A method and system for calculating hidden danger control rate

Abstract

The present application provides a method and system for calculating the hidden danger control rate, which determines a first control result based on the credibility coefficients of each characteristic of each pair of hidden danger data to be processed. The first control result is used to describe whether each pair of hidden danger data to be processed belongs to the same category. Determining whether each pair of hidden danger data to be processed belongs to the same category based on the credibility coefficients of the characteristics of each pair of hidden danger data to be processed is different from the related art in which the credibility coefficients between the same type of data knowledge characters of all hidden danger data to be processed are determined to determine whether each pair of hidden danger data to be processed belongs to the same category. When performing control processing on several hidden danger data to be processed, this solution does not require all hidden danger data to be processed to have the same type of data knowledge characters, and realizes the control processing of several hidden danger data to be processed covering different types of data knowledge characters, effectively improving the accuracy and reliability of data control.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to a method and system for calculating hidden danger control rates. Background Art

[0002] Conventional calculations of hidden danger detection and control rates use the current time or inspection time to calculate the current enterprise hidden danger rectification rate. However, this cannot fully reflect whether the hidden dangers have been rectified beyond the deadline. This makes it difficult to ensure the readiness and reliability of the data. Therefore, a technical solution is urgently needed to improve the above technical problems. Summary of the Invention

[0003] In order to improve the technical problems existing in the relevant technologies, this application provides a method and system for calculating the hidden danger control rate.

[0004] In a first aspect, a method for calculating a hidden danger control rate is provided, the method comprising: obtaining a number of hidden danger data to be processed, wherein each of the hidden danger data to be processed includes at least one data knowledge character, the several hidden danger data to be processed include at least one hidden danger subject data and at least one hidden danger event data, the hidden danger subject data includes at least one data knowledge character, the data knowledge characters of the hidden danger subject data tuple include at least element features, position features, description features and attribute local features, the hidden danger event data includes at least one data knowledge character, the data knowledge characters of the hidden danger event data tuple include at least element features, position features and attribute local features; determining a feature credibility coefficient of at least one data knowledge character for every two of the hidden danger data to be processed according to each of the data knowledge characters of each of the hidden danger data to be processed, and obtaining at least one feature credibility coefficient for every two of the hidden danger data to be processed; determining a first control result according to each of the feature credibility coefficients of every two of the hidden danger data to be processed, the first control result being used to represent the control result of the several hidden danger data to be processed.

[0005] In an independently implemented embodiment, before determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed based on each data knowledge character of each hidden danger data to be processed, it also includes: dividing the obtained multiple hidden danger data to be processed into X data categories, where X is an integer greater than 1, and each data category includes at least two hidden danger data to be processed; determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed based on each data knowledge character of each hidden danger data to be processed includes: determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed in each data category based on each data knowledge character of each hidden danger data to be processed; determining the first governance result based on each characteristic credibility coefficient of every two hidden danger data to be processed includes: determining the third governance result for each data category based on each characteristic credibility coefficient of every two hidden danger data to be processed in each data category; and determining the first governance result based on the third governance result for each data category.

[0006] In an independently implemented embodiment, each of the hidden danger data to be processed includes no less than one data item, and each of the data items includes no less than one data knowledge character; based on each of the data knowledge characters of each of the hidden danger data to be processed, determining a characteristic credibility coefficient of no less than one data knowledge character for every two of the hidden danger data to be processed in the plurality of hidden danger data to be processed, including: based on each of the data items of each of the hidden danger data to be processed, determining a target data item for each of the hidden danger data to be processed in the plurality of hidden danger data to be processed, wherein the target data item is a data item that needs to be governed; based on each of the data knowledge characters of the target data items of each of the hidden danger data to be processed, determining a characteristic credibility coefficient of no less than one data knowledge character for the target data items of every two of the hidden danger data to be processed in the plurality of hidden danger data to be processed, and obtaining no less than one characteristic credibility coefficient for the target data items of every two of the hidden danger data to be processed in the plurality of hidden danger data to be processed.

[0007] In an independently implemented embodiment, a first governance result is determined based on the respective characteristic credibility coefficients of each two of the hidden danger data to be processed, including: determining a second governance result for each two of the hidden danger data to be processed based on the respective characteristic credibility coefficients of each two of the hidden danger data to be processed, wherein the second governance result is used to represent the governance result of each two of the hidden danger data to be processed; and determining the first governance result based on the second governance result of each two of the hidden danger data to be processed.

