A method for monitoring platform user usage based on big data technology

Abstract

The embodiment of the present disclosure discloses a method for monitoring platform user usage based on big data technology. A specific implementation of the method includes: collecting a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform; performing data preprocessing on the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set; constructing a user portrait set based on the processed user operation behavior data set; determining the behavior path information for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain an analyzed user portrait behavior path information set; and making decisions and controlling the adjusted behavior path information set based on the analyzed result set. This implementation improves the comprehensiveness of the analysis results and improves the flexibility and responsiveness of the system.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method for monitoring platform user usage based on big data technology. Background Art

[0002] With the rapid development of Internet big data technology, the demand for analyzing the user usage corresponding to the big data technology monitoring platform is increasing. Monitoring the user usage of the platform based on big data technology is a technology for analyzing the user usage in the big data technology monitoring platform. Constructing user portraits for users and adjusting path information can improve the flexibility and responsiveness of the system. In addition, determining the path information of user portraits can improve the accuracy of path constraint information. In addition, dynamically generating an adjusted behavior path information set based on the analyzed user portrait behavior path information set can also improve the flexibility and responsiveness of the system. At present, the method commonly used to monitor the user usage of the platform based on big data technology is to realize the user usage of the platform based on big data technology by manually analyzing the user's operation behavior.

[0003] However, when the above method is adopted, the following technical problems often occur:

[0004] First, manual analysis of user behavior may lead to incomplete analysis results. Since the relationship between user behavior data and platform operation data is complex and the data volume is large, manual analysis will reduce the quality of monitoring data, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the system's flexibility and responsiveness.

[0005] Second, when determining the path information of the processed user portrait group set, path information extraction anomalies may occur, resulting in a longer cycle for determining the path information of the processed user portrait group set. When extracting constraint information from the path information, redundant information is easily generated, resulting in low accuracy of the path constraint information, which results in the obtained user portrait behavior path constraint information set being incomplete.

[0006] Third, since it is impossible to dynamically generate an adjusted behavior path information set based on the analyzed user portrait behavior path information set, the generated adjusted behavior path information set is not accurate enough, resulting in low data quality of the adjusted behavior path information set, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the flexibility and responsiveness of the system.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0008] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0009] Some embodiments of the present disclosure propose a method for monitoring platform user usage based on big data technology to solve one or more of the technical problems mentioned in the above background technology section.

[0010] In a first aspect, some embodiments of the present disclosure provide a method for monitoring platform user usage based on big data technology, the method comprising: collecting a user operation behavior data set corresponding to a user interface and an operation data set corresponding to a monitoring platform; performing data preprocessing on the above-mentioned user operation behavior data set and the above-mentioned operation data set to obtain a processed user operation behavior data set and a processed operation data set; constructing a user portrait set based on the above-mentioned processed user operation behavior data set; performing clustering processing on the above-mentioned user portrait set to obtain a processed user portrait group set; determining behavior path information for each processed user portrait group in the above-mentioned processed user portrait group set to generate analyzed user portrait behavior path information to obtain an analyzed user portrait behavior path information set; generating an adjusted behavior path information set based on the above-mentioned analyzed user portrait behavior path information set; performing data correlation analysis on the above-mentioned adjusted behavior path information set and the above-mentioned processed operation data set to obtain an analyzed result set, wherein the analyzed results in the above-mentioned analyzed result set represent data correlation consistent results and data correlation inconsistent results; performing decision control on the above-mentioned adjusted behavior path information set based on the above-mentioned analyzed result set.

