Material planning and warehousing expert intelligent matching and management system

Abstract

The invention discloses a material planning and warehousing expert intelligent matching and management system, and relates to the technical field of material management. Compared with a previous material planning and warehousing management technology, the problem that an existing material planning and warehousing management technology is relatively low in information cooperation efficiency and relatively poor in matching accuracy is solved; material plan information is shared, and experts can prepare materials in advance and accurately deal with the materials; the system is intercommunicated with purchasing and finance modules to form a whole-process collaborative ecology, so that the communication cost is reduced, and the overall efficiency of material management is improved; the experts and the material plans are accurately corresponding through an intelligent matching algorithm, the most adaptive expert is responsible for corresponding material acceptance, manual selection errors are reduced, the warehousing efficiency is improved, and the overall material turnover period is shortened; a unified quality detection standard is formulated; and market supply and project progress can be monitored in real time, and the system can quickly adjust a material plan, flexibly reconfigure experts, cope with sudden changes and enhance the toughness of enterprises in coping with complex and changeable business environments.

Description

Technical Field

[0001] The present invention relates to the technical field of material management, and in particular to a material planning and warehousing expert intelligent matching and management system. Background Art

[0002] In traditional material management processes, material planning and warehousing often present numerous challenges. Material planning typically involves considering multiple factors, such as project requirements, budget constraints, and market availability. However, in practice, the accuracy and scientific nature of material plans are difficult to ensure due to fragmented and inaccurate information. For example, poor communication between departments regarding material requirements can lead to over- or under-planning, resulting in wasted resources and project delays. Traditional management methods for material warehousing rely primarily on manual labor and empirical judgment. Warehousing specialists are required to individually inspect and verify material specifications, quality, and quantity. However, manual labor is prone to fatigue and negligence, which in turn impacts the accuracy and efficiency of incoming material inspections. Furthermore, the expertise and experience of different warehousing specialists vary significantly, and there is a lack of unified standards and norms to measure their work quality, which introduces uncertainty into material warehousing management.

[0003] In today's highly competitive market, companies are increasingly demanding the efficiency and quality of material management. Accurate material planning can ensure the smooth progress of projects and avoid increased costs and delays caused by material shortages or surpluses. Efficient and accurate material warehousing management can ensure that the quality and quantity of materials meet requirements, reducing risks in subsequent production operations. Different industries have specific requirements for material management. For example, in the manufacturing industry, strict control over the supply planning and warehousing quality of raw materials is required to ensure production continuity and product quality. In the construction industry, material planning and warehousing management for large-scale engineering projects involve huge capital investments and complex project schedules. The slightest mistake can lead to serious economic losses and project stagnation.

[0004] In order to solve the above problems, the present invention proposes a material planning and warehousing expert intelligent matching and management system. Summary of the Invention

[0005] The purpose of the present invention is to propose a material planning and warehousing expert intelligent matching and management system to solve the problems raised in the background technology:

[0006] The information coordination efficiency of existing material planning and warehousing management technologies is low and the matching accuracy is poor.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A material planning and warehousing expert intelligent matching and management system, including:

[0009] Expert classification module: used to classify neutral experts in material plan review according to their types;

[0010] The information collection module is used to design expert interaction information forms to collect expert information;

[0011] The entry system module is used to construct an expert entry evaluation index system based on the expert interaction information form and design corresponding entry evaluation standards;

[0012] Plan sorting module: used to sort out and analyze material plan batch information;

[0013] Intelligent matching module: used to analyze the material plan batch information and select the expert who best matches the material plan based on the intelligent matching algorithm;

[0014] Assessment system module: used to establish expert assessment and evaluation indicator system;

[0015] Expert Assessment Module: This module is used to design an assessment and evaluation mechanism for neutral experts in material plan review, implement a comprehensive assessment and evaluation process, establish an incentive and constraint mechanism, and continuously collect feedback;

[0016] The information collection module includes a form dimension unit and a form design unit;

[0017] The form dimension unit is used to determine the collection dimension of the expert interaction information form;

[0018] The form design unit is used to establish a neutral expert interaction information form for material procurement plan;

[0019] The plan combing module includes:

[0020] Batch classification unit: used to assign a unique number to each batch of materials, classify batch types according to procurement requirements, and select the corresponding procurement method;

[0021] Demand analysis unit: used to record and analyze the demand for each material planning batch;

[0022] Budget information unit: used to list and calculate the budget amount for each batch;

[0023] Supplier Information Unit: used to analyze supplier qualifications, selection criteria, delivery capabilities, credibility, environmental records, and social responsibility;

[0024] Procurement process unit: used to specify the material procurement process and conduct unified procurement management.

