Typical zero purchase equipment project reserve review method and system

Abstract

The invention discloses a typical zero-purchase equipment project reserve review method and system, and belongs to the technical field of zero-purchase equipment management. Information processing is performed on an entity proposal through an optical character recognition technology and a natural language processing technology to obtain accurate zero-purchase equipment structured data; the complete feature data is matched from the ledger database of the zero-purchase equipment, and the zero-purchase equipment is verified and marked through the typical equipment verification rule model to obtain the typical zero-purchase equipment, so that the verification accuracy of the typical zero-purchase equipment is improved, a standard is determined for the judgment of the typical zero-purchase equipment, and the verification efficiency of the typical zero-purchase equipment is improved. The consistency of equipment judgment is realized; besides, the configuration information of the typical zero-purchase equipment is matched and analyzed to generate corresponding review suggestions, the project reserve of the typical zero-purchase equipment is subjected to accurate standardized review, and an efficient, accurate and normative project reserve processing flow of the typical zero-purchase equipment is formed.

Description

Technical Field

[0001] This invention belongs to the field of retail equipment management technology, specifically relating to a review method and system for a typical retail equipment project reserve. Background Technology

[0002] In the operation and management of asset-intensive industries such as power, energy, and large industrial enterprises, equipment procurement projects are crucial for ensuring production safety, technological upgrades, and maintaining daily operations. However, these projects typically involve numerous specialties, a wide variety of equipment, and a massive number of annual applications, posing significant challenges to project review and approval processes. Traditional review methods rely heavily on manual operation. The core process involves review experts reading the diverse procurement project proposals submitted by various project units, combining their own work experience and professional knowledge to qualitatively assess the necessity and rationality of equipment purchases and configurations. This model has revealed several pain points in practice that urgently need to be addressed. First, information asymmetry and a lack of data support are key issues. Review experts struggle to accurately and quickly grasp the existing stock, distribution, technical status, and lifecycle costs of specific equipment across the entire enterprise. One unit may repeatedly apply for equipment it already possesses in large quantities, while another unit that truly needs it may fail to identify it, leading to an imbalance in resource allocation and compromising scientific rigor and accuracy. Secondly, the review standards are inconsistent and highly subjective. Due to the differences in technical parameters and model specifications of equipment purchased in small quantities, traditional methods lack a set of objective and quantifiable standards for identifying typical equipment. Furthermore, inconsistent evaluation criteria among different reviewers lead to large fluctuations in review results, making it difficult to guarantee fairness and introducing complexity and uncertainty into project management. In addition, traditional reviews are inefficient and struggle to handle large-scale reviews. Relying entirely on manual reading, understanding, and judgment is not only time-consuming and labor-intensive but also prone to oversights.

[0003] As mentioned above, how to provide a review method and system for typical retail equipment projects that can accurately identify typical retail equipment and conduct a comprehensive standardized review of equipment purchases has become an urgent problem to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a review method and system for typical retail equipment project reserves, in order to solve the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for reviewing typical equipment procurement project reserves, including: Obtain the reserve proposal for retail equipment projects, and use optical character recognition technology and natural language processing technology to extract the characteristic information of retail equipment from the reserve proposal. Then, perform structured processing on the characteristic information of retail equipment to generate corresponding structured data of retail equipment. The characteristic information of retail equipment includes retail equipment type information, retail equipment purchase quantity information and retail equipment purchase unit information. Using the structured data of the retail equipment as a query condition, the related data is matched in the retail equipment ledger database to obtain the related data of the retail equipment. Based on the structured data of the retail equipment and the related data of the retail equipment, complete feature data of the retail equipment is generated. Obtain a preset typical device verification rule model, input the complete feature data of the retail device into the typical device verification rule model, use the typical device verification rule model to perform typical device verification on the complete feature data of the retail device, and mark the retail device corresponding to the complete feature data of the retail device that has passed the typical device verification as a typical retail device; The typical retail purchase equipment is taken as the current typical retail purchase equipment, the retail purchase equipment type information is taken as the current typical retail purchase equipment type, the retail purchase equipment purchasing unit information corresponding to the typical retail purchase equipment is taken as the current typical retail purchase equipment purchasing unit, and the corresponding current typical retail purchase equipment configuration information is generated based on the current typical retail purchase equipment type and the current typical retail purchase equipment purchasing unit. Based on the current typical retail equipment configuration information, generate and issue review suggestions for the current typical retail equipment.

[0006] In one possible design, a project reserve proposal for retail equipment is obtained. Optical character recognition (OCR) and natural language processing (NLP) technologies are used to extract retail equipment feature information from the proposal. This feature information is then structured to generate corresponding structured retail equipment data, including: The data upload interface is used to obtain the reserve proposal for the equipment purchase project, which includes text data and / or image data. For the image data in the proposed reserve of equipment for retail purchase, image processing is performed on the image data to perform text recognition using optical character recognition technology, and the converted text data of the proposed reserve of equipment for retail purchase is generated based on the recognized text. The text data in the proposed reserve of equipment for retail purchases and the converted text data of the proposed reserve of equipment for retail purchases are integrated to form the text data of the proposed reserve of equipment for retail purchases. Obtain a pre-trained natural language processing model, and use the natural language processing model to perform regular expression matching and named entity recognition on the text data of the reserve proposal for the retail equipment project, so as to obtain the key matching information and key entity information of the retail equipment output by the natural language processing model. The key matching information and key entity information of the retail equipment are integrated to form the retail equipment feature information, wherein the retail equipment feature information includes retail equipment type information, retail equipment model information, retail equipment technical parameter information, retail equipment purchase quantity information and retail equipment purchasing unit information; Obtain a preset standard data structure, and combine the feature information of the retail equipment according to the standard data structure to generate structured data of the retail equipment.

