CN117634960A - Data processing method, device, equipment and storage medium - Google Patents
Data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a data processing method, a device, equipment and a storage medium. The method comprises the following steps: determining at least one assessment index corresponding to a target service, and determining an assessment level corresponding to the at least one assessment index; performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter; configuring processing rules for at least one node of the assessment index tree to update the assessment index tree; and processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result. The problem of low data processing efficiency is solved, and the data processing efficiency is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
Commercial banks are subjected to strict supervision and management, and the interior of the commercial banks is required to conform to a plurality of systems and specifications, so that a large number of assessment indexes with smaller granularity are related to different staff, but a plurality of assessment details are difficult to quantify and are pain points for formulating the assessment indexes.
In the technical scheme of related data processing, for assessment indexes with larger statistical ranges such as coverage rate, error rate, service continuity and the like, the result is quantized into service indexes such as the number of used hosts, working time length, error times and the like, and the related parameters of the service indexes are calculated based on a tree structure model. However, on one hand, because the calculation of part of the checking content is extremely complex, the user needs to pay attention to the parameter transmission correctness and the checking result correctness so as to meet the checking work requirement, and when the result is wrong, the error cause cannot be determined to be the parameter transmission error and the checking rule configuration error, or the checking result is checked to be in the correct interval but the parameter transmission error; on the other hand, only real-time determination of the automatic control rate and the stability rate is supported, and the application range is narrow. In summary, the efficiency of data processing is low.
Disclosure of Invention
The invention provides a data processing method, a device, equipment and a storage medium, which are used for improving the data processing efficiency.
According to an aspect of the present invention, there is provided a data processing method comprising:
determining at least one assessment index corresponding to a target service, and determining an assessment level corresponding to the at least one assessment index;
Performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter;
configuring processing rules for at least one node of the assessment index tree to update the assessment index tree;
and processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
Further, the performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree includes:
and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
Further, before configuring a processing rule for at least one node of the assessment indicator tree to update the assessment indicator tree, the method further includes:
and accessing a rule engine system to determine a target processing result corresponding to the service data based on the processing rule corresponding to each node called by the rule engine.
Further, after the assessment index tree is obtained, the method further includes:
Obtaining simulation parameters;
sequentially processing the simulation parameters based on the processing rules corresponding to each node in the assessment index tree to obtain a simulation processing result;
comparing the simulation processing result with a pre-input expected return value and an expected error code;
and updating the processing rules in the assessment index tree based on the comparison processing result.
Further, the updating the processing rule in the assessment index tree based on the comparison processing result includes:
and if the comparison processing result is that the simulation return value in the simulation processing result is different from the expected return value, and/or the simulation return code in the simulation processing result is different from the expected error code, the processing rule is reconfigured.
Further, the method further comprises:
and generating a monitoring report based on the comparison processing result, and distinguishing and displaying the differentiated information in the monitoring report.
According to another aspect of the present invention, there is provided a data processing apparatus comprising:
the assessment parameter determining module is used for determining at least one assessment index corresponding to the target service and determining an assessment level corresponding to the at least one assessment index;
The parameter processing module is used for performing tree diagram processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter;
the rule configuration module is used for configuring a processing rule for at least one node of the assessment index tree so as to update the assessment index tree;
and the data processing module is used for processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
Further, the parameter processing module is configured to:
and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
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, at least one assessment index corresponding to the target service is determined, and an assessment level corresponding to the at least one assessment index is determined; performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter; configuring processing rules for at least one node of the assessment index tree to update the assessment index tree; and processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result. The problem of low data processing efficiency is solved, and the data processing efficiency is improved.
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 provided according to an embodiment of the present invention;
FIG. 2 is a flow chart of a specific data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a specific assessment index tree according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a communication with a rules engine system provided in accordance with an embodiment of the present invention;
FIG. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 6 is a block diagram of an electronic device 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 is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, 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 or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the embodiment is applicable to a scenario of performing data processing based on a tree diagram, and the data processing method may be performed by a data processing apparatus, where the data processing apparatus may be implemented in a form of hardware and/or software and configured in a processor of an electronic device.
As shown in fig. 1, the data processing method includes the steps of:
s110, determining at least one assessment index corresponding to the target service, and determining an assessment level corresponding to the at least one assessment index.
