Engineering project bid inviting and tendering whole-process intelligent supervision system

Abstract

An engineering project bid invitation and bidding full-process intelligent supervision system relates to the technical field of engineering project management and comprises a project bid invitation data center which is in communication link with a client, a pre-bid supervision module, an intelligent feature analysis module, an in-bid supervision module and a full-process supervision module. The pre-bidding supervision module is used for performing pre-bidding multi-dimensional comparison and generating a pre-bidding scoring coefficient of a project bidder; the intelligent feature analysis module is used for acquiring weight coefficients of various data indexes of a project bidder for the associated feature factors; the in-bidding supervision module constructs an incidence relation graph, performs transverse feature comparison on bidding file data of each project bidder, performs cross-bidding behavior analysis on a plurality of historical project bidding data of each project bidder, and performs secondary judgment on a cross-bidding suspected object; and the whole-process supervision module monitors the pre-bidding scoring coefficient of each project bidder in real time, so that the efficiency and effect of bidding process supervision are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering project management, and in particular to an intelligent supervision system for the entire process of bidding and tendering for an engineering project. Background Art

[0002] A Chinese patent application with publication number CN114511162A discloses a method and system for assessing the risk of bidding for an engineering project, comprising the following steps: conducting a bidding risk analysis on the engineering project to be bid, establishing a game tree model based on the game logic relationship between the project integrator and the project contractor, obtaining the theoretical compliance probability of a qualified project contractor whose expected project revenue can meet the expected revenue demand of the project integrator based on the project stakeholder data determined by the game tree model, obtaining the actual existence probability of an actual qualified project contractor based on the Delphi method, and determining whether the qualified project contractor has a bidding risk based on the comparison result between the theoretical compliance probability and the actual existence probability.

[0003] There are many drawbacks in the bidding activities of the above-mentioned engineering projects. On the one hand, the supervision process relies on a large number of manual operations, which is inefficient and prone to omissions. For example, the review of the bidding documents needs to be manually checked word by word, and it is difficult to quickly discover the hidden restrictions on fair competition or illegal and irregular content. On the other hand, faced with massive transaction data and complex transaction relationships, regulatory measures cannot analyze potential risks in a timely and accurate manner, resulting in illegal and irregular behaviors such as bid rigging and collusion, which seriously undermine the market order. In addition, there is a serious phenomenon of information islands between the competent departments of different industries, and there is a lack of effective data sharing and collaborative supervision mechanisms, which leads to many loopholes in supervision. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent supervision system for the whole process of bidding and tendering for engineering projects, including a project bidding data center, wherein the project bidding data center is connected in communication with a client, a pre-bid supervision module, an intelligent feature analysis module, a bidding supervision module and a full-process supervision module; The client has two roles: a project tenderer and a project bidder. The project tenderer is used to upload project tender information to the project tender data center, and the project bidder is used to send an access request to the project tenderer through the project tender data center. The pre-bid supervision module is used to compare the project bidders and the project tenderers in multiple dimensions before bidding, and generate the pre-bid scoring coefficient of the project bidders based on the comparison results; The intelligent feature analysis module is used to perform intelligent feature analysis on several historical bid winning records during the multi-dimensional comparison process before bidding, and obtain the weight coefficients of various data indicators of the project bidders for the associated feature factors; The bidding supervision module is used to build a correlation map, compare the horizontal characteristics of the bidding document data of each project bidder, analyze the bidding collusion behavior of several historical project bidding data of each project bidder, and conduct a secondary judgment on the suspected collusion objects; The full-process supervision module is used to monitor the pre-bid scoring coefficients of each project bidder in real time and generate bid warning signals based on the monitoring results.

[0005] Furthermore, the pre-bid supervision module makes a pre-bid multi-dimensional comparison between the project bidder and the project tenderer, and generates a pre-bid scoring coefficient for the project bidder based on the comparison result, including: The project bidder logs into the project bidding data center by inputting the identity ID and password, and obtains the multi-dimensional data of the project bidder according to the identity ID through the project bidding data center, wherein the multi-dimensional data includes basic data, performance data and credit data, and inputs the identity ID of the project bidder that the project bidder needs to access into the project bidding data center according to the access request, obtains the project bidding information of the project bidder through the project bidding data center, extracts project features from the project bidding information, and obtains project bidding features; Extract various data indicators from the basic data, performance data and credit data of the project bidder as evaluation indicators, set the indicator weights of the evaluation indicators according to the project bidding characteristics, specifically extract various characteristic factors from the project bidding characteristics, obtain the data indicators of the basic data, performance data and credit data associated with each characteristic factor, obtain the weight coefficient of each data indicator for the associated characteristic factor, and obtain the membership matrix of the project bidder for the preset pre-bid scoring coefficient through fuzzy comprehensive evaluation; The pre-bid scoring coefficient of the project bidder is obtained according to the membership matrix and the indicator weight, and the pre-bid scoring coefficient of the project bidder is compared with the preset pre-bid scoring coefficient threshold. If the pre-bid scoring coefficient is less than the pre-bid scoring coefficient threshold, the access request of the project bidder is rejected. If the pre-bid scoring coefficient is greater than or equal to the pre-bid scoring coefficient threshold, the project bidding information of the project tenderer is sent to the project bidder.

