Information matching processing method and device oriented to bid procurement process

Abstract

The invention discloses an information matching processing method and device oriented to a bid procurement process. The method comprises the following steps: acquiring a supplier bidding data information set, a review expert information set and bid project demand information; the supplier bidding data information set comprises a qualification capability value set and a bidding technical index information set of each supplier; performing credible evaluation processing on the supplier bidding data information set and the bidding project demand information to obtain a credible supplier data information set; and based on the credible supplier data information set and the bid invitation project demand information, performing matching processing on the review expert information set to obtain a selected review expert information set.

Description

Technical Field

[0001] The present invention relates to the fields of data processing and bidding and procurement, and in particular to an information matching processing method and device oriented to a bidding and procurement process. Background Art

[0002] In the traditional bidding and procurement process, information matching faces numerous challenges. For one thing, supplier bid data is complex and potentially inaccurate, making it difficult to quickly and accurately determine whether suppliers truly possess the qualifications and capabilities to meet the project's requirements. When collecting supplier bid data, the lack of an effective screening mechanism allows some suppliers with substandard qualifications to be included. This not only increases the burden of subsequent reviews but can also hinder the smooth progress of the bidding project. For example, some suppliers may exaggerate their qualifications and capabilities, providing false bid technical indicators, making it difficult for purchasers to discern authenticity from the massive amount of data.

[0003] On the other hand, the selection of review experts can also be irrational. Accurately matching experts with appropriate bidding projects within a comprehensive expert information set has always been a challenge. Traditional matching methods often only consider the expert's technical field information, ignoring the deeper connection between the expert's competence indicator information set and the bidding project requirements. This can easily lead to the selected experts being unable to conduct a comprehensive and accurate review of supplier bid data, affecting the fairness and scientific nature of the bidding results. For example, in some complex technical bidding projects, the mismatch between the selected expert's competence indicators and the project requirements may lead to deviations in the evaluation of the supplier's technical proposal, thus affecting the ultimate quality of the project.

[0004] Furthermore, due to a lack of effective utilization of suppliers' historical bidding data, there's no solid basis for judging the credibility of current bids. Previously, bid data was often viewed in isolation, without mining potential insights from suppliers' historical bidding behavior. This made it impossible to accurately assess the stability and reliability of supplier bid data, which, to a certain extent, increased the risk of bidding and procurement. Summary of the Invention

[0005] The present invention mainly solves the problem of how to accurately and reliably evaluate bidder information and how to accurately select bid evaluation experts. The present invention discloses an information matching processing method and device for a bidding and procurement process.

[0006] In a first aspect, an embodiment of the present invention discloses an information matching processing method for a bidding and procurement process, comprising:

[0007] S1, obtaining a supplier bidding data information set, an evaluation expert information set, and bidding project requirement information; the supplier bidding data information set includes a qualification and capability value set and a bidding technical indicator information set of each supplier; the qualification and capability value set includes a plurality of qualification and capability values; the bidding technical indicator information set includes a plurality of bidding technical indicator information and bidding time; the evaluation expert information set includes a plurality of evaluation expert capability indicator information sets and technical field information; the bidding project requirement information includes the capability lower limit values of all qualifications, the standard values of all technical indicators, and a technical field information set; the technical field information set includes technical field information;

[0008] S2, performing a trustworthy evaluation process on the supplier bidding data information set and the bidding project demand information to obtain a trustworthy supplier data information set;

[0009] S3, based on the trusted supplier data information set and the bidding project requirement information, matching processing is performed on the review expert information set to obtain a selected review expert information set.

[0010] The trust evaluation process is performed on the supplier bidding data information set and the bidding project demand information to obtain a trustworthy supplier data information set, including:

[0011] S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determining whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, thereby obtaining a first identification result for the supplier;

[0012] S22, deleting information corresponding to suppliers for which the first judgment result is negative from the supplier bidding data information set, to obtain an updated supplier bidding data information set;

[0013] S23, obtaining a supplier's historical bidding data information set; the supplier's historical bidding data information set includes a set of historical bidding technical indicator information for each supplier; the set of historical bidding technical indicator information includes several pieces of historical bidding technical indicator information and corresponding bidding time information;

[0014] S24: Based on the supplier's historical bidding data information set, perform data credibility evaluation on the updated supplier's bidding data information set to obtain a trusted supplier data information set.

