Data statistical methods, systems, computer equipment and storage media

Abstract

This invention relates to a data statistics method, system, computer device, and storage medium. The key technical points are: establishing a many-to-many mapping relationship between multiple different services and multiple different indicators; determining all group IDs for each indicator based on the mapping relationship and a preset correspondence between services and popularity; determining the byte location of data storage for all indicators based on the preset correspondence between indicators and popularity; receiving indicator data statistics requests, each request including a target group ID and at least one target indicator corresponding to that target group ID; grouping the target indicators of all indicator data statistics requests according to the target group ID to obtain at least one indicator set; reading data from the byte location of the target indicators; and obtaining statistical data for each indicator set based on the data of the target indicators corresponding to each indicator set. This application significantly improves the efficiency of data statistics and facilitates rapid statistical analysis of indicators.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a data statistics method, system, computer device, and storage medium. Background Technology

[0002] Most existing data statistics are divided into real-time statistics and offline statistics. For example, in the advertising industry, the entire upstream and downstream (advertisers, channel partners and media, etc.) need to see their real-time data, such as revenue data, expenditure data, requests, responses, impressions, clicks, etc.

[0003] In the advertising industry, real-time statistical methods can provide real-time data support for business and operations personnel to make decisions, evaluate media, profits, and advertiser performance. However, real-time statistical methods are difficult to distinguish between fraudulent and fraudulent activities. If the current offline statistical methods are used, the statistical efficiency is low, and data correction is usually required every other day, resulting in a high delay. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a data statistics method, system, computer equipment and storage medium, which has the advantages of greatly improving the efficiency of data statistics and facilitating the rapid statistical analysis of indicators.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] A data statistics method, comprising:

[0007] In advance, establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators based on business needs, and determine all group IDs of each indicator based on the mapping relationship and the preset correspondence between business and popularity.

[0008] The byte location for storing all metrics is determined based on the pre-defined correspondence between metrics and popularity.

[0009] Receive at least one indicator data statistics request, the indicator data statistics request including: target group ID and at least one target indicator corresponding to the target group ID;

[0010] Based on the target group ID, group the target indicators of all indicator data statistical requests to obtain at least one indicator set;

[0011] Read the data in the byte position of each target indicator, and obtain the statistical data of each indicator set based on the data of the target indicators corresponding to each indicator set.

[0012] Optionally, determining the byte location of data for all indicators based on a preset correspondence between indicators and popularity includes:

[0013] The popularity value of each indicator is determined based on the preset correspondence between the indicators and popularity.

[0014] Based on the popularity values ​​of all indicators from high to low, the data of each corresponding indicator is stored in the database in order from the least significant byte position to the most significant byte position.

[0015] Optionally, the mapping relationship includes: a first mapping relationship established between each of the services and the corresponding indicators, and a second mapping relationship established between each of the indicators and the corresponding services; determining all group IDs of each indicator based on the mapping relationship and the preset correspondence between services and popularity includes:

[0016] Based on the first mapping relationship, all indicators are divided into multiple groups;

[0017] The popularity value of each group is determined based on the preset correspondence between business and popularity.

[0018] Based on the popularity values ​​of all groups from high to low, the corresponding groups are sorted in order from low ID to high ID to obtain the group ID of each business.

[0019] Based on the second mapping relationship, determine all services corresponding to each indicator, and based on all services corresponding to each indicator, determine all group IDs for each indicator.

[0020] Optionally, the step of reading data from the byte positions of each target indicator and obtaining statistical data for each indicator set based on the data of the target indicators corresponding to each indicator set includes:

[0021] Read the data from the byte positions of each target indicator sequentially, from the least significant byte to the most significant byte.

[0022] The order of the target group IDs corresponding to all indicator sets is determined from low to high based on the order of all group IDs;

[0023] The statistical data for each indicator set is obtained by sequentially aggregating the target indicator data corresponding to each indicator set according to the target group IDs in ascending order.

[0024] Optionally, after determining the group IDs of all metrics based on the mapping relationship, the method further includes:

[0025] Obtain the correlation between all metrics and real-time popularity;

[0026] The real-time popularity value of each indicator is determined based on the correspondence between all indicators and real-time popularity.

