Data compression method and device, data decompression method and device and electronic equipment

Abstract

The invention provides a data compression method and device, a data decompression method and device and electronic equipment, and relates to the technical field of computers, in particular to the application fields of e-commerce systems, recommendation systems, big data processing, data mining, cloud storage and the like. According to the specific implementation scheme, multiple pieces of target data are obtained; based on the multiple pieces of target data, obtaining reference data and a single data memory occupation amount; calculating the offset of each piece of target data in the plurality of pieces of target data relative to the reference data as compressed data to obtain a plurality of pieces of compressed data in one-to-one correspondence with the plurality of pieces of target data; and combining the reference data, the memory occupancy amount of the single data and the multiple pieces of compressed data to obtain a data compression result for the multiple pieces of target data.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to application fields such as e-commerce systems, recommendation systems, big data processing, data mining, and cloud storage, and specifically to a data compression method, a data decompression method, a device, and an electronic device. Background Art

[0002] With the advent of the big data era, the scale of data is growing exponentially. In order to meet the growing demand for data applications, it is usually necessary to compress the data in order to optimize the memory usage of the data and improve the storage efficiency of the data. Summary of the invention

[0003] The present disclosure provides a data compression method, a data decompression method, a device and an electronic device.

[0004] According to a first aspect of the present disclosure, there is provided a data compression method, comprising:

[0005] Get multiple target data;

[0006] Based on multiple target data, obtain benchmark data and single data memory usage;

[0007] Calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one;

[0008] The benchmark data, the memory usage of a single data and a plurality of compressed data are combined to obtain data compression results for a plurality of target data.

[0009] According to a second aspect of the present disclosure, a data decompression method is provided, comprising:

[0010] Get data compression results;

[0011] Decomposing a plurality of compressed data from the data compression result based on the memory occupation of a single data in the data compression result;

[0012] Based on the reference data in the data compression result, each compressed data in the plurality of compressed data is decompressed to obtain a plurality of target data corresponding to the plurality of compressed data one by one.

[0013] According to a third aspect of the present disclosure, there is provided a data compression device, comprising:

[0014] A target data acquisition unit, used for acquiring a plurality of target data;

[0015] An auxiliary data acquisition unit, used for obtaining benchmark data and single data memory usage based on multiple target data;

[0016] A compressed data calculation unit, used for calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one;

[0017] The data compression unit is used to combine the benchmark data, the memory usage of a single data and a plurality of compressed data to obtain data compression results for a plurality of target data.

[0018] According to a fourth aspect of the present disclosure, there is provided a data decompression device, comprising:

[0019] A compression result obtaining unit, used for obtaining a data compression result;

[0020] A data decomposition unit, used for decomposing a plurality of compressed data from the data compression result based on the memory occupation of a single data in the data compression result;

[0021] The data decompression unit is used to decompress each compressed data in the multiple compressed data based on the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data.

[0022] According to a fifth aspect of the present disclosure, there is provided an electronic device, including:

[0023] at least one processor;

[0024] a memory communicatively coupled to the at least one processor;

[0025] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided in the first aspect of the present disclosure.

[0026] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method provided according to the first aspect of the present disclosure.

[0027] According to a seventh aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the method provided according to the first aspect of the present disclosure when executed by a processor.

[0028] The use of the present disclosure can effectively reduce the memory usage of target data and improve the storage efficiency of target data.

[0029] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0031] Figure 1 A flowchart of a data compression method provided by an embodiment of the present disclosure;

[0032] Figure 2 An example diagram of a data compression process provided by an embodiment of the present disclosure;

[0033] Figure 3 An exemplary diagram of another data compression process provided by an embodiment of the present disclosure;

[0034] Figure 4 A complete flow chart of a data compression method provided by an embodiment of the present disclosure;

[0035] Figure 5 A flowchart of a data decompression method provided by an embodiment of the present disclosure;

[0036] Figure 6 An example diagram of a data decompression process provided by an embodiment of the present disclosure;

[0037] Figure 7 A schematic diagram of a scenario of a data compression method provided by an embodiment of the present disclosure;

[0038] Figure 8 A schematic diagram of a scenario of a data decompression method provided by an embodiment of the present disclosure;

[0039] Fig. 9 A schematic structural block diagram of a data compression device provided in an embodiment of the present disclosure;

[0040] Fig.10 A schematic structural block diagram of a data decompression device provided in an embodiment of the present disclosure;

[0041] Fig.11 A schematic structural block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0043] As described in the background technology, with the advent of the big data era, the scale of data is growing exponentially. In order to meet the growing demand for data applications, it is usually necessary to compress the data in order to optimize the memory usage of the data and improve the storage efficiency of the data.

[0044] At present, the commonly used data compression method is Huffman Coding, the core idea of ​​which is to assign variable-length codes to each character according to the frequency of the character in the data. Specifically, characters with higher frequencies use shorter codes, while characters with lower frequencies use longer codes. However, the inventors have found through research that Huffman Coding does not have strong applicability. Specifically, although Huffman Coding has a higher compression effect for data with uneven character distribution, it often has no obvious compression effect for data with even character distribution, and may even cause the memory usage of the data to become larger. Therefore, the memory usage of the data cannot be effectively reduced. In addition, for data with very small memory usage, the overhead of Huffman Coding may exceed the compression benefit, which also cannot effectively reduce the memory usage of the data.

[0045] When the memory usage of data is still large, it will naturally have a negative impact on the storage efficiency of data. Moreover, given the complex process of Huffman coding itself, it will further have a negative impact on the storage efficiency of data.

[0046] In view of the above problems, the embodiments of the present disclosure provide a data compression method, which can be applied to electronic devices. The electronic device can be a server, a workbench, a mainframe computer, a conventional computer (desktop computer, laptop computer, etc.) or other similar computing devices. Figure 1 The flowchart diagram shown in the figure illustrates a data compression method provided by an embodiment of the present disclosure. It should be noted that although the logical order is shown in the flowchart diagram, in some cases, the steps shown or described in the flowchart can also be performed in other orders.

[0047] Step S101, obtaining multiple target data.

[0048] The plurality of target data may be numerical data. Here, each of the plurality of target data may be any one of a feature set label, a commodity category label, and a user portrait label.

[0049] Step S102, based on multiple target data, obtain benchmark data and single data memory usage.

[0050] The reference data may be any value in the interval [X1, X2]. Here, X1 may be the minimum value among multiple target data; and X2 may be the maximum value among multiple target data.

[0051] In addition, it should be noted that, in the embodiment of the present disclosure, the memory usage of a single data can be used to characterize the memory usage of each compressed data in a plurality of compressed data corresponding one-to-one to a plurality of target data.

[0052] Step S103 , calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one.

[0053] The offset may be a difference.

[0054] Exemplarily, there are multiple target data N1, N2, N3 and N4, and the reference data is unique and is N1, then the multiple compressed data corresponding to the multiple target data are: N1-N1, N2-N1, N3-N1, N4-N1.

[0055] Step S104, combining the benchmark data, the memory usage of a single data and the multiple compressed data to obtain data compression results for the multiple target data.

[0056] In one example, the reference data, the memory usage of a single data and the multiple compressed data can be spliced ​​in a preset splicing order to obtain data compression results for multiple target data. The preset splicing order can be determined according to application requirements, and the embodiments of the present disclosure do not limit this.

[0057] The data compression method provided by the embodiment of the present disclosure can obtain the memory occupancy of the benchmark data and the single data based on the multiple target data after acquiring the multiple target data, and calculate the offset of each target data in the multiple target data relative to the benchmark data as the compressed data, so as to obtain multiple compressed data corresponding to the multiple target data one by one, and then combine the benchmark data, the memory occupancy of the single data and the multiple compressed data to obtain the data compression result for the multiple target data. That is to say, in the embodiment of the present disclosure, for each target data in the multiple target data, what is stored is the offset of the target data relative to the benchmark data, rather than the target data itself, and the offset can often be represented by fewer bytes relative to the target data itself, so the memory occupancy of the target data can be effectively reduced. At the same time, since the memory occupancy of the target data is smaller, the storage efficiency of the target data can be improved.

[0058] In some optional implementations, step S101, i.e., “obtaining multiple target data” may include:

[0059] Get multiple current tag data of the target user;

[0060] According to a preset time interval, multiple current tag data are updated to multiple target data.