The target service is a service to be checked, and may be determined according to a specific application scenario, for example, in order to check the bank staff, the target service may be determined according to a specific working content corresponding to each staff.
The assessment index is a quantifiable evaluation index associated with the target business.
It is understood that the assessment indicators for the same type of work content may be the same. In order to check the staff, a quantitative check index corresponding to the work content needs to be determined according to the specific work content of the staff. Considering that in some scenes, the importance degree of different assessment indexes is different, and/or some assessment indexes may also have more specific assessment indexes, so that the assessment level corresponding to the assessment indexes needs to be set.
The assessment level may be determined based on the importance and/or relevance of the assessment indicators.
Specifically, at least one assessment index corresponding to a target service is determined based on service content of the target service; and determining the importance degree and/or the association degree corresponding to each assessment index in all assessment indexes, and determining the assessment level corresponding to each assessment index.
Illustratively, determining at least one assessment index corresponding to the target service, and determining an assessment level corresponding to the at least one assessment index includes: firstly, determining a target service to be checked, wherein the check index can comprise a service description of the target service, for example, the check index can comprise at least one of a responsibility unit, a responsibility person and a service description, wherein the service description can comprise at least one of an index name, a check year, a weight score, an evaluation standard and a data source; further, for the convenience of computer calculation, a corresponding processing rule may be set for each index for converting the index into a number, for example, for an index name, a corresponding encoding rule may be set for determining an encoding corresponding to the index name. The method has the advantages that by setting the coding rule for the assessment index, the system development of research personnel can be facilitated, the assessment index is easily understood by related staff, the operation and the positioning are further performed on the assessment index, and the data source can be traced. Then, determining the importance degree corresponding to each assessment index for all the assessment indexes; and finally, based on the ranking results from high importance to low importance, taking one assessment index with the highest importance as a first-level assessment index, determining the number of assessment indexes set before ranking in the ranking results with the highest importance as a second-level assessment index, and removing the first-level assessment index and the remaining indexes of the second-level assessment index as third-level assessment indexes.
Optionally, the first-level assessment index is an overall assessment index of the target service, and the second-level assessment index is a specific assessment index corresponding to the overall assessment index. The tertiary assessment index may be an associated index to a particular assessment index.
Optionally, the assessment level is determined based on an organization structure corresponding to the target service, and the first-level assessment index is an assessment index of a company level, the second-level assessment index is an assessment index of a department level, and the third-level assessment index is an assessment index of an employee individual; further, an assessment weight corresponding to each level of assessment index is set for determining the contribution of each assessment index to the target service.
S120, performing tree diagram processing on at least one assessment index based on the assessment level to obtain an assessment index tree.
Wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter.
The check index parameter is a parameter associated with the check index of the leaf node of the last hierarchy, for example, the check index parameter may be a specific parameter required for calculating the check index.
In the tree diagram, each assessment index is taken as a basic unit of a tree, namely a Node (Node). Links between nodes are called branches. The nodes and branches form a tree graph. The tree graph may include root nodes, child nodes and leaf nodes, where the root node (root) is the top/beginning of the tree graph and nodes other than the root node are called child nodes (child). The child node of the last layer is called Leaf node (Leaf).
In this embodiment, performing tree graph processing on at least one assessment index based on an assessment level to obtain an assessment index tree, including: and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
Specifically, the first-level assessment index is used as a root node of the tree diagram; establishing connection between the first-level assessment index and the second-level assessment index to obtain a first-level child node of the tree diagram; establishing connection between the three-level assessment index and the second-level assessment index to obtain a next-level child node of the tree diagram; and determining the assessment index parameters corresponding to the three-level assessment indexes, taking each assessment index parameter as a leaf node of the three-level assessment index, and taking the tree diagram as an assessment index tree.