[0027] Based on the real-time popularity values ​​of all indicators from high to low, the indicators in each group are sorted in ascending order to determine the ranking position in each group.

[0028] Optionally, after obtaining the statistical data of each indicator set based on the data of the target indicator corresponding to each indicator set, the method further includes:

[0029] Determine whether a review request has been received. The review request includes at least one review indicator, which is selected from all target indicators. If yes, obtain the current data of the review indicator, compare the current data with the statistical data corresponding to the review indicator, obtain the comparison result, and generate the final statistical data based on the comparison result. If no, use the statistical data as the final statistical data.

[0030] A data statistics system, comprising:

[0031] The first determining module is used to pre-establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators according to business needs, and to determine all group IDs of each indicator according to the mapping relationship and the pre-set correspondence between business and popularity.

[0032] The second determining module is used to determine the byte location of data storage for all indicators based on the preset correspondence between indicators and popularity.

[0033] The request receiving module is used to receive at least one indicator data statistics request, wherein the indicator data statistics request includes: a target group ID and at least one target indicator corresponding to the target group ID;

[0034] The indicator grouping module is used to group the target indicators of all indicator data statistical requests according to the target group ID to obtain at least one indicator set.

[0035] The data reading module is used to read the data in the byte positions of each target indicator and obtain the statistical data of each indicator set based on the data of the target indicators corresponding to each indicator set.

[0036] Optionally, the second determining module includes:

[0037] The first unit is used to determine the popularity value of each indicator based on the preset correspondence between indicators and popularity.

[0038] The second unit is used to store the data of each corresponding indicator in the database in descending order of popularity value, in order from the least significant byte position to the most significant byte position.

[0039] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0040] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0041] In summary, the present invention has the following beneficial effects: when multiple indicator data statistics requests are received, the target indicators can be grouped according to the target group ID to obtain an indicator set, and then the data of each target indicator can be distributed to the indicator set according to the grouping situation to obtain the statistical data of each indicator set. This avoids reading the data of the target indicator corresponding to each indicator data statistics request once when there are multiple indicator data statistics requests, thereby preventing the situation of reading the data of the same indicator multiple times, greatly improving the efficiency of data statistics, and facilitating the rapid statistical data analysis of indicators. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the data statistics method provided by the present invention;

[0043] Figure 2 This is a structural block diagram of the data statistics system provided by the present invention;

[0044] Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0046] In this invention, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0047] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] This invention provides a data statistics method, such as... Figure 1 As shown, it includes:

[0049] Step 100: Establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators based on business needs in advance. Determine all group IDs for each indicator based on the mapping relationship and the preset correspondence between business and popularity. In practical applications, such as in the advertising industry, advertisers place advertisements through media, and the media displays the advertisers' advertisements to users. Advertisers typically need to judge the effectiveness of advertising by using data such as ad exposure requests, user click requests, download start, download completion, installation start, installation completion, and activation. Different businesses (such as operations departments and business departments) require different indicators. Given a first business, a second business, a third business, and indicators A, B, C, D, and E, a many-to-many mapping relationship can be formed by mapping the first business to indicators A, B, and C, the second business to indicators A, C, and E, and the third business to indicators D and E, based on the actual needs of the first, second, and third businesses.

[0050] Step 200: Determine the byte location of data storage for all indicators based on the preset correspondence between indicators and popularity. Specifically, the popularity of each indicator can be determined manually based on the estimated statistical frequency of each indicator. The higher the estimated statistical frequency, the higher the popularity, thereby determining the correspondence between each indicator and popularity.

[0051] Step 300: Receive at least one indicator data statistics request. The indicator data statistics request includes: a target group ID and at least one target indicator corresponding to the target group ID. In practical applications, indicator data statistics requests are issued by services. There may be a situation where only one service issues an indicator data statistics request, or there may be a situation where multiple services issue indicator data statistics requests.

[0052] Step 400: Group the target indicators of all indicator data statistics requests according to the target group ID to obtain at least one indicator set. In practical applications, when only one business issues an indicator data statistics request, all target indicators in that indicator data statistics request are taken as one indicator set. When multiple businesses issue indicator data statistics requests, the target indicators corresponding to each target group ID are taken as indicator sets to obtain multiple indicator sets. For example, if indicator A and indicator B are selected under the first business and indicator A and indicator C are selected under the second business, two indicator data statistics requests are generated. The target indicators corresponding to the target group ID of the first business are indicator A and indicator B, and the target indicators corresponding to the target group ID of the second business are indicator A and indicator C, thus obtaining one indicator set composed of indicator A and indicator B and another indicator set composed of indicator A and indicator C.