[0061] The preset time interval may be set according to application requirements, for example, may be set to 1 day, 3 days, or a week, etc., and the embodiments of the present disclosure do not limit this.

[0062] In addition, it should be noted that in the embodiments of the present disclosure, when each target data among multiple target data is a feature set label, each current label data among multiple current label data may also be a feature set label; when each target data among multiple target data is a product category label, each current label data among multiple current label data may also be a product category label; when each target data among multiple target data is a user portrait label, each current label data among multiple current label data may also be a user portrait label.

[0063] It should also be noted that after acquiring multiple current tag data of the target user and updating the multiple current tag data to multiple target data at preset time intervals, the multiple target data can be used as new multiple current tag data of the target user and wait for update.

[0064] Through the above method, in the embodiment of the present disclosure, after obtaining multiple current tag data of the target user, the multiple current tag data can be updated to multiple target data according to a preset time interval to ensure the timeliness of the updated multiple current tag data (that is, multiple target data).

[0065] In one example, each of the multiple target data is a feature set tag. In this case, “obtaining multiple current tag data of the target user” may include:

[0066] In response to the user matching request, obtaining first portrait data of the target user at a first moment; wherein the first moment is a time point when the user matching request is received;

[0067] Matching the first portrait data with the first target feature set to obtain a matching result; wherein the first target feature set is a feature set related to the target merchant at the first moment;

[0068] Based on the matching results, multiple historical tag data of the target user are updated to obtain multiple current tag data.

[0069] The user matching request may be issued by a target merchant, which may be any merchant among multiple platform merchants.

[0070] After receiving the user matching request, the receiving time of the user matching request can be recorded as the first moment, and the first portrait data of the target user at the first moment can be obtained. Among them, the first portrait data can be used to characterize the personalized characteristics of the target user, and can specifically include at least one of the basic feature data, e-commerce feature data and live broadcast feature data. Here, the basic feature data can include gender, age, region, etc.; the e-commerce feature data can include order frequency (Order Frequency, F-ORD), consumer will (Consumer Willingness, CON-WILL), average transaction size (Average Transaction Size, A-ST), etc.; the live broadcast feature data can include average viewing time, viewing time period preference, interactive behavior, etc.

[0071] After obtaining the first portrait data of the target user at the first moment, the first portrait data can be matched with the first target feature set to obtain a matching result, and based on the matching result, multiple historical tag data of the target user are updated to obtain multiple current tag data. The first target feature set can include multiple first preset features.

[0072] When the matching result is used to represent that the first portrait data successfully matches the first target feature set, and there are feature set labels corresponding to the target merchant in multiple historical label data, the multiple historical label data can be used as multiple current label data; when the matching result is used to represent that the first portrait data successfully matches the first target feature set, and there are no feature set labels corresponding to the target merchant in multiple historical label data, the feature set labels corresponding to the target merchant can be added to the multiple historical label data to obtain multiple current label data; when the matching result is used to represent that the first portrait data fails to match the first target feature set, and there are feature set labels corresponding to the target merchant in multiple historical label data, the feature set labels corresponding to the target merchant can be deleted from the multiple historical label data to obtain multiple current label data; when the matching result is used to represent that the first portrait data fails to match the first target feature set, and there are no feature set labels corresponding to the target merchant in multiple historical label data, the multiple historical label data can be used as multiple current label data.

[0073] Exemplarily, the first target feature set includes multiple first preset features, namely, gender is female, age is between 18 and 25 years old, region is Guangdong or Guangxi, and order frequency is high, and the feature set label corresponding to the target merchant is 10001.

[0074] Assuming that the first portrait data includes gender of female, age of 20, region of Guangdong, and high order frequency, then by matching the first portrait data with the first target feature set, a matching result can be obtained to characterize the successful matching of the first portrait data with the first target feature set. Thereafter, if there are feature set labels corresponding to the target merchant in the multiple historical label data of the target user, for example, when the multiple historical label data of the target user include 11002, 10001, 28002, 43197, 45000 and 65301, the multiple historical label data can be used as multiple current label data, that is, multiple current label data can be obtained: 11002, 10001, 28002, 43197, 45000 and 65301; if there are no feature set labels corresponding to the target merchant in the multiple historical label data of the target user, for example, when the multiple historical label data of the target user include 11002, 28002, 43197, 45000 and 65301, the feature set labels corresponding to the target merchant can be added to the multiple historical label data to obtain multiple current label data: 11002, 10001, 28002, 43197, 45000 and 65301.

[0075] Assume again that the first portrait data includes gender of male, age of 20, region of Guangdong, and high order frequency. Then, by matching the first portrait data with the first target feature set, a matching result can be obtained that characterizes the failure of the first portrait data to match the first target feature set. Thereafter, if there are feature set labels corresponding to the target merchant in the multiple historical label data of the target user, for example, when the multiple historical label data of the target user include 11002, 10001, 28002, 43197, 45000 and 65301, the feature set labels corresponding to the target merchant can be deleted from the multiple historical label data to obtain multiple current label data: 11002, 28002, 43197, 45000 and 65301; if there are no feature set labels corresponding to the target merchant in the multiple historical label data of the target user, for example, when the multiple historical label data of the target user include 11002, 28002, 43197, 45000 and 65301, the multiple historical label data can be used as multiple current label data, that is, multiple current label data: 11002, 28002, 43197, 45000 and 65301 can be obtained.

[0076] In the above example, as long as the target merchant issues a user matching request, it can respond to the user matching request in a timely manner, obtain the first portrait data of the target user at the first moment, and match the first portrait data with the first target feature set to obtain a matching result, and then based on the matching result, update multiple historical label data of the target user to obtain multiple current label data. In this way, the timeliness of each current label data in multiple current label data can be ensured, and then the high matching degree between the target user and the target merchant can be ensured, which can help the target merchant achieve accurate product promotion to increase the sales volume of the target merchant's products.

[0077] Further, in the case where each of the multiple target data is a feature set label, “updating the multiple current label data to the multiple target data at a preset time interval” may include:

[0078] In response to the matching update request triggered at the second moment, obtaining second portrait data of the target user at the second moment; wherein the second moment is a time point determined according to a preset time interval and located after the first moment;

[0079] Determining multiple second target feature sets matching the second portrait data from multiple candidate feature sets; wherein the multiple candidate feature sets are multiple feature sets determined at the second moment and corresponding one-to-one to multiple platform merchants;

[0080] Determine a plurality of target label data corresponding one-to-one to the plurality of second target feature sets as a plurality of target data;

[0081] Update multiple current label data to multiple target data.

[0082] Among them, the second portrait data can be used to characterize the personalized characteristics of the target user, which can be the same as the first portrait data, that is, it can include at least one of the basic feature data, e-commerce feature data and live feature data. Here, the basic feature data can include gender, age, region, etc.; the e-commerce feature data can include F-ORD, CON-WILL, A-ST, etc.; the live feature data can include average viewing time, viewing time period preference, interactive behavior, etc. Each candidate feature set in multiple candidate feature sets can include multiple candidate preset features; each second feature set in multiple second target feature sets can include multiple second preset features.

[0083] In response to the matching update request triggered at the second moment, the second portrait data of the target user at the second moment is obtained, and after determining multiple second target feature sets matching the second portrait data from multiple candidate feature sets, multiple target label data corresponding one-to-one to the multiple second target feature sets can be determined as multiple target data, and then the multiple current label data can be updated to multiple target data.

[0084] Exemplarily, the preset time interval is 1 day, and the target merchant sends a user matching request at 14:00 on December 3, 2024.

[0085] Thereafter, in response to the user matching request, the first portrait data of the target user at the first moment (14:00 on December 3, 2024) is obtained, and the first portrait data is matched with the first target feature set to obtain a matching result, and based on the matching result, multiple historical label data of the target user are updated to obtain multiple current label data: 11002, 10001, 28002, 43197, 45000 and 65301.

[0086] Then, according to the preset time interval (i.e., 1 day), the next time point that is after the first moment and closest to the first moment - 00:00 on December 4, 2024, can be determined as the second moment, and a matching update request can be triggered at the second moment. In response to the matching update request, the second portrait data of the target user at the second moment is obtained, and multiple second target feature sets that match the second portrait data are determined from multiple candidate feature sets, and then multiple target label data that correspond one-to-one to the multiple second target feature sets are determined as multiple target data, so as to update multiple current label data to multiple target data.

