Data acquisition methods, devices, equipment, and media based on user behavior analysis

Abstract

This invention relates to data acquisition technology, and discloses a data acquisition method based on user behavior analysis, comprising: performing high-frequency behavior analysis on behavioral data, generating commonly used query tags based on the analysis results; extracting a first acquisition field using the commonly used query tags, and filtering a selection field from the first acquisition field using a preset scenario; encrypting the selection field and displaying it on a terminal; receiving adjustment instructions returned by the user based on the displayed encrypted field, adjusting the first acquisition field according to the adjustment instructions to obtain a second acquisition field; sending a data acquisition approval request to the owner corresponding to the second acquisition field; and acquiring the second acquisition field when the owner returns an approval result of "approved". Furthermore, this invention also relates to blockchain technology, where user behavior data can be stored on blockchain nodes. This invention also proposes a data acquisition device, equipment, and medium based on user behavior analysis. This invention can improve the efficiency and accuracy of data acquisition.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, and in particular to a data acquisition method, apparatus, electronic device, and computer-readable storage medium based on user behavior analysis. Background Technology

[0002] Current industry practices for data collection typically involve user-submitted requests. These requests specify the fields to be collected, the data range, the collection time, and the collection frequency. After approval by designated personnel, the requests are submitted to the operations and maintenance department for either one-time data collection or scheduled periodic collection. This method has the following shortcomings:

[0003] 1. Users need to spend a lot of time organizing the fields to be collected before submitting an application;

[0004] 2. When collecting data from multiple systems, it is usually necessary to consult the data owner and approval personnel of each department.

[0005] 3. If the data collection results are found to be unsatisfactory after the data collection is completed, such as incorrect data dimensions, needing to add, reduce or change fields, etc., the report needs to be resubmitted for approval due to changes in the collected information.

[0006] In summary, existing data acquisition methods suffer from low acquisition efficiency and accuracy. Summary of the Invention

[0007] This invention provides a data acquisition method, device, and computer-readable storage medium based on user behavior analysis, with the main purpose of solving the problems of low data acquisition efficiency and accuracy.

[0008] To achieve the above objectives, the present invention provides a data collection method based on user behavior analysis, comprising:

[0009] Acquire user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data;

[0010] The high-frequency behavior data is sorted according to the frequency corresponding to the high-frequency behavior data, and commonly used query tags are generated based on the sorting results.

[0011] The system fields are queried using the commonly used query tags to obtain the first collection field. The first collection field is then filtered using a preset scenario to obtain the filter field.

[0012] The filtering field is encrypted to obtain an encrypted field, and the encrypted field is then displayed on the terminal.

[0013] Receive adjustment instructions from the user based on the displayed encrypted fields, and adjust the first collection field according to the adjustment instructions to obtain the second collection field;

[0014] Extract the owner of the second data collection field and send a data collection approval request to the owner;

[0015] Receive the approval result returned by the owner based on the data collection approval request. When the approval result is approved, collect the second collection field.

[0016] Optionally, the step of performing high-frequency behavior analysis on the behavior data to obtain high-frequency behavior data includes:

[0017] Frequency statistics are performed on the behavioral data to obtain each type of behavioral data and its corresponding frequency.

[0018] Behavioral data with a frequency greater than a preset threshold are selected as high-frequency behavioral data.

[0019] Optionally, sorting the high-frequency behavior data according to the frequency corresponding to the high-frequency behavior data includes:

[0020] Obtain the frequency corresponding to the high-frequency behavior data, and divide the high-frequency behavior data into an array of a preset number according to the frequency;

[0021] Select one array from the preset number of arrays one by one as the target array, and sort the high-frequency behavior data in the target array according to frequency;

[0022] Perform range sorting on all sorted arrays to obtain the sorting result of the high-frequency behavior data.

[0023] Optionally, generating commonly used query tags based on the sorted results includes:

[0024] The sorted results are used as sorting behavior data, and label attributes are set for the sorting behavior data.

[0025] Obtain the data characteristics of the sorting behavior data, name the tag attributes according to the data characteristics, and obtain commonly used query tags.

[0026] Optionally, the step of filtering the first collected fields using a preset scenario to obtain filtered fields includes:

[0027] Obtain the collection fields retrieved from the first collection fields for each preset scenario using commonly used query tags;

[0028] Select one of the scenarios as the target scenario, and randomly extract one collection field from each commonly used query tag as the target filtering field;

[0029] Summarize the target filter fields in each scenario as filter fields.