[0011] In a second aspect, some embodiments of the present disclosure provide a device for monitoring platform user usage based on big data technology, the device comprising: a collection unit, configured to collect a user operation behavior data set corresponding to a user interface and an operation data set corresponding to a monitoring platform; a first processing unit, configured to perform data preprocessing on the above-mentioned user operation behavior data set and the above-mentioned operation data set to obtain a processed user operation behavior data set and a processed operation data set; a construction unit, configured to construct a user portrait set based on the above-mentioned processed user operation behavior data set; a second processing unit, configured to perform clustering processing on the above-mentioned user portrait set to obtain a processed user portrait group set; a determination unit, configured to perform clustering processing on the above-mentioned user portrait set to obtain a processed user portrait group set; Determine the behavior path information of each processed user portrait group in the image group set to generate the analyzed user portrait behavior path information and obtain the analyzed user portrait behavior path information set; the generation unit is configured to generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set; the correlation analysis unit is configured to perform data correlation analysis on the adjusted behavior path information set and the processed running data set to obtain an analyzed result set, wherein the analyzed results in the analyzed result set represent data correlation consistent results and data correlation inconsistent results; the decision control unit is configured to perform decision control on the adjusted behavior path information set according to the analyzed result set.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0014] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method of monitoring platform user usage based on big data technology in some embodiments of the present disclosure, the comprehensiveness of the analysis results is improved, and the flexibility and responsiveness of the system are improved. Specifically, the reasons for the incomplete analysis results, the reduced quality of monitoring data, and the reduced flexibility and responsiveness of the system are: manual analysis of user behavior may lead to incomplete analysis results. Due to the complex association between user behavior data and platform operation data, and the large amount of data, manual analysis will reduce the quality of monitoring data, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the flexibility and responsiveness of the system. Based on this, the method of monitoring platform user usage based on big data technology in some embodiments of the present disclosure, first, collects the user operation behavior data set corresponding to the user interface and the operation data set corresponding to the monitoring platform. Thus, the analysis results can be combined with the operation data set to make it more comprehensive. Secondly, the above-mentioned user operation behavior data set and the above-mentioned operation data set are preprocessed to obtain the processed user operation behavior data set and the processed operation data set. Thus, the quality of the data can be improved. Thirdly, based on the above-mentioned processed user operation behavior data set, a user portrait set is constructed. Thus, a more complete user behavior analysis can be performed on the user. Then, clustering is performed on the user portrait set to obtain a processed user portrait group set. Thus, the complexity of the association between user behavior data and platform operation data can be reduced. Afterwards, behavior path information is determined for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain analyzed user portrait behavior path information set. Thus, user behavior can be comprehensively analyzed through path information, so that the system can adapt to changes in user needs in a timely manner. Secondly, based on the analyzed user portrait behavior path information set, an adjusted behavior path information set is generated. Thus, the flexibility and responsiveness of the system can be improved. The adjusted behavior path information set and the processed operation data set are subjected to data correlation analysis to obtain an analyzed result set, wherein the analyzed results in the analyzed result set represent data correlation consistent results and data correlation inconsistent results. Thus, the analysis results can be obtained more comprehensively. Finally, based on the analyzed result set, decision control is performed on the adjusted behavior path information set. Thus, the comprehensiveness of the analysis results is improved, and the flexibility and responsiveness of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0016] Figure 1 is a flowchart of some embodiments of a method for monitoring platform user usage based on big data technology according to the present disclosure;

[0017] Figure 2 It is a structural schematic diagram of some embodiments of the apparatus for monitoring platform user usage based on big data technology according to the present disclosure;

[0018] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0021] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0023] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0024] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0025] Figure 1 This is a process 100 of some embodiments of the method for monitoring platform user usage based on big data technology disclosed in the present invention. The method for monitoring platform user usage based on big data technology includes the following steps:

[0026] Step 101: Collect a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform.

[0027] In some embodiments, the executor (e.g., computing device) of the method for monitoring platform user usage based on big data technology can collect user operation behavior data sets corresponding to the user interface and operation data sets corresponding to the monitoring platform through data collectors and sensor devices respectively.

[0028] Here, the user interface is a user interface in the big data technology monitoring platform. The user operation behavior data set may include but is not limited to at least one of the following: user click behavior data, user scroll behavior data. The monitoring platform is a platform for monitoring building data. The building data may be data in a building. For example, the building data may be temperature data in a building. The operation data set may include but is not limited to at least one of the following: humidity data, energy consumption data.

[0029] Step 102 , preprocessing the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set.

[0030] In some embodiments, the execution entity may perform data preprocessing on the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set.

[0031] Here, the processed user operation behavior data set may refer to a set of data corresponding to user operation behaviors after data preprocessing. The processed operation data set may refer to a set of data on the operation of the monitoring platform after data preprocessing.

[0032] As an example, the execution subject may clean the user operation behavior data set and the operation data set to obtain a cleaned user operation behavior data set and a cleaned operation data set. Then, the cleaned user operation behavior data set and the cleaned operation data set are normalized to obtain a normalized user operation behavior data set and a normalized operation data set. Finally, the normalized user operation behavior data set and the normalized operation data set are normalized to obtain a normalized user operation behavior data set and a normalized operation data set as the processed user operation behavior data set and the processed operation data set.

[0033] Step 103: construct a user portrait set based on the processed user operation behavior data set.

[0034] In some embodiments, the above-mentioned execution entity can construct a user portrait set based on the above-mentioned processed user operation behavior data set.

[0035] Here, the user portrait in the user portrait set may include but is not limited to at least one of the following: user name, user gender, user occupation. The processed user operation behavior data set may refer to a collection of data on operation behaviors generated by a user within a period of time. For example, the processed user operation behavior data set may refer to a collection of data on user clicks on the user interface from January 3, 2024 to January 10, 2024.

[0036] As an example, the execution subject may first clean the processed user operation behavior data set to obtain a cleaned user operation behavior data set. Next, feature extraction is performed on each cleaned user operation behavior data in the cleaned user operation behavior data set to generate a cleaned user operation behavior data feature group to obtain a cleaned user operation behavior data feature group set. Finally, feature integration is performed on each cleaned user operation behavior data feature group in the cleaned user operation behavior data feature group set to obtain an integrated user operation behavior data feature set as a user portrait set.

[0037] Step 104: cluster the user portrait set to obtain a processed user portrait group set.

[0038] In some embodiments, the execution entity may perform clustering processing on the user portrait set to obtain a processed user portrait group set.

[0039] Here, the verification result may refer to a result in which the post-iteration application information corresponding to the post-iteration application and the application information corresponding to the post-addition application have a small significant difference in difference parameter information.

