E-commerce big data security evaluation system based on big data analysis

Abstract

The invention relates to the technical field of e-commerce data security evaluation, in particular to an e-commerce big data security evaluation system based on big data analysis. The system comprises a data integration unit, a division checking unit and a step-by-step evaluation unit. The e-commerce data is integrated and shunted through the data integration unit, the e-commerce data of the same type are combined together, virus checking is conducted on the e-commerce data after shunting is completed, after a file with viruses is determined, code format conversion is conducted on the e-commerce data through the division checking unit, segmentation is conducted after conversion, and therefore the file with the viruses is obtained. According to the method, the code converted from the e-commerce data is divided into multiple parts, and then the evaluation is performed through the step-by-step evaluation unit, so that the volume of a virus file is reduced, the deletion of a virus-free file is reduced, and the accuracy of searching the virus file is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce data security assessment, and in particular to an e-commerce big data security assessment system based on big data analysis. Background Art

[0002] With the further development of online consumption, the e-commerce industry is also becoming more and more prosperous. With the rise of the e-commerce industry, the data of e-commerce is also increasing. The data of each e-commerce reflects the sales situation of each e-commerce. Therefore, the data of e-commerce is vital to each e-commerce. If the e-commerce data is leaked, it is easy to cause economic losses to the e-commerce. At the same time, if data with viruses enters the location where the e-commerce stores data, the location where the e-commerce stores data will be damaged by the virus, which will cause data damage. Therefore, it is crucial to detect viruses in e-commerce data in order to evaluate the security of e-commerce data.

[0003] At present, in the process of evaluating e-commerce data, the e-commerce data will be checked for viruses. After the virus detection software detects viruses in the file, it will delete the entire folder containing the virus. However, not all files in the entire file are viruses. Directly deleting the entire folder will cause the e-commerce to lose the entire folder, which will cause the loss of e-commerce data. At the same time, after the e-commerce data is retrieved at a later stage, it is impossible to determine which files in the folder contain viruses. In this way, the e-commerce still cannot obtain the folder containing data with viruses. For this reason, we propose an e-commerce big data security assessment system based on big data analysis to determine the specific location of the virus in the process of evaluating e-commerce data. Summary of the invention

[0004] The purpose of the present invention is to provide an e-commerce big data security assessment system based on big data analysis to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, an e-commerce big data security assessment system based on big data analysis is provided, including a data integration unit, a division and verification unit, and a step-by-step assessment unit;

[0006] The data integration unit integrates the e-commerce data, divides the data into batches after integration, and performs virus detection on the divided data after data division;

[0007] The partitioning and checking unit converts the format of the data checked by the data integration unit so that all the data are converted into codes, and divides the converted codes. After the codes are divided, the divided codes are stored;

[0008] The step-by-step evaluation unit evaluates the codes stored in the partitioning and checking unit one by one. Meanwhile, during the evaluation process of the step-by-step evaluation unit, the code data stored in the partitioning and checking unit is transmitted to the data integration unit, and the data integration unit detects and kills viruses on the segmented codes. After the data integration unit detects and kills viruses, the structure of the virus detection and killing is fed back to the step-by-step evaluation unit. After the data integration unit detects and kills viruses, the step-by-step evaluation unit refines the designation of the segmentation strategy for the segmented codes after the viruses are detected and killed step by step, and transmits the code with the virus to the partitioning and checking unit, which re-segments and stores the code. After segmentation, the step-by-step evaluation unit performs evaluation again, and when the step-by-step evaluation unit detects and kills viruses, the result of the data evaluation is output.

[0009] As a further improvement of the technical solution, the data integration unit includes a data diversion module and a data virus detection module;

[0010] The data distribution module collects e-commerce data and integrates the collected data so that each type of data is integrated together, and after the data is integrated, the integrated data is distributed so that the integrated data is divided into multiple batches;

[0011] The data virus detection module detects and kills the viruses transmitted from the data diversion module, and after the detection and killing, distinguishes batches of data with viruses and without viruses, and transmits the batches with viruses to the partitioning and verification unit, and transmits the data without viruses to the step-by-step evaluation unit, which evaluates and exports them.