[0087] Assume that multiple candidate feature sets include:

[0088] A first candidate feature set corresponding to the first platform merchant, wherein the first candidate feature set includes a plurality of candidate preset features, namely, gender is female, age is between 18 and 40 years old, region is Guangdong, and order frequency is high, and the feature set label corresponding to the first platform merchant is 11002;

[0089] A second candidate feature set corresponding to the second platform merchant, wherein the second candidate feature set includes a plurality of candidate preset features, namely, gender is female, age is between 18 and 25 years old, region is Guangdong or Guangxi, and order frequency is high, and the feature set label corresponding to the second platform merchant is 10001;

[0090] A third candidate feature set corresponding to a third platform merchant, wherein the third candidate feature set includes a plurality of candidate preset features, namely, gender is female, age is between 22 and 30 years old, region is nationwide, and order frequency is high, and the feature set label corresponding to the third platform merchant is 28002;

[0091] A fourth candidate feature set corresponding to a fourth platform merchant, wherein the fourth candidate feature set includes a plurality of candidate preset features, namely, gender is male, age is between 18 and 25 years old, region is nationwide, and order frequency is medium, and the feature set label corresponding to the fourth platform merchant is 33100;

[0092] A fifth candidate feature set corresponding to a fifth platform merchant, wherein the fifth candidate feature set includes a plurality of candidate preset features, namely, gender is female, age is between 18 and 30 years old, region is nationwide, and order frequency is high, and the feature set label corresponding to the fifth platform merchant is 43197;

[0093] a sixth candidate feature set corresponding to a sixth platform merchant, wherein the sixth candidate feature set includes a plurality of candidate preset features, namely, gender is female, age is between 20 and 30 years old, region is nationwide, and order frequency is medium or low, and the feature set label corresponding to the sixth platform merchant is 45000;

[0094] A seventh candidate feature set corresponding to a seventh platform merchant, wherein the seventh candidate feature set includes a plurality of candidate preset features, namely, gender is male, age is between 18 and 50 years old, region is nationwide, and order frequency is medium or low, and the feature set label corresponding to the seventh platform merchant is 56050;

[0095] The eighth candidate feature set corresponding to the eighth platform merchant, the eighth candidate feature set includes multiple candidate preset features, namely, gender is female, age is 18 to 50 years old, region is nationwide, and order frequency is high, and the feature set label corresponding to the eighth platform merchant is 65301.

[0096] Assume again that the second portrait data includes gender of female, age of 20, region of Guangdong, and order frequency of high, then, the multiple second target feature sets determined from multiple candidate feature sets that match the second portrait data may include the first candidate feature set, the second candidate feature set, the fifth candidate feature set, and the eighth candidate feature set. Therefore, multiple target label data (11002, 10001, 43197, and 65301) corresponding one-to-one to the multiple second target feature sets can be determined as multiple target data, and the multiple current label data can be updated to multiple target data.

[0097] In the above example, as long as the matching update request is triggered, the second portrait data of the target user at the second moment can be obtained, and multiple second target feature sets matching the second portrait data can be determined from multiple candidate feature sets, and then multiple target label data corresponding to the multiple second target feature sets are determined as multiple target data to update the multiple current label data to multiple target data. In this way, even if the target merchant does not issue a user matching request in time, the timeliness of each current label data in the multiple current label data can be ensured, thereby ensuring a high degree of matching between the target user and the target merchant, which can better help the target merchant to achieve accurate product promotion, so as to further increase the target merchant's product sales.

[0098] In some optional implementations, step S102, that is, "obtaining benchmark data and single data memory usage based on multiple target data" may include:

[0099] Determine the data selection strategy;

[0100] According to the data selection strategy, based on multiple target data, determine the benchmark data and reference data;

[0101] Based on the benchmark data and reference data, the memory usage of a single data is obtained.

[0102] The data selection strategy may be determined from a plurality of candidate selection strategies, wherein the plurality of candidate selection strategies may include a first selection strategy and a second selection strategy.

[0103] In one example, the data selection strategy may be randomly determined from a plurality of candidate selection strategies.

[0104] Through the above manner, in the embodiment of the present disclosure, after determining the data selection strategy, the benchmark data and reference data can be determined based on multiple target data according to the data selection strategy, and the memory occupancy of a single data can be obtained based on the benchmark data and the reference data. That is to say, in the embodiment of the present disclosure, the optional data selection strategy is not unique. Only after determining the data selection strategy can the benchmark data and reference data be determined based on multiple target data according to the data selection strategy, and the memory occupancy of a single data can be obtained based on the benchmark data and the reference data, so as to calculate the offset of each target data in the multiple target data relative to the benchmark data as compressed data, so as to obtain multiple compressed data corresponding to the multiple target data one by one, and combine the benchmark data, the memory occupancy of a single data and the multiple compressed data to obtain data compression results for the multiple target data. In this way, the difficulty of decompressing the data compression results to obtain the decompression of the multiple target data can be improved, thereby improving the security of the multiple target data.

[0105] In one example, the data selection strategy is the first selection strategy. In this case, “determining the benchmark data and the reference data based on the data selection strategy and multiple target data” may include:

[0106] The minimum data among the plurality of target data is determined as the base data, and the maximum data among the plurality of target data is determined as the reference data.

[0107] In a specific example, after obtaining multiple target data, the arrangement order of the multiple target data can be adjusted in order from small to large, and then subsequent steps can be performed, for example, the minimum data among the multiple target data can be determined as the benchmark data, and the maximum data among the multiple target data can be determined as the reference data.

[0108] Please combine Figure 2 For example, the multiple target data include 11002, 10001, 43197 and 65301. Then, the arrangement order of the multiple target data can be adjusted from small to large, and then the minimum data 10001 among the multiple target data can be determined as the benchmark data, and the maximum data 65301 among the multiple target data can be determined as the reference data.

[0109] “Based on the benchmark data and reference data, obtaining the memory usage of a single data” may include:

[0110] Calculate the difference between the reference data and the benchmark data as the basis for memory calculation;

[0111] Based on the memory calculation basis, the memory usage of a single data is obtained.

[0112] Continuing with the above example, the difference between the reference data 65301 and the benchmark data 10001 can be calculated as the basis for memory calculation, that is, 65301-10001=55300 is used as the basis for memory calculation.

[0113] Afterwards, the memory occupancy of a single data can be obtained according to the first occupancy calculation logic and based on the memory calculation basis. The first occupancy calculation logic can be:

[0114] M1=(Log2Y1) / 8

[0115] Among them, M1 is used to represent the memory usage of a single data, in bytes; Y1 is used to represent the basis for memory calculation.

[0116] Continuing with the above example, the memory calculation basis is 55300. Then, by substituting the memory calculation basis 55300 into the first occupancy calculation logic, we can get M1=(Log255300) / 8≈1.96=2 (here, the specific value of M1 can be obtained by rounding up), that is, the memory occupancy of a single data is 2 bytes. Based on this, it can be understood that in the above example, when the memory calculation basis is in the interval [0, 255], the memory occupancy of a single data is 1 byte; when the memory calculation basis is in the interval [256, 65535], the memory occupancy of a single data is 2 bytes; when the memory calculation basis is in the interval [65536, 4294967295], the memory occupancy of a single data is 4 bytes, and so on.

[0117] In the above example, the minimum data among multiple target data can be determined as the benchmark data, and the maximum data among multiple target data can be determined as the reference data, and the difference between the reference data and the benchmark data can be calculated as the basis for memory calculation, and then the memory usage of a single data can be obtained based on the memory calculation basis. This process does not involve complex data processing logic, and can improve the efficiency of obtaining reference data and the memory usage of a single data, thereby improving data compression efficiency.

[0118] After obtaining the benchmark data and the memory usage of a single data, the offset of each target data in the multiple target data relative to the benchmark data can be calculated as compressed data to obtain multiple compressed data corresponding to the multiple target data, and the benchmark data, the memory usage of a single data and the multiple compressed data are combined to obtain data compression results for the multiple target data. For example, the benchmark data, the memory usage of a single data and the multiple compressed data, which are originally in decimal, can be converted into corresponding binary data respectively, and then combined to obtain data compression results for the multiple target data.

