CN117670128A

CN117670128A - Data processing method and device

Info

Publication number: CN117670128A
Application number: CN202311663382.1A
Authority: CN
Inventors: 巩力
Original assignee: Agricultural Bank of China
Current assignee: Agricultural Bank of China
Priority date: 2023-12-06
Filing date: 2023-12-06
Publication date: 2024-03-08

Abstract

The invention discloses a data processing method and device. The specific scheme is as follows: respectively determining an assessment index decomposition model corresponding to at least one service to be examined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter; configuring at least one processing rule for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule; and configuring a data warehouse for the assessment index decomposition model, so as to call the data to be processed from the data warehouse when the data processing conditions are met, and performing data processing on the data to be processed and the processing rules based on creating the calculation rules in the rule engine to obtain a target assessment result. The invention improves the accuracy of data processing and ensures the objective fairness of the target assessment result.

Description

Data processing method and device

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a data processing method and apparatus.

Background

The assessment is an important link of enterprise management, and mainly refers to a process that assessment personnel observe, collect, organize, extract and integrate assessment index related information (performance, achievement, actual serving as, etc.) of a behavior subject (can be an organization of specific staff or departments, etc.) by using scientific methods, standards and programs, and make accurate assessment as far as possible.

The prior art usually makes assessment indexes by assessment staff. And then, the assessment personnel collect the assessment data of the assessed departments, combine the historical experience and manually score the assessed personnel according to the relevant assessment indexes. Finally, the target assessment results are obtained through the steps of reporting, summarizing, opinion feedback and the like, but the manual intervention process is more, the target assessment results are influenced by human factors such as subjectivity, misoperation and the like of assessment personnel, and objective fairness of the assessment process is difficult to ensure. In addition, the above-mentioned assessment method cannot enable the assessment personnel and the assessed personnel to track and control the assessment process.

Disclosure of Invention

The invention provides a data processing method and a data processing device, which improve the accuracy of data processing and ensure the objective fairness of target assessment results.

According to an aspect of the present invention, there is provided a data processing method comprising:

respectively determining an assessment index decomposition model corresponding to at least one service to be examined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter;

configuring at least one processing rule for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule;

and configuring a data warehouse for the assessment index decomposition model, so as to call the data to be processed corresponding to the quantization parameters in the assessment index decomposition model from the data warehouse when the condition of data processing is detected to be met, and carrying out data processing on the called data to be processed and the processing rules corresponding to the quantization parameters based on the calculation rules created in the rule engine, thereby obtaining a target assessment result.

According to another aspect of the present invention, there is provided a data processing apparatus comprising:

the analysis model determining module is used for respectively determining an assessment index analysis model corresponding to at least one service to be examined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter;

The processing rule configuration module is used for configuring at least one processing rule for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule;

the assessment result acquisition module is used for configuring a data warehouse for the assessment index decomposition model, when the condition that the data processing condition is met is detected, retrieving the data to be processed corresponding to the quantization parameter in the assessment index decomposition model from the data warehouse, and carrying out data processing on the retrieved data to be processed and the processing rule corresponding to the quantization parameter based on the calculation rule created in the rule engine to obtain the target assessment result.

According to another aspect of the present invention, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a data processing method of any one of the embodiments of the present invention.

According to the technical scheme, the assessment index decomposition model corresponding to at least one service to be examined is respectively determined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter; then, at least one processing rule is configured for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule; further, a data warehouse is configured for the assessment index decomposition model, when the meeting of the data processing conditions is detected, the data to be processed corresponding to the quantization parameters in the assessment index decomposition model are called from the data warehouse, and based on the calculation rules established in the rule engine, the data to be processed and the processing rules corresponding to the quantization parameters are processed, so that the target assessment result is obtained. According to the invention, the manual intervention on the assessment process is reduced, the problems of subjectivity and misoperation in the assessment process are solved, the data is processed based on the data warehouse and the rule engine, the accuracy of data processing is ensured, and objective fairness of the target assessment result is realized.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a data processing method according to an embodiment of the present invention;

FIG. 2 is a diagram illustrating a structure of a quantization parameter model according to an embodiment of the present invention;

FIG. 3 is a diagram showing a correspondence between a quantization parameter model and quantization parameters and data to be processed according to an embodiment of the present invention;

FIG. 4 is a general block diagram of a multi-branch rule template rule item provided by an embodiment of the present invention;

FIG. 5 is a diagram showing an exemplary configuration of an automatic scoring module according to an embodiment of the present invention;

FIG. 6 is a flow chart of the execution of a data receiver provided by an embodiment of the present invention;

FIG. 7 is a flowchart illustrating the calculation of the target assessment results according to an embodiment of the present invention

FIG. 8 is a flow chart of a rule verification method provided by an embodiment of the present invention;

FIG. 9 is a flowchart illustrating rule verification provided by an embodiment of the present invention;

FIG. 10 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;

fig. 11 is a block diagram of an electronic device implementing a data processing method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the method may be implemented by a data processing device, which may be implemented in hardware and/or software, and the data processing device may be configured in an electronic device such as a mobile phone, a computer, or a server, where the data processing device may be implemented by using a data warehouse and a rule engine to process data to obtain a target evaluation result. As shown in fig. 1, the method includes:

s110, respectively determining an assessment index decomposition model corresponding to at least one service to be examined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment indexes of the last level are associated with at least one quantization parameter.