[0015] The step of performing data credibility evaluation on the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a trusted supplier data information set includes:

[0016] S241, performing a first credibility evaluation process on each supplier in the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a first credibility evaluation value of the supplier;

[0017] S242, constructing an indicator prediction model for each supplier based on the supplier's historical bidding data information set;

[0018] S243, performing a second credibility evaluation process on each supplier in the updated supplier bidding data information set based on the indicator prediction model of each supplier to obtain a second credibility evaluation value of the supplier;

[0019] S244, performing a weighted summation of the first credibility evaluation value and the second credibility evaluation value of each supplier to obtain a third credibility evaluation value of the supplier;

[0020] S245, determining whether the third credibility evaluation value of each supplier is greater than a preset credibility threshold, and obtaining a second discrimination result for each supplier;

[0021] S246, deleting the information corresponding to the supplier for which the second judgment result is negative from the updated supplier bidding data information set, to obtain a trusted supplier data information set.

[0022] The step of performing a first trustworthy evaluation on each supplier in the updated supplier bid data information set based on the supplier historical bid data information set to obtain a first trustworthy evaluation value for each supplier includes:

[0023] S2411, using the set of historical bidding technical indicator information of each supplier in the set of historical supplier bidding data information, construct a historical bidding matrix for each supplier; representing the set of bidding technical indicator information of each supplier in the updated set of supplier bidding data information as a bid vector of the supplier; the bid vector is a vector consisting of all the bidding technical indicator information of the supplier; the row vector of the historical bidding matrix is a vector consisting of all the bidding technical indicator information of the supplier in a bid;

[0024] S2412, performing deviation calculation on each supplier's historical bidding matrix and the corresponding supplier's bidding vector to obtain a first credible evaluation value of the supplier;

[0025] The step of performing deviation calculation on each supplier's historical bidding matrix and the corresponding supplier's bidding vector to obtain the first credible evaluation value of the supplier includes:

[0026] S24121, subtract each row vector of the supplier's historical bidding matrix from the corresponding supplier's bidding vector to obtain the corresponding difference vector;

[0027] S24122, constructing a difference matrix using the difference vector;

[0028] S24123, performing statistical processing on the difference matrix to obtain a statistical value set; the statistical value set includes the mean, variance, mode, and median of each row vector of the difference matrix;

[0029] S24124, calculating and obtaining the rank value and trace value of the difference matrix;

[0030] S24125, performing a first fusion calculation process on the statistical value set, the rank value, and the trace value to obtain a first trustworthy evaluation value of the supplier;

[0031] The expression of the first fusion calculation process is:

[0032]

[0033] Wherein, ke1 represents the first trustworthy evaluation value of the supplier, tra and ψ represent the trace value and rank value of the difference matrix respectively, N represents the column dimension of the difference matrix, μ j , σ j , ρ j and ν j are the mean, variance, median and mode of the j-th row vector of the difference matrix, and ρ0 is the mean of the median values of all row vectors of the difference matrix.

[0034] The matching process of the evaluation expert information set based on the trusted supplier data information set and the bidding project requirement information to obtain the selected evaluation expert information set includes:

[0035] S31, performing a first matching and screening process on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set;

[0036] S32: Using the pre-selected expert screening value set and the trusted supplier data information set, perform a second matching screening process on the pre-selected expert information set to obtain a selected review expert information set.

[0037] The first matching and screening process is performed on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set, including:

[0038] S311, based on the technical field information set in the bidding project requirement information, determining whether the technical field information of each review expert in the review expert information set is in the technical field information set, and obtaining a third determination result;

[0039] S312, deleting the information corresponding to the review experts whose third judgment result is negative from the review expert information set to obtain a pre-selected expert information set;

[0040] S313: Perform a first matching screening calculation process on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set.

[0041] The first matching screening calculation process is performed on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set, including:

[0042] S3131, representing the ability index information set of each review expert in the pre-selected expert information set as an ability index vector of the review expert; the ability index vector is a vector composed of all the ability index information of the ability index information set of the review expert;

[0043] S3132: construct a standard vector using all standard values of technical indicators in the bidding project requirement information; the standard vector is a vector composed of the standard values of all technical indicators;

[0044] S3133, performing matching screening calculation on the capability index vector and the standard vector to obtain the pre-selected expert screening value of the review expert;

[0045] S3134, using the pre-selected expert screening values of all review experts, construct a pre-selected expert screening value set;

[0046] The expression for the matching screening calculation is:

[0047]

[0048] Among them, nl i and bz i are the i-th item of the capability index vector and the standard vector respectively, nl0 and bz0 are the mean of the capability index vector and the mean of the standard vector respectively, ubz1 is the mean of the standard vector, N is the length of the capability index vector, and sx is the pre-selected expert screening value of the review expert.

