An information matching and processing method and apparatus for bidding and procurement processes

Abstract

This invention discloses an information matching processing method and apparatus for the bidding and procurement process. The method includes: acquiring a set of supplier bidding data, a set of review expert information, and bidding project requirements information; the set of supplier bidding data includes a set of qualification and capability values ​​and a set of bidding technical indicators for each supplier; performing a credibility assessment on the set of supplier bidding data and the bidding project requirements information to obtain a set of credible supplier data; and performing matching processing on the set of review expert information based on the set of credible supplier data and the bidding project requirements information to obtain a set of selected review experts.

Description

Technical Field

[0001] This invention relates to the fields of data processing and bidding and procurement, and specifically to an information matching and processing method and apparatus for bidding and procurement processes. Background Technology

[0002] In traditional bidding and procurement processes, information matching faces numerous challenges. On the one hand, supplier bid data is often complex and potentially inaccurate, making it difficult to quickly and accurately determine whether a supplier truly possesses the qualifications and capabilities to meet the project's requirements. Furthermore, the lack of an effective screening mechanism allows substandard suppliers to infiltrate the bidding process when acquiring bid data. This not only increases the burden of subsequent review work but may also hinder the smooth progress of the bidding project. For example, some suppliers may exaggerate their qualifications and provide false technical specifications, making it difficult for the procuring entity to distinguish truth from falsehood from massive amounts of data.

[0003] On the other hand, the selection of review experts also has its flaws. Accurately matching experts with suitable bidding projects from the available expert information has always been a challenge. Traditional matching methods often only consider the expert's technical field information, neglecting the deeper correlation between the expert's competency indicators and the requirements of the bidding project. This can easily lead to situations where the selected experts cannot conduct a comprehensive and accurate review of the supplier's bid data, affecting the fairness and scientific rigor of the bidding results. For example, in some complex technical bidding projects, a mismatch between the selected expert's competency indicators and the project requirements may result in biased reviews of the supplier's technical solutions, ultimately impacting the final quality of the project.

[0004] Furthermore, the lack of effective utilization of suppliers' historical bidding data leaves insufficient evidence for assessing the credibility of current bidding data. Previously, each bidding instance was often viewed in isolation, failing to uncover potential information from the supplier's historical bidding behavior. This hinders the accurate evaluation of the stability and reliability of supplier bidding data, which to some extent increases the risks associated with the bidding and procurement process. Summary of the Invention

[0005] This invention primarily addresses the issues of how to accurately and reliably assess bidder information and how to accurately select evaluation experts. This invention discloses an information matching and processing method and apparatus for the bidding and procurement process.

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

[0007] S1, acquire the supplier bidding data information set, the review expert information set, and the bidding project requirements 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 several qualification and capability values; the bidding technical indicator information set includes several bidding technical indicator information and the bidding time; the review expert information set includes a set of capability indicator information and technical field information for several review experts; the bidding project requirements information includes the capability minimum values ​​of all qualifications, the standard values ​​of all technical indicators, and a set of technical field information; the technical field information set includes technical field information.

[0008] S2, perform a credibility assessment on the supplier bidding data information set and the bidding project requirement information to obtain a credibility supplier data information set;

[0009] S3. Based on the set of trusted supplier data and the bidding project requirements, the set of review expert information is matched to obtain the set of selected review experts.

[0010] The process of performing a credibility assessment on the supplier bidding data information set and the bidding project requirement information to obtain a credibility supplier data information set includes:

[0011] S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determine whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, and obtain the first discrimination result of the supplier;

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

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

[0014] S24. Based on the historical bidding data set of suppliers, perform data credibility assessment on the updated supplier bidding data set to obtain a credible supplier data set.

[0015] The step of performing a data reliability assessment on the updated supplier bidding data set based on the supplier's historical bidding data set to obtain a reliable supplier data set includes:

[0016] S241, based on the supplier's historical bidding data information set, perform a first credibility assessment process on each supplier in the updated supplier bidding data information set to obtain the supplier's first credibility assessment value;

[0017] S242, Based on the set of historical bidding data of the suppliers, construct an indicator prediction model for each supplier;

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

[0019] S244, the first and second confidence assessment values ​​of each supplier are weighted and summed to obtain the third confidence assessment value of the supplier;

[0020] S245, determine whether the third credibility assessment value of each supplier is greater than the preset credibility threshold, and obtain the second discrimination result of each supplier;

[0021] S246, the information corresponding to the suppliers whose second judgment result is negative is deleted from the updated supplier bidding data information set to obtain a reliable supplier data information set.