[0119] Finally, the overall memory footprint of the data compression result can be:

[0120] MemoryUsage1=U11+U12+M1×N1

[0121] Among them, MemoryUsage1 is used to characterize the overall memory usage of the data compression result, in bytes; U11 is used to characterize the memory usage of the benchmark data; U12 is used to characterize the memory usage of a single data; M1 is used to characterize the memory usage of a single data; N1 is used to characterize the number of target data.

[0122] In a specific example, the memory usage of a single data can be used as the memory usage of the benchmark data; in another specific example, the memory usage of the benchmark data can also be obtained by calculation after the benchmark data is determined. For example, it can be calculated by the following usage calculation logic:

[0123] U11=(Log2B1) / 8

[0124] Among them, U11 is used to represent the memory usage of the benchmark data, in bytes; B1 is used to represent the benchmark data.

[0125] In yet another specific example, the memory footprint of a single data memory footprint may be 1 byte.

[0126] In addition, in the above example, the data compression result may also carry the memory usage of the benchmark data and the memory usage of a single data.

[0127] Continuing with the previous example, the offset of each target data in multiple target data (including 10001, 11002, 43197 and 65301) relative to the reference data (10001) is calculated as compressed data, and the obtained multiple compressed data corresponding to the multiple target data one by one include: 0, 1001, 33196 and 55300. After converting the original decimal benchmark data (10001), the memory usage of a single data (2) and multiple compressed data (including 0, 1001, 33196 and 55300) into corresponding binary data respectively and then combining them, the data compression results for multiple target data are: 10011100010001, 00000010, 0000000000000000, 0000001111101001, 1000001001011100 and 1101101111001100, and the overall memory usage of the data compression result is 11 bytes.

[0128] In another example, the data selection strategy is the second selection strategy. In this case, “determining the benchmark data and the reference data based on the data selection strategy and the plurality of target data” may include:

[0129] The median or average of the plurality of target data is determined as the reference data, the minimum data among the plurality of target data is determined as the first reference data among the reference data, and the maximum data among the plurality of target data is determined as the second reference data among the reference data.

[0130] In a specific example, after obtaining multiple target data, the multiple target data can be arranged in order from small to large, and then subsequent steps are performed, for example, the median or average of the multiple target data is determined as the reference data, the minimum data among the multiple target data is determined as the first reference data among the reference data, and the maximum data among the multiple target data is determined as the second reference data among the reference data. In a more specific example, when there is an odd number of target data, the median of the multiple target data can be determined as the reference data.

[0131] Please combine Figure 3 For example, the multiple target data include 11002, 10001, 43197 and 65301. Then, the arrangement order of the multiple target data can be adjusted from small to large, and then the average of the multiple target data 32375 can be determined as the benchmark data, 10001 can be determined as the first reference data, and 65301 can be determined as the second reference data.

[0132] “Based on the benchmark data and reference data, obtaining the memory usage of a single data” may include:

[0133] Calculate the difference between the benchmark data and the first reference data as a basis for first memory calculation;

[0134] Calculate the difference between the second reference data and the benchmark data as a basis for second memory calculation;

[0135] Based on the first memory calculation basis and the second memory calculation basis, a single data memory occupancy is obtained.

[0136] Continuing with the previous example, the difference between the benchmark data 32375 and the first reference data 10001 can be calculated as the first memory calculation basis, that is, 32375-10001=22374 is used as the first memory calculation basis, and the difference between the second reference data 65301 and the benchmark data 32375 is calculated as the second memory calculation basis, that is, 65301-32375=32926 is used as the second memory calculation basis.

[0137] Afterwards, the maximum data in the first memory calculation basis and the second memory calculation basis can be used as the target memory calculation basis, and the memory occupancy of a single data can be obtained based on the target memory calculation basis according to the second occupancy calculation logic. The second occupancy calculation logic can be:

[0138] M2=(Log2Y2) / 8

[0139] Among them, M2 is used to represent the memory usage of a single data, in bytes; Y2 is used to represent the calculation basis of the target memory.

[0140] Continuing with the above example, the target memory calculation basis is 32926. Then, by substituting the target memory calculation basis 32926 into the second occupancy calculation logic, we can get M2 = (Log232926) / 8≈1.88=2 (here, the specific value of M2 can be obtained by rounding up), that is, the single data memory occupancy is 2 bytes. Based on this, it can be understood that in the above example, when the target memory calculation basis is in the interval [0, 255], the single data memory occupancy is 1 byte; when the target memory calculation basis is in the interval [256, 65535], the single data memory occupancy is 2 bytes; when the target memory calculation basis is in the interval [65536, 4294967295], the single data memory occupancy is 4 bytes, and so on.

[0141] In the above example, the median or average of multiple target data can be determined as the benchmark data, the minimum data among the multiple target data can be determined as the first reference data among the reference data, and the maximum data among the multiple target data can be determined as the second reference data among the reference data, and the difference between the benchmark data and the first reference data can be calculated as the first memory calculation basis, and the difference between the second reference data and the benchmark data can be calculated as the second memory calculation basis, and then the memory occupancy of a single data can be obtained based on the first memory calculation basis and the second memory calculation basis. In this way, a relatively smaller memory occupancy of a single data can be obtained as much as possible, thereby further reducing the memory occupancy of the target data.

[0142] After obtaining the benchmark data and the memory usage of a single data, the offset of each target data among multiple target data relative to the benchmark data can be calculated as compressed data to obtain multiple compressed data corresponding one-to-one to the multiple target data, and the benchmark data, the memory usage of a single data and the multiple compressed data can be combined to obtain data compression results for the multiple target data.

[0143] In a specific example where the reference data is the median of a plurality of target data, “calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data to obtain a plurality of compressed data corresponding one-to-one to the plurality of target data” may include:

[0144] Determine a plurality of first target data whose arrangement order is before the reference data from the plurality of target data, and respectively calculate the difference between the reference data and the plurality of first target data as a plurality of first compressed data;

[0145] Determine a plurality of second target data whose arrangement order is after the reference data from the plurality of target data, and respectively calculate the difference between the plurality of second target data and the reference data as a plurality of second compressed data;

[0146] The plurality of first compressed data, the difference between the reference data and the reference data, and the plurality of second compressed data are collectively regarded as a plurality of compressed data.

[0147] The difference between the reference data and the reference data may be a value whose memory usage is the memory usage of a single data and whose value is 0. Here, the difference between the reference data and the reference data may be used as the first interval marker.

[0148] Thereafter, the originally decimal reference data, the memory usage of a single data, and the multiple compressed data may be converted into corresponding binary data respectively and then combined to obtain data compression results for multiple target data.

[0149] In a specific example where the reference data is an average of a plurality of target data, “calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data to obtain a plurality of compressed data corresponding one-to-one to the plurality of target data” may include:

[0150] Determine a plurality of third target data smaller than the reference data from the plurality of target data, and respectively calculate the difference between the reference data and the plurality of third target data as a plurality of third compressed data;

[0151] Determine a plurality of fourth target data that are greater than or equal to the reference data from the plurality of target data, and calculate differences between the plurality of fourth target data and the reference data as a plurality of fourth compressed data;

[0152] The plurality of third compressed data and the plurality of fourth compressed data are collectively regarded as a plurality of compressed data.

[0153] Thereafter, the originally decimal reference data, the memory usage of a single data, and the multiple compressed data can be converted into corresponding binary data respectively, and then combined with the second interval marker to obtain data compression results for multiple target data. The second interval marker can be a numerical value whose memory usage is the memory usage of a single data and whose value is 0. Here, the second interval marker can be placed between the first key data and the second key data. The first key data can be the compressed data corresponding to the target data that is smaller than the reference data among the multiple target data and has the smallest difference with the reference data; the second key data can be the compressed data corresponding to the target data that is greater than or equal to the reference data among the multiple target data and has the smallest difference with the reference data.

[0154] Finally, the overall memory footprint of the data compression result can be:

[0155] MemoryUsage2=U21+U22+M2×(N2+1)

[0156] Among them, MemoryUsage2 is used to characterize the overall memory usage of the data compression result, in bytes; U21 is used to characterize the memory usage of the benchmark data; U22 is used to characterize the memory usage of a single data; M2 is used to characterize the memory usage of a single data; N2 is used to characterize the number of target data.