[0049] According to a second aspect of an embodiment of the present invention, an information matching processing device for a bidding and procurement process is disclosed, the device comprising:

[0050] a memory storing executable program code;

[0051] a processor coupled to the memory;

[0052] The processor calls the executable program code stored in the memory to execute the information matching processing method oriented to the bidding and procurement process.

[0053] According to a third aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the information matching processing method for the bidding and procurement process.

[0054] According to a fourth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, which is used to implement the information matching processing method for the bidding and procurement process.

[0055] The beneficial effects of the present invention are:

[0056] The present invention can accurately screen out suppliers that meet qualification requirements and whose bidding data is credible by performing a credible assessment on supplier bid data information sets and tender project demand information, thereby obtaining a credible supplier data information set. In the process from S21 to S246, a preliminary screening is first performed by determining whether the qualification and capability values meet the standards, and then an in-depth data credibility assessment is performed using the supplier's historical bid data information set, greatly improving the credibility of the supplier information. This helps purchasers quickly identify truly capable and reputable suppliers, reduces project risks caused by inconsistent supplier qualifications or false data, and ensures the smooth implementation of the tender project.

[0057] The present invention matches the review expert information set based on the trusted supplier data information set and the bidding project requirement information, thereby obtaining a selected review expert information set that better meets the project requirements. This matching process fully considers the compatibility of the review expert's capability indicator information set and technical field information with the bidding project, making the review work more professional and accurate. Compared with traditional review expert selection methods, the present invention ensures that experts can leverage their professional advantages during the review process and conduct a comprehensive and objective evaluation of supplier bid data, thereby improving the fairness and scientific nature of the bidding results and providing strong support for selecting the most suitable suppliers for bidding projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0059] In order to better understand the content of the present invention, an embodiment is given here.

[0060] Figure 14 is an implementation flow chart of the method of the present invention.

[0061] In a first aspect, an embodiment of the present invention discloses an information matching processing method for a bidding and procurement process, comprising:

[0062] S1, obtaining a supplier bidding data information set, an evaluation expert information set, and bidding project requirement information; the supplier bidding data information set includes a qualification and capability value set and a bidding technical indicator information set of each supplier; the qualification and capability value set includes a plurality of qualification and capability values; the bidding technical indicator information set includes a plurality of bidding technical indicator information and bidding time; the evaluation expert information set includes a plurality of evaluation expert capability indicator information sets and technical field information; the bidding project requirement information includes the capability lower limit values of all qualifications, the standard values of all technical indicators, and a technical field information set; the technical field information set includes technical field information;

[0063] S2, performing a trustworthy evaluation process on the supplier bidding data information set and the bidding project demand information to obtain a trustworthy supplier data information set;

[0064] S3, based on the trusted supplier data information set and the bidding project requirement information, matching processing is performed on the review expert information set to obtain a selected review expert information set.

[0065] The trusted supplier data information set and the selected review expert information set are information matching processing results for the bidding and procurement process.

[0066] The trust evaluation process is performed on the supplier bidding data information set and the bidding project demand information to obtain a trustworthy supplier data information set, including:

[0067] S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determining whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, thereby obtaining a first identification result for the supplier;

[0068] S22, deleting information corresponding to suppliers for which the first judgment result is negative from the supplier bidding data information set, to obtain an updated supplier bidding data information set;

[0069] S23, obtaining a supplier's historical bidding data information set; the supplier's historical bidding data information set includes a set of historical bidding technical indicator information for each supplier; the set of historical bidding technical indicator information includes several pieces of historical bidding technical indicator information and corresponding bidding time information;

[0070] S24, performing a data credibility evaluation process on the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a trusted supplier data information set;

[0071] The step of performing data credibility evaluation on the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a trusted supplier data information set includes:

[0072] S241, performing a first credibility evaluation process on each supplier in the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a first credibility evaluation value of the supplier;

[0073] S242, constructing an indicator prediction model for each supplier based on the supplier's historical bidding data information set;

[0074] S243, performing a second credibility evaluation process on each supplier in the updated supplier bidding data information set based on the indicator prediction model of each supplier to obtain a second credibility evaluation value of the supplier;

[0075] S244, performing a weighted summation of the first credibility evaluation value and the second credibility evaluation value of each supplier to obtain a third credibility evaluation value of the supplier;

[0076] S245, determining whether the third credibility evaluation value of each supplier is greater than a preset credibility threshold, and obtaining a second discrimination result for each supplier;

[0077] S246, deleting the information corresponding to the supplier for which the second judgment result is negative from the updated supplier bidding data information set, to obtain a trusted supplier data information set.

