Business Update Method, Device, Electronic Device, and Storage Medium

Abstract

The present disclosure relates to a service update method, apparatus, electronic device, and storage medium. The method includes obtaining target associated index data of a plurality of associated indexes corresponding to a target service index within two preset periods; constructing, based on the target associated index data, index difference mapping data corresponding to the target service index within the two preset periods, which characterizes the corresponding relationship between the target service index and the plurality of associated indexes; performing attribution analysis on the target service index based on the index difference mapping data to obtain attribution analysis data characterizing the contributions of a plurality of preset influencing factors corresponding to the target service index to the target service index; and performing service update on the target service corresponding to the target service index based on the attribution analysis data. Using the embodiments of the present disclosure can accurately locate the factors causing changes in the target service index, improve the effectiveness of service update and service execution effect, reduce the situation of ineffective service update processing, reduce the waste of resources in the service system, and improve system performance.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technologies, and in particular, to a service update method, apparatus, electronic device, and storage medium. Background Art

[0002] With the development of Internet technologies, service processing forms based on the Internet have also become popular. For example, services for pushing multimedia resources on some Internet platforms. How to continuously upgrade and update services to better meet users is also one of the challenges faced by a large number of services.

[0003] In related technologies, service updates are often carried out in combination with service indicators that measure the execution effect of services. For example, in the multimedia resource push service, CPM (Cost Per Mille) is used as the service indicator, and the multimedia resource push service is adjusted and updated according to the change of CPM. However, the execution effect of services is often caused by multiple factors. In the related technologies, the solution of only using the service indicator representing the execution effect of services for service updates cannot accurately locate the factors causing the change of the service indicator, resulting in poor service execution effect, and there are situations of ineffective service update processing, which in turn causes problems such as waste of service system resources and reduction of system performance. Summary of the Invention

[0004] The present disclosure provides a service update method, apparatus, electronic device, and storage medium to at least solve the problems in the related technologies that the factors causing the change of the service indicator cannot be accurately located, resulting in poor service execution effect, and there are situations of ineffective service update processing, which in turn causes problems such as waste of service system resources and reduction of system performance. The technical solution of the present disclosure is as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a service update method is provided, including:

[0006] Obtaining target correlation index data of a plurality of correlation indexes corresponding to a target service index within two preset periods;

[0007] Based on the target correlation index data, constructing index difference mapping data corresponding to the target service index within the two preset periods, where the index difference mapping data is the corresponding relationship between the target service index and the plurality of correlation indexes, representing the index data difference corresponding to the target service index within the two preset periods;

[0008] Based on the index difference mapping data, performing attribution analysis on the target service index to obtain attribution analysis data, where the attribution analysis data represents the contribution of a plurality of preset influencing factors corresponding to the target service index to the target service index;

[0009] Based on the attribution analysis data, perform business updates on the target business corresponding to the target business metrics.

[0010] In an optional embodiment, the target correlation metric data includes the cumulative metric data of the multiple correlation metrics within any preset period; the constructing the metric difference mapping data of the target business metric corresponding to the two preset periods based on the target correlation metric data includes:

[0011] Construct the metric mapping data of the target business metric corresponding to each of the two preset periods according to the target correlation metric data, where the metric mapping data is the metric data of the target business metric corresponding to each of the two preset periods characterized by the corresponding relationship between the target business metric and the multiple correlation metrics;

[0012] Use the amplitude change mapping data between the metric mapping data corresponding to each of the two preset periods as the metric difference mapping data.

[0013] In an optional embodiment, the performing attribution analysis on the target business metric based on the metric difference mapping data to obtain the attribution analysis data includes:

[0014] Based on a preset splitting and conversion algorithm, convert the amplitude change mapping data into target mapping data;

[0015] Perform a splitting process on the target correlation metric data corresponding to the multiple correlation metrics in the target mapping data to obtain the first metric data items corresponding to the multiple correlation metrics respectively;

[0016] Determine the attribution analysis data according to the first metric data items and the target mapping data.

[0017] In an optional embodiment, the multiple preset influencing factors are the multiple correlation metrics, and the performing business updates on the target business corresponding to the target business metric based on the attribution analysis data includes:

[0018] Determine the target correlation metric from the multiple correlation metrics according to the attribution analysis data;

[0019] Perform business updates on the target business based on the target correlation metric.

[0020] In an optional embodiment, the target correlation metric data includes the cumulative metric data corresponding to each of the multiple sub - dimensions of the multiple correlation metrics in a preset analysis dimension within any preset period; the constructing the metric difference mapping data of the target business metric corresponding to the two preset periods based on the target correlation metric data includes:

[0021] Determine the target indicator data corresponding to the multiple associated indicators in the two preset periods according to the cumulative indicator data corresponding to the multiple sub-dimensions respectively;

[0022] Construct the indicator mapping data corresponding to the target business indicator in the two preset periods according to the target indicator data, where the indicator mapping data is the indicator data corresponding to the target business indicator in the two preset periods characterized by the corresponding relationship between the target business indicator and the multiple associated indicators;

[0023] Use the difference mapping data between the indicator mapping data corresponding to the two preset periods as the indicator difference mapping data.

[0024] In an optional embodiment, the attribution analysis of the target business indicator based on the indicator difference mapping data to obtain the attribution analysis data includes:

[0025] Perform a splitting process on the target associated indicator data corresponding to the multiple sub-dimensions in the difference mapping data to obtain the second indicator data items corresponding to the multiple sub-dimensions;

[0026] Determine the attribution analysis data according to the second indicator data items and the difference mapping data.

[0027] In an optional embodiment, the multiple preset influencing factors are the multiple sub-dimensions, and the business update of the target business corresponding to the target business indicator based on the attribution analysis data includes:

[0028] Determine the target analysis dimension from the multiple sub-dimensions according to the attribution analysis data;

[0029] Perform a business update on the target business based on the target analysis dimension.

[0030] In an optional embodiment, the two preset periods include the current period and the previous period of the current period; the obtaining of the target associated indicator data of the multiple associated indicators corresponding to the target business indicator within the two preset periods includes:

[0031] When the business indicator data of the target business indicator changes within the current period, obtain the target associated indicator data of the multiple associated indicators corresponding to the target business indicator within the current period and the previous period.

[0032] According to the second aspect of the embodiments of the present disclosure, a business update device is provided, including:

[0033] A target-related index data acquisition module, configured to acquire target-related index data of multiple related indexes corresponding to a target business index within two preset periods;

[0034] An index difference mapping data construction module, configured to construct index difference mapping data corresponding to the target business index within the two preset periods based on the target-related index data, where the index difference mapping data is the index data difference corresponding to the target business index within the two preset periods characterized by the corresponding relationship between the target business index and the multiple related indexes;

[0035] An attribution analysis module, configured to perform attribution analysis on the target business index based on the index difference mapping data to obtain attribution analysis data, where the attribution analysis data represents the contribution of multiple preset influencing factors corresponding to the target business index to the target business index;

[0036] A business update module, configured to perform business update on the target business corresponding to the target business index based on the attribution analysis data.

