Malicious bid invitation behavior detection method and system based on data analysis
By conducting multi-dimensional data analysis of successful bidders and related bidders in the bidding platform, potential malicious behaviors are detected, and the problem of malicious behavior detection in the bidding platform is solved, the detection efficiency and accuracy are improved, and the fairness of the bidding process is ensured.
Patent Information
- Application Number
- CN202411822485.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-11
- Publication Date
- 2025-03-28
AI Technical Summary
In the bidding platform, how to effectively detect and prevent malicious behavior between bidders, such as collusion to quotations to affect the winning results.
By obtaining the bidder's quotation from the winner and matching whether there are bidders whose quotations are within the threshold range in all bidders, a collection of bidders to be tested is generated. These bidders and their winning bidders will be subjected to first-level testing (based on this bid data, including overlapping unit personnel, same deposit remittance accounts, similarity in bid documents, etc.) and secondary testing (based on historical bid data, including similar contact information, taking turns to win bids, joint participation, etc.), and generate levels and prompt information for suspected malicious bidding.
It improves the efficiency and accuracy of bidding malicious behavior detection, reduces the false alarm rate, enhances the objectivity and reliability of the test results, and ensures the fairness, justice and transparency of the bidding process.
Smart Images
Figure CN119722260A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of bidding and procurement, and in particular to a method and system for detecting malicious bidding behavior based on data analysis, an electronic device and a storage medium. Background Art
[0002] At present, offline bidding has basically been transferred to the bidding platform. The bidding platform is an online platform dedicated to publishing and managing bidding information, helping the bidding party (usually government agencies, enterprises, institutions, etc.) and the bidder (suppliers, contractors, etc.) to achieve efficient communication and transactions. The bidding platform generally includes: information release, information collection, user registration and management, search and screening and other functions.
[0003] In the bidding process, there are currently two scoring methods stipulated by the state, one is the comprehensive scoring method, and the other is the lowest bid price method after evaluation. At present, most projects adopt the comprehensive scoring method. The comprehensive scoring method is to stipulate various comprehensive evaluation standards in the bidding documents. The judges score according to the comprehensive situation. The candidate with the highest score is the winning candidate. The bid price score accounts for about 50%-80% of the total score. Generally speaking, the bid price that is close to the average of all bid prices has a higher score. That is, since the bid price score occupies a dominant position in the total score, if the bidder's bid can be as close to the average of all bids as possible, the probability of winning the bid will be greatly increased. This is also why bidders will try their best to collude with as many bidders as possible to participate in the bidding, and agree on bids of similar amounts in advance. The average bid price of the bid-rigging group is almost the same as the bid price of the "winning bidder" agreed internally in advance.
[0004] To sum up, how to detect malicious bidding behaviors based on data analysis in the bidding platform is an issue that needs to be solved. Summary of the invention
[0005] In view of this, the embodiments of the present disclosure provide a method and system for detecting malicious bidding behavior based on data analysis, an electronic device and a storage medium, and detecting malicious bidding behavior based on data analysis.
[0006] In a first aspect, the present disclosure provides a method for detecting malicious bidding behavior based on data analysis, which adopts the following technical solutions: Obtain bids from successful bidders; Matching among all bidders’ quotations whether there is a bidder whose quotations are within the threshold range of the winning bidder; If yes, then a set of bidders to be tested is generated for bidders whose bids are within the threshold range of the winning bidder’s bid; Obtain the bidding data of each of the bidders to be tested and the successful bidder in the set of bidders to be tested, and conduct a first-level test based on the bidding data; Obtain historical bidding data for each of the bidders to be tested and the successful bidder in the set of bidders to be tested, and conduct secondary testing based on the historical bidding data; The level of suspected malicious bidding behavior is generated based on the results of the first-level detection or the second-level detection, and malicious bidding behavior prompt information is generated.
[0007] As an optional implementation, before matching all bidder quotations to see if there is a bidder whose quotations of the winning bidder are within a threshold range, the method further includes: Generate threshold ranges based on a preset ratio and winning bidders' bids.
[0008] As an optional implementation, the first-level detection based on the bidding data includes: Perform the following judgments one by one and score according to the judgment results: Determine one by one whether the personnel of the bidder to be tested overlap with those of the successful bidder; Determine one by one whether the deposit remittance account of the bidder to be tested is the same as the deposit remittance account of the successful bidder; Determine the similarity between the bid documents of the bidders to be tested and the bid documents of the successful bidder one by one.