[0025] Preferably, the types of neutral experts in material plan review include comprehensive business experts in material category, comprehensive business experts in service category, technical experts in material category and technical experts in service category.

[0026] Preferably, the expert interaction information form includes information in three dimensions: basic information, professional information and review experience.

[0027] Preferably, the entry indicators in the entry evaluation index system include highest academic qualification, professional title, honorary achievements, years of work experience, years of review, number of parameter reviews and number of reviewed bidding units.

[0028] Preferably, the intelligent matching module performs semantic analysis on the material plan batch information and expert information based on natural language processing technology, and the steps of selecting the expert that best matches the material plan based on the intelligent matching algorithm are as follows:

[0029] Extract the five most frequent keywords in the resource plan K = {k1, k2, k3, k4, k5}; the expert's research direction keyword set is R i ={r i1 ,r i2 ,Λ,r in}, where i is the expert; n is the number of keywords in the expert's research direction; define the function f(k j ,R i ) to measure keyword k j The degree of matching with expert i's research direction;

[0030]

[0031] Among them, k j is the jth keyword in the set of five keywords with the highest frequency extracted from the material plan; TF(k j ,R i ) is the keyword k j In the research direction keyword set R of expert i i The word frequency in IDF(k j ) is the inverse document frequency of the research method; N is the total number of experts; DF(k j ) contains the keyword k j The number of expert research direction keyword sets;

[0032] Calculate the comprehensive matching scores between the five keywords and the expert's research direction:

[0033]

[0034] Among them, ω j Keyword k j The weight of TF(k j) is the keyword k j Word frequency in material planning; TF(k l ) is the keyword k l word frequency in materiel planning;

[0035] The expert's review field keyword set is C i ={c i1 ,c i2 ,Λ,c id}, where d is the number of keywords in the expert review field;

[0036] Define the function g(k j ,C i ) to measure keyword k j The degree of matching with expert i's research direction;

[0037]

[0038] Among them, TF'(k j ,R i ) is the keyword k j In the review field of expert i, the keyword set R i The word frequency in IDF'(k j ) is the inverse document frequency of the review field; DF'(k j ) contains the keyword k j The number of expert review domain keyword sets;

[0039] Calculate the comprehensive matching scores between the five keywords and the expert's research direction:

[0040]

[0041] Among them, S Ci It is the comprehensive matching score between the five keywords and the expert's research direction;

[0042] According to the material plan, the material category Q1, planned implementation time range Q2, application project Q3, and expert personal information are collected, including the expert's expertise in material category E1, work experience time period E2, and participation project type E3;

[0043] Define the function h1(P1,E1) to measure the matching degree between the material category Q1 and the expert's expertise in the material category E1:

[0044]

[0045] Where count(·) is a statistical function; q 1x is the material category classification value; e 1x The classification value of the material category is good at;

[0046] Define the function h2(Q2,E2) to measure the matching degree between the planned implementation timeframe Q2 and the expert’s work experience timeframe E2:

[0047]

[0048] Among them, t1 is the start time of plan implementation, t2 is the end time of plan implementation; t3 is the start time of expert work experience, t4 is the end time of expert work experience;

[0049] Define the function h2(Q2,E2) to measure the matching degree between the planned application project Q3 and the expert application project Q3:

[0050]

[0051] Among them, q 3y Apply item classification values for material categories; e 3y Apply item classification values for experts;

[0052] Calculate the matching score between expert personal information and material plan:

[0053]

[0054] α+β+γ=1

[0055] Among them, α, β and γ are the weights of the corresponding dimensions;

[0056] Calculate the comprehensive matching score T of expert i i :

[0057]

[0058] λ+μ+δ=1

[0059] Among them, λ, μ and δ are adjustment weights, which are obtained by searching based on the harmony optimization algorithm; the comprehensive matching score T is finally selected. i The highest expert serves as the one that best matches the material plan.

[0060] Preferably, the expert assessment and evaluation index system includes personal qualities, professional qualities and work indicators; among which, personal qualities include age and academic qualifications; professional qualities include professional titles and years of experience; work indicators include professional ability, attendance, professional ethics and work discipline; the professional ability is the number of times participated in reviews, the attendance is the number of times taken leave, the professional ethics is the number of complaints and reports, and the work discipline is the number of conflicts of interest and violations.