[0157] In a specific example, the memory usage of a single data can be used as the memory usage of the benchmark data; in another specific example, the memory usage of the benchmark data can also be obtained by calculation after the benchmark data is determined. For example, it can be calculated by the following usage calculation logic:

[0158] U21=(Log2B2) / 8

[0159] Among them, U21 is used to represent the memory usage of the benchmark data, in bytes; B2 is used to represent the benchmark data.

[0160] In yet another specific example, the memory footprint of a single data memory footprint may be 1 byte.

[0161] In addition, in the above example, the data compression result can also carry the memory usage of the benchmark data and the memory usage of the single data.

[0162] Continuing with the above example, in this example, the benchmark data is the average of multiple target data, then, multiple third target data (including 10001 and 11002) smaller than the benchmark data (32375) can be determined from the multiple target data (including 10001, 11002, 43197 and 65301), and the differences between the benchmark data and the multiple third target data are calculated respectively as multiple third compressed data (including 22374 and 21373), and multiple fourth target data (including 43197 and 65301) greater than or equal to the benchmark data are determined from the multiple target data, and the differences between the multiple fourth target data and the benchmark data are calculated respectively as multiple fourth compressed data (including 10822 and 32926), and then the multiple third compressed data and the multiple fourth compressed data are collectively taken as multiple compressed data (including 22374, 21373, 10822 and 32926). Thereafter, the originally decimal reference data, the single data memory occupancy (2) and the multiple compressed data can be converted into corresponding binary data respectively, and then combined with the second interval marker to obtain the data compression result for the multiple target data. The second interval marker can be a numerical value whose memory occupancy is the single data memory occupancy and whose value is 0, that is, 0000000000000000. Here, the second interval marker can be placed between the first key data and the second key data. The first key data can be the compressed data corresponding to the target data which is smaller than the reference data and has the smallest difference with the reference data among the multiple target data, that is, 21373; the second key data can be the compressed data corresponding to the target data which is greater than or equal to the reference data and has the smallest difference with the reference data among the multiple target data, that is, 10822.

[0163] Therefore, the final data compression results for multiple target data are: 0111111011110011, 00000010, 0101011111110110, 0101001110010101, 0000000000000000, 0101010111011110, and 1000000111111110.

[0164] The following will be combined Figure 4 , the integrity process of a data compression method provided by an embodiment of the present disclosure is described.

[0165] First, in response to the user matching request, the user matching process is entered, that is, the first portrait data of the target user at the first moment is obtained, and the first portrait data is matched with the first target feature set to obtain a matching result, and then based on the matching result, multiple historical label data of the target user are updated to obtain multiple current label data. Among them, the first moment is the time point when the user matching request is received; the first target feature set is the feature set related to the target merchant at the first moment.

[0166] In addition, in the embodiment of the present disclosure, the user matching request may be issued by a target merchant, wherein the target merchant may be any merchant among multiple platform merchants.

[0167] After receiving the user matching request, the receiving time of the user matching request can be recorded as the first moment, and the first portrait data of the target user at the first moment can be obtained, and then the first portrait data can be processed (for example, vectorized) to obtain the processed first portrait data. Among them, the first portrait data can be used to characterize the personalized characteristics of the target user, and can specifically include at least one of the basic feature data, e-commerce feature data and live broadcast feature data. Here, the basic feature data can include gender, age, region, etc.; the e-commerce feature data can include F-ORD, CON-WILL, A-ST, etc.; the live broadcast feature data can include average viewing time, viewing time period preference, interactive behavior, etc.

[0168] After obtaining the first portrait data of the target user at the first moment (hereinafter, the first portrait data may refer to the processed first portrait data), the first portrait data may be matched with the first target feature set (for example, it may be the processed first target feature set, specifically, it may be the vectorized first target feature set) to obtain a matching result, and based on the matching result, multiple historical label data of the target user may be updated to obtain multiple current label data.

[0169] Among them, the first target feature set may include multiple first preset features, and the first preset features may also include at least one of basic feature data, e-commerce feature data and live broadcast feature data.

[0170] When the matching result is used to represent that the first portrait data successfully matches the first target feature set, and there are feature set labels corresponding to the target merchant in multiple historical label data, the multiple historical label data can be used as multiple current label data; when the matching result is used to represent that the first portrait data successfully matches the first target feature set, and there are no feature set labels corresponding to the target merchant in multiple historical label data, the feature set labels corresponding to the target merchant can be added to the multiple historical label data to obtain multiple current label data; when the matching result is used to represent that the first portrait data fails to match the first target feature set, and there are feature set labels corresponding to the target merchant in multiple historical label data, the feature set labels corresponding to the target merchant can be deleted from the multiple historical label data to obtain multiple current label data; when the matching result is used to represent that the first portrait data fails to match the first target feature set, and there are no feature set labels corresponding to the target merchant in multiple historical label data, the multiple historical label data can be used as multiple current label data.

[0171] Thereafter, in response to the matching update request triggered at the second moment, the feature set matching process can be entered, that is, the second portrait data of the target user at the second moment is obtained, and then the second portrait data is processed (for example, vectorized) to obtain the processed second portrait data, so as to determine multiple second target feature sets that match the second portrait data ((hereafter, the second portrait data may refer to the processed second portrait data)) from multiple candidate feature sets (for example, it may be a processed candidate feature set, specifically, it may be a vectorized candidate feature set), and then determine multiple target label data corresponding one-to-one to the multiple second target feature sets as multiple target data. Among them, the second moment is a time point determined according to a preset time interval and located after the first moment; the multiple candidate feature sets are multiple feature sets determined at the second moment and corresponding one-to-one to multiple platform merchants.

[0172] In addition, in the disclosed embodiments, the second portrait data can be used to characterize the personalized characteristics of the target user, which can be specifically the same as the first portrait data, that is, it can include at least one of the basic feature data, e-commerce feature data and live feature data. Here, the basic feature data can include gender, age, region, etc.; the e-commerce feature data can include F-ORD, CON-WILL, A-ST, etc.; the live feature data can include average viewing time, viewing time period preference, interactive behavior, etc. Each candidate feature set in multiple candidate feature sets can include multiple candidate preset features; each second feature set in multiple second target feature sets can include multiple second preset features.

[0173] In response to the matching update request triggered at the second moment, the second portrait data of the target user at the second moment is obtained, and after determining multiple second target feature sets matching the second portrait data from multiple candidate feature sets, multiple target tag data corresponding to the multiple second target feature sets can be determined as multiple target data. Each candidate feature set in the multiple candidate feature sets can include multiple candidate preset features, and the candidate preset features can also include at least one of basic feature data, e-commerce feature data, and live broadcast feature data.

[0174] Next, the data update process may be entered, that is, a plurality of current tag data may be updated to a plurality of target data.

[0175] After obtaining multiple target data, a compression process for the multiple target data may be performed. The compression process may include:

[0176] According to the first data selection strategy, based on the plurality of target data, determining the benchmark data and the reference data;

[0177] Based on the benchmark data and reference data, the memory usage of a single data is obtained.

[0178] Among them, the data selection strategy can be the first selection strategy.

[0179] In one example, “determining benchmark data and reference data based on a data selection strategy and a plurality of target data” may include:

[0180] The minimum data among the plurality of target data is determined as the base data, and the maximum data among the plurality of target data is determined as the reference data.

[0181] Among them, after obtaining multiple target data, the arrangement order of the multiple target data can be adjusted in order from small to large, and then subsequent steps can be performed, for example, the minimum data among the multiple target data can be determined as the benchmark data, and the maximum data among the multiple target data can be determined as the reference data.

[0182] Correspondingly, “obtaining the memory usage of a single data based on the benchmark data and the reference data” may include:

[0183] Calculate the difference between the reference data and the benchmark data as the basis for memory calculation;

[0184] Based on the memory calculation basis, the memory usage of a single data is obtained.

[0185] In a specific example, after obtaining the memory calculation basis, the memory occupancy of a single data can be obtained according to the first occupancy calculation logic based on the memory calculation basis. The first occupancy calculation logic can be:

[0186] M1=(Log2Y1) / 8

[0187] Among them, M1 is used to represent the memory usage of a single data, in bytes; Y1 is used to represent the basis for memory calculation.