[0040] As an example, the execution entity may perform a significance test on each difference parameter information in the difference parameter information set to generate tested difference parameter information, and parse the tested difference parameter information to generate a verification result to obtain a verification result set.

[0041] Optionally, the execution subject may perform clustering processing on the user portrait set through the following steps to obtain a processed user portrait group set:

[0042] In the first step, the following clustering steps are performed on the above user portrait set:

[0043] The first sub-step is to determine the similarity between every two user portraits in the user portrait set to obtain a similarity set.

[0044] Here, the similarity may refer to the Euclidean distance. The similarity may refer to the similarity between two users in terms of occupation, age or gender.

[0045] In the second sub-step, the first user portrait and the second user portrait corresponding to the largest similarity in the similarity set are determined as the user portrait class.

[0046] Here, the above user portrait class may refer to a class consisting of two user portraits with the greatest similarity.

[0047] In the third sub-step, the first user portrait and the second user portrait are deleted from the standardized user portrait set to obtain a deleted user portrait set.

[0048] Here, the above-mentioned deletion may refer to elimination.

[0049] In the fourth sub-step, each post-deletion user portrait in the post-deletion user portrait set is determined as a post-deletion user portrait class to obtain a post-deletion user portrait class set.

[0050] The fifth sub-step is to merge the user portrait class and the deleted user portrait class set into a clustered user portrait class set.

[0051] In a sixth sub-step, in response to determining that the number of clustered user portrait classes in the clustered user portrait class set is greater than or equal to the preset number of clusters, the clustered user portrait class set is determined as the user portrait set, and the above clustering processing step is performed again.

[0052] In the second step, in response to determining that the number of clustered user portrait classes in the clustered user portrait class set is less than the preset number of clusters, the obtained clustered user portrait class set is determined as a processed user portrait group set.

[0053] Here, the above-mentioned preset number of clusters may refer to 10.

[0054] Step 105 , determining the behavior path information of each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain the analyzed user portrait behavior path information set.

[0055] In some embodiments, the execution subject may determine the behavior path information for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain the analyzed user portrait behavior path information set.

[0056] Here, the analyzed user portrait behavior path information in the analyzed user portrait behavior path information set may refer to the path information of the user portrait corresponding to the user behavior node. For example, the analyzed user portrait behavior path information in the analyzed user portrait behavior path information set may refer to the path information of the user portrait corresponding to the user behavior of browsing node, scrolling node, and clicking node.

[0057] Optionally, the execution subject may determine the behavior path information of each processed user portrait group in the processed user portrait group set through the following steps to generate the analyzed user portrait behavior path information and obtain the analyzed user portrait behavior path information set:

[0058] In the first step, key information is extracted from key information corresponding to each processed user portrait group in the processed user portrait group set to generate path key information and obtain a path key information set.

[0059] As an example, the execution subject may perform data preprocessing (e.g., data cleaning) on ​​the processed user portrait group set to obtain a preprocessed user portrait group set. Then, natural language processing is used to extract key information from each preprocessed user portrait group in the preprocessed user portrait group set to generate path key information and obtain a path key information set. The key information may refer to category information of the user portrait group. For example, the key information may refer to information that the gender of the user portrait group is male.

[0060] The second step is to determine the data structure corresponding to each processed user portrait group in the processed user portrait group set based on the above path key information set to obtain a data structure set.

[0061] As an example, the execution subject may determine the node information of each path key information in the path key information set to obtain a path key information node set. The node information may represent the key attribute information of the path information. For example, the node information may refer to the name information of the path information. Then, the structural relationship between each path key information node in the path key information node set is determined, and the tree structure corresponding to each processed user portrait group in the processed user portrait group set is determined to obtain a tree structure set as a data structure set.

[0062] The third step is to determine the path information extraction algorithm based on the above path key information set and the above data structure set.

[0063] Here, the above-mentioned path information extraction algorithm may refer to an algorithm for extracting a path key information set.

[0064] As an example, the execution subject may fuse the path key information set and the data structure set to obtain a fused path key information data structure set. Then, the path information extraction algorithm is determined on the fused path key information data structure set to obtain a path information extraction algorithm.

[0065] In the fourth step, using the above-mentioned path information extraction algorithm, for each processed user portrait group in the above-mentioned processed user portrait group set, perform the following processing steps:

[0066] The first sub-step is to parse the path information of the user portrait group after the above processing to obtain a parsed user portrait path information group.

[0067] Here, the above-mentioned parsed user portrait path information group may refer to the user portrait path information group obtained after parsing the user portrait.

[0068] As an example, the execution subject may perform path information parsing on the processed user portrait group through a regular expression to obtain a parsed user portrait path information group.

[0069] The second sub-step is to integrate the individual parsed user portrait path information in the above parsed user portrait path information group to obtain integrated user portrait path information.

[0070] As an example, the execution entity may merge the individual pieces of parsed user portrait path information in the parsed user portrait path information group into merged key information as integrated user portrait path information.