[0037] In an optional embodiment, the target-related index data includes the cumulative index data of the multiple related indexes within any one preset period; the index difference mapping data construction module includes:

[0038] A first index mapping data construction unit, configured to construct index mapping data corresponding to the target business index within each of the two preset periods according to the target-related index data, where the index mapping data is the index data corresponding to the target business index within each of the two preset periods characterized by the corresponding relationship between the target business index and the multiple related indexes;

[0039] A first index difference mapping data determination unit, configured to use the amplitude change mapping data between the index mapping data corresponding to each of the two preset periods as the index difference mapping data.

[0040] In an optional embodiment, the attribution analysis module includes:

[0041] A splitting and conversion unit, configured to convert the amplitude change mapping data into target mapping data based on a preset splitting and conversion algorithm;

[0042] A first splitting processing unit, configured to perform splitting processing on the target-related index data corresponding to the multiple related indexes in the target mapping data to obtain first index data items corresponding to the multiple related indexes respectively;

[0043] The first attribution analysis data determination unit is configured to determine the attribution analysis data according to the first metric data item and the target mapping data.

[0044] In an alternative embodiment, the multiple preset influencing factors are the multiple associated metrics, and the service update module includes:

[0045] The target associated metric determination unit is configured to determine a target associated metric from the multiple associated metrics according to the attribution analysis data;

[0046] The first service update unit is configured to perform a service update on the target service based on the target associated metric.

[0047] In an alternative embodiment, the target associated metric data includes cumulative metric data corresponding to each of the multiple sub-dimensions of the multiple associated metrics in a preset analysis dimension within any preset period; the metric difference mapping data construction module includes:

[0048] The target metric data determination unit is configured to determine the target metric data corresponding to the multiple associated metrics in the two preset periods according to the cumulative metric data corresponding to each of the multiple sub-dimensions;

[0049] The first metric mapping data construction unit is configured to construct the metric mapping data corresponding to the target service metric in the two preset periods according to the target metric data, where the metric mapping data represents the metric data corresponding to the target service metric in the two preset periods in terms of the correspondence between the target service metric and the multiple associated metrics;

[0050] The first metric difference mapping data determination unit is configured to use the difference mapping data between the metric mapping data corresponding to the two preset periods as the metric difference mapping data.

[0051] In an alternative embodiment, the attribution analysis module includes:

[0052] The second splitting processing unit is configured to perform a splitting process on the target associated metric data corresponding to the multiple associated metrics in the difference mapping data to obtain second metric data items corresponding to the multiple sub-dimensions;

[0053] The second attribution analysis data determination unit is configured to determine the attribution analysis data according to the second metric data items and the difference mapping data.

[0054] In an alternative embodiment, the multiple preset influencing factors are the multiple sub-dimensions, and the business update of the target business corresponding to the target business indicator based on the attribution analysis data includes:

[0055] A target analysis dimension determination unit configured to determine a target analysis dimension from the multiple sub-dimensions according to the attribution analysis data;

[0056] A second business update unit configured to perform a business update on the target business based on the target analysis dimension.

[0057] In an alternative embodiment, the two preset periods include the current period and the previous period of the current period; the target associated indicator data acquisition module is specifically configured to acquire target associated indicator data of multiple associated indicators corresponding to the target business indicator within the current period and the previous period when the business indicator data of the target business indicator changes within the current period.

[0058] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method according to any one of the first aspects described above.

[0059] According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enabling the electronic device to execute the method according to any one of the first aspects of the embodiments of the present disclosure.

[0060] According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product containing instructions, when it runs on a computer, enabling the computer to execute the method according to any one of the first aspects of the embodiments of the present disclosure.

[0061] The technical solutions provided by the embodiments of the present disclosure at least bring the following beneficial effects:

[0062] Combining the target correlation index data of multiple correlation indexes corresponding to the target business index within two preset periods to construct index difference mapping data corresponding to the target business index in the two preset periods, which is characterized by the corresponding relationship between the target business index and the multiple correlation indexes, can greatly improve the convenience of attribution analysis of the target business index; then, combining the index difference mapping data to conduct attribution analysis on the target business index can obtain attribution analysis data representing the contributions of multiple preset influencing factors corresponding to the target business index to the target business index; and based on this attribution analysis data, accurately positioning the factors causing the change of the target business index and making targeted business updates to the target business corresponding to the target business index can greatly improve the effectiveness of business updates and the business execution effect, and further reduce the situation of ineffective business update processing, reduce the waste of resources in the business system, and improve system performance.

[0063] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0064] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0065] Figure 1 is a flowchart of a business update method shown according to an exemplary embodiment.

[0066] Figure 2 is a flowchart of constructing index difference mapping data corresponding to the target business index in two preset periods based on target correlation index data shown according to an exemplary embodiment;

[0067] Figure 3 is a flowchart of constructing index difference mapping data corresponding to the target business index in two preset periods based on target correlation index data shown according to an exemplary embodiment;

[0068] Figure 4 is a flowchart of conducting attribution analysis on the target business index based on the index difference mapping data to obtain attribution analysis data shown according to an exemplary embodiment;

[0069] Figure 5 is a flowchart of conducting attribution analysis on the target business index based on the index difference mapping data to obtain attribution analysis data shown according to an exemplary embodiment;

[0070] Figure 6 is a block diagram of a business update device shown according to an exemplary embodiment;

[0071] Figure 7 is a block diagram of an electronic device for service update shown according to an exemplary embodiment;

[0072] Figure 8 is a block diagram of an electronic device for service update shown according to an exemplary embodiment. Detailed implementation manners

[0073] To enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0074] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the present disclosure are all information and data that have been authorized by the user or fully authorized by all parties.

[0076] The present disclosure provides a service update method, and the service update method can be applied to electronic devices such as terminals or servers.

[0077] In an alternative embodiment, the terminal may include but is not limited to types of electronic devices such as smart phones, desktop computers, tablet computers, laptop computers, smart speakers, digital assistants, augmented reality (AR) / virtual reality (VR) devices, smart wearable devices, etc., and may also be software running on the above-mentioned electronic devices, such as application programs, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, linux, windows, etc.

[0078] In an alternative embodiment, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers.