[0188] After obtaining the benchmark data and the memory usage of a single data, the offset of each target data in the multiple target data relative to the benchmark data can be calculated as compressed data to obtain multiple compressed data corresponding to the multiple target data, and the benchmark data, the memory usage of a single data and the multiple compressed data are combined to obtain data compression results for the multiple target data. For example, the benchmark data, the memory usage of a single data and the multiple compressed data, which are originally in decimal, can be converted into corresponding binary data respectively, and then combined to obtain data compression results for the multiple target data.

[0189] Finally, the overall memory footprint of the data compression result can be:

[0190] MemoryUsage1=U11+U12+M1×N1

[0191] Among them, MemoryUsage1 is used to characterize the overall memory usage of the data compression result, in bytes; U11 is used to characterize the memory usage of the benchmark data; U12 is used to characterize the memory usage of a single data; M1 is used to characterize the memory usage of a single data; N1 is used to characterize the number of target data.

[0192] In a specific example, the memory usage of a single data can be used as the memory usage of the benchmark data; in another specific example, the memory usage of the benchmark data can also be obtained by calculation after the benchmark data is determined. For example, it can be calculated by the following usage calculation logic:

[0193] U11=(Log2B1) / 8

[0194] Among them, U11 is used to represent the memory usage of the benchmark data, in bytes; B1 is used to represent the benchmark data.

[0195] In addition, in the above example, the memory footprint of a single data memory footprint may be 1 byte.

[0196] Finally, the data storage process can be entered, that is, the data compression results for multiple target data are stored in the target memory to provide online services for at least one of the application fields such as e-commerce systems, recommendation systems, big data processing, data mining, cloud storage, etc.

[0197] For the specific functions and examples of each step in the embodiments of the present disclosure, reference can be made to the relevant descriptions of the relevant steps in the aforementioned data compression method embodiments, which will not be elaborated here.

[0198] The present disclosure provides a data decompression method, which can be applied to an electronic device. The electronic device can be a server, a workbench, a large computer, a conventional computer or other similar computing devices. Figure 5 The flowchart diagram shown illustrates a data decompression method provided by an embodiment of the present disclosure. It should be noted that although the logical order is shown in the flowchart diagram, in some cases, the steps shown or described in the flowchart can also be performed in other orders.

[0199] Step S501, obtaining data compression results.

[0200] The data compression result may be obtained by compressing the multiple target data. Here, the multiple target data may be numerical data, for example, each of the multiple target data may be any one of a feature set label, a product category label, and a user portrait label.

[0201] In addition, it should be noted that in the embodiment of the present disclosure, the data compression result can be obtained by compressing multiple target data using the aforementioned data compression method. Based on this, it can be understood that in the embodiment of the present disclosure, the data compression result can include benchmark data, single data memory usage and multiple compressed data.

[0202] Step S502: based on the memory usage of a single data in the data compression result, decompose the data compression result into a plurality of compressed data.

[0203] The memory footprint of a single data can be used to characterize the memory footprint of each compressed data in the multiple compressed data. Therefore, the memory footprint of a single data can be extracted from the data compression result, and based on the memory footprint of the single data, multiple compressed data can be decomposed from the data compression result.

[0204] Step S503: decompress each compressed data in the plurality of compressed data based on the reference data in the data compression result to obtain a plurality of target data corresponding to the plurality of compressed data.

[0205] The reference data may be any value in the interval [X1, X2]. Here, X1 may be the minimum value among multiple target data; and X2 may be the maximum value among multiple target data.

[0206] After the initial reference data is extracted from the data compression result, each compressed data in the plurality of compressed data may be decompressed based on the reference data to obtain a plurality of target data corresponding to the plurality of compressed data.

[0207] By using the data compression result provided by the embodiment of the present disclosure, after obtaining the data compression result, based on the memory usage of a single data in the data compression result, multiple compressed data can be decomposed from the data compression result, and based on the reference data in the data compression result, each compressed data in the multiple compressed data is decompressed to obtain multiple target data corresponding to the multiple compressed data. This process does not involve complex data processing logic, and therefore, can achieve rapid decompression of multiple target data, that is, can improve the decompression efficiency of multiple target data.

[0208] In some optional implementations, the data compression result may also carry the memory footprint of the benchmark data and the memory footprint of the single data memory footprint. Therefore, after obtaining the data compression result, the benchmark data may be obtained from the data compression result based on the memory footprint of the benchmark data, and the memory footprint of the single data may be obtained from the data compression result based on the memory footprint of the single data memory footprint, so as to perform step S502, that is, based on the memory footprint of the single data in the data compression result, multiple compressed data are decomposed from the data compression result.

[0209] Further, in some optional implementations, step S503, that is, "decompressing each compressed data in the multiple compressed data based on the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one" may include:

[0210] Obtaining a data selection strategy for determining reference data when compressing a plurality of target data to obtain data compression results;

[0211] Based on the data selection strategy, each compressed data in the multiple compressed data is decompressed using the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one.

[0212] The data selection strategy may be determined from a plurality of candidate selection strategies, wherein the plurality of candidate selection strategies may include a first selection strategy and a second selection strategy.

[0213] In one example, the data selection strategy is the first selection strategy. In this case, the reference data can be converted into decimal reference data, and at the same time, each compressed data in the plurality of compressed data can be converted into decimal compressed data, and then the converted plurality of compressed data can be added to the converted reference data to obtain a plurality of target data corresponding to the plurality of compressed data.

[0214] For example, Figure 6 The data compression results shown (10011100010001, 00000010, 00000000000000000, 0000001111101001, 1000001001011100 and 1101101111001100), and when multiple target data are compressed to obtain the data compression results, the data selection strategy for determining the benchmark data is the first selection strategy. Then, the benchmark data can be obtained from the data compression result based on the memory footprint of the benchmark data (2), that is, 10011100010001, and the memory footprint of the single data can be obtained from the data compression result based on the memory footprint of the single data (1), that is, 2, and the benchmark data is converted into decimal benchmark data, that is, 10001. At the same time, multiple compressed data (including 0000000000000000, 0000000111110 1001, 1000001001011100 and 1101101111001100) are converted into decimal compressed data, and a plurality of converted compressed data (including 0, 1001, 33196 and 55300) are obtained. Thereafter, the plurality of converted compressed data are respectively added to the converted reference data to obtain a plurality of target data corresponding to the plurality of compressed data, which are 10001, 11002, 43197 and 65301 respectively.

[0215] In another example, the data selection strategy is the second selection strategy, and the benchmark data is the median of multiple target data. In this case, the benchmark data can be converted into decimal benchmark data, and at the same time, each compressed data in the multiple compressed data is converted into decimal compressed data, and the first interval marker is determined from the converted multiple compressed data, and then the compressed data before the first interval marker in the converted multiple compressed data is determined as the first compressed data to obtain multiple first compressed data, and the compressed data after the first interval marker in the converted multiple compressed data is determined as the second compressed data to obtain multiple second compressed data. Thereafter, the difference between the benchmark data and the multiple first compressed data can be calculated respectively as multiple first target data, and the sum of the benchmark data and the multiple second compressed data can be calculated respectively as multiple second target data, and then the multiple first target data, the benchmark data and the multiple second compressed data are collectively used as multiple target data.

[0216] In another example, the data selection strategy is the second selection strategy, and the benchmark data is the average of multiple target data. In this case, the benchmark data can be converted into decimal benchmark data, and at the same time, each compressed data in the multiple compressed data is converted into decimal compressed data, and the second interval marker is determined in the data compression result, and then the compressed data before the second interval marker in the converted multiple compressed data is determined as the third compressed data to obtain multiple third compressed data, and the compressed data after the second interval marker in the converted multiple compressed data is determined as the fourth compressed data to obtain multiple fourth compressed data. Thereafter, the difference between the benchmark data and the multiple third compressed data can be calculated respectively as multiple third target data, and the sum of the benchmark data and the multiple fourth compressed data can be calculated respectively as multiple fourth target data, and then the multiple third target data and the multiple fourth compressed data are used together as multiple target data.

