Method and device for compressing burying point data

Abstract

The invention belongs to the field of data compression, and discloses a burying point data compression method and device. The method comprises the following steps: the client collects target burying point data of a user, wherein the target burying point data comprises user burying point data and / or system burying point data; according to the data classifier and the target burying point data, the client determines a target importance level corresponding to the target burying point data; the client queries a target compression method corresponding to the target importance level in a pre-stored corresponding relationship between importance levels and compression methods; the target compression method comprises a lossless compression method and a lossy compression method; and the client compresses the target burying point data according to the target compression method to obtain a target burying point compressed packet corresponding to the target burying point data. By adopting the method and the device, the target burying point data can be compressed.

Description

Technical Field

[0001] The present application relates to the technical field of data compression, and in particular to a method and device for compressing buried point data. Background Art

[0002] In large-scale Internet applications, data tracking systems collect massive amounts of data. For example, in an e-commerce application with millions of daily active users, each user's browsing, searching, purchasing and other behaviors will generate a large amount of tracking data. These tracking data are very important for analyzing user behavior paths and optimizing business processes, so they need to be transmitted to the server in a timely manner for real-time analysis in order to make quick business decisions.

[0003] In the prior art, after the data point system collects the data, it directly sends the original data to the server. For example, in the early data point systems of some small websites, when the number of users is small, the user's click, browse and other behavior data can be directly transmitted. However, with the rapid increase in the amount of data, if the original data is still transmitted directly, it will cause network congestion and result in low transmission efficiency. Therefore, a method for compressing data points is urgently needed. Summary of the invention

[0004] Based on this, it is necessary to provide a method and device for compressing buried point data in response to the above technical problems.

[0005] In a first aspect, a method for compressing buried data is provided, the method being applied to a buried data compression system, the system comprising a client and a server, the client being provided with a data classifier, the method comprising: The client collects target burying point data of the user, and the target burying point data includes user burying point data and / or system burying point data; According to the data classifier and the target buried point data, the client determines the target importance level corresponding to the target buried point data; In the pre-stored correspondence between importance levels and compression methods, the client queries the target compression method corresponding to the target importance level; the target compression method includes a lossless compression method and a lossy compression method; The client compresses the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

[0006] As an optional implementation manner, the client determines the target importance level corresponding to the target buried point data according to the data classifier and the target buried point data, including: The client classifies the target tracking data using a data classifier to obtain a plurality of current behavior data of the user and / or a plurality of current performance data and a plurality of current business process data of the system; In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

[0007] As an optional implementation manner, the client compresses the target burial point data according to the target compression method, including: When the target compression method is the lossless compression method, the target buried point data is compressed using the LZW algorithm; When the target compression method is the lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

[0008] As an optional implementation, the method further includes: Within a preset time window, the client aggregates multiple target embedding point compression packages from the same source and sends the aggregated data packets to the server.

[0009] As an optional implementation, the method further includes: After receiving the data packet, the server disaggregates the data packet to obtain a plurality of compressed packets of the target embedding points; The server side decompresses the target data according to the target decompression method corresponding to the target compression method, obtains the target buried point data, and stores it in the database according to a preset format.

[0010] In a second aspect, a device for compressing buried data is provided, the device being applied to a buried data compression system, the system comprising a client and a server, the client being provided with a data classifier, the device comprising: A collection module, used for the client to collect target buried point data of the user, wherein the target buried point data includes user buried point data and / or system buried point data; A determination module, configured to determine, by the client, a target importance level corresponding to the target buried point data according to the data classifier and the target buried point data; A query module, configured to query the target compression method corresponding to the target importance level by the client in the pre-stored correspondence between the importance level and the compression method; the target compression method includes a lossless compression method and a lossy compression method; A compression module is used for the client to compress the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

[0011] As an optional implementation manner, the determining module is specifically configured to: The client classifies the target tracking data using a data classifier to obtain a plurality of current behavior data of the user and / or a plurality of current performance data and a plurality of current business process data of the system; In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

[0012] As an optional implementation manner, the compression module is specifically used to: When the target compression method is the lossless compression method, the target buried point data is compressed using the LZW algorithm; When the target compression method is the lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

[0013] As an optional implementation, the device further includes: The aggregation module is used to enable the client to aggregate multiple target embedding point compression packages from the same source within a preset time window, and send the aggregated data packets to the server.