[0009] As an optional implementation, the secondary detection based on historical bidding data includes: Perform the following judgments one by one and score according to the judgment results: Determine whether the contact information of the bidder to be tested and the successful bidder in the historical bidding data is the same; Determine whether the bidder to be tested and the successful bidder in the historical bidding data win the bid in turns; Determine whether the bidder to be tested and the successful bidder in the historical bidding data participate together.
[0010] In a second aspect, the embodiment of the present disclosure further provides a bidding malicious behavior detection system based on data analysis, including: The successful bidder quotation acquisition module obtains the successful bidder's quotation; A matching module, which matches all bidders' quotations to see if there are bidders whose quotations are within a threshold range with those of the winning bidder; A module for generating a set of bidders to be tested, when the matching result of the matching module is yes, generates a set of bidders to be tested for bidders whose bids are within a threshold range of the bids of the successful bidder; The first-level detection module obtains the bidding data of the bidders to be tested and the successful bidders in the bidder set to be tested one by one, and performs first-level detection based on the bidding data; The secondary detection module obtains historical bidding data of the bidders to be detected and the successful bidders in the set of bidders to be detected one by one, and performs secondary detection based on the historical bidding data; The prompt information generation module generates a suspected malicious bidding behavior level according to the results of the first-level detection or the second-level detection, and generates malicious bidding behavior prompt information.
[0011] As an optional implementation, it also includes: The threshold range generation module generates a threshold range based on a preset ratio and the bid of the winning bidder.
[0012] As an optional implementation, the primary detection module includes: The first-level detection and scoring module executes the following modules one by one and scores according to the judgment results: Personnel overlap detection module, which determines whether the personnel of the bidder to be tested overlap with the personnel of the successful bidder one by one; The remittance account detection module determines whether the deposit remittance account of the bidder to be detected is the same as the deposit remittance account of the successful bidder; The bidding document similarity detection module determines the similarity between the bidding documents of the bidders to be detected and the bidding documents of the successful bidder one by one.
[0013] As an optional implementation, the secondary detection module includes: The secondary detection and scoring module executes the following modules one by one and scores according to the judgment results: The contact information similarity detection module determines whether the contact information of the bidder to be detected and the successful bidder in the historical bidding data is the same; The rotation winning bid detection module determines whether the bidder to be detected and the winning bidder in the historical bidding data win the bid in turns; The joint participation detection module determines whether the bidder to be detected and the successful bidder in the historical bidding data participate together.
[0014] In a third aspect, the present disclosure also provides an electronic device, which adopts the following technical solution: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned bidding malicious behavior detection methods based on data analysis.
[0015] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the above-mentioned bidding malicious behavior detection methods based on data analysis.
[0016] In summary, the technical effects of the bidding malicious behavior detection method and system based on data analysis provided by the present disclosure are: The bidding malicious behavior detection method based on data analysis effectively improves the efficiency and accuracy of detection through step-by-step, multi-dimensional data analysis. In particular, after the successful bidder is determined, the detection is carried out around the successful bidder, making the detection more focused, reducing the false alarm rate, improving the detection accuracy, and simplifying the detection process. The analysis method that combines this bidding data with historical bidding data not only verifies potential malicious behavior from multiple angles, but also provides more comprehensive and detailed evidence support, ensuring the objectivity and reliability of the detection results. This is of great significance for maintaining the fairness, justice and transparency of the bidding process.
[0017] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0019] Figure 1 A flowchart of a method for detecting malicious bidding behavior based on data analysis provided in an embodiment of the present disclosure; Figure 2 A schematic diagram of the structure of a bidding malicious behavior detection system based on data analysis provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0020] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
[0021] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.
[0022] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.
[0023] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.
[0024] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.
[0025] Reference Figure 1 The first aspect of the present invention provides a method for detecting malicious bidding behavior based on data analysis, comprising: Step S1, obtaining the bid of the successful bidder; Step S2, matching among all bidders' quotations whether there is a bidder whose quotations are within a threshold range with those of the successful bidder; This step can initially narrow down the scope of bidders to be tested through quotation matching and reduce the workload of subsequent analysis.
[0026] Step S3: if yes, generate a set of bidders to be tested for bidders whose bids are within the threshold range of the winning bidder's bid; Step S4, obtaining the bidding data of each of the bidders to be tested and the successful bidder in the set of bidders to be tested, and performing a first-level test based on the bidding data; Specifically, the first-level testing based on the bidding data includes: Perform the following judgments one by one and score them according to the judgment results. In the judgment process of the following steps, each step gives a score of suspected malicious bidding behavior, and finally adds them up to get the score of the first-level detection. The score can be expressed as a percentage or a natural number: Determine one by one whether the unit personnel of the bidder to be tested overlap with the unit personnel of the successful bidder; for example, the unit personnel of the bidder to be tested and the unit personnel of the successful bidder can be obtained through the national industrial and commercial information platform.