[0079] Figure 1 is a flowchart of a service update method shown according to an exemplary embodiment, asFigure 1 As shown, the service update method may include the following steps:

[0080] In step S101, obtain the target correlation index data of multiple correlation indexes corresponding to the target service index within two preset cycles.

[0081] In an optional embodiment, the update cycle of the index data of the target service index may be set in advance in combination with actual application requirements. The above two preset cycles may be two adjacent update cycles. Specifically, the above two preset cycles may include the current cycle and the previous cycle of the current cycle.

[0082] In a specific embodiment, the target service index may be an index that can measure the service execution effect of the target service. Optionally, the target service may vary according to different actual application requirements. The multiple correlation indexes corresponding to the target service index may be indexes having a corresponding relationship with the target service index. Optionally, in the case where the target service is a multimedia resource push service (a service for pushing multimedia resources based on a preset push platform to obtain multimedia resource exposure), the above target service index may be CPM. Correspondingly, the multiple correlation indexes corresponding to the above target service index may include the cumulative push resource consumption (the total virtual resources consumed by pushing multimedia resources) and the cumulative push exposure (the total exposure of multimedia resources after pushing multimedia resources). Specifically, the preset push platform may be a certain Internet platform or a user account capable of pushing multimedia resources (hereinafter referred to as a push account), etc.

[0083] Optionally, in the case where the target service is an object media resource push service (a service for pushing multimedia resources of a preset object based on a preset push platform to trigger an associated operation corresponding to the preset object), the above target service index may be the unit operation resource consumption (the average virtual resources consumed by a single associated operation brought by pushing multimedia resources of the preset object). Correspondingly, the multiple correlation indexes corresponding to the above target service index may include the cumulative operation resource consumption (the total virtual resources consumed by the associated operations brought by multimedia resources based on the preset object) and the cumulative associated operation data (the total number of associated operations triggered after pushing multimedia resources of the preset object). Specifically, the preset object may be an object to be recommended such as a commodity, an article, an application, etc. The associated operation may vary according to different actual applications. Optionally, the associated operation may include, but is not limited to, clicking, obtaining an operation of the preset object, etc.

[0084] In a specific embodiment, the above-mentioned target associated metric data may include the cumulative metric data of multiple associated metrics within any preset period. For example, in the scenario of object media resource push service, the target associated metric data corresponding to the associated metric "cumulative associated operation data" may include the total number of associated operations triggered after pushing the multimedia resources of a preset object within any preset period (i.e., the value of the cumulative associated operation data). The target associated metric data corresponding to the associated metric "cumulative operation resource consumption" may include the amount of resources of the total virtual resources consumed by the associated operations brought about by pushing the multimedia resources of a preset object within any preset period (the value of the cumulative operation resource consumption)

[0085] In an alternative embodiment, the attribution analysis of the target business metrics can be performed from multiple dimensions in combination with the actual application requirements. Correspondingly, the target associated metric data of the associated metrics can be obtained from multiple dimensions. Optionally, the above-mentioned target associated metric data may include the cumulative metric data corresponding to each of the multiple sub-dimensions of multiple associated metrics under a preset analysis dimension within any preset period. Specifically, the preset analysis dimension may represent the dimension for performing the attribution analysis of the target business metrics. Correspondingly, the multiple sub-dimensions under the preset analysis dimension may be the factors that affect the target business metrics under the preset analysis dimension.

[0086] In a specific embodiment, taking the object media resource push service as an example, the preset analysis dimension may include the dimension of the number of associated accounts of the preset push platform, the dimension of the platform type (such as tourism, sports, etc.), the dimension of the number of associated accounts and the platform type, etc. Optionally, taking the object media resource push service as an example, assuming that the preset analysis dimension is the dimension of the number of associated accounts of the preset push platform, the sub-dimensions under the dimension of the number of associated accounts of the preset push platform may include the number of associated accounts less than or equal to X1, the number of associated accounts greater than X1 and less than or equal to X2, the number of associated accounts greater than X2 and less than or equal to X3, and the number of associated accounts greater than X3, where X1 is less than X2 and X2 is less than X3.

[0087] In a specific embodiment, taking the object media resource push service as an example, in the case where the target associated metric data includes the cumulative metric data corresponding to each of the multiple sub-dimensions of multiple associated metrics under a preset analysis dimension within any preset period, the target associated metric data corresponding to the associated metric "cumulative associated operation data" may include the total number of associated operations triggered after pushing the multimedia resources of a preset object on the push platforms corresponding to the multiple sub-dimensions within any preset period (i.e., the value of the cumulative associated operation data). The target associated metric data corresponding to the associated metric "cumulative operation resource consumption" may include the amount of resources of the total virtual resources consumed by the associated operations brought about by pushing the multimedia resources of a preset object on the push platforms corresponding to the multiple sub-dimensions within any preset period (the value of the cumulative operation resource consumption)

[0088] In addition, it should be noted that in practical applications, the above are only examples of two target services. The target service can also include other services according to actual application requirements, such as object recommendation service (a service for recommending a preset object by pushing multimedia resources of the preset object). Correspondingly, the target service metric can be unit revenue resources (the average virtual resources brought by performing an operation to obtain a preset object based on the associated multimedia resources of the object); the multiple associated metrics can include operation data for obtaining the preset object and cumulative revenue resources (the total virtual resources brought by performing an operation to obtain the preset object based on the associated multimedia resources of the object).

[0089] In an alternative embodiment, when the metric data of the target service metric is determined in each update cycle, the process of whether to perform service update can be determined by combining whether the metric data of the target service metric has changed; correspondingly, the above-mentioned target associated metric data for obtaining multiple associated metrics corresponding to the target service metric within two preset cycles can include:

[0090] When the service metric data of the target service metric changes in the current cycle, obtain the target associated metric data of multiple associated metrics corresponding to the target service metric in the current cycle and the previous cycle.

[0091] In a specific embodiment, the change in the service metric data of the target service metric in the current cycle can be that the service metric data of the target service metric in the current cycle is larger or smaller than that in the previous cycle.

[0092] In the above embodiment, when the service metric data of the target service metric changes in the current cycle, obtaining the target associated metric data of multiple associated metrics corresponding to the target service metric in the current cycle and the previous cycle to enter the service update process can perform service update more pertinently, reduce ineffective service update operations, reduce resource waste of the service system, and improve system performance.

[0093] In step S103, based on the target associated metric data, construct the metric difference mapping data of the target service metric corresponding to two preset cycles.

[0094] In a specific embodiment, the above-mentioned metric difference mapping data can be the metric data difference of the target service metric corresponding to two preset cycles characterized by the corresponding relationship between the target service metric and multiple associated metrics.