[0014] As an optional implementation, the device further includes: A de-aggregation module, used for de-aggregating the data packet after the server receives the data packet, to obtain a plurality of compressed packets of the target embedding points; The decompression module is used for the server to decompress the target data according to the target decompression method corresponding to the target compression method, obtain the target buried point data, and store it in the database according to a preset format.

[0015] In a third aspect, a system for compressing buried point data is provided, and the system for compressing buried point data comprises: the method for compressing buried point data as described in the first aspect and the device for compressing buried point data as described in the second aspect.

[0016] In a fourth aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method steps described in the first aspect are implemented.

[0017] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method steps described in the first aspect are implemented.

[0018] The present application provides a method for compressing buried point data. The technical solution provided by the embodiment of the present application at least brings the following beneficial effects: the client collects the target buried point data of the user, and the target buried point data includes user buried point data and / or system buried point data; according to the data classifier and the target buried point data, the client determines the target importance level corresponding to the target buried point data; in the correspondence between the importance level and the compression method stored in advance, the client queries the target compression method corresponding to the target importance level; the target compression method includes lossless compression method and lossy compression method; the client compresses the target buried point data according to the target compression method to obtain the target buried point compression package corresponding to the target buried point data. In this way, when collecting the target buried point data of the client, the target buried point data is not directly obtained, and the target buried point data is compressed. In this way, it will not cause network congestion and will greatly improve the transmission efficiency. The importance level of the target buried point data can be determined by the data classifier, and different compression methods correspond to different importance levels. The buried point data with high importance level uses lossless compression method, and the buried point data with medium importance level uses lossy compression method. In this way, through a compression strategy with a focus, it is ensured that the compression accuracy of the buried data with a high importance level is high and it can be lossless during decompression. The compression of the buried data with a medium importance level will not occupy too many computing resources and can be fully compressed.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of the structure of a system for compressing buried point data provided in an embodiment of the present application; Figure 2 A flowchart of a method for compressing buried point data provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a device for compressing buried point data provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] The method for compressing buried point data provided in the embodiment of the present application can be applied to a system for compressing buried point data. Figure 1 As shown, the system for compressing buried point data includes a client 101 and a server 102, and the client 101 is connected to the server 102.

[0024] The client 101 is used to collect the user's target buried point data, and the target buried point data includes user buried point data and / or system buried point data. According to the data classifier and the target buried point data, the client 101 determines the target importance level corresponding to the target buried point data. In the correspondence between the importance level and the compression method stored in advance, the client 101 queries the target compression method corresponding to the target importance level. Among them, the target compression method includes lossless compression method and lossy compression method. The client 101 compresses the target buried point data according to the target compression method to obtain a target buried point compression package corresponding to the target buried point data. Within the preset time window, the client 101 aggregates multiple target buried point compression packages from the same source, and sends the aggregated data packets to the server 102.

[0025] The server 102 is used to receive the data packets sent by the client 101, and decompress the data packets to obtain multiple target point compression packets. The server 102 decompresses the data packets according to the target decompression method corresponding to the target compression method to obtain the target point data, and stores them in the database according to the preset format.

[0026] The following will describe in detail a method for compressing buried point data provided by an embodiment of the present application in combination with a specific implementation method. Figure 2 A flowchart of a method for compressing buried point data provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the specific steps are as follows: Step S201, the client collects the user's target buried data, and the target buried data includes user buried data and / or system buried data.

[0027] In implementation, in Internet applications, the data tracking system will collect massive amounts of tracking data from users and transmit these tracking data to the server. Tracking data are usually correlated, such as multiple behavioral data from the same user in a shopping process are interrelated. These related data are very important in analyzing user behavior paths and optimizing business processes. The server analyzes the tracking data in real time to make business decisions quickly. Among them, behavioral data can be browsing products, adding to shopping carts, and settlement. In order to avoid the client directly sending the collected raw data to the server to cause network congestion, and at the same time, the problem of low transmission efficiency, the user's target tracking data collected from the client can be compressed, so that the transmission efficiency is greatly improved and it will not cause network congestion. At the beginning, the client needs to collect the user's target tracking data, where the target tracking data includes user tracking data and / or system tracking data.

