Data processing method and device, electronic equipment and computer readable storage medium

Abstract

The present disclosure relates to the technical field of data processing, and provides a data processing method and device, electronic equipment and a computer readable storage medium. The method comprises: obtaining a target index table and an index indicator, the index indicator being a single index indicator or a multiple index indicator; obtaining a target data set corresponding to the index indicator in the target index table; and extracting a preset number of similar data from the target data set based on a preset number and a preset extraction strategy to obtain a similar data set. According to the present disclosure, the target data set corresponding to the index indicator in the target index table is obtained, and the similar data set is extracted from the target data set based on the extraction strategy, so that sorting when taking out data can be avoided, the response time is greatly reduced, and the efficiency of extracting data is greatly improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to data processing methods, apparatus, electronic devices and computer-readable storage media. Background Technology

[0002] With the rapid development of technology, databases are being used more and more in various industries, and the scale of database storage is also growing larger and larger. One method of data extraction involves first extracting a preset number of index tables from the database, and then retrieving the corresponding stored data from the database based on the extracted index data.

[0003] In existing technologies, the data in the index table is usually sorted first, and then extracted. When processing large amounts of data, the computational cost of sorting is very high, which greatly reduces the efficiency of data extraction. Summary of the Invention

[0004] In view of this, the present disclosure provides a data processing method, apparatus, electronic device, and computer-readable storage medium to solve the problem in the prior art where the computational cost of sorting functions is very high, resulting in a significant reduction in the efficiency of data extraction.

[0005] A first aspect of this disclosure provides a data processing method, including:

[0006] Obtain the target index table and index metrics, which may include single or multiple index metrics.

[0007] Retrieve the target dataset corresponding to the index metrics in the target index table;

[0008] Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0009] In some embodiments, the extraction strategy includes:

[0010] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0011] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0012] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0013] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0014] In some embodiments, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0015] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0016] In some embodiments, if the target dataset is a single-index dataset, the extraction strategy further includes:

[0017] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0018] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0019] In some embodiments, the extraction strategy includes:

[0020] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0021] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0022] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0023] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0024] In some embodiments, after updating the preset index to the adjacent high-order index and re-probing similar data, the method further includes:

[0025] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0026] In some embodiments, if the target dataset is a multi-indexed dataset, the extraction strategy further includes:

[0027] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0028] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0029] In some embodiments, the data processing method further includes:

[0030] Repeat the extraction strategy to obtain a second preset number of similar datasets.

[0031] A second aspect of this disclosure provides a data processing apparatus, comprising:

[0032] The first acquisition module is configured to acquire a target index table and index metrics, wherein the index metrics can be a single index metric or multiple index metrics.

[0033] The second acquisition module is configured to acquire the target dataset corresponding to the index indicator in the target index table;

[0034] The extraction module is configured to extract a preset number of similar data from the target dataset based on a preset quantity and a preset extraction strategy to obtain a similar dataset. The extraction strategy does not include sorting.

[0035] In some embodiments, if the target dataset is a single-index dataset, the extraction strategy includes:

[0036] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0037] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0038] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0039] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0040] In some embodiments, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0041] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0042] In some embodiments, if the target dataset is a single-index dataset, the extraction strategy further includes:

[0043] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0044] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0045] In some embodiments, if the target dataset is a multi-indexed dataset, the extraction strategy includes:

[0046] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0047] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0048] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0049] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0050] In some embodiments, after updating the preset index to the adjacent high-order index and re-probing similar data, the method further includes:

[0051] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0052] In some embodiments, if the target dataset is a multi-indexed dataset, the extraction strategy further includes:

[0053] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0054] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0055] In some embodiments, the data processing apparatus further includes:

[0056] The repeated execution module is configured to repeatedly execute the extraction strategy to obtain a second preset number of similar datasets.

[0057] A third aspect of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps implemented by the processor when executing the computer program include:

[0058] Retrieve the target index table and index metrics. Index metrics can be a single index metric or multiple index metrics.