[0012] As a further improvement of the technical solution, the partition checking unit includes a format conversion and segmentation module and a data dispersion storage module;

[0013] The format conversion and segmentation module converts the format of the virus-carrying data transmitted by the data virus detection module, converts all the data into code, and after the code conversion is completed, segments the code so that the code is divided into multiple parts;

[0014] The data dispersing storage module stores the codes segmented by the format conversion segmentation module, and after storing, transmits the newly stored data to the step-by-step evaluation unit.

[0015] As a further improvement of the technical solution, the method steps adopted by the format conversion and segmentation module when segmenting the code are as follows:

[0016] S1. Identify the codes converted from the e-commerce data after batching by the data diversion module, and mark the length of the code for each e-commerce data conversion format. Each e-commerce data conversion code is a segment, and then divide the batched e-commerce data conversion code into multiple segments;

[0017] S2. Extract the paragraph codes transmitted by the step-by-step evaluation unit and received by the format conversion segmentation module, and then segment the codes of each paragraph so that the multiple paragraph codes in each paragraph are segmented into two parts.

[0018] As a further improvement of the technical solution, the division verification unit further includes a traceability marking module;

[0019] After the data dispersing storage module receives the segmented codes transmitted by the format conversion segmentation module, the source tracing marking module marks the code data received by the data dispersing storage module, and marks the paragraph information corresponding to the code.

[0020] As a further improvement of the technical solution, the step-by-step evaluation unit includes a step-by-step refinement module and a data evaluation module;

[0021] The data evaluation module receives the code data stored in the data dispersion storage module, and after receiving the code data, transmits the received data to the data virus detection module. The data virus detection module detects and kills viruses on the data transmitted by the data evaluation module, and after the virus detection module detects and kills viruses, the virus detection result is evaluated;

[0022] The step-by-step refinement module receives the code data transmitted by the data evaluation module, reads the content of the code data, and then formulates a strategy for code data segmentation based on the length of the code data. After the strategy is determined, the received code data and the specified segmentation strategy are transmitted to the format conversion segmentation module.

[0023] As a further improvement of the technical solution, the operation steps of the data evaluation module in the process of evaluating the code data transmitted by the data dispersion storage module are:

[0024] S1, the data evaluation module receives the virus detection information from the data virus detection module, and determines whether there is a virus in the data received by the data evaluation module from the data distributed storage module according to the virus detection result of the data virus detection module;

[0025] S2. The data evaluation module processes the data accordingly according to the determination of whether there is a virus, wherein the data evaluation module exports the virus-free data from the data evaluation module, and combines the code information according to the information marked by the traceability marking module during the export, combines the codes segmented by the format conversion segmentation module, and exports the evaluation result of the combined code data together with the code data;

[0026] S3. The data evaluation module records the data with the virus and transmits the data with the virus to the step-by-step refinement module. At the same time, the data evaluation module temporarily stores the code converted from the same e-commerce data as the data with the virus.

[0027] As a further improvement of the present technical solution, after the format conversion and segmentation module receives the segmentation strategy and the virus-containing data transmitted by the step-by-step refinement module, it segments the virus-containing data according to the segmentation strategy, and imports the data into the data dispersion storage module after segmentation. The data dispersion storage module stores the data transmitted by the format conversion and segmentation module, and marks it through the traceability marking module, and then the data evaluation module evaluates the data transmitted by the data dispersion storage module.

[0028] As a further improvement of the present technical solution, during the process of data evaluation, if the data evaluation module found viruses in the data during the last evaluation, but the data virus detection module is unable to identify the virus after the segmentation strategy specified by the step-by-step refinement module and the format conversion segmentation module performs cutting, the data evaluation module stops segmenting the data and records and exports the data that was last evaluated as having viruses by the data evaluation module, and at the same time exports the code data that the data evaluation module evaluates as the same e-commerce data as the data having viruses and that has been stored by the data evaluation module.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In the e-commerce big data security assessment system based on big data analysis, the e-commerce data is integrated and diverted through the data integration unit, so that the e-commerce data of the same type are combined together, and the e-commerce data is checked for viruses after the diversion is completed. After the virus-containing files are determined, the e-commerce data is converted into a code format through the partitioning and checking unit, and the code is segmented after the conversion, so that the e-commerce data conversion code is divided into multiple copies, and then evaluated by the step-by-step evaluation unit, so as to reduce the volume of virus-containing files, reduce the deletion of virus-free files, and improve the accuracy of finding virus files.

