Data processing method, device, storage medium and computing device

Abstract

The embodiments of the present disclosure provide a data processing method, apparatus, storage medium and computing device. The method is applied to a client and includes: obtaining an analysis task; determining target data required for the analysis task and the data type of the target data; obtaining the target data from a local database corresponding to the data type; analyzing and processing the obtained target data based on the analysis task to obtain an analysis result; and executing an operation corresponding to the analysis result.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of data processing technology. More specifically, the embodiments of the present disclosure relate to a data processing method, apparatus, storage medium and computing device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the present disclosure. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] With the rapid development of information technology, enterprises and organizations are faced with massive data resources. In the process of collecting, analyzing and processing data, security issues such as data leakage may occur. Summary of the invention

[0004] In a first aspect of the embodiments of the present disclosure, a data processing method is provided, which is applied to a client. The method comprises:

[0005] Get analysis tasks;

[0006] Determining target data required for the analysis task and the data type of the target data;

[0007] Acquire the target data from a local database corresponding to the data type;

[0008] Analyze and process the acquired target data based on the analysis task to obtain an analysis result;

[0009] Execute the operation corresponding to the analysis result.

[0010] Optionally, the data type includes original data, the local database corresponding to the original data is a first database, and the method further includes:

[0011] Collect basic user data, user behavior data, and device status data;

[0012] The collected user basic data, user behavior data and device status data are stored as raw data in the first database.

[0013] Optionally, the data type further includes characteristic data, the local database corresponding to the characteristic data is a second database, and the method further includes:

[0014] Determine the raw data required for the characteristics of each dimension;

[0015] For each dimension feature, obtaining original data required for the dimension feature from the first database;

[0016] Performing feature analysis on the acquired original data to obtain feature data corresponding to the dimensional feature;

[0017] The feature data is stored in the second database.

[0018] Optionally, also include:

[0019] Determine the amount of the original data in the first database;

[0020] In response to the data volume being greater than a data volume threshold, the original data in the first database is cleaned up based on a cleansing rule.

[0021] Optionally, when the analysis task is a risk analysis, the performing of an operation corresponding to the analysis result includes:

[0022] If the analysis result is risky, the target data and the analysis result obtained from the local database are sent to the server, so that the server can review the analysis result based on the target data;

[0023] Receive the review result returned by the server;

[0024] When the review result is also risky, risk interception is performed.

[0025] Optionally, when the analysis task is object recommendation, the performing of the operation corresponding to the analysis result includes:

[0026] Obtaining a target recommendation object from the analysis result;

[0027] The target recommended object is pushed to the user.

[0028] Optionally, when the analysis task is page layout recommendation, the performing of the operation corresponding to the analysis result includes:

[0029] Obtaining a target page layout from the analysis results;

[0030] The current page layout is adjusted to the target page layout.

[0031] Optionally, when the analysis task is stability analysis, the performing of the operation corresponding to the analysis result includes:

[0032] When the analysis result shows that the stability index is less than the index threshold, the operation strategy is adjusted to reduce system resource consumption.

[0033] Optionally, the adjustment operation strategy includes one or more of the following:

[0034] Reduce code execution, reduce network requests, and optimize memory usage.

[0035] In a second aspect of the embodiments of the present disclosure, a data processing device is provided, which is applied to a client, and the device includes:

[0036] A task acquisition unit, which acquires analysis tasks;

[0037] A data determination unit, which determines the target data required for the analysis task and the data type of the target data;

[0038] A data acquisition unit, which acquires the target data from a local database corresponding to the data type;

[0039] A task analysis unit, which analyzes and processes the acquired target data based on the analysis task to obtain an analysis result;

[0040] The result execution unit executes the operation corresponding to the analysis result.

[0041] In a third aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, comprising:

[0042] When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method as described in any of the preceding items.

[0043] In a fourth aspect of the embodiments of the present disclosure, there is provided a computing device, comprising:

[0044] processor;

[0045] a memory for storing instructions executable by the processor;

[0046] Wherein, the processor is configured to execute the executable instructions to implement the data processing method as described in any of the preceding items.

