Workload assessment method, apparatus, device, and storage medium

Abstract

The application discloses a workload evaluation method and device, equipment and a storage medium, relates to the technical field of data processing, and is used for improving the efficiency of network parameter management and control evaluation, and comprises the following steps: after a target service is published, the number of times of calling and callers of a target data asset corresponding to the target service are counted, the callers including internal systems and external systems; based on the number of times of calling of the target data asset, the importance level of the target data asset is determined, and based on the importance level of the target data asset, whether the target service is a core service is determined; and based on the usage amount of components of each process in at least one process included in the target service, the number of asset categories included in the target data asset, the number of times of calling of the target data asset, the importance level of the target data asset, whether the target service is a core service, the workload corresponding to the target service is determined. The application is applied to the scene of network parameter management and control evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a workload evaluation method and device, equipment and a storage medium. BACKGROUND

[0002] Currently, with the continuous development of science and technology, especially the emergence of digital technologies such as big data, artificial intelligence, cloud computing, blockchain and the Internet of Things, data has become a new production factor, and communication operators with massive information resources are also evolving towards the form of production factors. However, the evaluation of data R&D workload within the communication operator is still mainly based on system function implementation, and there is a lack of a system for distributing the data asset income generated by the R&D process. For example, a project team of a certain company had 260 R&D tasks in 2021, generated 18 effective data assets, and the R&D task asset conversion rate was 6.9%, which was much lower than the industry standard level of 10%.

[0003] Therefore, R&D personnel cannot generate data assets from intermediate results in the R&D process, which indirectly reduces the company's ability to monetize data assets. Therefore, the current evaluation of R&D workload is inefficient, and the evaluation of data asset value is poor. SUMMARY

[0004] The present application provides a workload evaluation method, device, equipment and storage medium for improving the efficiency of network parameter management and control evaluation.

[0005] To achieve the above purpose, the present application adopts the following technical solution:

[0006] In a first aspect, a workload evaluation method is provided, which includes: after a target service is published, counting the number of calls and callers of a target data asset corresponding to the target service, the callers including internal systems and external systems, the number of calls of the target data asset including: the number of calls of the target data asset corresponding to each day, the total number of calls of the target data asset by internal systems in a single month, and the total number of calls of the target data asset by external systems in a single month, the target service including at least one process, each process being implemented through a plurality of components; determining the importance level of the target data asset based on the number of calls of the target data asset, and determining whether the target service is a core service based on the importance level of the target data asset; determining the workload of the target service based on the usage of the components in each process of the target service, the number of asset categories included in the target data asset, the number of calls of the target data asset, the importance level of the target data asset, and whether the target service is a core service.

[0007] In a possible implementation, the method further includes: generating a target data asset corresponding to the target service based on the target business requirement information and the plurality of preset components, and determining a usage amount of each of at least one process included in the target service to the component; publishing the target data asset corresponding to the target service to an internal system and publishing the target service to an external system, and determining an asset category included in the target data asset, the asset category including: a label, a data application layer ADS, a data service layer DWS, a data intermediate layer DWM, and a data detail layer DWD.

[0008] In a possible implementation, the method further includes: determining a calling parameter corresponding to the target data asset based on the number of times of calling the target data asset; and determining the importance level of the target data asset based on the calling parameter corresponding to the target data asset and a preset data asset importance level table.

[0009] In a possible implementation, the method further includes: determining a calling parameter corresponding to the target data asset based on the number of times of calling the target data asset; and determining the importance level of the target data asset based on the calling parameter corresponding to the target data asset and a preset data asset importance level table.

[0010] In a possible implementation, the method further includes: determining a data asset conversion rate based on an asset category included in a data asset corresponding to each of a plurality of services in a target time period; and determining a core service proportion in the plurality of services based on a number of core services included in the plurality of services.

[0011] In a second aspect, a workload evaluation apparatus is provided, which comprises: a processing unit; the processing unit is configured to, after a target service is published, count a calling frequency of a target data asset corresponding to the target service and a caller, the caller comprising an internal system and an external system, the calling frequency of the target data asset comprising: a corresponding calling frequency of the target data asset per day, a total number of times that the target data asset is called by the internal system in a single month, and a total number of times that the target data asset is called by the external system in a single month, the target service comprising at least one process, and each process being implemented by a plurality of components; the processing unit is configured to determine an importance level of the target data asset based on the calling frequency of the target data asset, and determine whether the target service is a core service based on the importance level of the target data asset; and the processing unit is configured to determine a workload corresponding to the target service based on the usage amount of the components by each process in the at least one process of the target service, a number of asset categories included in the target data asset, the calling frequency of the target data asset, the importance level of the target data asset, and whether the target service is the core service.