[0008] In an independently implemented embodiment, each of the hidden danger data to be processed includes no less than one target data item, wherein the target data item is a data item that needs to be factor detected; based on the respective characteristic credibility coefficients of each two of the hidden danger data to be processed, the second governance result of each two of the hidden danger data to be processed is determined, including: based on the respective characteristic credibility coefficients of no less than one target data item of each two of the hidden danger data to be processed, the second governance result of no less than one target data item of each two of the hidden danger data to be processed is determined; based on the second governance result of each two of the hidden danger data to be processed, the first governance result is determined, including: based on the second governance result of no less than one target data item of each two of the hidden danger data to be processed, the first governance result is determined.

[0009] In an independently implemented embodiment, the second governance result includes a determinable governance result and an undeterminable governance result, and the determinable governance result includes those belonging to the same category and those belonging to different categories; according to the second governance result of each two of the pending hidden danger data, the first governance result is determined, including: for the two pending hidden danger data whose governance results are undeterminable, two data tuples whose governance results are determinable are determined, wherein one of the two data tuples includes one pending hidden danger data and one remaining pending hidden danger data of the two pending hidden danger data whose governance results are undeterminable, and the other data tuple includes another pending hidden danger data and the one remaining pending hidden danger data of the two pending hidden danger data whose governance results are undeterminable; according to the second governance results of the two data tuples, the second governance result of the two pending hidden danger data whose governance results are undeterminable is determined.

[0010] In an independently implemented embodiment, any two adjacent data types include at least one identical hidden danger data to be processed.

[0011] In a second aspect, a hidden danger control rate calculation system is provided, comprising a processor and a memory communicating with each other, wherein the processor is configured to read a computer program from the memory and execute the program to implement the above method.

[0012] The embodiment of the present application provides a method and system for calculating the hidden danger control rate, which obtains a number of hidden danger data to be processed, wherein each hidden danger data to be processed includes no less than one data knowledge character; based on each data knowledge character of each hidden danger data to be processed, a characteristic credibility coefficient of no less than one data knowledge character of every two hidden danger data to be processed is determined; based on each characteristic credibility coefficient of every two hidden danger data to be processed, a first control result is determined, and the first control result is used to describe whether each hidden danger data to be processed belongs to the same category. Based on the characteristic credibility coefficient of every two hidden danger data to be processed, it is determined whether each hidden danger data to be processed belongs to the same category. Compared with the related art of determining whether each hidden danger data to be processed belongs to the same category by determining the credibility coefficient between the same category of data knowledge characters of all hidden danger data to be processed, this solution does not require all hidden danger data to be processed to have the same category of data knowledge characters when performing control control of several hidden danger data to be processed, and realizes control control of several hidden danger data to be processed covering different categories of data knowledge characters, effectively improving the accuracy and reliability of data control. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is a flowchart of a method for calculating a hidden danger control rate provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0016] See also Figure 1 , shows a method for calculating a hidden danger control rate, which may include the technical solutions described in the following steps 201 to 203.

[0017] Step 201: obtain a number of hidden danger data to be processed, wherein each of the hidden danger data to be processed includes at least one data knowledge character, the hidden danger data to be processed include at least one hidden danger subject data and at least one hidden danger event data, the hidden danger subject data include at least one data knowledge character, the data knowledge characters of the hidden danger subject data binary group include at least element features, position features, description features and attribute local features, the hidden danger event data include at least one data knowledge character, the data knowledge characters of the hidden danger event data binary group include at least element features, position features and attribute local features.

[0018] For example, each acquired hidden danger data item to be processed may include several data items. Based on the data processing objectives, a coarse governance method is used to identify one or several target data items from these data items, with the target data items representing all the hidden danger data to be processed. In subsequent steps such as data knowledge character credibility coefficient analysis, data credibility coefficient determination, and data factor detection, the target data items are analyzed, and the remaining data items in the hidden danger data to be processed are not considered. The coarse governance method can be implemented using existing methods and is not described in detail in this application.

[0019] By determining the target data items of each hidden danger data to be processed, and in subsequent steps determining the first governance results of several hidden danger data to be processed based on the data knowledge characters of the target data items of each hidden danger data to be processed, this can effectively reduce the workload of data governance, help improve the processing efficiency of data governance, and at the same time, help improve the accuracy of data governance.

[0020] Step 202: Determine the characteristic credibility coefficient of at least one data knowledge character for every two of the plurality of hidden danger data to be processed based on each of the data knowledge characters of the plurality of hidden danger data to be processed, and obtain at least one characteristic credibility coefficient for every two of the plurality of hidden danger data to be processed.

[0021] In this embodiment, specifically, each target data item of each hidden danger data to be processed includes no less than one data knowledge character, and a feature credibility coefficient of no less than one data knowledge character is calculated for every two target data items.