For each first-level assessment index, determining two assessment indexes with the highest degree of association with the first-level assessment index, taking the two assessment indexes as second-level assessment indexes, taking the first-level assessment index as a root node of a tree diagram, and establishing connection between the first-level assessment index and each second-level assessment index to obtain a first-level child node of the tree diagram; for each secondary assessment index, determining two assessment indexes with the highest association degree with the secondary assessment index from assessment indexes except the primary assessment index and the secondary assessment index, taking the two assessment indexes as tertiary assessment indexes, and establishing connection between the secondary assessment index and each tertiary assessment index corresponding to the secondary assessment index to obtain a next-layer child node of the tree diagram; further, for each three-level assessment index, determining a formula required for calculating the three-level assessment index, determining two specific parameters related to the three-level assessment index based on the formula, taking each parameter as an assessment index parameter, and establishing connection between the assessment index parameter and the three-level assessment index corresponding to the assessment index parameter to obtain leaf nodes of a tree diagram so as to obtain the tree diagram corresponding to the first-level assessment index corresponding to the target service; and taking the tree diagram corresponding to the first-level assessment index corresponding to the target service as an assessment index tree.
S130, configuring processing rules for at least one node of the assessment index tree to update the assessment index tree.
The processing rules include processing rules corresponding to each node in the assessment index tree, for example, for a parent node corresponding to a leaf node, the processing rules of the parent node may include calculation rules of assessment index parameters corresponding to all leaf nodes corresponding to the parent node.
Optionally, the processing rule may further include a maximum numerical range corresponding to the processing result, and if the maximum numerical range is exceeded, determining that the processing result is an erroneous result, and further determining a cause of the erroneous result.
It can be understood that, in order to obtain the assessment result corresponding to the target service, a corresponding node processing rule needs to be configured for each node in the assessment index tree to obtain the assessment index corresponding to the node, so as to obtain the assessment result corresponding to the target service.
Specifically, for each node in the assessment index tree, determining a calculation mode and/or a maximum value (i.e., a maximum value and a minimum value) of an assessment index corresponding to the node, and adding the calculation mode and/or the maximum value to an attribute of the node to update the assessment index tree.
For each node of the assessment index tree except for the leaf node of the last level, the processing rule is to calculate the assessment index corresponding to the node based on a weighted calculation mode. Specifically, for each node in the assessment index tree, determining a weight value corresponding to each child node of the node, and adding the weight value corresponding to the child node to the attribute of the child node to update the assessment index tree.
In this embodiment, before configuring a processing rule for at least one node of the assessment indicator tree to update the assessment indicator tree, the method further includes: and accessing a rule engine system to determine a target processing result corresponding to the service data based on the processing rule corresponding to each node called by the rule engine.
It can be understood that, for the same type of assessment index, the processing rules may be the same, so that the corresponding relationship between the data type and the processing rules may be determined, so as to obtain the corresponding relationship between the assessment index and the processing rules, and further determine the corresponding relationship between the nodes and the processing rules.
The rule engine comprises processing rules corresponding to all nodes, and the service data are data related to the target service and data corresponding to the assessment index parameters of the assessment index tree; the target processing result is a data processing result obtained by performing data processing on the service data based on the processing rules of all nodes in the assessment index tree.
Specifically, communication connection is established with a rule engine system, the rule engine determines a processing rule corresponding to the data type of each node in the assessment index tree based on the corresponding relation between the data type and the processing rule, adds the processing rule to the attribute of the corresponding node in the assessment index tree, and updates the assessment index tree; and based on the updated assessment index tree, carrying out data processing on the service data based on the processing rules in the assessment index tree to obtain a target processing result corresponding to the service data.
Illustratively, the assessment index is divided into a target class index and a process class index, wherein the target class index is a target progress that the assessment index should reach at a target time point, and for example, the target class index may be an assessment index corresponding to a range of years, quarters, months, and/or the like; the process class index may be a business continuity of the check index in a time period corresponding to the year, quarter and/or month, or may be an index related to data security or production operation in general if a target event occurs in the corresponding time period; setting a processing rule corresponding to the target class index under the condition of accessing the rule engine system, so that the target processing result is the result of a transaction associated with the target service; for the process type index, setting the corresponding processing rule so that the target processing result is the result of a plurality of transactions associated with the target service, namely, assuming that the index score is m i The result of the transaction is n i Its parameter is a i And b i Representing calculation among parameters, the target processing result corresponding to the target class index is: { m 1 ＝n 1 ,n 1 ＝a 1 *b 1 -a }; the target processing result corresponding to the process type index is as follows: { m i ＝∑n i ,n 1 ＝a 1 *b 1 ,n 2 ＝a 2 *b 2 ,……}。
The target class index is a relevant assessment index which is suitable for the specific assessment standard in the time frequency of the year, the quarter, the month and the like, wherein the assessment index is required to reach a certain progress at the time point of the milestone or to achieve a certain standard; the process type index is an index related to the business continuity of the check index in a certain time period or the data safety and the production operation, or a certain event is avoided in a certain time period.