[0006] Furthermore, the process of obtaining the pre-bid scoring coefficient of the project bidder according to the membership matrix and the index weight includes: The indicator weights and the membership matrix of the evaluation indicators are integrated through a formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicators, the membership of the project bidder to different pre-bid scoring coefficients is obtained according to the fuzzy comprehensive evaluation matrix, the pre-bid scoring coefficient with the highest membership corresponding to the project bidder is screened out, and the pre-bid scoring coefficient with the highest membership corresponding to the project bidder is used as the pre-bid scoring coefficient of the project bidder; Wherein, the formula is: ;in, is the fuzzy comprehensive evaluation matrix of the evaluation index, is the indicator weight of the evaluation indicator, is the membership matrix, " represents the multiplication of the weight matrix of the evaluation index and the elements at the corresponding positions of the membership matrix, It is a weighted parameter for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0007] Furthermore, the process of the intelligent feature analysis module performing intelligent feature analysis on a number of historical bid winning records includes: Extracting a number of historical bid winning records from a project bidding data center, the historical bid winning records include project bidding features, basic data, performance data and credit data of the project bidder and each project bidder, extracting each characteristic factor in the project bidding features, obtaining data indicators of the basic data, performance data and credit data associated with each characteristic factor, performing cluster analysis on the project bidding features in the several historical bid winning records according to the project bidding features, obtaining the similarity between the project bidding features in each historical bid winning record and the current project bidding features, performing clustering according to the similarity of the project bidding features in each historical bid winning record, obtaining a project bidding feature clustering circle, wherein the similarity corresponding to the project bidding features in each historical bid winning record in the project bidding feature clustering circle is greater than a similarity threshold; Conduct statistical analysis on several historical winning bid records in the project bidding feature clustering circle to obtain the Pearson correlation coefficient between the data indicators of the project winning bidder and each project bidder and the associated characteristic factors. At the same time, conduct difference analysis on the data indicators of the project winning bidder and each project bidder to obtain the difference coefficient between the data indicators for the associated characteristic factors. Data distribution analysis is performed on various data indicators in the project bidding feature clustering circle to obtain frequency distribution histograms of various data indicators in the project winning party and the project bidding party respectively. The frequency distribution histogram uses the literal value of the data indicator as the horizontal axis and the frequency as the vertical axis. Feature analysis is performed on the frequency distribution histogram to obtain the trend distribution deviation coefficient of the associated feature factors between various data indicators.

[0008] Furthermore, the process of obtaining the trend distribution deviation coefficients of the associated characteristic factors between the various data indicators includes: Obtain the frequency of different literal values ​​of each data indicator in the frequency distribution histogram of the project bidder, mark the frequency as the first frequency, and the frequency of different literal values ​​in the frequency distribution histogram of the project bidder, mark the frequency as the second frequency, obtain the trend distribution coefficient of each data indicator in the project bidder and the project bidder respectively according to the first frequency and the second frequency corresponding to the different literal values ​​of each data indicator, compare the trend distribution coefficient of each data indicator in the project bidder and the project bidder respectively, and obtain the trend distribution deviation coefficient of each data indicator for associated characteristic factors.

[0009] Furthermore, the specific process of obtaining the trend distribution deviation coefficient for the associated characteristic factors includes: ; ,in, For the associated characteristic factors The trend distribution deviation coefficient, Indicates The frequency of literal values, is the number of literal values, is the bias coefficient.

[0010] Furthermore, the process of obtaining the weight coefficient of each data indicator for the associated characteristic factor includes: The Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors are standardized, and the weight coefficients of the data indicators for the associated characteristic factors are obtained based on the Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors after the standardized processing.

[0011] Furthermore, the process of obtaining the weight coefficients of various data indicators includes: Shared Data indicators, characteristic factors. Indicates the first Data indicators and related characteristic factors The Pearson correlation coefficient between Indicates the project bidder Data indicators and related characteristic factors The Pearson correlation coefficient between Indicates the project bidder and the bidders of each project in the For each data indicator, the correlation characteristic factors The coefficient of variation, Indicates Data indicators for related characteristic factors The difference of skewness coefficient; right , , , Standardization was performed separately, and the Pearson correlation coefficient , the standardized coefficient ,in , Represents characteristic factors The corresponding minimum and maximum values ​​of all Pearson correlation coefficients. Similarly, obtain the standardized coefficients , , ; ; in, Data indicators For the associated characteristic factors The weight coefficient of , , is an adjustment parameter used to adjust the relative importance of the difference in Pearson correlation coefficient, difference coefficient, and skewness coefficient in determining the weight, and The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0012] Furthermore, the process of constructing the association relationship map by the bid supervision module includes: Obtain the subjects of the current project bidding process and the relationship information between the subjects, the subjects include project bidders, project tenderers, tendering agencies, and bid evaluation experts, and use each subject as a node in the relationship graph. Each node contains the attribute information of the corresponding subject. According to the relationship information between the subjects, different types of edges are constructed between the nodes. Each edge contains a corresponding relationship. Weight labels corresponding to different relationships are preset, and weight labels are set on each edge according to the relationship corresponding to each edge.