[0030] Optionally, encrypting the filtering field to obtain an encrypted field includes:

[0031] Retrieve the sensitive fields from the filter fields;

[0032] The sensitive field is masked to obtain the encrypted field.

[0033] Optionally, adjusting the first acquisition field according to the adjustment instruction to obtain the second acquisition field includes:

[0034] The adjustment instruction is parsed to obtain the execution code corresponding to the adjustment instruction;

[0035] The execution code performs at least one of the following operations on the first collection field: deletion, addition, and sorting, to obtain the second collection field.

[0036] To address the above problems, the present invention also provides a data acquisition device based on user behavior analysis, the device comprising:

[0037] The high-frequency behavior data generation module is used to acquire user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data.

[0038] The common query tag generation module is used to sort the high-frequency behavior data according to the frequency corresponding to the high-frequency behavior data, and generate common query tags based on the sorting results.

[0039] The filter field generation module is used to query system fields using the commonly used query tags to obtain the first collection field, and to filter the first collection field using a preset scenario to obtain the filter field.

[0040] The data collection field adjustment module is used to encrypt the filtering field to obtain an encrypted field and display the encrypted field on the terminal; receive adjustment instructions returned by the user based on the displayed encrypted field, and adjust the first data collection field according to the adjustment instructions to obtain a second data collection field;

[0041] The data collection and approval module is used to extract the owner of the second data collection field and send a data collection approval request to the owner.

[0042] The field collection module is used to receive the approval result returned by the owner based on the data collection approval request. When the approval result is approved, the second collection field is collected.

[0043] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0044] At least one processor; and,

[0045] A memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the data acquisition method based on user behavior analysis described above.

[0047] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the aforementioned data acquisition method based on user behavior analysis.

[0048] This invention improves data collection efficiency by performing high-frequency analysis and sorting of user behavior data to generate frequently used query tags. By filtering and encrypting the first collection field before displaying it on the terminal, users are informed of the collection results in advance, facilitating adjustments and improving data collection accuracy. Furthermore, data collection is ensured by approving the attributes of the second collection field before data collection. Therefore, the data collection method, device, electronic device, and computer-readable storage medium based on user behavior analysis proposed in this invention can solve the problems of low data collection efficiency and accuracy. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a data collection method based on user behavior analysis provided in an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram of a process for sorting high-frequency behavioral data according to an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of a process for filtering the first collection field according to an embodiment of the present invention;

[0052] Figure 4 This is a functional block diagram of a data acquisition device based on user behavior analysis provided in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the data acquisition method based on user behavior analysis, according to an embodiment of the present invention.

[0054] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] This application provides a data collection method based on user behavior analysis. The execution entity of the data collection method based on user behavior analysis includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the data collection method based on user behavior analysis can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0057] Reference Figure 1 The diagram shown is a flowchart illustrating a data collection method based on user behavior analysis according to an embodiment of the present invention. In this embodiment, the data collection method based on user behavior analysis includes:

[0058] S1. Obtain user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data;

[0059] In this embodiment of the invention, the user's behavior data refers to the user's operational behavior when the system collects data. For example, the behavior data may be query fields of certain business operations.

[0060] In this embodiment of the invention, computer statements with crawling capabilities (e.g., Java statements, Python statements, etc.) or web crawling technology can be used to crawl behavioral data from storage areas (e.g., blockchain nodes, databases, system logs, etc.) during each data collection, thereby obtaining user behavioral data.

[0061] In this embodiment of the invention, performing high-frequency behavior analysis on the behavioral data to obtain high-frequency behavior data includes:

[0062] Frequency statistics are performed on the behavioral data to obtain each type of behavioral data and its corresponding frequency.

[0063] Behavioral data with a frequency greater than a preset threshold are selected as high-frequency behavioral data.

[0064] In this embodiment of the invention, filtering out high-frequency behavioral data can save users a lot of time in organizing and processing the fields of the collected data, thus improving the efficiency of data collection.

[0065] S2. Sort the high-frequency behavior data according to the frequency corresponding to the high-frequency behavior data, and generate commonly used query tags based on the sorting results;

[0066] In this embodiment of the invention, the obtained high-frequency behavioral data also have different usage frequencies. When using them, they can be sorted in an orderly manner according to different frequencies, and common behavioral query tags can be used to facilitate subsequent data collection by users.