[0061] Preferably, the assessment and evaluation system includes four aspects: professional ability, review quality, work attitude and comprehensive evaluation.

[0062] Preferably, the assessment and evaluation process includes self-evaluation, project assessment, peer review, comprehensive evaluation and public disclosure of results.

[0063] Preferably, the incentives in the incentive and constraint mechanism include issuing honorary certificates, providing material rewards, and offering professional training opportunities; the constraints include reminder talks; and taking measures to suspend qualifications when experts fail to perform their duties or violate regulations.

[0064] Preferably, the material procurement process includes a demand proposal stage, a demand approval link, a procurement implementation stage and a material acceptance environment.

[0065] Compared with the existing technology, the present invention provides a material planning and warehousing expert intelligent matching and management system, which has the following beneficial effects:

[0066] The present invention shares material plan information, allowing experts to prepare in advance and respond accurately; the system communicates with procurement, finance and other modules to form a full-process collaborative ecosystem, reduce communication costs, and improve the overall efficiency of material management; through intelligent matching algorithms, experts and material plans are accurately matched, and the most suitable experts are responsible for the corresponding material acceptance, reducing manual selection errors, improving warehousing efficiency, and shortening the overall material turnover cycle; unified quality inspection standards are also formulated; and market supply and project progress can be monitored in real time. The system can quickly adjust material plans, flexibly reallocate experts, respond to sudden changes, ensure the timeliness and adaptability of material supply, and enhance the resilience of enterprises in dealing with complex and changing business environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is the system block diagram mentioned in Example 1 of the present invention;

[0068] Figure 2 This is a schematic diagram of the neutral expert interaction information form for the material procurement plan mentioned in Example 1 of the present invention;

[0069] Figure 3 This is a diagram of the evaluation index system for the neutral expert capabilities in the material plan review mentioned in Example 1 of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0071] The present invention shares material plan information, allowing experts to prepare in advance and respond accurately. The system communicates with procurement, finance and other modules to form a full-process collaborative ecosystem, reduce communication costs, and improve the overall efficiency of material management. Experts are accurately matched with material plans through intelligent matching algorithms, allowing the most suitable experts to be responsible for corresponding material acceptance, reducing manual selection errors, improving warehousing efficiency, and shortening the overall material turnover cycle. Unified quality inspection standards are also formulated. Market supply and project progress can also be monitored in real time. The system can quickly adjust material plans, flexibly reallocate experts, respond to sudden changes, ensure the timeliness and adaptability of material supply, and enhance the resilience of enterprises in complex and changing business environments. Specifically, it includes the following content.

[0072] Example 1:

[0073] See also Figure 1-3 The present invention provides a material planning and warehousing expert intelligent matching and management system, including:

[0074] Expert classification module 100: used to classify neutral experts in material plan review according to types; neutral experts in material plan review are divided into types such as comprehensive business experts in material category, comprehensive business experts in service category, technical experts in material category and technical experts in service category.

[0075] The information collection module 200 is used to design an expert interaction information form to collect expert information;

[0076] The information collection module 200 includes a form dimension unit 210 and a form design unit 220. The form dimension unit 210 is used to determine the collection dimensions for the expert interaction information form. The collection dimensions include four dimensions: basic information, professional skills, work experience, and honors and achievements. Basic information includes, but is not limited to, core elements such as the expert's name, gender, age, work unit, position, and contact information. This basic information is not only crucial for quickly identifying and effectively contacting experts, but also serves as the foundation for building and improving expert profiles. Professional skills include, but are not limited to, whether the expert is proficient in specific software operations, such as CAD drawing, ERP system management, and data analysis software; whether they possess proficient equipment maintenance skills and can handle various complex mechanical equipment issues; and whether they master efficient project management methods, such as Agile Management and Six Sigma. This detailed information helps us accurately portray the expert's technical strength and expertise, providing companies with precise expert matching when needed. Work experience includes, but is not limited to, setting project goals, developing plans, building teams, tracking progress, controlling quality, and evaluating results. Furthermore, the system records the challenges encountered by experts during project implementation, the effective measures taken, problem-solving strategies, and the resulting achievements or breakthroughs. This information is crucial for assessing experts' practical skills, teamwork, problem-solving abilities, and insight and decision-making abilities in complex situations. Honorary achievements include national, provincial, ministerial, and industry-wide awards for outstanding engineers, scientific and technological progress, and technological inventions, as well as international recognition, honors, and patents, such as invention patents, utility model patents, and design patents. This information provides a more comprehensive understanding of their professional background and capabilities when selecting experts, ensuring they can provide cutting-edge technical support and innovation momentum to the company.