Optionally, a processing rule corresponding to each assessment level is set in the rule engine, so that the corresponding processing rule is configured for the node of each assessment level in the assessment index tree to update the assessment index tree.
In this embodiment, after obtaining the assessment index tree, the method further includes: obtaining simulation parameters; processing the simulation parameters in sequence based on the processing rules corresponding to each node in the assessment index tree to obtain a simulation processing result; comparing the simulation processing result with a pre-input expected return value and an expected error code; and updating the processing rules in the assessment index tree based on the comparison processing result.
It can be appreciated that after the assessment index tree is obtained, the processing rule of the assessment index tree needs to be improved based on the simulation parameters and the ideal output results corresponding to the simulation parameters, so as to improve the accuracy of the assessment index tree, and further, the assessment index tree is applied to the real scene.
Wherein the simulation parameters are parameters corresponding to the assessment index parameters of the assessment index tree; the simulation processing result refers to processing the simulation parameters based on the processing rules corresponding to each node in the assessment index tree, and the processing result corresponding to each node in the assessment index tree is obtained; the expected return value is a theoretical value of the assessment result corresponding to the simulation parameter; the expected error code is a code corresponding to the type of error that occurs during processing of the analog parameters; the comparison process results may include agreement and disagreement.
Specifically, according to the boundary condition corresponding to each assessment index parameter, determining a value range corresponding to the assessment index parameter, determining a parameter corresponding to the assessment index parameter based on the value range, and taking the parameter as a simulation parameter to obtain a simulation parameter set, wherein the simulation parameter set can comprise simulation parameters in the value range and simulation parameters not in the value range; for each simulation parameter in the simulation parameter set, processing the simulation parameter based on a processing rule corresponding to each node in the assessment index tree to obtain a processing result corresponding to each node, further obtaining a return value and an error code corresponding to the target service, and taking the return value and the error code as simulation processing results; determining whether the return value in the simulation processing result is the same as a pre-input expected return value, and whether the error code in the simulation processing result is the same as the pre-input expected error code; if the return value in the simulation processing result is the same as the expected return value input in advance, and the error code in the simulation processing result is the same as the expected error code input in advance, the processing rule in the assessment index tree is not updated.
Further, if the comparison processing result is that the simulation return value in the simulation processing result is different from the expected return value, and/or the simulation return code in the simulation processing result is different from the expected error code, the processing rule is reconfigured.
The simulation return value is a return value corresponding to the target service, which is obtained by processing the simulation parameters based on the processing rule corresponding to each node in the assessment index tree. The analog return code includes a code corresponding to the type of error that occurred during processing of the analog parameters.
Specifically, if the return value in the simulation result is different from the expected return value input in advance, and/or if the error code in the simulation result is different from the expected error code input in advance, the processing rule in the assessment index tree is adjusted, and the adjusted processing rule is configured to each node in the assessment index tree again, so that the return value in the simulation result is the same as the expected return value input in advance, and the error code in the simulation result is the same as the expected error code input in advance.
In this embodiment, after obtaining the assessment index tree, the method further includes: and generating a monitoring report based on the comparison processing result, and distinguishing and displaying the differential information in the monitoring report.
The monitoring report may include a simulation result, an expected result, and a comparison result corresponding to each simulation parameter, that is, the monitoring report may include a simulation return value, a simulation return code, an expected return value, an expected return code, a comparison result of the simulation return value and the expected return value, and a comparison result of the simulation return code and the expected return code corresponding to each simulation parameter. The differencing information may be a result of a comparison of the simulated return value to the expected return value and a result of a comparison of the simulated return code to the expected return code. The differential display may be in different fonts, for example, the comparison of the analog parameters may be displayed in different font colors in the monitoring report.