[0047] According to the data processing solution provided by the embodiment of the present disclosure, by storing the target data locally on the client, it is possible to effectively solve the security issues such as data leakage and attack on the server. At the same time, the client can perform analysis tasks based on local data without interacting with the server, which can effectively avoid the problem of untimely execution of analysis tasks caused by server failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0049] Figure 1The system diagram of the data processing system provided by the present disclosure is schematically shown;

[0050] Figure 2 The data processing method provided by the present disclosure is schematically shown;

[0051] Figure 3 The schematic diagram of the original data storage method provided by the present disclosure is schematically shown;

[0052] Figure 4 The data storage method provided by the present invention is schematically shown;

[0053] Figure 5 A schematic diagram of a characteristic data storage method provided by the present disclosure is schematically shown;

[0054] Figure 6 The following schematically shows a schematic diagram of the analysis result review method provided by the present disclosure;

[0055] Figure 7 The medium schematic diagram provided by the present disclosure is schematically shown;

[0056] Figure 8 The data processing device provided by the present disclosure is schematically shown;

[0057] Fig. 9 The schematic diagram of the computing device provided by the present disclosure is schematically shown.

[0058] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0059] The principles and spirit of the present disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0060] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0061] According to an embodiment of the present disclosure, a data processing method, a computer-readable storage medium, an apparatus, and a computing device are proposed.

[0062] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0063] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0064] The data involved in the present disclosure may be data authorized by the user or fully authorized by all parties. The collection, dissemination, and use of the data shall comply with the requirements of relevant national laws and regulations. The implementation methods / examples of the present disclosure may be combined with each other. SUMMARY OF THE INVENTION

[0066] The present disclosure aims to provide a data processing solution, whereby a client can determine the target data required for an analysis task and the data type of the target data, and then obtain the target data from a local database corresponding to the data type, so as to analyze and process the obtained target data based on the analysis task, and execute operations corresponding to the analysis results. The present disclosure can effectively solve security issues such as data leakage and attacks on the server side by storing the target data locally on the client side. At the same time, the client can perform analysis tasks based on local data without interacting with the server side, which can effectively avoid the problem of untimely execution of analysis tasks caused by server side failures.

[0067] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0068] Application Scenario Overview

[0069] Figure 1 The system diagram of the data processing system provided by the present disclosure is schematically shown.

[0070] Please refer to Figure 1 , the data processing system may include a client and a server.

[0071] The client can be installed in electronic devices such as mobile phones, PCs (Personal Computers), tablet devices, laptops, PDAs (Personal Digital Assistants), wearable devices (such as smart glasses, smart watches, etc.), etc.

[0072] The client can obtain user data such as user behavior data and user basic data. The client can also obtain device status data of the electronic device, such as network status, CPU occupancy rate, etc.

[0073] The physical carrier of the server may be a physical server including an independent host, or a virtual server carried by a host cluster.

[0074] As for the network for interaction between the client and the server, the communication can be realized by wired or wireless network based on the communication method supported by the corresponding electronic equipment. For example, PC can support both wired and wireless communication, so wired or wireless network can be used for communication as needed, while mobile phones usually only support wireless communication, so wireless network can be used for communication.

[0075] In related technologies, after the client collects user data, device status data and other data, it will send these data to the server for storage. When the server performs analysis tasks, it can obtain the stored data, then analyze and process the data to obtain the processing results, and can instruct the client to perform operations corresponding to the analysis results.

[0076] However, there are many problems in this implementation:

[0077] On the one hand, storing data on the server side may lead to security issues such as data leakage and data attacks. For example, the server side may become the target of hacker attacks. Once the server side is breached, a large amount of data will be stolen or tampered with. For another example, internal personnel on the server side may abuse access rights.

[0078] On the other hand, data may be intercepted, stolen or cracked during transmission from the client to the server. Even if the security of data during transmission can be ensured, if there is a network failure, insufficient bandwidth or high latency, it will affect the speed of data upload and download, and may cause the data to be unable to be stored or retrieved in time.

[0079] On the other hand, the server may encounter software failures, device hardware failures, device maintenance and upgrades, etc., which may cause the analysis task to fail to execute in a timely manner, thus affecting the user experience.

