A method and device for automatically intercepting abnormal data

Abstract

This invention provides a method and apparatus for automatically intercepting abnormal data. The method includes: upon receiving first objection data submitted by a user terminal regarding a bidding project object, determining the bidding object and the submission time; if the first objection data meets preset objection rules, determining the objection credit value of the bidding object; if the objection credit value meets preset malicious user conditions, determining the degree of suspicion of the first objection data; and if a preset malicious objection event is triggered based on the degree of suspicion, intercepting the first objection data. This achieves the identification and automatic interception of malicious objection data, saving manpower consumption.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for automatically intercepting abnormal data. Background Art

[0002] In the process of online bidding, once the winning candidate is determined, it is often announced publicly for the sake of fairness and justice. During the announcement period, it is common for the unsuccessful candidates to make arbitrary speculations about the winning candidate and raise malicious objections to the winning candidate.

[0003] The current method of handling objections is usually manual processing, which involves verifying the objection evidence submitted by the objector. However, this requires a lot of human resources, and it is difficult to deal effectively with malicious objections. Summary of the invention

[0004] In view of the above problems, a method and device for automatically intercepting abnormal data is proposed to overcome the above problems or at least partially solve the above problems, including:

[0005] A method for automatically intercepting abnormal data, the method comprising:

[0006] Upon receiving first objection data submitted by a user terminal to a bidding project object, determining a bidding object submitting the first objection data and a submission time of submitting the objection data;

[0007] In the case where it is determined according to the submission time and the bidder that the first objection data meets the preset objection regulations, determining a set of failed bidding project objects of the bidder that participated in the bidding but did not close the deal, and determining a set of invalid objection project objects of the bidder that raised objections and whose objections were invalid from the set of failed bidding project objects;

[0008] Determining the objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set;

[0009] In the case where the objection credit value meets the preset malicious user condition, collecting the second objection data of the bidding object on the objection invalid project object to obtain a second objection data set, and determining a third objection data set with the same attribute as the first objection data from the second objection data set;

[0010] The suspiciousness of the first objection data is determined according to the third objection data set and the second objection data set, and the first objection data is intercepted when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

[0011] Optionally, the step of determining the objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set includes:

[0012] Determine a first proportion of the objection invalid project objects in the objection invalid project object set to the bid failed project object set;

[0013] An objection credit value for the bidding object is determined based on the first proportion.

[0014] Optionally, the step of determining the suspiciousness of the first objection data according to the third objection data set and the second objection data set includes:

[0015] determining a second proportion of third objection data in the third objection data set in the second objection data set;

[0016] The suspiciousness level of the first objection data is determined based on the second ratio.

[0017] Optionally, before determining the set of failed bidding project objects in which the bidder participated in the bidding but did not win the bid, the method further includes:

[0018] Obtaining the quotation data of the bidding project object, and searching for the quotation information of the bidding object in the quotation information;

[0019] When the quotation information of the bidding object is found, determining whether the submission time is within the public notice period of the preset objection regulations;

[0020] It is determined whether the first objection data complies with a preset objection regulation according to the determination result.

[0021] Optionally, also include:

[0022] In a case where it is determined that the first objection data does not comply with a preset objection provision, the first objection data is intercepted.

[0023] A device for automatically intercepting abnormal data, the device comprising:

[0024] A receiving module, configured to, upon receiving first objection data submitted by a user terminal to a bidding project object, determine a bidding object that submitted the first objection data and a submission time of the objection data;

[0025] A bidding failure project object module is used to determine a bidding failure project object set of the bidding object that participated in the bidding and failed to complete the transaction, and determine an objection invalid project object set of the bidding object that has raised an objection and the objection is invalid from the bidding failure project object set, when the first objection data meets the preset objection regulations according to the submission time and the bidding object.

[0026] An objection credit value module, used for determining an objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set;

[0027] a third objection data set, for collecting second objection data of the bidding object on the object of the invalid objection project to obtain a second objection data set when the objection credit value meets a preset malicious user condition, and determining a third objection data set having the same attributes as the first objection data from the second objection data set;

[0028] The suspiciousness module is used to determine the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercept the first objection data when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

[0029] Optionally, the objection credit value module includes:

[0030] A first proportion submodule, used to determine a first proportion of the objection-invalid project objects in the objection-invalid project object set in the objection-failed project object set;

[0031] The objection credit value submodule is used to determine the objection credit value for the bidding object according to the first proportion.

[0032] Optionally, the suspicious degree module includes:

[0033] A second proportion submodule, configured to determine a second proportion of the third objection data in the third objection data set in the second objection data set;

[0034] The suspicion level submodule is used to determine the suspicion level of the first objection data according to the second proportion.