[0028] Step S202: Based on the data classifier and the target buried point data, the client determines the target importance level corresponding to the target buried point data.

[0029] In the implementation, in the acquired target buried data, the data types of the corresponding multiple behavior data of the user and the multiple performance data of the system are different, and the corresponding data have different importance levels. For example, the data of user login, payment and other operations are marked as user behavior data of high importance, so that the data of key business operations are of high importance. The data of page scrolling, mouse movement and other data are marked as user behavior data of medium importance, so that the data of ordinary browsing behavior is of medium importance. In this way, the importance levels of different data types can be divided, and different compression strategies can be adopted for buried data of different importance levels. Therefore, the target buried data can be classified according to the data classifier to obtain multiple current behavior data of the user and / or multiple current performance data and multiple current business process data of the system, and then the target importance levels of multiple current behavior data of the user and / or multiple current performance data of the system are determined respectively. Among them, the current business process data can be order creation, payment process, etc. In this way, the subsequent steps can determine different compression strategies according to the buried data of importance level. Therefore, according to the data classifier and the target buried data, the client determines the target importance level corresponding to the target buried data.

[0030] Specifically, the specific process of executing step S202 is as follows: Step 1: The client classifies the target tracking data using a data classifier to obtain multiple current behavior data of the user and / or multiple current performance data and multiple current business process data of the system.

[0031] In the implementation, the client uses a data classifier to classify the target buried data, and obtains multiple current behavior data of the user and / or multiple current performance data and multiple current business process data of the system. Among them, the function of the data classifier is to classify the types of buried data. For example, when collecting buried data of the login page, a data classifier is set on the client program, and a ticket query page carrying data_type='login' is collected, which carries a data_type='search_ticket'. Through the data classifier, the data_type of the buried data is judged, thereby determining the data type of the buried data.

[0032] Step 2: In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

[0033] During implementation, technical personnel determine the corresponding importance levels in advance for various behavior data of users, various performance data of the system, and various business process data, and determine the importance levels of various behavior data, various system data of the system, and various business process data according to the importance levels. For example, the importance of system performance tracking data of core business is higher than that of system performance tracking data of non-core business. Record the correspondence between behavior data, performance data, business process data, and importance levels. Later, when determining the importance levels of target tracking data, the client can query the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and business process data in the pre-stored correspondence between behavior data, performance data, business process data, and importance levels.

[0034] Step S203: the client searches for a target compression method corresponding to a target importance level in the pre-stored correspondence between importance levels and compression methods; the target compression method includes a lossless compression method and a lossy compression method.

[0035] In implementation, if the client compresses all the collected target buried data with a unified compression method, it is obvious that the diversity of buried data and the importance differences of different buried data are not taken into account. The compression degree of the same compression method is used for high-value critical buried data and relatively low-value ordinary buried data. For high-value critical buried data, it may lead to insufficient precision of key data after decompression or over-compression of transaction data, affecting the accuracy of data transmission; for relatively low-value ordinary data, too many computing resources are wasted or browsing behavior data is not fully compressed, resulting in limited bandwidth savings. Therefore, different compression strategies can be adopted for buried data of different importance levels, and adaptive compression strategies can be adopted for different importance levels of buried data. Among them, the importance levels include high importance and medium importance, and the target compression methods include lossless compression and lossy compression. The technicians determine the corresponding compression methods for different importance levels in advance to ensure that the compression accuracy of the buried data of high importance level is high, the decompression can be lossless, and the compression of the buried data of medium importance level will not take up too many computing resources, and can be fully compressed. The target compression method for the buried point data with a high importance level is lossless compression, and the target compression method for the buried point data with a medium importance level is lossy compression. When determining the compression method for the target buried point data, the client can query the target compression method corresponding to the target importance level in the pre-stored correspondence between the importance level and the compression method.