[0007] In one possible design, the structured data of the retail equipment is used as a query condition to perform related data matching in the retail equipment ledger database to obtain related data of the retail equipment. Based on the structured data of the retail equipment and the related data of the retail equipment, complete feature data of the retail equipment is generated, including: Use the structured data of the retail equipment as query conditions, and generate related data query statements based on the query conditions; The related data query statement is input into the retail equipment ledger database to perform a similarity query on each piece of retail equipment data in the retail equipment ledger database, to obtain multiple pieces of similar retail equipment data, and to integrate the pieces of similar retail equipment data to form pre-retail equipment related data; A preset similarity threshold is obtained, and the pre-purchase equipment associated data is screened using the similarity threshold. Similar purchase equipment data with a similarity lower than the similarity threshold to the associated data query statement is filtered out, and similar purchase equipment data with a similarity not lower than the similarity threshold to the associated data query statement is integrated to form purchase equipment associated data. Obtain preset key data extraction conditions, extract key data of retail purchase equipment from the associated data and structured data of retail purchase equipment according to the key data extraction conditions, and concatenate the structured data, associated data and key data of retail purchase equipment into complete feature data of retail purchase equipment.

[0008] In one possible design, the preset method for the typical device verification rule model includes: From the enterprise rule configuration database, a preset set of typical equipment identification rules is extracted in real time. The set of typical equipment identification rules includes rules for identifying high-value retail equipment, rules for identifying high-quantity retail equipment, and rules for identifying technical parameters of retail equipment. Each rule corresponds to a scoring function. Obtain a preset rule scoring weight matrix, and use the rule scoring weight matrix to assign a rule scoring weight to each rule in the typical device identification rule set. A typical device verification scoring threshold is obtained. Based on the typical device verification scoring threshold, each rule in the typical device identification rule set, and the rule scoring weight corresponding to each rule, a typical device verification rule model is constructed.

[0009] In one possible design, the complete feature data of the retail purchase equipment is input into the typical equipment verification rule model. The typical equipment verification rule model is then used to perform typical equipment verification on the complete feature data of the retail purchase equipment. Retail purchase equipment whose complete feature data passes the typical equipment verification is marked as typical retail purchase equipment. This includes: The complete feature data of the retail purchase device is input into the typical device verification rule model. The complete feature data of the retail purchase device is matched with each rule in the typical device identification rule set to obtain the retail purchase device rule features corresponding to each rule. Based on the scoring function corresponding to each rule, the corresponding rule score is calculated for the rule features of the retail equipment; By using the rule scoring weights corresponding to each rule, the rule scores of each rule are weighted and summed to obtain the total score of the zero-purchase equipment; Based on the typical equipment verification scoring threshold, the total score of the retail equipment is verified. Retail equipment with a total score lower than the typical equipment verification scoring threshold is marked as atypical retail equipment, and retail equipment with a total score not lower than the typical equipment verification scoring threshold is marked as typical retail equipment.

[0010] In one possible design, the typical retail purchase equipment is taken as the current typical retail purchase equipment, the retail purchase equipment type information is taken as the current typical retail purchase equipment type, and the retail purchase unit information corresponding to the typical retail purchase equipment is taken as the current typical retail purchase equipment purchase unit. Based on the current typical retail purchase equipment type and the current typical retail purchase equipment purchase unit, corresponding current typical retail purchase equipment configuration information is generated, including: Obtain a preset information table of retail equipment purchasing units, wherein the information table of retail equipment purchasing units includes unit level information and unit affiliation information of each retail equipment purchasing unit; The typical retail equipment is taken as the current typical retail equipment, the retail equipment type information corresponding to the current typical retail equipment is taken as the current typical retail equipment type, the retail equipment purchasing unit information corresponding to the current typical retail equipment is taken as the current typical retail equipment purchasing unit, and according to the retail equipment purchasing unit information table, all retail equipment purchasing units at the same level as the current typical retail equipment purchasing unit are selected as the current typical retail equipment purchasing unit at the same level. Based on the current typical retail equipment type and the current typical retail equipment purchasing unit at the same level, generate query conditions for the configuration information of typical retail equipment of the current purchasing unit at the same level; Input the query conditions for the typical retail equipment configuration information of the current peer-level purchasing unit into the retail equipment ledger database to extract the typical retail equipment configuration information of the current peer-level purchasing unit. The typical retail equipment configuration information of the current peer-level purchasing unit includes the configuration quantity and configuration rate of each current typical retail equipment purchasing peer-level unit for the current typical retail equipment. The complete feature data of the retail equipment corresponding to the current typical retail equipment is used as the current typical retail equipment information. The current typical retail equipment information and the configuration information of the current peer-level purchasing unit's typical retail equipment are integrated to form the corresponding current typical retail equipment configuration information.