[0022] Step 203: Determine a first governance result based on the characteristic credibility coefficients of every two of the hidden danger data to be processed, where the first governance result is used to represent the governance result of the plurality of hidden danger data to be processed.

[0023] Furthermore, according to each characteristic credibility coefficient of each pair of hidden danger data to be processed, a second governance result of each pair of hidden danger data to be processed is determined, wherein the second governance result is the governance result of each pair of hidden danger data to be processed.

[0024] The second governance outcome for each of the two target data items of the potential danger data to be processed is determined using the respective feature credibility coefficients. When determining the second governance outcome, the only requirement is that the target data items of the two potential danger data items undergoing factor detection have at least one data knowledge character of the same type; there is no restriction that all target data items of the potential danger data to be processed have the same data knowledge character. The feature credibility coefficients offer greater flexibility and diversity, effectively expanding the precision and reliability of data governance and significantly improving its accuracy.

[0025] Exemplarily, the second governance result includes a determinable governance result and an undeterminable governance result. The determinable governance result includes belonging to the same category and belonging to different categories. The undeterminable governance result indicates that it is impossible to determine whether the two target data items are of the same category. The second governance result is determined based on the credibility coefficients of each feature of each two target data items. For any two target data items with the same category of data knowledge characters, the corresponding second governance result is determinable. For any two target data items that do not have the same category of data knowledge characters, the corresponding second governance result is undeterminable, that is, it is impossible to determine whether any two target data items that do not cover the same category of data knowledge characters belong to the same category.

[0026] Determining a first governance result based on the second governance result of each pair of pending hidden danger data includes: for two pending hidden danger data with uncertain governance results, determining any two data binary pairs with determinable governance results, wherein one of the two data binary pairs includes one pending hidden danger data of the two pending hidden danger data with uncertain governance results and one remaining pending hidden danger data, and the other of the two data binary pairs includes the other pending hidden danger data of the two pending hidden danger data with uncertain governance results and the aforementioned one remaining pending hidden danger data; and determining a second governance result for the two pending hidden danger data with uncertain governance results based on the second governance results of the two data binary pairs. When any two pending hidden danger data do not cover the same type of data knowledge characters, the feature credibility coefficients of the two pending hidden danger data are both zero. At this time, it is impossible to determine whether the two pending hidden danger data belong to the same type based on the feature credibility coefficients of the two pending hidden danger data, and the second governance result for the two pending hidden danger data is that the governance result is uncertain. For any two pending hidden danger data whose governance results are uncertain, two data tuples that respectively cover one of the two pending hidden danger data and at the same time cover the governance results of the same one pending hidden danger data are used to re-determine the second governance result of any two pending hidden danger data, thereby realizing the governance processing of the pending hidden danger data that do not cover the same type of data knowledge characters, effectively expanding the accuracy and reliability of data governance, and realizing the governance processing of abnormal data.

[0027] Optionally, this embodiment further includes: dividing the obtained plurality of hidden danger data to be processed into X data categories, where X is an integer greater than 1, each data category includes at least two hidden danger data to be processed, and any two adjacent data categories include at least one identical hidden danger data to be processed; then, based on each data knowledge character of each hidden danger data to be processed in each data category, determining the characteristic credibility coefficient of at least one data knowledge character of every two hidden danger data to be processed in each data category; based on each characteristic credibility coefficient of every two hidden danger data to be processed in each data category, determining the third governance result of each data category; and determining the first governance result based on the third governance result of each data category.

[0028] The obtained several hidden danger data to be processed are grouped and processed, and the third governance results of each data type are determined respectively, which can significantly reduce the workload of data processing and help ensure the accuracy of the data type results. Since any two adjacent data types include at least one identical hidden danger data to be processed, the third governance results of any two adjacent data types can still be transmitted to each other. The interaction of the third governance results of different data types can be used to further optimize the third governance results, and then the first governance results are obtained by combining the optimized third governance results of each data type. Since this embodiment does not require that all hidden danger data to be processed include the same type of data knowledge characters, there may be hidden danger data to be processed that do not include the same type of data knowledge characters. This part of the hidden danger data to be processed is abnormal data that cannot be directly compared with the credibility coefficient. This embodiment realizes the governance of abnormal data.