[0217] Through the above manner, in the embodiment of the present disclosure, when compressing multiple target data to obtain data compression results, a data selection strategy for determining the reference data can be obtained, and based on the data selection strategy, each compressed data in the multiple compressed data is decompressed using the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data. That is to say, in the embodiment of the present disclosure, only when the data selection strategy is obtained can the multiple target data corresponding to the multiple compressed data be accurately obtained, so the security of the multiple target data can be improved.

[0218] For the specific functions and examples of each step in the embodiments of the present disclosure, please refer to the relevant descriptions of the relevant steps in the aforementioned data decompression method embodiment, which will not be repeated here.

[0219] See also Figure 7 , which is a scenario diagram of a data compression method provided in an embodiment of the present disclosure.

[0220] As mentioned above, the data compression method provided by the embodiment of the present disclosure is applied to an electronic device, wherein the electronic device may be a server, a workbench, a large computer, a conventional computer or other similar computing devices.

[0221] Electronic devices are used for:

[0222] Get multiple target data;

[0223] Based on multiple target data, obtain benchmark data and single data memory usage;

[0224] Calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one;

[0225] The benchmark data, the memory usage of a single data and a plurality of compressed data are combined to obtain data compression results for a plurality of target data.

[0226] It should be noted that in the embodiments of the present disclosure, Figure 7 The scene diagram shown is only illustrative and not restrictive. Those skilled in the art can Figure 7 Various obvious changes and / or substitutions are made to the examples, and the obtained technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0227] See also Figure 8 , which is a scenario diagram of a data decompression method provided in an embodiment of the present disclosure.

[0228] As mentioned above, the data decompression method provided by the embodiment of the present disclosure is applied to an electronic device, wherein the electronic device may be a server, a workbench, a large computer, a conventional computer or other similar computing devices.

[0229] Electronic devices are used for:

[0230] Get data compression results;

[0231] Decomposing a plurality of compressed data from the data compression result based on the memory occupation of a single data in the data compression result;

[0232] Based on the reference data in the data compression result, each compressed data in the plurality of compressed data is decompressed to obtain a plurality of target data corresponding to the plurality of compressed data one by one.

[0233] It should be noted that in the embodiments of the present disclosure, Figure 8 The scene diagram shown is only illustrative and not restrictive. Those skilled in the art can Figure 8 Various obvious changes and / or substitutions are made to the examples, and the obtained technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0234] In order to better implement the data compression method, the embodiment of the present disclosure also provides a data compression device, which can be integrated in an electronic device. The electronic device can be a server, a workbench, a large computer, a conventional computer or other similar computing devices. Fig. 9 The schematic structural block diagram shown illustrates a data compression device 900 provided in the disclosed embodiment.

[0235] The data compression device 900 comprises:

[0236] The target data acquisition unit 901 is used to acquire multiple target data;

[0237] An auxiliary data acquisition unit 902, used to obtain reference data and single data memory usage based on multiple target data;

[0238] A compressed data calculation unit 903 is used to calculate the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one;

[0239] The data compression unit 904 data compression unit 904900 is used to combine the benchmark data, the memory usage of a single data and the multiple compressed data to obtain the data compression results for the multiple target data.

[0240] In some optional implementations, the auxiliary data acquisition unit 902 is used to:

[0241] Determine the data selection strategy;

[0242] According to the data selection strategy, based on multiple target data, determine the benchmark data and reference data;

[0243] Based on the benchmark data and reference data, the memory usage of a single data is obtained.

[0244] In some optional implementations, the auxiliary data acquisition unit 902 is used to:

[0245] When the data selection strategy is determined to be the first selection strategy, the minimum data among the plurality of target data is determined as the benchmark data, and the maximum data among the plurality of target data is determined as the reference data.

[0246] In some optional implementations, the auxiliary data acquisition unit 902 is used to:

[0247] Calculate the difference between the reference data and the benchmark data as the basis for memory calculation;

[0248] Based on the memory calculation basis, the memory usage of a single data is obtained.

[0249] In some optional implementations, the auxiliary data acquisition unit 902 is used to:

[0250] When the data selection strategy is determined to be the second selection strategy, the median or average of multiple target data is determined as the benchmark data, the minimum data among the multiple target data is determined as the first reference data among the reference data, and the maximum data among the multiple target data is determined as the second reference data among the reference data.

[0251] In some optional implementations, the auxiliary data acquisition unit 902 is used to:

[0252] Calculate the difference between the benchmark data and the first reference data as a basis for first memory calculation;

[0253] Calculate the difference between the second reference data and the benchmark data as a basis for second memory calculation;

[0254] Based on the first memory calculation basis and the second memory calculation basis, a single data memory occupancy is obtained.

[0255] In some optional implementations, the target data acquisition unit 901 is used to:

[0256] Acquire multiple current tag data of the target user; wherein each current tag data of the multiple current tag data corresponds to a category of goods of interest;

[0257] According to a preset time interval, multiple current tag data are updated to multiple target data.

[0258] In some optional implementations, the target data acquisition unit 901 is used to:

[0259] In response to the user matching request, obtaining first portrait data of the target user at a first moment; wherein the first moment is a time point when the user matching request is received;

[0260] Matching the first portrait data with the first target feature set to obtain a matching result; wherein the first target feature set is a feature set related to the target merchant at the first moment;

[0261] Based on the matching results, multiple historical tag data of the target user are updated to obtain multiple current tag data.

[0262] In some optional implementations, the target data acquisition unit 901 is used to:

[0263] In response to the matching update request triggered at the second moment, obtaining second portrait data of the target user at the second moment; wherein the second moment is a time point determined according to a preset time interval and located after the first moment;

[0264] Determining multiple second target feature sets matching the second portrait data from multiple candidate feature sets; wherein the multiple candidate feature sets are multiple feature sets determined at the second moment and corresponding one-to-one to multiple platform merchants;

[0265] Determine a plurality of target label data corresponding one-to-one to the plurality of second target feature sets as a plurality of target data;

[0266] Update multiple current label data to multiple target data.

[0267] In the embodiment of the present disclosure, the specific functions and examples of each unit in the data compression device 900 can be found in the relevant description of the relevant steps in the aforementioned data compression method embodiment, and will not be repeated here.

[0268] In order to better implement the data compression method, the embodiment of the present disclosure also provides a data decompression device 1000, which can be integrated in an electronic device. The electronic device can be a server, a workbench, a large computer, a conventional computer or other similar computing devices. Fig.10 The schematic structural block diagram shown is used to illustrate a data decompression device 1000 provided in the disclosed embodiment.

[0269] The data decompression device 1000 comprises:

[0270] The compression result obtaining unit 1001 is used to obtain the data compression result;

[0271] A data decomposition unit 1002 is used to decompose a plurality of compressed data from the data compression result based on the memory usage of a single data in the data compression result;

[0272] The data decompression unit 1003 is used to decompress each compressed data in the multiple compressed data based on the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data.

[0273] In some optional implementations, the data decompression unit 1003 is used to:

[0274] Obtaining a data selection strategy for determining reference data when compressing target data to obtain a data compression result;

[0275] Based on the data selection strategy, each compressed data in the multiple compressed data is decompressed using the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one.

[0276] In the embodiment of the present disclosure, the specific functions and examples of each unit in the data decompression device 1000 can be found in the relevant description of the relevant steps in the aforementioned data compression method embodiment, and will not be repeated here.

[0277] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0278] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0279] Fig.11A schematic block diagram of an example electronic device 1100 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as vehicle-mounted computing devices, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0280] like Fig.11 As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 to a random access memory (RAM) 1103. In RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, ROM 1102, and RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0281] A number of components in the electronic device 1100 are connected to the I / O interface 1105, including: an input unit 1106, such as a keyboard, a mouse, etc.; an output unit 1107, such as various types of renderers, speakers, etc.; a storage unit 1108, such as a disk, an optical disk, etc.; and a communication unit 1109, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1109 allows the electronic device 1100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0282] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a GPU, various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above, such as data compression methods and / or data decompression methods. For example, in some embodiments, the data compression method and / or data decompression method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1100 via the ROM 1102 and / or the communication unit 1109. When the computer program is loaded into the RAM 1103 and executed by the computing unit 1101, one or more steps in the data compression method and / or data decompression method described above may be executed. Alternatively, in other embodiments, the computing unit 1101 may be configured as a data compression method and / or a data decompression method in any other appropriate manner (for example, by means of firmware).