[0067] In this embodiment of the invention, the high-frequency behavior data can be sorted using basic sorting algorithms, including but not limited to quicksort, selection sort, counting sort, radix sort, insertion sort, bubble sort, merge sort, shell sort, heap sort, and bucket sort.

[0068] In the embodiments of this invention, please refer to Figure 2 As shown, sorting the high-frequency behavior data according to the frequency corresponding to the high-frequency behavior data includes:

[0069] S21. Obtain the frequency corresponding to the high-frequency behavior data, and divide the high-frequency behavior data into an array of a preset number according to the frequency;

[0070] S22. Select one array from the preset number of arrays as the target array, and sort the high-frequency behavior data in the target array according to frequency;

[0071] S23. Perform interval sorting on all sorted arrays to obtain the sorting result of the high-frequency behavior data.

[0072] For example, suppose the frequencies of high-frequency behavioral data A, B, C, D, E, F, and G are 2, 6, 8, 7, 3, 10, and 4, respectively. Given that the frequency range of array a is [0, 3], array b is [4, 7], and array c is [8, 10], the high-frequency behavioral data in array a are A and E, array b is B, D, and G, and array c is C and F. Sorting the high-frequency behavioral data within each array yields the following results: in array a, the sorted high-frequency behavioral data are A and E; in array b, G, B, and D; and in array c, C and F. Further sorting the arrays by interval yields the following sorted array order: array a, array b, array c. The final sorted result of the high-frequency behavioral data is: high-frequency data A, E, G, B, D, C, and F.

[0073] In this embodiment of the invention, the high-frequency behavior data is sorted using the above sorting method, and several sub-tasks of a total task are processed simultaneously and then merged, which can improve sorting efficiency.

[0074] In this embodiment of the invention, after sorting the high-frequency behavior data, a priority level is set for the high-frequency behavior data. The high-frequency behavior data may represent query fields, execution code, etc., which have weak identification meaning. Therefore, common query tags can be set according to the high-frequency behavior data to facilitate use when collecting data.

[0075] In this embodiment of the invention, generating commonly used query tags based on the sorted results includes:

[0076] The sorted results are used as sorting behavior data, and label attributes are set for the sorting behavior data.

[0077] Obtain the data characteristics of the sorting behavior data, name the tag attributes according to the data characteristics, and obtain commonly used query tags.

[0078] In this embodiment of the invention, the sorting behavior data is the sorted high-frequency behavior data. The data features of the sorting behavior data can be the operation and target of the sorting behavior data. For example, there is high-frequency behavior data that is to obtain the voucher image in each user file in the system one by one. The corresponding data feature can be to extract the voucher image. Commonly used query tags are to extract the voucher image.

[0079] S3. Use the commonly used query tags to query system fields to obtain the first collection field, and use a preset scenario to filter the first collection field to obtain the filter field;

[0080] In this embodiment of the invention, after generating commonly used query tags, the corresponding data fields can be easily queried in different systems (e.g., project approval system, contract loan system, etc.) based on the query tags, and the queried data fields are the first collection fields.

[0081] In this embodiment of the invention, after obtaining the first collection field using common query tags, due to differences between data from different systems or different data that the user wants to obtain, the first collection field may not be the data that the user wants to collect. It is necessary to select a portion of the fields as representatives and then have the user confirm them.

[0082] In the embodiments of this invention, please refer to Figure 3 As shown, the filtering of the first collected fields using a preset scenario to obtain the filtered fields includes:

[0083] S31. Obtain the collection fields queried from the commonly used query tags in each preset scenario from the first collection fields;

[0084] S32. Select one of the scenarios as the target scenario, and randomly extract one collection field obtained from each commonly used query tag as the target filtering field;

[0085] S33. Summarize the target filter fields in each scenario as filter fields.

[0086] In this embodiment of the invention, the scenario can be specifically different systems, such as a project approval system, a contract loan disbursement system, etc.

[0087] S4. Encrypt the filtering field to obtain the encrypted field, and display the encrypted field on the terminal;

[0088] In this embodiment of the invention, the filtered fields need to be encrypted before being displayed on the terminal to ensure data security.

[0089] In this embodiment of the invention, the encrypted fields are displayed on the terminal to allow users to view the collected data, thereby enabling the processing or confirmation of the collected data.