[0012] In a possible implementation, the processing unit is configured to generate the target data asset corresponding to the target service based on the target business requirement information and the plurality of components, and determine the usage amount of the components by each process in the at least one process of the target service; the processing unit is configured to publish the target data asset corresponding to the target service to the internal system, publish the target service to the external system, and determine an asset category included in the target data asset, the asset category comprising: a tag, an application data layer (ADS), a data service layer (DWS), a data middleware layer (DWM), and a data detail layer (DWD).

[0013] In a possible implementation, the processing unit is configured to determine a calling parameter corresponding to the target data asset based on the calling frequency of the target data asset; and the processing unit is configured to determine the importance level of the target data asset based on the calling parameter corresponding to the target data asset and a preset data asset importance level table.

[0014] In a possible implementation, the processing unit is configured to determine the basic workload corresponding to the target service based on the usage amount of the component in each of the at least one process included in the target service; determine the asset category income corresponding to the target service based on the number of asset categories included in the target data asset and corresponding coefficients; determine the asset invocation income corresponding to the target service based on the number of invocations of the target data asset and corresponding coefficients; determine the important asset increment income corresponding to the target service based on the important level of the target data asset and the basic workload; when the target service is a core service, determine the core service increment income corresponding to the target service based on the basic workload and a coefficient corresponding to the core service; and determine the workload corresponding to the target service based on the basic workload, the asset category income, the asset invocation income, the important asset increment income, the core service increment income, and a highest income coefficient.

[0015] In a possible implementation, the processing unit is configured to determine the data asset conversion rate based on the asset categories included in the data asset corresponding to each of the plurality of services in the target time period; and determine the proportion of core services in the plurality of services based on the number of core services included in the plurality of services.

[0016] In a third aspect, an electronic device is provided, including a processor and a memory; the memory is configured to store one or more programs including computer execution instructions; when the electronic device is running, the processor executes the computer execution instructions stored in the memory, so that the electronic device executes the workload evaluation method in the first aspect.

[0017] In a fourth aspect, a computer readable storage medium storing one or more programs is provided, the one or more programs including instructions that, when executed by a computer, cause the computer to perform the workload evaluation method in the first aspect.

[0018] The application provides a workload evaluation method and device, equipment and a storage medium, and is applied to a scenario of network parameter management and control evaluation. After a target service is published, the number of invocations and invokers of a target data asset corresponding to the target service including at least one process can be counted, then the importance level of the target data asset is determined based on the number of invocations of the target data asset, and whether the target service is a core service is determined based on the importance level of the target data asset; thus, the workload corresponding to the target service is determined based on the usage amount of components of each process in the target service including at least one process, the number of asset categories included in the target data asset, the number of invocations of the target data asset, the importance level of the target data asset, whether the target service is a core service, and the like. The application determines the importance level of the target data asset and whether the target service is a core service by counting the number of invocations and invokers of the target data asset corresponding to the target service, and thus the workload corresponding to the target service can be determined. Therefore, the efficiency of network parameter management and control evaluation can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A workload evaluation system structure schematic diagram provided for an embodiment of the application;

[0020] Figure 2 A workload evaluation method flowchart provided for an embodiment of the application Figure One ;

[0021] Figure 3 A workload evaluation method flowchart provided for an embodiment of the application Figure Two ;

[0022] Figure 4 A workload evaluation method flowchart provided for an embodiment of the application Figure Three ;

[0023] Figure 5 A workload evaluation method flowchart provided for an embodiment of the application Figure Four ;

[0024] Figure 6 A workload evaluation method flowchart provided for an embodiment of the application Figure Five ;

[0025] Figure 7 A workload evaluation method flowchart provided for an embodiment of the application Figure Six

[0026] Figure 8 A workload evaluation device structure schematic diagram provided for an embodiment of the application;

[0027] Figure 9An electronic device structure schematic diagram is provided by the embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0029] In the description of the present application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this document only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can mean that A exists alone, A and B exist together, and B exists alone. In addition, "at least one" and "multiple" mean two or more. "First", "second", and the like do not limit the quantity and execution order, and "first", "second", and the like do not necessarily mean different.

[0030] The work load evaluation method provided by the embodiment of the present application can be applied to a work load evaluation system. Figure 1 A structure schematic diagram of the work load evaluation system is shown. As shown in Figure 1 The work load evaluation system 20 includes a demand management module 21, a data research and development control module 22, and a work load evaluation module 23.

[0031] The demand management module 21 is used to store detailed business demand information proposed by a demand department. The data research and development control module 22 includes six modules: demand distribution, task research and development, process execution, data registration (asset release), service release, asset use measurement, and six record tables: process single component use amount table, task single component use amount table, data asset information registration table, data asset service packaging table, demand single data table, and demand completion record table.