[0095] In an alternative embodiment, when the target associated metric data includes the cumulative metric data of multiple associated metrics in any preset cycle, such as Figure 2As shown, the steps of constructing the index difference mapping data corresponding to the target business index in two preset cycles based on the target correlation index data may include the following steps:

[0096] In step S201, according to the target correlation index data, construct the index mapping data corresponding to the target business index in each of the two preset cycles;

[0097] In step S203, use the amplitude change mapping data between the index mapping data corresponding to each of the two preset cycles as the index difference mapping data corresponding to the two preset cycles.

[0098] In a specific embodiment, the above index mapping data is the index data corresponding to the target business index in each of the two preset cycles, represented by the corresponding relationship between the target business index and multiple correlation indexes;

[0099] In a specific embodiment, taking the object media resource push service as an example, the corresponding relationship between the target business index and multiple correlation indexes may be: unit operation resource consumption = cumulative operation resource consumption / cumulative associated operation data. Optionally, assume that the values of the cumulative operation resource consumption corresponding to two preset cycles (the current cycle and the previous cycle) are A1 and A2 in sequence, and the values of the cumulative associated operation data corresponding to two preset cycles (the current cycle and the previous cycle) are B1 and B2 in sequence; correspondingly, the index mapping data corresponding to the target business index in two preset cycles (the current cycle and the previous cycle) may be: Y1 = A1 / B1, Y2 = A2 / B2; where Y1 represents the value (index data) of the unit operation resource consumption in the current cycle; Y2 represents the value (index data) of the unit operation resource consumption in the next cycle.

[0100] In a specific embodiment, the amplitude change mapping data between the index mapping data corresponding to each of the two preset cycles may represent the change amplitude (increase or decrease) between the index mapping data corresponding to the two preset cycles; optionally, the index mapping data corresponding to a certain preset cycle can be divided by the index mapping data corresponding to another preset cycle to obtain the above amplitude change mapping data. Optionally, combining the above embodiment of the object media resource push service, the amplitude change mapping data, that is, the index difference mapping data = A1 / B1 / A2 / B2; where " / " represents the division sign.

[0101] In the above embodiments, based on the target correlation index data of multiple correlation indexes combined with the target business index, an index mapping data representing the corresponding relationship between the target business index and multiple correlation indexes and the corresponding index data of the target business index in two preset periods is constructed; and the amplitude change mapping data between the index mapping data corresponding to the two preset periods is used as the index difference mapping data corresponding to the two preset periods, which can accurately reflect the index data difference of the target business index in the two preset periods. At the same time, it is also convenient to conduct attribution analysis on the target business index, improve the effectiveness of the analysis, and then update the business more pertinently, reduce the resource waste of the business system, and improve the system performance.

[0102] In an alternative embodiment, when the above target correlation index data includes the cumulative index data corresponding to each of multiple sub-dimensions of multiple correlation indexes in a preset analysis dimension within any preset period. Optionally, as Figure 3 shown, constructing the index difference mapping data corresponding to the target business index in two preset periods based on the target correlation index data may include the following steps:

[0103] In step S301, according to the cumulative index data corresponding to each of the multiple sub-dimensions, determine the target index data corresponding to the multiple correlation indexes in each of the two preset periods.

[0104] In step S303, according to the target index data, construct the index mapping data corresponding to the target business index in each of the two preset periods.

[0105] In step S305, use the difference mapping data between the index mapping data corresponding to the two preset periods as the index difference mapping data corresponding to the two preset periods.

[0106] In a specific embodiment, after adding up the cumulative index data corresponding to each associated index in multiple sub-dimensions within each preset period, the obtained data can be used as the target index data corresponding to the associated index within the preset period. Optionally, taking the associated account number dimension including 4 sub-dimensions as an example above, assume that within a certain preset period (the current period), the index data corresponding to multiple sub-dimensions of the cumulative operation resource consumption under the associated account number dimension (associated account number less than or equal to X1, associated account number greater than X1 and less than or equal to X2, associated account number greater than X2 and less than or equal to X3, associated account number greater than X3) can be: a1, a2, a3, a4 in sequence; within a certain preset period (the current period), the index data corresponding to multiple sub-dimensions of the cumulative associated operation data under the associated account number dimension (associated account number less than or equal to X1, associated account number greater than X1 and less than or equal to X2, associated account number greater than X2 and less than or equal to X3, associated account number greater than X3) can be: b1, b2, b3, b4 in sequence; within another preset period (the next period), the index data corresponding to multiple sub-dimensions of the cumulative operation resource consumption under the associated account number dimension (associated account number less than or equal to X1, associated account number greater than X1 and less than or equal to X2, associated account number greater than X2 and less than or equal to X3, associated account number greater than X3) can be: c1, c2, c3, c4 in sequence; within another preset period (the next period), the index data corresponding to multiple sub-dimensions of the cumulative associated operation data under the associated account number dimension (associated account number less than or equal to X1, associated account number greater than X1 and less than or equal to X2, associated account number greater than X2 and less than or equal to X3, associated account number greater than X3) can be: d1, d2, d3, d4 in sequence. Correspondingly, the target index data corresponding to the cumulative operation resource consumption in the current period can be a1 + a2 + a3 + a4; the target index data corresponding to the cumulative operation resource consumption in the next period can be c1 + c2 + c3 + c4; the target index data corresponding to the cumulative associated operation data in the current period can be b1 + b2 + b3 + b4; the target index data corresponding to the cumulative associated operation data in the next period can be d1 + d2 + d3 + d4.

[0107] In a specific embodiment, the index mapping data can be the index data corresponding to the target business index in two preset periods, represented by the corresponding relationship between the target business index and multiple associated indexes; optionally, in combination with the above embodiment of the associated account number dimension including 4 sub-dimensions in the object media resource push service, the index mapping data corresponding to the target business index in two preset periods can be: Y1 = (a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4), Y2 = (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4). Among them, Y1 represents the value (index data) of the unit operation resource consumption in the current period; Y2 represents the value (index data) of the unit operation resource consumption in the next period.

[0108] In a specific embodiment, the differential mapping data between the index mapping data corresponding to two preset cycles can represent the difference between the index mapping data corresponding to the two preset cycles; optionally, the differential mapping data can be obtained by subtracting the index mapping data corresponding to one preset cycle from the index mapping data corresponding to another preset cycle. Optionally, in combination with the above embodiment of the object media resource pushing service, the differential mapping data, that is, the index difference mapping data = (a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4).