In the embodiment of the invention, the assessment index decomposition model can be used for decomposing and refining the assessment index corresponding to at least one service to be examined. The business to be checked may be a related business that needs to be evaluated and checked, for example, the business to be checked may be performance, achievement, actual use, etc. of the staff to be checked, which is not limited in this embodiment. Accordingly, the assessment index may be understood as a specific standard for measuring the service to be assessed. The quantization parameter may be a parameter corresponding to the assessment index, that is, the quantization parameter corresponding to the assessment index is a parameter value that converts the assessment index into a specific number or value for assessment. Accordingly, the quantization parameter may be a data parameter obtained from a corresponding data warehouse. For example, when the assessment index is the old system migration modification condition, the corresponding quantization parameter may be the migration completion rate. In addition, the examination index decomposition model corresponds to a tree diagram, and hierarchical nodes in the tree diagram represent nodes of a certain hierarchy of the tree diagram.

Specifically, in the actual scene, when the service of the checked person is checked and evaluated, at least one service to be checked can be determined according to the actual requirement in order to ensure the integrity and accuracy of the final check result. And then, determining the assessment index corresponding to each service to be assessed based on the service to be assessed so as to determine an assessment index decomposition model based on the assessment index. The examination index decomposition model can be represented by a tree diagram, wherein the tree diagram can comprise at least three levels, and each level comprises at least one level node. Correspondingly, the hierarchical nodes correspond to the assessment indicators. The assessment index of the last level may be associated with a quantization parameter in the respective data warehouse, i.e. the assessment index may be associated with at least one quantization parameter in order to achieve an overall assessment of the assessment index based on one or more quantization parameters.

Optionally, determining the assessment index decomposition model corresponding to at least one service to be examined respectively includes: for each service to be checked, determining at least one check index associated with the current service to be checked, and refining the at least one check index to obtain a sub-check index corresponding to the check index; taking the sub-assessment index as an assessment index, refining the assessment index to obtain a sub-assessment index of the next level, and repeatedly executing the operation of determining the assessment index of the next level by taking the sub-assessment index as the assessment index until the assessment index of the last level is obtained; and configuring at least one quantization parameter for the assessment index of the last level to obtain a tree diagram corresponding to the current service to be assessed, and taking the tree diagram as an assessment index decomposition model.

In the embodiment of the invention, the sub-assessment index may be an index obtained based on refinement of the current assessment index, and one assessment index may be refined into at least one corresponding sub-assessment index.

Specifically, for each service to be checked, at least one check index corresponding to each service to be checked may be determined, where the check index corresponding to the service to be checked may be set according to actual requirements, and the number of check indexes may be one or multiple, which is not limited in this embodiment. And then, each assessment index can be refined to obtain at least one sub-assessment index after the current assessment index is refined. Further, the sub-assessment index can be refined as an assessment index, and accordingly, the sub-assessment index of the next level can be obtained, so that the operation of using the sub-assessment index as the assessment index to determine the assessment index of the next level is repeatedly performed until the assessment index of the last level is obtained. At least one quantization parameter may be configured for the assessment indicator of the last level. Based on the above, a tree diagram corresponding to the service to be checked can be obtained, and the tree diagram is used as an assessment index decomposition model so as to perform data processing based on the assessment index decomposition model.

For example, one assessment index may be decomposed down into several first-level sub-indexes, i.e., sub-assessment indexes, and one first-level sub-index may be decomposed down into several second-level sub-indexes, i.e., the above-mentioned operation of determining the next-level assessment index by using the sub-assessment indexes as the assessment indexes. Correspondingly, the sub-index at the bottom layer, namely the assessment index at the last layer, can be decomposed by the same way. The corresponding tree diagram may also be represented in tabular form, for example, as shown in table 1 below.

TABLE 1 schematic table of analysis model of assessment index

It should be noted that the present invention is not limited to the representation and hierarchy of the tree diagram.

S120, configuring at least one processing rule for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule.