[0013] Furthermore, the process of comparing the horizontal characteristics of the bidding document data of each project bidder by the bidding supervision module includes: Obtain the bidding document data of each project bidder in the association relationship map, represent the text content in the bidding document data by word vector, generate a word vector sequence, compare the word vector sequence corresponding to each bidding document data by cosine similarity, obtain the text similarity between each bidding document data, and compare the text similarity between each bidding document data with a preset text similarity threshold; If the text similarity between the bidding document data is greater than the text similarity threshold, the numerical key indicators (including bidding quotations, bills of quantities, etc.) in the bidding document data are extracted, the numerical key indicators between the bidding document data are compared and analyzed, the deviation values ​​between the various types of numerical key indicators are obtained, and the deviation values ​​between the various types of numerical key indicators are weighted averaged to obtain the comprehensive deviation rate between the bidding document data; A standard threshold interval corresponding to the preset comprehensive deviation rate is selected within the standard threshold interval to divide the sub-intervals into different reliability levels, and the reliability level corresponding to the comprehensive deviation rate between the bidding document data is obtained. According to the reliability level and the weight labels of all edges between the nodes to which the bidding document data belongs, the risk loss value of the node to which the bidding document data belongs is obtained, and the pre-bid scoring coefficient of the node is updated according to the risk loss value. The updated pre-bid scoring coefficient = the pre-bid scoring coefficient before updating - the risk loss value.

[0014] Furthermore, the process of obtaining the risk loss value of the node to which the bidding document data belongs includes: Let the number of edges between nodes be , No. The weight label of the edge is , is the reliability level of the bidding document data, is the total number of reliability levels; ; in, represents the risk loss value, is an adjustment coefficient used to adjust the overall size of the risk loss value. .

[0015] Furthermore, the bidding supervision module analyzes the bidding collusion behavior of several historical project bidding data of each project bidder, including: Extract several historical project bidding data of each project bidder in the association relationship map, pre-process several historical project bidding data, use normalization method to convert bidding price into relative value, so as to eliminate the influence of project scale, type and other factors on price, obtain the bid price difference ratio between each project bidder and other project bidders for each project according to several pre-processed historical project bidding data, and calculate the formula as follows: price difference ratio = (bidder A's bid price - bidder B's bid price) / bidder B's bid price, conduct correlation analysis on the bid price difference ratio between each project bidder and other project bidders in each project, and obtain Take the Pearson correlation coefficient between each project bidder and other project bidders. For example, there are m projects. For bidders A and B, their price difference ratios in each project are (x1, x2, ..., xm) and (y1, y2, ..., ym) respectively. The correlation coefficient r is calculated according to the Pearson correlation coefficient calculation method. The strength of the correlation is judged according to the value of r. The closer r is to 1 or (-1), the stronger the correlation is; the closer r is to 0, the weaker the correlation is. A first correlation coefficient threshold is preset. If the Pearson correlation coefficient between the project bidder and other project bidders is greater than the first correlation coefficient threshold, the project bidder and other project bidders are marked as suspected collusion.

[0016] Furthermore, the process of conducting a secondary judgment on the suspected bid-rigging object includes: Construct a behavior trend prediction model based on deep learning, extract project bidding features and time features from several historical project bidding data of suspected bid-collusion objects, obtain the bidding price time series of the suspected bid-collusion objects for different project bidding features, use the bidding price time series of the suspected bid-collusion objects for different project bidding features as training sets and test sets, input the training set into the behavior trend prediction model for training until the loss function training is stable, save the model parameters, test the behavior trend prediction model through the test set until it meets the preset requirements, and output the behavior trend prediction model; Output the estimated bid price of the suspected bidder for the current project bidding characteristics according to the behavioral trend prediction model, compare the bid price of the suspected bidder for the current project bidding characteristics with the estimated bid price, and obtain the bid price deviation value, and obtain the risk loss value of the node to which the suspected bidder belongs based on the bid price deviation value and the weight labels of all edges between the nodes to which the suspected bidder belongs, and update the pre-bid scoring coefficient of the node according to the risk loss value.

[0017] Furthermore, the full-process supervision module monitors the pre-bid scoring coefficients of each project bidder in real time, and the process of generating a bidding early warning signal based on the monitoring results includes: A preset bid warning score is used to compare the pre-bid score coefficient of each project bidder with the bid warning score in real time. When the pre-bid score coefficient of the project bidder is less than the bid warning score, a bid warning signal is generated and fed back to the project tenderer.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. Multi-dimensional comprehensive evaluation: The pre-bid supervision module obtains multi-dimensional data of project bidders, compares it with the project bidding information of the project bidder in multiple dimensions, and uses fuzzy comprehensive evaluation to obtain the pre-bid scoring coefficient. This method changes the limitations of traditional single indicator evaluation, comprehensively considers more project-related factors, makes the initial screening of bidders more scientific and reasonable, and can more accurately judge whether the bidders meet the project requirements, thereby increasing the probability of screening out high-quality bidders.

