Main risk assessment method and device for scientific research purchase bidding

Abstract

The invention discloses a subject risk assessment method and device for scientific research purchase bidding. The method comprises the steps of obtaining an operation information set and a bidding information set of scientific research bidding subjects; the operation information set comprises a qualification capability value, credit investigation information, bank fund flow information, stock equity architecture change information, historical punishment frequency information and credit information; the bidding information set comprises bidding technical index information and bidding price information; preprocessing the operation information set and the bidding information set to obtain a preprocessed information set; and performing risk assessment processing on the pre-processing information set to obtain a risk assessment value of the scientific research bidding subject.

Description

Technical Field

[0001] The present invention relates to the fields of bidding and procurement technology and data processing, and in particular to a method and device for assessing subject risk in scientific research procurement bidding. Background Art

[0002] Currently, risk assessment methods in the field of scientific research procurement bidding generally have the problems of single technical solutions and one-sided assessment dimensions. Traditional methods usually rely on the price competitiveness of the bidding entity and the review of basic qualification documents, while ignoring the correlation analysis between its dynamic operating status and potential risk factors. For example, existing technologies often make judgments based on static credit ratings or historical bidding records, but fail to effectively integrate information such as bank cash flow and equity structure stability that reflect the company's real-time operating capabilities, resulting in deviations in the prediction of the bidding entity's performance ability. In addition, the correlation assessment between bidding technical indicators and price information often uses manual experience judgment or simple linear weighting, lacks a unified pre-processing mechanism for multi-source heterogeneous data, and is difficult to eliminate the interference of data noise or outliers on the evaluation results. These problems make it easy for bidding entities to miss or misjudge risks when identifying risks, and are unable to dynamically reflect the comprehensive risk level of the bidding entity, which in turn affects the execution efficiency and financial security of scientific research procurement projects. Summary of the Invention

[0003] The present invention mainly solves the problem of how to evaluate the comprehensive risk level of bidding entities in the field of scientific research procurement bidding. The present invention discloses a subject risk assessment method and device for scientific research procurement bidding.

[0004] In a first aspect, an embodiment of the present invention discloses a method for risk assessment of a subject in a scientific research procurement bidding, comprising:

[0005] S1, obtaining the operational information set and bidding information set of the scientific research bidding entity; the operational information set includes qualification and capability values, credit information, bank fund flow information, equity structure change information, historical penalty information, and credit information; the bidding information set includes bidding technical indicator information and bidding price information;

[0006] S2, preprocessing the operation information set and the bidding information set to obtain a preprocessed information set;

[0007] S3, performing risk assessment processing on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity.

[0008] The preprocessing of the operation information set and the bidding information set to obtain a preprocessed information set includes:

[0009] S21, performing category check processing on the operation information set and the bidding information set to obtain a first information set;

[0010] S22: Perform value discrimination processing on the first information set to obtain a preprocessing information set.

[0011] The risk assessment process is performed on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity, including:

[0012] S31, performing an operation risk assessment process on the operation information set in the pre-processed information set to obtain an operation risk assessment value;

[0013] S32, performing comprehensive risk assessment processing on the operational risk assessment value and the bidding information set to obtain a risk assessment value of the scientific research bidding entity.

[0014] The performing operation risk assessment processing on the operation information set in the pre-processed information set to obtain an operation risk assessment value includes:

[0015] S311, performing a positive evaluation process on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value;

[0016] S312, performing reverse evaluation processing on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value;

[0017] S313: Perform a fusion risk assessment calculation on the first assessment value and the second assessment value to obtain an operational risk assessment value.

[0018] The performing positive evaluation processing on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value includes:

[0019] S3111, performing a first qualification evaluation process on the qualification capability value and credit information in the operation information set in the pre-processed information set to obtain a credit qualification evaluation value;

[0020] S3112, performing a second qualification evaluation on the credit qualification evaluation value and credit information to obtain a first evaluation value.

[0021] The reverse evaluation process is performed on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value, including:

[0022] S3121, performing a first fluctuation estimation process on the bank fund flow information in the operation information set in the pre-processed information set to obtain a fund fluctuation value;

[0023] S3122, performing a second fluctuation estimation process on the equity structure change information in the operation information set in the pre-processed information set to obtain an equity fluctuation value;

[0024] S3123: Perform a first fusion weighted processing on the capital fluctuation value, equity fluctuation value and historical penalty number information to obtain a second evaluation value.