In the embodiment of the present invention, the processing rule may be a scoring rule for performing calculation processing on data corresponding to the quantization parameter. Alternatively, the processing rules may be a function of processing the data. The data corresponding to the quantization parameter is to-be-processed data, and the to-be-processed data can be data obtained from a corresponding data warehouse.

Specifically, a corresponding processing rule may be configured for an assessment index corresponding to a level node of a last level in the assessment index decomposition model, where the assessment index may be configured with at least one processing rule, and the number of processing rules configured by the assessment index may be defined according to an actual service requirement. After the configuration of the processing rule is completed, the data to be processed obtained from the corresponding data warehouse, which corresponds to the quantization parameter, may be processed based on the processing rule.

Illustratively, as shown in Table 1, rule A, which is the processing rule, is configured for three levels of sub-metrics 1-1-1. Rule B is configured for the secondary sub-index 2-1, and correspondingly, the rule B is also a processing rule.

Optionally, configuring at least one processing rule for a node of a last level in the assessment index decomposition model includes: configuring event types for the assessment indexes of the last hierarchy, and configuring at least one corresponding processing rule based on the event types; the event type comprises a target type or an event type, the quantization parameters of the event type comprise at least one group, and the processing rule is a processing function for processing data to be processed corresponding to the quantization parameters.

In the embodiment of the invention, the event type may be the type corresponding to the event generated by the last-level assessment index when reaching a certain condition or triggering a certain action. Among event types, a target type and an event type may be included. The target type may be an event triggered when the data to be processed corresponding to the assessment index of the last level reaches or exceeds a preset target value. The event type may be an event triggered when the data to be processed corresponding to the assessment index of the last hierarchy reaches a specific behavior. The processing function may be a calculation formula for processing the data to be processed corresponding to the quantization parameter.

Specifically, the event type may be configured for the assessment index of the last level according to the nature of the assessment index of the last level. Among them, event types can be classified into a target type and an event type. At least one processing rule may then be configured separately for each event type. In addition, the number of sets of quantization parameters corresponding to the assessment indicators for different event types is different. The quantization parameters for the event type include at least one group, and the quantization parameters for the target type may include one group. In contrast, when processing data to be processed using processing rules, only one processing result is obtained. When processing the data to be processed corresponding to the event type quantization parameters, the processing result may be the sum of a plurality of sub-processing results obtained after processing the data to be processed of a plurality of groups of quantization parameters based on the processing rule. Alternatively, there may be no case for the event-type quantization parameter, and the processing result obtained at this time is regarded as zero.

For example, the event types corresponding to the bottom sub-index, i.e., the last-level check index, may be classified into a calculation type and a fixed type. Wherein, the sub index value corresponding to the fixed type, namely the data to be processed corresponding to the fixed type is a fixed value. The processing rule between the calculation type assessment index and the quantization parameter is complex, and the calculation needs to be configured by means of the rule engine, so that the processing rule needs to be configured for the calculation type assessment index. The computing type assessment indicators can be classified into a target type and an event type based on the properties of the bottom sub-indicators or the number of sets of quantization parameters corresponding to the assessment indicators. There are only one set of quantization parameters of the target type, and there may be n sets of quantization parameters of the event type, where n > =0. Correspondingly, when the data to be processed corresponding to the n groups of quantization parameters are processed based on the calculation rule, the processing result is equal to the sum of the processing results of the data to be processed corresponding to the n groups of quantization parameters. For example, in table 1, the three-level sub-index 1-1 is of a target type, the two-level sub-index 2-1 is of an event type, the two-level sub-index 2-2 is of a fixed type, and processing rules can be respectively configured for the two-level sub-index based on different event types, namely, the processing rules configured for the three-level sub-index 1-1 are the processing rules configured for the two-level sub-index 2-1 are the rules B.

S130, configuring a data warehouse for the assessment index decomposition model, so as to retrieve data to be processed corresponding to the quantization parameters in the assessment index decomposition model from the data warehouse when the condition of data processing is detected to be met, and carrying out data processing on the retrieved data to be processed and the processing rules corresponding to the quantization parameters based on the calculation rules created in the rule engine, so as to obtain a target assessment result.

In an embodiment of the present invention, the data warehouse is a centralized storage structured data and semi-structured data store for reporting and analysis. The data in the data warehouse may be obtained from data stored in different business systems. Accordingly, when importing various data into the data warehouse, it is necessary to clean and normalize it to obtain the data after the above preprocessing. The preprocessed data is the data to be processed. The calculation rule may be a rule corresponding to how the rule engine is invoked to work. The target assessment result may be a processing result obtained after processing the data to be processed based on the processing rule by using the rule engine. Alternatively, the target assessment result may be a score value. It should be noted that, the rule engine is a component embedded in the application program, and may be used to input data to be processed, and obtain the target assessment result according to the processing rule and the calculation rule.