[0035] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, one or more methods for automatically intercepting abnormal data as described above are implemented.

[0036] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements one or more methods for automatically intercepting abnormal data as described above.

[0037] The embodiments of the present invention have the following advantages:

[0038] In an embodiment of the present invention, when receiving first objection data submitted by a user terminal for a bidding project object, determining the bidding object that submitted the first objection data and the submission time of the objection data; when judging that the first objection data meets the preset objection regulations according to the submission time and the bidding object, determining the set of failed bidding project objects of the bidding object that participated in the bidding but did not close the deal, and determining the set of invalid objection project objects for which the bidding object has raised objections and the objections are invalid from the set of failed bidding project objects; determining the objection credit value for the bidding object according to the set of failed bidding project objects and the set of invalid objection project objects; when the objection credit value meets the preset malicious user condition, collecting the second objection data of the bidding object on the invalid objection project object to obtain a second objection data set, and determining a third objection data set with the same attributes as the first objection data from the second objection data set; determining the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercepting the first objection data when judging that a preset malicious objection event is triggered according to the suspiciousness, thereby realizing the identification of malicious objection data and automatically intercepting malicious objection data, which can save human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0040] Figure 1 It is a flowchart of the steps of a method for automatically intercepting abnormal data provided by an embodiment of the present invention;

[0041] Figure 2 It is a flowchart of the steps of another method for automatically intercepting abnormal data provided by an embodiment of the present invention;

[0042] Figure 3 It is a structural block diagram of a device for automatically intercepting abnormal data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Reference Figure 1 , shows a flowchart of a method for automatically intercepting abnormal data provided by an embodiment of the present invention, which may specifically include the following steps:

[0045] Step 101, upon receiving first objection data submitted by a user terminal to a bidding project object, determining the bidding object submitting the first objection data and the submission time of the objection data;

[0046] After the bidding project object is in a public announcement state, a bidder who has quoted on the bidding project object but has not won the bid can raise an objection. He or she can log in to a client installed in a user terminal and submit first objection data for the bidding project object. The bidder identifier can be included when submitting the first objection data.

[0047] In order to identify malicious objections, when the first objection data submitted by the user terminal to the bidding project object is received, the bidding object corresponding to the bidding object identifier carried by the first objection data can be determined, and the submission time of the first objection data submitted by the bidding object can be determined based on the system time.

[0048] Step 102, when it is determined that the first objection data meets the preset objection regulations according to the submission time and the bidder, a set of failed bidding project objects of the bidder that participated in the bidding but did not win the transaction is determined, and from the set of failed bidding project objects, a set of invalid objection project objects for which the bidder has raised objections and the objections are invalid is determined;

[0049] In a specific implementation, objection regulations may be preset for the bidding project object. For example, the objection regulations include that the object of raising an objection must be the bidding object participating in the quotation, and the time of raising an objection must be within the specified time.

[0050] To identify malicious objections, after determining the submission time of the first objection data and the bidding object that raised the first objection data, it is possible to determine whether the first objection data complies with the preset objection regulations based on the bidding object and the submission time.

[0051] When it is determined that the first objection data complies with the preset objection regulations, in order to identify malicious objections, the set of failed bidding project objects that participated in the bidding of the bidding object but did not close the deal can be further determined, and then, within the set of failed bidding project objects, the set of invalid objection project objects for which the bidding object has initiated objections and the objections are invalid can be determined.

[0052] Specifically, feedback for objection data may be provided. When the objection data raised by the bidding object is determined to be invalid (rejected), invalidation mark information may be added.

[0053] The historical behavior data of the bidding object can be obtained, and the failed bidding project objects in which the bidding object has participated in the bidding but did not win the bid can be determined from the historical behavior data. Furthermore, it can be determined from the objection information of the failed bidding project objects whether the bidding object has raised an objection and whether the raised objection data has been rejected. A set of invalid objection project objects in which the bidding object has raised an objection and the objection is invalid can be obtained.

[0054] Step 103, determining the objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set;

[0055] In order to identify malicious objections, the objection credit value of the bidding object can be analyzed based on the objection raised by the bidding object against the project object for which it failed to bid.

[0056] The more project objects a bidding object has raised objections to and whose objections are invalid among the project objects for which it has failed in its bidding, the lower the objection credit value representing the bidding object will be.

[0057] In a specific implementation, the objection credit value for the bidding object can be determined according to the proportion of the objection invalid project objects in the objection invalid project object set in the bid failure project object set.