[0109] In the above embodiment, in a scenario where it is necessary to perform attribution analysis of target service indicators in combination with multiple sub-dimensions under a preset analysis dimension, the cumulative indicator data corresponding to multiple associated indicators in multiple sub-dimensions can be combined to determine the target indicator data corresponding to multiple associated indicators in each of the two preset cycles; and based on the target indicator data, an index mapping data representing the corresponding relationship between the target service indicator and multiple associated indicators in each of the two preset cycles is constructed; then, using the differential mapping data between the index mapping data corresponding to the two preset cycles as the index difference mapping data corresponding to the two preset cycles can accurately reflect the difference in the index data corresponding to the target service indicator in the two preset cycles, and at the same time facilitate the attribution analysis of the target service indicator in multiple sub-dimensions, improve the effectiveness and flexibility of the attribution analysis, and thus perform service updates more targeted, reduce the waste of resources in the service system, and improve the system performance.

[0110] In step S105, based on the index difference mapping data, perform attribution analysis on the target service indicator to obtain attribution analysis data.

[0111] In a specific embodiment, the above attribution analysis data can represent the contributions of multiple preset influencing factors corresponding to the target service indicator to the target service indicator; optionally, the attribution analysis data can include values representing the contribution degrees of multiple preset influencing factors to the target service indicator, and optionally, the attribution analysis data can include the character-based representations of the contribution degrees of multiple preset influencing factors to the target service indicator, such as high, medium, low, etc.

[0112] In an alternative embodiment, when the above target associated indicator data includes the cumulative indicator data of multiple associated indicators within any preset cycle, the multiple preset influencing factors can be multiple associated indicators of the target service indicator. Correspondingly, the index difference mapping data can be the amplitude change mapping data. As Figure 4 shown, the above steps of performing attribution analysis on the target service indicator based on the index difference mapping data to obtain attribution analysis data can include the following steps:

[0113] In step S401, based on a preset splitting and conversion algorithm, the amplitude mapping data is converted into target mapping data;

[0114] In step S403, the target associated index data corresponding to multiple associated indexes in the target mapping data is split to obtain the first index data items corresponding to each associated index;

[0115] In step S405, the attribution analysis data is determined according to the first index data items and the target mapping data.

[0116] In a specific embodiment, the preset splitting and conversion algorithm can be an algorithm capable of converting the amplitude mapping data into splittable data. Optionally, taking the above amplitude mapping data = A1 / B1 / A2 / B2 as an example, the preset splitting and conversion algorithm can be a logarithmic function. Correspondingly, the logarithmic function can convert the amplitude mapping data into lg(A1 / B1 / A2 / B2). Further, lg(A1 / B1 / A2 / B2) can be split as follows:

[0117] lg(A1 / B1 / A2 / B2) = lg(A1) - lg(B1) - lg(A2) + lg(B2)

[0118] Among them, lg(A1) - lg(A2) is the first index data item corresponding to the cumulative operation resource consumption, and -lg(B1) + lg(B2) is the first index data item corresponding to the cumulative associated operation data. Further, in the process of determining the attribution analysis data according to the first index data items and the target mapping data, the first index data item corresponding to each associated index is divided by the target mapping data to obtain the above attribution analysis data. Optionally, in combination with the above example, when the attribution analysis data includes values representing the contribution degrees of multiple preset influencing factors (multiple associated indexes) to the target business index, (lg(A1) - lg(A2)) / lg(A1 / B1 / A2 / B2) can be used as the attribution analysis data corresponding to the cumulative operation resource consumption; (-lg(B1) + lg(B2)) / lg(A1 / B1 / A2 / B2) can be used as the attribution analysis data corresponding to the cumulative associated operation data. Optionally, when the attribution analysis data includes the characterizations of the contribution degrees of multiple preset influencing factors (multiple associated indexes) to the target business index, (lg(A1) - lg(A2)) / lg(A1 / B1 / A2 / B2) and (-lg(B1) + lg(B2)) / lg(A1 / B1 / A2 / B2) can be respectively converted into corresponding characterizations in combination with preset rules.

[0119] In the above embodiments, when the index difference mapping data is amplitude change mapping data, first, the amplitude change mapping data is converted into target mapping data that can be split by combining a preset splitting and conversion algorithm, and by splitting the target associated index data corresponding to multiple associated indexes in the target mapping data, the first index data items corresponding to each associated index can be obtained. Furthermore, the contribution degree of each associated index to the target business index can be accurately determined, the effectiveness of subsequent business updates and the business execution effect can be improved, the resource waste of the business system can be reduced, and the system performance can be improved.

[0120] In an alternative embodiment, when the above target associated index data includes the cumulative index data corresponding to each of multiple sub-dimensions of multiple associated indexes under a preset analysis dimension within any preset period, the multiple preset influencing factors can be the above multiple sub-dimensions. Correspondingly, the index difference mapping data can be difference mapping data. As Figure 5 shown, the above-mentioned attribution analysis of the target business index based on the index difference mapping data to obtain the attribution analysis data may include the following steps:

[0121] In step S501, the target associated index data corresponding to multiple sub-dimensions in the difference mapping data is split to obtain the second index data items corresponding to the multiple sub-dimensions;

[0122] In step S503, the attribution analysis data is determined according to the second index data items and the difference mapping data.

[0123] In a specific embodiment, the target associated index data corresponding to multiple associated indexes in the difference mapping data can be split to obtain the second index data items corresponding to each of the multiple associated indexes under multiple sub-dimensions. Specifically, taking the above difference mapping data = (a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4) as an example, (a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4) can be split as follows:

[0124] (a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4) = a1 / (b1 + b2 + b3 + b4) + a2 / (b1 + b2 + b3 + b4) + a3 / (b1 + b2 + b3 + b4) + a4 / (b1 + b2 + b3 + b4) - c1 / (d1 + d2 + d3 + d4) - c2 / (d1 + d2 + d3 + d4) - c3 / (d1 + d2 + d3 + d4) - c4 / (d1 + d2 + d3 + d4)

[0125] Specifically, the second index data item corresponding to the sub-dimension "the number of associated accounts is less than or equal to X1" can be a1 / (b1 + b2 + b3 + b4) - c1 / (d1 + d2 + d3 + d4), the second index data item corresponding to the sub-dimension "the number of associated accounts is greater than X1 and less than or equal to X2" can be a2 / (b1 + b2 + b3 + b4) - c2 / (d1 + d2 + d3 + d4), the second index data item corresponding to the sub-dimension "the number of associated accounts is greater than X2 and less than or equal to X3" can be a3 / (b1 + b2 + b3 + b4) - c3 / (d1 + d2 + d3 + d4), and the second index data item corresponding to the sub-dimension "the number of associated accounts is greater than X3" can be a4 / (b1 + b2 + b3 + b4) - c4 / (d1 + d2 + d3 + d4).