[0031] 2. In the e-commerce big data security assessment system based on big data analysis, after the data assessment module evaluates the data transmitted from the data dispersion storage module, the file containing the virus is transmitted to the step-by-step refinement module, and the segmentation strategy specified by the step-by-step refinement module is used, and then the format conversion segmentation module performs segmentation. After segmentation by the format conversion segmentation module, the data is evaluated again by the data assessment module to find the smallest unit where the virus exists, and after finding the exact location of the virus, the assessment result is exported to facilitate the user to obtain the exact location of the virus and facilitate the user to process the file containing the virus. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is the overall structural diagram of the present invention;

[0033] Figure 2 The figure is a detailed block diagram of the overall structure of the present invention.

[0034] The meaning of each number in the figure is:

[0035] 1. Data integration unit; 11. Data diversion module; 12. Data virus detection module;

[0036] 2. Division and verification unit; 21. Format conversion and segmentation module; 22. Data dispersion storage module; 23. Traceability marking module;

[0037] 3. Step-by-step evaluation unit; 31. Step-by-step refinement module; 32. Data evaluation module. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0040] Example 1

[0041] In order to accurately obtain the specific files with viruses in e-commerce data and reduce the amount of data that is accidentally deleted, please refer to Figure 1-Figure 2 As shown, the purpose of this embodiment is to provide an e-commerce big data security assessment system based on big data analysis, including a data integration unit 1, a partitioning and checking unit 2, and a step-by-step assessment unit 3;

[0042] The data integration unit 1 integrates the e-commerce data, divides the data into batches after the integration, and performs virus detection on the divided data after the data division;

[0043] The data integration unit 1 includes a data distribution module 11 and a data virus detection module 12;

[0044] The data diversion module 11 collects e-commerce data. The data collected by the data diversion module 11 is data that the e-commerce company organizes itself or that is stored by the system itself. After collecting the e-commerce data, the data diversion module 11 integrates the collected data so that each type of data is integrated together, that is, text data is integrated together, image data is integrated together, and audio files are integrated together. By combining different types of data together, the difficulty of later processing can be reduced and the speed of data processing can be accelerated. After the data is integrated, the integrated data is diverted and divided into multiple batches. By diverting the integrated data into multiple batches, the file size of the file data is reduced, so as to facilitate the data virus detection module 12 to process the data. When dividing the data into batches, it should be noted that the integrity of the file itself cannot be destroyed, otherwise the file with the virus may be split into multiple parts, and when the file is processed by the data virus detection module 12, it is impossible to determine whether the file has a virus.

[0045] The data virus detection module 12 detects and kills the viruses transmitted by the data diversion module 11, and kills the viruses on the batched data through the data virus detection module 12 to determine which files have viruses and which files do not have viruses. After the virus detection and killing, the batch data with viruses and those without viruses are distinguished, and the batches with viruses are transmitted to the partitioning and verification unit 2, so that the partitioning and verification unit 2 further processes the data with viruses, and the data without viruses are transmitted to the step-by-step evaluation unit 3, which evaluates and exports them, so as to reduce the probability of a large amount of data accumulating inside the system itself and causing load, thereby ensuring that the system can run stably, and quickly output files without viruses, so that users can quickly understand which files have no problems.