[0019] 2. In-depth exploration of the value of historical data: The intelligent feature analysis module conducts in-depth analysis of several historical bidding records, extracts characteristic factors from project bidding characteristics, and obtains related data indicators. Through cluster analysis, statistical analysis and other methods, the Pearson correlation coefficient, difference coefficient and trend distribution deviation coefficient are obtained, and then the weight coefficient of each data indicator for the related characteristic factors is determined. Compared with the fuzzy comprehensive evaluation process of multi-dimensional matching in the prior art, the weights of the evaluation indicators are determined based on the experience of experts. The present invention can more accurately grasp the key factors when evaluating bidders, provide a more targeted and scientific basis for pre-bid decision-making, and help select the bidder that best suits the project.

[0020] 3. Multi-dimensional detection of bid collusion: When comparing the horizontal features of the bidding document data, not only the text similarity is compared, but also the numerical key indicators are compared and analyzed to obtain the comprehensive deviation rate and calculate the risk loss value, and the pre-bid scoring coefficient of the node is updated. At the same time, the bid collusion behavior analysis is conducted on the bidding data of historical projects, and the correlation of the bid price difference ratio is calculated to mark the suspected bid collusion objects and make a secondary judgment. This multi-dimensional detection method can more comprehensively and accurately identify violations such as bid collusion, reduce the possibility of violations, and ensure the fairness of the bidding process.

[0021] 4. Promote the continuity and comprehensiveness of supervision: The system covers the entire bidding process, from pre-bidding, bidding to full-process supervision, forming a complete supervision system. The various modules cooperate and complement each other, achieving continuous and comprehensive supervision of the bidding process, helping to timely discover and solve problems in each link, and improving the efficiency and effectiveness of supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1This is a schematic diagram of an intelligent supervision system for the entire process of bidding and tendering for an engineering project according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0024] like Figure 1 As shown, a full-process intelligent supervision system for bidding and tendering of engineering projects includes a project bidding data center, wherein the project bidding data center is connected in communication with a client, a pre-bid supervision module, an intelligent feature analysis module, a bidding supervision module and a full-process supervision module; The client has two roles: a project tenderer and a project bidder. The project tenderer is used to upload project tender information to the project tender data center, and the project bidder is used to send an access request to the project tenderer through the project tender data center. The pre-bid supervision module is used to compare the project bidders and the project tenderers in multiple dimensions before bidding, and generate the pre-bid scoring coefficient of the project bidders based on the comparison results; The intelligent feature analysis module is used to perform intelligent feature analysis on several historical bid winning records during the multi-dimensional comparison process before bidding, and obtain the weight coefficients of various data indicators of the project bidders for the associated feature factors; The bidding supervision module is used to build a correlation map, compare the horizontal characteristics of the bidding document data of each project bidder, analyze the bidding collusion behavior of several historical project bidding data of each project bidder, and conduct a secondary judgment on the suspected collusion objects; The full-process supervision module is used to monitor the pre-bid scoring coefficients of each project bidder in real time and generate bid warning signals based on the monitoring results.

[0025] It should be further explained that, in the specific implementation process, the pre-bid supervision module conducts a multi-dimensional comparison between the project bidder and the project tenderer before bidding, and generates the pre-bid scoring coefficient of the project bidder based on the comparison results, including: The project bidder logs into the project bidding data center by inputting the identity ID and password, and obtains the basic data, performance data and credit data of the project bidder according to the identity ID through the project bidding data center, and inputs the identity ID of the project bidder that the project bidder needs to access into the project bidding data center according to the access request, obtains the project bidding information of the project bidder through the project bidding data center, extracts project features from the project bidding information, and obtains project bidding features, wherein the project bidding features include the complexity of technical requirements, accuracy requirements, the level of difference from existing technologies, contract type, payment method, deposit requirements and bid evaluation method; Extract various data indicators from the basic data, performance data and credit data of the project bidders as evaluation indicators, including setting scoring standards based on registered capital, years of establishment, etc. For example, companies with high registered capital and long establishment time may perform better in terms of stability and obtain higher scores. Qualification level is also an important indicator. High-level qualifications can correspond to higher scores. Scoring standards are set according to the bid amount, number of projects, project difficulty, etc. For example, high scores will be awarded for bids with a cumulative bid amount of a certain scale in the past three years, and extra points can be awarded for undertaking similar large and complex projects. The quality assessment results of the project can also be used as a basis for scoring. Projects that have won the Quality Engineering Award can get extra points. Scoring standards are set according to credit data, such as AAA credit ratings can be given higher Scores. If there is a bad credit record, points will be deducted according to the severity. If there is no legal dispute, the enterprise can score points. If it is involved in major legal disputes and is in a disadvantageous position, it should be deducted a lot of points. The indicator weights of the evaluation indicators are set according to the project bidding characteristics. Specifically, the various characteristic factors in the project bidding characteristics are extracted, and the data indicators of the basic data, performance data and credit data associated with each characteristic factor are obtained. The weight coefficient of each data indicator for the associated characteristic factor is obtained. For example, for technically complex projects, the weight of the relevant technical performance in the performance data can be appropriately increased. For projects with high requirements for corporate reputation, the weight of credit data can be increased. The membership matrix of the project bidders for the preset pre-bid scoring coefficients is obtained through fuzzy comprehensive evaluation; The pre-bid scoring coefficient of the project bidder is obtained according to the membership matrix and the indicator weight, and the pre-bid scoring coefficient of the project bidder is compared with the preset pre-bid scoring coefficient threshold. If the pre-bid scoring coefficient is less than the pre-bid scoring coefficient threshold, the access request of the project bidder is rejected. If the pre-bid scoring coefficient is greater than or equal to the pre-bid scoring coefficient threshold, the project bidding information of the project tenderer is sent to the project bidder.