[0080] The present disclosure provides a data processing solution that can store user basic data, user behavior data, device status data and other data locally on the client, thereby solving problems such as insecure data transmission and server-side data leakage.

[0081] Exemplary Methods

[0082] In combination with the above application scenarios, Figure 2To describe the method of data processing according to an exemplary embodiment of the present disclosure. Among them, data processing includes but is not limited to operations such as data storage and data analysis. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of the present disclosure, and the embodiments of the present disclosure are not limited in this regard. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0083] like Figure 2 As shown, the data processing method can be applied to the aforementioned client and may include the following steps:

[0084] Step 210: Obtain analysis tasks.

[0085] In an exemplary embodiment, the analysis task is generally a task of performing analysis using data, and may include risk analysis, object recommendation, page layout recommendation, stability analysis, and the like.

[0086] The analysis task may be triggered by a user. For example, when a user uploads an image, a risk analysis task may be triggered, and the client may then obtain a corresponding risk analysis task to perform risk analysis on the image uploaded by the user.

[0087] The analysis task may also be triggered by the server. For example, the server may periodically send an object recommendation task to the client to recommend products to the user. The client may obtain the object recommendation task sent by the server and then recommend products to the user.

[0088] The analysis task may also be triggered by the client itself under specified conditions. For example, the client may trigger a stability analysis task when it detects a drop in network speed, and then perform a stability analysis.

[0089] Step 220: Determine the target data required for the analysis task and the data type of the target data.

[0090] In an exemplary embodiment, the target data and data types required for different analysis tasks are often different. After obtaining the analysis task, the client can determine the data required for the analysis task, which is hereinafter referred to as target data in this disclosure, and can determine the data type of the target data.

[0091] In an exemplary embodiment, the data type can be divided into raw data and feature data. The raw data is usually data collected by the client, such as user basic data, user behavior data, and device status data collected by the client. The feature data is usually data obtained after analyzing the raw data.

[0092] For example, the raw data collected by the client may include: the user's element click behavior in the client, the user's page sliding behavior, the user's entry or exit behavior of the client, etc. Based on these raw data, the user's willingness to pay, the user's willingness to click, etc. can be analyzed as feature data.

[0093] Step 230: Acquire the target data from a local database corresponding to the data type.

[0094] In an exemplary embodiment, the client may store the acquired data in a local database, which may be divided based on data types. For example, the local database may include a first database for storing raw data and a second database for storing a feature database.

[0095] Based on the aforementioned step 220, after determining the target data and the data type of the target data required for the analysis task, the target data can be acquired from the corresponding database.

[0096] For example, assuming that the target data required for the analysis task includes both original data and feature data, the corresponding original data can be obtained from the local first database, and the corresponding feature data can be obtained from the local second database to obtain the target data for the analysis task.

[0097] Step 240: Analyze and process the acquired target data based on the analysis task to obtain an analysis result.

[0098] In an exemplary embodiment, the client may analyze and process the acquired target data based on the analysis task to obtain analysis results, such as risk analysis results, stability analysis results, object recommendation results, etc.

[0099] Step 250: Execute an operation corresponding to the analysis result.

[0100] In an exemplary embodiment, the client can perform operations corresponding to the aforementioned analysis results. For example, if the analysis result of the risk analysis task is risky, the client can intercept the image uploaded by the user. For another example, if the analysis result of the object recommendation task is product A, the client can recommend product A to the user.

[0101] It can be seen that the data processing solution provided by the present disclosure allows the client to determine the target data required for the analysis task and the data type of the target data, and then obtain the target data from the local database corresponding to the data type, so as to analyze and process the obtained target data based on the analysis task, and perform operations corresponding to the analysis results. The present disclosure can effectively solve security issues such as data leakage and attacks on the server by storing the target data locally on the client. At the same time, the client can perform analysis tasks based on local data without interacting with the server, which can effectively avoid problems such as untimely execution of analysis tasks caused by server failures.

[0102] The implementation process of the present disclosure is described below from two aspects: data storage and data analysis scenarios.