[0046] The partitioning and checking unit 2 converts the format of the data checked and killed by the data integration unit 1 so that all the data are converted into codes, that is, all the data transmitted by the data integration unit 1 are converted into codes. By converting all the data into codes, the data is refined, which is helpful to find the location of the file with the virus, and after all the data are converted into codes, the converted codes are divided into multiple parts, and after the codes are divided, the divided codes are stored;

[0047] The partition checking unit 2 includes a format conversion and segmentation module 21 and a data dispersion storage module 22;

[0048] The format conversion and segmentation module 21 converts the format of the virus-carrying data transmitted by the data virus detection module 12, converts all the data into code, and after the code conversion is completed, segments the code into multiple parts;

[0049] The method and steps adopted by the format conversion and segmentation module 21 when segmenting the code are as follows:

[0050] S1. Identify the codes converted from the e-commerce data after batching by the data diversion module 11, and mark the code length of each e-commerce data conversion format. Each e-commerce data conversion code is a segment, and then the batched e-commerce data conversion code is divided into multiple segments. When segmenting, ensure that the code of each segment is complete. For example, there are 10 e-commerce data. After being converted into codes, they are 10 segments of codes. Then, the 10 segments of codes are divided into paragraphs. When dividing, try to ensure that the code of each paragraph is the same. For example, if it is divided into 2 paragraphs, each paragraph has 5 segments of code. If it is divided into 5 paragraphs, each paragraph has 2 segments of code. If the code is not evenly divided, the remaining code is the last paragraph.

[0051] The above step S1 is to perform preliminary segmentation on the data transmitted by the data virus detection module 12. After segmentation, the segmented data can be transmitted to the data distribution storage module 22.

[0052] S2, extracting the paragraph code transmitted by the step-by-step evaluation unit 3 and received by the format conversion and segmentation module 21, and then segmenting the code of each paragraph, so that the multiple paragraph codes in each paragraph are divided into two parts, and if the code transmitted by the step-by-step evaluation unit 3 is not a paragraph code, the format conversion and segmentation module 21 directly divides the code into multiple parts by evenly dividing the code;

[0053] The step in S2 is that after the subsequent step-by-step evaluation unit 3 feeds back the data to the division and verification unit 2, the division and verification unit 2 processes the data again;

[0054] The data dispersing storage module 22 stores the codes segmented by the format conversion segmentation module 21 , and after storage, transmits the newly stored data to the step-by-step evaluation unit 3 .

[0055] In order to facilitate the system to separate the traceability code from those paragraphs, the division and verification unit 2 also includes a traceability marking module 23. After the data dispersion storage module 22 receives the segmented code transmitted by the format conversion segmentation module 21, the traceability marking module 23 marks the code data received by the data dispersion storage module 22, and marks the paragraph information corresponding to the code. At the same time, when the data dispersion storage module 22 stores data, the data stored in the data dispersion storage module 22 is marked, so as to achieve the data segmentation directory of the format conversion segmentation module 21, which is convenient for the subsequent step-by-step evaluation unit 3 to export the data.

[0056] The step-by-step evaluation unit 3 evaluates the codes stored in the partitioning and checking unit 2 one by one. Meanwhile, during the evaluation process of the step-by-step evaluation unit 3, the code data stored in the partitioning and checking unit 2 is transmitted to the data integration unit 1, and the data integration unit 1 detects and kills viruses on the segmented codes. After the data integration unit 1 detects and kills viruses, the structure of the virus detection is fed back to the step-by-step evaluation unit 3. After the data integration unit 1 detects and kills viruses, the step-by-step evaluation unit 3 refines the designation of the segmentation strategy for the segmented codes after the viruses are detected and killed step by step, and transmits the code with the virus to the partitioning and checking unit 2, which re-segments and stores it. After segmentation, the step-by-step evaluation unit 3 evaluates it again. When the step-by-step evaluation unit 3 detects and kills viruses, the result of the data evaluation is output.

[0057] Wherein, the step-by-step evaluation unit 3 includes a step-by-step refinement module 31 and a data evaluation module 32;

[0058] The data evaluation module 32 receives the code data stored in the data dispersion storage module 22, and after receiving the code data, transmits the received data to the data virus detection module 12. The data virus detection module 12 performs virus detection on the data transmitted by the data evaluation module 32, determines whether the data received by the data evaluation module 32 contains viruses, and after the data virus detection module 12 detects and kills the viruses, the data virus detection module 12 transmits the virus detection result to the data evaluation module 32, and the data evaluation module 32 evaluates the virus detection result.