[0011] In one possible design, based on the current typical retail equipment configuration information, a review suggestion for the current typical retail equipment is generated and issued, including: Based on the current typical retail equipment configuration information of the same level purchasing unit, calculate the total configuration of typical retail equipment of the current typical retail equipment type in each same level purchasing unit; The average value of the total configuration of typical retail equipment of the current typical retail equipment type in each purchasing unit at the same level is calculated to obtain the average value of typical retail equipment in the purchasing unit at the same level. The average value of typical retail equipment purchased by the same level purchasing unit is used as the configuration benchmark value. The current typical retail equipment information is reviewed using the configuration benchmark value, and review suggestions are generated. If the number of currently typical retail purchase equipment purchased in the current typical retail purchase equipment information is higher than the configuration benchmark value, then the purchase limit is calculated based on the number of currently typical retail purchase equipment purchased and the configuration benchmark value, and a reduction control instruction is generated and issued based on the purchase limit as a review suggestion for the current typical retail purchase equipment; If the number of currently purchased typical retail equipment in the current typical retail equipment information is not higher than the configuration benchmark value, then an approval instruction is generated and issued as a review suggestion for the current typical retail equipment.

[0012] Secondly, this invention provides a review system for typical retail equipment procurement project reserves, including... The proposal processing unit is used to obtain the proposal for the reserve of equipment purchase projects, and to extract the feature information of the equipment purchase from the proposal using optical character recognition technology and natural language processing technology. The feature information of the equipment purchase is then processed in a structured manner to generate corresponding structured data of the equipment purchase. The feature information of the equipment purchase includes equipment type information, equipment purchase quantity information, and equipment purchase unit information. The complete feature extraction unit is used to use the structured data of the retail equipment as query conditions, perform related data matching in the retail equipment ledger database to obtain the retail equipment related data, and generate complete feature data of the retail equipment based on the structured data of the retail equipment and the retail equipment related data. A typical device determination unit is used to obtain a preset typical device verification rule model, input the complete feature data of the retail device into the typical device verification rule model, use the typical device verification rule model to perform typical device verification on the complete feature data of the retail device, and mark the retail device corresponding to the complete feature data of the retail device that has passed the typical device verification as a typical retail device. A typical equipment configuration identification unit is used to identify the typical retail equipment as the current typical retail equipment, correspondingly identify the retail equipment type information as the current typical retail equipment type, identify the retail equipment purchasing unit information corresponding to the typical retail equipment as the current typical retail equipment purchasing unit, and generate corresponding current typical retail equipment configuration information based on the current typical retail equipment type and the current typical retail equipment purchasing unit. The review suggestion generation unit is used to generate and issue review suggestions for the current typical retail equipment based on the current typical retail equipment configuration information.

[0013] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the review method for a typical retail equipment project reserve as described in the first aspect or any possible design of the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the review method for a typical retail equipment project reserve as described in the first aspect or any possible design of the first aspect.

[0015] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform a review method for a typical inventory of retail equipment items as described in the first aspect or any possible design of the first aspect.

[0016] Beneficial Effects: This invention provides a method and system for reviewing typical retail equipment procurement project reserves, including: First, obtaining a retail equipment procurement project reserve proposal; using optical character recognition technology and natural language processing technology to extract retail equipment feature information from the proposal; and performing structured processing on the retail equipment feature information to generate corresponding structured data of retail equipment, wherein the retail equipment feature information includes retail equipment type information, retail equipment purchase quantity information, and retail equipment purchase unit information; Second, using the structured data of retail equipment as query conditions, performing related data matching in a retail equipment ledger database to obtain retail equipment related data; and generating complete feature data of retail equipment based on the structured data and the related data; and then, obtaining a preset typical equipment... A verification rule model is prepared. The complete feature data of the retail equipment is input into the typical equipment verification rule model. The typical equipment verification rule model is used to verify the complete feature data of the retail equipment. The retail equipment corresponding to the complete feature data of the retail equipment that passes the typical equipment verification is marked as typical retail equipment. Then, the typical retail equipment is used as the current typical retail equipment. Correspondingly, the retail equipment type information is used as the current typical retail equipment type, and the retail equipment purchasing unit information corresponding to the typical retail equipment is used as the current typical retail equipment purchasing unit. Based on the current typical retail equipment type and the current typical retail equipment purchasing unit, the corresponding current typical retail equipment configuration information is generated. Finally, based on the current typical retail equipment configuration information, the current typical retail equipment review suggestion is generated and issued. By employing optical character recognition (OCR) and natural language processing (NLP) technologies, the entity's proposal is processed to obtain accurate structured data on retail equipment. Complete feature data is then matched against the retail equipment ledger database. Furthermore, the retail equipment is verified and marked using a typical equipment verification rule model to identify typical retail equipment. This not only improves the accuracy of typical retail equipment verification but also establishes standards for its identification, ensuring consistency in equipment assessment. In addition, the configuration information of typical retail equipment is matched and analyzed to generate corresponding review recommendations. This allows for accurate and standardized review of the typical retail equipment project reserve, forming an efficient, accurate, and standardized process for handling typical retail equipment project reserves. Attached Figure Description