[0036] Step S204: the client compresses the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

[0037] In implementation, after determining the target compression method of the target burial point data, the client compresses the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

[0038] Specifically, the target compression method includes a lossless compression method and a lossy compression method. The method for executing the steps of compressing the target embedding point data by the client according to the target compression method is as follows: Method A: When the target compression method is lossless compression, the LZW algorithm is used to compress the target buried point data.

[0039] In the implementation, the target compression method for the high-importance buried data is lossless compression. When the target compression method is lossless compression, a lossless compression algorithm is used for data compression. The LZW algorithm can be used to compress the target buried data. For example, when the target buried data is user login data, the target buried data contains key information such as user name and login time, and the LZW algorithm is used to compress it. Assuming that the original user login data is a JSON string containing multiple repeated user names and time formats, the LZW algorithm will build the repeated strings into a dictionary and replace the repeated parts with dictionary indexes to reduce the data length. The core of the LZW algorithm is to use the repeated patterns in the data to reduce data redundancy through dictionary encoding. If there are a large number of repeated strings or patterns in the data, the algorithm can replace these repeated parts with shorter codes to obtain a higher compression rate. Original data: {"login_name":"zhangsan","login_time":"2025-04-02 16:49:17","user_name":"zhangsan"}. Compressed data: [34, 108, 111, 103, 105, 110, 95, 110, 97, 109, 101, 34, 58, 34, 122, 104, 97, 110, 103, 115, 273, 34, 44, 257, 259, 261, 95, 116, 105, 266, 268, 34, 50, 48, 50, 53, 45, 48, 52, 293, 50, 32, 49, 54, 58, 52, 57, 58, 49, 55, 278, 34, 117, 115, 101, 114, 263, 265, 267, 269, 271, 273, 275, 277, 125]. The data after decompression on each other's server: {"login_name":"zhangsan","login_time":"2025-04-02 16:49:17","user_name":"zhangsan"}.

[0040] Method B: When the target compression method is a lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

[0041] In implementation, the target compression method for the buried point data of medium importance level is lossy compression method. When the target compression method is lossy compression method, the target buried point data is preprocessed by discrete cosine transform to remove some high-frequency information that has little impact on the analysis. Then, Huffman coding is used to compress the preprocessed target buried point data. Among them, the buried point data of medium importance level can be page browsing behavior data, such as the user's stay time on the page, the page area browsed, etc.

[0042] Furthermore, after the client compresses the target tracking data, it is also necessary to aggregate the compressed data and send the aggregated data blocks to the server for decompression.

[0043] Within the preset time window, the client aggregates multiple target point compression packages from the same source and sends the aggregated data packets to the server.

[0044] In implementation, after the target tracking data is compressed, it can also be aggregated according to the timestamp and source of the data. The timestamp is the time within the preset time window, where the preset time window can be 10 seconds. The source is the same user or the same device. In this way, within the preset time window, the client aggregates multiple target tracking compressed packages from the same source and sends the aggregated data packets to the server. For example, within a time window (such as 10 seconds), all compressed tracking data from the same user device are combined into one data packet. Assume that within this time window, there are tracking data of 5 different behaviors of the user (such as clicking a button, browsing a page, entering content, etc.), which are aggregated and sent after compression.

[0045] Furthermore, after the compressed data is aggregated, the aggregated data blocks are sent to the server for decompression. The specific process is as follows: Step a: After receiving the data packet, the server disaggregates the data packet to obtain multiple target point compressed packets.

[0046] In implementation, after receiving the data packet, the server disaggregates the data packet and can separate the data according to the identification information in the data packet to obtain multiple target embedding compressed packets. The identification information can be the source and timestamp of each data.

[0047] Step b: the server side decompresses the target data according to the target decompression method corresponding to the target compression method, obtains the target buried point data, and stores it in the database according to the preset format.

[0048] In the implementation, the server-side decompresses the target data according to the target decompression method corresponding to the target compression method. Corresponding decompression is performed for different compression methods. For target data with high importance level compressed by LZW, the LZW decompression algorithm is used for restoration; for target data with medium importance level, Huffman decoding is performed first, and then the approximate original state of the data is restored according to the inverse transform of DCT. Then, the data is stored in the database according to the preset format, and the restored data is stored in the database according to the predetermined format for subsequent analysis and processing.

