filtered by combining natural speech processing entity matching model. Finally, the full information of the procurement equipment involved in the contract can be accurately located and structured and output, that is, the equipment benchmark information corresponding to each procurement equipment can be determined.

[0028] Optionally, information can be extracted from the procurement contract documents to identify the equipment involved in the procurement contract documents and to determine the equipment baseline information corresponding to each piece of equipment. This includes extracting information from the contract text, contract forms, equipment list, technical appendices, acceptance clauses, and delivery clauses in the procurement contract documents to identify the equipment involved in the procurement contract documents and to determine the equipment baseline information corresponding to each piece of equipment.

[0029] The equipment baseline information includes at least one of the following: equipment name, equipment model, equipment brand, purchase quantity, specifications, technical parameters, delivery requirements, acceptance standards, after-sales requirements, and contract reference location. The contract reference location is used for tracing issues in subsequent audit reports.

[0030] Optionally, based on a preset mapping relationship, the associated fields in the equipment baseline information and the actual procurement information can be normalized or standardized. For example, "specification model", "equipment model" and "model parameters" can be unified into "model"; "64G", "64GB" and "64G memory" can be uniformly identified as 64G memory; and units such as "unit" and "set" can be uniformly processed.

[0031] For example, the preset mapping relationship between equipment baseline information and actual procurement information can be shown in Table 1 below: For example, when purchasing equipment including servers and switches, the corresponding equipment baseline information can be shown in Table 2 below: S102. Match the equipment with the equipment baseline information and the actual procurement information. If the match is successful, check each field to determine the field check results corresponding to the accepted equipment.

[0032] Optionally, equipment matching is performed based on equipment baseline information and actual procurement information, including: for each accepted equipment, determining the similarity between the actual procurement information and the corresponding equipment baseline information of each procured equipment; and determining whether there is any procured equipment that matches each accepted equipment based on the correlation between the similarity and a preset threshold, so as to perform equipment matching.

[0033] The similarity includes at least one of the following: device name similarity, device model similarity, brand similarity, quantity similarity, configuration description similarity, and technical parameter semantic similarity.

[0034] Optionally, the similarity of equipment model, brand, and quantity can be set to 100% or 0%. For example, if the equipment model of the accepted equipment is the same as that of the purchased equipment, the similarity is 100%; otherwise, it is 0%.

[0035] Optionally, the similarity of device names, configuration descriptions, and semantic similarity of technical parameters can be determined by using a preset semantic similarity calculation method, combined with common similarity relationships between devices.

[0036] Optionally, if no matching equipment can be found in the purchased equipment, the verification result of the purchased equipment can be directly determined as "no corresponding equipment found in the contract".

[0037] Optionally, for each accepted device, based on the correlation between similarity and a preset threshold, if there is a purchased device that matches the accepted device, then the accepted device is determined to be a successful match.

[0038] Optionally, a field-by-field verification is performed to determine the field verification results corresponding to the accepted equipment, including: determining the successfully matched target accepted equipment and target purchased equipment, and determining the target equipment baseline information corresponding to the target purchased equipment and the target actual purchase information corresponding to the target accepted equipment; comparing at least one of the following information among the target equipment baseline information and the target actual purchase information in terms of semantics, format, value, and unit to determine the field verification results corresponding to the accepted equipment.

[0039] Optionally, for each target acceptance device, the benchmark information corresponding to the target purchased device can be used as a basis to verify each field in the target time procurement information. Fields to be verified may include device name, device model, brand, quantity, specifications, technical parameters, delivery requirements, acceptance standards, and remarks. Verification results can be categorized as completely consistent, semantically consistent, different formats but consistent meaning, inconsistent values, inconsistent units, missing fields, lack of contractual basis, and suspected errors. For example, if the procurement contract document records a quantity of 5 units, while the procurement acceptance document records a quantity of 3 units, the field verification result could be "quantity inconsistent."

[0040] S103. Determine the confidence level of the field verification results for each accepted equipment, and generate an equipment acceptance report based on the actual procurement information, field verification results, and confidence level for each accepted equipment.

[0041] The confidence level represents the degree of consistency between the actual procurement information of the accepted equipment and the corresponding equipment baseline information. The confidence level can be a value between 0 and 1.

[0042] Optionally, the confidence level of the field verification results for each accepted device is determined, including: determining the verification item score based on the field verification results for each accepted device, and determining the confidence level of the field verification results for each accepted device based on the verification item score and the preset verification item weight.