[0126] Further, in the process of determining the attribution analysis data based on the second indicator data item and the difference mapping data, divide the second indicator data item corresponding to each sub-dimension by the difference mapping data to obtain the above-mentioned attribution analysis data. Optionally, in combination with the above example, when the attribution analysis data includes values representing the contribution degrees of multiple preset influencing factors (multiple sub-dimensions) to the target business indicator, ((a1 / (b1 + b2 + b3 + b4) - c1 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) can be used as the attribution analysis data corresponding to the sub-dimension "the number of associated accounts is less than or equal to X1"; ((a2 / (b1 + b2 + b3 + b4) - c2 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) can be used as the attribution analysis data corresponding to the sub-dimension "the number of associated accounts is greater than X1 and less than or equal to X2"; ((a3 / (b1 + b2 + b3 + b4) - c3 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) can be used as the attribution analysis data corresponding to the sub-dimension "the number of associated accounts is greater than X2 and less than or equal to X3"; ((a4 / (b1 + b2 + b3 + b4) - c4 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) can be used as the attribution analysis data corresponding to the sub-dimension "the number of associated accounts is greater than X3". Optionally, when the attribution analysis data includes the character representation of the contribution degrees of multiple preset influencing factors (multiple sub-dimensions) to the target business indicator, taking the attribution analysis data corresponding to the sub-dimension "the number of associated accounts is less than or equal to X1": ((a1 / (b1 + b2 + b3 + b4) - c1 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) as an example, ((a1 / (b1 + b2 + b3 + b4) - c1 / (d1 + d2 + d3 + d4)) / ((a1 + a2 + a3 + a4) / (b1 + b2 + b3 + b4) - (c1 + c2 + c3 + c4) / (d1 + d2 + d3 + d4))) can be converted into the corresponding character representation in combination with the preset rules.

[0127] In the above embodiments, when the index difference mapping data is differential mapping data, by splitting the target associated index data corresponding to multiple sub-dimensions in the differential mapping data, multiple second index data items corresponding to the sub-dimensions can be obtained, and then the contribution degree of each sub-dimension to the target business index can be accurately determined, improving the effectiveness of subsequent business updates and business execution effects, reducing resource waste in the business system, and improving system performance.

[0128] In step S107, based on the attribution analysis data, perform business updates on the target business corresponding to the target business index.

[0129] In an optional embodiment, when multiple preset influencing factors are multiple associated indexes of the target business index, the above-mentioned performing business updates on the target business corresponding to the target business index based on the attribution analysis data may include:

[0130] According to the attribution analysis data, determine the target associated index from multiple associated indexes;

[0131] Based on the target associated index, perform business updates on the target business.

[0132] In an optional embodiment, the associated index with the highest contribution degree to the target business index can be used as the target associated index in combination with the attribution analysis data. Specifically, when determining the target associated index, during the process of performing business updates, update and adjustment can be performed in combination with the business operations that affect the target associated index.

[0133] In a specific embodiment, taking the scenario of object media resource push as an example, the associated indexes may include cumulative operation resource consumption and cumulative associated operation data. Assuming that the cumulative operation resource consumption is the target associated index, since adjusting the unit virtual resource required for the push platform to receive the multimedia resource push of a preset object will affect the cumulative operation resource consumption, business updates can be performed by operations such as adjusting the unit virtual resource required for the push platform to receive the multimedia resource push of a preset object. Assuming that the cumulative associated operation data is the target associated index, since adjusting the multimedia resource push volume of the push platform to receive a preset object will affect the cumulative associated operation data, business updates can be performed by adjusting the multimedia resource push volume of the push platform to receive a preset object.

[0134] In the above embodiments, when multiple preset influencing factors are multiple associated indexes of the target business index, through the attribution analysis results representing the contributions of multiple associated indexes to the target business index, the target associated index with a greater contribution to the target business index can be accurately located, and then targeted business updates can be performed, greatly improving the effectiveness of business updates and business execution effects, reducing resource waste in the business system, and improving system performance.

[0135] In an alternative embodiment, when the multiple preset influencing factors are multiple sub-dimensions, the above-mentioned business update of the target business corresponding to the target business indicator based on the attribution analysis data may include:

[0136] Determine a target analysis dimension from the multiple sub-dimensions according to the attribution analysis data;

[0137] Based on the target analysis dimension, perform a business update on the target business.

[0138] In an alternative embodiment, the sub-dimension with the highest contribution degree to the target business indicator can be used as the target analysis dimension in combination with the attribution analysis data. Specifically, when the target analysis dimension is determined, corresponding business adjustment and update can be performed on the target analysis dimension. Taking the associated account number dimension including 4 sub-dimensions as an example, assuming the target analysis dimension is "the associated account number is greater than X2 and less than or equal to X3", correspondingly, business update and adjustment can be performed on the push accounts with "the associated account number is greater than X2 and less than or equal to X3".

[0139] In the above embodiment, when the multiple preset influencing factors are multiple sub-dimensions under the preset analysis dimension, through the attribution analysis result representing the contributions of the multiple sub-dimensions to the target business indicator, the target analysis dimension with a greater contribution to the target business indicator can be accurately located, and then business update can be carried out targeted, greatly improving the effectiveness of business update and the business execution effect, reducing the resource waste of the business system, and improving the system performance.

[0140] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification combines the target association indicator data of multiple association indicators corresponding to the target business indicator within two preset periods to construct the target business indicator in the form of the corresponding relationship between the target business indicator and the multiple association indicators, representing the indicator difference mapping data corresponding to the target business indicator within two preset periods, which can greatly improve the convenience of performing attribution analysis on the target business indicator; then, combining the indicator difference mapping data, performing attribution analysis on the target business indicator can obtain the attribution analysis data representing the contributions of multiple preset influencing factors corresponding to the target business indicator to the target business indicator; and based on this attribution analysis data, accurately locate the factors causing the change of the target business indicator, and perform business update on the target business corresponding to the target business indicator targeted, greatly improving the effectiveness of business update and the business execution effect, and further reducing the situation of ineffective business update processing, reducing the resource waste of the business system, and improving the system performance.

[0141] Figure 6 is a block diagram of a business update device shown according to an exemplary embodiment. Refer to Figure 6 and the device includes:

[0142] A target associated metric data acquisition module 610, configured to acquire target associated metric data of a plurality of associated metrics corresponding to a target business metric within two preset periods;

[0143] An index difference mapping data construction module 620, configured to construct index difference mapping data corresponding to the target business metric in two preset periods based on the target associated metric data, where the index difference mapping data is the index data difference corresponding to the target business metric in two preset periods characterized by the corresponding relationship between the target business metric and a plurality of associated metrics;

[0144] An attribution analysis module 630, configured to perform attribution analysis on the target business metric based on the index difference mapping data to obtain attribution analysis data, where the attribution analysis data represents the contributions of a plurality of preset influencing factors corresponding to the target business metric to the target business metric;

[0145] A service update module 640, configured to perform service update on the target service corresponding to the target business metric based on the attribution analysis data.