[0053] Step 500: Read the data from the byte positions of each target indicator. Based on the data of the target indicators corresponding to each indicator set, obtain the statistical data for each indicator set. Specifically, read the data from the corresponding byte positions of indicators A, B, and C respectively. The statistical data for the indicator set composed of indicators A and B consists of the data of indicators A and B, and the statistical data for the indicator set composed of indicators A and C consists of the data of indicators A and C. When multiple indicator data statistics requests are received, the target indicators can be grouped according to the target group ID to obtain indicator sets. Then, the data of each target indicator is read, and the data of each target indicator is distributed to the indicator sets according to the grouping, obtaining the statistical data for each indicator set. This avoids reading the data of the target indicators corresponding to each indicator data statistics request once when there are multiple indicator data statistics requests, thus preventing multiple readings of the same indicator's data, greatly improving the efficiency of data statistics, and facilitating rapid statistical analysis of indicators.

[0054] Furthermore, determining the byte location of data storage for all indicators based on a preset correspondence between indicators and popularity includes:

[0055] The popularity value of each indicator is determined based on the preset correspondence between indicators and popularity. In practical applications, this means that after determining the correspondence between indicators and popularity based on the statistical frequency of each indicator, the popularity value of each indicator can be determined based on the correspondence between indicators and popularity. The popularity value is related to the statistical frequency of each indicator; the higher the statistical frequency, the higher the popularity value.

[0056] Based on the popularity values ​​of all indicators from highest to lowest, the data for each indicator is stored in the database in ascending order of byte position. Indicator data with high popularity values ​​is stored in the lowest byte position, and indicator data with low popularity values ​​is stored in the highest byte position. When reading data, it is usually done from the lowest byte position to the highest byte position. Storing data for high-popularity indicators in the lowest byte position makes storage more efficient and facilitates better data statistics.

[0057] Furthermore, the mapping relationship includes: a first mapping relationship established between each of the services and its corresponding indicators, such as the first service corresponding to indicators A, B and C, the second service corresponding to indicators A, C and E, and the third service corresponding to indicators D and E; and a second mapping relationship established between each of the indicators and its corresponding services, such as indicator A corresponding to the first and second services, indicator B corresponding to the first service, indicator C corresponding to the first and second services, indicator D corresponding to the third service, and indicator E corresponding to the second and third services.

[0058] The process of determining all group IDs for each indicator based on the mapping relationship and the preset correspondence between business and popularity includes:

[0059] Based on the first mapping relationship, all indicators are divided into multiple groups; when the first mapping relationship is that the first business corresponds to indicators A, B and C, the second business corresponds to indicators A, C and E, and the third business corresponds to indicators D and E, the grouping of indicators A, B and C, the grouping of indicators A, C and E, and the grouping of indicators D and E are obtained.

[0060] The popularity value of each group is determined based on the preset correspondence between business and popularity. In practical applications, the popularity of each business can be determined based on the statistical request frequency. For example, if the statistical request frequency of the first business is higher than that of the second business, and the statistical request frequency of the second business is higher than that of the third business, then the popularity of the first business is greater than that of the second business, and the popularity of the second business is greater than that of the third business. In other words, the popularity value of the first business is greater than that of the second business, and the popularity value of the second business is greater than that of the third business.

[0061] Based on the popularity values ​​of all groups from high to low, the corresponding groups are sorted into groups with IDs from low to high to obtain the group IDs of each business. If the popularity value of the first business is greater than that of the second business, and the popularity value of the second business is greater than that of the third business, then the group ID of the first business is 1, the group ID of the second business is 2, and the group ID of the third business is 3.