[0025] The calculation expression of the first fluctuation estimation process is:

[0026]

[0027] in, is the distribution probability of the outflow of bank funds in the i-th value interval, is the distribution probability of the inflow of bank funds in the i-th value interval, K1 and K2 are the number of value intervals of the outflow and inflow of bank funds in the flow information respectively, μ1 and μ2 are the preset weight values, and zjb is the fund fluctuation value;

[0028] The distribution probability of the outflow or inflow of bank funds flow information within the value interval is obtained by evenly segmenting the total value interval to obtain several value intervals, and then counting the number of data points of outflow or inflow within the value intervals;

[0029] The calculation expression of the second fluctuation estimation process is:

[0030]

[0031] Among them, p i is the distribution probability of the i-th value of the equity structure change information set, Y is the total number of equity structure change information values, and gqb is the equity fluctuation value; the distribution probability of each value in the value information set is obtained by statistically analyzing all equity structure change information;

[0032] The expression of the first fusion weighted processing is:

[0033]

[0034] Among them, zpg2 is the second evaluation value, cfn is the historical penalty number information, and t is the calculation independent variable.

[0035] In a second aspect of an embodiment of the present invention, a risk assessment device for a subject in a scientific research procurement bidding is disclosed, the device comprising:

[0036] a memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the subject risk assessment method of scientific research procurement bidding.

[0039] 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 subject risk assessment method for scientific research procurement bidding.

[0040] 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 subject risk assessment method of scientific research procurement bidding.

[0041] The beneficial effects of the present invention are:

[0042] The present invention effectively solves the problems of mixed data types and inconsistent dimensions in operation information and bidding information through a preprocessing process that combines category checking with value discrimination, provides standardized input for subsequent risk assessment, and avoids model failure due to data format errors or missing values.

[0043] The present invention constructs a two-way assessment system for operational risks: positive indicators such as qualifications, capabilities, and credit information are separated and evaluated from negative indicators such as capital fluctuations and equity changes. This not only strengthens the positive incentives for the core competitiveness of the enterprise, but also captures potential operational risks through negative indicators, solving the problem of distorted assessment results caused by a single scoring dimension in traditional methods.

[0044] This method uses a first-class fusion weighting process to nonlinearly weight capital fluctuation, equity fluctuation, and historical penalty counts. This method then combines forward assessment results with bidding technical indicators to construct a multidimensional risk assessment model. This method overcomes the limitations of traditional linear superposition and can adaptively adjust the weighting of different risk factors. For example, it automatically increases the weight of reverse assessments when the capital chain fluctuates abnormally, thereby more sensitively reflecting the real-time risk status of the bidder.

[0045] During the comprehensive risk assessment stage, the present invention incorporates bidding technical indicators and price information into the evaluation framework. By quantitatively analyzing the matching relationship between the technical compliance rate and the rationality of the quotation, it can effectively identify malicious low-price bidding or technical false bidding, providing dual guarantees for the technical feasibility and cost controllability of scientific research procurement projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] In order to better understand the content of the present invention, an embodiment is given here.

[0048] Figure 1 4 is an implementation flow chart of the method of the present invention.

[0049] In a first aspect, an embodiment of the present invention discloses a method for risk assessment of a subject in a scientific research procurement bidding, comprising:

[0050] S1, obtaining the operational information set and bidding information set of the scientific research bidding entity; the operational information set includes qualification and capability values, credit information, bank fund flow information, equity structure change information, historical penalty information, and credit information; the bidding information set includes bidding technical indicator information and bidding price information;

[0051] S2, preprocessing the operation information set and the bidding information set to obtain a preprocessed information set;

[0052] S3, performing risk assessment processing on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity.

[0053] The preprocessing of the operation information set and the bidding information set to obtain a preprocessed information set includes:

[0054] S21, performing category checking processing on the operation information set and the bidding information set to obtain a first information set;

[0055] S22: Perform value discrimination processing on the first information set to obtain a preprocessing information set.