[0146] In an optional embodiment, the target associated metric data includes the cumulative metric data of a plurality of associated metrics within any one preset period; the index difference mapping data construction module 620 includes:

[0147] A first index mapping data construction unit, configured to construct index mapping data corresponding to the target business metric in each of the two preset periods according to the target associated metric data, where the index mapping data is the index data corresponding to the target business metric in each of the two preset periods characterized by the corresponding relationship between the target business metric and a plurality of associated metrics;

[0148] A first index difference mapping data determination unit, configured to use the amplitude change mapping data between the index mapping data corresponding to each of the two preset periods as the index difference mapping data.

[0149] In an optional embodiment, the attribution analysis module 630 includes:

[0150] A splitting and conversion unit, configured to convert the amplitude change mapping data into target mapping data based on a preset splitting and conversion algorithm;

[0151] A first splitting processing unit, configured to perform splitting processing on the target associated metric data corresponding to a plurality of associated metrics in the target mapping data to obtain first metric data items corresponding to each of the plurality of associated metrics;

[0152] A first attribution analysis data determination unit, configured to determine the attribution analysis data according to the first metric data items and the target mapping data.

[0153] In an alternative embodiment, the multiple preset influencing factors are multiple associated metrics, and the service update module 640 includes:

[0154] A target associated metric determination unit, configured to determine a target associated metric from the multiple associated metrics according to the attribution analysis data;

[0155] A first service update unit, configured to perform a service update on the target service based on the target associated metric.

[0156] In an alternative embodiment, the target associated metric data includes the cumulative metric data corresponding to each of the multiple sub-dimensions of the multiple associated metrics in a preset analysis dimension within any preset period; the metric difference mapping data construction module 620 includes:

[0157] A target metric data determination unit, configured to determine the target metric data corresponding to the multiple associated metrics in two preset periods according to the cumulative metric data corresponding to each of the multiple sub-dimensions;

[0158] A first metric mapping data construction unit, configured to construct the metric mapping data corresponding to the target service metric in two preset periods according to the target metric data, where the metric mapping data is the metric data corresponding to the target service metric in two preset periods characterized by the corresponding relationship between the target service metric and the multiple associated metrics;

[0159] A first metric difference mapping data determination unit, configured to use the difference mapping data between the metric mapping data corresponding to the two preset periods as the metric difference mapping data.

[0160] In an alternative embodiment, the attribution analysis module 630 includes:

[0161] A second splitting processing unit, configured to perform a splitting process on the target associated metric data corresponding to the multiple associated metrics in the difference mapping data to obtain the second metric data items corresponding to the multiple sub-dimensions;

[0162] A second attribution analysis data determination unit, configured to determine the attribution analysis data according to the second metric data items and the difference mapping data.

[0163] In an alternative embodiment, the multiple preset influencing factors are multiple sub-dimensions, and performing a service update on the target service corresponding to the target service metric based on the attribution analysis data includes:

[0164] A target analysis dimension determination unit, configured to determine a target analysis dimension from the multiple sub-dimensions according to the attribution analysis data;

[0165] The second service update unit is configured to perform service update on the target service based on the target analysis dimension.

[0166] In an optional embodiment, the two preset periods include the current period and the previous period of the current period; the target associated index data acquisition module 610 is specifically configured to obtain the target associated index data of multiple associated indexes corresponding to the target service index in the current period and the previous period when the service index data of the target service index changes within the current period.

[0167] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be elaborated here.

[0168] Figure 7 is a block diagram of an electronic device for service update shown according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as Figure 7 shown. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a service update method. The display screen of the electronic device may be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0169] Figure 8 is a block diagram of an electronic device for service update shown according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as Figure 8 shown. The electronic device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a service update method.

[0170] Those skilled in the art can understand,Figure 7 or Figure 8 The structure shown is only a block diagram of some of the structures related to the present disclosure, and does not constitute a limitation on the electronic device to which the present disclosure is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0171] In an exemplary embodiment, an electronic device is further provided, including: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the service update method in the embodiments of the present disclosure.

[0172] In an exemplary embodiment, a computer-readable storage medium is further provided. When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute the service update method in the embodiments of the present disclosure.

[0173] In an exemplary embodiment, a computer program product containing instructions is further provided. When it runs on a computer, the computer is enabled to execute the service update method in the embodiments of the present disclosure.

[0174] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application may include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0175] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known or customary technical means in the technical field not disclosed herein. The specification and examples are only to be considered exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0176] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A business update method, characterized in that, comprising: Obtaining target associated index data of a plurality of associated indexes corresponding to a target business index within two preset periods, where the plurality of associated indexes are indexes having a corresponding relationship with the target business index, and the corresponding relationship is a relationship representing the target business index with the plurality of associated indexes; the target associated index data corresponding to any one of the preset periods includes the cumulative index values of the plurality of associated indexes within any one of the preset periods; Based on the target associated index data, constructing index difference mapping data of the target business index corresponding to the two preset periods, where the index difference mapping data is the index data difference of the target business index corresponding to the two preset periods characterized by the corresponding relationship between the target business index and the plurality of associated indexes; Based on the index difference mapping data, performing attribution analysis on the target business index to obtain attribution analysis data, where the attribution analysis data represents the contributions of a plurality of preset influencing factors corresponding to the target business index to the target business index, and the plurality of preset influencing factors include the plurality of associated indexes; Based on the attribution analysis data, performing business update on the target business corresponding to the target business index.

2. The business update method according to claim 1, characterized in that, the target associated index data includes the cumulative index data of the plurality of associated indexes within any one of the preset periods; the constructing the index difference mapping data of the target business index corresponding to the two preset periods based on the target associated index data includes: According to the target associated index data, constructing index mapping data of the target business index corresponding to each of the two preset periods, where the index mapping data is the index data of the target business index corresponding to each of the two preset periods characterized by the corresponding relationship between the target business index and the plurality of associated indexes; Taking the amplitude change mapping data between the index mapping data corresponding to each of the two preset periods as the index difference mapping data.

3. The business update method according to claim 2, characterized in that, the performing attribution analysis on the target business index based on the index difference mapping data to obtain attribution analysis data includes: Based on a preset splitting and conversion algorithm, converting the amplitude change mapping data into target mapping data; Performing a splitting process on the target associated index data corresponding to the plurality of associated indexes in the target mapping data to obtain first index data items corresponding to the plurality of associated indexes respectively; Determining the attribution analysis data according to the first index data items and the target mapping data.