[0103] 1. Data Storage

[0104] Please refer to Figure 3 and Figure 4 The original data storage method provided by the present disclosure may include the following steps:

[0105] Step 310, collecting user basic data, user behavior data and device status data.

[0106] In an exemplary embodiment, the client may collect user-related data and device-related data.

[0107] The user-related data may include user basic data and user behavior data. The user basic data may include: user age, user gender, user location, user hobbies, user occupation, etc. The user behavior data is usually data of user operation of the client, and may include: user entry into the client, user leaving the client, user browsed pages, user clicked page elements, etc.

[0108] The device-related data is usually device status data, which may include: device model, system model, device location, device network status, device CPU occupancy rate, etc.

[0109] In an exemplary embodiment, for data with higher stability such as device model, user age, and user gender, the client can collect corresponding data at certain time intervals. Since the data is relatively stable, the interval can be slightly longer, such as 3 months, 6 months, etc.

[0110] For data that may change in real time, such as network status and device CPU usage, the client can also collect corresponding data at certain time intervals. Since the data changes quickly, the intervals are short, such as 1 minute, 3 minutes, etc.

[0111] For user behavior data such as the pages browsed by users and the page elements clicked by users, the corresponding data can be collected in response to the occurrence of user behavior.

[0112] Of course, in other embodiments, other strategies may be used to collect the above data, and the present disclosure does not impose any special restrictions on this.

[0113] Step 320: Store the collected user basic data, user behavior data and device status data as raw data in a first database.

[0114] In an exemplary embodiment, the collected user basic data, user behavior data and device status data can be used as raw data and stored in a local first database. When storing these raw data, they can be pre-processed and converted into a standard format before being stored in the first database for subsequent analysis and processing.

[0115] Optionally, before storing the original data, it can also be filtered and integrated. For example, the original data can be screened based on preset rules to filter out erroneous or irrelevant original data. For another example, some relevant original data can be appropriately integrated to facilitate subsequent analysis, etc. The present disclosure does not impose any special restrictions on this.

[0116] Please refer to Figure 4 and Figure 5 The characteristic data storage method provided by the present disclosure may include the following steps:

[0117] Step 510: determine the original data required for each dimensional feature.

[0118] In an exemplary embodiment, based on the collected raw data, feature analysis of multiple dimensions can be performed to obtain feature data. The feature dimensions can be pre-specified, for example: user's willingness to click on a certain element, willingness to pay, user consumption level, user preference, user activity level, user behavior characteristics, etc.

[0119] Generally speaking, feature analysis of different dimensions often requires different raw data. For example, when analyzing the features of the dimension of user willingness to pay for a certain element, raw data such as the user's click behavior and browsing time for the element are often required; when analyzing the features of the dimension of user consumption level, raw data such as user occupation and user income range are often required.

[0120] In an exemplary embodiment, the original data required for each dimensional feature may be preset, and when analyzing the features of a certain dimension, the required original data may be determined first according to the settings.

[0121] Step 520: For each dimensional feature, the original data required for the dimensional feature is obtained from the first database.

[0122] In an exemplary embodiment, when analyzing a feature of a certain dimension, after determining the required original data, the original data required for the feature of the dimension may be acquired from the first database.

[0123] Still taking the feature analysis of the dimension of user consumption level as an example, original data such as user occupation, user income range, etc. can be obtained from the first database.

[0124] Step 530: Perform feature analysis on the acquired original data to obtain feature data corresponding to the dimensional feature.

[0125] In an exemplary embodiment, after the original data required for feature analysis is obtained, feature analysis may be performed on the obtained original data to obtain feature data corresponding to the dimensional feature.

[0126] Taking the feature analysis of the user consumption level as an example, assuming that the user consumption level is divided into level 1, level 2 and level 3, the original data such as the user occupation and user income range can be analyzed to obtain the feature data corresponding to the user consumption level, such as level 2. In other words, the user's user consumption level feature is level 2.

[0127] In an exemplary embodiment, the trained machine learning model may be used to analyze the raw data to obtain feature data. Of course, in other examples, other methods may be used to analyze the raw data to obtain feature data, and the present disclosure does not impose any special restrictions on this.

