CN112686527B - Service data quality checking method and system - Google Patents
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Abstract
The invention discloses a business data quality checking method and a system, comprising the following steps: acquiring a data quality rule set of a service data set to be checked; constructing a multi-layer tree structure diagram according to rules in the data quality rule set and service types to which the rules belong; in the multi-layer tree structure diagram, sub-node local weights are distributed according to the number of rules and service types, sub-node global weights are obtained according to father-level node global weights and sub-node local weights, and sub-node scores are obtained according to father-level node total scores and sub-node local weights; and carrying out quality check on the service data set to be checked according to the sub-node global weight and the sub-node score corresponding to each rule and the service type to which each rule belongs. And automatically generating a multi-layer tree structure diagram with a default duty ratio according to the checked service data set and the data quality rule set thereof, and flexibly adjusting the duty ratio of the child nodes according to the weight and the score of the parent node as required, thereby improving the flexibility of data quality check.
Description
Technical Field
The present invention relates to the field of data management technologies, and in particular, to a method and a system for checking quality of service data.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In enterprise data normalization processes, it is desirable to manage feedback value to business through data normalization, emphasizing the importance of data quality. In this process, low quality data is inevitably generated, and the quality of the data is affected by large-scale data initialization, problem diffusion caused by unprocessed historical data and low quality data generated by emergency service. The method controls the generation probability of low-quality data, discovers the low-quality data in time and processes the low-quality data effectively, and is a measure which can be organized and developed by enterprises. Therefore, the accurate understanding of enterprise data quality management does not generate low-quality data, but reduces and controls the generation rate and the existence rate of the low-quality data through scientific, effective and professional management and technical support, discovers the low-quality data in time and performs effective processing, and controls the high health degree of a standard coding library.
In the process of data management, the overall quality condition and various quality problems of all the current data are required to be known, and only the quality condition of all the current data is completely known, data adjustment and data problem prevention can be performed in a targeted manner.
In many data quality management software at present, comprehensive scoring of data quality and effective flow mechanisms for deeply tracking each data quality problem according to scoring are imperfect, and especially the ratio of scoring for the severity of each data quality problem cannot be effectively controlled, even once the ratio of the problem is determined, the scoring scheme cannot be adjusted again, self-adaptive adjustment cannot be performed on the scoring model according to actual business data volume, and the overall data quality checking mode has lower flexibility, so that the data quality checking accuracy is lower.
Disclosure of Invention
In order to solve the above problems, the present invention provides a service data quality checking method and system, which automatically generates a multi-layer tree structure diagram with default duty ratio according to the checked service data set and the data quality rule set thereof, and can flexibly adjust the duty ratio of the child nodes according to the father node weight and score as required, thereby improving the flexibility of data quality checking.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for checking quality of service data, including:
acquiring a data quality rule set of a service data set to be checked;
constructing a multi-layer tree structure diagram according to rules in the data quality rule set and service types to which the rules belong;
in the multi-layer tree structure diagram, sub-node local weights are distributed according to the number of rules and service types, sub-node global weights are obtained according to father-level node global weights and sub-node local weights, and sub-node scores are obtained according to father-level node total scores and sub-node local weights;
and carrying out quality check on the service data set to be checked according to the sub-node global weight and the sub-node score corresponding to each rule and the service type to which each rule belongs.
In a second aspect, the present invention provides a service data quality checking system, including:
the data acquisition module is configured to acquire a data quality rule set of a business data set to be checked;
the model construction module is configured to construct a multi-layer tree structure diagram according to rules in the data quality rule set and the service type to which the rules belong;
the weight distribution module is configured to distribute sub-node local weights according to the number of rules and service types in the multi-layer tree structure diagram, obtain sub-node global weights according to the parent node global weights and the sub-node local weights, and obtain sub-node scores according to the parent node total scores and the sub-node local weights;
and the quality checking module is configured to perform quality checking on the service data set to be checked according to the global weight and the sub node score of the sub node corresponding to each rule and the service type to which the rule belongs.
In a third aspect, the invention provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention automatically generates the service data quality check scoring model in a mode, does not need to be added and set one by an operator, effectively reduces the workload of the operator, reduces the data errors generated in the operation process of the operator, and integrally improves the working efficiency and the working quality of the operator.
In the service data quality check scoring model, the scoring weight and the total score of each item such as the service type or rule can be readjusted and set according to the requirements and the application scenes, and the scoring model with default duty ratio can be automatically generated according to the checked quality rule, so that the controllability of operators on the scoring model is integrally improved.