[0027] Determine one by one whether the deposit remittance account of the bidder to be tested is the same as the deposit remittance account of the successful bidder; this step can be obtained by retrieving the pre-stored information of each bidder.
[0028] Determine the similarity between the bid documents of the bidders to be tested and the bid documents of the successful bidder one by one; Specifically, the trained model can be used to identify the similarity between the bid documents of the bidder to be tested and the bid documents of the successful bidder to obtain a similarity score. For example, when identifying the similarity, it can be identified from two aspects: text similarity and format similarity.
[0029] This step can quickly discover possible malicious behaviors in the current bidding process through real-time analysis of the bidding data. For example, overlapping personnel in units, the same deposit remittance account, and high similarity of bidding documents are all direct evidence of malicious behaviors such as bid rigging and collusion. Since it is based on the current bidding data, it has high real-time performance. This means that detection can be initiated immediately before the bidding results are announced, and potential malicious behaviors can be discovered and handled in a timely manner to ensure the fairness and transparency of the bidding process.
[0030] At the same time, the first-level detection covers multiple dimensions such as unit personnel, deposit remittance accounts and bidding documents, ensuring comprehensive verification of potential malicious behavior from different angles. This multi-dimensional verification method can reduce false positives or omissions caused by inaccurate judgment of a single feature.
[0031] Each judgment result has a corresponding score, which can quantify the degree of abnormality of each feature. This scoring mechanism not only provides a more objective basis for detection, but also helps to comprehensively evaluate the risk level of each bidder and improve the reliability and operability of the detection results.
[0032] The operation process of the first-level test is relatively simple, requiring only matching and analysis of the bidding data, without the need for complex calculation models and long-term data processing. This makes the test method easier to implement and suitable for bidding projects of all sizes.
[0033] The first-level test provides basic data and preliminary conclusions for the subsequent second-level test, enhancing the credibility of the entire test process. Even if new evidence is found later, the results of the first-level test can serve as an important reference to ensure the coherence and consistency of the test process.
[0034] Step S5, obtaining historical bidding data of the bidders to be tested and the successful bidders in the set of bidders to be tested one by one, and performing secondary testing based on the historical bidding data; Specifically, the secondary detection based on historical bidding data includes: Perform the following judgments one by one and score according to the judgment results: Determine whether the contact information of the bidder to be tested and the successful bidder in the historical bidding data is the same. Specifically, this can be achieved through a string similarity algorithm; Determine whether the bidder to be detected and the successful bidder in the historical bidding data take turns to win the bid; specifically, this can be achieved through pattern recognition algorithms or time series analysis.
[0035] Determine whether the bidder to be detected and the successful bidder in the historical bidding data participate together. Specifically, this can be achieved through regular matching.
[0036] This step can verify whether potential malicious behavior persists in multiple biddings by acquiring and analyzing historical bidding data, thereby enhancing the credibility of the detection results.
[0037] At the same time, historical data can reveal long-term cooperative relationships between bidders, such as similar contact information, taking turns to win bids, joint participation, etc. This information helps to identify malicious behaviors such as bid rigging and collusion.
[0038] Step S6, generating a suspected malicious bidding behavior level according to the results of the first-level detection or the second-level detection, and generating malicious bidding behavior prompt information.
[0039] Specifically, this step is to add the scores of the first-level test and the second-level test to obtain a total score, and match the level of suspected malicious bidding behavior according to the total score. The higher the score, the higher the level of suspected malicious bidding behavior. When any one of the test scores reaches the highest standard, they do not need to be added together.
[0040] This step can more comprehensively evaluate the malicious behavior risk level of each bidder by combining the scoring results of the first-level and second-level tests, ensuring the objectivity and accuracy of the evaluation results. Generate the level of suspected malicious behavior to help decision makers quickly determine whether malicious behavior exists and improve decision-making efficiency. Generate detailed prompt information, list specific test results and evidence, provide clear guidance for subsequent investigations and handling, and ensure the transparency and fairness of the handling process.
[0041] The bidding malicious behavior detection method based on data analysis provided in this embodiment has the following technical effects: After the winning bidder is determined, the detection can be carried out around the winning bidder, which can significantly reduce the detection scope and improve the detection speed and efficiency. There is no need to thoroughly analyze all the bidding data, only the information related to the winning bidder needs to be focused, thus saving time and resources. As the detection scope is reduced, the data of each bidder to be detected can be analyzed more carefully, reducing the possibility of false positives. At the same time, through multi-dimensional detection, the accuracy and reliability of the detection results are ensured. The winning bidder is the ultimate beneficiary. Carrying out detection around the winning bidder can more directly discover potential malicious behavior and improve the pertinence and effectiveness of detection.