[0049] The embodiment of the present application provides a method for compressing buried data, which can significantly reduce the amount of data transmitted over the network through adaptive compression and aggregation. Compared with the unoptimized transmission method, experimental data show that when the amount of data is large, up to 60%-80% of the network bandwidth can be saved. Lossless compression is used for buried data with high importance levels to ensure the integrity of key information. For buried data with medium importance levels, although lossy compression is used, the accuracy of the data can be guaranteed within the accuracy range required for analysis through reasonable preprocessing and restoration mechanisms. Due to the use of reasonable data classification and time window aggregation strategies, the real-time nature of the data can be guaranteed while reducing bandwidth occupancy. In the actual test of the data buried system, the transmission delay of the data can be controlled within an acceptable range, meeting the needs of real-time analysis.

[0050] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0051] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0052] The present application also provides a device for compressing buried point data. Figure 3 As shown, the device comprises: The collection module 301 is used for the client to collect the target buried point data of the user, and the target buried point data includes the user buried point data and / or the system buried point data; A determination module 302, configured to determine, by the client, a target importance level corresponding to the target buried point data according to the data classifier and the target buried point data; A query module 303 is used for querying the target compression method corresponding to the target importance level by the client in the pre-stored correspondence between the importance level and the compression method; the target compression method includes a lossless compression method and a lossy compression method; The compression module 304 is used for the client to compress the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

[0053] As an optional implementation manner, the determining module 302 is specifically configured to: The client classifies the target tracking data using a data classifier to obtain a plurality of current behavior data of the user and / or a plurality of current performance data and a plurality of current business process data of the system; In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

[0054] As an optional implementation manner, the compression module 304 is specifically configured to: When the target compression method is the lossless compression method, the target buried point data is compressed using the LZW algorithm; When the target compression method is the lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

[0055] As an optional implementation, the device further includes: The aggregation module is used to enable the client to aggregate multiple target embedding point compression packages from the same source within a preset time window, and send the aggregated data packets to the server.

[0056] As an optional implementation, the device further includes: A de-aggregation module, used for de-aggregating the data packet after the server receives the data packet, to obtain a plurality of compressed packets of the target embedding points; The decompression module is used for the server to decompress the target data according to the target decompression method corresponding to the target compression method, obtain the target buried point data, and store it in the database according to a preset format.

[0057] The embodiment of the present application provides a device for compressing buried data, which can significantly reduce the amount of data transmitted over the network through adaptive compression and aggregation. Compared with the unoptimized transmission method, experimental data show that when the amount of data is large, up to 60%-80% of the network bandwidth can be saved. Lossless compression is used for buried data of high importance level to ensure the integrity of key information. For buried data of medium importance level, although lossy compression is used, the accuracy of the data can be guaranteed within the accuracy range required for analysis through reasonable preprocessing and restoration mechanisms. Due to the use of reasonable data classification and time window aggregation strategies, the real-time nature of the data can be guaranteed while reducing bandwidth occupancy. In the actual test of the data buried system, the transmission delay of the data can be controlled within an acceptable range, meeting the needs of real-time analysis.

[0058] For the specific definition of the device for compressing buried data, please refer to the definition of the method for compressing buried data above, which will not be repeated here. Each module in the above-mentioned device for compressing buried data can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0059] In one embodiment, a computer device is provided, such as Figure 4 As shown, it includes a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the above-mentioned method steps for compressing buried point data when executing the computer program. Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Figure 4As shown, the computer device may include a processor 401, a system bus 402, a non-volatile storage medium 403, a memory 404, a network interface 405, a display screen 406, and an input device 407. Among them, the non-volatile storage medium 403 stores an operating system 4031 and a computer program 4032. The processor 401 is used to execute the computer program 4032 to implement the above-mentioned method steps for compressing the buried data. The system bus 402 is used to connect the processor 401, the non-volatile storage medium 403, the memory 404, the network interface 405, the display screen 406, and the input device 407 to ensure efficient communication between the components. The memory 404 is used to temporarily store the running programs and data, helping the processor 401 to quickly access the required information, thereby improving the overall system performance. The network interface 405 (such as a network card) enables the computer device to be connected to a local area network or the Internet to realize data transmission and remote communication. The display screen 406 is used to present the information of the buried data compression processed by the computer device to the user in the form of graphics or text. The input device 407 (such as a keyboard, a mouse, a touch screen, etc.) is used to allow the user to input data and commands for compressed point data into the computer device to implement interactive operations with the computer device.