[0029] This embodiment obtains a number of hidden danger data to be processed, wherein each hidden danger data to be processed includes no less than one data knowledge character; based on each data knowledge character of each hidden danger data to be processed, determines a feature credibility coefficient of no less than one data knowledge character for every two hidden danger data to be processed; based on each feature credibility coefficient of every two hidden danger data to be processed, determines a second governance result for every two hidden danger data to be processed; based on the second governance result for every two hidden danger data to be processed, determines a first governance result. By utilizing at least one characteristic credibility coefficient of every two hidden danger data to be processed, the second governance result corresponding to every two hidden danger data to be processed is determined. When two hidden danger data to be processed have at least one data knowledge character of the same type, the governance results of the two hidden danger data to be processed can be determined. The data type process does not require all hidden danger data to be processed to have the same type of data knowledge characters, which can effectively improve the accuracy and reliability of the data type; when determining the first governance result, the second governance result of every two hidden danger data to be processed is utilized. Since the second governance result is interactive, this realizes the determination of factor detection results between hidden danger data to be processed that do not have the same type of data knowledge characters, effectively improves the accuracy and reliability of data types, and greatly reduces the requirements of the data governance process on data content and data format; utilizing the interaction of the second governance result, the data governance result can be self-checked and corrected, which is conducive to improving the accuracy of data governance.

[0030] Based on the above, a hidden danger control rate calculation device 200 is provided, which includes:

[0031] a data acquisition module, configured to obtain a plurality of hidden danger data to be processed, wherein each of the plurality of hidden danger data to be processed includes at least one data knowledge character, the plurality of hidden danger data to be processed includes at least one hidden danger theme data and at least one hidden danger event data, the hidden danger theme data includes at least one data knowledge character, the data knowledge character of the hidden danger theme data binary includes at least element features, position features, description features, and attribute local features, the hidden danger event data includes at least one data knowledge character, the data knowledge character of the hidden danger event data binary includes at least element features, position features, and attribute local features;

[0032] a data obtaining module, configured to determine, based on each of the data knowledge characters of each of the hidden danger data to be processed, a feature credibility coefficient of at least one of the data knowledge characters for every two of the hidden danger data to be processed, and obtain at least one feature credibility coefficient for every two of the hidden danger data to be processed;

[0033] The result management module is used to determine a first management result based on the characteristic credibility coefficients of every two of the hidden danger data to be processed, and the first management result is used to represent the management result of the multiple hidden danger data to be processed.

[0034] Based on the above, a hidden danger control rate calculation system is shown, which includes a processor and a memory that communicate with each other. The processor is used to read a computer program from the memory and execute it to implement the above method.

[0035] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0036] In summary, based on the above scheme, a number of hidden danger data to be processed are obtained, wherein each hidden danger data to be processed includes no less than one data knowledge character; based on each data knowledge character of each hidden danger data to be processed, a characteristic credibility coefficient of no less than one data knowledge character of every two hidden danger data to be processed is determined; based on each characteristic credibility coefficient of every two hidden danger data to be processed, a first governance result is determined, and the first governance result is used to describe whether each hidden danger data to be processed belongs to the same category. Based on the characteristic credibility coefficient of every two hidden danger data to be processed, it is determined whether each hidden danger data to be processed belongs to the same category. Compared with the related art of determining whether each hidden danger data to be processed belongs to the same category by determining the credibility coefficient between the same type of data knowledge characters of all hidden danger data to be processed, this scheme does not require all hidden danger data to be processed to have the same type of data knowledge characters when performing governance processing on several hidden danger data to be processed, and realizes the governance processing of several hidden danger data to be processed covering different types of data knowledge characters, effectively improving the accuracy and reliability of data governance.

[0037] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0038] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0039] The basic concepts have been described above. It will be apparent to those skilled in the art that the detailed disclosure above is merely illustrative and does not limit the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to the present application. Such modifications, improvements, and amendments are suggested in the present application and remain within the spirit and scope of the exemplary embodiments of the present application.

[0040] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0041] In addition, it will be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0042] A computer storage medium may include a propagated data signal embodying the computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, or any suitable combination thereof. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transfer the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of these.

[0043] The computer program coding required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program coding can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or be connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0044] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0045] Similarly, it should be noted that, in order to simplify the presentation of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0046] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0047] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this application, as well as documents (currently or subsequently attached to this application) that limit the broadest scope of the claims of this application. It should be noted that if the descriptions, definitions, and / or use of terms in the accompanying materials of this application are inconsistent or conflicting with the content of this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0048] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other variations may also fall within the scope of this application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this application may be considered consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly introduced and described in this application.