[0128] In an exemplary embodiment, for each dimension, feature analysis may be performed based on the required original data to obtain corresponding feature data, so that a plurality of feature data may be obtained.

[0129] Step 540: store the feature data in the second database.

[0130] In an exemplary embodiment, after the characteristic data is obtained through analysis, the characteristic data may be stored in a local second database for subsequent use.

[0131] It can be seen that the data storage solution provided by the present invention can store both the original data and the feature data in the local database without uploading them to the server for storage, thereby solving the problems of data being intercepted and cracked during transmission and data being leaked on the server caused by storing the original data on the server, thereby protecting the privacy and security of users.

[0132] It should be noted that in the above embodiment, steps 510-540 of performing feature analysis on the original data to obtain feature data and storing the feature data in the second database can be performed periodically or after receiving a server instruction, and the present disclosure does not impose any special restrictions on this.

[0133] In an exemplary embodiment, since the storage capacity of the electronic device where the client is located is often limited, the original data stored in the first database may be properly cleaned up.

[0134] In one example, the amount of raw data stored in the first database can be determined, for example, the amount of raw data in the first database can be periodically determined, and then it can be determined whether the determined amount of data is greater than a preset data amount threshold. If it is greater, it can be said that the amount of data in the first database is large, and then the raw data in the first database can be cleaned up based on the cleaning rule. If the determined amount of data is less than or equal to the data amount threshold, there is no need to clean up the raw data.

[0135] The cleaning rules may also be preset, for example, deleting raw data whose storage time exceeds a specified time period. For example, assuming that the specified time period is 7 days, the raw data whose storage time exceeds 7 days may be deleted. Another example is deleting raw data of a specified size in order of storage time from earliest to latest. For example, deleting raw data of 5MB in size in order of storage time from earliest to latest.

[0136] In another example, the amount of raw data stored in the first database may not be taken into consideration, and only raw data within a specified period of time may be stored in the first database, for example, only raw data within 7 days may be stored in the first database. For example, raw data stored for more than 7 days may be deleted at a specified time point every day (such as 00:00 every day).

[0137] Of course, in other examples, the original data in the first database may be cleaned up based on other methods, and the present disclosure does not impose any special limitation on this.

[0138] In an exemplary embodiment, the feature data in the second database may be cleaned up using a method similar to that used to clean up the original data in the first database. Optionally, since the feature data itself is relatively small, the feature data in the second database may not be cleaned up, and the present disclosure does not impose any special restrictions on this.

[0139] 2. Data Analysis Scenario

[0140] In an exemplary embodiment, the analysis task acquired by the client is a risk analysis task, for example: performing risk analysis on a user's payment operation, performing risk analysis on a picture uploaded by a user, and the like.

[0141] Taking the risk analysis of the user's payment operation as an example, this analysis task may require some raw data, such as user behavior data such as the elements clicked by the user before triggering the payment operation and the pages browsed by the user, and may also require device status data such as device model. The client can then obtain these raw data from the first database, and then perform risk analysis on the payment operation in combination with payment information (payment amount, payee), etc.

[0142] The risk analysis results usually include: risky and no risk. If the risk analysis result is determined to be no risk, the payment operation can continue. If the analysis result is risky, risk interception is performed, such as intercepting the payment operation to avoid the risk.

[0143] Optionally, if the risk analysis result is risky, the client can request the server to review the risk analysis result. Please refer to Figure 6 The analysis result review method may include the following steps:

[0144] Step 610, when the analysis result indicates that there is a risk, the target data obtained from the local database and the analysis result are sent to the server, so that the server can review the analysis result based on the target data.

[0145] In this embodiment, if the risk analysis result is risky, the client can send the target data for this risk analysis, the risk analysis results with risks, and other information required to execute this risk analysis task obtained from the local database to the server, so that the server can re-execute the analysis task to perform risk analysis and obtain a review result of the analysis result.

[0146] The review result also includes risk or no risk, and the server can return the review result to the client.

[0147] Step 620, receiving the review result returned by the server.