[0017] Figure 1A flowchart illustrating the review method for a typical reserve of retail equipment projects provided in this embodiment of the invention; Figure 2 A functional structure diagram of a typical review system for reserve of equipment purchase projects provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is 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. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0019] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0020] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0021] Example: like Figure 1 As shown, the first aspect of this embodiment provides a method for reviewing a typical reserve of retail equipment projects, which may include, but is not limited to, the following steps: S1. Obtain the reserve proposal for retail equipment projects, and extract the retail equipment feature information from the reserve proposal using optical character recognition technology and natural language processing technology. Then, perform structured processing on the retail equipment feature information to generate corresponding structured data of retail equipment. The retail equipment feature information includes retail equipment type information, retail equipment purchase quantity information, and retail equipment purchase unit information. S2. Using the structured data of the retail equipment as a query condition, perform related data matching in the retail equipment ledger database to obtain the related data of the retail equipment. Based on the structured data of the retail equipment and the related data of the retail equipment, generate complete feature data of the retail equipment. S3. Obtain a preset typical device verification rule model, input the complete feature data of the retail device into the typical device verification rule model, use the typical device verification rule model to perform typical device verification on the complete feature data of the retail device, and mark the retail device corresponding to the complete feature data of the retail device that has passed the typical device verification as a typical retail device. S4. The typical retail purchase equipment is taken as the current typical retail purchase equipment, the retail purchase equipment type information is taken as the current typical retail purchase equipment type, the retail purchase equipment purchasing unit information corresponding to the typical retail purchase equipment is taken as the current typical retail purchase equipment purchasing unit, and the corresponding current typical retail purchase equipment configuration information is generated based on the current typical retail purchase equipment type and the current typical retail purchase equipment purchasing unit. S5. Based on the current typical retail equipment configuration information, generate and issue review suggestions for the current typical retail equipment.

[0022] In one possible implementation, step S1 involves obtaining a project reserve proposal for retail equipment, extracting retail equipment feature information from the proposal using optical character recognition (OCR) and natural language processing (NLP) technologies, and then performing structured processing on the feature information to generate corresponding structured data for the retail equipment. This step can be broken down into, but is not limited to, the following steps S11-S16, specifically including: S11. Obtain the reserve proposal for the retail equipment project through the data upload interface, wherein the reserve proposal for the retail equipment project includes text data and / or image data; S12. For the image data in the proposed reserve of equipment for retail purchase, perform image processing on the image data to perform text recognition on the image data after image processing using optical character recognition technology, and generate converted text data of the proposed reserve of equipment for retail purchase based on the recognized text. S13. Integrate the text data in the proposed reserve of equipment for retail purchases and the converted text data of the proposed reserve of equipment for retail purchases to form text data of the proposed reserve of equipment for retail purchases. S14. Obtain a pre-trained natural language processing model, and use the natural language processing model to perform regular expression matching and named entity recognition on the text data of the reserve proposal for the zero-purchase equipment project, so as to obtain the key matching information and key entity information of the zero-purchase equipment output by the natural language processing model. S15. Integrate the key matching information of the retail equipment and the key entity information of the retail equipment to form retail equipment feature information, wherein the retail equipment feature information includes retail equipment type information, retail equipment model information, retail equipment technical parameter information, retail equipment purchase quantity information and retail equipment purchasing unit information; S16. Obtain a preset standard data structure, combine the retail equipment feature information according to the standard data structure, and generate structured data for the retail equipment.

[0023] In one possible implementation, step S2 involves using the structured data of the retail equipment as a query condition to perform related data matching in the retail equipment ledger database to obtain related data of the retail equipment. Based on the structured data of the retail equipment and the related data of the retail equipment, complete feature data of the retail equipment is generated. This can be, but is not limited to, decomposed into the following steps S21-S24, specifically including: S21. Using the structured data of the retail equipment as query conditions, generate a related data query statement based on the query conditions; S22. Input the associated data query statement into the retail equipment ledger database to perform a similarity query on each piece of retail equipment data in the retail equipment ledger database, obtain multiple pieces of similar retail equipment data, and integrate each piece of similar retail equipment data to form pre-retail equipment associated data; S23. Obtain a preset similarity threshold, use the similarity threshold to screen the pre-purchase equipment associated data, filter out similar purchase equipment data whose similarity to the associated data query statement is lower than the similarity threshold, and integrate similar purchase equipment data whose similarity to the associated data query statement is not lower than the similarity threshold to form purchase equipment associated data; S24. Obtain preset key data extraction conditions, extract key data of retail purchase equipment from the related data and structured data of retail purchase equipment according to the key data extraction conditions, and concatenate the structured data, related data and key data of retail purchase equipment into complete feature data of retail purchase equipment.

[0024] It should be noted that the data in the retail equipment purchase ledger database in this embodiment is the historical retail equipment purchase data entered by the enterprise (unit), including the configuration of each unit for each piece of equipment.

[0025] In one possible implementation, the preset method for the typical device verification rule model in step S3 may include, but is not limited to, the following steps S301-S303, specifically: S301. Extract a preset set of typical equipment identification rules from the enterprise rule configuration database in real time. The set of typical equipment identification rules includes rules for identifying high-value retail equipment, rules for identifying high-quantity retail equipment, and rules for identifying technical parameters of retail equipment. Each rule corresponds to a scoring function. S302. Obtain a preset rule scoring weight matrix, and use the rule scoring weight matrix to assign a rule scoring weight to each rule in the typical device identification rule set. S303. Obtain the typical device verification scoring threshold, and construct a typical device verification rule model based on the typical device verification scoring threshold, each rule in the typical device identification rule set and the rule scoring weight corresponding to each rule.