[0026] It should be further explained that, in the specific implementation process, the process of obtaining the pre-bid scoring coefficient of the project bidder based on the membership matrix and indicator weights includes: The indicator weights and the membership matrix of the evaluation indicators are integrated through a formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicators, the membership of the project bidder to different pre-bid scoring coefficients is obtained according to the fuzzy comprehensive evaluation matrix, the pre-bid scoring coefficient with the highest membership corresponding to the project bidder is screened out, and the pre-bid scoring coefficient with the highest membership corresponding to the project bidder is used as the pre-bid scoring coefficient of the project bidder; Wherein, the formula is: ; in, is the fuzzy comprehensive evaluation matrix of the evaluation index, is the indicator weight of the evaluation indicator, is the membership matrix, " represents the multiplication of the weight matrix of the evaluation index and the elements at the corresponding positions of the membership matrix, It is a weighted parameter for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0027] It should be further explained that, in the specific implementation process, the process of the intelligent feature analysis module performing intelligent feature analysis on a number of historical bid winning records includes: Extracting a number of historical bid winning records from the project bidding data center, the historical bid winning records include project bidding features, basic data of the project bid winner and each project bidder, performance data and credit data, extracting each characteristic factor in the project bidding features, the characteristic factors include the complexity of technical requirements, accuracy requirements, the level of difference from existing technologies, contract type, payment method, deposit requirements and bid evaluation method, and obtaining data indicators of basic data, performance data and credit data associated with each characteristic factor. For example, for the bidding feature of technical difficulty, the relevant indicators may include basic data such as the proportion of technical personnel of the bid winner and the bidder, technical research and development investment, and performance data such as the performance completion of projects with similar technical difficulty; performing cluster analysis on the project bidding features in a number of historical bid winning records according to the project bidding features, obtaining the similarity between the project bidding features in each historical bid winning record and the current project bidding features, performing clustering according to the similarity of the project bidding features in each historical bid winning record, and obtaining a project bidding feature clustering circle, wherein the similarity corresponding to the project bidding features in each historical bid winning record in the project bidding feature clustering circle is greater than the similarity threshold; Conduct statistical analysis on several historical winning bid records in the project bidding feature clustering circle to obtain the Pearson correlation coefficient between the data indicators of the project winning bidder and each project bidder and the associated characteristic factors. At the same time, conduct difference analysis on the data indicators of the project winning bidder and each project bidder to obtain the difference coefficient between the data indicators for the associated characteristic factors. Data distribution analysis is performed on various data indicators in the project bidding feature clustering circle to obtain frequency distribution histograms of various data indicators in the project winning party and the project bidding party respectively. The frequency distribution histogram uses the literal value of the data indicator as the horizontal axis and the frequency as the vertical axis. Feature analysis is performed on the frequency distribution histogram to obtain the trend distribution deviation coefficient of the associated feature factors between various data indicators.

[0028] It should be further explained that, in the specific implementation process, the process of obtaining the trend distribution deviation coefficient of the associated characteristic factors between various data indicators includes: Obtain the frequency of different literal values ​​of each data indicator in the frequency distribution histogram of the project bidder, mark the frequency as the first frequency, and the frequency of different literal values ​​in the frequency distribution histogram of the project bidder, mark the frequency as the second frequency, obtain the trend distribution coefficient of each data indicator in the project bidder and the project bidder respectively according to the first frequency and the second frequency corresponding to the different literal values ​​of each data indicator, compare the trend distribution coefficient of each data indicator in the project bidder and the project bidder respectively, and obtain the trend distribution deviation coefficient of each data indicator for associated characteristic factors.

[0029] It should be further explained that, in the specific implementation process, the specific process of obtaining the trend distribution deviation coefficient for the associated characteristic factors includes: ; ,in, For the associated characteristic factors The trend distribution deviation coefficient, Indicates The frequency of literal values, is the number of literal values, is the bias coefficient.

[0030] It should be further explained that, in the specific implementation process, the process of obtaining the weight coefficient of each data indicator for the associated characteristic factor includes: The Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors are standardized, and the weight coefficients of the data indicators for the associated characteristic factors are obtained based on the Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors after the standardized processing.