[0043] The verification item scoring includes at least one of the following: equipment name matching score, model consistency score, brand consistency score, quantity consistency score, configuration consistency score, technical parameter similarity score, contract basis completeness score, and conflict penalty score.

[0044] Optionally, the weighted sum of the scores of each verification item and the preset weights of the verification items can be used to determine the confidence level of the verification results of the corresponding fields of each accepted device.

[0045] Optionally, the audit conclusion for the accepted equipment can be determined based on the correlation between the confidence level and the preset confidence level range, and the corresponding handling method can be determined. Based on the actual procurement information, field verification results, confidence level, audit conclusion and handling method of each accepted equipment, an equipment acceptance report can be generated.

[0046] For example, the relationship between the preset confidence level range, the review conclusion, and the corresponding processing method can be shown in Table 3 below: For example, the equipment acceptance report can be in tabular format, and the report content includes at least one of the following: procurement project number, contract number, table row number, name of accepted equipment, name of the matched procured equipment, cell value (i.e., actual procurement information), equipment baseline information, abnormal fields, field verification results, confidence level, risk level, problem description, contract basis location, and suggested modifications.

[0047] For example, the equipment acceptance report can be shown in Table 4 below: For example, after generating an equipment acceptance report using an AI agent, the report can be returned to the RPA. Based on the return path and recipient information in the audit task package, the RPA automatically logs into the in-house email platform and sends the audit report to the designated account. The email content may include the procurement project number, contract number, audit completion time, total number of lines for the equipment, number of lines approved, number of lines recommended for review, number of high-risk anomalies, and audit report attachments. If high-risk anomalies exist, the RPA can send the report to the procurement manager, acceptance manager, and manual reviewer; if all lines are approved, it is sent to the acceptance confirmation personnel.

[0048] Optionally, after the report is sent, RPA can archive the relevant documents and logs from this procurement acceptance verification process. The archived content includes the procurement contract, procurement acceptance documents, equipment acceptance report, document download logs, audit logs, email sending records, and exception handling records, thus completing an intelligent equipment procurement acceptance verification process based on the collaboration of RPA and AI agents.

[0049] It should be noted that this invention clearly defines the responsibilities of RPA and AI agents in the procurement acceptance and verification process. RPA is responsible for procedural, deterministic, and cross-platform operations, including obtaining procurement acceptance tasks, logging into procurement systems and other platforms, downloading documents, submitting review tasks, receiving review reports, sending emails, and archiving process logs. The AI ​​agent performs intelligent, semantic, and judgmental processing, including parsing documents, establishing baseline information, completing field mapping, matching equipment entries, verifying equipment information field by field, and calculating the confidence level of each accepted device. Through this division of labor, this invention achieves collaboration between RPA and AI agents, avoiding the problem that traditional RPA can only execute fixed rules and cannot understand document semantics, and also avoiding the lack of business process closure and operational implementation capabilities when using AI agents alone.

[0050] It should be noted that this invention, through setting field mapping and semantic normalization, can unify the expression of unstructured or semi-structured content in contracts with the structured fields in procurement and acceptance documents, thereby achieving accurate verification. This solves the problem of inconsistent field names and expression methods between contract documents and tables.

[0051] The technical solution of this invention, when detecting a procurement equipment acceptance task, determines the equipment baseline information corresponding to the procurement equipment based on the procurement contract documents, and determines the actual procurement information corresponding to the acceptance equipment based on the procurement acceptance documents; equipment matching is performed based on the equipment baseline information and the actual procurement information; if the matching is successful, field-by-field verification is performed to determine the field verification results corresponding to the acceptance equipment; the confidence level of the field verification results corresponding to each acceptance equipment is determined, and an equipment acceptance report is generated based on the actual procurement information, field verification results, and confidence level of each acceptance equipment. By automatically associating procurement contract documents to extract equipment baseline information and extracting actual procurement information from procurement acceptance documents, replacing the traditional manual document sifting mode, the main equipment matching is completed first, and then deviation items are locked through field-by-field verification. At the same time, a confidence quantification mechanism is introduced to give the verification results a credible basis, and finally the data is automatically integrated to generate a standardized equipment acceptance report. This not only significantly reduces the amount of repetitive manual work and shortens the acceptance cycle, but also avoids the omissions and subjective errors of manual verification, realizing full automation of the equipment acceptance process and making the results traceable and auditable.