[0062] Based on the second mapping relationship, all services corresponding to each indicator are determined, and all group IDs for each indicator are determined based on all services corresponding to each indicator. In the case where the second mapping relationship is that indicator A corresponds to the first and second services, indicator B corresponds to the first service, indicator C corresponds to the first and second services, indicator D corresponds to the third service, and indicator E corresponds to the second and third services, it is determined that indicator A corresponds to the first and second services, indicator B corresponds to the first service, indicator C corresponds to the first and second services, and indicator D corresponds to the third service. Therefore, it is determined that indicator A corresponds to group IDs 1 and 3, indicator B corresponds to group ID 1, indicator C corresponds to group IDs 1 and 2, and indicator D corresponds to group ID 3.

[0063] Further, the step of reading data from the byte positions of each target indicator and obtaining statistical data for each indicator set based on the data of the target indicators corresponding to each indicator set includes:

[0064] Read the data in the byte positions of each target indicator sequentially from the least significant bit to the most significant bit. If the byte positions of all indicators are A, B, C, D, and E from the least significant bit to the most significant bit, and the target indicators are A, B, and C, then read the data in the byte positions of A, B, and C sequentially to obtain the data of A, B, and C.

[0065] The target group IDs corresponding to all indicator sets are determined in ascending order based on the order of all group IDs. For example, if the group ID of the first business is 1, the group ID of the second business is 2, and the group ID of the third business is 3, and all indicator sets include the indicator set consisting of indicator A and indicator B corresponding to the first business and the indicator set consisting of indicator A and indicator C corresponding to the second business, then the target group ID corresponding to the indicator set consisting of indicator A and indicator B is 1, and the target group ID corresponding to the indicator set consisting of indicator A and indicator C is 2.

[0066] Based on the target group IDs corresponding to all indicator sets in ascending order, the data of the target indicators corresponding to each indicator set are aggregated to obtain the statistical data of each indicator set. In the case where the target group ID corresponding to the indicator set composed of indicator A and indicator B is 1, and the target group ID corresponding to the indicator set composed of indicator A and indicator C is 2, the data of indicator A and indicator B are aggregated first to form the statistical data corresponding to the indicator set with target group ID 1, so that the first business can know the statistical data about indicator A and indicator B. Then, the data of indicator A and indicator C are aggregated to form the statistical data corresponding to the indicator set with target group ID 2, so that the second business can know the statistical data about indicator A and indicator C.

[0067] Furthermore, after determining the group IDs of all indicators based on the mapping relationship, the method further includes:

[0068] Obtain the correspondence between all indicators and real-time popularity; in practical applications, the estimated statistical frequency of each indicator may differ from the actual statistical frequency at different stages. At this time, the popularity of each indicator will change. By obtaining the correspondence between all indicators and real-time popularity, it is easier to carry out subsequent processes.

[0069] The real-time popularity value of each indicator is determined based on the correspondence between all indicators and real-time popularity.

[0070] Based on the real-time popularity values ​​of all indicators, the indicators in each group are sorted in ascending order to determine their ranking position within each group. For example, if all groups are grouped as Indicator A, Indicator B, and Indicator C; grouped as Indicator A, Indicator C, and Indicator E; and grouped as Indicator D and Indicator E, the ranking position of the grouped Indicator A, Indicator B, and Indicator C, determined by the estimated statistical frequency, is Indicator A, Indicator B, and Indicator C. Indicator A, Indicator B, and Indicator C can then be displayed to the personnel in the first business unit based on this ranking position. If the real-time popularity values ​​change, and the indicator ranking based on the real-time popularity values ​​becomes Indicator B, Indicator C, Indicator A, Indicator D, and Indicator E, then the ranking position of the grouped Indicator A, Indicator B, and Indicator C becomes Indicator B, Indicator C, and Indicator A. Indicator B, Indicator C, and Indicator A can then be displayed to the personnel in the first business unit based on this ranking position, making it easier for them to select indicators with higher real-time popularity values.

[0071] Furthermore, after obtaining the statistical data of each indicator set based on the data of the target indicator corresponding to each indicator set, the process further includes:

[0072] The system determines whether a review request has been received. The review request can be issued manually or by a scheduled task. The review request includes at least one review indicator, selected from all target indicators. For example, if the target indicators are indicator A, indicator B, and indicator C, the review indicator can be any one or more of these indicators. If yes, the system obtains the current data of the review indicator, which can be obtained from a third-party platform. The current data is compared with the statistical data corresponding to the review indicator to obtain a comparison result, and the final statistical data is generated based on the comparison result. If no, the statistical data is used as the final statistical data.