[0022] The first credibility assessment process, based on the historical bidding data set of suppliers, is performed on each supplier in the updated supplier bidding data set to obtain a first credibility assessment value for each supplier, including:

[0023] S2411, using the historical bidding technical indicator information set of each supplier in the historical bidding data information set, a historical bidding matrix for each supplier is constructed; the bidding technical indicator information set of each supplier in the updated supplier bidding data information set is represented as the supplier's bidding vector; the bidding vector is a vector composed of all the supplier's bidding technical indicator information; the row vector of the historical bidding matrix is ​​a vector composed of all the supplier's bidding technical indicator information in a single bidding process;

[0024] S2412, For each supplier's historical bidding matrix, the deviation from the corresponding supplier's bidding vector is calculated to obtain the first credible evaluation value of the supplier;

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

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

[0027] S24122, Using the aforementioned difference vector, a difference matrix is ​​constructed;

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

[0029] S24124, calculate the rank and trace of the difference matrix;

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

[0031] The expression for the first fusion calculation is:

[0032]

[0033] Where ke1 represents the supplier's first confidence assessment value, tra and ψ represent the trace and rank of the difference matrix, respectively, N represents the column dimension of the difference matrix, and μ j σ j ρ j and v j ρj represents the mean, variance, median, and mode of the j-th row vector of the difference matrix, respectively, and ρ0 represents the mean of the medians of all row vectors of the difference matrix.

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

[0035] S31, based on the bidding project requirements information, perform a first matching and filtering process on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values;

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

[0037] Based on the bidding project requirements information, the first matching and filtering process is performed on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values, including:

[0038] S311, Based on the set of technical field information in the bidding project requirements information, determine whether the technical field information of each review expert in the set of review expert information is in the set of technical field information to obtain a third discrimination result;

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

[0040] S313, perform a first matching and filtering calculation 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 and filtering calculation process of the pre-selected expert information set and the bidding project requirement information to obtain the pre-selected expert screening value set includes:

[0042] S3131, for each set of competency indicator information of the pre-selected expert information set, it is represented as the competency indicator vector of the review expert; the competency indicator vector is a vector composed of all competency indicator information of the competency indicator information set of the review expert.

[0043] S3132, using all the standard values ​​of technical indicators in the bidding project requirements information, a standard vector is constructed; the standard vector is a vector composed of all the standard values ​​of technical indicators.

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

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

[0046] The expression for calculating the matching filter is:

[0047]

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

[0049] A second aspect of the present invention discloses an information matching and processing device for a bidding and procurement process, the device comprising:

[0050] Memory containing 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 for the bidding and procurement process.

[0053] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are invoked by a computer, they are used to execute the information matching processing method for the bidding and procurement process.

[0054] In a fourth aspect of this 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 this invention are as follows:

[0056] This invention, through a credibility assessment of supplier bidding data and project requirements, accurately identifies suppliers who meet the qualification requirements and whose bidding data is reliable, resulting in a credible supplier data set. In steps S21 to S246, initial screening is performed by determining whether the supplier's qualification and capability values ​​meet the standards. Then, a deeper data credibility assessment is conducted using the supplier's historical bidding data, significantly improving the credibility of the supplier information. This helps the procuring entity quickly identify truly capable and reputable suppliers, reducing project risks caused by unqualified suppliers or falsified data, and ensuring the smooth progress of the bidding project.

[0057] This invention matches a set of review expert information with a set of trusted supplier data and bidding project requirements, resulting in a more suitable set of selected review experts. This matching process fully considers the compatibility of the review experts' competency indicators and technical expertise with the bidding project, making the review process more professional and accurate. Compared to traditional expert selection methods, this invention ensures that experts leverage their professional expertise to provide a comprehensive and objective evaluation of supplier bids, thereby improving the fairness and scientific rigor of the bidding results and providing strong support for selecting the most suitable suppliers for bidding projects. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0059] To better understand the content of this invention, an embodiment is provided here.