[0059] Retrieve the target dataset corresponding to the index metrics in the target index table;

[0060] Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0061] In some embodiments, the extraction strategy includes:

[0062] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0063] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0064] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0065] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0066] In some embodiments, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0067] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0068] In some embodiments, if the target dataset is a single-index dataset, the extraction strategy further includes:

[0069] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0070] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0071] In some embodiments, the extraction strategy includes:

[0072] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0073] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0074] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0075] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0076] In some embodiments, after updating the preset index to the adjacent high-order index and re-probing similar data, the method further includes:

[0077] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0078] In some embodiments, if the target dataset is a multi-indexed dataset, the extraction strategy further includes:

[0079] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0080] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0081] In some embodiments, the data processing method further includes:

[0082] Repeat the extraction strategy to obtain a second preset number of similar datasets.

[0083] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program, the steps of which, when executed by a processor, include:

[0084] Retrieve the target index table and index metrics. Index metrics can be a single index metric or multiple index metrics.

[0085] Retrieve the target dataset corresponding to the index metrics in the target index table;

[0086] Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0087] In some embodiments, the extraction strategy includes:

[0088] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0089] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0090] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0091] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0092] In some embodiments, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0093] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0094] In some embodiments, if the target dataset is a single-index dataset, the extraction strategy further includes:

[0095] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0096] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0097] In some embodiments, the extraction strategy includes:

[0098] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0099] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0100] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0101] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0102] In some embodiments, after updating the preset index to the adjacent high-order index and re-probing similar data, the method further includes:

[0103] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0104] In some embodiments, if the target dataset is a multi-indexed dataset, the extraction strategy further includes:

[0105] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0106] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0107] In some embodiments, the data processing method further includes:

[0108] Repeat the extraction strategy to obtain a second preset number of similar datasets.

[0109] Beneficial effects

[0110] The beneficial effects of the embodiments disclosed herein compared with the prior art include at least the following: by obtaining the target dataset corresponding to the index index in the target index table and extracting similar datasets from the target dataset based on the extraction strategy, sorting can be avoided when extracting data, greatly reducing response time and thus greatly increasing the efficiency of data extraction. Attached Figure Description

[0111] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0112] Figure 1 This is a schematic diagram of an application scenario of the data processing method provided according to the embodiments of this disclosure;

[0113] Figure 2 This is a flowchart of a second embodiment of a data processing method provided according to the present disclosure;

[0114] Figure 3 This is a flowchart of Embodiment 3 of another data processing method provided according to the embodiments of this disclosure;

[0115] Figure 4 This is a simplified structural diagram of a data processing apparatus provided according to an embodiment of the present disclosure;

[0116] Figure 5 This is a schematic diagram of an electronic device provided according to an embodiment of the present disclosure. Detailed Implementation

[0117] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0118] It should also be noted that, for ease of description, only the parts relevant to this disclosure are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0119] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different systems, devices, modules or units, and are not used to limit the order of functions performed by these systems, devices, modules or units or their interdependencies.

[0120] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0121] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0122] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0123] Example 1:

[0124] Figure 1 This is a schematic diagram of an application scenario of the data processing method according to Embodiment 1 of this disclosure.

[0125] exist Figure 1 In the application scenario, firstly, the computing device 101 can obtain the target index table 102 and the index index 103. The index index can be a single index index or multiple index indexes.

[0126] Secondly, the computing device 101 can obtain the target dataset 104 corresponding to the index index 103 in the target index table 102.

[0127] Finally, the computing device 101 can extract a preset number of similar data from the target dataset 102 based on a preset quantity and a preset extraction strategy 105 to obtain a similar dataset 106. The extraction strategy does not include sorting.

[0128] It should be noted that the aforementioned computing device 101 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed within the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0129] It should be understood that Figure 1 The number of computing devices shown is merely illustrative. Any number of computing devices can be used depending on implementation needs.

[0130] Example 2:

[0131] Continue to refer to Figure 2 The diagram illustrates flow 200 of a second embodiment of the data processing method according to this disclosure. This method can be performed by... Figure 1 The data processing method is performed by the computing device 101. The method includes the following steps:

[0132] Step 201: Obtain the target index table and index metrics. Index metrics can be single index metrics or multiple index metrics.