[0073] Specifically, upon receiving a review request, if the current data of the review indicator is the same as the statistical data, that is, the comparison result is the same, then the final statistical data generated is the statistical data; if the current data of the review indicator is different from the statistical data, that is, the comparison result is different, then the final statistical data generated is the current data. This application can review statistical data, thereby improving the accuracy of statistical data.

[0074] The data statistics method of the present invention, when receiving multiple indicator data statistics requests, can group the target indicators according to the target group ID to obtain an indicator set, and then distribute the data of each target indicator to the indicator set according to the grouping situation to obtain the statistical data of each indicator set. This avoids reading the data of the target indicator corresponding to each indicator data statistics request once when there are multiple indicator data statistics requests, thereby preventing the situation of reading the data of the same indicator multiple times, greatly improving the efficiency of data statistics, and facilitating the rapid statistical data analysis of indicators.

[0075] like Figure 2 As shown, the present invention also provides a data statistics system, comprising:

[0076] The first determining module 10 is used to pre-establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators according to business needs, and to determine all group IDs of each indicator according to the mapping relationship and the preset correspondence between business and popularity.

[0077] The second determining module 20 is used to determine the byte location of data storage for all indicators based on the preset correspondence between indicators and popularity.

[0078] The request receiving module 30 is used to receive at least one indicator data statistics request, the indicator data statistics request including: target group ID and at least one target indicator corresponding to the target group ID;

[0079] The indicator grouping module 40 is used to group the target indicators of all indicator data statistical requests according to the target grouping ID to obtain at least one indicator set.

[0080] The data reading module 50 is used to read the data in the byte position of each target indicator and obtain the statistical data of each indicator set based on the data of the target indicators corresponding to each indicator set.

[0081] In one embodiment, the second determining module 20 includes:

[0082] The first unit is used to determine the popularity value of each indicator based on the preset correspondence between indicators and popularity.

[0083] The second unit is used to store the data of each corresponding indicator in the database in descending order of popularity value, in order from the least significant byte position to the most significant byte position.

[0084] For specific limitations regarding the data statistics system, please refer to the limitations on data statistics methods mentioned above, which will not be repeated here. Each module of the aforementioned data statistics system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0085] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a data statistics method.

[0086] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps:

[0088] In advance, establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators based on business needs, and determine all group IDs of each indicator based on the mapping relationship and the preset correspondence between business and popularity.

[0089] The byte location for storing all metrics is determined based on the pre-defined correspondence between metrics and popularity.

[0090] Receive at least one indicator data statistics request, the indicator data statistics request including: target group ID and at least one target indicator corresponding to the target group ID;

[0091] Based on the target group ID, group the target indicators of all indicator data statistical requests to obtain at least one indicator set;

[0092] Read the data in the byte position of each target indicator, and obtain the statistical data of each indicator set based on the data of the target indicators corresponding to each indicator set.

[0093] In one embodiment, determining the byte location of data storage for all indicators based on a preset correspondence between indicators and popularity includes:

[0094] The popularity value of each indicator is determined based on the preset correspondence between the indicators and popularity.

[0095] Based on the popularity values ​​of all indicators from high to low, the data of each corresponding indicator is stored in the database in order from the least significant byte position to the most significant byte position.

[0096] In one embodiment, the mapping relationship includes: a first mapping relationship established between each of the services and its corresponding metrics, and a second mapping relationship established between each of the metrics and its corresponding services; determining all group IDs for each metric based on the mapping relationship and a preset correspondence between services and popularity includes:

[0097] Based on the first mapping relationship, all indicators are divided into multiple groups;

[0098] The popularity value of each group is determined based on the preset correspondence between business and popularity.

[0099] Based on the popularity values ​​of all groups from high to low, the corresponding groups are sorted in order from low ID to high ID to obtain the group ID of each business.

[0100] Based on the second mapping relationship, determine all services corresponding to each indicator, and based on all services corresponding to each indicator, determine all group IDs for each indicator.

[0101] In one embodiment, reading data from the byte positions of each target indicator and obtaining statistical data for each indicator set based on the data of the target indicators corresponding to each indicator set includes:

[0102] Read the data from the byte positions of each target indicator sequentially, from the least significant byte to the most significant byte.