[0026] In one possible implementation, step S3 involves inputting the complete feature data of the retail purchase equipment into the typical equipment verification rule model, using the typical equipment verification rule model to perform typical equipment verification on the complete feature data of the retail purchase equipment, and marking the retail purchase equipment corresponding to the complete feature data of the retail purchase equipment that has passed the typical equipment verification as a typical retail purchase equipment. This can be decomposed into, but is not limited to, the following steps S31-S34, specifically including: S31. Input the complete feature data of the retail purchase equipment into the typical equipment verification rule model, and use each rule in the typical equipment identification rule set to perform data matching on the complete feature data of the retail purchase equipment to obtain the retail purchase equipment rule features corresponding to each rule; S32. Calculate the corresponding rule score for the rule features of the retail equipment based on the scoring function corresponding to each rule; S33. Using the rule scoring weights corresponding to each rule, the rule scores of each rule are weighted and summed to obtain the total score of the zero-purchase equipment; S34. Based on the typical equipment verification scoring threshold, verify the total score of the retail equipment, mark the retail equipment with a total score lower than the typical equipment verification scoring threshold as atypical retail equipment, and mark the retail equipment with a total score not lower than the typical equipment verification scoring threshold as typical retail equipment.

[0027] It should be noted that the typical device verification rule model in this embodiment actually scores multiple rules simultaneously, and then sums the scores of each rule with weights to obtain a total score. The total score is then used to determine a threshold based on the typical device verification score threshold, thereby verifying the typical device. In practical applications, the typical device verification rule model determines whether a retail device belongs to the typical retail device category and outputs "is_typical_device" (a Boolean value used to mark whether a retail device is a typical retail device). Based on a preset rule weight matrix, the rule weights of each rule are obtained to arrive at the total score. ; in, For the rule index number, For the number of rules, The rule weights for each rule, Each rule is scored (1 for meeting the rule requirements, 0 for not meeting the rule requirements).

[0028] With this typical device verification rule model, when the rules change, only the typical device identification rule set needs to be updated, and the typical device verification rule model will be updated accordingly to adapt to the impact of the new rules, thus achieving flexible and adjustable device verification. In addition, the rule weight matrix can be obtained through multiple training sessions to ensure the fairness of the final score. Accurate score calculation and weighted summation are performed through the typical device verification rule model to obtain accurate verification results.

[0029] Specifically, for example, the structured fields for obtaining complete characteristic data of a certain piece of equipment are: {"Equipment ID: EQP-2023002"; "Equipment Type: Circuit Breaker"; "Technical Parameters: {"Rated Current: 2000A"; "Operating Mechanism Type: Spring Energy Storage"; "Arc Extinguishing Medium: SF6"}; "Purchase Quantity: 8"; "Equipment Unit Price: 450000"; "Manufacturer: YY Electric"}; Using the typical equipment verification rule model, three rules R1 (rated voltage less than 220kV), R2 (purchase quantity greater than 10 units), and R3 (unit price of equipment not higher than 450,000 yuan) were matched with a certain piece of equipment purchased in a retail setting. The rule score weights of these three rules were 0.4, 0.3, and 0.3, respectively. The complete feature data of the retail equipment is scored and calculated according to each rule: R1: Rated voltage 220kV 110kV, meets the requirements, score is 1 (r1=1) R2: Purchase quantity 8 10. Does not meet the rule requirements, score is 0 (r2=0) R3: The unit price of the equipment is 450,000, which is greater than or equal to 300,000, meeting the rule requirements. The score is 1 (r3=1). By weighting and summing the scores of the three rules according to their respective weights, the total score (Score) for the specific retail device can be calculated as follows: Score = 0.4 * 1 + 0.3 * 0 + 0.3 * 1 = 0.7 The preset typical device verification score threshold (0.6) is obtained. Since Score=0.7>0.6, the retail device of type "circuit breaker" passes the verification and is marked as a typical retail device.

[0030] In one possible implementation, in step S4, the typical retail purchase equipment is taken as the current typical retail purchase equipment, the retail purchase equipment type information is taken as the current typical retail purchase equipment type, and the retail purchase unit information corresponding to the typical retail purchase equipment is taken as the current typical retail purchase equipment purchase unit. Based on the current typical retail purchase equipment type and the current typical retail purchase equipment purchase unit, corresponding current typical retail purchase equipment configuration information is generated. This can be, but is not limited to, decomposed into the following steps S41-S45, specifically including: S41. Obtain a preset information table of retail equipment purchasing units, wherein the information table of retail equipment purchasing units includes unit level information and unit affiliation information of each retail equipment purchasing unit; S42. The typical retail equipment is taken as the current typical retail equipment, the retail equipment type information corresponding to the current typical retail equipment is taken as the current typical retail equipment type, the retail equipment purchasing unit information corresponding to the current typical retail equipment is taken as the current typical retail equipment purchasing unit, and according to the retail equipment purchasing unit information table, all retail equipment purchasing units at the same level as the current typical retail equipment purchasing unit are selected as current typical retail equipment purchasing units at the same level. S43. Based on the current typical retail equipment type and the current typical retail equipment purchasing unit at the same level, generate query conditions for the configuration information of typical retail equipment of the current purchasing unit at the same level; S44. Input the query conditions for the typical retail equipment configuration information of the current peer-level purchasing unit into the retail equipment ledger database to extract the typical retail equipment configuration information of the current peer-level purchasing unit, wherein the typical retail equipment configuration information of the current peer-level purchasing unit includes the configuration quantity and configuration rate of each current typical retail equipment purchasing peer-level unit for the current typical retail equipment; S45. Take the complete feature data of the retail equipment corresponding to the current typical retail equipment as the current typical retail equipment information, and integrate the current typical retail equipment information and the configuration information of the current peer-level purchasing unit typical retail equipment to form the corresponding current typical retail equipment configuration information.