[0133] In some alternative implementations, the execution body of the data processing method (such as...) Figure 1 The computing device 101 shown can connect to the target device via a wired or wireless connection, and then obtain the target index table and index metrics. Index metrics can refer to metrics used to limit the index selection method. Index metrics can include single index metrics or multiple index metrics. The target index table can refer to a data storage structure composed of multiple index data entries. Based on each index data entry in the target index table, one corresponding stored data entry can be obtained from the database, resulting in multiple stored data entries. As an example, as shown in the table below, each column of data represents the same index type, and each row of index data can refer to one index data entry. The target index table can refer to one of the index tables in the database, or it can refer to an index table retrieved from the database and temporarily stored in a cache, or it can refer to other types of tables that store multiple index data entries; there are no restrictions here. The aforementioned execution device can filter data from it. Index metrics can refer to metrics used to determine the dataset used as an index. Please see the table below:

[0134] Serial Number Index type 1 Index type 2 Index type 3 Index type 4 Index type 5 Index type 6 Index type 7 1 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) 2 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) 3 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted)

[0135] In some optional implementations, the index metric can be a single index metric. A single index metric refers to a metric that uses the indexed data of a single data column in the indexed table as the index. For example, the data corresponding to data type 1 could be used as the index, or the data corresponding to data type 3 could also be used as the index. It should be noted that the value of each indexed data in the data column used as a single index metric is distinct.

[0136] In some alternative implementations, the index metric can be a multi-index metric. A multi-index metric refers to an index that uses a combination of data from at least two columns (or rows) in the indexed table as the index, where each combination of data in the multi-index is unique. A unique combination means that at least one column in each indexed combination is different. For example, a multi-index metric could be a combination of data corresponding to data type 1, data type 2, and data type 3, such as ("Data type 1, 55", "Data type 2, 23.5", "Data type 3, 15"). Furthermore, no other combination of data in the multi-index can have the same data as each of the three data types in the current multi-index; at least one column (row) must be different.

[0137] In addition, the data types that can be used as indexes include commonly used index data types such as numbers, strings, binary numbers, and dates. The settings are as needed and no specific restrictions are imposed here.

[0138] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0139] In some alternative implementations, the rows and columns of the index table described above can be interchanged, allowing each column of data to be treated as a single row, with each row containing data of the same type. This can be configured as needed, and no specific restrictions are imposed here.

[0140] Step 202: Obtain the target dataset corresponding to the index metric in the target index table.

[0141] In some optional implementations, the aforementioned execution entity can obtain the target dataset corresponding to the index metric from the target index table. The target dataset can refer to the dataset corresponding to the index extracted from the target index table. This target dataset corresponds to the index metric and can be a single-index dataset or a multi-index dataset. Data in a single-index dataset is single-index data, and data in a multi-index dataset is multi-index data.

[0142] Step 203: Based on a preset quantity and a preset extraction strategy, extract a preset quantity of similar data from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0143] The preset quantity refers to the predetermined number of data points to be extracted. For example, a preset quantity of 100 means 100 data points will be extracted from the target dataset. This preset quantity can also be other numbers, such as 3, 1235, 10000, etc. The preset quantity can be a positive integer. Similar data refers to a preset number of data points that are closest to the minimum or maximum value selected from the target dataset. The extraction strategy refers to the steps or methods used to extract the preset number of similar data points from the target dataset. This extraction strategy does not include sorting processes to save computational resources.

[0144] In some alternative implementations, the above method further includes: repeatedly executing the extraction strategy to obtain a second preset number of similar datasets.

[0145] The second preset quantity refers to the number of similar datasets to be retrieved, which is set in advance. This second preset quantity can be obtained in various ways. For example, in the index metric, it could refer to retrieving 1000 similar datasets from the target index table, with each dataset containing 500 similar data points. As another example, the index metric could refer to retrieving 10000 grouped data points from the target index table, with each group containing 100 data points; in this case, the second preset quantity would be calculated as 10000 / 100 = 100. This second preset quantity can also be obtained through other methods, which can be set as needed; no specific restrictions are imposed here.

[0146] The beneficial effects of one of the above implementation methods in the present disclosure include at least the following: by obtaining the target dataset corresponding to the index index in the target index table and extracting similar datasets from the target dataset based on the extraction strategy, sorting can be avoided when extracting data, which greatly reduces the response time and thus greatly increases the efficiency of data extraction.

[0147] Example 3:

[0148] Continue to refer to Figure 3 The diagram illustrates flow 300 of a third embodiment of the data processing method according to this disclosure, which can be performed by... Figure 1 The data processing method is executed by the computing device 101 in the computer. The data processing method includes:

[0149] Step 301: Obtain the target index table and index metrics. Index metrics can be single index metrics or multiple index metrics.