[0060] Figure 1This is a flowchart illustrating the implementation of the method of the present invention.

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

[0062] S1, acquire the supplier bidding data information set, the review expert information set, and the bidding project requirements 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 several qualification and capability values; the bidding technical indicator information set includes several bidding technical indicator information and the bidding time; the review expert information set includes a set of capability indicator information and technical field information for several review experts; the bidding project requirements information includes the capability minimum values ​​of all qualifications, the standard values ​​of all technical indicators, and a set of technical field information; the technical field information set includes technical field information.

[0063] S2, perform a credibility assessment on the supplier bidding data information set and the bidding project requirement information to obtain a credibility supplier data information set;

[0064] S3. Based on the set of trusted supplier data and the bidding project requirements, the set of review expert information is matched to obtain the set of selected review experts.

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

[0066] The process of performing a credibility assessment on the supplier bidding data information set and the bidding project requirement information to obtain a credibility supplier data information set includes:

[0067] S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determine whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, and obtain the first discrimination result of the supplier;

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

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

[0070] S24, Based on the historical bidding data information set of suppliers, perform data credibility assessment processing on the updated supplier bidding data information set to obtain a credible supplier data information set;

[0071] The step of performing a data reliability assessment on the updated supplier bidding data set based on the supplier's historical bidding data set to obtain a reliable supplier data set includes:

[0072] S241, based on the supplier's historical bidding data information set, perform a first credibility assessment process on each supplier in the updated supplier bidding data information set to obtain the supplier's first credibility assessment value;

[0073] S242, Based on the set of historical bidding data of the suppliers, construct an indicator prediction model for each supplier;

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

[0075] S244, the first and second confidence assessment values ​​of each supplier are weighted and summed to obtain the third confidence assessment value of the supplier;

[0076] S245, determine whether the third credibility assessment value of each supplier is greater than the preset credibility threshold, and obtain the second discrimination result of each supplier;

[0077] S246, the information corresponding to the suppliers whose second judgment result is negative is deleted from the updated supplier bidding data information set to obtain a reliable supplier data information set.

[0078] The first credibility assessment process, based on the historical bidding data set of suppliers, is performed on each supplier in the updated supplier bidding data set to obtain a first credibility assessment value for each supplier, including:

[0079] S2411, using the historical bidding technical indicator information set of each supplier in the historical bidding data information set, a historical bidding matrix for each supplier is constructed; the bidding technical indicator information set of each supplier in the updated supplier bidding data information set is represented as the supplier's bidding vector; the bidding vector is a vector composed of all the supplier's bidding technical indicator information; the row vector of the historical bidding matrix is ​​a vector composed of all the supplier's bidding technical indicator information in a single bidding process;

[0080] S2412, For each supplier's historical bidding matrix, the deviation from the corresponding supplier's bidding vector is calculated to obtain the first credible evaluation value of the supplier.

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

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

[0083] S24122, Using the aforementioned difference vector, a difference matrix is ​​constructed;

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

[0085] S24124, calculate the rank and trace of the difference matrix;

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

[0087] The expression for the first fusion calculation is:

[0088]

[0089] Where ke1 represents the supplier's first confidence assessment value, tra and ψ represent the trace and rank of the difference matrix, respectively, N represents the column dimension of the difference matrix, and μ j σ j ρ j and v j ρj represents the mean, variance, median, and mode of the j-th row vector of the difference matrix, respectively, and ρ0 represents the mean of the medians of all row vectors of the difference matrix.

[0090] The expression in the first fusion calculation fully integrates various statistical features of the difference matrix, including the trace, rank, and the mean, variance, median, and mode of each row vector. The trace reflects the sum of the elements on the main diagonal of the matrix and can reflect the overall trend of the data; the rank represents the degree of linear independence of the matrix and can measure the structural stability of the data. By integrating this multi-dimensional information, the differences between the supplier's current bidding data and historical bidding data can be comprehensively characterized, avoiding the one-sidedness caused by evaluation based on a single indicator, and more accurately assessing the credibility of the supplier's bidding data.