[0058] Step 104, when the objection credit value meets the preset malicious user condition, collecting the second objection data of the bidding object on the objection invalid project object to obtain a second objection data set, and determining a third objection data set having the same attribute as the first objection data from the second objection data set;

[0059] Malicious user conditions may be preset. If the objection information value of a bidding object satisfies the preset malicious user conditions, it may indicate that the bidding object is suspected of malicious objection.

[0060] In order to identify malicious objections, when the objection information value of the bidding object meets the preset malicious user conditions, the second objection data raised by the bidding object in its objection invalid project object can be collected. Each objection invalid project object can correspond to one or more second objection data. Based on one or more objection invalid project objects, a second objection data set is obtained, and then a third objection data set with the same attributes as the first objection data can be determined from the second objection data set.

[0061] Specifically, the format of the objection data may be preset, which may include multiple attribute items, such as objection reasons, objection evidence, objection purpose, and the like.

[0062] When raising an objection, the bidder may fill in the corresponding attribute information for the attribute item.

[0063] Thus, according to the attribute information, the second objection data in the second objection data set can be keyword matched with the first objection data respectively, and whether the attributes are the same can be determined based on the matching similarity, and the second objection data with the same attributes can be marked as third objection data, and then the third objection data set can be obtained from the second objection data set.

[0064] Step 105: Determine the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercept the first objection data when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

[0065] Among them, the preset malicious objection event can represent that the objection data raised by the bidding object suspected of malicious objection is triggered as malicious objection data.

[0066] In order to identify malicious objections, the suspiciousness of the first objection data may be determined based on the third objection data set and the second objection data set. The suspiciousness may indicate the degree of suspicion that the first objection data is malicious objection data.

[0067] Specifically, the suspiciousness of the first objection data can be determined based on the proportion of the third objection data in the third objection data set in the second objection data set, and then it can be judged whether a preset malicious objection event is triggered based on the suspiciousness. If it is determined that the preset malicious objection event is triggered, the first objection data can be intercepted.

[0068] In an embodiment of the present invention, when receiving first objection data submitted by a user terminal for a bidding project object, determining the bidding object that submitted the first objection data and the submission time of the objection data; when judging that the first objection data meets the preset objection regulations according to the submission time and the bidding object, determining the set of failed bidding project objects of the bidding object that participated in the bidding but did not close the deal, and determining the set of invalid objection project objects for which the bidding object has raised objections and the objections are invalid from the set of failed bidding project objects; determining the objection credit value for the bidding object according to the set of failed bidding project objects and the set of invalid objection project objects; when the objection credit value meets the preset malicious user condition, collecting the second objection data of the bidding object on the invalid objection project object to obtain a second objection data set, and determining a third objection data set with the same attributes as the first objection data from the second objection data set; determining the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercepting the first objection data when judging that a preset malicious objection event is triggered according to the suspiciousness, thereby realizing the identification of malicious objection data and automatically intercepting malicious objection data, which can save human resources.

[0069] Reference Figure 2 , shows a flowchart of another method for automatically intercepting abnormal data provided by an embodiment of the present invention, which may specifically include the following steps:

[0070] Step 201, upon receiving first objection data submitted by a user terminal to a bidding project object, determining the bidding object submitting the first objection data and the submission time of the objection data;

[0071] Step 202, when it is determined that the first objection data meets the preset objection regulations according to the submission time and the bidder, a set of failed bidding project objects of the bidder that participated in the bidding but did not win the transaction is determined, and from the set of failed bidding project objects, a set of invalid objection project objects for which the bidder has raised objections and the objections are invalid is determined;

[0072] In one embodiment of the present invention, step 202 may further include the following steps:

[0073] Step a1, obtaining the quotation data of the bidding project object, and searching for the quotation information of the bidding object in the quotation information;

[0074] Step a2, when the quotation information of the bidding object is found, determining whether the submission time is within the public notice period of the preset objection regulations;

[0075] Step a3: judging whether the first objection data complies with preset objection regulations according to the judgment result.

[0076] In a specific implementation, the quotation information of the bidding object can be searched in the quotation data of the bidding project object. When the quotation information of the bidding object is found, it can be explained that the bidding object is the bidding object that has quoted for the bidding project object and is eligible to be the subject of raising an objection.

[0077] Furthermore, based on the submission time of the first objection data, it can be judged whether the time when the bidder raised the first objection data is within the public disclosure period of the objection regulations. Furthermore, based on whether the submission time is within the public disclosure period of the objection regulations, it can be judged whether the first objection data complies with the preset objection regulations.

[0078] In one embodiment of the present invention, the following steps may also be included:

[0079] Step b1: intercepting the first objection data when it is determined that the first objection data does not comply with a preset objection regulation.