[0060] In one embodiment, a computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned method for compressing buried point data when executed by a processor.

[0061] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0062] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0063] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0064] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0065] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for compressing buried point data, characterized in that: The method is applied to a buried point data compression system, the system includes a client and a server, the client is provided with a data classifier, and the method includes: The client collects target burial point data of the user, and the target burial point data includes user burial point data and / or system burial point data; According to the data classifier and the target buried point data, the client determines the target importance level corresponding to the target buried point data; In the pre-stored correspondence between importance levels and compression methods, the client queries the target compression method corresponding to the target importance level; the target compression method includes a lossless compression method and a lossy compression method; The client compresses the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

2. The method according to claim 1, characterized in that: The client determines, according to the data classifier and the target buried point data, a target importance level corresponding to the target buried point data, including: The client classifies the target tracking data using a data classifier to obtain a plurality of current behavior data of the user and / or a plurality of current performance data and a plurality of current business process data of the system; In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

3. The method according to claim 1, characterized in that The client compresses the target buried point data according to the target compression method, including: When the target compression method is the lossless compression method, the target buried point data is compressed using the LZW algorithm; When the target compression method is the lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

4. The method according to claim 1, characterized in that The method further comprises: Within a preset time window, the client aggregates multiple target embedding point compression packages from the same source and sends the aggregated data packets to the server.

5. The method according to claim 4, characterized in that The method further comprises: After receiving the data packet, the server disaggregates the data packet to obtain a plurality of compressed packets of the target embedding points; The server side decompresses the target data according to the target decompression method corresponding to the target compression method, obtains the target buried point data, and stores it in the database according to a preset format.

6. A device for compressing buried data, characterized in that: The device is applied to a buried point data compression system, the system includes a client and a server, the client is provided with a data classifier, and the device includes: A collection module, used for the client to collect target buried point data of the user, wherein the target buried point data includes user buried point data and / or system buried point data; A determination module, configured to determine, by the client, a target importance level corresponding to the target buried point data according to the data classifier and the target buried point data; A query module, configured to query the target compression method corresponding to the target importance level by the client in the pre-stored correspondence between the importance level and the compression method; the target compression method includes a lossless compression method and a lossy compression method; A compression module is used for the client to compress the target burial point data according to the target compression method to obtain a target burial point compression package corresponding to the target burial point data.

7. The device according to claim 6, characterized in that The determination module is specifically used for: The client classifies the target tracking data using a data classifier to obtain a plurality of current behavior data of the user and / or a plurality of current performance data and a plurality of current business process data of the system; In the pre-stored correspondence between behavior data, performance data, business process data and importance levels, the client queries the target importance level corresponding to each current behavior data and / or the target importance level corresponding to each current performance data and each current business process data.

8. The device according to claim 6, characterized in that The compression module is specifically used for: When the target compression method is the lossless compression method, the target buried point data is compressed using the LZW algorithm; When the target compression method is the lossy compression method, the target buried point data is preprocessed by discrete cosine transform, and the preprocessed target buried point data is compressed by Huffman coding.

9. The device according to claim 6, characterized in that The device also includes: The aggregation module is used to enable the client to aggregate multiple target embedding point compression packages from the same source within a preset time window, and send the aggregated data packets to the server.

10. The device according to claim 9, characterized in that The device also includes: A de-aggregation module, used for de-aggregating the data packet after the server receives the data packet, to obtain a plurality of compressed packets of the target embedding points; The decompression module is used for the server to decompress the target data according to the target decompression method corresponding to the target compression method, obtain the target buried point data, and store it in the database according to a preset format.

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