[0150] Step 302: Obtain the target dataset corresponding to the index metric in the target index table.

[0151] Step 303: Determine whether the target dataset is a single-index dataset or a multi-index dataset.

[0152] In some alternative implementations, the aforementioned execution entity may determine whether the target dataset is a single-index dataset or a multi-index dataset.

[0153] Step 304: If the target dataset is a single-index dataset, start probing for similar data from the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position higher than the preset data position.

[0154] In some optional implementations, if the target dataset is a multi-indexed dataset, the aforementioned execution entity can start probing for similar data from the smallest data in the target dataset. Similar data includes the smallest data, as well as data that differs from the smallest data at a preset data position but is identical at data positions higher than the preset data position. The preset data position can refer to the rightmost data position (i.e., the last position of the data). The data can be stored in the index table in binary, decimal, hexadecimal, string, date, etc., with each data position representing a different meaning. For example, different digits in different bases represent different numerical orders. Different data positions in a string represent different characters; different data positions in a date represent different meanings such as hours, minutes, seconds, etc. As an example, the smallest data could be 12543, so probing can start from the rightmost data position 3. Data similar to the smallest data could be 12544, 12545, 12546, 12547, 12548, or 12549, resulting in 6 similar data detected. The minimum data (i.e., the data in the target dataset) can also be other forms of data, which can be set as needed, without specific restrictions here.

[0155] Step 305: If the number of similar data detected is less than the preset number, shift the preset data to the adjacent high data bit and then re-detect similar data.

[0156] In some optional implementations, if the number of similar data detected is less than a preset number, the execution entity can shift the preset data to an adjacent high data bit and then re-detect similar data. Referring to the example of step 304, if the preset number is 10 and the number of similar data detected is 6 (i.e., the number of similar data detected is less than the preset number), then it can continue to detect the number of data that are different from the second data bit from the right and the same as the data to the left (higher bit) of the second data bit.

[0157] In some preferred implementations, if the number of detected similar data exceeds a preset number, the forward adjacent data of the smallest data is obtained and added to the similar data. Similar data is then re-detected until the number of similar data is the same as the preset number, resulting in a similar dataset. Forward adjacent data can refer to the smallest data adjacent to the smallest data.

[0158] Step 306: If the number of similar data detected again is less than the preset number, continue to shift the preset data to the adjacent high data bit and then detect similar data again until the number of similar data detected again is greater than or equal to the preset number.

[0159] In some alternative implementations, if the number of similar data re-probeed is less than a preset number, the execution entity may continue to shift the preset data to the adjacent high data bits and then re-probe similar data until the number of similar data re-probeed is greater than or equal to the preset number.

[0160] Step 307: If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previously detected similar data and add the positive neighbor data to the similar data.

[0161] In some optional implementations, if the number of re-probeed similar data exceeds a preset number, the execution entity can obtain the forward adjacent data of the largest data among the previously probed similar data and add the forward adjacent data to the similar data. Forward adjacent data can refer to the smallest data adjacent to the smallest data.

[0162] Step 308: Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0163] In some alternative implementations, the aforementioned execution entity can re-probe similar data until the number of similar data is the same as the preset number, thus obtaining a similar dataset.

[0164] Step 309: If the target dataset is a multi-index dataset, start probing similar data from the minimum combination data determined in the target dataset. Similar data includes the minimum combination data and data that are different from the minimum combination data at a preset index but the same at an index higher than the preset index.

[0165] In some optional implementations, if the target dataset is a multi-indexed dataset, the aforementioned execution entity can start probing similar data from the minimum combination data determined in the target dataset. Similar data includes the minimum combination data, as well as data that is different from the minimum combination data at a preset index but identical at indices higher than the preset index. The minimum combination data can refer to the minimum data in each combination of data in the target dataset (the data in each unit is the minimum data). As an example, the minimum combination data could be ("data type 1, 01", "data type 2, 02", "data type 3, 09"). Then, the combination data similar to the minimum combination data would be data where data type 1 is 01, data type 2 is 02, and data type 3 is greater than 09.

[0166] Step 310: If the number of similar data detected is less than the preset number, update the preset index to the adjacent high-order index and then re-detect similar data.