4. The business update method according to claim 2, characterized in that, the plurality of preset influencing factors are the plurality of associated indexes, and the performing business update on the target business corresponding to the target business index based on the attribution analysis data includes: According to the attribution analysis data, determining a target associated index from the plurality of associated indexes; Based on the target associated index, performing business update on the target business.

5. The service update method according to claim 1, wherein, the target associated metric data includes the cumulative metric data corresponding to each of the multiple sub-dimensions of the multiple associated metrics under a preset analysis dimension within any preset period; the constructing of the metric difference mapping data of the target service metric corresponding to the two preset periods based on the target associated metric data includes: determining the target metric data corresponding to the multiple associated metrics in the two preset periods respectively according to the cumulative metric data corresponding to each of the multiple sub-dimensions; constructing the metric mapping data of the target service metric corresponding to the two preset periods respectively according to the target metric data, where the metric mapping data is the metric data of the target service metric corresponding to the two preset periods respectively characterized by the corresponding relationship between the target service metric and the multiple associated metrics; taking the difference mapping data between the metric mapping data corresponding to the two preset periods respectively as the metric difference mapping data.

6. The service update method according to claim 5, wherein, the performing of the attribution analysis on the target service metric based on the metric difference mapping data to obtain the attribution analysis data includes: performing a splitting process on the target associated metric data corresponding to the multiple sub-dimensions in the difference mapping data to obtain the second metric data items corresponding to the multiple sub-dimensions; determining the attribution analysis data according to the second metric data items and the difference mapping data.

7. The service update method according to claim 6, wherein, the multiple preset influencing factors are the multiple sub-dimensions, and the performing of the service update on the target service corresponding to the target service metric based on the attribution analysis data includes: determining a target analysis dimension from the multiple sub-dimensions according to the attribution analysis data; performing a service update on the target service based on the target analysis dimension.

8. The service update method according to any one of claims 1 to 7, wherein, the two preset periods include the current period and the previous period of the current period; the obtaining of the target associated metric data of the multiple associated metrics corresponding to the target service metric within the two preset periods includes: when the service metric data of the target service metric changes within the current period, obtaining the target associated metric data of the multiple associated metrics corresponding to the target service metric within the current period and the previous period.

9. A service update device, wherein, it includes: a target associated metric data acquisition module configured to perform the acquisition of the target associated metric data of the multiple associated metrics corresponding to a target service metric within two preset periods, where the multiple associated metrics are metrics having a corresponding relationship with the target service metric, and the corresponding relationship is a relationship representing the target service metric by the multiple associated metrics; the target associated metric data corresponding to any preset period includes the cumulative metric value of the multiple associated metrics within the any preset period. An index difference mapping data construction module, configured to construct index difference mapping data corresponding to the target business index in the two preset periods based on the target associated index data, where the index difference mapping data is the index data difference corresponding to the target business index in the two preset periods characterized by the corresponding relationship between the target business index and the multiple associated indexes; An attribution analysis module, configured to perform attribution analysis on the target business index based on the index difference mapping data to obtain attribution analysis data, where the attribution analysis data represents the contribution of multiple preset influencing factors corresponding to the target business index to the target business index, and the multiple preset influencing factors include the multiple associated indexes; A service update module, configured to perform service update on the target service corresponding to the target business index based on the attribution analysis data.

10. The service update device according to claim 9, wherein, the target associated index data includes the cumulative index data of the multiple associated indexes in any preset period; the index difference mapping data construction module includes: A first index mapping data construction unit, configured to construct index mapping data corresponding to the target business index in each of the two preset periods according to the target associated index data, where the index mapping data is the index data corresponding to the target business index in each of the two preset periods characterized by the corresponding relationship between the target business index and the multiple associated indexes; A first index difference mapping data determination unit, configured to use the amplitude change mapping data between the index mapping data corresponding to each of the two preset periods as the index difference mapping data.

11. The service update device according to claim 10, wherein, the attribution analysis module includes: A splitting and conversion unit, configured to convert the amplitude change mapping data into target mapping data based on a preset splitting and conversion algorithm; A first splitting processing unit, configured to perform splitting processing on the target associated index data corresponding to the multiple associated indexes in the target mapping data to obtain first index data items corresponding to each of the multiple associated indexes; A first attribution analysis data determination unit, configured to determine the attribution analysis data according to the first index data items and the target mapping data.

12. The service update device according to claim 10, wherein, the multiple preset influencing factors are the multiple associated indexes, and the service update module includes: A target associated index determination unit, configured to determine a target associated index from the multiple associated indexes according to the attribution analysis data; A first service update unit, configured to perform service update on the target service based on the target associated index.

13. The service update device according to claim 9, wherein, the target associated index data includes the cumulative index data corresponding to each of the multiple sub-dimensions of the multiple associated indexes in a preset analysis dimension in any preset period; The above-mentioned index difference mapping data construction module includes: A target index data determination unit configured to determine the target index data corresponding to the multiple associated indexes in the two preset periods according to the cumulative index data corresponding to the multiple sub-dimensions respectively; A first index mapping data construction unit configured to construct the index mapping data corresponding to the target business index in the two preset periods according to the target index data, where the index mapping data is the index data corresponding to the target business index in the two preset periods characterized by the corresponding relationship between the target business index and the multiple associated indexes; A first index difference mapping data determination unit configured to use the difference mapping data between the index mapping data corresponding to the two preset periods respectively as the index difference mapping data.

14. The service update device according to claim 13, wherein, the attribution analysis module includes: A second splitting processing unit configured to perform splitting processing on the target associated index data corresponding to the multiple associated indexes in the difference mapping data to obtain the second index data items corresponding to the multiple sub-dimensions; A second attribution analysis data determination unit configured to determine the attribution analysis data according to the second index data items and the difference mapping data.

15. The service update device according to claim 13, wherein, the multiple preset influencing factors are the multiple sub-dimensions, and the service update of the target service corresponding to the target business index based on the attribution analysis data includes: A target analysis dimension determination unit configured to determine a target analysis dimension from the multiple sub-dimensions according to the attribution analysis data; A second service update unit configured to perform service update on the target service based on the target analysis dimension.

16. The service update device according to any one of claims 9 to 15, wherein, the two preset periods include the current period and the previous period of the current period; the target associated index data acquisition module is specifically configured to acquire the target associated index data of the multiple associated indexes corresponding to the target business index in the current period and the previous period when the service index data of the target business index changes in the current period.

17. An electronic device, wherein, it includes: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to execute the instructions to implement the service update method according to any one of claims 1 to 8.

18. A computer-readable storage medium, wherein, when the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute the service update method according to any one of claims 1 to 8.

19. A computer program product, wherein, when the computer program product runs on a computer, the computer executes the service update method according to any one of claims 1 to 8.

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