[0056] The risk assessment process is performed on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity, including:

[0057] S31, performing an operation risk assessment process on the operation information set in the pre-processed information set to obtain an operation risk assessment value;

[0058] S32, performing comprehensive risk assessment processing on the operational risk assessment value and the bidding information set to obtain a risk assessment value of the scientific research bidding entity.

[0059] The performing operation risk assessment processing on the operation information set in the pre-processed information set to obtain an operation risk assessment value includes:

[0060] S311, performing a positive evaluation process on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value;

[0061] S312, performing reverse evaluation processing on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value;

[0062] S313: Perform a fusion risk assessment calculation on the first assessment value and the second assessment value to obtain an operational risk assessment value.

[0063] The performing positive evaluation processing on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value includes:

[0064] S3111, performing a first qualification evaluation process on the qualification capability value and credit information in the operation information set in the pre-processed information set to obtain a credit qualification evaluation value;

[0065] S3112, performing a second qualification evaluation on the credit qualification evaluation value and credit information to obtain a first evaluation value.

[0066] The expression for the first qualification evaluation process is:

[0067]

[0068] Among them, zzp is the credit qualification assessment value, is the qualification capability value of the jth qualification at the i-th collection moment, M1 and M2 are the total number of collection moments and the total number of qualification types, respectively. is the maximum value of the j-th qualification capability value at all acquisition moments, ε is the preset deviation value, is the mean of the qualification capability values ​​of the jth qualification at all collection moments.

[0069] The expression of the second qualification evaluation process is:

[0070]

[0071] Among them, zpg1 is the first evaluation value, xy i is the credit information at the i-th collection moment, and xy0 is the preset standard value of the credit information.

[0072] A type of qualification capability value corresponds to a qualification type;

[0073] The reverse evaluation process is performed on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value, including:

[0074] S3121, performing a first fluctuation estimation process on the bank fund flow information in the operation information set in the pre-processed information set to obtain a fund fluctuation value;

[0075] S3122, performing a second fluctuation estimation process on the equity structure change information in the operation information set in the pre-processed information set to obtain an equity fluctuation value;

[0076] S3123: Perform a first fusion weighted processing on the capital fluctuation value, equity fluctuation value and historical penalty number information to obtain a second evaluation value.

[0077] The calculation expression of the first fluctuation estimation process is:

[0078]

[0079] in, is the distribution probability of the outflow of bank funds in the i-th value interval, is the distribution probability of the inflow of bank funds in the i-th value interval, K1 and K2 are the number of value intervals of the outflow and inflow of bank funds in the flow information respectively, μ1 and μ2 are the preset weight values, and zjb is the fund fluctuation value;

[0080] The distribution probability of the outflow or inflow of bank funds flow information within the value interval is obtained by evenly segmenting the total value interval to obtain several value intervals, and then counting the number of outflow or inflow data within the value intervals.

[0081] The calculation expression of the first volatility estimation process divides the bank's cash flow into two dimensions, outflow and inflow, and calculates them separately, and dynamically adjusts the importance of the two through preset weights. In the scientific research procurement scenario, this design can effectively identify abnormal fund operations (such as concentrated inflows but dispersed outflows, which may indicate misappropriation of funds). The cash flow is divided into intervals and the distribution probability is calculated, and the original cash flow data is converted into probability density features. Its technical advantages include: eliminating scale differences: the absolute value differences of cash flow of enterprises of different sizes are standardized, making the evaluation fairer; highlighting fluctuation patterns: focusing on the distribution ratio of funds in each interval rather than the specific amount, can discover hidden regular fluctuations (such as large outflows on fixed dates every month).

[0082] The calculation expression of the second fluctuation estimation process is:

[0083]

[0084] Among them, p i is the distribution probability of the i-th value of the equity structure change information set, Y is the total number of equity structure change information values, and gqb is the equity fluctuation value. The distribution probability of each value in the value information set is obtained by statistically analyzing all equity structure change information;

[0085] The calculation expression of the second fluctuation estimation process introduces the combination number and exponential functions, mapping the probability distribution of equity changes into a nonlinear space. This design is particularly well-suited to capturing complex shifts in ownership structures: when minority shareholders suddenly increase or decrease their holdings, the sin and exp functions are more sensitive to such changes. Calculating the likelihood of different shareholder combinations by counting combinations can identify potential risks of control transfers (e.g., simultaneous changes in multiple minority shareholders may indicate behind-the-scenes collusion). While the formula does not explicitly include a time variable, it indirectly reflects the frequency and stability of equity changes by statistically analyzing the distribution probabilities of various equity states. In scientific research procurement, frequent equity changes may indicate strategic instability or financial pressure, and this formula can effectively quantify such risks.