[0090] In this embodiment of the invention, encrypting the filtering field to obtain an encrypted field includes:

[0091] Retrieve the sensitive fields from the filter fields;

[0092] The sensitive field is masked to obtain the encrypted field.

[0093] In this embodiment of the invention, the masking operation can achieve the purpose of masking by obtaining new operands through bitwise operations or logical operations. The bitwise operations can be OR operations and AND operations, using ASCII codes to change uppercase letters to lowercase letters, or performing direct symbol masking operations on some positions, etc.

[0094] S5. Receive the adjustment instruction returned by the user based on the displayed encrypted field, and adjust the first collection field according to the adjustment instruction to obtain the second collection field;

[0095] In this embodiment of the invention, after the user views the encrypted fields displayed on the terminal, they can issue an instruction to adjust the collected fields according to specific needs in order to achieve the final collection requirements.

[0096] In this embodiment of the invention, adjusting the first acquisition field according to the adjustment instruction to obtain the second acquisition field includes:

[0097] The adjustment instruction is parsed to obtain the execution code corresponding to the adjustment instruction;

[0098] The execution code performs at least one of the following operations on the first collection field: deletion, addition, and sorting, to obtain the second collection field.

[0099] In this embodiment of the invention, the execution code can be a specific operation instruction field, which can perform field deletion operations, field sorting operations, field addition operations, etc. on the first collected field.

[0100] Further, the step of parsing the adjustment instruction to obtain the execution code corresponding to the adjustment instruction includes:

[0101] Extract the data type field of the adjustment instruction;

[0102] Select a parser corresponding to the data type field to parse the adjustment instruction and obtain parsed data;

[0103] Data at a preset position in the parsed data is extracted as the execution code corresponding to the adjustment instruction.

[0104] Specifically, the data type field is a field used to identify the type of data in the adjustment instruction. The data type field of the adjustment instruction can be extracted using a pre-built rule expression, which is a pre-compiled function that can recall data in a fixed format.

[0105] In this embodiment of the invention, the data packet obtained by the parser contains data corresponding to the execution code, and its position in the parsed data can be fixed at a certain preset position. The parser includes, but is not limited to, C language and Java.

[0106] S6. Extract the owner of the second collection field and send a data collection approval request to the owner;

[0107] In this embodiment of the invention, the management personnel corresponding to the data collection fields may be different. For example, depending on the system of the data collection field, the corresponding approver may be different. That is, the owners of different data collection fields may be different, and the approval result of the owner is required before further data can be collected.

[0108] In this embodiment of the invention, fields may have different owner attributes depending on the system they belong to, and the owner can be obtained by directly extracting the owner attribute corresponding to the field.

[0109] S7. Receive the approval result returned by the owner based on the data collection approval request. When the approval result is approved, collect the second collection field.

[0110] In this embodiment of the invention, the owner may return an approval result based on the data collection approval request, or may not return an approval result in a timely manner based on the data collection approval request; the approval result returned by the owner based on the data collection approval request may be either "agree" or "disagree"; only when the owner returns an approval result based on the data collection approval request, and the returned approval result is "agree", can the fields corresponding to the owner be collected.

[0111] In this embodiment of the invention, the security of the data collection process is ensured by collecting fields after the owner's consent.

[0112] In one practical application scenario of this invention, the data collection may involve multiple systems. It is not necessary for the owners of all systems to agree to collect all the data. Once an owner agrees, the data corresponding to that owner can be collected, which improves the efficiency of data collection.

[0113] In this embodiment of the invention, after collecting the second collected field, the second collected field can be uploaded to the blockchain to ensure its security. The methods of uploading to the blockchain may include, but are not limited to: content storage, hash storage, link storage, privacy storage, and shared privacy storage. Content storage refers to directly placing the data to be uploaded to the blockchain. Hash storage involves saving the hash value of the data to be uploaded to the blockchain. The hash value of the data to be uploaded is also commonly referred to as a "digital fingerprint," which can be obtained by performing a hash operation on the data. Link storage is equivalent to adding the URI (Uniform Resource Identifier) ​​address of the data to be uploaded to the blockchain on top of hash storage, so that the blockchain stores the address and digital fingerprint of the data to be uploaded, and the two can mutually verify each other. Privacy storage involves encrypting the data to be uploaded before storing it on the blockchain; this is a strategy adopted to address the fact that data on the blockchain is publicly transparent. Shared privacy involves encrypting the data to be uploaded to the blockchain and then further encrypting the encryption key using asymmetric encryption. In other words, the first encryption of the file content uses symmetric encryption, while the second encryption of the key uses asymmetric encryption.