[0167] In some optional implementations, if the number of detected similar data is less than a preset number, the execution entity can update the preset index to an adjacent high-order index and then re-detect similar data. The high-order index refers to the sorting of different index types among multiple indexes; indexes sorted later are relatively in the lower rank, and indexes sorted earlier are relatively in the higher rank. Furthermore, this order is not fixed and can be set as needed.

[0168] In some preferred implementations, if the number of similar data exceeds a preset number, the forward adjacent data of the largest data at the preset index is obtained and added to the similar data. The process of re-probing for similar data continues until the number of similar data equals the preset number, thus obtaining a similar dataset.

[0169] Step 311: If the number of similar data detected again is less than the preset number, continue to move the preset index to the adjacent high index position and detect similar data again until the number of similar data detected again is greater than or equal to the preset number.

[0170] In some optional implementations, if the number of similar data re-probeed is less than a preset number, the execution entity may continue to move the preset index to an adjacent high index position and re-probe similar data until the number of similar data re-probeed is greater than or equal to the preset number.

[0171] Step 312: If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data detected last time, and add the positive neighbor data to the similar data; detect similar data again until the number of similar data is the same as the preset number, and obtain the similar dataset.

[0172] In some optional implementations, if the number of re-probeed similar data exceeds a preset number, the execution entity can obtain the forward neighbor data of the largest data in the last probe and add the forward neighbor data to the similar data; re-probe similar data until the number of similar data is the same as the preset number, thus obtaining a similar dataset. This step can be processed in the manner described in steps 304 to 308 above until a similar dataset is obtained, and will not be elaborated further here.

[0173] It should be noted that in this embodiment, data probing begins with the minimum value. In other possible implementations, this disclosure may also begin data probing with the maximum value, similar to the description above, and will not be elaborated further here.

[0174] The beneficial effects of one of the above embodiments of this disclosure include at least the following: through the above steps, similar data that meet a preset number can be accurately detected, thereby increasing the practicality of this disclosure.

[0175] In some alternative implementations, the specific implementation of steps 301-302 and the resulting technical effects can be found by referring to Figure 2 Steps 201-202 in the corresponding embodiment 2 will not be repeated here.

[0176] All of the above-mentioned optional technical solutions can be combined in any way to form the optional implementation methods of this application, and will not be described in detail here.

[0177] Example 4:

[0178] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0179] Further reference Figure 4 As an implementation of the above figures and methods, this disclosure provides an embodiment of a data processing apparatus, which is similar to... Figure 2 This corresponds to the second embodiment described above.

[0180] like Figure 4 As shown, the data processing apparatus 400 of this embodiment includes:

[0181] The first acquisition module 401 is configured to acquire a target index table and index indicators, wherein the index indicators can be a single index indicator or multiple index indicators.

[0182] In some alternative implementations, the execution body of the data processing method (such as...) Figure 1 The computing device 101 shown can connect to the target device via a wired or wireless connection, and then obtain the target index table and index metrics. Index metrics can refer to metrics used to limit the index selection method. Index metrics can include single index metrics or multiple index metrics. The target index table can refer to a data storage structure composed of multiple index data entries. Based on each index data entry in the target index table, one corresponding stored data entry can be obtained from the database, resulting in multiple stored data entries. As an example, as shown in the table below, each column of data represents the same index type, and each row of index data can refer to one index data entry. The target index table can refer to one of the index tables in the database, or it can refer to an index table retrieved from the database and temporarily stored in a cache, or it can refer to other types of tables that store multiple index data entries; there are no restrictions here. The aforementioned execution device can filter data from it. Index metrics can refer to metrics used to determine the dataset used as an index. Please see the table below:

[0183] Serial Number Index type 1 Index type 2 Index type 3 Index type 4 Index type 5 Index type 6 Index type 7 1 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) 2 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) 3 (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted) (Index data omitted)

[0184] In some optional implementations, the index metric can be a single index metric. A single index metric refers to a metric that uses the indexed data of a single data column in the indexed table as the index. For example, the data corresponding to data type 1 could be used as the index, or the data corresponding to data type 3 could also be used as the index. It should be noted that the value of each indexed data in the data column used as a single index metric is distinct.