Specifically, a data warehouse is configured for the assessment index decomposition model, wherein data in the data warehouse can be imported from databases of other business systems. For example, the service systems may include service system a, service system B, service system C, and the like. And then, cleaning and standardizing the data in the data warehouse until the preprocessed data meet the data processing conditions, and calling the data corresponding to the quantization parameters in the assessment index decomposition model from the data warehouse, namely the data to be processed. And creating a calculation rule in the rule engine so as to call the rule engine to perform data processing on the data to be processed by using the processing rule according to the calculation rule, thereby obtaining a target assessment result.

For example, the rules engine may implement specific computational logic in the computational rules deployed to the rules engine, and then obtain target assessment results by invoking the computational rules in the rules engine using the rule entry parameters. Wherein the above procedure may be implemented using a quantization parameter model (XOM). The structure of the quantization parameter model may be as shown in fig. 2. A 3-part, preset variable mapper, branch selector and auxiliary calculator may be included in the quantization parameter model. The three parts can be utilized to realize that the data to be processed corresponding to the quantization parameter is transmitted when the calculation rule is called. That is, the data to be processed of the data warehouse may be loaded into the XOM, and then the corresponding data to be processed may be obtained in the rule engine by using the XOM, so as to load the corresponding calculation rule by using the rule to be processed, thereby implementing processing of the data to be processed by using the processing rule, so as to obtain the target assessment result.

Optionally, the method further comprises: the calculation rule of the rule engine is configured so as to carry out data processing on the fetched data to be processed and the processing rule corresponding to the quantization parameter based on the calculation rule created in the rule engine, and a target assessment result is obtained.

Specifically, computing logic within the rules engine is set or defined to implement configuration of the computing rules. And processing the data according to the calculation rules configured in the rule engine, wherein the processing rules correspond to the called data to be processed and the quantization parameters, so as to obtain a target assessment result after processing the data.

Optionally, the method further comprises: and determining a parameter identifier of at least one quantization parameter in the assessment index decomposition model, and storing data in the data warehouse according to the parameter identifier so as to call the data to be processed from the data warehouse based on the parameter identifier of the target quantization parameter of the target assessment task when the target assessment task is received.

In an embodiment of the invention, the parameter identification is a symbol or mark for identifying or referring to the quantization parameter. Alternatively, the parameter identifier may be a character such as an letter, a number, or the like, which is not limited in this embodiment. The target assessment task may be a task corresponding to a service to be processed that needs to be performed at present to obtain a target assessment result. The target quantization parameter may be a quantization parameter corresponding to the target assessment task.

Specifically, in the assessment index decomposition model, a corresponding parameter identifier is determined for each quantization parameter, and correspondingly, data in the data warehouse is stored according to the parameter identifier, so that when a target assessment task is received, corresponding data to be processed is called from the data warehouse according to the parameter identifier of the target quantization parameter corresponding to the target assessment task.

For example, to facilitate management of the rules engine and reduce the number of corresponding rule entries, a quantization parameter model (XOM) and multi-branch template rule entries may be utilized, as there may be an excessive number of calculation rules. The parameter identification can be used for establishing a corresponding relation. As shown in fig. 3, fig. 3 is a corresponding relationship diagram between the quantization parameter model and the quantization parameter, and the data to be processed. In fig. 3, the index decomposition model is an assessment index decomposition model, in which three levels of sub-indexes are assessment indexes of the last level. Wherein 1, 2, … …, 5 are parameter identifiers. The parameter index decomposition model obtains the data to be processed corresponding to the parameter identification in the data file of the data warehouse by utilizing the parameter identification of the quantization parameter. In addition, the parameter identification can be utilized to establish a corresponding relation between the quantization parameter model and the quantization parameter in the parameter index decomposition model. The XOM is composed of a preset variable mapper (for quantization parameter mapping, done by a decomposition model), a branch selector (providing branch routing information in rule entries), an auxiliary calculator (e.g., error code, message, intermediate variable), etc. When the calculation rules of the rule engine are configured, multi-branch rule template rule items deployed in the rule engine need to be modified in order to realize the calculation relation between the assessment index and the quantization parameter. At this time, the corresponding branch route information may be acquired based on the branch selector in the XOM so as to determine the corresponding calculation rule based on the branch route information. The rule items of the multi-branch rule template adopt a multi-branch overall structure (the specific calculation rule is empty), so that the number of the rule items in the rule engine can be greatly reduced, and the assessment personnel can more flexibly distribute the rule items. The general structure of the multi-branch rule template rule item described above may be as shown in fig. 4. Wherein, rule 1 to rule 7 are all calculation rules. When the calculation rule is configured, an assessment staff can modify and deploy multi-branch template rule items in the rule engine so as to realize the calculation relation between assessment indexes and quantization parameters; and binding the rule item parameters to the assessment index of the last level of the assessment index decomposition model.