[0114] In this embodiment of the invention, after collecting the second collection field, the collection process and subsequent forwarding process can be automatically recorded. In addition, the security department conducts real-time monitoring and alerts users when there may be violations.

[0115] This invention improves data collection efficiency by performing high-frequency analysis and sorting of user behavior data to generate frequently used query tags. Furthermore, by filtering and encrypting the first collection field before displaying it on the terminal, users are informed of the collection results in advance, facilitating adjustments and improving data collection accuracy. Finally, data collection is performed after the attributes of the second collection field are approved, ensuring the security of the data collection process. Therefore, the user behavior analysis-based data collection method proposed in this invention can solve the problems of low data collection efficiency and accuracy.

[0116] like Figure 4 The diagram shown is a functional block diagram of a data acquisition device based on user behavior analysis provided in an embodiment of the present invention.

[0117] The user behavior analysis-based data acquisition device 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the user behavior analysis-based data acquisition device 100 may include a high-frequency behavior data generation module 101, a commonly used query tag generation module 102, a filter field generation module 103, a collection field adjustment module 104, a collection approval module 105, and a field collection module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0118] In this embodiment, the functions of each module / unit are as follows:

[0119] The high-frequency behavior data generation module 101 is used to acquire user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data.

[0120] The commonly used query tag generation module 102 is used to sort the high-frequency behavior data according to the frequency corresponding to the high-frequency behavior data, and generate commonly used query tags based on the sorting results.

[0121] The filtering field generation module 103 is used to query system fields using the common query tags to obtain a first collection field, and to filter the first collection field using a preset scenario to obtain a filtering field.

[0122] The data collection field adjustment module 104 is used to encrypt the filter field to obtain an encrypted field and display the encrypted field on the terminal; receive the adjustment instruction returned by the user based on the displayed encrypted field, and adjust the first data collection field according to the adjustment instruction to obtain a second data collection field;

[0123] The data collection approval module 105 is used to extract the owner of the second data collection field and send a data collection approval request to the owner.

[0124] The field acquisition module 106 is used to receive the approval result returned by the owner according to the data acquisition approval request, and when the approval result is approved, the second acquisition field is acquired.

[0125] In detail, the modules in the data acquisition device 100 based on user behavior analysis described in this embodiment of the invention employ the same methods as described above. Figures 1 to 3 The data collection method based on user behavior analysis described herein uses the same technical means and can produce the same technical effect, so it will not be repeated here.

[0126] like Figure 5The diagram shown is a structural schematic of an electronic device that implements a data acquisition method based on user behavior analysis, according to an embodiment of the present invention.

[0127] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a data acquisition program based on user behavior analysis.

[0128] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing data acquisition programs based on user behavior analysis) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0129] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a data acquisition program based on user behavior analysis, but also to temporarily store data that has been output or will be output.

[0130] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0131] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0132] Figure 5 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 5 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0133] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0134] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0135] The data acquisition program based on user behavior analysis stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0136] Acquire user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data;

[0137] The high-frequency behavior data is sorted according to the frequency corresponding to the high-frequency behavior data, and commonly used query tags are generated based on the sorting results.

[0138] The system fields are queried using the commonly used query tags to obtain the first collection field. The first collection field is then filtered using a preset scenario to obtain the filter field.

[0139] The filtering field is encrypted to obtain an encrypted field, and the encrypted field is then displayed on the terminal.

[0140] Receive adjustment instructions from the user based on the displayed encrypted fields, and adjust the first collection field according to the adjustment instructions to obtain the second collection field;

[0141] Extract the owner of the second data collection field and send a data collection approval request to the owner;

[0142] Receive the approval result returned by the owner based on the data collection approval request. When the approval result is approved, collect the second collection field.

[0143] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0144] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0145] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0146] Acquire user behavior data, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data;

[0147] The high-frequency behavior data is sorted according to the frequency corresponding to the high-frequency behavior data, and commonly used query tags are generated based on the sorting results.

[0148] The system fields are queried using the commonly used query tags to obtain the first collection field. The first collection field is then filtered using a preset scenario to obtain the filter field.

[0149] The filtering field is encrypted to obtain an encrypted field, and the encrypted field is then displayed on the terminal.