[0103] The order of the target group IDs corresponding to all indicator sets is determined from low to high based on the order of all group IDs;

[0104] The statistical data for each indicator set is obtained by sequentially aggregating the target indicator data corresponding to each indicator set according to the target group IDs in ascending order.

[0105] In one embodiment, after determining the group IDs of all metrics according to the mapping relationship, the method further includes:

[0106] Obtain the correlation between all metrics and real-time popularity;

[0107] The real-time popularity value of each indicator is determined based on the correspondence between all indicators and real-time popularity.

[0108] Based on the real-time popularity values ​​of all indicators from high to low, the indicators in each group are sorted in ascending order to determine the ranking position in each group.

[0109] In one embodiment, after obtaining the statistical data of each indicator set based on the data of the target indicator corresponding to each indicator set, the method further includes:

[0110] Determine whether a review request has been received. The review request includes at least one review indicator, which is selected from all target indicators. If yes, obtain the current data of the review indicator, compare the current data with the statistical data corresponding to the review indicator, obtain the comparison result, and generate the final statistical data based on the comparison result. If no, use the statistical data as the final statistical data.

[0111] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0112] In advance, establish a many-to-many mapping relationship between multiple different businesses and multiple different indicators based on business needs, and determine all group IDs of each indicator based on the mapping relationship and the preset correspondence between business and popularity.

[0113] The byte location for storing all metrics is determined based on the pre-defined correspondence between metrics and popularity.

[0114] Receive at least one indicator data statistics request, the indicator data statistics request including: target group ID and at least one target indicator corresponding to the target group ID;

[0115] Based on the target group ID, group the target indicators of all indicator data statistical requests to obtain at least one indicator set;

[0116] Read the data in the byte position of each target indicator, and obtain the statistical data of each indicator set based on the data of the target indicators corresponding to each indicator set.

[0117] In one embodiment, determining the byte location of data storage for all indicators based on a preset correspondence between indicators and popularity includes:

[0118] The popularity value of each indicator is determined based on the preset correspondence between the indicators and popularity.

[0119] Based on the popularity values ​​of all indicators from high to low, the data of each corresponding indicator is stored in the database in order from the least significant byte position to the most significant byte position.

[0120] In one embodiment, the mapping relationship includes: a first mapping relationship established between each of the services and its corresponding metrics, and a second mapping relationship established between each of the metrics and its corresponding services; determining all group IDs for each metric based on the mapping relationship and a preset correspondence between services and popularity includes:

[0121] Based on the first mapping relationship, all indicators are divided into multiple groups;

[0122] The popularity value of each group is determined based on the preset correspondence between business and popularity.

[0123] Based on the popularity values ​​of all groups from high to low, the corresponding groups are sorted in order from low ID to high ID to obtain the group ID of each business.

[0124] Based on the second mapping relationship, determine all services corresponding to each indicator, and based on all services corresponding to each indicator, determine all group IDs for each indicator.

[0125] In one embodiment, reading data from the byte positions of each target indicator and obtaining statistical data for each indicator set based on the data of the target indicators corresponding to each indicator set includes:

[0126] Read the data from the byte positions of each target indicator sequentially, from the least significant byte to the most significant byte.

[0127] The order of the target group IDs corresponding to all indicator sets is determined from low to high based on the order of all group IDs;

[0128] The statistical data for each indicator set is obtained by sequentially aggregating the target indicator data corresponding to each indicator set according to the target group IDs in ascending order.

[0129] In one embodiment, after determining the group IDs of all metrics according to the mapping relationship, the method further includes:

[0130] Obtain the correlation between all metrics and real-time popularity;

[0131] The real-time popularity value of each indicator is determined based on the correspondence between all indicators and real-time popularity.

[0132] Based on the real-time popularity values ​​of all indicators from high to low, the indicators in each group are sorted in ascending order to determine the ranking position in each group.