Optionally, the method further comprises: setting data processing conditions; wherein setting the data processing conditions includes: configuring and determining a triggering condition and an assessment constraint condition of a target assessment result; the assessment constraint condition comprises at least one of an assessment period, an assessment task switch, an assessment expiration date and an assessment project.

In the embodiment of the invention, the data processing conditions can comprise a triggering condition and an assessment constraint condition of the target assessment result. The trigger condition of the target checking result may be a trigger condition set according to an actual requirement. Alternatively, the triggering condition may be an instant trigger, a timing trigger, an associated trigger, or the like. The examination constraint conditions can be dynamically configured, and the selected constraint conditions can be set according to actual requirements. The evaluation constraint condition includes at least one of an evaluation period, an evaluation task switch, an evaluation expiration date, and an evaluation item. The evaluation period may be understood as a time range of evaluation, for example, evaluation is performed by day, week, month, quarter, or year, for example. The assessment task switch may be a control parameter for starting or stopping the assessment task. The assessment expiration date may be a deadline by which the assessment task must be completed. The assessment item may be specific assessment content.

Specifically, when data processing is performed on data to be processed, data processing conditions may be set first, so that standardization and flexibility of data processing are ensured based on the data processing conditions. The data processing conditions may include trigger conditions for configuring the target assessment results and assessment constraints. The assessment constraint condition comprises at least one of an assessment period, an assessment task switch, an assessment expiration date and an assessment project. The examination constraint condition can be configured in the early stage or in the data processing process of the data to be processed. Correspondingly, the examination constraint conditions can be modified later according to the requirements.

For example, this may be accomplished using an automatic scoring module when setting data processing conditions. As shown in fig. 5, fig. 5 is a diagram showing an exemplary structure of the automatic scoring module. The scoring trigger in fig. 5 can be used to implement configuration of the triggering conditions of the target assessment result, and the scoring trigger supports multiple modes such as instant triggering, timing triggering, association triggering and the like. The scoring configurator can control assessment constraint conditions such as assessment period, assessment task switch, assessment expiration date and the like. In addition, for the data receiver, it may be for receiving and loading data to be processed transmitted in the data warehouse. Because of the large amount of data, the data receiver only loads the latest received data file and checks the abnormal data to be processed in the loading process. Wherein the execution flow chart of the data receiver may be as shown in fig. 6. The data in fig. 6 are the data to be processed mentioned above.

Optionally, the method further comprises: when the condition that the data processing condition is met is detected, determining a target assessment index decomposition model, and determining an event type corresponding to the assessment index of the last level based on the target assessment index decomposition model; based on the event type, the quantization parameter corresponding to the assessment index of the last level is accessed into a rule engine for processing; and determining a target assessment result based on the processing result of the rule engine.

In the embodiment of the invention, the target assessment index decomposition model can be an assessment index decomposition model determined based on the current service to be examined.

Specifically, when the condition of meeting the data processing is detected, a target assessment index decomposition model can be determined based on the current service to be assessed, so that the event type of the assessment index of the last level of the target assessment index decomposition model is determined. And then, according to the event types, the assessment indexes of the last level are respectively processed along with the corresponding quantization parameters in the rule engine, wherein the assessment indexes of different event types have different processing modes of the data to be processed of the corresponding quantization parameters. Based on this, the processing result of the rule engine can be obtained, thereby obtaining the target assessment result.

Optionally, based on the event type, the quantization parameter corresponding to the assessment index of the last level is accessed into a rule engine for processing, including: if the event type is the event type, extracting an event type quantization parameter data set from a data warehouse, and carrying quantization parameters corresponding to the event type into a rule engine for calculation to obtain each sub-evaluation result, so as to obtain a target assessment result through accumulation of the sub-evaluation results; and if the event type is the target type, extracting the data to be processed corresponding to the target type quantization parameter from the data warehouse so as to process the data to be processed based on the rule engine.

In the embodiment of the present invention, the event type quantization parameter data set may be data to be processed of at least one set of quantization parameters corresponding to the event type assessment index. The sub-evaluation result may be a processing result obtained after processing the data to be processed of each set of quantization parameters.

Specifically, according to different event types, the quantization parameters corresponding to the assessment indexes of the last level are respectively accessed into a rule engine for processing. When the event type is event type, an event type quantization parameter data set can be extracted from the data warehouse, the data to be processed corresponding to the corresponding quantization parameter is brought into the rule engine for calculation, so that a plurality of sub-evaluation results are obtained, and the plurality of sub-evaluation results are accumulated, so that a target assessment result can be obtained. If the event type is the target type, the data to be processed corresponding to the target type assessment index can be extracted from the data warehouse and is brought into the rule engine for calculation, so that a target assessment result is obtained.