[0148] In this step, the client can receive the review result returned by the server.

[0149] Step 630, when the review result is also risky, risk interception is performed.

[0150] In this step, if the review result is that there is a risk, it means that the previous analysis result of the client is correct, and risk interception can be performed, such as intercepting payment operations.

[0151] If the review result is no risk, it means that the previous analysis result of the client is wrong. The review result of the server shall prevail and there is no need to conduct risk interception.

[0152] It can be seen from this that for risk analysis tasks, the client can request the server to review the analysis result if the analysis result is risky. If the server's review result is also risky, risk interception will be performed. If the server's review result is no risk, there is no need to intercept the risk, so as to improve the accuracy of risk analysis and avoid problems such as user business obstruction caused by the client misjudging the risk analysis result and intercepting it without reason.

[0153] In an exemplary embodiment, the analysis task acquired by the client is an object recommendation task, such as product recommendation, music recommendation, etc.

[0154] Taking music recommendation for users as an example, this analysis task may require both raw data and feature data. For example, user behavior data such as pages browsed by users, basic user data such as user age, feature data such as user preferences, etc. The client can then obtain the required raw data from the first database and the required feature data from the second database to make music recommendations.

[0155] The analysis result of the object recommendation task usually includes a target recommended object, which is usually an object that the user may be interested in. The client can then obtain the target recommended object and push it to the user. Taking music recommendation as an example, the analysis result of the analysis task includes target music. After obtaining the target music, the client can recommend the target music to the user.

[0156] In an exemplary embodiment, the analysis task acquired by the client is a page layout recommendation task, and the page part may include: list format layout, thumbnail format layout, etc.

[0157] The analysis task may require raw data such as user behavior data, and the client may then obtain the required raw data from the first database to make page layout recommendations.

[0158] The analysis result of the page layout recommendation task usually includes a target page layout, such as a list-like part. The client can then obtain the target page layout, and if the current page layout is not the target page layout, adjust the current page layout to the target page layout.

[0159] Of course, in other examples, before adjusting the page layout, an adjustment prompt may be output to the user, such as "Do you want to adjust the page layout to a partial list format?" If the user selects yes, the current page layout may be adjusted to the target page layout; if the user selects no, there is no need to adjust the current page layout.

[0160] In this example, the client can quickly adjust the page layout by analyzing data such as user behavior, so that the adjusted page layout is more in line with the user's behavioral characteristics, greatly improving the user experience.

[0161] In an exemplary embodiment, the analysis task acquired by the client is a stability analysis task, and the stability analysis task can be used to analyze the stability of the client operation.

[0162] The stability analysis task may require raw data representing the device status, such as network status and CPU occupancy rate, and the client may then obtain the required raw data from the first database for stability analysis.

[0163] The analysis result of the stability analysis task is usually a stability index that can represent the stability of the client. After obtaining the analysis result, it can be determined whether the stability index in the analysis result is less than a preset index threshold. If it is less than, it can indicate that the current stability of the client is poor, and the client's operating strategy can be adjusted. For example, some background tasks that users are not aware of can be not executed to reduce the amount of client code execution, the amount of network requests sent to the server can be reduced, and memory usage can be optimized. By adjusting these operating strategies, the consumption of system resources can be reduced, thereby improving the stability of the client and enhancing the user experience.

[0164] If the stability index in the analysis result is greater than or equal to the preset index threshold, it can be said that the current client operation is relatively stable, and there is no need to reduce system resource consumption by adjusting the client operation strategy. If the current client operation strategy is already an operation strategy that reduces system resource consumption such as reducing the amount of code execution, it can be adjusted to a regular operation strategy. If the current client operation strategy is the regular operation strategy, no adjustment is required.

[0165] Of course, in other examples, the stability analysis result may be directly stable or unstable. If the stability analysis result is unstable, the client may adjust the operation strategy to reduce system resource consumption.

[0166] It can be seen from the above description that the disclosed client can analyze and process the original data and / or feature data stored in the local database, and can perform operations corresponding to the analysis results. The entire process does not require interaction with the server, so that the execution of the analysis task is not restricted by the network status, and can be executed normally in the case of poor network status or even no network, and the user experience is better. At the same time, since the entire process does not require interaction with the server, the efficiency of task execution can be greatly improved, allowing the client to respond quickly, further improving the user experience.