[0052] Example 2 Figure 2 This is a structural block diagram of an equipment acceptance device provided in an embodiment of the present invention. This embodiment is applicable to situations where the equipment body is matched first, then the deviation items are checked and locked field by field to obtain the equipment acceptance verification result. At the same time, a confidence quantification mechanism is introduced to give the verification result a basis for credibility, and finally a comprehensive equipment acceptance report is generated. The equipment acceptance device provided by the present invention can execute the equipment acceptance method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. The equipment acceptance device can be implemented in hardware and / or software and configured in an electronic device with equipment acceptance function, such as... Figure 2 As shown, the equipment acceptance device may specifically include: The determination module 201 is used to determine the equipment baseline information corresponding to the purchased equipment based on the purchase contract documents and the actual purchase information corresponding to the accepted equipment based on the purchase acceptance documents if the purchase equipment acceptance task is detected. The matching module 202 is used to match equipment based on equipment baseline information and actual procurement information. If the match is successful, it performs field-by-field verification to determine the field verification results corresponding to the accepted equipment. The generation module 203 is used to determine the confidence level of the field verification results for each accepted device, and generate an equipment acceptance report based on the actual procurement information, field verification results and confidence level for each accepted device.

[0053] The technical solution of this invention, when detecting a procurement equipment acceptance task, determines the equipment baseline information corresponding to the procurement equipment based on the procurement contract documents, and determines the actual procurement information corresponding to the acceptance equipment based on the procurement acceptance documents; equipment matching is performed based on the equipment baseline information and the actual procurement information; if the matching is successful, field-by-field verification is performed to determine the field verification results corresponding to the acceptance equipment; the confidence level of the field verification results corresponding to each acceptance equipment is determined, and an equipment acceptance report is generated based on the actual procurement information, field verification results, and confidence level of each acceptance equipment. By automatically associating procurement contract documents to extract equipment baseline information and extracting actual procurement information from procurement acceptance documents, replacing the traditional manual document sifting mode, the main equipment matching is completed first, and then deviation items are locked through field-by-field verification. At the same time, a confidence quantification mechanism is introduced to give the verification results a credible basis, and finally the data is automatically integrated to generate a standardized equipment acceptance report. This not only significantly reduces the amount of repetitive manual work and shortens the acceptance cycle, but also avoids the omissions and subjective errors of manual verification, realizing full automation of the equipment acceptance process and making the results traceable and auditable.

[0054] Furthermore, module 201 is specifically used for: Information is extracted from the procurement contract documents to identify the equipment involved in the procurement contract documents and to determine the equipment baseline information corresponding to each piece of equipment. The procurement and acceptance documents are parsed to identify the equipment to be accepted and to determine the actual procurement information corresponding to each piece of equipment.

[0055] Furthermore, the procurement contract documents are in text format, while the procurement acceptance documents are in tabular format. The determination module 201 is also used for: Information was extracted from the main body of the procurement contract, contract forms, equipment list, technical appendices, acceptance clauses, and delivery clauses to identify the equipment to be procured in the procurement contract. Determine the equipment baseline information corresponding to each purchased piece of equipment; the equipment baseline information includes at least one of the following: equipment name, equipment model, equipment brand, purchase quantity, specifications, technical parameters, delivery requirements, acceptance standards, after-sales requirements, and contract basis location.

[0056] Furthermore, the matching module 202 is specifically used for: For each accepted piece of equipment, the similarity between the actual procurement information and the corresponding equipment baseline information is determined; the similarity includes at least one of the following: equipment name similarity, equipment model similarity, brand similarity, quantity similarity, configuration description similarity, and technical parameter semantic similarity; Based on the correlation between similarity and preset thresholds, it is determined whether there are any purchased devices that match each accepted device, so as to perform device matching.

[0057] Furthermore, the matching module 202 is also used for: Identify the successfully matched target acceptance equipment and target procurement equipment, and determine the target equipment baseline information corresponding to the target procurement equipment and the target actual procurement information corresponding to the target acceptance equipment; Compare at least one of the following information: semantics, format, value, and unit, among the fields of the target equipment's baseline information and the target's actual procurement information, to determine the field verification results corresponding to the accepted equipment.

[0058] Furthermore, the generation module 203 is specifically used for: Based on the field verification results corresponding to each accepted device, the verification item score is determined; the verification item score includes at least one of the following: device name matching score, model consistency score, brand consistency score, quantity consistency score, configuration consistency score, technical parameter similarity score, contract basis completeness score, and conflict penalty score. Based on the scores of each verification item and the preset weights of the verification items, the confidence level of the verification results for the corresponding fields of each accepted device is determined.

[0059] Example 3 Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0060] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0061] Multiple components in electronic device 10 are connected to input / output 15, including: input unit 16, such as a keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as a disk, optical disk, etc.; and communication unit 19, such as a network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0062] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as device acceptance methods.