[0031] It should be further explained that, in the specific implementation process, the process of obtaining the weight coefficients of various data indicators includes: Shared Data indicators, characteristic factors. Indicates the first Data indicators and related characteristic factors The Pearson correlation coefficient between Indicates the project bidder Data indicators and related characteristic factors The Pearson correlation coefficient between Indicates the project bidder and the bidders of each project in the For each data indicator, the correlation characteristic factors The coefficient of variation, Indicates Data indicators for related characteristic factors The difference of skewness coefficient; right , , , Standardization was performed separately, and the Pearson correlation coefficient , the standardized coefficient ,in , Represents characteristic factors The corresponding minimum and maximum values ​​of all Pearson correlation coefficients. Similarly, obtain the standardized coefficients , , ; ; in, Data indicators For the associated characteristic factors The weight coefficient of , , is an adjustment parameter used to adjust the relative importance of the difference in Pearson correlation coefficient, difference coefficient, and skewness coefficient in determining the weight, and The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0032] It should be further explained that, in the specific implementation process, the process of the regulatory module in the bid to build the association relationship map includes: Obtain the subjects of the current project bidding process and the relationship information between the subjects, the subjects include project bidders, project tenderers, bidding agencies, and evaluation experts. The relationship information includes business dealings (business cooperation records between the subjects, such as bidders participating in the tenderer's project bidding, bidding agencies providing bidding agency services to the tenderer, evaluation experts participating in the evaluation of bidding projects, etc., business dealings time, project name and other information), equity relations, personnel positions, etc. Each subject is taken as a node of the relationship map, each node contains the attribute information of the corresponding subject, and according to the relationship information between the subjects, different types of edges are constructed between the nodes, each edge contains a corresponding relationship, weight labels corresponding to different relationships are preset, and weight labels are set on each edge according to the relationship corresponding to each edge.

[0033] It should be further explained that, in the specific implementation process, the bidding supervision module compares the horizontal characteristics of the bidding document data of each project bidder in the following process: Obtain the bidding document data of each project bidder in the association relationship map, represent the text content in the bidding document data with word vectors, generate a word vector sequence, compare the word vector sequence corresponding to each bidding document data with cosine similarity, and obtain the text similarity between each bidding document data. For example, if the text similarity of key parts such as construction steps and resource allocation plans in the construction organization design of two project bidders exceeds 80%, there may be suspicion of plagiarism or collusion. Compare the text similarity between each bidding document data with a preset text similarity threshold; If the text similarity between the bidding document data is greater than the text similarity threshold, the numerical key indicators (including bidding quotations, bills of quantities, etc.) in the bidding document data are extracted, and the numerical key indicators between the bidding document data are compared and analyzed to obtain the deviation values ​​between the various types of numerical key indicators. The deviation values ​​between the various types of numerical key indicators are weighted averaged to obtain the comprehensive deviation rate between the bidding document data. For example, in the bills of quantities of different bidders, the quantities of multiple projects are exactly the same, which may be signs of collusion in bidding; A standard threshold interval corresponding to the preset comprehensive deviation rate is selected within the standard threshold interval to divide the sub-intervals into different reliability levels, and the reliability level corresponding to the comprehensive deviation rate between the bidding document data is obtained. According to the reliability level and the weight labels of all edges between the nodes to which the bidding document data belongs, the risk loss value of the node to which the bidding document data belongs is obtained, and the pre-bid scoring coefficient of the node is updated according to the risk loss value. The updated pre-bid scoring coefficient = the pre-bid scoring coefficient before updating - the risk loss value.

[0034] It should be further explained that, in the specific implementation process, the process of obtaining the risk loss value of the node to which the bidding document data belongs includes: Let the number of edges between nodes be , No. The weight label of the edge is , is the reliability level of the bidding document data, is the total number of reliability levels; ; in, represents the risk loss value, is an adjustment coefficient used to adjust the overall size of the risk loss value. .

[0035] It should be further explained that, in the specific implementation process, the bidding supervision module analyzes the bidding collusion behavior of several historical project bidding data of each project bidder in the following process: Extract several historical project bidding data of each project bidder in the association relationship map, pre-process several historical project bidding data, use normalization method to convert bidding price into relative value, so as to eliminate the influence of project scale, type and other factors on price, obtain the bid price difference ratio between each project bidder and other project bidders for each project according to several pre-processed historical project bidding data, and calculate the formula as follows: price difference ratio = (bidder A's bid price - bidder B's bid price) / bidder B's bid price, conduct correlation analysis on the bid price difference ratio between each project bidder and other project bidders in each project, and obtain Take the Pearson correlation coefficient between each project bidder and other project bidders. For example, there are m projects. For bidders A and B, their price difference ratios in each project are (x1, x2, ..., xm) and (y1, y2, ..., ym) respectively. The correlation coefficient r is calculated according to the Pearson correlation coefficient calculation method. The strength of the correlation is judged according to the value of r. The closer r is to 1 or (-1), the stronger the correlation is; the closer r is to 0, the weaker the correlation is. A first correlation coefficient threshold is preset. If the Pearson correlation coefficient between the project bidder and other project bidders is greater than the first correlation coefficient threshold, the project bidder and other project bidders are marked as suspected collusion.