[0077] The form design unit 220 is used to establish a neutral expert interaction information form for material procurement plan; Figure 2Expert interaction information form, in which the experts' research directions are set as power system planning and design, power grid operation and management, power market analysis and policy research, power equipment and material research, power electronics and power transmission, high voltage and insulation technology, power system automation and intelligence, distributed generation and microgrid technology, new energy power generation technology (such as wind energy, solar energy, etc.), power system safety, stability and control, power system relay protection, power communication and information processing, power demand side management, energy conservation and emission reduction and environmental protection, power engineering management and cost, supply chain management and logistics, quality management and certification, human resources management, financial management and auditing, laws, regulations and policy research, power system reliability analysis, power grid fault analysis and prevention, power system restoration and emergency response, power engineering geology and geotechnical engineering, power engineering design standards and specifications, power engineering project risk assessment and management, power system simulation and modeling, power system status monitoring and fault diagnosis, power system optimization and dispatching, power grid asset management and maintenance, aging and life assessment of power infrastructure, environmental monitoring and pollution control of power systems, analysis and application of big data in power systems, application of cloud computing and Internet of Things in power systems, network security of power systems, economic analysis of power systems, power market trading strategies and models, mathematical methods for power system planning and optimization, dynamic behavior and stability analysis of power systems, transient process analysis of power systems, harmonic analysis and control of power systems, reactive power and voltage control of power systems, analysis and limitation of short-circuit current in power systems, long-distance transmission technology of power systems, ultra-high voltage and ultra-high voltage technology, cable line design and laying technology, design and application of transformers and reactors, switchgear and circuit breaker technology, power electronic devices and applications, electric vehicles and charging infrastructure, energy storage system technology and applications, smart grid and smart home technology, international cooperation and exchange in power systems, education and training in power systems, standardization and certification of power systems and others, a total of 56 research directions, each expert can choose up to 5.The expert review areas are set as power grid infrastructure construction projects (such as transmission lines, substations, etc.), power system upgrade and transformation projects, new energy power generation projects (wind power, solar energy, biomass energy, etc.), energy storage system construction projects, smart grid and distribution automation projects, power system safety and stability control projects, power market operation support systems, power information and communication network construction projects, power system monitoring and dispatching automation projects, power system environmental protection and pollution control projects, power facility earthquake disaster reduction projects, power system lightning protection projects, power system noise control projects, power system water saving and water resources management projects, power engineering design and consulting services, power equipment procurement and supply chain management, power system maintenance and repair services, power system operation analysis and optimization services, power system load forecasting and demand side management, power system energy conservation and emission reduction technical services, power system quality and performance evaluation, power system accident analysis and investigation, power system emergency plan and drill evaluation, power system training and education services, power system R&D and technological innovation projects, power system standardization and specification formulation, power system international The review covers 50 areas, including cooperation and exchange projects, power system laws, regulations, and policies research, power system financial analysis and investment evaluation, power system auditing and risk management, power system human resources management and services, power system marketing and brand building, power system social responsibility and sustainable development projects, power system public health and employee health projects, power system culture and history research projects, power system social impact assessment, power system engineering geology and geotechnical engineering assessment, power system engineering supervision and quality control, power system engineering safety assessment and supervision, power system materials science research and application, power system application research of new technologies, new processes, and new materials, power system engineering bidding and contract management, power system engineering insurance and guarantee services, power system engineering environmental impact assessment, power system engineering ecological protection and restoration, power system engineering social responsibility assessment, power system engineering community relations and public affairs, power system engineering intellectual property management and protection, power system engineering compliance with international standards and specifications, and power system engineering cross-border and transnational project management. Each expert can select up to five areas.

[0078] The entry system module 300 is used to construct an expert entry evaluation index system based on the expert interaction information form and design corresponding entry evaluation standards; the constructed expert entry evaluation index system and the corresponding entry evaluation standards can be referred to Table 1.