[0042] Advantages compared to pre-bid testing: 1. Reduce computing resource consumption: Before winning a bid, all bidders’ data needs to be fully analyzed, which consumes a lot of computing resources. However, after winning a bid, the detection only needs to focus on the winning bidder and its potential related bidders, which reduces the consumption of computing resources.
[0043] 2. Reduce the risk of false alarms: The pre-bid inspection may be difficult to accurately analyze due to the large amount of data, resulting in a high false alarm rate. The post-bid inspection can analyze each data point more carefully due to the reduced scope, reducing the risk of false alarms.
[0044] 3. Improve detection accuracy: After winning the bid, the detection can be more focused, and the detection accuracy and precision can be improved through multi-dimensional data analysis. Even if there is malicious behavior, it is easier to be discovered and confirmed.
[0045] 4. Simplify the process: Pre-bid testing may require complex pre-processing and data cleaning steps, while the post-bid testing process is relatively simple and the data of the winning bidder and related bidders can be directly used for analysis.
[0046] In summary, the bidding malicious behavior detection method based on data analysis effectively improves the efficiency and accuracy of detection through step-by-step, multi-dimensional data analysis. In particular, after the successful bidder is determined, the detection is carried out around the successful bidder, making the detection more focused, reducing the false alarm rate, improving the detection accuracy, and simplifying the detection process. The analysis method that combines this bidding data with historical bidding data not only verifies potential malicious behavior from multiple angles, but also provides more comprehensive and detailed evidence support, ensuring the objectivity and reliability of the detection results. This is of great significance for maintaining the fairness, justice and transparency of the bidding process.
[0047] As an optional implementation, before matching all bidder quotations to see if there is a bidder whose quotations of the winning bidder are within a threshold range, the method further includes: Generate threshold ranges based on a preset ratio and winning bidders' bids.
[0048] On the other hand, refer to Figure 2 The present invention provides a bidding malicious behavior detection system based on data analysis, comprising: The successful bidder quotation acquisition module obtains the successful bidder's quotation; A matching module, which matches all bidders' quotations to see if there are bidders whose quotations are within a threshold range with those of the winning bidder; A module for generating a set of bidders to be tested, when the matching result of the matching module is yes, generates a set of bidders to be tested for bidders whose bids are within a threshold range of the bids of the successful bidder; The first-level detection module obtains the bidding data of the bidders to be tested and the successful bidders in the bidder set to be tested one by one, and performs first-level detection based on the bidding data; The secondary detection module obtains historical bidding data of the bidders to be detected and the successful bidders in the set of bidders to be detected one by one, and performs secondary detection based on the historical bidding data; The prompt information generation module generates a suspected malicious bidding behavior level according to the results of the first-level detection or the second-level detection, and generates malicious bidding behavior prompt information.
[0049] As an optional implementation, it also includes: The threshold range generation module generates a threshold range based on a preset ratio and the bid of the winning bidder.
[0050] As an optional implementation, the primary detection module includes: The first-level detection and scoring module executes the following modules one by one and scores according to the judgment results: Personnel overlap detection module, which determines whether the personnel of the bidder to be tested overlap with the personnel of the successful bidder one by one; The remittance account detection module determines whether the deposit remittance account of the bidder to be detected is the same as the deposit remittance account of the successful bidder; The bidding document similarity detection module determines the similarity between the bidding documents of the bidders to be detected and the bidding documents of the successful bidder one by one.
[0051] As an optional implementation, the secondary detection module includes: The secondary detection and scoring module executes the following modules one by one and scores according to the judgment results: The contact information similarity detection module determines whether the contact information of the bidder to be detected and the successful bidder in the historical bidding data is the same; The rotation winning bid detection module determines whether the bidder to be detected and the winning bidder in the historical bidding data win the bid in turns; The joint participation detection module determines whether the bidder to be detected and the successful bidder in the historical bidding data participate together.
[0052] The electronic device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc.
[0053] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the electronic device performs all or part of the steps of the bidding malicious behavior detection method based on data analysis of the aforementioned embodiments of the present disclosure.
[0054] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.
[0055] like Figure 3 The present invention provides a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, which is suitable for implementing the electronic device in the embodiment of the present invention. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.
[0056] like Figure 3 As shown, the electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.
[0057] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes, hard disks, etc.; and communication devices. The communication device allows the electronic device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 3 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.
[0058] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the bidding malicious behavior detection method based on data analysis of an embodiment of the present disclosure are executed.