[0080] Specifically, when it is determined that the first objection data does not comply with the preset objection regulations, the first objection data may be considered invalid and the first objection data may be intercepted to prevent the first objection data from being sent to the contact person corresponding to the bidding project object.

[0081] Step 203, determining a first proportion of the objection-invalidated project objects in the objection-invalidated project object set in the bid-failed project object set;

[0082] In order to identify whether the bidding object is suspected of malicious objection, the first proportion of invalid objection project objects in the invalid objection project object set in the failed bidding project object set can be determined, and then the objection credit value for the bidding object can be determined based on the first proportion.

[0083] Step 204: determining an objection credit value for the bidding object according to the first proportion;

[0084] Step 205, when the objection credit value meets the preset malicious user condition, collect the second objection data of the bidding object on the object of the invalid objection project to obtain a second objection data set, and determine a third objection data set with the same attribute as the first objection data from the second objection data set;

[0085] Step 206, determining a second proportion of the third objection data in the third objection data set in the second objection data set;

[0086] In order to improve the accuracy of identifying malicious objection data, when a bidding object is suspected of malicious objection, the second proportion of the third objection data in the third objection data set in the second objection data set can be determined, so that the degree of suspicion of the first objection data can be determined based on the second proportion.

[0087] Step 207: determining the suspiciousness of the first objection data according to the second proportion, and intercepting the first objection data when a preset malicious objection event is determined to be triggered according to the suspiciousness.

[0088] Specifically, the suspiciousness of the first objection data may be determined based on the second ratio, and then it may be determined whether a preset malicious objection event is triggered based on the suspiciousness. If it is determined that the preset malicious objection event is triggered, the first objection data may be intercepted.

[0089] In an embodiment of the present invention, when receiving first objection data submitted by a user terminal for a bidding project object, determining the bidding object that submitted the first objection data and the submission time of the objection data, and when judging that the first objection data meets the preset objection regulations according to the submission time and the bidding object, determining the set of failed bidding project objects that participated in the bidding and did not close the deal, and determining from the set of failed bidding project objects a set of invalid objection project objects for which the bidding object has raised objections and whose objections are invalid, determining a first proportion of the invalid objection project objects in the invalid objection project object set in the set of failed bidding project objects, determining an objection credit value for the bidding object according to the first proportion, and when the objection credit value meets a preset malicious user condition, collecting second objection data of the bidding object on the invalid objection project objects to obtain a second objection data set, and determining from the second objection data set a third objection data set with the same attributes as the first objection data, determining a second proportion of the third objection data in the third objection data set in the second objection data set, determining the suspiciousness of the first objection data according to the second proportion, and when judging that a preset malicious objection event is triggered according to the suspiciousness, intercepting the first objection data, thereby realizing identification of malicious objection data, automatically intercepting malicious objection data, and saving human resources.

[0090] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0091] Reference Figure 3 , shows a schematic diagram of the structure of a device for automatically intercepting abnormal data provided by an embodiment of the present invention, which may specifically include the following modules:

[0092] The receiving module 301 is used to determine the bidding object submitting the first objection data and the submission time of the objection data when receiving the first objection data submitted by the user terminal to the bidding project object;

[0093] The bidding failure project object module 302 is used to determine a bidding failure project object set of the bidding object that participated in the bidding and did not close the deal, and determine an objection invalid project object set of the bidding object that has raised an objection and the objection is invalid from the bidding failure project object set, when the first objection data meets the preset objection regulations according to the submission time and the bidding object.

[0094] An objection credit value module 303 is used to determine an objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set;

[0095] A third objection data set 304 is used to collect the second objection data of the bidding object on the objection invalid project object when the objection credit value meets the preset malicious user condition, obtain a second objection data set, and determine a third objection data set with the same attributes as the first objection data from the second objection data set;

[0096] The suspiciousness module 305 is used to determine the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercept the first objection data when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

[0097] In one embodiment of the present invention, the objection credit value module 303 may include:

[0098] A first proportion submodule, used to determine a first proportion of the objection-invalid project objects in the objection-invalid project object set in the objection-failed project object set;

[0099] The objection credit value submodule is used to determine the objection credit value for the bidding object according to the first proportion.

[0100] In one embodiment of the present invention, the suspicious degree module 305 may include:

[0101] A second proportion submodule, configured to determine a second proportion of the third objection data in the third objection data set in the second objection data set;

[0102] The suspicion level submodule is used to determine the suspicion level of the first objection data according to the second proportion.