[0185] In some alternative implementations, the index metric can be a multi-index metric. A multi-index metric refers to an index that uses a combination of data from at least two columns (or rows) in the indexed table as the index, where each combination of data in the multi-index is unique. A unique combination means that at least one column in each indexed combination is different. For example, a multi-index metric could be a combination of data corresponding to data type 1, data type 2, and data type 3, such as ("Data type 1, 55", "Data type 2, 23.5", "Data type 3, 15"). Furthermore, no other combination of data in the multi-index can have the same data as each of the three data types in the current multi-index; at least one column (row) must be different.

[0186] In addition, the data types that can be used as indexes include commonly used index data types such as numbers, strings, binary numbers, and dates. The settings are as needed and no specific restrictions are imposed here.

[0187] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0188] In some alternative implementations, the rows and columns of the index table described above can be interchanged, allowing each column of data to be treated as a single row, with each row containing data of the same type. This can be configured as needed, and no specific restrictions are imposed here.

[0189] The second acquisition module 402 is configured to acquire the target dataset corresponding to the index index in the target index table.

[0190] In some optional implementations, the aforementioned execution entity can obtain the target dataset corresponding to the index metric from the target index table. The target dataset can refer to the dataset corresponding to the index extracted from the target index table. This target dataset corresponds to the index metric and can be a single-index dataset or a multi-index dataset. Data in a single-index dataset is single-index data, and data in a multi-index dataset is multi-index data.

[0191] The extraction module 403 is configured to extract a preset number of similar data from the target dataset based on a preset quantity and a preset extraction strategy to obtain a similar dataset, wherein the extraction strategy does not include a sorting process.

[0192] The preset quantity refers to the predetermined number of data points to be extracted. For example, a preset quantity of 100 means 100 data points will be extracted from the target dataset. This preset quantity can also be other numbers, such as 3, 1235, 10000, etc. The preset quantity can be a positive integer. Similar data refers to a preset number of data points that are closest to the minimum or maximum value selected from the target dataset. The extraction strategy refers to the steps or methods used to extract the preset number of similar data points from the target dataset. This extraction strategy does not include sorting processes to save computational resources.

[0193] In some alternative implementations, the above method further includes: repeatedly executing the extraction strategy to obtain a second preset number of similar datasets.

[0194] The second preset quantity refers to the number of similar datasets to be retrieved, which is set in advance. This second preset quantity can be obtained in various ways. For example, in the index metric, it could refer to retrieving 1000 similar datasets from the target index table, with each dataset containing 500 similar data points. As another example, the index metric could refer to retrieving 10000 grouped data points from the target index table, with each group containing 100 data points; in this case, the second preset quantity would be calculated as 10000 / 100 = 100. This second preset quantity can also be obtained through other methods, which can be set as needed; no specific restrictions are imposed here.

[0195] In some optional implementations, if the target dataset is a single-index dataset, the extraction strategy includes:

[0196] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0197] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0198] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0199] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0200] In some optional implementations, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0201] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0202] In some optional implementations, the extraction strategy further includes, if the target dataset is a single-index dataset:

[0203] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0204] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0205] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy includes:

[0206] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0207] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0208] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0209] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0210] In some optional implementations, after updating the preset index to the adjacent high-order index and re-probing similar data, the following additional steps are also included:

[0211] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0212] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy also includes:

[0213] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0214] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0215] In some alternative implementations, the data processing apparatus further includes:

[0216] The repeated execution module is configured to repeatedly execute the extraction strategy to obtain a second preset number of similar datasets.

[0217] It is understandable that the modules described in the device 400 are similar to those in the reference. Figure 2 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to device 400 and the modules contained therein, and will not be repeated here.

[0218] Example 5:

[0219] like Figure 5 As shown, electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. RAM 503 also stores various programs and data required for the operation of electronic device 500. Processing device 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504. In some cases, electronic device 500 may be computing device 101 as described in the foregoing embodiments.