[0078] The step of performing a first trustworthy evaluation on each supplier in the updated supplier bid data information set based on the supplier historical bid data information set to obtain a first trustworthy evaluation value for each supplier includes:

[0079] S2411, using the set of historical bidding technical indicator information of each supplier in the set of historical supplier bidding data information, construct a historical bidding matrix for each supplier; representing the set of bidding technical indicator information of each supplier in the updated set of supplier bidding data information as a bid vector of the supplier; the bid vector is a vector consisting of all the bidding technical indicator information of the supplier; the row vector of the historical bidding matrix is a vector consisting of all the bidding technical indicator information of the supplier in a bid;

[0080] S2412: Perform deviation calculation on the historical bidding matrix of each supplier and the corresponding supplier's bidding vector to obtain a first credible evaluation value of the supplier.

[0081] The step of performing deviation calculation on each supplier's historical bidding matrix and the corresponding supplier's bidding vector to obtain the first credible evaluation value of the supplier includes:

[0082] S24121, subtract each row vector of the supplier's historical bidding matrix from the corresponding supplier's bidding vector to obtain the corresponding difference vector;

[0083] S24122, constructing a difference matrix using the difference vector;

[0084] S24123, performing statistical processing on the difference matrix to obtain a statistical value set; the statistical value set includes the mean, variance, mode, and median of each row vector of the difference matrix;

[0085] S24124, calculating and obtaining the rank value and trace value of the difference matrix;

[0086] S24125: Perform a first fusion calculation on the statistical value set, rank value, and trace value to obtain a first trustworthy assessment value of the supplier.

[0087] The expression of the first fusion calculation process is:

[0088]

[0089] Wherein, ke1 represents the first trustworthy evaluation value of the supplier, tra and ψ represent the trace value and rank value of the difference matrix respectively, N represents the column dimension of the difference matrix, μ j , σ j , ρ j and v j are the mean, variance, median and mode of the j-th row vector of the difference matrix, and ρ0 is the mean of the median values of all row vectors of the difference matrix.

[0090] The expression processed by the first fusion calculation fully incorporates the various statistical characteristics of the difference matrix, including trace value, rank value, and the mean, variance, median, and mode of each row vector. The trace value reflects the sum of the elements on the main diagonal of the matrix and can reflect the overall trend of the data; the rank value indicates the degree of linear independence of the matrix and measures the structural stability of the data. By integrating this multi-dimensional information, it is possible to comprehensively characterize the differences between the supplier's current bid data and historical bid data, avoiding the one-sidedness caused by single-metric evaluation and more accurately assessing the credibility of the supplier's bid data.

[0091] Using exponential and sine functions to perform nonlinear transformations on data can amplify the impact of key differential features. When the mean-to-variance ratio is large, the exponential function's value increases significantly, indicating a high degree of data deviation in that dimension and assigning a higher weight in the credibility assessment. The sine function, on the other hand, flexibly adjusts weights based on the ratio of the median to the mode, further highlighting anomalies in the data distribution and making the assessment more sensitive to subtle changes in the data.

[0092] The median value is normalized using the geometric mean term to avoid significant interference from individual extreme values in the evaluation results, ensuring the stability of the evaluation results. Furthermore, the combination of the various components of the expression allows the calculation results to fluctuate within a reasonable range, facilitating horizontal comparisons of the first trustworthy evaluation values of different suppliers, thereby efficiently screening for trusted suppliers.

[0093] The method of constructing an indicator prediction model for each supplier based on the supplier's historical bidding data information set includes:

[0094] Using all the bidding historical technical indicator information of each supplier in the supplier's bidding historical technical indicator information set as a multivariate dependent variable, and using the bidding time information of the supplier's bidding historical technical indicator information set as an independent variable, performing multivariate polynomial fitting on the multivariate dependent variable and the independent variable to obtain an indicator prediction model for the supplier;

[0095] The multivariate polynomial fitting may adopt the best consistent linear approximation method.