[0091] Using exponential and sine functions to perform nonlinear transformations on data can amplify the impact of key discrepancies. When the ratio of mean to variance is large, the value of the exponential function increases significantly, indicating a large deviation in data along that dimension, and thus assigning it higher weight in credibility assessments. The sine function, on the other hand, can flexibly adjust its weight based on the ratio of the median to the mode, further highlighting anomalies in the data distribution and making the assessment results more sensitive to subtle changes in the data.

[0092] By using a geometric mean term to normalize the median value, excessive interference from individual extreme values ​​can be avoided in the evaluation results, ensuring their stability. Furthermore, the combination of components in the expression allows the calculation results to fluctuate within a reasonable range, facilitating horizontal comparisons of the first-confidence evaluation values ​​of different suppliers and thus efficiently selecting reliable suppliers.

[0093] The step of constructing a predictive model for each supplier's metrics based on the set of historical bidding data includes:

[0094] Using all the historical technical indicators of each supplier in the historical bidding data set as the multivariate dependent variable and the bidding time information of the historical technical indicators of each supplier as the independent variable, a multivariate multinomial fitting is performed on the multivariate dependent variable and the independent variable to obtain the indicator prediction model of the supplier.

[0095] The multivariate polynomial fitting can be performed using the best uniform linear approximation method.

[0096] The indicator prediction model based on each supplier performs a second credibility assessment on each supplier in the updated supplier bidding data information set to obtain a second credibility assessment value for the supplier, including:

[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 the 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] The predicted value of each bidding technical indicator is subtracted from the corresponding bidding technical indicator of the corresponding supplier in the updated supplier bidding data information set to obtain the difference value of the corresponding bidding technical indicator.

[0099] The differences in all the bid technical specifications are summed to obtain the supplier's second credible evaluation value.

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

[0101] S31, based on the bidding project requirements information, perform a first matching and filtering process on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values;

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

[0103] Based on the bidding project requirements information, the first matching and filtering process is performed on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values, including:

[0104] S311, Based on the set of technical field information in the bidding project requirements information, determine whether the technical field information of each review expert in the set of review expert information is in the set of technical field information to obtain a third discrimination result;

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

[0106] S313, perform a first matching and filtering calculation 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 and filtering calculation process of the pre-selected expert information set and the bidding project requirement information to obtain the pre-selected expert screening value set includes:

[0108] S3131, for each set of competency indicator information of the pre-selected expert information set, it is represented as the competency indicator vector of the review expert; the competency indicator vector is a vector composed of all competency indicator information of the competency indicator information set of the review expert.

[0109] S3132, using all the standard values ​​of technical indicators in the bidding project requirements information, a standard vector is constructed; the standard vector is a vector composed of all the standard values ​​of technical indicators.

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

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

[0112] The expression for calculating the matching filter is:

[0113]

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

[0115] The matching and screening calculation expression uses logarithmic and exponential functions to perform a deep quantitative comparison between the capability indicator vector of the review experts and the standard vector of the bidding project requirements. The logarithmic function can sensitively capture the proportional relationship of the capability indicator differences; when the difference between the capability indicator and the standard value is small, its value approaches 0; the larger the difference, the more obvious the change in value. The exponential function further strengthens the impact of the differences, especially the penalty for larger differences, thereby accurately quantifying the degree of matching between the review experts' capabilities and the bidding project requirements.

[0116] The expression used for matching and screening calculations incorporates the mean of the capability indicator vector, the mean of the standard vector, and the mean of the standard vector. This approach not only considers the individual differences of each capability indicator but also the overall average level of the vectors. By doing so, it avoids the excessive influence of extreme differences in individual indicators on the overall matching assessment, while also highlighting the fit between the overall capability level and project requirements, making the matching of review experts with bidding projects more scientific and reasonable.

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

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

[0119] S321, the set of bidding technical indicator information of all suppliers in the trusted supplier data information set is represented as a supply indicator matrix; the row vector of the supply indicator matrix is ​​a vector composed of all bidding technical indicator information of a supplier.