Illustratively, in connection with the above example, the scoring actuator of FIG. 5 may implement the operations described above, wherein a particular computational flow may be as shown in FIG. 7. The scoring executor is responsible for executing the overall scoring task, namely, obtaining the target assessment result. After the scoring actuator is triggered, the scoring actuator firstly loads the examination constraint conditions in the scoring configurator, and the range of the data to be processed is determined; invoking a data receiver to load data to be processed; finally, the target assessment index decomposition model is brought into a score calculator, and a recursion algorithm is used for calculating the assessment index of the last level of the assessment index of the last level; classifying and calculating the assessment index of the last level: the fixed type directly obtains the numerical value, namely directly obtains the target examination result; the target type extracts the data to be processed corresponding to the quantization parameter from the data warehouse, and carries the data to be processed into a configured rule engine for calculation to obtain a bottom sub index value, namely a target assessment result; the event type extracts an event type quantization parameter n (n > =0) group from the data warehouse, namely an event type quantization parameter data group, the data to be processed corresponding to the n groups of quantization parameters are respectively put into a configured rule engine, and finally n groups of calculation results are summed to obtain an event type bottom sub-index value, namely a target assessment index. Among them, a recursive algorithm can be understood as a method of solving a problem by repetitively decomposing the problem into sub-problems of the same kind.

According to the technical scheme, an assessment index decomposition model corresponding to at least one service to be examined is respectively determined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter; then, at least one processing rule is configured for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule; further, a data warehouse is configured for the assessment index decomposition model, when the meeting of the data processing conditions is detected, the data to be processed corresponding to the quantization parameters in the assessment index decomposition model are called from the data warehouse, and based on the calculation rules established in the rule engine, the data to be processed and the processing rules corresponding to the quantization parameters are processed, so that the target assessment result is obtained. According to the invention, the manual intervention on the assessment process is reduced, the problems of subjectivity and misoperation in the assessment process are solved, the data is processed based on the data warehouse and the rule engine, the accuracy of data processing is ensured, and objective fairness of the target assessment result is realized.

Example two

Fig. 8 is a flowchart of a rule verification method according to an embodiment of the present invention, where, based on the above embodiment, after determining a processing rule and a calculation rule, a test case is required to verify the rule, so as to process data to be processed subsequently. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein. As shown in fig. 8, the method includes:

s210, at least one test case is obtained, wherein the test case comprises expected results.

In embodiments of the present invention, test cases may be used to verify that the calculation rules and the processing rules are in compliance with expectations. The desired results may be set according to actual requirements, with the results expected to be obtained after processing the test cases.

Specifically, data corresponding to at least one test case can be obtained from the corresponding data warehouse, wherein when the data corresponding to the test case is obtained, a data range corresponding to the test case can be set according to actual requirements so as to obtain the test case data meeting the requirements. For test cases, the expected results may be included so that after the test cases are processed, comparisons may be made based on the expected results.

Illustratively, as shown in fig. 9, fig. 9 is an exemplary diagram of a flow of rule verification. In fig. 9, a test case range may be selected first, so as to obtain a test case meeting the requirement.

S220, processing at least one test case to obtain an actual result.

In the embodiment of the invention, the actual result may be a calculation result obtained after the test case is processed.

Specifically, the data of the quantization parameter corresponding to the test case can be brought into the rule engine for calculation, and the data of the quantization parameter corresponding to the test case and the processing rule corresponding to the quantization parameter are subjected to data processing based on the calculation rule in the rule engine, so that an actual result is obtained.

For example, in combination with the above example, as shown in fig. 9, after the test case is acquired, it may be determined whether the test processing of the test case is finished. If not, the data of the quantization parameter of the test case can be brought into the XOM, and transmitted into the rule engine through the XOM, and then the data of the quantization parameter corresponding to the test case and the processing rule corresponding to the quantization parameter can be processed based on the calculation rule in the rule engine. Thereby obtaining a return value, i.e. an actual result.

And S230, when the actual result and the expected result are inconsistent, processing the processing rule and the calculation rule so as to process the data to be processed when the actual result and the expected result are consistent.

Specifically, the actual result calculated by the rule engine is compared with the expected result, if the actual result is inconsistent with the expected result, the processing rule and the calculation rule can be verified or verified and modified one by one until the actual result is consistent with the expected result. If the test cases are consistent, the test cases are successfully executed, and the data to be processed can be processed based on the processing rules and the calculation rules.