[0032] The work load evaluation module 23 calculates the work load of a research and development task according to information such as the process single component use amount table, the task single component use amount table, and the data asset information registration table formed by the asset release, the service release, and the asset use measurement of the data research and development control module 22, and information such as the data asset use detail table. The work load evaluation module 23 includes six modules: permission setting, key process identification, key task identification, asset importance calculation, work load calculation, and report generation, and eight record tables: data asset use detail table, data asset use summary table, data asset importance summary table, core process single record table, core task single record table, task single data asset conversion rate, core task single proportion statistical table, and account record table.

[0033] Specifically, as shown in Figure 2As shown, the workload evaluation method involves 11 modules, including demand assignment, task research and development, process execution, asset release, service release, asset usage measurement, asset importance calculation, key process identification, key task identification, workload calculation, and report generation.

[0034] The workload evaluation method provided by the embodiment of the present application is described below with reference to the accompanying drawings. As shown in Figure 3 The workload evaluation method provided by the embodiment of the present application includes S201-S203:

[0035] S201, after the target service is released, the number of times of calling the target data asset corresponding to the target service and the caller are counted.

[0036] The caller includes an internal system and an external system, and the number of times of calling the target data asset includes the number of times of calling the target data asset per day, the total number of times of calling the target data asset by the internal system in a single month, and the total number of times of calling the target data asset by the external system in a single month. The target service includes at least one process, and each process is implemented through a plurality of components.

[0037] Optionally, the target data asset corresponding to the target service after being released can be called by the internal system or the external application system. Through the asset usage measurement module in the data research and control module, the number of times of calling the target data asset per day can be collected. The internal calling record process number and the external system calling record subscriber number are formed into a data asset usage detail table, and the data asset usage detail table is sent to the workload evaluation module.

[0038] Optionally, the data asset usage detail table includes date number, data asset number, asset name, service type, number of times of calling, user, and other key fields.

[0039] S202, based on the number of times of calling the target data asset, the importance level of the target data asset is determined, and based on the importance level of the target data asset, it is determined whether the target service is a core service.

[0040] In one design, as shown in Figure 4 The workload evaluation method provided by the embodiment of the present application includes steps S301-S302 for determining the importance level of the target data asset based on the number of times of calling the target data asset in the step S202.

[0041] S301, based on the number of times of calling the target data asset, the calling parameters corresponding to the target data asset are determined.

[0042] S302, based on the calling parameters corresponding to the target data asset and the preset data asset importance level table, determine the importance level of the target data asset.

[0043] Optionally, the data asset usage detail table needs to be combined for calculation to summarize and count the number of processes corresponding to each data asset called internally each month, referred to as task association count, denoted as Uai, wherein i is the number of data assets.

[0044] Specifically, the calculation method is as follows:

[0045] Select data asset number, count(distinct process number) Ua

[0046] from data asset usage detail table group by data asset number;

[0047] And, combined with the data asset usage detail table, the number of calls corresponding to each of the four service modes of each data asset called by the external application system each month is calculated and summarized, and the four service modes include: database mode, file mode, Restful API and WebService API, and the corresponding number of calls is counted as Ub1i, Ub2i, Ub3i, Ub4i, wherein i is the data asset number. Thus, the data asset usage summary table is generated monthly.

[0048] Specifically, the calculation method is as follows:

[0049] Select data asset number, service type, sum(call number)

[0050] From data asset usage detail table Group by data asset number;

[0051] Further, based on the following formula, Ua, Ub1, Ub2, Ub3, Ub4 are normalized to generate Va, Vb1, Vb2, Vb3, Vb4 respectively.

[0052]

[0053] In addition, based on formula two, the call score Zi corresponding to each data asset is calculated, i is the data asset number.

[0054]

[0055] Wherein, The value range of Zi is 0-1.

[0056] Further, according to the calling score corresponding to the data asset, referring to the data asset importance score table (i.e., the data asset importance level table) in Table 1, the importance level corresponding to the data asset is calculated.

[0057] Table 1

[0058] Score Interval Data Asset Importance 0.5-1 A 0.3-0.5 B 0.1-0.3 C 0-0.1 D

[0059] Optionally, in combination with the data asset information registration table, the data asset use summary table, and the importance level corresponding to the data asset, a data asset importance summary table is generated, and the data asset importance summary table includes: month, data asset number, asset name, process number, task association count, task association normalization, calling score, asset importance score, and R&D personnel.

[0060] Further, based on the data in the data asset importance summary table, if the calling score corresponding to the process number is greater than 0.3, it is directly determined that the process is a key process; if the task association count Ua corresponding to the process is greater than 10 and the task association normalization is greater than 0.5, it is also determined to be a key process.

[0061] Optionally, based on the task single component use amount table and the data asset importance summary table, a core process single record table can also be generated, and the core process single record table includes: month, process number, task association count, key process flag, task number, and R&D personnel key field.