[0071] The third sub-step is to perform key information verification on the above-mentioned integrated user portrait path information to obtain the verified user portrait path information.

[0072] As an example, the above-mentioned execution entity can verify the key information corresponding to the above-mentioned integrated user portrait path information, and obtain the verified user portrait path information as the verified user portrait path information.

[0073] The fifth step is to fill in the missing information of each post-inspection user portrait path information in the obtained post-inspection user portrait path information set to obtain a filled user portrait path information set.

[0074] Here, the missing information mentioned above may refer to the constraint information of missing post-verification user portrait path information in the post-verification user portrait path information set.

[0075] As an example, the execution subject may perform information recognition on each post-verification user portrait path information in the post-verification user portrait path information set to obtain a post-verification user portrait path information set. Then, the missing information of each post-verification user portrait path information in the post-verification user portrait path information set may be filled using interpolation technology to obtain a filled user portrait path information set.

[0076] The sixth step is to deduplicate the padded user portrait path information set to obtain a deduplicated user portrait path information set.

[0077] As an example, the above-mentioned execution entity can deduplicate the above-mentioned padded user portrait path information set through a hash table to obtain a deduplicated user portrait path information set.

[0078] The seventh step is to perform a randomly generated path test on the above-mentioned user portrait path information set after deduplication, and obtain the user portrait path information set after path test as the user portrait behavior path information set after analysis.

[0079] The relevant contents in the first step to the seventh step mentioned above are an inventive point of the present disclosure, which solves the second technical problem mentioned in the background technology: "When determining the path information of the processed user portrait group set, path information extraction anomalies may occur, resulting in a longer period for determining the path information of the processed user portrait group set. Redundant information is easily generated when the constraint information is extracted from the path information, resulting in low accuracy of the path constraint information, which results in the obtained user portrait behavior path constraint information set being not comprehensive enough." The factors that lead to a longer period for determining the path information of the processed user portrait group set and an incomplete set of user portrait behavior path constraint information are often as follows: When determining the path information of the processed user portrait group set, path information extraction anomalies may occur, resulting in a longer period for determining the path information of the processed user portrait group set. Redundant information is easily generated when the constraint information is extracted from the path information, resulting in low accuracy of the path constraint information, which results in the obtained set of user portrait behavior path constraint information being not comprehensive enough. If the above factors are solved, the period for determining the path information of the processed user portrait group set can be shortened, so that the obtained set of user portrait behavior path constraint information is more comprehensive. In order to achieve this effect, the first step is to extract key information corresponding to each processed user portrait group in the above-mentioned processed user portrait group set to generate path key information and obtain a path key information set. The second step is to determine the data structure corresponding to each processed user portrait group in the above-mentioned processed user portrait group set based on the above-mentioned path key information set to obtain a data structure set. The third step is to determine the path information extraction algorithm based on the above-mentioned path key information set and the above-mentioned data structure set. The fourth step is to use the above-mentioned path information extraction algorithm to perform the following processing steps for each processed user portrait group in the above-mentioned processed user portrait group set: the first sub-step is to parse the path information of the above-mentioned processed user portrait group to obtain a parsed user portrait path information group. The second sub-step is to integrate the information of each parsed user portrait path information in the above-mentioned parsed user portrait path information group to obtain the integrated user portrait path information. The third sub-step is to perform key information inspection on the above-mentioned integrated user portrait path information to obtain the inspected user portrait path information. The fifth step is to fill in the missing information of each post-test user portrait path information in the obtained post-test user portrait path information set to obtain a filled user portrait path information set. The sixth step is to deduplicate the above-mentioned filled user portrait path information set to obtain a deduplicated user portrait path information set. The seventh step is to perform a randomly generated path test on the above-mentioned deduplicated user portrait path information set to obtain a user portrait path information set after path test as the analyzed user portrait behavior path information set. In this way, the cycle of determining the path information of the processed user portrait group set can be shortened, so that the obtained user portrait behavior path constraint information set is more comprehensive.

[0080] Step 106, generating an adjusted behavior path information set based on the above analyzed user portrait behavior path information set.

[0081] In some embodiments, the above-mentioned execution entity can generate an adjusted behavior path information set based on the above-mentioned analyzed user portrait behavior path information set.

[0082] Optionally, the execution subject may generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set through the following steps:

[0083] The first step is to perform access permission control on the user portrait behavior path information set after the above analysis to obtain the controlled user portrait behavior path information set.

[0084] As an example, the execution subject may control the access rights corresponding to the analyzed user profile behavior path information set to obtain the controlled user profile behavior path information set. The access rights may refer to the user's access rights to the monitoring platform.

[0085] The second step is to encrypt the above-mentioned controlled user portrait behavior path information set to obtain the encrypted user portrait behavior path information set.

[0086] As an example, the execution subject may encrypt the controlled user profile behavior path information set using an encryption algorithm to obtain an encrypted user profile behavior path information set. The encryption algorithm may be a Data Encryption Standard (DES) algorithm.

[0087] The third step is to verify the identity authority of the user in response to determining that the user has accessed the encrypted user portrait behavior path information set to obtain a verification result.