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

[0121] S323, Sort all review experts according to their information matching values ​​from largest to smallest; Filter out the top ZJ review experts by value ranking to form the selected review expert information set;

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

[0123]

[0124] Among them, xpp i Let sx be the information matching value for the i-th review expert. i Let gy be the pre-selected expert screening value for the i-th review expert. ki β1 and β2 are the elements in the k-th row and i-th column of the supply index matrix, where K is the row dimension of the supply index matrix, and β1 and β2 are the preset first weighting value and second weighting value, respectively.

[0125] ZJ represents the number of selected review experts.

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

[0127] The first judgment result is negative, which means that there is at least one qualification capability value that is not greater than the corresponding qualification capability lower limit value in the bidding project requirement information;

[0128] This invention fully utilizes historical supplier bidding data sets, enhancing the accuracy of supplier bidding data evaluation. By constructing historical bidding matrices and bidding vectors, and performing deviation calculations and other operations, the credibility of supplier bidding data can be assessed from multiple dimensions. This historical data-based analysis method can uncover potential patterns in supplier bidding behavior, determine the stability and reliability of their current bidding data, further reduce risks in the bidding and procurement process, and improve the quality and efficiency of the entire bidding and procurement process.

[0129] A second aspect of the present invention discloses an information matching and processing device for a bidding and procurement process, the device comprising:

[0130] Memory containing 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 for the bidding and procurement process.

[0133] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are invoked by a computer, they are used to execute the information matching processing method for the bidding and procurement process.

[0134] In a fourth aspect of this 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 above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. An information matching and processing method for a bidding and procurement process, characterized in that, include: S1, obtain the supplier bidding data set, the review expert information set, and the bidding project requirements 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 several qualification and capability values; the bidding technical indicator information set includes several bidding technical indicator information and the bidding time; the review expert information set includes several sets of review expert capability indicator information and technical field information; the bidding project requirement information includes the capability minimum values ​​of all qualifications, the standard values ​​of all technical indicators, and a set of technical field information; the technical field information set includes technical field information. S2, perform a reliability assessment on the supplier bidding data information set and the bidding project requirement information to obtain a reliable supplier data information set, including: S21, for each supplier's qualification and capability value set in the supplier bidding data information set, determine whether each qualification and capability value is greater than the corresponding qualification and capability lower limit value in the bidding project requirement information, and obtain the first discrimination result of the supplier; S22, delete the information corresponding to the suppliers whose first judgment result is negative from the supplier bidding data information set to obtain an updated supplier bidding data information set; S23, Obtain the supplier's historical bidding data information set; the supplier's historical bidding data information set includes the bidding historical technical indicator information set for each supplier; the bidding historical technical indicator information set includes several historical bidding technical indicator information and corresponding bidding time information; S24, based on the aforementioned historical supplier bidding data information set, perform data reliability assessment processing on the updated supplier bidding data information set to obtain a reliable supplier data information set, including: S241, based on the historical bidding data set of suppliers, perform a first credibility assessment on each supplier in the updated supplier bidding data set to obtain a first credibility assessment value for the supplier, including: S2411, using the historical bidding technical indicator information set of each supplier in the historical bidding data information set, a historical bidding matrix for each supplier is constructed; the bidding technical indicator information set of each supplier in the updated supplier bidding data information set is represented as the supplier's bidding vector; the bidding vector is a vector composed of all the supplier's bidding technical indicator information; the row vector of the historical bidding matrix is ​​a vector composed of all the supplier's bidding technical indicator information in a single bidding process; S2412, For each supplier's historical bidding matrix, the deviation from the corresponding supplier's bidding vector is calculated to obtain the first credible evaluation value of the supplier; The step of calculating the deviation between the historical bidding matrix of each supplier and the corresponding supplier's bidding vector to obtain the first credible evaluation value of the supplier includes: S24121, for each row vector of the supplier's historical bidding matrix, subtract the corresponding supplier's bidding vector to obtain the corresponding difference vector; S24122, Using the aforementioned difference vector, a difference matrix is ​​constructed; S24123, Perform statistical processing on the difference matrix to obtain a set of statistical values; the set of statistical values ​​includes the mean, variance, mode, and median of each row vector of the difference matrix; S24124, calculate the rank and trace of the difference matrix; S24125, Perform a first fusion calculation on the statistical value set, rank value and trace value to obtain the first reliable evaluation value of the supplier; The expression for the first fusion calculation is: Where ke1 represents the supplier's first confidence assessment value, tra and ψ represent the trace and rank of the difference matrix, respectively, N represents the column dimension of the difference matrix, and μ j σ j ρ j and v j ρj represents the mean, variance, median, and mode of the j-th row vector of the difference matrix, respectively, and ρ0 represents the mean of the median values ​​of all row vectors of the difference matrix. S242, Based on the set of historical bidding data of the suppliers, construct an indicator prediction model for each supplier; S243, based on the indicator prediction model for each supplier, perform a second credible assessment process on each supplier in the updated supplier bidding data information set to obtain the second credible assessment value of the supplier, including: 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 the 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. The predicted value of each bidding technical indicator is subtracted from the corresponding bidding technical indicator of the corresponding supplier in the updated supplier bidding data information set to obtain the difference value of the corresponding bidding technical indicator. The differences in all the bid technical specifications are summed to obtain the supplier's second credible evaluation value. S244, the first and second confidence assessment values ​​of each supplier are weighted and summed to obtain the third confidence assessment value of the supplier; S245, determine whether the third credibility assessment value of each supplier is greater than the preset credibility threshold, and obtain the second discrimination result of each supplier; S246, the information corresponding to the suppliers whose second judgment result is negative is deleted from the updated supplier bidding data information set to obtain a reliable supplier data information set; S3. Based on the set of trusted supplier data and the bidding project requirements, the set of review expert information is matched to obtain the set of selected review experts.