[0062] Further, based on the key process flag recorded in the core process single record table, the task number with the flag being "yes" is directly determined to be a key task; or in combination with the core process single record table, the task number is classified and calculated based on Formula 1 and Formula 2 to obtain a Ua summary value, and normalization is performed to obtain Vt. The requirement task with Ua>10 and task association normalization Vt>0.5 is identified as a core data requirement task, and a core task single record table is generated, and the core task single record table includes: month, task number, core requirement task flag, requirement number, and R&D personnel.

[0063] S203, based on the use amount of the component in each process in the target service, the number of asset categories included in the target data asset, the number of times the target data asset is called, the importance level of the target data asset, and whether the target service is a core service, determining the workload corresponding to the target service.

[0064] In one design, as shown in FIG. Figure 5 The method for workload evaluation provided by the embodiments of the present application can specifically include steps S401-S406 in the method in step S203.

[0065] S401. Based on the usage of components by each process in at least one process of the target service, determine the basic workload corresponding to the target service.

[0066] Optionally, first calculate the basic workload of the R&D data task. Based on the data in the process sheet component usage table, calculate the basic workload of each task flowchart using Formula 3, denoted as Task Flowchart Workload F1, where Hi is the standard component workload coefficient. Furthermore, based on the task sheet component usage table, if a data R&D task is completed by one or more task flowcharts, calculate the total basic workload of the data R&D task, denoted as T1.

[0067]

[0068] S402. Based on the number of asset categories and corresponding coefficients included in the target data assets, determine the asset category revenue corresponding to the target service.

[0069] Optionally, it is also necessary to calculate the data asset registration revenue in the R&D data task, based on the data asset information registration form and formula four, to calculate the process for each task. Figure Five The revenue from registering data assets is denoted as F2, where ki is the revenue coefficient for each data asset category. Furthermore, based on the task sheet component usage table, a data development task is completed by one or more task flowcharts, and the revenue from registering the five types of data assets for each data development task is calculated as T2.

[0070]

[0071] S403. Based on the number of times the target data asset is invoked and the corresponding coefficient, determine the asset invocation revenue corresponding to the target service.

[0072] Optionally, it is also necessary to calculate the revenue from the use of four types of data assets in the R&D data task. Based on the data asset usage details table and combined with Formula 5, the revenue from the use of data assets is calculated and denoted as F3, where li is the service type revenue coefficient. Furthermore, based on the task sheet component usage table, a data R&D task is completed by one or more task flowcharts, and the revenue from the use of the four types of data assets in the data R&D task is calculated in summary as T3.

[0073]

[0074] S404. Based on the importance level and basic workload of the target data assets, determine the incremental revenue of the important assets corresponding to the target service.

[0075] Optionally, the incremental revenue of the important asset in the R&D data task also needs to be calculated, which is calculated based on the task single component usage table and the data asset importance summary table, taking the data asset calling score max(Z) of the task flow chart associated with the R&D data task as the incremental revenue coefficient of the important asset of the R&D data task, and combining formula six to calculate the incremental revenue of the important asset in the R&D data task as T4.

[0076] T4=T1*Max(Z) Formula six

[0077] S405, when the target service is a core service, based on the basic workload and the coefficient corresponding to the core service, the incremental revenue of the core service corresponding to the target service is determined.

[0078] Optionally, the incremental revenue of the core task in the R&D data task also needs to be calculated, according to the core R&D task table record information, a R&D data task is identified as a core task, combining formula seven to calculate the incremental revenue of the core task, which is T5. And also need to set the core processing task revenue coefficient ma, the range of ma is between 0 and 1.

[0079] T5=T1*ma Formula seven

[0080] S406, based on the basic workload, asset category revenue, asset calling revenue, important asset incremental revenue, core service incremental revenue and maximum revenue coefficient, the workload corresponding to the target service is determined.

[0081] Optionally, based on the basic workload, asset category revenue, asset calling revenue, important asset incremental revenue, core service incremental revenue and maximum revenue coefficient, combining formula eight, the final workload of a R&D task is calculated, which is Tt. And also need to set the data processing task maximum revenue sharing coefficient mb.

[0082]

[0083] In one design, as shown in Figure 6 , the work load evaluation method provided by the embodiment of the application can further include S501-S502.

[0084] S501, based on the target business requirement information and the preset plurality of components, the target data asset corresponding to the target service is generated, and the usage amount of each process in at least one process included in the target service to the component is determined.

[0085] Optionally, the data research and development control module extracts the business requirement information of the requirement management module, captures the key fields: requirement number, requirement title, requirement description, business domain, requirement proposer, expected online time, etc., and distributes the business requirement to specific developers for software development through requirement distribution, and forms a requirement data table.

[0086] Optionally, the main information of the requirement data table includes: task number, requirement number, requirement title, requirement description, developer, business domain, requirement proposer, and expected online time.