[0096] The step of performing a second credibility evaluation on each supplier in the updated supplier bidding data information set based on the indicator prediction model for each supplier to obtain a second credibility evaluation value of the supplier includes:

[0097] Based on the indicator prediction model for each supplier, the bidding time of the corresponding supplier in the updated supplier bidding data information set is calculated and processed to obtain a predicted value of each bidding technical indicator information; the predicted value of each bidding technical indicator information is a multivariate dependent variable of the indicator prediction model;

[0098] Using the predicted value of each bidding technical indicator information, subtract the corresponding bidding technical indicator information of the corresponding supplier in the updated supplier bidding data information set to obtain a difference value of the corresponding bidding technical indicator information;

[0099] The difference values of all bidding technical indicator information are accumulated to obtain a second credible evaluation value of the supplier.

[0100] The matching process of the evaluation expert information set based on the trusted supplier data information set and the bidding project requirement information to obtain the selected evaluation expert information set includes:

[0101] S31, performing a first matching and screening process on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set;

[0102] S32: Using the pre-selected expert screening value set and the trusted supplier data information set, perform a second matching screening process on the pre-selected expert information set to obtain a selected review expert information set.

[0103] The first matching and screening process is performed on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set, including:

[0104] S311, based on the technical field information set in the bidding project requirement information, determining whether the technical field information of each review expert in the review expert information set is in the technical field information set, and obtaining a third determination result;

[0105] S312, deleting the information corresponding to the review experts whose third judgment result is negative from the review expert information set to obtain a pre-selected expert information set;

[0106] S313: Perform a first matching screening calculation process on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set.

[0107] The first matching screening calculation process is performed on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set, including:

[0108] S3131, representing the ability index information set of each review expert in the pre-selected expert information set as an ability index vector of the review expert; the ability index vector is a vector composed of all the ability index information of the ability index information set of the review expert;

[0109] S3132: construct a standard vector using all standard values of technical indicators in the bidding project requirement information; the standard vector is a vector composed of the standard values of all technical indicators;

[0110] S3133, performing matching screening calculation on the capability index vector and the standard vector to obtain the pre-selected expert screening value of the review expert;

[0111] S3134, using the pre-selected expert screening values of all review experts, construct a pre-selected expert screening value set.

[0112] The expression for the matching screening calculation is:

[0113]

[0114] Among them, nl i and bz i are the i-th item of the capability index vector and the standard vector respectively, nl0 and bz0 are the mean of the capability index vector and the mean of the standard vector respectively, ubz1 is the mean of the standard vector, N is the length of the capability index vector, and sx is the pre-selected expert screening value of the review expert.

[0115] The matching screening calculation expression uses logarithmic and exponential functions to perform a deep quantitative comparison between the evaluation expert's ability index vector and the standard vector of the bidding project requirements. The logarithmic function sensitively captures the proportional relationship between the difference in ability index. When the difference between the ability index and the standard value is small, its value approaches 0; the larger the difference, the more significant the change in value. The exponential function further intensifies the impact of the difference, especially the penalty for large discrepancies, thereby accurately quantifying the degree of match between the evaluation expert's ability and the bidding project requirements.

[0116] The matching screening calculation formula incorporates the capability indicator vector mean, the standard vector mean, and the standard vector mean. This not only considers the individual differences of each capability indicator, but also the average level of the vectors at the overall level. This approach prevents extreme differences in individual indicators from excessively impacting the overall matching assessment, while also highlighting the fit between the overall capability level and project requirements, making the matching of evaluation experts and bidding projects more scientific and reasonable.

[0117] The length of the capability indicator vector is the same as the column dimension of the difference matrix.