For example, in combination with the above example, after the rule engine calculates the return value, the return value and the expected value may be compared, where the expected value is the expected result. Therefore, whether the test cases are successfully executed is judged, and the test results are recorded until all the test cases are executed. By testing whether the return value and the expected value recorded in the result are consistent, it can be determined whether verification modification of the processing rule and the calculation rule is required.

According to the technical scheme, at least one test case is obtained, wherein the test case comprises expected results. And then, processing the acquired at least one test case to obtain an actual result, and processing the processing rule and the calculation rule when the actual result is inconsistent with the expected result so as to process the data to be processed when the actual result is consistent with the expected result. The verification of the processing rules and the calculation rules is realized, so that the data processing efficiency is improved, and the accuracy of the subsequent data processing of the data to be processed is ensured.

Example III

Fig. 10 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention. As shown in fig. 10, the apparatus includes: the analysis model determination module 310, the processing rule configuration module 320, and the assessment result acquisition module 330.

A decomposition model determining module 310, configured to determine an assessment index decomposition model corresponding to at least one service to be examined respectively; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter; the processing rule configuration module 320 is configured to configure at least one processing rule for a node of a last level in the assessment index decomposition model, so as to process data to be processed corresponding to the quantization parameter based on the processing rule; the assessment result obtaining module 330 is configured to configure a data warehouse for the assessment index decomposition model, so as to retrieve data to be processed corresponding to the quantization parameter in the assessment index decomposition model from the data warehouse when the meeting of the data processing condition is detected, and perform data processing on the retrieved data to be processed and the processing rule corresponding to the quantization parameter based on the calculation rule created in the rule engine, so as to obtain the target assessment result.

On the basis of the above embodiment, optionally, the decomposition model determining module is configured to determine, for each service to be checked, at least one check index associated with the current service to be checked, and refine the at least one check index to obtain a sub-check index corresponding to the check index; taking the sub-assessment index as an assessment index, refining the assessment index to obtain a sub-assessment index of the next level, and repeatedly executing the operation of determining the assessment index of the next level by taking the sub-assessment index as the assessment index until the assessment index of the last level is obtained; and configuring at least one quantization parameter for the assessment index of the last level to obtain a tree diagram corresponding to the current service to be assessed, and taking the tree diagram as an assessment index decomposition model.

Optionally, the processing rule configuration module is configured to configure an event type for the assessment index of the last level, and configure at least one corresponding processing rule based on the event type; the event type comprises a target type or an event type, the quantization parameters of the event type comprise at least one group, and the processing rule is a processing function for processing data to be processed corresponding to the quantization parameters.

Optionally, the apparatus further comprises: and the calculation rule configuration module is used for configuring calculation rules of the rule engine so as to perform data processing on the fetched data to be processed and the processing rules corresponding to the quantization parameters based on the calculation rules established in the rule engine, and obtain a target assessment result.

Optionally, the apparatus further comprises: and the parameter identification determining module is used for determining the parameter identification of at least one quantization parameter in the assessment index decomposition model, and storing the data in the data warehouse according to the parameter identification so as to call the data to be processed from the data warehouse based on the parameter identification of the target quantization parameter of the target assessment task when the target assessment task is received.

Optionally, the apparatus further comprises: the rule verification module is used for acquiring at least one test case, wherein the test case comprises an expected result; processing at least one test case to obtain an actual result; and when the actual result is inconsistent with the expected result, processing the processing rule and the calculation rule, so as to process the data to be processed when the actual result is consistent with the expected result.

Optionally, the apparatus further comprises: the data processing condition setting module is used for configuring and determining the triggering condition and the examination constraint condition of the target examination result; the assessment constraint condition comprises at least one of an assessment period, an assessment task switch, an assessment expiration date and an assessment project.

Optionally, the apparatus further comprises: an event type determination module, the module comprising: the event type determining unit is used for determining a target assessment index decomposition model when the condition that the data processing condition is met is detected, and determining the event type corresponding to the assessment index of the last level based on the target assessment index decomposition model; the quantization parameter processing unit is used for accessing the quantization parameter corresponding to the assessment index of the last level into the rule engine for processing based on the event type; and the assessment result determining unit is used for determining a target assessment result based on the processing result of the rule engine.

Optionally, the quantization parameter processing unit is configured to extract an event type quantization parameter data set from the data warehouse if the event type is an event type, and bring quantization parameters corresponding to the event type into the rule engine for calculation, so as to obtain each sub-evaluation result, so that a target assessment result is obtained by accumulating the sub-evaluation results; and if the event type is the target type, extracting the data to be processed corresponding to the target type quantization parameter from the data warehouse so as to process the data to be processed based on the rule engine.

The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 11, the electronic device 10 includes at least one processor 11, and a memory such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as data processing methods.