[0063] In some embodiments, the device acceptance method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into random access memory 13 and executed by processor 11, one or more steps of the device acceptance method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the device acceptance method by any other suitable means (e.g., by means of firmware).

[0064] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), system-on-a-chip (SoCs), complex programmable logic devices (PLCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0065] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0067] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (e.g., voice input, speech input, or tactile input).

[0068] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0069] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual reality services, such as high management difficulty and weak business scalability.

[0070] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the device acceptance method of any embodiment of the present invention.

[0071] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0072] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0073] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for equipment acceptance, characterized in that, include: If a procurement equipment acceptance task is detected, the equipment baseline information corresponding to the procurement equipment is determined according to the procurement contract documents, and the actual procurement information corresponding to the accepted equipment is determined according to the procurement acceptance documents. Equipment is matched based on the equipment baseline information and the actual procurement information. If the match is successful, a field-by-field check is performed to determine the field check results corresponding to the accepted equipment. Determine the confidence level of the field verification results for each accepted device, and generate an equipment acceptance report based on the actual procurement information, field verification results, and confidence level for each accepted device.

2. The method according to claim 1, characterized in that, Based on the procurement contract documents, determine the equipment baseline information corresponding to the procured equipment, and based on the procurement acceptance documents, determine the actual procurement information corresponding to the accepted equipment, including: Information is extracted from the procurement contract documents to identify the equipment involved in the procurement contract documents and to determine the equipment baseline information corresponding to each piece of equipment. The procurement and acceptance documents are parsed to identify the equipment to be accepted and to determine the actual procurement information corresponding to each piece of equipment.

3. The method according to claim 2, characterized in that, in, The procurement contract documents are in text format; the procurement acceptance documents are in tabular format. Accordingly, information is extracted from the procurement contract documents to identify the equipment involved in the procurement contract documents and to determine the equipment baseline information corresponding to each piece of equipment, including; Information was extracted from the main body of the procurement contract, contract forms, equipment list, technical appendices, acceptance clauses, and delivery clauses to identify the equipment to be procured in the procurement contract. Determine the equipment baseline information corresponding to each purchased piece of equipment; the equipment baseline information includes at least one of the following: equipment name, equipment model, equipment brand, purchase quantity, specifications, technical parameters, delivery requirements, acceptance standards, after-sales requirements, and contract basis location.

4. The method according to claim 1, characterized in that, Equipment matching is performed based on equipment baseline information and actual procurement information, including: For each accepted piece of equipment, the similarity between the actual procurement information and the corresponding equipment baseline information is determined; the similarity includes at least one of the following: equipment name similarity, equipment model similarity, brand similarity, quantity similarity, configuration description similarity, and technical parameter semantic similarity; Based on the correlation between similarity and preset thresholds, it is determined whether there are any purchased devices that match each accepted device, so as to perform device matching.

5. The method according to claim 1, characterized in that, Perform a field-by-field check to determine the field check results corresponding to the accepted equipment, including: Identify the successfully matched target acceptance equipment and target procurement equipment, and determine the target equipment baseline information corresponding to the target procurement equipment and the target actual procurement information corresponding to the target acceptance equipment; Compare at least one of the following information: semantics, format, value, and unit, among the fields of the target equipment's baseline information and the target's actual procurement information, to determine the field verification results corresponding to the accepted equipment.

6. The method according to claim 1, characterized in that, Determine the confidence level of the verification results for each field of the accepted equipment, including: Based on the field verification results corresponding to each accepted device, the verification item score is determined; the verification item score includes at least one of the following: device name matching score, model consistency score, brand consistency score, quantity consistency score, configuration consistency score, technical parameter similarity score, contract basis completeness score, and conflict penalty score. Based on the scores of each verification item and the preset weights of the verification items, the confidence level of the verification results for the corresponding fields of each accepted device is determined.

7. An equipment acceptance device, characterized in that, include: The determination module is used to determine the equipment baseline information corresponding to the purchased equipment based on the purchase contract documents and the actual purchase information corresponding to the accepted equipment based on the purchase acceptance documents if a purchase equipment acceptance task is detected. The matching module is used to match equipment based on equipment baseline information and actual procurement information. If the match is successful, it performs field-by-field verification to determine the field verification results corresponding to the accepted equipment. The generation module is used to determine the confidence level of the field verification results for each accepted device, and generate an equipment acceptance report based on the actual procurement information, field verification results, and confidence level of each accepted device.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the device acceptance method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the device acceptance method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the equipment acceptance method according to any one of claims 1-6.

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