CN-121998747-A - Multi-dimensional intelligent analysis integrated bid-picking evaluation risk prevention and control system and method

CN121998747ACN 121998747 ACN121998747 ACN 121998747ACN-121998747-A

Abstract

The invention relates to the technical field of bid evaluation risk prevention and control, in particular to a bid evaluation risk prevention and control system and method integrating multidimensional intelligent analysis, wherein the method comprises the steps of carrying out semantic vector mapping and fine granularity similarity calculation on bidding texts through a pre-training language model, identifying hidden Lei-with content and generating a semantic-structure two-dimensional Lei-with-segment set; the method comprises the steps of calculating a text similarity matrix between bidding subjects and a key term matching offset, screening abnormal association subject clusters, further extracting business information, behavior time sequence and network topology data, constructing a subject association evolution track chain, finally measuring semantic space distance and behavior association critical distance by combining a risk threshold benchmark, and screening a high risk bidder ring path. The full-chain prevention and control of risk deduction from text detection is realized, and the identification accuracy and the early warning capability of the hidden bidder ring string mark behavior are obviously improved.

Inventors

FENG WEI
ZHAO NANPING
YU CHANGMING
CHENG SHOUWU
HU HEXIN
LIU ZHENGTIAN

Assignees

杭州高达软件系统股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260119

Claims (10)