Specifically, for each simulation parameter, determining a simulation return value, a simulation return code, an expected return value, an expected return code, a comparison result of the simulation return value and the expected return value, and a comparison result of the simulation return code and the expected return code corresponding to the simulation parameter; the results are used as monitoring results corresponding to the simulation parameters and are displayed in a monitoring report; the comparison result of the simulation return value and the expected return value, and the comparison result of the simulation return code and the expected return code, which are different, are taken as the highlighting comparison result, and highlighting is carried out in the monitoring report; and highlighting the simulation parameters and the error types corresponding to the highlighting comparison result at the set positions in the monitoring report.
For each simulation parameter, determining a simulation return value, a simulation return code, an expected return value, an expected return code, a comparison result of the simulation return value and the expected return value and a comparison result of the simulation return code and the expected return code corresponding to the simulation parameter; the results are used as monitoring results corresponding to the simulation parameters and are displayed in a monitoring report; the comparison result that the simulation return value is different from the expected return value and the comparison result that the simulation return code is different from the expected return code are taken as the outstanding comparison result; the highlighting comparison result, the simulation parameters, the error types and the corresponding processing rules corresponding to the highlighting comparison result are all displayed in the monitoring report in red fonts, and the advantage of this is that the business personnel can correct the input parameters or the processing rules in time.
S140, processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
The service data may include real data corresponding to an assessment index parameter related to the target service, and the target processing result includes a processing result obtained by processing the service data related to the target service based on a processing rule of the assessment index tree, for example, the target processing result may be an output value, that is, a return value and an error code, of the assessment index tree corresponding to the service data.
Specifically, for each data in the service data related to the target service, determining an assessment index parameter corresponding to the data; and processing the assessment index parameters based on the processing rules corresponding to the updated assessment index tree to obtain a return value and an error code corresponding to the assessment index parameters, and taking the return value and the error code as a target processing result associated with the target service.
According to the technical scheme, on one hand, the processing rules are set on the basis of the assessment level, and the relation between assessment indexes is described on the basis of the tree diagram, so that assessment index parameters and corresponding assessment rules can be determined more intuitively, effectively and accurately, and the accuracy of data processing results is improved; on the other hand, based on the comparison result, a monitoring report is generated, and the processing result which is different from the expected value is highlighted, so that service personnel can correct the input parameters or the processing rules of the assessment index tree in time, the data processing efficiency is improved, the target processing result can be monitored regularly or according to the requirement, and the normal development of the assessment work is ensured.
Fig. 2 is a flowchart of a specific data processing method according to an embodiment of the present invention, and as shown in fig. 2, the data processing method includes:
S210, determining at least one assessment index corresponding to the target service, and determining an assessment level corresponding to the at least one assessment index.
Specifically, determining a service description and a quantized assessment index corresponding to a target service, wherein the assessment index comprises a responsibility unit, a responsibility person and the service description, wherein the service description comprises key fields such as index numbers, index names, assessment years, weight scores, assessment standards, data sources and the like, and the design requirements of data contents corresponding to the fields comprise convenience for research and development personnel to carry out system development, operation and positioning of staff responsible for assessment, reading understanding of the personnel to be assessed, data sources tracing and the like. The data of each field needs to be described by mathematical language so that staff in charge of checking can modify the data of each node of the checking index tree. Based on the correlation degree among the assessment indexes, parameters corresponding to the assessment indexes are independent, and a business principle of covering all evaluation standards of the first-level assessment indexes is needed, so that assessment in different fields is facilitated. And determining the importance degree and/or the association degree corresponding to each assessment index in all assessment indexes, and determining the assessment level corresponding to each assessment index.
S220, performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree.
Referring to fig. 3, each assessment index is determined as a tree structure, the tree degree is generally within 4, the left subtree and the right subtree in the assessment index tree can be assessment indexes with smaller granularity, and the leaf nodes are parameters required for calculating the assessment index of the previous layer; the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter, namely, the specific parameter required by calculating the three-level assessment index; establishing connection between the assessment index parameters and the three-level assessment indexes corresponding to the assessment index parameters to obtain leaf nodes of a tree diagram so as to obtain a tree diagram corresponding to the first-level assessment index corresponding to the target service; and taking the tree diagram corresponding to the first-level assessment index corresponding to the target service as an assessment index tree.
S230, accessing a rule engine system, and calling a processing rule corresponding to each node based on the rule engine.