In some embodiments, the data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the data processing method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out data processing methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

Example five

The embodiment of the invention also provides a computer readable storage medium, the computer readable storage medium stores computer instructions for causing a processor to execute a data processing method, the method comprising:

respectively determining an assessment index decomposition model corresponding to at least one service to be examined; the assessment index decomposition model corresponds to a tree diagram, the tree diagram comprises at least three levels, each level comprises at least one level node, the level nodes correspond to assessment indexes, and the assessment index of the last level is associated with at least one quantization parameter; configuring at least one processing rule for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule; and configuring a data warehouse for the assessment index decomposition model, so as to call the data to be processed corresponding to the quantization parameters in the assessment index decomposition model from the data warehouse when the condition of data processing is detected to be met, and carrying out data processing on the called data to be processed and the processing rules corresponding to the quantization parameters based on the calculation rules created in the rule engine, thereby obtaining a target assessment result.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of data processing, comprising:

At least one processing rule is configured for the last level node in the assessment index decomposition model so as to process the data to be processed corresponding to the quantization parameter based on the processing rule;

2. The method of claim 1, wherein the determining the assessment indicator decomposition model corresponding to the at least one service to be examined, respectively, comprises:

for each service to be checked, determining at least one check index associated with the current service to be checked, and refining the at least one check index to obtain a sub-check index corresponding to the check index;

taking the sub-assessment index as the assessment index, refining the assessment index to obtain a sub-assessment index of the next level, and repeatedly executing the operation of taking the sub-assessment index as the assessment index to determine the assessment index of the next level until the assessment index of the last level is obtained;

And configuring at least one quantization parameter for the assessment index of the last level to obtain a tree diagram corresponding to the current service to be assessed, and taking the tree diagram as the assessment index decomposition model.

3. The method of claim 1, wherein configuring at least one processing rule for a last level node in the assessment-index decomposition model comprises:

configuring event types for the assessment indexes of the last hierarchy, and configuring at least one corresponding processing rule based on the event types;

the event type comprises a target type or an event type, the quantization parameters of the event type comprise at least one group, and the processing rule is a processing function for processing data to be processed corresponding to the quantization parameters.

4. The method as recited in claim 1, further comprising:

and configuring calculation rules of a rule engine to perform data processing on the fetched data to be processed and the processing rules corresponding to the quantization parameters based on the calculation rules established in the rule engine, so as to obtain a target assessment result.

5. The method as recited in claim 1, further comprising:

And determining a parameter identifier of at least one quantization parameter in the assessment index decomposition model, and storing data in the data warehouse according to the parameter identifier so as to call the data to be processed from the data warehouse based on the parameter identifier of the target quantization parameter of the target assessment task when the target assessment task is received.

6. The method as recited in claim 1, further comprising:

acquiring at least one test case, wherein the test case comprises a desired result;

processing the at least one test case to obtain an actual result;

and when the actual result and the expected result are inconsistent, processing the processing rule and the calculation rule, so as to process the data to be processed when the actual result and the expected result are consistent.

7. The method as recited in claim 1, further comprising:

setting data processing conditions;

the setting of the data processing conditions includes:

configuring and determining a triggering condition and an assessment constraint condition of a target assessment result;

the assessment constraint condition comprises at least one of an assessment period, an assessment task switch, an assessment expiration date and an assessment project.

8. The method as recited in claim 1, further comprising:

when the condition that the data processing condition is met is detected, determining a target assessment index decomposition model, and determining an event type corresponding to the assessment index of the last level based on the target assessment index decomposition model;

based on the event type, the quantization parameter corresponding to the assessment index of the last level is accessed into a rule engine for processing;

and determining the target assessment result based on the processing result of the rule engine.

9. The method of claim 8, wherein the accessing quantization parameters corresponding to the assessment indicator of the last hierarchy into a rules engine for processing based on the event type comprises:

if the event type is event type, extracting event type quantized parameter data sets from the data warehouse, and carrying quantized parameters corresponding to the event type into a rule engine for calculation to obtain each sub-evaluation result, so as to obtain the target assessment result through accumulation of the sub-evaluation results;

and if the event type is the target type, extracting the data to be processed corresponding to the target type quantization parameter from the data warehouse so as to process the data to be processed based on the rule engine.

10. A data processing apparatus, comprising:

the assessment result acquisition module is used for configuring a data warehouse for the assessment index decomposition model, when the condition that the data processing condition is met is detected, retrieving data to be processed corresponding to the quantization parameter in the assessment index decomposition model from the data warehouse, and carrying out data processing on the retrieved data to be processed and the processing rule corresponding to the quantization parameter based on the calculation rule created in the rule engine to obtain a target assessment result.