[0031] In one possible implementation, step S5, based on the current typical retail equipment configuration information, generates and issues a review suggestion for the current typical retail equipment. This can be broken down into, but is not limited to, the following steps S51-S55, specifically including: S51. Based on the current typical retail equipment configuration information of the same level purchasing unit in the current typical retail equipment configuration information, calculate the total configuration of typical retail equipment of the current typical retail equipment type in each same level purchasing unit; S52. Calculate the average value of the total configuration of typical retail equipment of the current typical retail equipment type in each purchasing unit at the same level, and obtain the average value of typical retail equipment in the purchasing unit at the same level; S53. Use the average value of typical retail equipment purchased by the same level purchasing unit as the configuration benchmark value, and use the configuration benchmark value to review the current typical retail equipment information and generate review suggestions; S54. If the number of currently typical retail purchase equipment purchased in the current typical retail purchase equipment information is higher than the configuration benchmark value, then the purchase limit is calculated based on the number of currently typical retail purchase equipment purchased and the configuration benchmark value, and a reduction control instruction is generated and issued based on the purchase limit as a review suggestion for the current typical retail purchase equipment. S55. If the number of currently typical retail purchase equipment purchased in the current typical retail purchase equipment information is not higher than the configuration benchmark value, then generate and issue an approval instruction as a review suggestion for the current typical retail purchase equipment.

[0032] It should be noted that the current typical retail equipment review recommendations described in this embodiment not only include reduction control instructions and approval instructions, but also include a variety of instructions for regulating the purchase quantity, price, etc., and require analysis of specific information such as the purchase quantity and price of the current typical retail equipment in the current typical retail equipment information to issue corresponding review recommendations.

[0033] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the review method for typical retail equipment project reserves described in the first aspect of the embodiment, including: The proposal processing unit is used to obtain the proposal for the reserve of equipment purchase projects, and to extract the feature information of the equipment purchase from the proposal using optical character recognition technology and natural language processing technology. The feature information of the equipment purchase is then processed in a structured manner to generate corresponding structured data of the equipment purchase. The feature information of the equipment purchase includes equipment type information, equipment purchase quantity information, and equipment purchase unit information. The complete feature extraction unit is used to use the structured data of the retail equipment as query conditions, perform related data matching in the retail equipment ledger database to obtain the retail equipment related data, and generate complete feature data of the retail equipment based on the structured data of the retail equipment and the retail equipment related data. A typical device determination unit is used to obtain a preset typical device verification rule model, input the complete feature data of the retail device into the typical device verification rule model, use the typical device verification rule model to perform typical device verification on the complete feature data of the retail device, and mark the retail device corresponding to the complete feature data of the retail device that has passed the typical device verification as a typical retail device. A typical equipment configuration identification unit is used to identify the typical retail equipment as the current typical retail equipment, correspondingly identify the retail equipment type information as the current typical retail equipment type, identify the retail equipment purchasing unit information corresponding to the typical retail equipment as the current typical retail equipment purchasing unit, and generate corresponding current typical retail equipment configuration information based on the current typical retail equipment type and the current typical retail equipment purchasing unit. The review suggestion generation unit is used to generate and issue review suggestions for the current typical retail equipment based on the current typical retail equipment configuration information.

[0034] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0035] like Figure 3 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the review method for typical retail equipment project reserves as described in the first aspect of the embodiment.

[0036] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0037] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee transceiver (a low-power LAN protocol based on the IEEE 802.15.4 standard), a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0038] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0039] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the review method for a typical retail equipment project reserve as described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when executed on a computer, perform the review method for a typical retail equipment project reserve as described in the first aspect of the embodiment.

[0040] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0041] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0042] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform a review method for a typical retail equipment project reserve as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0043] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of reviewing typical zero-based equipment project reserves, characterized by, The method comprises the following steps: obtaining a zero-purchase equipment project reserve proposal, extracting zero-purchase equipment feature information from the zero-purchase equipment project reserve proposal by using optical character recognition technology and natural language processing technology, and structuring the zero-purchase equipment feature information to generate corresponding zero-purchase equipment structured data, wherein the zero-purchase equipment feature information comprises zero-purchase equipment type information, zero-purchase equipment purchase quantity information, and zero-purchase equipment purchase unit information; associating and matching data in a zero-purchase equipment account database by taking the zero-purchase equipment structured data as a query condition to obtain zero-purchase equipment associated data, and generating complete feature data of the zero-purchase equipment based on the zero-purchase equipment structured data and the zero-purchase equipment associated data; inputting the complete feature data of the zero-purchase equipment into a preset typical equipment verification rule model, verifying the complete feature data of the zero-purchase equipment by using the typical equipment verification rule model, and marking the zero-purchase equipment corresponding to the complete feature data of the zero-purchase equipment that passes the typical equipment verification as a typical zero-purchase equipment; taking the typical zero-purchase equipment as a current typical zero-purchase equipment, correspondingly taking the zero-purchase equipment type information as a current typical zero-purchase equipment type, taking the zero-purchase equipment purchase unit information corresponding to the typical zero-purchase equipment as a current typical zero-purchase equipment purchase unit, and generating corresponding current typical zero-purchase equipment configuration information based on the current typical zero-purchase equipment type and the current typical zero-purchase equipment purchase unit; generating and issuing a current typical zero-purchase equipment review suggestion based on the current typical zero-purchase equipment configuration information.