[0167] Exemplary Media

[0168] After introducing the method of the exemplary embodiment of the present disclosure, next, refer to Figure 7 A medium according to an exemplary embodiment of the present disclosure is described.

[0169] In this exemplary embodiment, the above method can be implemented by a program product, such as a portable compact disk read-only memory (CD-ROM) and including program code, and can be run on a device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus, or a device.

[0170] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable 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 of the above.

[0171] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which 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 of the above. Readable signal media may also be any readable medium other than a 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.

[0172] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RE, etc., or any suitable combination of the foregoing.

[0173] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0174] In summary, the present disclosure may provide a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device may be enabled to execute the aforementioned data processing method embodiment.

[0175] Exemplary Devices

[0176] After introducing the medium of the exemplary embodiment of the present disclosure, next, reference is made to Figure 8 A device according to an exemplary embodiment of the present disclosure is described.

[0177] Figure 8 A block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown, corresponding to the aforementioned Figure 2 The method embodiment shown. For optional application, the data processing device may include:

[0178] A task acquisition unit 810 acquires an analysis task;

[0179] A type determination unit 820 is configured to determine target data required for the analysis task and a data type of the target data;

[0180] A data acquisition unit 830 acquires the target data from a local database corresponding to the data type;

[0181] A task analysis unit 840 analyzes and processes the acquired target data based on the analysis task to obtain an analysis result;

[0182] The result execution unit 850 executes the operation corresponding to the analysis result.

[0183] Optionally, the data type includes original data, the local database corresponding to the original data is a first database, and the device further includes:

[0184] Data collection unit 860, collects user basic data, user behavior data and device status data;

[0185] The data storage unit 870 stores the collected user basic data, the user behavior data and the device status data as raw data in the first database.

[0186] Optionally, the data type further includes characteristic data, the local database corresponding to the characteristic data is a second database, and the device further includes:

[0187] The feature analysis unit 880 determines the original data required for each dimensional feature, obtains the original data required for the dimensional feature from the first database for each dimensional feature, performs feature analysis on the obtained original data, and obtains feature data corresponding to the dimensional feature;

[0188] The data storage unit 870 stores the feature data in the second database.

[0189] Optionally, the device further comprises:

[0190] The data cleaning unit 890 determines the data volume of the original data in the first database; in response to the data volume being greater than a data volume threshold, cleans the original data in the first database based on a cleaning rule.

[0191] Optionally, when the analysis task is a risk analysis, the result execution unit 850 includes:

[0192] The sending subunit 851 sends the target data and the analysis result obtained from the local database to the server, if the analysis result is risky, so that the server can review the analysis result based on the target data;

[0193] The receiving subunit 852 receives the verification result returned by the server;

[0194] The interception subunit 853 performs risk interception when the review result also indicates risk.

[0195] Optionally, when the analysis task is object recommendation, the result execution unit 850 includes:

[0196] The object acquisition subunit 854 acquires a target recommended object from the analysis result;

[0197] The object recommendation subunit 855 pushes the target recommended object to the user.

[0198] Optionally, when the analysis task is page layout recommendation, the result execution unit 850 includes:

[0199] A layout acquisition subunit 856 acquires a target page layout from the analysis result;

[0200] The layout adjustment subunit 857 adjusts the current page layout to the target page layout.

[0201] Optionally, when the analysis task is stability analysis, the result execution unit 850 includes:

[0202] The operation adjustment subunit 858 adjusts the operation strategy to reduce system resource consumption when the analysis result shows that the stability index is less than the index threshold.

[0203] Optionally, the adjustment operation strategy includes one or more of the following:

[0204] Reduce code execution, reduce network requests, and optimize memory usage.

[0205] Exemplary Computing Devices

[0206] After introducing the method, medium and apparatus of the exemplary embodiments of the present disclosure, next, reference is made to Fig. 9 A computing device according to an exemplary embodiment of the present disclosure is described.