[0086] The expression of the first fusion weighted processing is:

[0087]

[0088] Among them, zpg2 is the second evaluation value, cfn is the historical penalty number information, and t is the calculation independent variable.

[0089] The expression for the first fusion weighted process nonlinearly integrates capital and equity fluctuations through an integral operation. This design offers the following advantages: When both capital and equity fluctuations are high, the double integration results in an exponential increase in the final VaR, consistent with the reality that "multiple risks lead to a sharp increase in overall risk." Furthermore, the risk offset effect occurs when the fluctuations of the two are complementary (e.g., stable capital but frequent equity fluctuations), the integral operation may partially offset the VaR, which is more consistent with the logic of risk assessment. The introduction of a sinusoidal integral function imbues the impact of the number of historical penalties (cfn) on the final VaR with a cyclical nature. The cyclical variation of the sine integral creates a "decay-recovery" cycle in the impact of historical penalties, preventing a single penalty from excessively impacting long-term assessments.

[0090] The expression for the fusion risk assessment calculation process is:

[0091] yfe=D2(zpg1+zpg2),

[0092] Where D2() represents the second-order Weibull function, and yfe represents the operational risk assessment value.

[0093] The comprehensive risk assessment process of the operational risk assessment value and the bidding information set to obtain the risk assessment value of the scientific research bidding entity includes:

[0094] S321, performing bid risk calculation processing on the bid information set to obtain a bid risk value;

[0095] S322, performing a second fusion weighted processing on the bidding risk value and the operational risk assessment value to obtain a risk assessment value of the scientific research bidding entity;

[0096] The second fusion weighted processing is to use a preset weighting vector to perform weighted summation on the bidding risk value and the operational risk assessment value to obtain a risk assessment value of the scientific research bidding entity.

[0097] The preset weight vectors may be 0.35 and 0.15.

[0098] The step of performing bid risk calculation processing on the bid information set to obtain a bid risk value includes:

[0099] S3211, obtain the standard value of price and standard values ​​of various bidding technical indicators;

[0100] S3212: Subtract each piece of information in the bid information set from the corresponding standard value to obtain a difference value set; the difference value set includes the difference value of each bid technical indicator information and the difference value of the bid price information;

[0101] S3213, performing statistical calculation on the difference value to obtain a bid risk value;

[0102] The expression for the statistical calculation process is:

[0103]

[0104] Among them, TBF is the bid risk value, nzb is the total number of bid technical indicator information, θ i is the difference value of the i-th bidding technical indicator information, and prc is the difference value of the bidding price information.

[0105] The equity structure change information refers to the average time interval between changes in the equity structure.

[0106] The credit information is obtained based on the credit assessment report of the central bank.

[0107] The credit information is obtained by weighted summation of social credit, bidding credit, cross-regional credit, performance credit and cross-industry credit.

[0108] The scientific research bidding entity may be an enterprise unit, a legal person organization, etc.

[0109] The performing category checking on the operation information set and the bidding information set to obtain a first information set includes:

[0110] Using a preset category set, determining whether the data category of each data of each category of information in the operation information set and the bidding information set is consistent with the corresponding data category in the category set, deleting inconsistent data from the corresponding information set to obtain a first information set;

[0111] Each type of information in the operation information set and the bidding information set has a corresponding data category in the category set.

[0112] The performing value discrimination processing on the first information set to obtain a preprocessed information set includes:

[0113] For each type of information data in the first information set, determine whether its value is within the corresponding value range of the type of information, and delete the data that is not within the value range from the first information set to obtain a preprocessed information set.

[0114] The value range of each type of information is preset.

[0115] In a second aspect of an embodiment of the present invention, a risk assessment device for a subject in a scientific research procurement bidding is disclosed, the device comprising:

[0116] a memory storing executable program code;

[0117] a processor coupled to the memory;

[0118] The processor calls the executable program code stored in the memory to execute the subject risk assessment method of scientific research procurement bidding.

[0119] 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 subject risk assessment method for scientific research procurement bidding.