[0087] Further, the developers write processing flows based on the 21 types of general standard components provided by the data research and development control module, such as processing mapping, conversion mapping, asset registration, etc., and the subsequent different standard components are represented by J1, J2,..., J21, and the processing flow is published and verified through the data research and development control module, and a flow component usage table is generated.

[0088] Specifically, the flow component usage table includes: flow number, data set access, processing mapping, conversion mapping, etc. The number of general standard components called by the flow is represented by Ji1, Ji2,..., Ji21.

[0089] It should be noted that one development task (e.g. target service) can be performed by one flow alone or multiple flows in combination. Based on the flow component usage table, a task number column can be further added to generate a task component usage table. The task component usage table includes: task number, flow number, data set access, processing mapping, conversion mapping, etc.

[0090] Optionally, the work load coefficients of the 21 types of general standard components also need to be set. The work load coefficients of the 21 types of general standard components J1, J2, J3,..., J21 in the data research and development control module are set as H1, H2, H3,..., H21, respectively. The coefficient range is between 0 and 1.

[0091] Optionally, through the flow scheduling engine of the data research and development control module, the specific processing flow is executed according to the day, month, etc. period, and the requirement completion time is extended based on the requirement data table. The last successful execution time of all processing flows corresponding to a certain requirement number is taken as the completion time of the requirement, and a requirement completion record table is generated.

[0092] Specifically, the requirement completion record table includes 8 key fields: requirement number, requirement title, requirement description, developer, business domain, requirement proposer, expected online time, and requirement completion time.

[0093] S502, publish the target data asset corresponding to the target service to the internal system, publish the target service to the external system, and determine an asset category included in the target data asset.

[0094] The asset category includes a label, an application layer of data (ADS), a service layer of data (DWS), a middle layer of data (DWM), and a detail layer of data (DWD).

[0095] Optionally, through an asset publishing function of the data development and control module, a temporary data model generated in a specific process execution process is officially published to the data development and control module, a belonging category of the data asset is determined according to a layered attribute of the data asset, and a data asset information login table is generated.

[0096] Specifically, the data asset information login table includes a data asset number, an asset name, an asset category, an asset update cycle, an asset state, a process number, a task order number, a development personnel, and a registration time, and the data asset information login table is sent to the workload assessment module.

[0097] It should be noted that the asset category includes five categories: a label, ADS, DWS, DWM, and DWD, and five data asset category corresponding benefit coefficients need to be set, and the benefit coefficients corresponding to the label, ADS, DWS, DWM, and DWD are k1, k2, k3, k4, and k5 respectively, where k1 < k2 < k3 < k4 < k5.

[0098] Optionally, through a service publishing function of the data development and control module, the data asset is encapsulated as a service, the data development and control module can provide four encapsulation modes: a database mode, a file mode, a Restful API, and a WebService API. One data asset can be encapsulated as one or more, forming a data asset service encapsulation table.

[0099] Specifically, the data asset service encapsulation table includes a data asset number, an asset name, a service type, validity, a development personnel, and an encapsulation date field.

[0100] Optionally, four data asset service type corresponding benefit coefficients need to be set, and the four service types: the database mode, the file mode, the Restful API, and the WebService API corresponding benefit coefficients are l1, l2, l3, and l4 respectively, where l1 > l2 > l3 > l4.

[0101] In one design, as shown in FIG. 6, Figure 7 The workload assessment method provided by the embodiment of the application can further include S601-S602.

[0102] S601, determining a data asset conversion rate based on an asset category included in each of a plurality of services in a target time period.

[0103] Optionally, a research and development task data asset conversion rate can also be generated, by extracting a plurality of month data asset information registration table information and calculating and summarizing to obtain a task sheet data asset conversion rate report. The task sheet data asset conversion rate report includes: month, research and development personnel, cumulative research and development demand quantity, cumulative data asset quantity, data asset conversion rate key field.

[0104] Further, a core research and development task proportion statistical table is generated, by extracting a plurality of month core task sheet record table, a core task proportion statistical table can be summarized, the core task proportion statistical table includes: research and development team, research and development personnel, cumulative research and development demand quantity, cumulative core task quantity, core task proportion, etc.

[0105] S602, determining a core service proportion in the plurality of services based on a core service quantity included in the plurality of services.

[0106] Optionally, the permissions can also be divided and managed into three levels from low to high: research and development personnel, research and development manager, and manager. The research and development personnel can only view their own research and development task records, the research and development manager can view the research and development task records of all research and development personnel in the research and development team, and the manager can view the research and development task records of all research and development personnel in the department.

[0107] Optionally, the research and development personnel can export their own report, the research and development manager can export the report of all research and development personnel in the research and development team, and the manager can export the report of all research and development personnel in the department.