[0207] Fig. 9 The computing device 1500 shown is merely an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0208] like Fig. 9 As shown, computing device 1500 is in the form of a general computing device. Components of computing device 1500 may include, but are not limited to: at least one processing unit 1501, at least one storage unit 1502, and a bus 1503 connecting different system components (including processing unit 1501 and storage unit 1502).

[0209] The bus 1503 includes a data bus, a control bus, and an address bus.

[0210] The storage unit 1502 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 15021 and / or a cache memory 15022 , and may further include a readable medium in the form of a non-volatile memory, such as a read-only memory (ROM) 15023 .

[0211] The storage unit 1502 may also include a program / utility 15025 having a set (at least one) of program modules 15024, such program modules 15024 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0212] The computing device 1500 may also communicate with one or more external devices 1504 (eg, a keyboard, pointing device, etc.).

[0213] Such communication may be performed via input / output (I / O) interface 1505. In addition, computing device 1500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via network adapter 1506. Fig. 9 As shown, network adapter 1506 communicates with other modules of computing device 1500 via bus 1503. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with computing device 1500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0214] Through Fig. 9 The computing device 1500 shown can implement the aforementioned data processing method. More specifically, the storage unit 1502 stores instructions executable by the processing unit 1501. When the processing unit 1501 executes the instructions, the aforementioned data processing method is implemented.

[0215] It should be noted that, although several units / modules or sub-units / modules of the data processing device are mentioned in the above detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above may be embodied in one unit / module. Conversely, the features and functions of one unit / module described above may be further divided into multiple units / modules to be embodied.

[0216] In addition, although the operations of the disclosed method are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0217] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims.

Claims

1. A data processing method, applied to a client, comprising: Get analysis tasks; Determining target data required for the analysis task and the data type of the target data; Acquire the target data from a local database corresponding to the data type; Analyze and process the acquired target data based on the analysis task to obtain an analysis result; Execute the operation corresponding to the analysis result.

2. The method according to claim 1, wherein the data type includes original data, the local database corresponding to the original data is a first database, and the method further comprises: Collect basic user data, user behavior data, and device status data; The collected user basic data, user behavior data and device status data are stored as raw data in the first database.

3. The method according to claim 2, wherein the data type further comprises characteristic data, the local database corresponding to the characteristic data is a second database, and the method further comprises: Determine the raw data required for the characteristics of each dimension; For each dimension feature, obtaining original data required for the dimension feature from the first database; Performing feature analysis on the acquired original data to obtain feature data corresponding to the dimensional feature; The feature data is stored in the second database.

4. According to the method of claim 1, when the analysis task is risk analysis, the performing of the operation corresponding to the analysis result comprises: If the analysis result is risky, the target data and the analysis result obtained from the local database are sent to the server, so that the server can review the analysis result based on the target data; Receive the review result returned by the server; When the review result is also risky, risk interception is performed.

5. According to the method of claim 1, when the analysis task is object recommendation, the performing of the operation corresponding to the analysis result comprises: Obtaining a target recommendation object from the analysis result; The target recommended object is pushed to the user.

6. According to the method of claim 1, when the analysis task is page layout recommendation, the performing of the operation corresponding to the analysis result comprises: Obtaining a target page layout from the analysis results; The current page layout is adjusted to the target page layout.

7. The method according to claim 1, wherein when the analysis task is stability analysis, the performing of the operation corresponding to the analysis result comprises: When the analysis result shows that the stability index is less than the index threshold, the operation strategy is adjusted to reduce system resource consumption.

8. A data processing device, applied to a client, comprising: A task acquisition unit, which acquires analysis tasks; A type determination unit, which determines the target data required for the analysis task and the data type of the target data; A data acquisition unit, which acquires the target data from a local database corresponding to the data type; A task analysis unit, which analyzes and processes the acquired target data based on the analysis task to obtain an analysis result; The result execution unit executes the operation corresponding to the analysis result.

9. A computer-readable storage medium, comprising: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method according to any one of claims 1 to 7.

10. A computing device comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the executable instructions to implement the data processing method according to any one of claims 1 to 7.

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