[0036] It should be further explained that, in the specific implementation process, the process of secondary judgment on the suspected bid-rigging object includes: Construct a behavior trend prediction model based on deep learning, extract project bidding features and time features from several historical project bidding data of suspected bid-collusion objects, obtain the bidding price time series of the suspected bid-collusion objects for different project bidding features, use the bidding price time series of the suspected bid-collusion objects for different project bidding features as training sets and test sets, input the training set into the behavior trend prediction model for training until the loss function training is stable, save the model parameters, test the behavior trend prediction model through the test set until it meets the preset requirements, and output the behavior trend prediction model; Output the estimated bid price of the suspected bidder for the current project bidding characteristics according to the behavioral trend prediction model, compare the bid price of the suspected bidder for the current project bidding characteristics with the estimated bid price, and obtain the bid price deviation value, and obtain the risk loss value of the node to which the suspected bidder belongs based on the bid price deviation value and the weight labels of all edges between the nodes to which the suspected bidder belongs, and update the pre-bid scoring coefficient of the node according to the risk loss value.

[0037] Building a behavior trend prediction model based on deep learning is a complex process, which involves multiple steps such as model selection, training, verification and testing. The following is a detailed supplementary explanation of this process: The present invention selects a convolutional neural network (CNN) suitable for time series analysis as a deep learning architecture, selects a mean square error loss function as an optimization target, and then inputs the prepared training set into the selected deep learning model to start training. During the training process, the weights are continuously updated through the back propagation algorithm, so that the loss function gradually decreases until a stable state is reached. During this period, techniques such as Early Stopping are used to avoid overfitting. In addition to the basic training process, various parameters of the model are tuned through Grid Search, including learning rate, batch size, regularization coefficient, etc.

[0038] After the model training is completed and the parameters are adjusted, the final evaluation is performed through the test set to obtain the evaluation results of the model, which include classification indicators such as accuracy, recall, F1 score, etc. Based on the evaluation results on the test set, it is judged whether the model has reached the expected standards. If the requirements are met, the model parameters are saved and ready for deployment; if not ideal, it is necessary to return to a previous stage to re-examine issues such as data quality, model structure or training strategy.

[0039] It should be further explained that, in the specific implementation process, the full-process supervision module conducts real-time monitoring of the pre-bid scoring coefficients of each project bidder, and the process of generating a bidding early warning signal based on the monitoring results includes: A preset bid warning score is used to compare the pre-bid score coefficient of each project bidder with the bid warning score in real time. When the pre-bid score coefficient of the project bidder is less than the bid warning score, a bid warning signal is generated and fed back to the project tenderer.

[0040] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent supervision system for the entire process of bidding for engineering projects, characterized in that: It includes a project bidding data center, wherein the communication links of the project bidding data center include a client, a pre-bid supervision module, an intelligent feature analysis module, a bidding supervision module and a full-process supervision module; The client includes the project tenderer and the project bidder. The project tenderer is used to upload the project tender information to the project tender data center, and the project bidder is used to send an access request to the project tenderer through the project tender data center. The pre-bid supervision module is used to compare the project bidders and the project tenderers in multiple dimensions before bidding, and generate the pre-bid scoring coefficients of the project bidders; The intelligent feature analysis module is used to perform intelligent feature analysis on several historical bid winning records to obtain the weight coefficients of various data indicators of the project bidders for the associated feature factors; The bidding supervision module is used to build a correlation map, compare the horizontal characteristics of the bidding document data of each project bidder, analyze the bidding collusion behavior of several historical project bidding data of each project bidder, and make a secondary judgment on the suspected collusion objects; The full-process supervision module is used to monitor the pre-bid scoring coefficients of each project bidder in real time.

2. According to claim 1, a whole-process intelligent supervision system for bidding and tendering of engineering projects is characterized in that: The pre-bid supervision module conducts a multi-dimensional comparison between the project bidder and the project tenderer before bidding, including: Obtain multi-dimensional data of project bidders and project bidding information of project tenderers, extract project features from the project bidding information, and obtain project bidding features; Extract various data indicators from multi-dimensional data as evaluation indicators, set the indicator weights of the evaluation indicators according to the project bidding characteristics, and obtain the membership matrix of the project bidders for the preset pre-bid scoring coefficients through fuzzy comprehensive evaluation; The pre-bid scoring coefficient of the project bidder is obtained according to the membership matrix and the indicator weight, and a pre-bid scoring coefficient threshold is preset. If the pre-bid scoring coefficient is less than the pre-bid scoring coefficient threshold, the access request of the project bidder is rejected. If the pre-bid scoring coefficient is greater than or equal to the pre-bid scoring coefficient threshold, the project bidding information is sent to the project bidder.

3. According to claim 2, a whole-process intelligent supervision system for bidding and tendering of engineering projects is characterized in that: The process of the intelligent feature analysis module performing intelligent feature analysis on several historical bid winning records includes: Extracting a number of historical bid winning records, wherein the historical bid winning records include project bidding characteristics, multi-dimensional data of the project winning party and each project bidder, extracting each characteristic factor in the project bidding characteristics, obtaining data indicators of the multi-dimensional data associated with each characteristic factor, performing cluster analysis on the number of historical bid winning records according to the project bidding characteristics, and obtaining a project bidding characteristic clustering circle; Conduct statistical analysis on each historical winning bid record in the project bidding feature clustering circle to obtain the Pearson correlation coefficient between each data indicator of the project winning bidder and each project bidder and the associated characteristic factors. At the same time, conduct difference analysis on each data indicator of the project winning bidder and each project bidder to obtain the difference coefficient between each data indicator for the associated characteristic factors. The distribution characteristics of various data indicators in the project bidding feature clustering circle are analyzed to obtain the trend distribution deviation coefficient of the associated characteristic factors between various data indicators.