[0133] In one embodiment, after obtaining the statistical data of each indicator set based on the data of the target indicator corresponding to each indicator set, the method further includes:

[0134] Determine whether a review request has been received. The review request includes at least one review indicator, which is selected from all target indicators. If yes, obtain the current data of the review indicator, compare the current data with the statistical data corresponding to the review indicator, obtain the comparison result, and generate the final statistical data based on the comparison result. If no, use the statistical data as the final statistical data.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A data statistics method, characterized by, The method comprises the following steps: pre-mapping multiple different services with multiple different indicators according to business needs to establish a many-to-many mapping relationship, determining all group IDs of each indicator according to the mapping relationship and a preset business-heat correspondence; determining the byte position of data storage of all indicators according to a preset indicator-heat correspondence; receiving at least one indicator data statistics request, which includes a target group ID and at least one target indicator corresponding to the target group ID; grouping the target indicators of all indicator data statistics requests according to the target group ID to obtain at least one indicator set; reading the data in the byte position of each target indicator, and obtaining the statistical data of each indicator set according to the data of the target indicators corresponding to each indicator set; determining the byte position of data storage of all indicators according to a preset indicator-heat correspondence, which comprises the following steps: determining the heat value of each indicator according to a preset indicator-heat correspondence; storing the data of each indicator in the database in the order from low byte position to high byte position according to the order from high to low of the heat values of all indicators; the mapping relationship includes: the first mapping relationship established by each service and the corresponding indicator, and the second mapping relationship established by each indicator and the corresponding service; determining all group IDs of each indicator according to the mapping relationship and a preset business-heat correspondence, which comprises the following steps: dividing all indicators according to the first mapping relationship to obtain multiple groups; determining the heat value of each group according to a preset business-heat correspondence; obtaining the group ID of each business in the order from low ID to high ID according to the order of each group from low to high according to the heat values of all groups; determining all group IDs of each indicator according to the second mapping relationship and all businesses corresponding to each indicator; reading the data in the byte position of each target indicator, and obtaining the statistical data of each indicator set according to the data of the target indicators corresponding to each indicator set, which comprises the following steps: reading the data in the byte position of each target indicator in the order from low to high according to the order of all indicators from low byte position to high byte position; determining the order of the target group ID of all indicator sets from low to high according to the order of all group IDs; obtaining the statistical data of each indicator set by gathering the data of the target indicators corresponding to each indicator set in the order from low to high according to the order of the target group ID of all indicator sets; after determining the group ID of all indicators according to the mapping relationship, the method further comprises the following steps: obtaining the correspondence between all indicators and real-time heat; determining the real-time heat value of each indicator according to the correspondence between all indicators and real-time heat; determining the sorting position in each group in the order from low to high according to the order of the indicators in each group from low to high according to the order of all indicators from high to low according to the real-time heat values of all indicators.

2. The data statistical method according to claim 1, characterized in that, after obtaining the statistical data of each indicator set according to the data of the target indicators corresponding to each indicator set, the method further comprises the following steps: The method comprises the following steps: judging whether a review request is received or not, wherein the review request comprises at least one review index selected from all target indexes; if yes, obtaining current data of the review index, comparing the current data with statistical data corresponding to the review index to obtain a comparison result, and generating final statistical data according to the comparison result; if no, taking the statistical data as the final statistical data.

3. A data statistical system characterized by, The method comprises the following steps: The first determining module is configured to pre-establish a plurality of mapping relationships between a plurality of different businesses and a plurality of different indexes according to business demands, and determine all group IDs of each index according to the mapping relationships and a preset correspondence between businesses and hotness; The second determining module is configured to determine byte positions of data storage of all indexes according to a preset correspondence between indexes and hotness; The request receiving module is configured to receive at least one index data statistical request, wherein the index data statistical request comprises a target group ID and at least one target index corresponding to the target group ID; The index grouping module is configured to group target indexes of all index data statistical requests according to the target group ID to obtain at least one index set; The data reading module is configured to read data in byte positions of each target index, and obtain statistical data of each index set according to data of target indexes corresponding to each index set. The data statistical system is configured to execute steps of the method of claim 1 or 2.

4. The data statistical system of claim 3, wherein, The second determining module comprises: The first unit is configured to determine hotness values of each index according to a preset correspondence between indexes and hotness; The second unit is configured to store data of each index in a database in a sequence from low byte positions to high byte positions according to a sequence from high to low of hotness values of all indexes. 5.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-4 when the computer program is executed by the processor. The processor executes the computer program to implement steps of the method of claim 1 or 2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement steps of the method of claim 1 or 2.

Images