[0059] The operation steps of the data evaluation module 32 in the process of evaluating the code data transmitted by the data dispersion storage module 22 are as follows:

[0060] S1, the data evaluation module 32 receives the virus detection information of the data virus detection module 12, and determines whether there is a virus in the data received by the data evaluation module 32 from the data distributed storage module 22 according to the virus detection result of the data virus detection module 12;

[0061] S2, the data evaluation module 32 processes the data accordingly according to the determination of whether there is a virus, wherein the data evaluation module 32 exports the virus-free data from the data evaluation module 32, and combines the code information according to the information marked by the traceability marking module 23 during the export, combines the codes segmented by the format conversion segmentation module 21 together, and exports the evaluation result of the combined code data together with the code data; by combining the codes segmented by the format conversion segmentation module 21 together, the content of the exported code data will not be scattered, so as to facilitate the viewing of the user and speed up the user's determination of which files are virus-free;

[0062] S3. The data evaluation module 32 records the data with viruses and transmits the data with viruses to the step-by-step refinement module 31. Meanwhile, the data evaluation module 32 temporarily stores the code converted from the same e-commerce data as the data with viruses.

[0063] The step-by-step refinement module 31 receives the code data transmitted by the data evaluation module 32, and reads the content of the code data, and then formulates a strategy for code data segmentation based on the length of the code data. After the strategy is determined, the received code data and the specified segmentation strategy are transmitted to the format conversion segmentation module 21.

[0064] When formulating the segmentation strategy for the code transmitted by the data evaluation module 32, the step-by-step refinement module 31 adopts different cutting methods to form different strategies to better obtain the location of the virus, wherein the steps of formulating the strategy are:

[0065] Recognize the code size of the data transmitted by the data evaluation module 32;

[0066] When the code is too long and is formed by converting multiple data formats, a strategy is developed to divide the code into multiple codes according to the position of each code segment based on the boundaries of each data conversion code, so that each code is separated from each other;

[0067] When the code is a code of a data, the step-by-step refinement module 31 copies the code transmitted by the data evaluation module 32 to form a plurality of identical codes, and at the same time, the codes are segmented one by one by using a permutation and combination method. At this time, the segmentation position used is a two-segment segmentation, that is, a code is segmented into two parts. For example, there is a string of codes: SHEUIHKEIDH. The strategies for segmenting it currently include: S / HEUIHKEIDH, SH / EUIHKEIDH, ..., SHEUIHKEID / H, and the segmentation strategy formed is transmitted to the format conversion segmentation module 21;

[0068] After the format conversion segmentation module 21 receives the segmentation strategy and the virus-containing data transmitted by the step-by-step refinement module 31, it segments the virus-containing data according to the segmentation strategy, and imports the data into the data dispersion storage module 22 after segmentation. The data dispersion storage module 22 stores the data transmitted by the format conversion segmentation module 21, and marks it through the traceability marking module 23, and then the data evaluation module 32 evaluates the data transmitted by the data dispersion storage module 22.

[0069] After the format conversion segmentation module 21 segments the code according to the segmentation strategy of the step-by-step refinement module 31, the data evaluation module 32 uses the data virus detection module 12 to detect viruses on the several codes segmented by the format conversion segmentation module 21. During the process of evaluating the data, if the evaluated data contains viruses during the last evaluation by the data evaluation module 32, but the data virus detection module 12 cannot identify the virus after the format conversion segmentation module 21 performs cutting according to the segmentation strategy specified by the step-by-step refinement module 31, the data evaluation module 32 stops segmenting the data, and records and exports the data that was last evaluated as having viruses by the data evaluation module 32, and at the same time exports the code data that the data evaluation module 32 evaluates and stores as the same e-commerce data as the data having viruses.