[0103] In one embodiment of the present invention, the method may further include:

[0104] A quotation information module, used for obtaining quotation data of the bidding project object and searching for quotation information of the bidding object in the quotation information before determining the set of failed bidding project objects in which the bidding object participated in the bidding and did not win the bid;

[0105] When the quotation information of the bidding object is found, determining whether the submission time is within the public notice period of the preset objection regulations;

[0106] It is determined whether the first objection data complies with a preset objection regulation according to the determination result.

[0107] In one embodiment of the present invention, the method may further include:

[0108] The interception module is used to intercept the first objection data if it is determined that the first objection data does not comply with a preset objection regulation.

[0109] An embodiment of the present invention further provides an electronic device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor, and when the computer program is executed by the processor, the method for automatically intercepting abnormal data as described above is implemented.

[0110] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for automatically intercepting abnormal data as described above is implemented.

[0111] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0112] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0113] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0114] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0117] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0118] Finally, it should be noted that, in this article, 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 any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0119] The above is a detailed introduction to the method and device, electronic device, and storage medium for automatically intercepting abnormal data. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for automatically intercepting abnormal data, characterized in that: The method comprises: Upon receiving first objection data submitted by a user terminal to a bidding project object, determining a bidding object submitting the first objection data and a submission time of submitting the objection data; In the case where it is determined according to the submission time and the bidder that the first objection data meets the preset objection regulations, determining a set of failed bidding project objects of the bidder that participated in the bidding but did not close the deal, and determining a set of invalid objection project objects of the bidder that raised objections and whose objections were invalid from the set of failed bidding project objects; Determining the objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set; In the case where the objection credit value meets the preset malicious user condition, collecting the second objection data of the bidding object on the objection invalid project object to obtain a second objection data set, and determining a third objection data set with the same attribute as the first objection data from the second objection data set; The suspiciousness of the first objection data is determined according to the third objection data set and the second objection data set, and the first objection data is intercepted when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

2. The method according to claim 1, characterized in that The step of determining the objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set comprises: Determine a first proportion of the objection-invalidated project objects in the objection-invalidated project object set to the bid-failed project object set; An objection credit value for the bidding object is determined based on the first proportion.

3. The method according to claim 1 or 2, characterized in that: The step of determining the suspiciousness of the first objection data according to the third objection data set and the second objection data set comprises: determining a second proportion of third objection data in the third objection data set in the second objection data set; The suspiciousness level of the first objection data is determined according to the second ratio.

4. The method according to claim 1, characterized in that: Before determining the set of failed bidding project objects in which the bidding object participated in the bidding but did not win the bid, the method further includes: Obtaining the quotation data of the bidding project object, and searching for the quotation information of the bidding object in the quotation information; When the quotation information of the bidding object is found, determining whether the submission time is within the public notice period of the preset objection regulations; It is determined whether the first objection data complies with a preset objection regulation according to the determination result.

5. The method according to claim 1 or 4, characterized in that: Also includes: In a case where it is determined that the first objection data does not comply with a preset objection provision, the first objection data is intercepted.

6. A device for automatically intercepting abnormal data, characterized in that: The device comprises: A receiving module, configured to, upon receiving first objection data submitted by a user terminal to a bidding project object, determine a bidding object that submitted the first objection data and a submission time of the objection data; A bidding failure project object module is used to determine a bidding failure project object set of the bidding object that participated in the bidding and failed to complete the transaction, and determine an objection invalid project object set of the bidding object that has raised an objection and the objection is invalid from the bidding failure project object set, when the first objection data meets the preset objection regulations according to the submission time and the bidding object. An objection credit value module, used for determining an objection credit value for the bidding object according to the bidding failed project object set and the objection invalid project object set; a third objection data set, for collecting second objection data of the bidding object on the object of the invalid objection project to obtain a second objection data set when the objection credit value meets a preset malicious user condition, and determining a third objection data set having the same attributes as the first objection data from the second objection data set; The suspiciousness module is used to determine the suspiciousness of the first objection data according to the third objection data set and the second objection data set, and intercept the first objection data when it is determined that a preset malicious objection event is triggered according to the suspiciousness.

7. The device according to claim 6, characterized in that The objection credit value module includes: A first proportion submodule, used to determine a first proportion of the objection-invalid project objects in the objection-invalid project object set in the objection-failed project object set; The objection credit value submodule is used to determine the objection credit value for the bidding object according to the first proportion.

8. The device according to claim 6 or 7, characterized in that The suspicious degree module includes: A second proportion submodule, configured to determine a second proportion of the third objection data in the third objection data set in the second objection data set; The suspicion level submodule is used to determine the suspicion level of the first objection data according to the second proportion.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the method for automatically intercepting abnormal data as claimed in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for automatically intercepting abnormal data according to any one of claims 1 to 5 is implemented.

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