[0088] Here, the above verification result can represent verification success and verification failure.

[0089] In the fourth step, in response to determining that the above verification result indicates a verification failure, the encrypted user portrait behavior path information set is subjected to user isolation to obtain an isolated user portrait behavior path information set.

[0090] Here, the user isolation may refer to adding the user to a blacklist. The isolated user portrait behavior path information set may refer to the user portrait behavior path information set after isolating the encrypted user portrait behavior path information set.

[0091] The fifth step is to perform real-time detection on the above-mentioned isolated user portrait behavior path information set to obtain the detection results.

[0092] In a sixth step, in response to determining that the detection result indicates a detection abnormality, abnormal alarm information corresponding to the detection result indicating the detection abnormality is sent to the user interface for the user to perform a preset behavior operation.

[0093] Here, the above-mentioned preset behavior may refer to the behavior of the user verifying his or her own identity.

[0094] The seventh step, in response to determining that the above-mentioned user has performed the preset behavioral operation, performs data cleaning on the above-mentioned isolated user portrait behavior path information set to obtain a cleaned behavior path information set.

[0095] In the eighth step, the cleaned behavior path information set is adjusted to obtain an adjusted behavior path information set.

[0096] As an example, the execution subject may screen the cleaned behavior path information set to obtain a screened behavior path information set, and then encrypt the screened behavior path information set to obtain an encrypted behavior path information set as the adjusted behavior path information set.

[0097] Optionally, the execution subject may generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set through the following steps:

[0098] The first step is to perform static analysis on the user portrait behavior path information set after the above analysis to obtain the user portrait behavior path information set after static analysis.

[0099] As an example, the above-mentioned execution subject can perform symbolic execution on the above-mentioned analyzed user portrait behavior path information set to obtain the executed user portrait behavior path information set as the static analyzed user portrait behavior path information set.

[0100] The second step is to perform path optimization on the user portrait behavior path information set after the above static analysis to obtain an optimized user portrait behavior path information set.

[0101] As an example, the execution subject may utilize a path planning algorithm to perform path optimization on the user portrait behavior path information set after the static analysis to obtain an optimized user portrait behavior path information set.

[0102] The third step is to generate data for the above-mentioned optimized user portrait behavior path information set based on the above-mentioned optimized user portrait behavior path information set to obtain a generated data set.

[0103] As an example, the execution subject may parse the feasible solution space information corresponding to the optimized user portrait behavior path information set to obtain the parsed feasible solution space information. Then, the rand() function is used in the parsed feasible solution space information to generate random data for the optimized user portrait behavior path information set to obtain a randomly generated data set as the generated data set.

[0104] The fourth step is to filter the generated data set to obtain a filtered data set.

[0105] As an example, the execution subject may remove dirty data from the generated data set to obtain a randomly generated data set after removal, which serves as a filtered data set.

[0106] The fifth step is to convert the data format of the filtered data set to obtain a converted data set.

[0107] As an example, the execution subject may use the pandas library in Python to convert the data format of the filtered data set to obtain a converted data set. The converted data set may refer to a data set whose format is converted into a JSON format.

[0108] The sixth step is to discretize the converted data set according to the boundary condition information corresponding to the converted data set to obtain a discretized data set.

[0109] As an example, the execution subject may first determine the boundary value of the converted data set through the boundary condition information corresponding to the converted data set. Then, the converted data set is discretized in equal frequency according to the boundary value to obtain a discretized data set. The boundary value may be 5.

[0110] The seventh step is to perform data balancing on the discretized data set to obtain a balanced data set.

[0111] As an example, the execution subject may perform class weighting on the discretized data in the discretized data set to obtain a weighted data set as a balanced data set.

[0112] In step 8, in response to determining that the path coverage corresponding to the balanced data set is not equal to the preset coverage, the following processing steps are performed:

[0113] In the first sub-step, the difference between the path coverage rate corresponding to the balanced data set and the preset coverage rate is determined as coverage rate difference information.

[0114] The second sub-step is to perform data sampling adjustment on the balanced data set according to the coverage difference information to obtain an adjusted data set.

[0115] As an example, in response to determining that the coverage difference information is greater than a preset difference threshold, the execution subject may undersample the balanced data set to obtain an undersampled data set as the adjusted data set. In response to determining that the coverage difference information is less than or equal to the preset difference threshold, the balanced data set may be oversampled to obtain an oversampled data set as the adjusted data set.

[0116] The third sub-step is to perform a significance test on the adjusted data set to obtain a tested data set.

[0117] The fourth sub-step is to compare the coverage information corresponding to the above-mentioned verified data set with the coverage information corresponding to the above-mentioned adjusted data set to obtain compared coverage information.

[0118] As an example, the execution entity may compare the difference rate between the coverage rate information corresponding to the verified data set and the coverage rate information corresponding to the adjusted data set to obtain the compared difference rate information as the compared coverage rate information.

[0119] In a ninth step, in response to determining that the coverage rate corresponding to the compared coverage rate information is greater than or equal to a preset value, the verified data set is determined as an adjusted behavior path information set.