[0220] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0221] Specifically, according to this embodiment, the process described in the above-referenced flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In this embodiment, the computer program can be downloaded and installed from a network via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, the following steps can be performed:

[0222] Exclusive access: Obtain the target index table and index metrics, which can be a single index metric or multiple index metrics;

[0223] Retrieve the target dataset corresponding to the index metrics in the target index table;

[0224] Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0225] In some alternative implementations, the extraction strategy includes:

[0226] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0227] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0228] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0229] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0230] In some optional implementations, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0231] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0232] In some optional implementations, if the target dataset is a single-index dataset, the extraction strategy also includes:

[0233] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0234] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0235] In some alternative implementations, the extraction strategy includes:

[0236] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0237] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0238] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0239] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0240] In some optional implementations, after updating the preset index to the adjacent high-order index and re-probing similar data, the following additional steps are also included:

[0241] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0242] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy also includes:

[0243] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0244] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0245] In some alternative implementations, the data processing method also includes:

[0246] Repeat the extraction strategy to obtain a second preset number of similar datasets.

[0247] It should be noted that the computer-readable medium described above in this embodiment can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this embodiment, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0248] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0249] The aforementioned computer-readable medium may be included in the aforementioned device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the following steps:

[0250] Retrieve the target index table and index metrics. Index metrics can be a single index metric or multiple index metrics.

[0251] Retrieve the target dataset corresponding to the index metrics in the target index table;

[0252] Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting.

[0253] In some alternative implementations, the extraction strategy includes:

[0254] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0255] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0256] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0257] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0258] In some optional implementations, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0259] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0260] In some optional implementations, if the target dataset is a single-index dataset, the extraction strategy also includes:

[0261] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0262] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0263] In some alternative implementations, the extraction strategy includes:

[0264] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0265] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0266] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0267] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0268] In some optional implementations, after updating the preset index to the adjacent high-order index and re-probing similar data, the following additional steps are also included:

[0269] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0270] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy also includes:

[0271] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0272] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0273] In some alternative implementations, the data processing method also includes:

[0274] Repeat the extraction strategy to obtain a second preset number of similar datasets.

[0275] Computer program code for performing the operations of this embodiment can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0276] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0277] The modules described in this embodiment can be implemented in software or hardware. The described modules can also be located in a processor; for example, they can be described as:

[0278] The third acquisition module 401 is configured to acquire the target index table and index indicators, wherein the index indicators can be single index indicators or multiple index indicators.

[0279] The fourth acquisition module 402 is configured to acquire the target dataset corresponding to the index index in the target index table.

[0280] The second extraction module 403 is configured to extract a preset number of similar data from the target dataset based on a preset quantity and a preset extraction strategy to obtain a similar dataset, wherein the extraction strategy does not include a sorting process.

[0281] In some optional implementations, if the target dataset is a single-index dataset, the extraction strategy includes:

[0282] The detection of similar data begins with the minimum data in the target dataset. Similar data includes the minimum data and data that are different from the minimum data at a preset data position but the same at a data position above the preset data position.

[0283] If the number of similar data detected is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again.

[0284] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the previous detection of similar data, and add the positive neighbor data to the similar data;

[0285] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0286] In some optional implementations, after shifting the preset data to adjacent high data bits and re-probing similar data, the method further includes:

[0287] If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

[0288] In some optional implementations, the extraction strategy further includes, if the target dataset is a single-index dataset:

[0289] If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the smallest data and add the positive neighbor data to the similar data;

[0290] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0291] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy includes:

[0292] The detection of similar data begins with the minimum combination of data determined from the target dataset. Similar data includes the minimum combination of data and data that is different from the minimum combination of data at a preset index but the same at an index higher than the preset index.

[0293] If the number of similar data detected is less than the preset number, the preset index is updated to the adjacent high-order index and similar data is detected again.

[0294] If the number of similar data detected again is greater than the preset number, obtain the positive neighbor data of the largest data in the last detection and add the positive neighbor data to the similar data;

[0295] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0296] In some optional implementations, after updating the preset index to the adjacent high-order index and re-probing similar data, the following additional steps are also included:

[0297] If the number of similar data detected is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected is greater than or equal to the preset number.

[0298] In some optional implementations, if the target dataset is a multi-indexed dataset, the extraction strategy also includes:

[0299] If the number of similar data exceeds the preset number, obtain the forward adjacent data of the largest data at the preset index, and add the forward adjacent data to the similar data;

[0300] Re-probe similar data until the number of similar data is the same as the preset number, and obtain a similar dataset.

[0301] In some alternative implementations, the data processing apparatus further includes:

[0302] The second repetitive execution module is configured to repeatedly execute the extraction strategy to obtain a second preset number of similar datasets.