2. The information matching and processing method for the bidding and procurement process as described in claim 1, characterized in that, The process of matching the set of review expert information based on the set of trusted supplier data and the bidding project requirements to obtain the set of selected review expert information includes: S31, based on the bidding project requirements information, perform a first matching and filtering process on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values; S32, using the pre-selected expert screening value set and the trusted supplier data information set, perform a second matching and screening process on the pre-selected expert information set to obtain the selected review expert information set.

3. The information matching and processing method for the bidding and procurement process as described in claim 2, characterized in that, Based on the bidding project requirements information, the first matching and filtering process is performed on the set of review experts to obtain a set of pre-selected expert information and a set of pre-selected expert screening values, including: S311, Based on the set of technical field information in the bidding project requirements information, determine whether the technical field information of each review expert in the review expert information set is in the set of technical field information to obtain a third discrimination result; S312, delete the information corresponding to the review experts whose third judgment result is negative from the review expert information set to obtain the pre-selected expert information set; S313, perform a first matching and filtering calculation on the pre-selected expert information set and the bidding project requirement information to obtain a pre-selected expert screening value set.

4. The information matching and processing method for the bidding and procurement process as described in claim 3, characterized in that, The first matching and filtering calculation process of the pre-selected expert information set and the bidding project requirement information to obtain the pre-selected expert screening value set includes: S3131, for each set of competency indicator information of the pre-selected expert information set, it is represented as the competency indicator vector of the review expert; the competency indicator vector is a vector composed of all competency indicator information of the competency indicator information set of the review expert. S3132, using all the standard values ​​of technical indicators in the bidding project requirements information, a standard vector is constructed; the standard vector is a vector composed of all the standard values ​​of technical indicators. S3133, perform matching and filtering calculations on the capability index vector and the standard vector to obtain the pre-selected expert screening value of the review experts; S3134, using the pre-selected expert screening values ​​of all review experts, construct a set of pre-selected expert screening values; The expression for calculating the matching filter is: Among them, nl i and bz i Let be the i-th term of the capability index vector and the standard vector, respectively; nl0 and bz0 be the mean of the capability index vector and the mean of the standard vector, respectively; ubz1 be the mean of the standard vector; N be the length of the capability index vector; and sx be the pre-selected expert screening value of the review experts.

5. An information matching and processing device for a bidding and procurement process, characterized in that, The device includes: Memory containing 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 as described in any one of claims 1 to 4.

6. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the information matching processing method for the bidding and procurement process as described in any one of claims 1 to 4.

7. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the information matching and processing method for the bidding and procurement process as described in any one of claims 1 to 4.

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