[0120] Here, the above-mentioned preset value may refer to 0.5.

[0121] The relevant contents in the first step to the ninth step above are an inventive point of the present disclosure, which solves the technical problem three mentioned in the background technology: "Since it is impossible to dynamically generate the adjusted behavior path information set based on the analyzed user portrait behavior path information set, the generated adjusted behavior path information set is not accurate enough, which makes the data quality of the adjusted behavior path information set low, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the flexibility and responsiveness of the system." The factors that cause the generated adjusted behavior path information set to be inaccurate and the flexibility and responsiveness of the system to be reduced are often as follows: Since it is impossible to dynamically generate the adjusted behavior path information set based on the analyzed user portrait behavior path information set, the generated adjusted behavior path information set is not accurate enough, which makes the data quality of the adjusted behavior path information set low, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the flexibility and responsiveness of the system. If the above factors are solved, the accuracy of the generated adjusted behavior path information set can be improved, and the flexibility and responsiveness of the system can be improved. In order to achieve this effect, in the first step, the above-mentioned analyzed user portrait behavior path information set is statically analyzed to obtain the statically analyzed user portrait behavior path information set. The second step is to optimize the path of the user portrait behavior path information set after the static analysis to obtain the optimized user portrait behavior path information set. The third step is to generate data for the optimized user portrait behavior path information set according to the optimized user portrait behavior path information set to obtain a generated data set. The fourth step is to filter the generated data set to obtain a filtered data set. The fifth step is to convert the data format of the filtered data set to obtain a converted data set. The sixth step is to discretize the converted data set according to the boundary condition information corresponding to the converted data set to obtain a discretized data set. The seventh step is to perform data balancing on the discretized data set to obtain a balanced data set. The eighth step is to perform the following processing steps in response to determining that the path coverage rate corresponding to the balanced data set is not equal to the preset coverage rate: the first sub-step is to determine the difference between the path coverage rate corresponding to the balanced data set and the preset coverage rate as the coverage rate difference information. The second sub-step is to adjust the data sampling of the balanced data set according to the coverage rate difference information to obtain the adjusted data set. The third sub-step is to perform a significance test on the adjusted data set to obtain the tested data set. The fourth sub-step is to compare the coverage information corresponding to the tested data set with the coverage information corresponding to the adjusted data set to obtain the compared coverage information. The ninth step is to determine that the coverage corresponding to the compared coverage information is greater than or equal to a preset value, and determine the tested data set as the adjusted behavior path information set. In this way, the accuracy of the generated adjusted behavior path information set is improved, and the flexibility and responsiveness of the system are improved.

[0122] Step 107 , performing data correlation analysis on the adjusted behavior path information set and the processed operation data set to obtain an analyzed result set.

[0123] In some embodiments, the execution entity may perform data correlation analysis on the adjusted behavior path information set and the processed running data set to obtain an analysis result set, wherein the analysis results in the analysis result set represent data correlation consistent results and data correlation inconsistent results.

[0124] Optionally, the execution subject may perform data correlation analysis on the adjusted behavior path information set and the processed operation data set through the following steps to obtain an analysis result set:

[0125] The first step is to integrate the adjusted behavior path information set and the processed operation data set to obtain an integrated data set.

[0126] Here, the above data integration may refer to merging.

[0127] The second step is to perform feature engineering on the above integrated data set to obtain the processed data set.

[0128] As an example, the execution subject may first perform type screening on each integrated data in the integrated data set to obtain a screened data type group set. Finally, each screened data type group in the screened data type group set may be merged to obtain a merged data feature set as the processed data set.

[0129] The third step is to clean the processed data set to obtain a cleaned data set.

[0130] The fourth step is to construct a relational table for the above cleaned data set to obtain a behavior path information relational table.

[0131] The fifth step is to determine at least one data in the behavior path information relationship table whose similarity between the corresponding adjusted behavior path information set and the corresponding processed running data set is greater than a preset similarity threshold as an associated data group.

[0132] Here, the preset similarity threshold may refer to a preset maximum value corresponding to the similarity. For example, the preset similarity threshold may refer to 0.7.

[0133] The sixth step is to parse the above-mentioned associated data groups to obtain a parsed result set as the analyzed result set.

[0134] Step 108: Based on the analysis result set, decision control is performed on the adjusted behavior path information set.

[0135] In some embodiments, the execution entity may perform decision control on the adjusted behavior path information set based on the analysis result set.

[0136] As an example, the execution subject may store the adjusted behavior path information set in response to determining that the analysis results in the analysis result set represent consistent data association results. In response to determining that the analysis results in the analysis result set represent inconsistent data association results, the adjusted behavior path information set is screened for the adjusted behavior path information corresponding to the inconsistent data association results.