[0059] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.
[0060] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the bidding malicious behavior detection method based on data analysis of the above-mentioned embodiments of the present disclosure are executed.
[0061] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).
[0062] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.
[0063] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.
[0064] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.
[0065] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.
[0066] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.
[0067] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.
[0068] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
[0069] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.
Claims
1. A method for detecting malicious bidding behavior based on data analysis, characterized in that: include: Obtain bids from successful bidders; Matching among all bidders’ quotations whether there is a bidder whose quotations are within the threshold range of the winning bidder; If yes, a set of bidders to be tested is generated for bidders whose bids are within the threshold range of the winning bidder's bid; Obtain the bidding data of each of the bidders to be tested and the successful bidder in the set of bidders to be tested, and conduct a first-level test based on the bidding data; Obtain historical bidding data for each of the bidders to be tested and the successful bidder in the set of bidders to be tested, and conduct secondary testing based on the historical bidding data; The level of suspected malicious bidding behavior is generated based on the results of the first-level detection or the second-level detection, and malicious bidding behavior prompt information is generated.
2. The method for detecting malicious bidding behavior based on data analysis according to claim 1 is characterized in that: Before matching all bidders to see if there is a bidder within the threshold range of the winning bidder's bid, it also includes: Generate threshold ranges based on a preset ratio and winning bidders' bids.
3. The method for detecting malicious bidding behavior based on data analysis according to claim 2 is characterized in that: The first-level testing based on the bidding data includes: Perform the following judgments one by one and score according to the judgment results: Determine one by one whether the personnel of the bidder to be tested overlap with those of the successful bidder; Determine one by one whether the deposit remittance account of the bidder to be tested is the same as the deposit remittance account of the successful bidder; Determine the similarity between the bid documents of the bidders to be tested and the bid documents of the successful bidder one by one.
4. The method for detecting malicious bidding behavior based on data analysis according to claim 3 is characterized in that: The secondary detection based on historical bidding data includes: Perform the following judgments one by one and score according to the judgment results: Determine whether the contact information of the bidder to be tested and the successful bidder in the historical bidding data is the same; Determine whether the bidder to be tested and the successful bidder in the historical bidding data win the bid in turns; Determine whether the bidder to be tested and the successful bidder in the historical bidding data participate together.
5. A bidding malicious behavior detection system based on data analysis, characterized in that: include: The successful bidder quotation acquisition module obtains the successful bidder's quotation; A matching module, which matches all bidders' quotations to see if there are bidders whose quotations are within a threshold range with those of the winning bidder; A module for generating a set of bidders to be tested, when the matching result of the matching module is yes, generates a set of bidders to be tested for bidders whose bids are within a threshold range of the bids of the successful bidder; The first-level detection module obtains the bidding data of the bidders to be tested and the successful bidders in the bidder set to be tested one by one, and performs first-level detection based on the bidding data; The secondary detection module obtains historical bidding data of the bidders to be detected and the successful bidders in the set of bidders to be detected one by one, and performs secondary detection based on the historical bidding data; The prompt information generation module generates a suspected malicious bidding behavior level according to the results of the first-level detection or the second-level detection, and generates malicious bidding behavior prompt information.
6. The bidding malicious behavior detection system based on data analysis according to claim 7 is characterized in that: Also includes: The threshold range generation module generates a threshold range based on a preset ratio and the bid of the winning bidder.
7. The method for detecting malicious bidding behavior based on data analysis according to claim 4 is characterized in that: The primary detection module comprises: The first-level detection and scoring module executes the following modules one by one and scores according to the judgment results: Personnel overlap detection module, which determines whether the personnel of the bidder to be tested overlap with the personnel of the successful bidder one by one; The remittance account detection module determines whether the deposit remittance account of the bidder to be detected is the same as the deposit remittance account of the successful bidder; The bidding document similarity detection module determines the similarity between the bidding documents of the bidders to be detected and the bidding documents of the successful bidder one by one.
8. The method for detecting malicious bidding behavior based on data analysis according to claim 5 is characterized in that: The secondary detection module comprises: The secondary detection and scoring module executes the following modules one by one and scores according to the judgment results: The contact information similarity detection module determines whether the contact information of the bidder to be detected and the successful bidder in the historical bidding data is the same; The module for detecting the successive bids is used to determine whether the bidder to be detected and the successful bidder in the historical bidding data have won the bids in turns; The joint participation detection module determines whether the bidder to be detected and the successful bidder in the historical bidding data participate together.
9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the bidding malicious behavior detection method based on data analysis as described in any one of claims 1-4.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the bidding malicious behavior detection method based on data analysis as described in any one of claims 1-4.