[0283] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0284] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0285] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM) or a 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 foregoing.

[0286] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a rendering device (e.g., a cathode ray tube (CRT) renderer or a liquid crystal display (LCD)) for rendering information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices are also used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0287] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), and the Internet.

[0288] The computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0289] The embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute a data compression method and / or a data decompression method.

[0290] The embodiments of the present disclosure also provide a computer program product, including a computer program, which implements a data compression method and / or a data decompression method when executed by a processor.

[0291] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and this document is not limited here. In addition, in the present disclosure, relational terms such as "first", "second", "third", etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. In addition, "multiple" in the present disclosure can be understood as at least two.

[0292] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data compression method, comprising: Get multiple target data; Based on the multiple target data, obtain benchmark data and single data memory usage; Calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one; The reference data, the single data memory occupancy, and the plurality of compressed data are combined to obtain data compression results for the plurality of target data.

2. The method according to claim 1, wherein: The obtaining of the benchmark data and the memory usage of a single data based on the multiple target data includes: Determine the data selection strategy; According to the data selection strategy, based on the multiple target data, determine the benchmark data and the reference data; Based on the benchmark data and the reference data, a single data memory occupancy is obtained.

3. The method according to claim 2, wherein: The determining of the benchmark data and the reference data based on the data selection strategy and the plurality of target data includes: When the data selection strategy is determined to be the first selection strategy, the minimum data among the plurality of target data is determined as the benchmark data, and the maximum data among the plurality of target data is determined as the reference data.

4. The method according to claim 3, wherein: The obtaining, based on the benchmark data and the reference data, a memory usage of a single data, comprises: Calculate the difference between the reference data and the benchmark data as a basis for memory calculation; Based on the memory calculation basis, the memory usage of a single data is obtained.

5. The method according to claim 2, wherein: The determining of the benchmark data and the reference data based on the data selection strategy and the plurality of target data includes: When the data selection strategy is determined to be the second selection strategy, the median or average of the multiple target data is determined as the benchmark data, the minimum data among the multiple target data is determined as the first reference data among the reference data, and the maximum data among the multiple target data is determined as the second reference data among the reference data.

6. The method according to claim 5, wherein: The obtaining, based on the benchmark data and the reference data, a memory usage of a single data, comprises: Calculating a difference between the benchmark data and the first reference data as a first memory calculation basis; Calculating a difference between the second reference data and the benchmark data as a second memory calculation basis; Based on the first memory calculation basis and the second memory calculation basis, a single data memory occupancy is obtained.

7. The method according to any one of claims 1 to 6, wherein: The acquiring of multiple target data includes: Get multiple current tag data of the target user; The multiple current tag data are updated to multiple target data at preset time intervals.

8. The method according to claim 7, wherein: The step of obtaining multiple current tag data of the target user includes: In response to the user matching request, obtaining first portrait data of the target user at a first moment; wherein the first moment is the time point when the user matching request is received; Matching the first portrait data with a first target feature set to obtain a matching result; wherein the first target feature set is a feature set related to the target merchant at the first moment; Based on the matching result, multiple historical tag data of the target user are updated to obtain multiple current tag data.

9. The method according to claim 8, wherein: The updating of the plurality of current tag data into a plurality of target data at a preset time interval includes: In response to the matching update request triggered at the second moment, obtaining the second portrait data of the target user at the second moment; wherein the second moment is a time point determined according to the preset time interval and located after the first moment; Determining a plurality of second target feature sets matching the second portrait data from a plurality of candidate feature sets; wherein the plurality of candidate feature sets are a plurality of feature sets determined at the second moment and corresponding one-to-one to a plurality of platform merchants; Determine a plurality of target label data corresponding one-to-one to the plurality of second target feature sets as a plurality of target data; The multiple current tag data are updated to the multiple target data.

10. A data decompression method, comprising: Get data compression results; Decomposing a plurality of compressed data from the data compression result based on the memory usage of a single data in the data compression result; Based on the reference data in the data compression result, each compressed data in the plurality of compressed data is decompressed to obtain a plurality of target data corresponding to the plurality of compressed data one by one.

11. The method according to claim 10, wherein: The decompression process is performed on each compressed data in the plurality of compressed data based on the reference data in the data compression result to obtain a plurality of target data corresponding to the plurality of compressed data one by one, including: Acquire a data selection strategy for determining the reference data when compressing the plurality of target data to obtain the data compression result; Based on the data selection strategy, each compressed data in the multiple compressed data is decompressed using the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one.

12. A data compression device, comprising: A target data acquisition unit, used for acquiring a plurality of target data; An auxiliary data acquisition unit, used for obtaining reference data and a memory occupancy amount of a single data based on the plurality of target data; A compressed data calculation unit, used for calculating the offset of each target data in the plurality of target data relative to the reference data as compressed data, so as to obtain a plurality of compressed data corresponding to the plurality of target data one by one; The data compression unit is used to combine the reference data, the single data memory occupancy and the multiple compressed data to obtain data compression results for the multiple target data.

13. The device according to claim 12, wherein: The auxiliary data acquisition unit is used for: Determine the data selection strategy; According to the data selection strategy, based on the multiple target data, determine the benchmark data and the reference data; Based on the benchmark data and the reference data, a single data memory occupancy is obtained.

14. The device according to claim 13, wherein: The auxiliary data acquisition unit is used for: When the data selection strategy is determined to be the first selection strategy, the minimum data among the plurality of target data is determined as the benchmark data, and the maximum data among the plurality of target data is determined as the reference data.

15. The device according to claim 14, wherein: The auxiliary data acquisition unit is used for: Calculate the difference between the reference data and the benchmark data as a basis for memory calculation; Based on the memory calculation basis, the memory usage of a single data is obtained.

16. The device according to claim 13, wherein: The auxiliary data acquisition unit is used for: When the data selection strategy is determined to be the second selection strategy, the median or average of the multiple target data is determined as the benchmark data, the minimum data among the multiple target data is determined as the first reference data among the reference data, and the maximum data among the multiple target data is determined as the second reference data among the reference data.

17. The device according to claim 16, wherein: The auxiliary data acquisition unit is used for: Calculating a difference between the benchmark data and the first reference data as a first memory calculation basis; Calculating a difference between the second reference data and the benchmark data as a second memory calculation basis; Based on the first memory calculation basis and the second memory calculation basis, a single data memory occupancy is obtained.

18. The device according to any one of claims 12 to 17, wherein: The target data acquisition unit is used for: Get multiple current tag data of the target user; The multiple current tag data are updated to multiple target data at preset time intervals.

19. The device according to claim 18, wherein: The target data acquisition unit is used for: In response to the user matching request, obtaining first portrait data of the target user at a first moment; wherein the first moment is the time point when the user matching request is received; Matching the first portrait data with a first target feature set to obtain a matching result; wherein the first target feature set is a feature set related to the target merchant at the first moment; Based on the matching result, multiple historical tag data of the target user are updated to obtain multiple current tag data.

20. The device according to claim 19, wherein The target data acquisition unit is used for: In response to the matching update request triggered at the second moment, obtaining the second portrait data of the target user at the second moment; wherein the second moment is a time point determined according to the preset time interval and located after the first moment; Determining a plurality of second target feature sets matching the second portrait data from a plurality of candidate feature sets; wherein the plurality of candidate feature sets are a plurality of feature sets determined at the second moment and corresponding one-to-one to a plurality of platform merchants; Determine a plurality of target label data corresponding one-to-one to the plurality of second target feature sets as a plurality of target data; The multiple current tag data are updated to the multiple target data.

21. A data decompression device, comprising: A compression result obtaining unit, used for obtaining a data compression result; A data decomposition unit, configured to decompose the data compression result into a plurality of compressed data based on the memory usage of a single data in the data compression result; The data decompression unit is used to decompress each compressed data in the multiple compressed data based on the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one.

22. The device according to claim 21, wherein The data decompression unit is used for: Acquire a data selection strategy for determining the reference data when compressing the plurality of target data to obtain the data compression result; Based on the data selection strategy, each compressed data in the multiple compressed data is decompressed using the reference data in the data compression result to obtain multiple target data corresponding to the multiple compressed data one by one.

23. An electronic device, comprising: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 11.

25. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 11.

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