[0108] Optionally, the manager can set an account record table, the account record table includes: department name, account, password, name, identity (including research and development personnel, research and development manager, and manager), and belonging research and development team, etc. Information. And the manager can modify H1, H2, H3,..., H21, k1, k2, k3, k4, k5, l1, l2, l3, l4, ma, mb, etc. Parameters, the research and development manager can modify H1, H2, H3,..., H21, ma, etc. Parameters.

[0109] Optionally, the research and development manager can report the task sheet data asset conversion rate as a monthly KPI evaluation index of the research and development personnel.

[0110] The work load evaluation method provided by the embodiment of the application determines the rules for identifying core data assets and core processing tasks, introduces a data element income distribution system, sets five types of data asset registration income, four types of data asset calling incremental income, core data asset additional income and core data task additional income, and defines the core processing task R&D proportion, important data asset R&D proportion and R&D task data asset conversion rate as the evaluation indexes of R&D personnel, thereby providing evaluation basis for the management department. The R&D personnel can be motivated to identify potential data assets, improve the R&D task asset conversion rate, and increase the external supply and realization capacity of data assets.

[0111] The application provides a work load evaluation method. After a target service is published, the called times and callers of a target data asset corresponding to the target service including at least one flow can be counted. Then, the important level of the target data asset is determined based on the called times of the target data asset, and whether the target service is a core service is determined based on the important level of the target data asset. Thus, the work load corresponding to the target service is determined based on the usage amount of components in each flow in the target service including at least one flow, the number of asset categories included in the target data asset, the called times of the target data asset, the important level of the target data asset, whether the target service is a core service. The called times and callers of the target data asset corresponding to the target service are counted, the important level of the target data asset and whether the target service is a core service are determined, and thus the work load corresponding to the target service can be determined. Therefore, the efficiency of network parameter management and control evaluation can be improved.

[0112] The above mainly describes the scheme provided by the embodiment of the application from the method aspect. To implement the above functions, the hardware structure and / or software module corresponding to each function are included. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the examples described in the embodiments disclosed in the present text, the embodiments of the application can be implemented in the form of hardware or the combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical scheme. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0113] The embodiments of the present application can divide the functional modules of the workload evaluation device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated in one processing module. The above integrated module can be realized in the form of hardware or in the form of a software functional module. Optionally, the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, another division manner can be used.

[0114] Figure 8 A structural schematic diagram of a workload evaluation device provided by the embodiments of the present application is shown in FIG. 1. As shown in FIG. 1, a workload evaluation device 40 is used to improve the efficiency of network parameter management and control evaluation, for example, used to execute the workload evaluation method shown in FIG. 2. Figure 8 Figure 3 The workload evaluation device 40 includes a processing unit 401.

[0115] The processing unit 401 is configured to, after a target service is published, count a calling frequency of a target data asset corresponding to the target service and a caller of the target data asset, the caller including an internal system and an external system, the calling frequency of the target data asset including a calling frequency of the target data asset corresponding to each day, a total calling frequency of the target data asset by the internal system in a single month, and a total calling frequency of the target data asset by the external system in the single month, the target service including at least one process, and each process being implemented by a plurality of components.

[0116] The processing unit 401 is configured to determine an importance level of the target data asset based on the calling frequency of the target data asset, and determine whether the target service is a core service based on the importance level of the target data asset.

[0117] The processing unit 401 is configured to determine a workload of the target service based on the usage amount of the components in each process of the at least one process included in the target service, a number of asset categories included in the target data asset, the calling frequency of the target data asset, the importance level of the target data asset, and whether the target service is the core service.

[0118] In a possible implementation, in the workload evaluation device 40 provided by the embodiments of the present application, the processing unit 401 is configured to generate the target data asset corresponding to the target service based on target business requirement information and the plurality of components, and determine the usage amount of the components in each process of the at least one process included in the target service.

[0119] ​The processing unit 401 is configured to publish a target data asset corresponding to a target service to an internal system, publish the target service to an external system, and determine an asset category included in the target data asset, the asset category including a label, a data application layer ADS, a data service layer DWS, a data intermediate layer DWM, and a data detail layer DWD.

[0120] In a possible implementation, in the workload evaluation device 40 provided in the embodiment of the present application, the processing unit 401 is configured to determine a calling parameter corresponding to the target data asset based on a calling frequency of the target data asset.

[0121] The processing unit 401 is configured to determine an importance level of the target data asset based on the calling parameter corresponding to the target data asset and a preset data asset importance level table.

[0122] In a possible implementation, in the workload evaluation device 40 provided in the embodiment of the present application, the processing unit 401 is configured to determine a basic workload corresponding to the target service based on an amount of use of a component in each process in at least one process included in the target service.