Specifically, communication connection is established with a rule engine system, and the assessment indexes are divided into target class indexes and process class indexes, wherein the target class indexes are related assessment indexes which are suitable for specific assessment standards in time frequency of years, quarters, months and the like, and the assessment indexes are required to reach a certain progress or achieve a certain standard at a milestone time point; the process type index is an index related to the service continuity of the check index in a certain time period or the process type index is usually data safety and production operation, or a certain event is avoided in a certain time period. The rule engine is provided with a target class index and a corresponding processing rule thereof, a first-level process class index and a corresponding processing rule thereof, an index type corresponding to each node in the check index tree is determined, and the processing rule corresponding to the index type is determined based on the rule engine so as to configure the processing rule of the node.
Illustratively, determining, based on the rule engine, a processing rule corresponding to the index type to configure the processing rule of the node includes: the staff needs to configure the relevant calculation rules in the rule engine system, and the rule engine system is used for deducting the calculation rules described by natural language or pseudo codes to carry out mathematical calculation on the input parameters, for example, a parameter module is used for carrying out operation modes such as dragging, pulling and/or dragging, or a text editor with specific parameters is used for setting the corresponding calculation rules.
S240, obtaining simulation parameters, and processing the simulation parameters in sequence based on processing rules corresponding to each node in the assessment index tree to obtain a simulation return value and a simulation return code.
Specifically, according to the value range of each parameter, determining the simulation parameter, and an expected return value and an expected error code corresponding to the simulation parameter; the simulation parameters include an upper boundary value and a lower boundary value corresponding to the value range, and values within the value range and values outside the value range, for example, the simulation parameters may include parameters with 0 or parameters close to 0, and may also include simulation parameters related to precision calculation.
And processing the simulation parameters corresponding to the target class indexes and the simulation parameters corresponding to the process class indexes in sequence based on the processing rules corresponding to each node in the assessment index tree respectively to obtain simulation return values and simulation return codes corresponding to the simulation parameters.
S250, respectively comparing the simulated return value with the expected return value input in advance and the simulated return code with the expected error code to obtain a comparison processing result.
Referring to fig. 4, the expected return value of the target class indicator should be the result of a single transaction, and the expected return value of the process class indicator should be the sum of the results of multiple transactions and their counterparts. The error code can be a representative error code which is arranged according to the business rule, is convenient to map and has high readability.
Specifically, the analog return value and the expected return value inputted in advance, and the analog return code and the expected error code are compared, respectively, to determine whether the analog return value is identical to the expected return value, and whether the analog return code is identical to the expected error code inputted in advance.
And S260, updating the processing rules in the assessment index tree based on the comparison processing result, generating a monitoring report based on the comparison processing result, and displaying the differentiated information in the monitoring report in a distinguishing way.
In one aspect, if the comparison result is that the simulated return value is different from the expected return value and/or the simulated return code is different from the expected error code in the simulated result, the processing rule is adjusted and reconfigured based on the rule engine system until the comparison result is that the simulated return value is the same as the expected return value in the simulated result and the simulated return code is the same as the expected error code in the simulated result.
On the other hand, for each simulation parameter, determining a simulation return value, a simulation return code, an expected return value, an expected return code, a comparison result of the simulation return value and the expected return value and a comparison result of the simulation return code and the expected return code corresponding to the simulation parameter; the results are used as monitoring results corresponding to the simulation parameters and are displayed in a monitoring report; the comparison result of the simulation return value and the expected return value, and the comparison result of the simulation return code and the expected return code, which are different, are taken as the highlighting comparison result, and highlighting is carried out in the monitoring report; the simulation parameters and error types corresponding to the highlighting comparison result are also highlighted at the set positions in the monitoring report, so that service personnel can correct the input parameters or configured rules in time.
S270, processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
Specifically, for service data related to a target service, determining an assessment index parameter corresponding to the service data; and processing the assessment index parameters based on the processing rules corresponding to the updated assessment index tree to obtain a return value corresponding to the assessment index parameters, and taking the return value as a target processing result associated with the target service.
Fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention, where the embodiment is applicable to a scenario of performing data processing based on a tree diagram, and the apparatus may be implemented in hardware and/or software, and integrated into a processor of an electronic device with an application development function.