1. The method for preventing and controlling risk of bid evaluation of recruitment integrating multi-dimensional intelligent analysis is characterized by comprising the following steps: According to multi-source heterogeneous text data in the recruitment file library and the bidding file library, semantic vector mapping is carried out through a pre-training language model, fine-granularity text similarity calculation is carried out, hidden identical contents of suspected plagiarism paragraphs and avoidance keyword replacement are identified, and a semantic-structure two-dimensional identical-segment set is generated; Calculating the matching offset of a text similarity matrix and key terms among bidding subjects based on each piece of information in the semantic-structure two-dimensional identical-segment set, screening bidding subject clusters meeting the multi-dimensional abnormal association condition, and generating multi-subject collaborative bidder ring node clusters; Extracting relevant subject business registration information, bidding behavior time sequences and relevant network topological structures according to each node cluster in the multi-subject collaborative bidder ring node clusters, constructing a subject relevant evolution track chain, and generating bidder ring string bidding behavior propulsion path information sets; Based on the bidder ring string behaviors, the path end node information in the path information set is advanced, a corresponding risk-recruiting threshold benchmark set is extracted, semantic space distance from the path end node to a bidder ring behavior critical point is measured, the semantic space distance is compared with the behavior association critical distance, a path set meeting bidder ring penetration conditions is screened, and a high-risk bidder ring string-label path list is generated.
2. The method for controlling risk of bid evaluation in bid evaluation integrated with multi-dimensional intelligent analysis according to claim 1 is characterized in that the semantic-structure two-dimensional Lei-Tong segment set comprises semantic similarity values, structure similarity indexes and implicit Lei-Tong continuous segments in Lei Tong segments, the multi-main-body collaborative bidder ring node cluster comprises bid main body numbers, inter-main-body association strength indexes and bid space continuity identifications meeting multi-dimensional abnormal association conditions, the bidder ring string bid behavior propulsion path information set comprises business information topological distance values, behavior collaborative time delay values and associated propagation direction sequences of all nodes in a track chain, and the high-risk bidder ring string bid path list comprises main body business registration coordinate information, risk threshold distance measurement values and corresponding bid item grade codes of path terminal nodes.
3. The method for risk prevention and control of bid evaluation of bid acceptance of integrated multidimensional intelligent analysis of claim 2, wherein the step of acquiring the semantic-structural two-dimensional radon segment set is specifically as follows: According to the text content of technical specifications and business clauses in the recruitment file library, performing hierarchical semantic coding through a BERT pre-training model, performing dynamic sliding window semantic similarity calculation on each text segment, judging similarity change fluctuation characteristics, identifying abnormal similar segments with fluctuation amplitude exceeding a set threshold, and generating a text semantic identical segment set; Extracting keyword replacement mode data in a corresponding bidding document based on each segment in the text semantic identical segment set, performing keyword distribution entropy calculation on each segment text, evaluating hidden thunder identical degree of avoidance detection, screening key abnormal segments by setting a hidden thunder identical threshold value, and generating a structure avoidance feature set; and carrying out space-time association analysis on the identical text segment according to the text semantic identical segment set and the structure evading feature set, and screening the segments meeting bidder ring behavior feature conditions by judging the coupling relation between the semantic similarity and the structure evading degree to generate a semantic-structure double-dimensional identical segment set.
4. The method for risk prevention and control of bid evaluation in integrated multidimensional intelligent analysis as recited in claim 3, wherein the step of acquiring the multi-subject collaborative bidder ring node cluster is specifically as follows: Based on each piece of information in the semantic-structure two-dimensional identical-segment set, extracting a corresponding bidding subject identifier, bidding document submitting time and identical-segment start-stop positions, calculating distribution density of identical-segments in bidding documents, establishing a topological combination list among any associated subjects according to the spatial topological relation of business registration information among bidding subjects, sequentially calling identical distribution density values of the subjects in the combination, calculating density difference values, and generating an identical-density difference value sequence of the associated subjects; Acquiring text matching initial position offset between each pair of main bodies according to initial positions of the radar same segments of the associated main bodies, constructing a position offset sequence, calling the radar same density difference value sequence of the associated main bodies and the text matching initial position offset sequence, respectively comparing the radar same density difference value sequence of the associated main bodies with a preset radar same density tolerance threshold value and a preset position synchronization tolerance threshold value, screening main body combinations which are simultaneously smaller than the two types of tolerance threshold values, and obtaining a multidimensional abnormal association meeting combination index set; And calling the multi-dimensional abnormal association to meet the number identification of each group of main body combinations in the combined index set, extracting corresponding industrial and commercial information codes of main bodies in the original identical-section information, constructing a main body topology cluster conforming to the multi-dimensional abnormal association condition, calculating to obtain an association cluster intensity value, screening according to whether the cluster intensity value falls into a mining risk intensity interval, obtaining main body combinations passing the screening, and establishing a multi-main body collaborative bidder ring node cluster.
5. The method for risk prevention and control of bid evaluation in integrated multidimensional intelligent analysis according to claim 4, wherein the step of obtaining the bidder ring bid-string behavior propulsion path information set is specifically as follows: Based on each node cluster in the multi-main body collaborative bidder ring node clusters, extracting azimuth vectors, associated information space coordinate values and behavior starting time of main bodies in an industrial and commercial registration network, constructing a pairwise combination of the main bodies, determining an associated network arrangement sequence, calculating the difference between the space Euclidean distance of adjacent main bodies in the combination and the behavior collaborative starting time, and generating an associated behavior extension distance-time difference set; invoking a main body combination in the related behavior extension distance-time difference set, judging whether the included angle between the related network direction difference vector and the Euclidean line direction is smaller than the related path consistency angle threshold, judging whether the behavior coordination time strictly meets the network increment trend, screening the main body sequence combination meeting double constraint, and obtaining a network increment extension combination index set; According to the main body combination index in the network incremental expansion combination index set, a behavior track segment list of network series connection is established, bidder ring string mark behavior propulsion path chains are established, accumulated correlation propagation distances and behavior time domain window widths of track segments are recorded, maximum correlation propagation lengths and minimum behavior time domain window values are extracted, and bidder ring string mark behavior propulsion path information sets are obtained.
6. The method for risk prevention and control of bid evaluation in integrated multidimensional intelligent analysis according to claim 5, wherein the step of obtaining the high risk bidder ring bid-string path list is specifically as follows: Extracting corresponding main body business information codes and space coordinates of end nodes based on track end node information in the bidder ring string mark behavior propulsion path information set, searching a risk threshold value reference set of the affiliated recruitment item according to the information codes, calling coordinate values to calculate space Euclidean distance between the end nodes and all critical points in the risk threshold value reference set, and generating path end-to-risk threshold value point distance information; Extracting the minimum distance value corresponding to each path end node according to the path end-to-risk threshold point distance information, constructing an end node risk distance sequence, calling a behavior association critical distance reference value to compare each item value in the distance sequence, screening path combination indexes smaller than or equal to the reference value, and obtaining bidder ring penetration risk path index sets; And extracting corresponding track segment information from the bidder ring string marking behavior propulsion path information set according to the path numbers marked in the bidder ring penetration risk path index set, reconstructing and constructing a bidder ring risk penetration path list, marking the shortest penetration distance value between the path tail end and the risk threshold point, and obtaining a high risk bidder ring string marking path list.
7. The method for risk prevention and control of bid evaluation in integrated multidimensional intelligent analysis of claim 6, wherein after obtaining the high risk bidder ring bid-string path list, further comprises: For each path in the high risk bidder ring string path list, extracting the maximum semantic similarity change amplitude in the same segment of the path starting point main body, the total time domain span of path evolution and the number of abnormal associated main bodies in the path, calculating a corresponding risk index, marking the treatment priority according to the grade of the belonging risk item, and generating a multi-dimensional risk calibration result; The multidimensional risk-recruitment calibration result comprises a risk level label, a risk-recruitment item identifier corresponding to the level and a risk-recruitment index code.
8. The method for controlling risk of bid evaluation integrated with multi-dimensional intelligent analysis according to claim 7, wherein the step of obtaining the multi-dimensional bid evaluation calibration result comprises the following steps: extracting a Lei-segment semantic similarity sequence of a main body corresponding to a path starting point based on each path in the high-risk bidder ring string mark path list, identifying a difference value between a maximum value in the sequence and a historical base line minimum value, carrying out normalization processing based on the complexity of a recruitment item, acquiring a normalized risk amplitude of each path, and generating a path risk amplitude sequence; Invoking path information corresponding to the path risk amplitude sequences, extracting starting and stopping moments in each path, calculating time domain spans, counting the number of bidding subjects marked as abnormal association states in the paths, combining three indexes, weighting to construct a multi-parameter risk assessment set, calculating to obtain a risk-recruitment index value, and generating a risk-recruitment index value set; And searching a grade identifier of the recruitment project to which the path belongs according to the index corresponding to each path in the recruitment risk index value set, performing grading judgment on the recruitment risk index according to a preset risk grading reference value interval in the grade of the recruitment project, marking a corresponding treatment priority grade, and obtaining a multi-dimensional recruitment risk calibration result.
9. A system for controlling risk of bid evaluation in integrated multi-dimensional intelligent analysis, comprising a memory, a processor, and a bid evaluation risk control program for integrated multi-dimensional intelligent analysis stored on the memory and operable on the processor, wherein the bid evaluation risk control program for integrated multi-dimensional intelligent analysis is configured to implement the steps of the bid evaluation risk control method for integrated multi-dimensional intelligent analysis as claimed in any one of claims 1 to 8.
10. A medium, wherein a bid evaluation risk prevention and control program for integrated multi-dimensional intelligent analysis is stored on the medium, and the bid evaluation risk prevention and control program for integrated multi-dimensional intelligent analysis realizes the steps of the bid evaluation risk prevention and control method for integrated multi-dimensional intelligent analysis according to any one of claims 1 to 8 when being executed by a processor.