[0123] The processing unit 401 is configured to determine an asset category income corresponding to the target service based on a quantity of asset categories included in the target data asset and a corresponding coefficient.

[0124] The processing unit 401 is configured to determine an asset calling income corresponding to the target service based on the calling frequency of the target data asset and a corresponding coefficient.

[0125] The processing unit 401 is configured to determine an important asset incremental income corresponding to the target service based on the importance level of the target data asset and the basic workload.

[0126] The processing unit 401 is configured to, when the target service is a core service, determine a core service incremental income corresponding to the target service based on the basic workload and a coefficient corresponding to the core service.

[0127] The processing unit 401 is configured to determine a workload corresponding to the target service based on the basic workload, the asset category income, the asset calling income, the important asset incremental income, the core service incremental income, and a highest income coefficient.

[0128] In a possible implementation, in the workload evaluation device 40 provided in the embodiment of the present application, the processing unit 401 is configured to determine a data asset conversion rate based on an asset category included in a data asset corresponding to each service in a plurality of services in a target time period.

[0129] The processing unit 401 is configured to determine a core service proportion in the plurality of services based on a quantity of core services included in the plurality of services.

[0130] In the case of implementing the functions of the above-mentioned integrated modules in the form of hardware, the embodiments of the present application provide another possible structural diagram of the electronic device involved in the above-mentioned embodiments. As shown in the figure, an electronic device 60 is configured to improve the efficiency of network parameter management and control evaluation, for example, to perform the workload evaluation method shown in the figure. The electronic device 60 includes a processor 601, a memory 602 and a bus 603. The processor 601 and the memory 602 can be connected through the bus 603. Figure 9 Figure 3 The processor 601 is the control center of the communication device, which can be one processor or a general term of multiple processing elements. For example, the processor 601 can be a general central processing unit (CPU), or other general-purpose processors, etc. Among them, the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0131] As an embodiment, the processor 601 can include one or more CPUs, such as the CPU 0 and the CPU 1 shown in the figure.

[0132] The memory 602 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage devices, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but not limited to this. Figure 9 As a possible implementation, the memory 602 can exist independently of the processor 601, and the memory 602 can be connected to the processor 601 through the bus 603, used to store instructions or program codes. When the processor 601 calls and executes the instructions or program codes stored in the memory 602, the workload evaluation method provided by the embodiments of the present application can be implemented.

[0133] In another possible implementation, the memory 602 can also be integrated with the processor 601.

[0134]

[0135]

[0136] ​​​The bus 603 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 9 Only one thick line is used to represent the bus in the middle, but it does not mean that there is only one bus or only one type of bus.

[0137] It should be noted that, Figure 9 The structure shown does not constitute a limitation on the electronic device 60. In addition to Figure 9 the components shown, the electronic device 60 can include more or fewer components than shown, or combine some components, or different component arrangements.

[0138] As an example, in combination with Figure 8 , the processing unit 401 in the electronic device implements the same function as the processor 601 in Figure 9 .

[0139] Optionally, as Figure 9 shown, the electronic device 60 provided by the embodiments of the present application can further include a communication interface 604.

[0140] The communication interface 604 is used to connect with other devices through a communication network. The communication network can be an Ethernet, a wireless access network, a wireless local area network (WLAN), and the like. The communication interface 604 can include a receiving unit for receiving data, and a sending unit for sending data.

[0141] In one design, in the electronic device provided by the embodiments of the present application, the communication interface can also be integrated in the processor.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units is taken as an example. In actual application, the above-mentioned functions can be completed by different functional units according to needs, that is, the internal structure of the device is divided into different functional units to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0143] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores instructions. When a computer executes the instructions, the computer executes each step in the method flow shown in the method embodiment.

[0144] The embodiment of the present application provides a computer program product containing instructions, which, when executed on a computer, cause the computer to perform the workload evaluation method in the method embodiment.

[0145] The computer readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any other suitable combination of the above, or any other medium from which the program can be derived.

[0146] An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Of course, the storage medium can be part of the processor. Consistent with the teachings of the exemplary embodiments described herein, a processor can be implemented using a combination of general and specific purpose processors, or any other combination of hardware and / or software.

[0147] In the embodiment of the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or apparatus.

[0148] Since the electronic device, the computer readable storage medium and the computer program product in the embodiment of the present application can be applied to the above method, the technical effects that can be obtained are also referable to the method embodiment, and the embodiment of the present application will not be described here.

[0149] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application.