[0079] Table 1 Expert database evaluation index system and standards

[0080]

[0081]

[0082] Plan sorting module 400: used to sort out and analyze material plan batch information;

[0083] The planning module 400 includes a batch classification unit 410, which assigns a unique number to each batch of materials, ensuring that each batch can be quickly located and identified within the vast material system. Each batch can also be named, with the name corresponding to the number, further enhancing identification and facilitating effective tracking and management within the complex supply chain. Batch types are also categorized based on procurement needs, including regular procurement, emergency procurement, and strategic reserves. This classification helps companies more accurately grasp procurement priorities, ensuring the timely supply of critical materials to meet the diverse needs of power grid operations. The appropriate procurement method is then selected, including open bidding, competitive negotiation, and single-source procurement. Each procurement method is strictly implemented in accordance with national laws and regulations and internal company regulations to ensure the legality, transparency, and fairness of procurement activities. This refined management strategy not only improves procurement efficiency but also saves costs and reduces procurement risks for the company.

[0084] Demand Analysis Unit 420: This unit records and analyzes the demand for each planned batch of materials, ensuring accurate and efficient material procurement. This record includes, but is not limited to, key information such as the material's name, specifications, model, quantity, intended use, and timeframe, as well as any special technical parameters or quality requirements. This detailed record ensures that purchased materials fully meet actual operational needs and avoids procurement errors caused by incomplete information.

[0085] Budget Information Unit 430: This unit lists and calculates the budget for each batch, including the total budget and specific budget allocations for each material. This unit accurately controls costs, ensuring that every penny is allocated to the areas where it is most needed, effectively avoiding unnecessary overspending and improving fund efficiency. It also records the source of the budget, such as internal funds, project-specific funds, or government subsidies, helping companies better track fund flows and ensure transparency and compliance in fund use.

[0086] Supplier Information Unit 440: This unit analyzes supplier qualifications, selection criteria, delivery capabilities, reputation, environmental record, and social responsibility. Supplier qualifications include aspects such as company size, financial status, production capacity, and technical level. These qualifications are designed to screen suppliers with sufficient strength and capabilities, laying a solid foundation for subsequent procurement activities. Selection criteria include quality standards, price competitiveness, and service levels. Quality standards are the core of supplier evaluation. Companies require suppliers to provide products that meet national or industry standards and obtain relevant quality certifications. Price competitiveness requires suppliers to offer competitive prices while ensuring quality. Service levels include supplier responsiveness, after-sales service, and customer satisfaction. Delivery capabilities ensure that suppliers can complete orders on time and in full, avoiding disruptions to the company's production schedule. Reputation reflects a supplier's reputation within the industry and past cooperation experience. Environmental record and social responsibility are key considerations in modern society regarding a company's sustainable development capabilities.

[0087] Procurement process unit 450: used to define the material procurement process and conduct unified procurement management. The material procurement process includes:

[0088] During the demand proposal stage, the using department fills out the demand application form in detail according to actual work needs, including detailed information such as the type, specifications, quantity, technical parameters, etc. of the materials.

[0089] During the demand approval phase, a dedicated procurement management department will assume the review responsibility. Approved demands will formally enter the procurement planning stage. The procurement management department will formulate a detailed procurement plan based on the approval results and clarify the implementation schedule of the plan to ensure that procurement activities proceed as planned.

[0090] During the procurement implementation phase, the process is further broken down into multiple sub-steps, including preparation of bidding documents, publication of the bidding notice, supplier qualification review, bid opening and evaluation, bid determination, and contract signing. Each sub-step has clear responsibilities and operating procedures.

[0091] During the material acceptance phase, the company establishes a dedicated acceptance team to conduct a comprehensive inspection of incoming materials. Inspection details include quantity, quality, and technical specifications. Only after passing the acceptance test can the materials be stored or put into use. If the acceptance fails, the acceptance team will take appropriate measures as agreed in the contract, including returns, claims, or requests for rectification from the supplier, to protect the company's legitimate rights and interests.

[0092] Intelligent matching module 500: used to analyze the material plan batch information and select the expert who best matches the material plan based on the intelligent matching algorithm;

[0093] The steps for performing semantic analysis of material plan batch information and expert information based on natural language processing technology and selecting the expert that best matches the material plan based on an intelligent matching algorithm are as follows:

[0094] Extract the five most frequent keywords in the resource plan K = {k1, k2, k3, k4, k5}; the expert's research direction keyword set is R i ={r i1 ,r i2 ,Λ,r in}, where i is the expert; n is the number of keywords in the expert's research direction; define the function f(k j ,R i ) to measure keyword k j The degree of matching with expert i's research direction;

[0095]

[0096] Among them, k j is the jth keyword in the set of five keywords with the highest frequency extracted from the material plan; TF(k j ,R i ) is the keyword k j In the research direction keyword set R of expert i i The word frequency in, when the keyword k j Not in set R i In the equation, TF(k j ,R i )=0;IDF(k j ) is the inverse document frequency of the research method, which is used to measure the keyword k j The rarity of the expert; N is the total number of experts; DF(k j ) contains the keyword k j The number of expert research direction keyword sets;

[0097] Calculate the comprehensive matching scores between the five keywords and the expert's research direction:

[0098]

[0099] Among them, ω j Keyword k j The weight of TF(k j ) is the keyword k j Word frequency in material planning; TF(k l ) is the keyword k lThe frequency of keywords in the material plan is then matched against the experts' research areas to initially identify experts aligned with the project's research direction. This step ensures the expert's professional background is highly relevant to the research project, laying the foundation for subsequent accurate matching.