Description

Multi-dimensional intelligent analysis integrated bid-picking evaluation risk prevention and control system and method Technical Field The invention relates to the technical field of bid evaluation risk prevention and control, in particular to a bid evaluation risk prevention and control system and method integrating multidimensional intelligent analysis. Background In the current large-scale and high-frequency bidding purchasing activities, the improper competitive behaviors such as bidder ring, serial bidding and the like show the trend of concealment, organization and intelligence, seriously disturb the market order of fair competition and influence the reasonable configuration of public resources. The traditional bid evaluation risk prevention and control means mainly rely on manual examination and simple keyword comparison technology, so that hidden similar contents realized by means of synonym replacement, sentence pattern reconstruction, paragraph order adjustment and the like are difficult to effectively identify, and a collaborative behavior mode among cross-subjects cannot be found. With the continuous increase in the number and complexity of bidding documents, methods based on rule or shallow text matching have been difficult to meet the need for accurate and efficient risk identification. In recent years, natural Language Processing (NLP) technology has made remarkable progress in semantic understanding and text similarity calculation, and a pre-trained language model (such as BERT, roBERTa and the like) can capture text deep semantic information, so that technical possibility is provided for identifying plagiarism behaviors avoiding keyword replacement. Meanwhile, the Graph Neural Network (GNN) and the complex network analysis method show strong capability in terms of mining potential association relations among entities, can be used for constructing association graphs among bidding subjects, and reveal abnormal collaborative modes of the Graph Neural Network (GNN) and the complex network analysis method in terms of business registration, bidding time sequence, geographic position and the like. However, existing research has focused on risk detection in a single dimension, lacks a systematic approach to organically combine text semantic analysis with subject behavior association mining, and particularly still has a significantly short panel in chain construction deduced from local text merging to global bidder ring paths. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The invention mainly aims to provide a bid evaluation risk prevention and control system and method integrating multi-dimensional intelligent analysis, and aims to solve the technical problem that the existing bid evaluation risk prevention and control technology is difficult to effectively identify hidden text radar and complex main body cooperative behaviors, and a systematic method integrating deep semantic analysis and associated network mining is lacked to realize full-chain intelligent prevention and control of bidder ring bid-string behaviors. In order to achieve the above purpose, the invention provides a method for controlling risk of bid evaluation of mining integrated with multi-dimensional intelligent analysis, which comprises the following steps: According to multi-source heterogeneous text data in the recruitment file library and the bidding file library, semantic vector mapping is carried out through a pre-training language model, fine-granularity text similarity calculation is carried out, hidden identical contents of suspected plagiarism paragraphs and avoidance keyword replacement are identified, and a semantic-structure two-dimensional identical-segment set is generated; Calculating the matching offset of a text similarity matrix and key terms among bidding subjects based on each piece of information in the semantic-structure two-dimensional identical-segment set, screening bidding subject clusters meeting the multi-dimensional abnormal association condition, and generating multi-subject collaborative bidder ring node clusters; Extracting relevant subject business registration information, bidding behavior time sequences and relevant network topological structures according to each node cluster in the multi-subject collaborative bidder ring node clusters, constructing a subject relevant evolution track chain, and generating bidder ring string bidding behavior propulsion path information sets; Based on the bidder ring string behaviors, the path end node information in the path information set is advanced, a corresponding risk-recruiting threshold benchmark set is extracted, semantic space distance from the path end node to a bidder ring behavior critical point is measured, the semantic space distance is compared with the behavior