[0303] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0304] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A data processing method, characterized in that, include: Obtain the target index table and index metrics, wherein the index metrics include single index metrics or multiple index metrics; Obtain the target dataset corresponding to the index metric in the target index table; Based on a preset quantity and a preset extraction strategy, a preset quantity of similar data is extracted from the target dataset to obtain a similar dataset. The extraction strategy does not include sorting. If the target dataset is a single-index dataset, the extraction strategy includes: starting from the smallest data in the target dataset, probing similar data, where the similar data includes the smallest data and data that is different from the smallest data at a preset data position but the same at a data position higher than the preset data position; if the number of similar data detected is less than the preset number, shifting the preset data to an adjacent higher data position and re-probing similar data; if the number of similar data re-probing is greater than the preset number, obtaining the positive adjacent data of the largest data in the previously probing similar data and adding the positive adjacent data to the similar data; re-probing similar data until the number of similar data is the same as the preset number, thus obtaining the similar dataset; If the target dataset is a multi-index dataset, the extraction strategy includes: starting from the minimum combination data determined in the target dataset, probing similar data, the similar data including the minimum combination data, and data that is different from the minimum combination data at a preset index but the same at an index higher than the preset index; if the number of similar data detected is less than the preset number, updating the preset index to the adjacent high-order index and re-probing similar data; if the number of re-probing similar data is greater than the preset number, obtaining the positive adjacent data of the largest data detected last time, and adding the positive adjacent data to the similar data; re-probing similar data until the number of similar data is the same as the preset number, thus obtaining the similar dataset.

2. The method according to claim 1, characterized in that, After shifting the preset data to adjacent high data bits and then re-detecting similar data, the method further includes: If the number of similar data detected again is less than the preset number, the preset data is shifted to the adjacent high data bit and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

3. The method according to claim 1, characterized in that, The method further includes: If the number of similar data detected is greater than the preset number, obtain the positive neighbor data of the minimum data and add the positive neighbor data to the similar data; The similar data is re-probened until the number of similar data is the same as the preset number, thus obtaining the similar dataset.

4. The method according to claim 1, characterized in that, After updating the preset index to the adjacent high-order index and re-probing similar data, the process further includes: If the number of similar data detected again is less than the preset number, the preset index is moved to the adjacent high index position and similar data is detected again until the number of similar data detected again is greater than or equal to the preset number.

5. The method according to claim 1, characterized in that, The method further includes: If the number of similar data is greater than the preset number, obtain the positive adjacent data of the largest data at the preset index, and add the positive adjacent data to the similar data; The similar data is re-probened until the number of similar data is the same as the preset number, thus obtaining the similar dataset.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Repeat the extraction strategy to obtain a second preset number of similar datasets.

7. A data processing apparatus, characterized in that, include: The first acquisition module is configured to acquire a target index table and index metrics, wherein the index metrics can be a single index metric or multiple index metrics. The second acquisition module is configured to acquire the target dataset corresponding to the index indicator in the target index table; The extraction module is configured to extract a preset number of similar data from the target dataset based on a preset quantity and a preset extraction strategy to obtain a similar dataset. The extraction strategy does not include sorting. If the target dataset is a single-index dataset, the extraction strategy includes: starting from the smallest data in the target dataset, probing similar data, where the similar data includes the smallest data and data that is different from the smallest data at a preset data position but the same at a data position higher than the preset data position; if the number of similar data detected is less than the preset number, shifting the preset data to an adjacent higher data position and re-probing similar data; if the number of similar data re-probing is greater than the preset number, obtaining the positive adjacent data of the largest data in the previously probing similar data and adding the positive adjacent data to the similar data; re-probing similar data until the number of similar data is the same as the preset number, thus obtaining the similar dataset; If the target dataset is a multi-index dataset, the extraction strategy includes: starting from the minimum combination data determined in the target dataset, probing similar data, the similar data including the minimum combination data, and data that is different from the minimum combination data at a preset index but the same at an index higher than the preset index; if the number of similar data detected is less than the preset number, updating the preset index to the adjacent high-order index and re-probing similar data; if the number of re-probing similar data is greater than the preset number, obtaining the positive adjacent data of the largest data detected last time, and adding the positive adjacent data to the similar data; re-probing similar data until the number of similar data is the same as the preset number, thus obtaining the similar dataset.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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