[0100] The expert's review field keyword set is C i ={c i1 ,c i2 ,Λ,c id}, where d is the number of keywords in the expert review field;

[0101] Define the function g(k j ,C i ) to measure keyword k j The degree of matching with expert i's research direction;

[0102]

[0103] Among them, TF'(k j ,R i ) is the keyword k j In the review field of expert i, the keyword set R i The word frequency in, when the keyword k j Not in set R i Then TF'(k j ,R i )=0;IDF'(k j ) is the inverse document frequency of the review field, which is used to measure the keyword k j The rarity of DF'(k j ) contains the keyword k j The number of expert review domain keyword sets;

[0104] Calculate the comprehensive matching scores between the five keywords and the expert's research direction:

[0105]

[0106] Among them, S Ci A comprehensive score is calculated based on the matching of the five keywords with the expert's research direction. Further matching is performed between the project's keywords and the expert's review area. Again, the maximum number of keywords is limited to five. This allows for more precise targeting of experts whose research direction not only aligns with the project's, but also possesses expertise and experience in the review area. This step helps to improve the relevance and effectiveness of the expert review.

[0107] According to the material plan, the material category Q1, planned implementation time range Q2, application project Q3, and expert personal information are collected, including the expert's expertise in material category E1, work experience time period E2, and participation project type E3;

[0108] Define the function h1(P1,E1) to measure the matching degree between the material category Q1 and the expert's expertise in the material category E1:

[0109]

[0110] Where count(·) is a statistical function; q 1x is the material category classification value; e 1x The classification value of the material category is good at;

[0111] Define the function h2(Q2,E2) to measure the matching degree between the planned implementation time range Q2 and the expert work experience time range E2:

[0112]

[0113] Among them, t1 is the start time of plan implementation, t2 is the end time of plan implementation; t3 is the start time of expert work experience, t4 is the end time of expert work experience;

[0114] Define the function h2(Q2,E2) to measure the matching degree between the planned application project Q3 and the expert application project Q3:

[0115]

[0116] Among them, q 3y Apply item classification values for material categories; e 3y Apply item classification values for experts;

[0117] Calculate the matching score between expert personal information and material plan:

[0118]

[0119] α+β+γ=1

[0120] Among them, α, β and γ are the weights of the corresponding dimensions;

[0121] Calculate the comprehensive matching score T of expert i i :

[0122]

[0123] λ+μ+δ=1

[0124] Among them, λ, μ and δ are adjustment weights, which are obtained by searching based on the harmony optimization algorithm; the comprehensive matching score T is finally selected. iThe highest-ranking expert is chosen as the one that best matches the material plan. Finally, a comprehensive analysis of the expert's personal information and the specific information of the project batch is used to calculate and analyze the method that best matches the material plan. This ensures that each expert can maximize their contribution within their field of expertise and that each project receives review and guidance from the most appropriate expert.

[0125] Assessment system module 600: used to establish an expert assessment and evaluation indicator system;

[0126] When constructing the comprehensive quality evaluation system of neutral experts in power grid material plan review, we should first establish an evaluation index system in the management of neutral experts in material plan review, which can be referred to Figure 3 Combined with big data from past bid evaluation projects, this system provides an objective and comprehensive evaluation of the professional qualities and attendance of neutral experts in material planning review during bid evaluation activities. The established expert assessment and evaluation indicator system and standards can be found in Table 2.

[0127] Table 2 Expert assessment and evaluation system and standards

[0128]

[0129]

[0130]

[0131] The evaluation method for neutral experts in material plan review can be that the tenderer can make an objective and comprehensive evaluation of the experts' technical level, work attitude, etc.; at the same time, the expert database managers can also use various channels and methods to fully and deeply understand the experts' work situation, actively listen to feedback from others, and make objective and fair evaluations of the experts on this basis.