[0049] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for calculating hidden danger control rate, characterized in that: The method comprises: Obtaining a plurality of hidden danger data to be processed, wherein each of the plurality of hidden danger data to be processed includes at least one data knowledge character, the plurality of hidden danger data to be processed includes at least one type of hidden danger subject data and at least one type of hidden danger event data, the hidden danger subject data includes at least one data knowledge character, the data knowledge character of the hidden danger subject data binary includes at least element features, position features, description features, and attribute local features, the hidden danger event data includes at least one data knowledge character, the data knowledge character of the hidden danger event data binary includes at least element features, position features, and attribute local features; Determining, based on each of the data knowledge characters of each of the hidden danger data to be processed, a feature credibility coefficient of at least one of the data knowledge characters for every two of the hidden danger data to be processed, thereby obtaining at least one feature credibility coefficient for every two of the hidden danger data to be processed; determining a first governance result according to each of the characteristic credibility coefficients of every two of the hidden danger data to be processed, wherein the first governance result is used to represent a governance result of the plurality of hidden danger data to be processed; Before determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed based on the data knowledge characters of the hidden danger data to be processed, the method further includes: dividing the obtained plurality of hidden danger data to be processed into X data categories, where X is an integer greater than 1, and each data category includes at least two hidden danger data to be processed; The method of determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed based on each data knowledge character of each hidden danger data to be processed includes: determining the characteristic credibility coefficient of at least one data knowledge character for every two hidden danger data to be processed in each data type based on each data knowledge character of each hidden danger data to be processed in each data type.

2. The method according to claim 1, characterized in that The first governance result is determined based on the characteristic credibility coefficients of each pair of the hidden danger data to be processed, including: determining the third governance result of each data type based on the characteristic credibility coefficients of each pair of the hidden danger data to be processed of each data type; and determining the first governance result based on the third governance result of each data type.

3. The method according to claim 2, characterized in that Each of the hidden danger data to be processed includes at least one data item, and each of the data items includes at least one data knowledge character; determining, based on each of the data knowledge characters of each of the hidden danger data to be processed, a feature credibility coefficient of at least one of the data knowledge characters for every two of the hidden danger data to be processed, including: Determining, based on each data item of each hidden danger data to be processed, a target data item of each hidden danger data to be processed among the plurality of hidden danger data to be processed, wherein the target data item is a data item that needs to be managed; According to each of the data knowledge characters of the target data items of each of the hidden danger data to be processed, the characteristic credibility coefficient of at least one of the data knowledge characters of the target data items of every two of the hidden danger data to be processed in the several hidden danger data to be processed is determined, and at least one characteristic credibility coefficient of the target data items of every two of the hidden danger data to be processed in the several hidden danger data to be processed is obtained.

4. The method according to claim 1, wherein Determining a first treatment result according to each of the characteristic credibility coefficients of each of the two hidden danger data to be processed includes: determining, based on each of the characteristic credibility coefficients of each pair of the hidden danger data to be processed, a second governance result for each pair of the hidden danger data to be processed, wherein the second governance result is used to represent a governance result for each pair of the hidden danger data to be processed; The first governance result is determined according to the second governance results of every two of the hidden danger data to be processed.

5. The method according to claim 4, characterized in that Each of the hidden danger data to be processed includes at least one target data item, wherein the target data item is a data item that needs to be factor-detected; and determining the second governance result for each of the two hidden danger data to be processed based on the respective feature credibility coefficients of each of the two hidden danger data to be processed includes: determining the second governance result for the at least one target data item for each of the two hidden danger data to be processed based on the respective feature credibility coefficients of the at least one target data item for each of the two hidden danger data to be processed; Determining the first governance result based on the second governance result of every two of the hidden danger data to be processed includes: determining the first governance result based on the second governance result of at least one target data item of every two of the hidden danger data to be processed.

6. The method according to claim 4, characterized in that The second governance result includes a determinable governance result and an indeterminate governance result, and the determinable governance result includes belonging to the same category and belonging to different categories; Determining the first governance result according to the second governance results of every two of the hidden danger data to be processed includes: For the two pending hidden danger data whose governance results are uncertain, determine two data 2-tuples whose governance results are determinable, wherein one of the two data 2-tuples includes one pending hidden danger data of the two pending hidden danger data whose governance results are uncertain and one remaining pending hidden danger data, and the other of the two data 2-tuples includes the other pending hidden danger data of the two pending hidden danger data whose governance results are uncertain and the one remaining pending hidden danger data; According to the second governance results of the two data tuples, the second governance results of the two to-be-processed hidden danger data whose governance results are uncertain are determined.

7. The method according to claim 1, characterized in that Any two adjacent data types include at least one identical hidden danger data to be processed.

8. A hidden danger control rate calculation system, characterized in that: The method comprises a processor and a memory communicating with each other, wherein the processor is configured to read a computer program from the memory and execute the program to implement the method according to any one of claims 1 to 7.

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