[0137] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method of monitoring platform user usage based on big data technology in some embodiments of the present disclosure, the comprehensiveness of the analysis results is improved, and the flexibility and responsiveness of the system are improved. Specifically, the reasons for the incomplete analysis results, the reduced quality of monitoring data, and the reduced flexibility and responsiveness of the system are: manual analysis of user behavior may lead to incomplete analysis results. Due to the complex association between user behavior data and platform operation data, and the large amount of data, manual analysis will reduce the quality of monitoring data, making it difficult for the system to adapt to changes in user needs in a timely manner, thereby reducing the flexibility and responsiveness of the system. Based on this, the method of monitoring platform user usage based on big data technology in some embodiments of the present disclosure, first, collects the user operation behavior data set corresponding to the user interface and the operation data set corresponding to the monitoring platform. Thus, the analysis results can be combined with the operation data set to make it more comprehensive. Secondly, the above-mentioned user operation behavior data set and the above-mentioned operation data set are preprocessed to obtain the processed user operation behavior data set and the processed operation data set. Thus, the quality of the data can be improved. Thirdly, based on the above-mentioned processed user operation behavior data set, a user portrait set is constructed. Thus, a more complete user behavior analysis can be performed on the user. Then, clustering is performed on the user portrait set to obtain a processed user portrait group set. Thus, the complexity of the association between user behavior data and platform operation data can be reduced. Afterwards, behavior path information is determined for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain analyzed user portrait behavior path information set. Thus, user behavior can be comprehensively analyzed through path information, so that the system can adapt to changes in user needs in a timely manner. Secondly, based on the analyzed user portrait behavior path information set, an adjusted behavior path information set is generated. Thus, the flexibility and responsiveness of the system can be improved. The adjusted behavior path information set and the processed operation data set are subjected to data correlation analysis to obtain an analyzed result set, wherein the analyzed results in the analyzed result set represent data correlation consistent results and data correlation inconsistent results. Thus, the analysis results can be obtained more comprehensively. Finally, based on the analyzed result set, decision control is performed on the adjusted behavior path information set. Thus, the comprehensiveness of the analysis results is improved, and the flexibility and responsiveness of the system are improved.

[0138] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a method for monitoring platform user usage based on big data technology. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0139] like Figure 2 As shown, in some embodiments, the apparatus 200 for monitoring platform user usage based on big data technology includes: a collection unit 201, a first processing unit 202, a construction unit 203, a second processing unit 204, a determination unit 205, a generation unit 206, a correlation analysis unit 207 and a decision control unit 208. Among them, the collection unit 201 is configured to collect a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform; the first processing unit 202 is configured to perform data preprocessing on the above user operation behavior data set and the above operation data set to obtain a processed user operation behavior data set and a processed operation data set; the construction unit 203 is configured to construct a user portrait set based on the above processed user operation behavior data set; the second processing unit 204 is configured to perform clustering processing on the above user portrait set to obtain a processed user portrait group set; the determination unit 205 is configured to perform clustering processing on each processed user portrait group in the above processed user portrait group set; The path information is determined to generate the analyzed user portrait behavior path information and obtain the analyzed user portrait behavior path information set; the generation unit 206 is configured to generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set; the correlation analysis unit 207 is configured to perform data correlation analysis on the adjusted behavior path information set and the processed running data set to obtain an analyzed result set, wherein the analyzed results in the analyzed result set represent data correlation consistent results and data correlation inconsistent results; the decision control unit 208 is configured to perform decision control on the adjusted behavior path information set according to the analyzed result set.

[0140] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be described in detail here.

[0141] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0142] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 304. In RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, ROM 302, and RAM 304 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0143] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0144] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the method of some embodiments of the present disclosure are executed.

[0145] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0146] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0147] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: collects a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform; performs data preprocessing on the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set; constructs a user portrait set based on the processed user operation behavior data set; performs clustering processing on the user portrait set to obtain a processed user portrait group set; determines the behavior path information of each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information to obtain an analyzed user portrait behavior path information set; generates an adjusted behavior path information set based on the analyzed user portrait behavior path information set; performs data correlation analysis on the adjusted behavior path information set and the processed operation data set to obtain an analyzed result set, wherein the analyzed results in the analyzed result set represent data correlation consistent results and data correlation inconsistent results; and performs decision control on the adjusted behavior path information set based on the analyzed result set.

[0148] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising: an acquisition unit, a first processing unit, a construction unit, a second processing unit, a determination unit, a generation unit, a correlation analysis unit and a decision control unit. Among them, the names of these units do not constitute a limitation on the unit itself in certain cases, for example, the acquisition unit may also be described as "a unit for collecting a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform".