[0118] The method of performing a second matching and screening process on the pre-selected expert information set using the pre-selected expert screening value set and the trusted supplier data information set to obtain a selected review expert information set includes:

[0119] S321, representing the bid technical indicator information set of all suppliers in the trusted supplier data information set as a supply indicator matrix; the row vector of the supply indicator matrix is a vector consisting of all bid technical indicator information of a supplier;

[0120] S322, performing information matching calculation processing on the capability index vector, the supply index matrix, and the pre-selected expert screening value set to obtain information matching values of all review experts;

[0121] S323, sorting all the review experts according to their information matching values from large to small; selecting the review experts with the top ZJ values as the selected review expert information set;

[0122] The expression for the information matching calculation process is:

[0123]

[0124] Among them, xpp i is the information matching value of the i-th review expert, sx i is the pre-selected expert screening value of the i-th review expert, gy ki is the element in the kth row and ith column of the supply indicator matrix, K is the row dimension of the supply indicator matrix, β1 and β2 are the preset first weighted value and second weighted value respectively.

[0125] Among them, ZJ is the number of selected review experts.

[0126] The first judgment result is yes, which means that each qualification capability value is greater than the capability lower limit value of the corresponding qualification in the bidding project requirement information;

[0127] The first determination result is no, which means that there is at least one qualification capability value that is not greater than the capability lower limit value of the corresponding qualification in the bidding project requirement information;

[0128] This invention fully utilizes historical supplier bid data to enhance the accuracy of supplier bid data assessment. By constructing a historical bid matrix and bid vector, and performing a series of operations such as deviation calculation, the credibility of supplier bid data can be assessed from multiple dimensions. This historical data-based analysis method can uncover underlying patterns in supplier bidding behavior and determine the stability and reliability of their current bid data, further reducing risks in the bidding and procurement process and improving the quality and efficiency of the entire process.

[0129] According to a second aspect of an embodiment of the present invention, an information matching processing device for a bidding and procurement process is disclosed, the device comprising:

[0130] a memory storing executable program code;

[0131] a processor coupled to the memory;

[0132] The processor calls the executable program code stored in the memory to execute the information matching processing method oriented to the bidding and procurement process.

[0133] According to a third aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the information matching processing method for the bidding and procurement process.

[0134] According to a fourth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, which is used to implement the information matching processing method for the bidding and procurement process.

[0135] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. An information matching processing method for a bidding and procurement process, characterized in that: include: S1, obtain supplier bidding data information set, evaluation expert information set and bidding project demand information; The supplier bidding data information set includes a set of qualification and capability values and a set of bidding technical indicator information for each supplier; the qualification and capability value set includes a plurality of qualification and capability values; the bidding technical indicator information set includes a plurality of bidding technical indicator information and bidding time; the review expert information set includes a set of capability indicator information and technical field information for a plurality of review experts; the bidding project requirement information includes a set of capability lower limits for all qualifications, standard values for all technical indicators, and a set of technical field information; the technical field information set includes technical field information; S2, performing a trustworthy evaluation process on the supplier bidding data information set and the bidding project demand information to obtain a trustworthy supplier data information set; S3, based on the trusted supplier data information set and the bidding project requirement information, matching processing is performed on the review expert information set to obtain a selected review expert information set.

2. The information matching processing method for the bidding and procurement process according to claim 1, characterized in that: The trusted supplier data information set and the bidding project demand information are subjected to a trustworthy evaluation process to obtain a trusted supplier data information set, including: S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determining whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, thereby obtaining a first identification result for the supplier; S22, deleting information corresponding to suppliers for which the first judgment result is negative from the supplier bidding data information set, to obtain an updated supplier bidding data information set; S23, obtaining a supplier's historical bidding data information set; the supplier's historical bidding data information set includes a set of historical bidding technical indicator information for each supplier; the set of historical bidding technical indicator information includes several pieces of historical bidding technical indicator information and corresponding bidding time information; S24: Based on the supplier's historical bidding data information set, perform data credibility evaluation on the updated supplier's bidding data information set to obtain a trusted supplier data information set.

3. The information matching processing method for the bidding and procurement process according to claim 2, characterized in that: The step of performing data credibility evaluation on the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a trusted supplier data information set includes: S241, performing a first credibility evaluation process on each supplier in the updated supplier bidding data information set based on the supplier historical bidding data information set to obtain a first credibility evaluation value of the supplier; S242, constructing an indicator prediction model for each supplier based on the supplier's historical bidding data information set; S243, performing a second credibility evaluation process on each supplier in the updated supplier bidding data information set based on the indicator prediction model of each supplier to obtain a second credibility evaluation value of the supplier; S244, performing a weighted summation of the first credibility evaluation value and the second credibility evaluation value of each supplier to obtain a third credibility evaluation value of the supplier; S245, determining whether the third credibility evaluation value of each supplier is greater than a preset credibility threshold, and obtaining a second discrimination result for each supplier; S246, deleting the information corresponding to the supplier for which the second judgment result is negative from the updated supplier bidding data information set, to obtain a trusted supplier data information set.