[0070] When the data evaluation module 32 evaluates the data of a single code, the data evaluation module 32 evaluates the multiple identical codes divided by the data virus detection module 12 in a sequential manner, that is, the data evaluation module 32 uniformly transmits the two parts of a string of codes to the data virus detection module 12, and the data virus detection module 12 detects and kills viruses, and then the data virus detection module 12 determines which part of the code divided into the two parts has a virus, and then the data virus detection module 12 transmits the result to the data evaluation module 32, and the data evaluation module 32 records the code information of the virus part, and then the data evaluation module 32 transmits multiple groups of codes divided by a string of codes to the data virus detection module 12, and then the data evaluation module 32 records the virus part, and then the data evaluation module 32 combines the virus-containing codes with each other, and then excludes the redundant codes, so as to obtain accurate data of virus-containing codes.

[0071] The e-commerce data is integrated and diverted through the data integration unit 1, so that the e-commerce data of the same type are combined together, and the e-commerce data is checked for viruses after the diversion is completed. After the virus-containing files are determined, the e-commerce data is converted into a code format through the partitioning and checking unit 2, and the code is segmented after the conversion, so that the e-commerce data conversion code is divided into multiple parts, and then evaluated by the step-by-step evaluation unit 3, so as to reduce the volume of virus-containing files, reduce the deletion of virus-free files, and improve the accuracy of finding virus files.

[0072] After the data evaluation module 32 evaluates the data transmitted from the data dispersion storage module 22, the file containing the virus is transmitted to the step-by-step refinement module 31, and is segmented by the format conversion segmentation module 21 according to the segmentation strategy specified by the step-by-step refinement module 31. After segmentation by the format conversion segmentation module 21, the data evaluation module 32 performs another evaluation to find the smallest unit where the virus exists, and after finding the exact location of the virus, the evaluation result is exported to facilitate the user to obtain the exact location of the virus and facilitate the user to process the file containing the virus.

[0073] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. E-commerce big data security assessment system based on big data analysis, characterized by: It includes a data integration unit (1), a division and verification unit (2) and a step-by-step evaluation unit (3); The data integration unit (1) integrates the e-commerce data, divides the data into batches after the integration, and performs virus detection on the divided data after the data is divided; The partition checking unit (2) converts the format of the data checked by the data integration unit (1) so that all the data are converted into codes, and divides the converted codes. After the codes are divided, the divided codes are stored; The step-by-step evaluation unit (3) evaluates the codes stored in the partitioning and checking unit (2) one by one. Meanwhile, during the evaluation process of the step-by-step evaluation unit (3), the code data stored in the partitioning and checking unit (2) is transmitted to the data integration unit (1). The data integration unit (1) detects and kills viruses in the segmented codes. After the data integration unit (1) detects and kills viruses, a structure for feedback of the detection and killing is provided to the step-by-step evaluation unit (3). After the data integration unit (1) detects and kills viruses, the step-by-step evaluation unit (3) refines the designation of the segmentation strategy for the segmented codes after the viruses are detected and killed, and transmits the virus-carrying codes to the partitioning and checking unit (2). The partitioning and checking unit (2) re-segments and stores the codes, and after segmentation, the step-by-step evaluation unit (3) performs evaluation again. When the step-by-step evaluation unit (3) detects and kills viruses, the result of the data evaluation is output.

2. The e-commerce big data security assessment system based on big data analysis according to claim 1 is characterized by: The data integration unit (1) comprises a data diversion module (11) and a data virus checking module (12); The data distribution module (11) collects e-commerce data and integrates the collected data so that each type of data is integrated together, and after the data is integrated, distributes the integrated data so that the integrated data is divided into multiple batches; The data virus detection module (12) detects and kills the viruses transmitted by the data diversion module (11), and after the detection and killing, distinguishes batches of data with viruses and those without viruses, and transmits the batches with viruses to the partitioning and checking unit (2), and transmits the data without viruses to the step-by-step evaluation unit (3), which evaluates and exports them.

3. The e-commerce big data security assessment system based on big data analysis according to claim 2 is characterized by: The partition checking unit (2) comprises a format conversion and segmentation module (21) and a data dispersion storage module (22); The format conversion and segmentation module (21) performs format conversion on the virus-carrying data transmitted by the data virus detection module (12), converts all the data into code form, and after the code conversion is completed, segments the code so that the code is divided into multiple parts; The data dispersion storage module (22) stores the codes segmented by the format conversion segmentation module (21), and after storage, transmits the newly stored data to the step-by-step evaluation unit (3).