2. The review method for typical retail equipment project reserves according to claim 1, characterized in that, obtaining a zero-purchase equipment project reserve proposal, extracting zero-purchase equipment feature information from the zero-purchase equipment project reserve proposal by using optical character recognition technology and natural language processing technology, and structuring the zero-purchase equipment feature information to generate corresponding zero-purchase equipment structured data, comprising: obtaining a zero-purchase equipment project reserve proposal through a data uploading interface, wherein the zero-purchase equipment project reserve proposal comprises text data and / or picture data; for picture data in the zero-purchase equipment project reserve proposal, performing image processing on the picture data to recognize text from the image-processed picture data by using optical character recognition technology, and generating zero-purchase equipment project reserve proposal converted text data according to the recognized text; integrating the text data in the zero-purchase equipment project reserve proposal and the zero-purchase equipment project reserve proposal converted text data to form zero-purchase equipment project reserve proposal text data; obtaining a pre-trained natural language processing model, performing regular expression matching and named entity recognition on the zero-purchase equipment project reserve proposal text data by using the natural language processing model to obtain zero-purchase equipment key matching information and zero-purchase equipment key entity information output by the natural language processing model; Integrate the key matching information of the zero-purchase equipment and the key entity information of the zero-purchase equipment to form zero-purchase equipment feature information, wherein the zero-purchase equipment feature information includes zero-purchase equipment type information, zero-purchase equipment model information, zero-purchase equipment technical parameter information, zero-purchase equipment purchase quantity information and zero-purchase equipment purchase unit information; Obtain a preset standard data structure, combine the zero-purchase equipment feature information according to the standard data structure, and generate zero-purchase equipment structured data.

3. The review method for typical retail equipment project reserves according to claim 1, characterized in that, Use the zero-purchase equipment structured data as a query condition to perform associated data matching in a zero-purchase equipment account database to obtain zero-purchase equipment associated data, and generate complete zero-purchase equipment feature data based on the zero-purchase equipment structured data and the zero-purchase equipment associated data, including: Use the zero-purchase equipment structured data as a query condition to generate an associated data query statement according to the query condition; Input the associated data query statement into the zero-purchase equipment account database to perform similarity query on each piece of zero-purchase equipment data in the zero-purchase equipment account database to obtain multiple pieces of similar zero-purchase equipment data, and integrate each piece of similar zero-purchase equipment data to form pre-zero-purchase equipment associated data; Obtain a preset similarity threshold, use the similarity threshold to screen the pre-zero-purchase equipment associated data, exclude similar zero-purchase equipment data with a similarity to the associated data query statement lower than the similarity threshold, and integrate similar zero-purchase equipment data with a similarity to the associated data query statement not lower than the similarity threshold to form zero-purchase equipment associated data; Obtain a preset key data extraction condition, extract zero-purchase equipment key data from the zero-purchase equipment associated data and the zero-purchase equipment structured data according to the key data extraction condition, and splice the zero-purchase equipment structured data, the zero-purchase equipment associated data and the zero-purchase equipment key data into complete zero-purchase equipment feature data.

4. The review method for typical retail equipment project reserves according to claim 1, characterized in that, The preset method of the typical equipment verification rule model includes: Real-time extract a preset typical equipment identification rule set from an enterprise rule configuration database, wherein the typical equipment identification rule set includes high-value zero-purchase equipment identification rules, high-quantity zero-purchase equipment identification rules and zero-purchase equipment technical parameter identification rules, and each rule corresponds to a scoring function; Obtain a preset rule scoring weight matrix, and use the rule scoring weight matrix to assign a rule scoring weight to each rule in the typical equipment identification rule set one by one; Obtain a typical equipment verification scoring threshold, and construct a typical equipment verification rule model based on the typical equipment verification scoring threshold, each rule in the typical equipment identification rule set and the rule scoring weight corresponding to each rule.

5. The review method for typical retail equipment project reserves according to claim 4, characterized in that, Input the complete zero-purchase equipment feature data into the typical equipment verification rule model, use the typical equipment verification rule model to verify the complete zero-purchase equipment feature data, and mark the zero-purchase equipment corresponding to the complete zero-purchase equipment feature data that passes the typical equipment verification as a typical zero-purchase equipment, including: inputting the complete feature data of the zero-purchase equipment into the typical equipment verification rule model, performing data matching on the complete feature data of the zero-purchase equipment by using each rule in the rule set of the typical equipment, and obtaining zero-purchase equipment rule features corresponding to each rule; calculating rule scores corresponding to the zero-purchase equipment rule features according to a scoring function corresponding to each rule; performing weighted summation on the rule scores of each rule by using a rule score weight corresponding to each rule, to obtain a total score of the zero-purchase equipment; verifying the total score of the zero-purchase equipment according to the typical equipment verification score threshold, marking the zero-purchase equipment corresponding to the total score of the zero-purchase equipment that is lower than the typical equipment verification score threshold as a non-typical zero-purchase equipment, and marking the zero-purchase equipment corresponding to the total score of the zero-purchase equipment that is not lower than the typical equipment verification score threshold as a typical zero-purchase equipment.