4. According to claim 3, the intelligent supervision system for the whole process of bidding and tendering for engineering projects is characterized in that: The process of obtaining the trend distribution deviation coefficients of the associated characteristic factors between various data indicators includes: Obtain frequency distribution histograms corresponding to the project winning party and the project bidder for each data indicator, the frequency distribution histogram uses the literal value of the data indicator as the horizontal coordinate and the frequency as the vertical coordinate, obtain the frequency of different literal values ​​of each data indicator in the frequency distribution histogram of the project winning party, mark the frequency as the first frequency, and the frequency of different literal values ​​in the frequency distribution histogram of the project bidder, mark the frequency as the second frequency, and obtain the trend distribution deviation coefficient of the associated characteristic factors between the data indicators according to the first frequency and the second frequency corresponding to the different literal values ​​of each data indicator.

5. According to claim 4, a whole-process intelligent supervision system for bidding and tendering of engineering projects is characterized in that: The process of obtaining the weight coefficients of various data indicators for associated characteristic factors includes: The Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors are standardized, and the weight coefficients of the data indicators for the associated characteristic factors are obtained based on the Pearson correlation coefficients between the data indicators of the project bidder and each project bidder and the associated characteristic factors, the difference coefficients between the data indicators for the associated characteristic factors, and the trend distribution deviation coefficients between the data indicators for the associated characteristic factors after the standardized processing.

6. According to claim 5, the intelligent supervision system for the whole process of bidding and tendering for engineering projects is characterized in that: The process of constructing the association relationship map by the bidding supervision module includes: Obtain the subjects of the current project bidding process and the relationship information between each subject, wherein the subjects include the project bidders, and use each subject as a node of the relationship graph. According to the relationship information between each subject, construct different types of edges between each node, and set a weight label on each edge.

7. According to claim 6, a whole-process intelligent supervision system for bidding and tendering of engineering projects is characterized in that: The process of the bidding supervision module comparing the horizontal characteristics of the bidding document data of each project bidder includes: Obtain the bidding document data of each project bidder in the association relationship map, perform cosine similarity comparison on the text content in the bidding document data, obtain the text similarity between each bidding document data, and preset a text similarity threshold; If the text similarity between the bidding document data is greater than the text similarity threshold, the numerical key indicators in the bidding document data are extracted, and the numerical key indicators between the bidding document data are compared and analyzed to obtain the comprehensive deviation rate between the bidding document data; A standard threshold interval corresponding to the preset comprehensive deviation rate is set, and threshold points are selected within the standard threshold interval to divide sub-intervals into different reliability levels. The reliability level corresponding to the comprehensive deviation rate between the bidding document data is obtained, and the risk loss value of the node to which the bidding document data belongs is obtained based on the reliability level and the weight labels of all edges between the nodes to which the bidding document data belongs. The pre-bid scoring coefficient of the node is updated based on the risk loss value.

8. According to claim 7, a whole-process intelligent supervision system for bidding and tendering of engineering projects is characterized in that: The process of the bidding supervision module analyzing the bidding behavior of several historical project bidding data of each project bidder includes: Extract several historical project bidding data of each project bidder in the association relationship map and preprocess them, obtain the bid price difference ratio between each project bidder and other project bidders for each project based on the preprocessed historical project bidding data, perform correlation analysis on the bid price difference ratio, obtain the Pearson correlation coefficient between each project bidder and other project bidders, preset a first correlation coefficient threshold, and if the Pearson correlation coefficient between the project bidder and other project bidders is greater than the first correlation coefficient threshold, mark the project bidder and other project bidders as suspected collusion objects.

9. The intelligent supervision system for the whole process of bidding and tendering for engineering projects according to claim 8 is characterized in that: The process of conducting a secondary judgment on a suspected bid-rigging object includes: Based on deep learning, a behavioral trend prediction model is constructed to extract project bidding features and time features from several historical project bidding data of suspected bid-collusion objects, obtain the bidding price time series of the suspected bid-collusion objects for different project bidding features, and use the bidding price time series of the suspected bid-collusion objects for different project bidding features as training sets and test sets to obtain a trained behavioral trend prediction model; Output the estimated bid price of the suspected bidder for the current project bidding characteristics according to the behavioral trend prediction model, compare the bid price of the suspected bidder for the current project bidding characteristics with the estimated bid price, and obtain the bid price deviation value, and obtain the risk loss value of the node to which the suspected bidder belongs based on the bid price deviation value and the weight labels of all edges between the nodes to which the suspected bidder belongs, and update the pre-bid scoring coefficient of the node according to the risk loss value.

10. The intelligent supervision system for the whole process of bidding and tendering for engineering projects according to claim 9 is characterized in that: The whole process supervision module conducts real-time monitoring of the pre-bid scoring coefficients of each project bidder, including: A preset bid warning score is used to compare the pre-bid score coefficient of each project bidder with the bid warning score in real time. When the pre-bid score coefficient of the project bidder is less than the bid warning score, a bid warning signal is generated and fed back to the project tenderer.

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