[0151] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0152] The above descriptions are only some preferred embodiments of the present disclosure and the explanation of the technical principles used above. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for monitoring platform user usage based on big data technology, comprising: Collect the user operation behavior data set corresponding to the user interface and the operation data set corresponding to the monitoring platform; Performing data preprocessing on the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set; Based on the processed user operation behavior data set, construct a user portrait set; Performing clustering processing on the user portrait set to obtain a processed user portrait group set; Determining behavior path information for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information, and obtaining an analyzed user portrait behavior path information set; Generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set, wherein generating an adjusted behavior path information set according to the analyzed user portrait behavior path information set includes: Performing access permission control on the analyzed user portrait behavior path information set to obtain a controlled user portrait behavior path information set; Encrypting the controlled user portrait behavior path information set to obtain an encrypted user portrait behavior path information set; In response to determining that the user has accessed the encrypted user portrait behavior path information set, verifying the identity authority of the user to obtain a verification result; In response to determining that the verification result indicates a verification failure, performing user isolation on the encrypted user portrait behavior path information set to obtain an isolated user portrait behavior path information set; Performing real-time detection on the isolated user portrait behavior path information set to obtain a detection result; In response to determining that the detection result represents a detection abnormality, sending abnormality alarm information corresponding to the detection result representing the detection abnormality to the user interface for the user to perform a preset behavior operation; In response to determining that the user has performed a preset behavior operation, data cleaning is performed on the isolated user portrait behavior path information set to obtain a cleaned behavior path information set; Adjusting the cleaned behavior path information set to obtain an adjusted behavior path information set; Performing data correlation analysis on the adjusted behavior path information set and the processed operation data set to obtain a post-analysis result set, wherein the post-analysis results in the post-analysis result set represent data correlation consistent results and data correlation inconsistent results; According to the analyzed result set, decision control is performed on the adjusted behavior path information set.

2. The method according to claim 1, wherein: The clustering process is performed on the user portrait set to obtain a processed user portrait group set, including: The following clustering processing steps are performed on the user portrait set: Determine the similarity between every two user portraits in the user portrait set to obtain a similarity set; Determine the first user portrait and the second user portrait corresponding to the maximum similarity in the similarity set as the user portrait class; Deleting the first user portrait and the second user portrait from the standardized user portrait set to obtain a deleted user portrait set; Determine each post-deletion user portrait in the post-deletion user portrait set as a post-deletion user portrait class, and obtain a post-deletion user portrait class set; Combine the user portrait class and the deleted user portrait class set into the clustered user portrait class set; In response to determining that the number of clustered user portrait classes in the clustered user portrait class set is greater than or equal to the preset number of clusters, determining the clustered user portrait class set as the user portrait set, and performing the clustering processing step again; In response to determining that the number of clustered user portrait classes in the clustered user portrait class set is less than the preset number of clusters, the obtained clustered user portrait class set is determined as a processed user portrait group set.

3. The method according to claim 1, wherein: The step of performing data correlation analysis on the adjusted behavior path information set and the processed operation data set to obtain an analysis result set includes: Integrate the adjusted behavior path information set and the processed operation data set to obtain an integrated data set; Performing feature engineering processing on the integrated data set to obtain a processed data set; Performing data cleaning on the processed data set to obtain a cleaned data set; Constructing a relational table for the cleaned data set to obtain a behavior path information relational table; Determine at least one data whose similarity between the corresponding adjusted behavior path information set and the corresponding processed running data set in the behavior path information relationship table is greater than a preset similarity threshold as an associated data group; The associated data group is parsed to obtain a parsed result set as an analyzed result set.

4. A device for monitoring platform user usage based on big data technology, comprising: A collection unit configured to collect a user operation behavior data set corresponding to the user interface and an operation data set corresponding to the monitoring platform; A first processing unit is configured to perform data preprocessing on the user operation behavior data set and the operation data set to obtain a processed user operation behavior data set and a processed operation data set; A construction unit, configured to construct a user portrait set based on the processed user operation behavior data set; A second processing unit is configured to perform clustering processing on the user portrait set to obtain a processed user portrait group set; a determination unit configured to determine behavior path information for each processed user portrait group in the processed user portrait group set to generate analyzed user portrait behavior path information and obtain an analyzed user portrait behavior path information set; A generating unit is configured to generate an adjusted behavior path information set according to the analyzed user portrait behavior path information set, wherein the generating of the adjusted behavior path information set according to the analyzed user portrait behavior path information set includes: performing access permission control on the analyzed user portrait behavior path information set to obtain a controlled user portrait behavior path information set; encrypting the controlled user portrait behavior path information set to obtain an encrypted user portrait behavior path information set; in response to determining that a user accesses the encrypted user portrait behavior path information set, verifying the identity permission of the user to obtain a verification result; in response to determining that the verification result indicates a verification result, If the authentication fails, user isolation is performed on the encrypted user portrait behavior path information set to obtain an isolated user portrait behavior path information set; real-time detection is performed on the isolated user portrait behavior path information set to obtain a detection result; in response to determining that the detection result represents a detection abnormality, abnormal alarm information corresponding to the detection result representing the detection abnormality is sent to the user interface for the user to perform a preset behavior operation; in response to determining that the user has performed the preset behavior operation, data cleaning is performed on the isolated user portrait behavior path information set to obtain a cleaned behavior path information set; the cleaned behavior path information set is adjusted to obtain an adjusted behavior path information set; A correlation analysis unit is configured to perform data correlation analysis on the adjusted behavior path information set and the processed operation data set to obtain a post-analysis result set, wherein the post-analysis results in the post-analysis result set represent data correlation consistent results and data correlation inconsistent results; A decision control unit is configured to perform decision control on the adjusted behavior path information set according to the analyzed result set.

5. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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