[0120] 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 subject risk assessment method of scientific research procurement bidding.

[0121] 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. A method for risk assessment of scientific research procurement bidding, characterized by: include: S1, obtaining the operational information set and bidding information set of the scientific research bidding entity; the operational information set includes qualification and capability values, credit information, bank fund flow information, equity structure change information, historical penalty number information, and credit information; The bidding information set includes bidding technical indicator information and bidding price information; S2, preprocessing the operation information set and the bidding information set to obtain a preprocessed information set; S3, performing risk assessment processing on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity.

2. The subject risk assessment method for scientific research procurement bidding according to claim 1 is characterized in that: The preprocessing of the operation information set and the bidding information set to obtain a preprocessed information set includes: S21, performing category check processing on the operation information set and the bidding information set to obtain a first information set; S22: Perform value discrimination processing on the first information set to obtain a preprocessing information set.

3. The subject risk assessment method for scientific research procurement bidding according to claim 1 is characterized in that: The risk assessment process is performed on the pre-processed information set to obtain a risk assessment value of the scientific research bidding entity, including: S31, performing an operation risk assessment process on the operation information set in the pre-processed information set to obtain an operation risk assessment value; S32, performing comprehensive risk assessment processing on the operational risk assessment value and the bidding information set to obtain a risk assessment value of the scientific research bidding entity.

4. The subject risk assessment method for scientific research procurement bidding according to claim 3 is characterized in that: The performing operation risk assessment processing on the operation information set in the pre-processed information set to obtain an operation risk assessment value includes: S311, performing a positive evaluation process on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value; S312, performing reverse evaluation processing on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value; S313: Perform a fusion risk assessment calculation on the first assessment value and the second assessment value to obtain an operational risk assessment value.

5. The subject risk assessment method for scientific research procurement bidding according to claim 4 is characterized in that: The performing positive evaluation processing on the qualification and capability values, credit information, and credit information in the operation information set in the pre-processed information set to obtain a first evaluation value includes: S3111, performing a first qualification evaluation process on the qualification capability value and credit information in the operation information set in the pre-processed information set to obtain a credit qualification evaluation value; S3112, performing a second qualification evaluation on the credit qualification evaluation value and credit information to obtain a first evaluation value.

6. The subject risk assessment method for scientific research procurement bidding according to claim 4 is characterized in that: The reverse evaluation process is performed on the bank fund flow information, equity structure change information, and historical penalty number information in the operation information set in the pre-processed information set to obtain a second evaluation value, including: S3121, performing a first fluctuation estimation process on the bank fund flow information in the operation information set in the pre-processed information set to obtain a fund fluctuation value; S3122, performing a second fluctuation estimation process on the equity structure change information in the operation information set in the pre-processed information set to obtain an equity fluctuation value; S3123: Perform a first fusion weighted processing on the capital fluctuation value, equity fluctuation value and historical penalty number information to obtain a second evaluation value.

7. The subject risk assessment method for scientific research procurement bidding according to claim 6 is characterized in that: The calculation expression of the first fluctuation estimation process is: in, is the distribution probability of the outflow of bank funds in the i-th value interval, is the distribution probability of the inflow of bank funds in the i-th value interval, K1 and K2 are the number of value intervals of the outflow and inflow of bank funds in the flow information respectively, μ1 and μ2 are the preset weight values, and zjb is the fund fluctuation value; The distribution probability of the outflow or inflow of bank funds flow information within the value interval is obtained by evenly segmenting the total value interval to obtain several value intervals, and then counting the number of data points of outflow or inflow within the value intervals; The calculation expression of the second fluctuation estimation process is: Among them, p i is the distribution probability of the i-th value of the equity structure change information set, Y is the total number of equity structure change information values, and gqb is the equity fluctuation value; the distribution probability of each value in the value information set is obtained by statistically analyzing all equity structure change information; The expression of the first fusion weighted processing is: Among them, zpg2 is the second evaluation value, cfn is the historical penalty number information, and t is the calculation independent variable.

8. A risk assessment device for scientific research procurement bidding, characterized by: 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 subject risk assessment method for scientific research procurement bidding as described in 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 subject risk assessment method for scientific research procurement bidding as described in 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 subject risk assessment method for scientific research procurement bidding as described in any one of claims 1 to 7.

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