6. The method of claim 1, wherein the exemplary zero-based equipment item inventory review is performed by a computer system. taking the typical zero-purchase equipment as a current typical zero-purchase equipment, taking the zero-purchase equipment type information corresponding to the typical zero-purchase equipment as a current typical zero-purchase equipment type, taking the zero-purchase equipment procurement unit information corresponding to the typical zero-purchase equipment as a current typical zero-purchase equipment procurement unit, and generating corresponding current typical zero-purchase equipment configuration information based on the current typical zero-purchase equipment type and the current typical zero-purchase equipment procurement unit, including: obtaining a preset zero-purchase equipment procurement unit information table, wherein the zero-purchase equipment procurement unit information table includes unit level information and unit affiliation information of each zero-purchase equipment procurement unit; taking the typical zero-purchase equipment as a current typical zero-purchase equipment, taking the zero-purchase equipment type information corresponding to the current typical zero-purchase equipment as a current typical zero-purchase equipment type, taking the zero-purchase equipment procurement unit information corresponding to the current typical zero-purchase equipment as a current typical zero-purchase equipment procurement unit, and selecting, according to the zero-purchase equipment procurement unit information table, all zero-purchase equipment procurement units at the same level as the current typical zero-purchase equipment procurement unit as current typical zero-purchase equipment procurement units at the same level; generating a current typical zero-purchase equipment configuration information query condition of the current typical zero-purchase equipment procurement units at the same level based on the current typical zero-purchase equipment type and the current typical zero-purchase equipment procurement units at the same level; inputting the current typical zero-purchase equipment configuration information query condition of the current typical zero-purchase equipment procurement units at the same level into the zero-purchase equipment ledger database, to extract current typical zero-purchase equipment configuration information of the current typical zero-purchase equipment procurement units at the same level, wherein the current typical zero-purchase equipment configuration information of the current typical zero-purchase equipment procurement units at the same level includes a configuration quantity and a configuration rate of the current typical zero-purchase equipment for each current typical zero-purchase equipment procurement unit at the same level; taking the complete feature data of the current typical zero-purchase equipment as current typical zero-purchase equipment information, and integrating the current typical zero-purchase equipment information and the current typical zero-purchase equipment configuration information of the current typical zero-purchase equipment procurement units at the same level, to form corresponding current typical zero-purchase equipment configuration information.

7. The review method for typical retail equipment project reserves according to claim 6, characterized in that, generating and issuing a current typical zero-purchase equipment review suggestion based on the current typical zero-purchase equipment configuration information, including: According to the current typical zero-purchase equipment configuration information in the current typical zero-purchase equipment configuration information, the total amount of typical zero-purchase equipment of the current typical zero-purchase equipment type in each same-level purchase unit is calculated; The total amount of typical zero-purchase equipment of the current typical zero-purchase equipment type in each same-level purchase unit is averaged to obtain a typical zero-purchase equipment average value of the same-level purchase unit; The typical zero-purchase equipment average value of the same-level purchase unit is used as a configuration reference value, and the current typical zero-purchase equipment information is reviewed using the configuration reference value to generate a review suggestion; If the current typical zero-purchase equipment purchase quantity in the current typical zero-purchase equipment information is higher than the configuration reference value, a purchase upper limit is calculated according to the current typical zero-purchase equipment purchase quantity and the configuration reference value, and a reduction control instruction is generated and issued based on the purchase upper limit as a current typical zero-purchase equipment review suggestion; If the current typical zero-purchase equipment purchase quantity in the current typical zero-purchase equipment information is not higher than the configuration reference value, a pass instruction is generated and issued as a current typical zero-purchase equipment review suggestion.

8. A system for reviewing typical zero-based equipment project reserves, comprising: The application is applied to the review method of the typical zero-purchase equipment project reserve of any one of claims 1-7, comprising: A proposal processing unit is configured to obtain a zero-purchase equipment project reserve proposal, extract zero-purchase equipment feature information from the zero-purchase equipment project reserve proposal using optical character recognition technology and natural language processing technology, and generate corresponding zero-purchase equipment structured data by structuring the zero-purchase equipment feature information, wherein the zero-purchase equipment feature information includes zero-purchase equipment type information, zero-purchase equipment purchase quantity information, and zero-purchase equipment purchase unit information; A complete feature extraction unit is configured to use the zero-purchase equipment structured data as a query condition to perform associated data matching in a zero-purchase equipment account database to obtain zero-purchase equipment associated data, and generate zero-purchase equipment complete feature data based on the zero-purchase equipment structured data and the zero-purchase equipment associated data; A typical equipment determination unit is configured to obtain a preset typical equipment verification rule model, input the zero-purchase equipment complete feature data into the typical equipment verification rule model, use the typical equipment verification rule model to verify the zero-purchase equipment complete feature data, and mark the zero-purchase equipment corresponding to the zero-purchase equipment complete feature data that passes the typical equipment verification as a typical zero-purchase equipment; A typical equipment configuration identification unit is configured to use the typical zero-purchase equipment as a current typical zero-purchase equipment, correspondingly use the zero-purchase equipment type information as a current typical zero-purchase equipment type, use the zero-purchase equipment purchase unit information corresponding to the typical zero-purchase equipment as a current typical zero-purchase equipment purchase unit, and generate corresponding current typical zero-purchase equipment configuration information based on the current typical zero-purchase equipment type and the current typical zero-purchase equipment purchase unit; An review suggestion generation unit is configured to generate and issue a current typical zero-purchase equipment review suggestion based on the current typical zero-purchase equipment configuration information.

9. An electronic device, comprising: The computer program or the instruction, when executed by the computer, implements the typical zero-purchase equipment project reserve review method according to any one of claims 1-7.

10. A computer program product comprising computer programs or instructions, characterized in that, The computer program or the instruction, when executed by the computer, implements the typical zero-purchase equipment project reserve review method according to any one of claims 1-7.

Images