[0132] Expert Assessment Module 700: Designs an assessment and evaluation mechanism for neutral experts in material plan review, implements a comprehensive assessment and evaluation process, builds an incentive and constraint mechanism, and continuously collects feedback;

[0133] The assessment and evaluation system encompasses four aspects: professional competence, review quality, work attitude, and a comprehensive evaluation. Professional competence assessment focuses on the expert's professional knowledge, skill proficiency, and industry experience. Review quality examines the expert's performance in specific review projects, including accuracy, efficiency, and attention to detail. Work attitude assessment focuses on the expert's sense of responsibility, professionalism, and cooperative attitude. The comprehensive evaluation provides a comprehensive assessment of the expert's overall performance.

[0134] The assessment and evaluation process includes:

[0135] Self-evaluation: Experts complete a self-evaluation form based on their work performance, project participation, and personal development goals, rating and describing their professional capabilities, work achievements, learning and growth, and other aspects. This step helps experts gain self-awareness and provides basic information for subsequent evaluations.

[0136] Project Evaluation: Evaluators will score and evaluate the expert's performance based on factors such as project completion, quality standards, time efficiency, and cost control. This phase focuses on the expert's actual work ability and project contribution.

[0137] Peer review: This process is conducted by peers with similar professional backgrounds and experience to the expert being evaluated. Peer review focuses not only on the expert's professionalism but also on their reputation within the industry, collaborative spirit, and academic contributions. The results of peer review provide a professional and authoritative basis for the evaluation.

[0138] Comprehensive Evaluation: The evaluation committee or team will comprehensively consider the results of self-evaluation, project assessment, and peer review, and combine the overall performance and contributions of the experts to provide a comprehensive evaluation conclusion. This step is intended to ensure the comprehensiveness and balance of the evaluation results.

[0139] Public disclosure of results: The evaluation results will be made public within a preset timeframe, subject to public oversight and review, to facilitate comments and suggestions from stakeholders and the public.

[0140] Incentive measures include: issuing honorary certificates to enhance the social recognition and professional honor of experts; providing material rewards, including bonuses, prizes, etc., to affirm the contributions of experts in the form of material incentives; and providing professional training opportunities to help experts continuously improve their professional capabilities and business levels, and promote the sustainable development of their careers.

[0141] The restrictive measures include: conducting reminder talks, providing timely communication and guidance on problems encountered by experts in their work, and urging them to correct them; suspending the qualifications of experts when they fail to perform their duties or violate regulations, and temporarily restricting their participation in review activities as a warning; for experts who seriously violate regulations or professional ethics, we will cancel their qualifications and remove them from the expert team to maintain the professional standards and ethical standards of the entire expert team.

[0142] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A material planning and warehousing expert intelligent matching and management system, characterized by: include: Expert classification module (100): used to classify neutral experts in material plan review according to types; The information collection module (200) is used to design an expert interaction information form to collect expert information; The entry system module (300) is used to construct an expert entry evaluation index system based on the expert interaction information form and design corresponding entry evaluation standards; Plan sorting module (400): used to sort out and analyze the material plan batch information; Intelligent matching module (500): used to analyze the material plan batch information and select the expert who best matches the material plan based on the intelligent matching algorithm; Assessment system module (600): used to establish an expert assessment and evaluation indicator system; Expert Assessment Module (700): used to design an assessment and evaluation mechanism for neutral experts in material plan review, implement a comprehensive assessment and evaluation process, build an incentive and constraint mechanism, and continuously collect feedback; The information collection module (200) includes a form dimension unit (210) and a form design unit (220); The form dimension unit (210) is used to determine the collection dimension of the expert interaction information form; The form design unit (220) is used to establish a neutral expert interaction information form for material procurement plan; The plan sorting module (400) includes: Batch classification unit (410): used to assign a unique number to each batch of materials, classify the batch types according to procurement requirements, and select the corresponding procurement method; Demand analysis unit (420): used to record and analyze the demand for each material planning batch; Budget information unit (430): used to list and calculate the budget amount for each batch; Supplier information unit (440): used to analyze supplier qualifications, selection criteria, delivery capabilities, credibility, environmental records and social responsibility; Procurement process unit (450): used to define the material procurement process and conduct unified procurement management.

2. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The types of neutral experts for material plan review include comprehensive business experts for materials, comprehensive business experts for services, technical experts for materials and technical experts for services.

3. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The expert interaction information form includes information in three dimensions: basic information, professional information and review experience.

4. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The entry indicators in the entry evaluation index system include highest academic qualification, professional title, honorary achievements, years of work experience, years of review, number of parameter reviews and the number of reviewed bidding units.

5. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The intelligent matching module performs semantic analysis on the material plan batch information and expert information based on natural language processing technology, and selects the expert who best matches the material plan based on the intelligent matching algorithm. The specific steps are as follows: Extract the five most frequent keywords in the resource plan K = {k1, k2, k3, k4, k5}; the expert's research direction keyword set is R i ={r i1 ,r i2 ,Λ,r in }, where i is the expert; n is the number of keywords in the expert's research direction; define the function f(k j ,R i ) to measure keyword k j The degree of matching with expert i's research direction; Among them, k j is the jth keyword in the set of five keywords with the highest frequency extracted from the material plan; TF(k j ,R i ) is the keyword k j In the research direction keyword set R of expert i i The word frequency in IDF(k j ) is the inverse document frequency of the research method; N is the total number of experts; DF(k j ) contains the keyword k j The number of expert research direction keyword sets; Calculate the comprehensive matching scores between the five keywords and the expert's research direction: Among them, ω j Keyword k j The weight of TF(k j ) is the keyword k j Word frequency in material planning; TF(k l ) is the keyword k l word frequency in materiel planning; The expert's review field keyword set is C i ={c i1 ,c i2 ,Λ,c id }, where d is the number of keywords in the expert review field; Define the function g(k j ,C i ) to measure keyword k j The degree of matching with expert i's research direction; Among them, TF'(k j ,R i ) is the keyword k j In the expert i's review field keyword set R i The word frequency in IDF'(k j ) is the inverse document frequency of the review field; DF'(k j ) contains the keyword k j The number of expert review domain keyword sets; Calculate the comprehensive matching scores between the five keywords and the expert's research direction: Among them, S Ci It is the comprehensive matching score between the five keywords and the expert's research direction; According to the material plan, the material category Q1, planned implementation time range Q2, application project Q3, and expert personal information are collected, including the expert's expertise in material category E1, work experience time period E2, and participation project type E3; Define the function h1(P1, E1) to measure the matching degree between the material category Q1 and the expert's expertise in the material category E1: Where count(·) is a statistical function; q 1x is the material category classification value; e 1x The classification value of the material category is good at; Define the function h2(Q2,E2) to measure the matching degree between the planned implementation timeframe Q2 and the expert’s work experience timeframe E2: Among them, t1 is the start time of plan implementation, t2 is the end time of plan implementation; t3 is the start time of expert work experience, t4 is the end time of expert work experience; Define the function h2(Q2,E2) to measure the matching degree between the planned application project Q3 and the expert application project Q3: Among them, q 3y Apply item classification values for material categories; e 3y Apply item classification values for experts; Calculate the matching score between expert personal information and material plan: <h2 style=";text-align:left;direction:ltr">S<h2 style=";text-align:left;direction:ltr"> Ii <h2 style=";text-align:left;direction:ltr"> =α×h1(Q1,E1)+β×h2(Q2,E2)+γ×h3(Q3,E3) α+β+γ=1 Among them, α, β and γ are the weights of the corresponding dimensions; Calculate the comprehensive matching score T of expert i i : λ+μ+δ=1 Among them, λ, μ and δ are adjustment weights, which are obtained by searching based on the harmony optimization algorithm; the comprehensive matching score T is finally selected. i The highest expert serves as the one that best matches the material plan.

6. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The expert assessment and evaluation index system includes personal qualities, professional qualities and work indicators; among them, personal qualities include age and academic qualifications; professional qualities include professional titles and years of experience; work indicators include professional ability, attendance, professional ethics and work discipline; the professional ability refers to the number of times participated in reviews, the attendance refers to the number of times taken leave, the professional ethics refers to the number of complaints and reports, and the work discipline refers to the number of conflicts of interest and violations.

7. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The assessment and evaluation system includes four aspects: professional ability, review quality, work attitude and comprehensive evaluation.

8. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The assessment and evaluation process includes self-evaluation, project assessment, peer review, comprehensive evaluation and public disclosure of results.

9. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The incentives in the incentive and constraint mechanism include issuing honorary certificates, providing material rewards, and offering professional training opportunities; constraints include reminder talks; and taking measures to suspend qualifications when experts fail to perform their duties or violate regulations.

10. The intelligent matching and management system for material planning and warehousing experts according to claim 1 is characterized in that: The material procurement process includes the demand proposal stage, demand approval link, procurement implementation stage and material acceptance environment.

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