4. The information matching processing method for the bidding and procurement process according to claim 3 is characterized in that: The step of performing a first trustworthy evaluation process on each supplier in the updated supplier bid data information set based on the supplier historical bid data information set to obtain a first trustworthy evaluation value for each supplier includes: S2411, using the set of historical bidding technical indicator information of each supplier in the set of historical supplier bidding data information, construct a historical bidding matrix for each supplier; representing the set of bidding technical indicator information of each supplier in the updated set of supplier bidding data information as a bid vector of the supplier; the bid vector is a vector consisting of all the bidding technical indicator information of the supplier; the row vector of the historical bidding matrix is a vector consisting of all the bidding technical indicator information of the supplier in a bid; S2412, performing deviation calculation on each supplier's historical bidding matrix and the corresponding supplier's bidding vector to obtain a first credible evaluation value of the supplier; The step of performing deviation calculation on each supplier's historical bidding matrix and the corresponding supplier's bidding vector to obtain the first credible evaluation value of the supplier includes: S24121, subtract each row vector of the supplier's historical bidding matrix from the corresponding supplier's bidding vector to obtain the corresponding difference vector; S24122, constructing a difference matrix using the difference vector; S24123, performing statistical processing on the difference matrix to obtain a statistical value set; the statistical value set includes the mean, variance, mode, and median of each row vector of the difference matrix; S24124, calculating and obtaining the rank value and trace value of the difference matrix; S24125, performing a first fusion calculation process on the statistical value set, the rank value, and the trace value to obtain a first trustworthy evaluation value of the supplier; The expression of the first fusion calculation process is: Wherein, ke1 represents the first trustworthy evaluation value of the supplier, tra and ψ represent the trace value and rank value of the difference matrix respectively, N represents the column dimension of the difference matrix, μ j , σ j , ρ j and ν j are the mean, variance, median and mode of the j-th row vector of the difference matrix, and ρ0 is the mean of the median values of all row vectors of the difference matrix.

5. The information matching processing method for bidding and procurement process according to claim 1, characterized in that: The matching process of the evaluation expert information set based on the trusted supplier data information set and the bidding project requirement information to obtain the selected evaluation expert information set includes: S31, performing a first matching and screening process on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set; S32: Using the pre-selected expert screening value set and the trusted supplier data information set, perform a second matching screening process on the pre-selected expert information set to obtain a selected review expert information set.

6. The information matching processing method for bidding and procurement process according to claim 1, characterized in that: The first matching and screening process is performed on the evaluation expert information set based on the bidding project requirement information to obtain a pre-selected expert information set and a pre-selected expert screening value set, including: S311, based on the technical field information set in the bidding project requirement information, determining whether the technical field information of each review expert in the review expert information set is in the technical field information set, and obtaining a third determination result; S312, deleting the information corresponding to the review experts whose third judgment result is negative from the review expert information set to obtain a pre-selected expert information set; S313: Perform a first matching screening calculation process on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set.

7. The information matching processing method for bidding and procurement process according to claim 6, characterized in that: The first matching screening calculation process is performed on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set, including: S3131, representing the ability index information set of each review expert in the pre-selected expert information set as an ability index vector of the review expert; the ability index vector is a vector composed of all the ability index information of the ability index information set of the review expert; S3132: construct a standard vector using all standard values of technical indicators in the bidding project requirement information; the standard vector is a vector composed of the standard values of all technical indicators; S3133, performing matching screening calculation on the capability index vector and the standard vector to obtain the pre-selected expert screening value of the review expert; S3134, using the pre-selected expert screening values of all review experts, construct a pre-selected expert screening value set; The expression for the matching screening calculation is: Among them, nl i and bz i are the i-th item of the capability index vector and the standard vector respectively, nl0 and bz0 are the mean of the capability index vector and the mean of the standard vector respectively, ubz1 is the mean of the standard vector, N is the length of the capability index vector, and sx is the pre-selected expert screening value of the review expert.

8. An information matching processing device for bidding and procurement process, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the information matching processing method for the bidding and procurement process according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the information matching processing method for the bidding and procurement process according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the information matching processing method for the bidding and procurement process according to any one of claims 1 to 7.

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