Claims

1. A workload assessment method, characterized in that, The method includes: After the target service is released, the number of times the target data asset corresponding to the target service is called and the callers are counted. The callers include internal systems and external systems. The number of times the target data asset is called includes: the number of times the target data asset is called every day, the total number of times the target data asset is called by the internal system in a single month, and the total number of times the target data asset is called by the external system in a single month. The target service includes at least one process, and each process is implemented through multiple preset components. Based on the number of times the target data asset is invoked, the importance level of the target data asset is determined, and based on the importance level of the target data asset, it is determined whether the target service is a core service. Based on the usage of components by each process in at least one process of the target service, the basic workload corresponding to the target service is determined. Based on the number of asset categories included in the target data assets and the corresponding coefficients, the asset category revenue corresponding to the target service is determined; Based on the number of times the target data asset is invoked and the corresponding coefficient, the asset invocation revenue corresponding to the target service is determined; Based on the importance level of the target data asset and the basic workload, determine the incremental revenue of the important asset corresponding to the target service; When the target service is a core service, the incremental revenue of the core service corresponding to the target service is determined based on the basic workload and the coefficient corresponding to the core service. Based on the basic workload, the asset category revenue, the asset call revenue, the incremental revenue of the important assets, the incremental revenue of the core service, and the highest revenue coefficient, the workload corresponding to the target service is determined.

2. The method according to claim 1, characterized in that, The method further includes: Based on the target business requirements information and the preset multiple components, generate the target data assets corresponding to the target service, and determine the usage of each component in at least one process included in the target service. The target data asset corresponding to the target service is published to the internal system, and the target service is published to the external system. The asset categories included in the target data asset are determined, including: tags, data application layer (ADS), data service layer (DWS), data middleware layer (DWM), and data detail layer (DWD).

3. The method according to claim 1 or 2, characterized in that, The determination of the importance level of the target data asset based on the number of times the target data asset is accessed includes: Based on the number of times the target data asset is invoked, the invocation parameters corresponding to the target data asset are determined; Based on the calling parameters corresponding to the target data asset and the preset data asset importance level table, the importance level of the target data asset is determined.

4. The method according to claim 1, characterized in that, The method further includes: The data asset conversion rate is determined based on the asset categories included in the data assets corresponding to each of the multiple services within the target time period. Based on the number of core services included in the multiple services, the proportion of core services in the multiple services is determined.

5. A workload assessment device, characterized in that, The workload assessment device includes: a processing unit; The processing unit is used to count the number of times the target data asset corresponding to the target service is called and the callers after the target service is published. The callers include internal systems and external systems. The number of times the target data asset is called includes: the number of times the target data asset is called every day, the total number of times the target data asset is called by the internal system in a single month, and the total number of times the target data asset is called by the external system in a single month. The target service includes at least one process, and each process is implemented through multiple preset components. The processing unit is used to determine the importance level of the target data asset based on the number of times the target data asset is invoked, and to determine whether the target service is a core service based on the importance level of the target data asset. The processing unit is configured to determine the basic workload corresponding to the target service based on the usage of components by each process in at least one process of the target service. The processing unit is used to determine the asset category revenue corresponding to the target service based on the number of asset categories included in the target data asset and the corresponding coefficients. The processing unit is used to determine the asset call revenue corresponding to the target service based on the number of times the target data asset is called and the corresponding coefficient. The processing unit is used to determine the incremental revenue of the important assets corresponding to the target service based on the importance level of the target data asset and the basic workload. The processing unit is used to determine the incremental revenue of the core service corresponding to the target service based on the basic workload and the coefficient corresponding to the core service when the target service is a core service. The processing unit is used to determine the workload corresponding to the target service based on the basic workload, the asset category revenue, the asset call revenue, the incremental revenue of the important assets, the incremental revenue of the core service, and the highest revenue coefficient.

6. The workload assessment device according to claim 5, characterized in that, The processing unit is used to generate target data assets corresponding to the target service based on the target business requirement information and the preset multiple components, and to determine the usage of each component in at least one process included in the target service. The processing unit is configured to publish the target data asset corresponding to the target service to the internal system, publish the target service to the external system, and determine the asset categories included in the target data asset, wherein the asset categories include: tags, data application layer (ADS), data service layer (DWS), data middleware layer (DWM), and data detail layer (DWD).

7. The workload assessment device according to claim 5 or 6, characterized in that, The processing unit is used to determine the calling parameters corresponding to the target data asset based on the number of times the target data asset is called; The processing unit is used to determine the importance level of the target data asset based on the calling parameters corresponding to the target data asset and a preset data asset importance level table.

8. The workload assessment device according to claim 5, characterized in that, The processing unit is used to determine the data asset conversion rate based on the asset categories included in the data assets corresponding to each of the multiple services in the target time period. The processing unit is used to determine the proportion of core services among the multiple services based on the number of core services included in the multiple services.

9. An electronic device, characterized in that, include: A processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer execution instructions, wherein when the electronic device is running, the processor executes the computer execution instructions stored in the memory to cause the electronic device to perform a workload assessment method according to any one of claims 1-4.

10. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computer, cause the computer to perform a workload assessment method as described in any one of claims 1-4.

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