As shown in fig. 5, the data processing apparatus includes: an assessment parameter determining module 501, configured to determine at least one assessment index corresponding to a target service, and determine an assessment level corresponding to the at least one assessment index; the parameter processing module 502 is configured to perform a tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter; a rule configuration module 503, configured to configure a processing rule for at least one node of the assessment index tree, so as to update the assessment index tree; and the data processing module 504 is configured to process the received service data related to the target service based on the updated assessment index tree, so as to obtain a target processing result. The problem of low data processing efficiency is solved, and the data processing efficiency is improved.
Optionally, the parameter processing module 502 is specifically configured to:
and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
And sending the certificate to be updated and the signature information to the zero trust proxy based on the data processing terminal so that the zero trust proxy receives the signature information and the certificate to be updated.
Optionally, the apparatus further comprises an engine access module, where the engine access module is configured to:
and accessing a rule engine system to determine a target processing result corresponding to the service data based on the processing rule corresponding to each node called by the rule engine.
Optionally, the device further includes a rule updating module, where the rule updating module is specifically configured to:
obtaining simulation parameters;
sequentially processing the simulation parameters based on the processing rules corresponding to each node in the assessment index tree to obtain a simulation processing result;
comparing the simulation processing result with a pre-input expected return value and an expected error code;
and updating the processing rules in the assessment index tree based on the comparison processing result.
Optionally, the rule updating module includes a rule updating unit, where the rule updating unit is specifically configured to:
and if the comparison processing result is that the simulation return value in the simulation processing result is different from the expected return value, and/or the simulation return code in the simulation processing result is different from the expected error code, the processing rule is reconfigured.
Optionally, the rule updating module further includes a report generating unit, where the report generating unit is specifically configured to:
and generating a monitoring report based on the comparison processing result, and distinguishing and displaying the differentiated information in the monitoring report.
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.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention. Electronic devices are 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. 6, 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, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may 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 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.
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 (10)
1. A method of data processing, comprising:
determining at least one assessment index corresponding to a target service, and determining an assessment level corresponding to the at least one assessment index;
performing tree graph processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter;
Configuring processing rules for at least one node of the assessment index tree to update the assessment index tree;
and processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
2. The method of claim 1, wherein the performing tree graph processing on the at least one assessment indicator based on the assessment level to obtain an assessment indicator tree comprises:
and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
3. The method of claim 1, further comprising, prior to said configuring processing rules for at least one node of the assessment indicator tree to update the assessment indicator tree:
and accessing a rule engine system to determine a target processing result corresponding to the service data based on the processing rule corresponding to each node called by the rule engine.
4. The method of claim 1, wherein after obtaining the assessment index tree, the method further comprises:
obtaining simulation parameters;
Sequentially processing the simulation parameters based on the processing rules corresponding to each node in the assessment index tree to obtain a simulation processing result;
comparing the simulation processing result with a pre-input expected return value and an expected error code;
and updating the processing rules in the assessment index tree based on the comparison processing result.
5. The method of claim 4, wherein updating the processing rules in the assessment index tree based on the comparison processing results comprises:
and if the comparison processing result is that the simulation return value in the simulation processing result is different from the expected return value, and/or the simulation return code in the simulation processing result is different from the expected error code, the processing rule is reconfigured.
6. The method as recited in claim 4, further comprising:
and generating a monitoring report based on the comparison processing result, and distinguishing and displaying the differentiated information in the monitoring report.
7. A data processing apparatus, comprising:
the assessment parameter determining module is used for determining at least one assessment index corresponding to the target service and determining an assessment level corresponding to the at least one assessment index;
The parameter processing module is used for performing tree diagram processing on the at least one assessment index based on the assessment level to obtain an assessment index tree; wherein, the leaf node of the last level of the assessment index tree corresponds to the assessment index parameter;
the rule configuration module is used for configuring a processing rule for at least one node of the assessment index tree so as to update the assessment index tree;
and the data processing module is used for processing the received service data related to the target service based on the updated assessment index tree to obtain a target processing result.
8. The apparatus of claim 7, wherein the parameter processing module is configured to:
and determining each level of the assessment index tree according to the order of the assessment levels from high to low to obtain the assessment index tree.
9. An electronic device, the electronic device comprising:
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 claims 1-6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing a processor to implement the data processing method of any one of claims 1-6 when executed.
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