[0150] Receive adjustment instructions from the user based on the displayed encrypted fields, and adjust the first collection field according to the adjustment instructions to obtain the second collection field;

[0151] Extract the owner of the second data collection field and send a data collection approval request to the owner;

[0152] Receive the approval result returned by the owner based on the data collection approval request. When the approval result is approved, collect the second collection field.

[0153] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0154] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0156] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0158] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0159] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data collection method based on user behavior analysis, characterized by, The method comprises: obtaining behavior data of a user, performing high-frequency behavior analysis on the behavior data to obtain high-frequency behavior data; dividing the high-frequency behavior data into a preset number of arrays according to frequencies corresponding to the high-frequency behavior data, sorting the high-frequency behavior data in each array according to the frequencies, performing interval sorting on all sorted arrays to obtain a sorting result of the high-frequency behavior data, and generating a commonly used query label according to the sorting result; querying a system field by using the commonly used query label to obtain a first collected field, and filtering the first collected field by using a preset scene to obtain a filtered field; encrypting the filtered field to obtain an encrypted field, and displaying the encrypted field on a terminal; receiving an adjustment instruction returned by a user according to the displayed encrypted field, adjusting the first collected field according to an execution code corresponding to the adjustment instruction to obtain a second collected field; extracting an owner of the second collected field, and sending a data collection approval request to the owner; receiving an approval result returned by the owner according to the data collection approval request, and collecting the second collected field when the approval result is passed.

2. The data collection method based on user behavior analysis of claim 1, wherein, The high-frequency behavior analysis on the behavior data to obtain high-frequency behavior data comprises: performing frequency statistics on the behavior data to obtain each kind of behavior data and a corresponding frequency; selecting behavior data with a frequency greater than a preset threshold as high-frequency behavior data.

3. The data collection method based on user behavior analysis of claim 1, wherein, The generation of a commonly used query label according to the sorting result comprises: using the sorting result as sorted behavior data, setting a label attribute of the sorted behavior data; obtaining a data feature of the sorted behavior data, naming the label attribute according to the data feature to obtain a commonly used query label.

4. The data collection method based on user behavior analysis of claim 1, wherein, The filtering of the first collected field by using a preset scene to obtain a filtered field comprises: obtaining a collected field queried by a commonly used query label in each preset scene from the first collected field; selecting one of the scenes as a target scene, and randomly extracting a collected field queried by each kind of commonly used query label as a target filtered field; summarizing the target filtered fields in each scene as a filtered field.

5. The data collection method based on user behavior analysis of claim 1, wherein, The encryption of the filtered field to obtain an encrypted field comprises: obtaining a sensitive field in the filtered field; performing a mask operation on the sensitive field to obtain an encrypted field.

6. The data collection method based on user behavior analysis according to any one of claims 1 to 5, wherein, The adjustment of the first collected field according to an execution code corresponding to the adjustment instruction to obtain a second collected field comprises: parsing the adjustment instruction to obtain an execution code corresponding to the adjustment instruction; performing at least one of deleting, adding and sorting on the first collected field according to the execution code to obtain a second collected field.

7. A data collection device based on user behavior analysis, characterized by, The device comprises: a high-frequency behavior data generation module configured to obtain behavior data of a user, perform high-frequency behavior analysis on the behavior data, and obtain high-frequency behavior data. The common query label generation module is configured to divide the high-frequency behavior data into a preset number of arrays according to frequencies corresponding to the high-frequency behavior data, sort the high-frequency behavior data in each array according to the frequencies, perform interval sorting on all sorted arrays, obtain a sorting result of the high-frequency behavior data, and generate a common query label according to the sorting result; The screening field generation module is configured to query system fields by using the common query label, obtain a first collection field, and screen the first collection field by using a preset scene to obtain a screening field; The collection field adjustment module is configured to encrypt the screening field to obtain an encrypted field, display the encrypted field on a terminal, receive an adjustment instruction returned by a user according to the displayed encrypted field, and adjust the first collection field according to an execution code corresponding to the adjustment instruction to obtain a second collection field; The collection approval module is configured to extract an owner of the second collection field, and send a data collection approval request to the owner; The field collection module is configured to receive an approval result returned by the owner according to the data collection approval request, and collect the second collection field when the approval result is passed.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data collection method based on user behavior analysis according to any one of claims 1 to 6.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the data collection method based on user behavior analysis according to any one of claims 1 to 6.

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