4. The e-commerce big data security assessment system based on big data analysis according to claim 3 is characterized by: The method steps adopted by the format conversion and segmentation module (21) when segmenting the code are as follows: S1, identifying the codes converted by the data diversion module (11) after the e-commerce data is divided into batches, and marking the length of the code for each e-commerce data conversion format, each e-commerce data conversion code is a section, and then dividing the codes converted by the e-commerce data after the batches into multiple sections; S2. Extract the paragraph codes transmitted by the step-by-step evaluation unit (3) and received by the format conversion segmentation module (21), and then segment the codes of each paragraph so that the multiple paragraph codes in each paragraph are segmented into two parts.

5. The e-commerce big data security assessment system based on big data analysis according to claim 3 is characterized by: The division and verification unit (2) further comprises a traceability marking module (23); After the data dispersing storage module (22) receives the segmented code transmitted by the format conversion segmentation module (21), the source tracing marking module (23) marks the code data received by the data dispersing storage module (22) and marks the paragraph information corresponding to the code.

6. The e-commerce big data security assessment system based on big data analysis according to claim 3 is characterized by: The step-by-step evaluation unit (3) comprises a step-by-step refinement module (31) and a data evaluation module (32); The data evaluation module (32) receives the code data stored in the data distribution storage module (22), and after receiving the code data, transmits the received data to the data virus detection module (12). The data virus detection module (12) detects and kills viruses in the data transmitted by the data evaluation module (32), and after the virus detection module (12) detects and kills viruses, the virus detection result is evaluated; The step-by-step refinement module (31) receives the code data transmitted by the data evaluation module (32), reads the content of the code data, and then formulates a strategy for segmenting the code data based on the length of the code data. After the strategy is determined, the received code data and the specified segmentation strategy are transmitted to the format conversion segmentation module (21).

7. The e-commerce big data security assessment system based on big data analysis according to claim 6 is characterized by: The operation steps of the data evaluation module (32) in the process of evaluating the code data transmitted by the data distribution storage module (22) are as follows: S1, the data evaluation module (32) receives the information of virus detection by the data virus detection module (12), and judges whether there is virus in the data received by the data distributed storage module (22) by the data evaluation module (32) according to the result of virus detection by the data virus detection module (12); S2, the data evaluation module (32) processes the data accordingly according to the determination of whether there is a virus, wherein the data evaluation module (32) exports the virus-free data from the data evaluation module (32), and when exporting, combines the code information according to the information marked by the source tracing marking module (23), combines the codes segmented by the format conversion segmentation module (21), and exports the evaluation result of the combined code data together with the code data; S3, the data evaluation module (32) records the data with viruses and transmits the data with viruses to the step-by-step refinement module (31). At the same time, the data evaluation module (32) temporarily stores the code converted from the same e-commerce data as the data with viruses.

8. The e-commerce big data security assessment system based on big data analysis according to claim 7 is characterized by: After receiving the segmentation strategy and the virus-containing data transmitted by the step-by-step refinement module (31), the format conversion segmentation module (21) segments the virus-containing data according to the segmentation strategy, and imports the data into the data dispersion storage module (22) after segmentation. The data dispersion storage module (22) stores the data transmitted by the format conversion segmentation module (21), and marks the data through the traceability marking module (23). Then, the data evaluation module (32) evaluates the data transmitted by the data dispersion storage module (22).

9. The e-commerce big data security assessment system based on big data analysis according to claim 8 is characterized by: During the process of evaluating data, if the data evaluation module (32) last evaluated the data and found that the data contained viruses, but the data virus detection module (12) is unable to identify the viruses after the data evaluation module (32) performs segmentation according to the segmentation strategy specified by the step-by-step refinement module (31) and the format conversion segmentation module (21), the data evaluation module (32) stops segmenting the data and records and exports the data that was last evaluated by the data evaluation module (32) as containing viruses, and at the same time exports the code data of the same e-commerce data that the data evaluation module (32) evaluated as containing viruses and that has been stored by the data evaluation module (32).

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