According to the invention, a plurality of sets of scoring models can be generated according to the rule set for checking the data quality, and after the scoring proportion of the father level is adjusted in the scoring models, each item subset can be dynamically adjusted and allocated again according to the change of the father level, so that the whole scoring model is simple, flexible and diversified in operation and management and convenient to use.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a method for checking quality of service data according to embodiment 1 of the present invention.
The specific embodiment is as follows:
the invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1, this embodiment provides a service data quality checking method, including:
s1: acquiring a data quality rule set of a service data set to be checked;
s2: constructing a multi-layer tree structure diagram according to rules in the data quality rule set and service types to which the rules belong;
s3: in the multi-layer tree structure diagram, sub-node local weights are distributed according to the number of rules and service types, sub-node global weights are obtained according to father-level node global weights and sub-node local weights, and sub-node scores are obtained according to father-level node total scores and sub-node local weights;
s4: and carrying out quality check on the service data set to be checked according to the sub-node global weight and the sub-node score corresponding to each rule and the service type to which each rule belongs.
In the step S1, the data quality rule set is a rule set packet in which a series of rules for data quality verification are packaged and stored according to the data quality control heavy point, and the information of the rules includes the name of the rule, the category of the rule, the service classification to which the rule belongs, and the like.
In the step S2, a four-layer tree structure system for grading the data quality, namely a grading model is constructed according to the first-level service classification, the second-level service classification and the specific rule information of the rules in the data quality rule set, and the grading model is used for grading the data quality grading system automatically generated according to the check data quality rule set to be maintained.
The four-layer tree structure system comprises names, total scores, rule set packages and the like; each layer of nodes comprises a scoring node number, a scoring node name, a scoring node local weight, a scoring node global weight and a scoring node total score;
the partial weight of the scoring node is the weight ratio of the scoring node to the node of the node level; the global weight of the scoring node is the weight ratio of the node to the whole scoring model.
Specifically:
the first layer node is the root node of the scoring model: the node name of the root node is the name of the scoring model; the local weight of the node is set to be 1, the global weight of the node is set to be 1, and the total score of the node is the total score of the model.
The second-layer node is a first-level traffic classification of all rules in the rule set package: the node name is the name of the service class, the node local weight is a weight value which is distributed evenly according to the number of the first-level service classes, the node global weight is the global weight of the father-level node multiplied by the local weight value of the node, and the node total score is the total score of the father-level node multiplied by the local weight value of the node.
The third level node is a second level traffic classification for all rules in the rule set package: the node name is the name of the service class, the node local weight is a weight value which is evenly distributed according to the number of the two-level service classes of the node level, the node global weight is the global weight of the father level node multiplied by the local weight value of the node, and the node total score is the total score of the father level node multiplied by the local weight value of the node.
The fourth layer node is all specific rules in the rule set package: the node name is a rule name, the node local weight is a weight value which is evenly distributed according to the number of rules of the node level, the node global weight is a total weight of a father level node multiplied by a local weight value of the node, and the node total score is a score obtained by multiplying a total score of the father level node by the local weight value of the node.
Preferably, the local weight of the parent node, the global weight of the parent node and the total score of the parent node can be adaptively changed, in this embodiment, the score and the score are distributed evenly, if the score of the default average setting cannot meet the requirement, the score of the corresponding node can be adjusted according to the requirement, and if the score of the parent is adjusted, each subset under the parent is dynamically distributed again according to the change of the score of the parent.
Preferably, if multiple sets of scoring schemes are required to be formulated according to the same set of rules for checking, a new set of scoring models can be generated again according to the set of rules for checking the quality of data, and the scoring proportion meeting the requirements can be prepared again according to the model automatically generated by the system.
Example 2
The present embodiment provides a service data quality checking system, including:
the data acquisition module is configured to acquire a data quality rule set of a business data set to be checked;
the model construction module is configured to construct a multi-layer tree structure diagram according to rules in the data quality rule set and the service type to which the rules belong;
the weight distribution module is configured to distribute sub-node local weights according to the number of rules and service types in the multi-layer tree structure diagram, obtain sub-node global weights according to the parent node global weights and the sub-node local weights, and obtain sub-node scores according to the parent node total scores and the sub-node local weights;
and the quality checking module is configured to perform quality checking on the service data set to be checked according to the global weight and the sub node score of the sub node corresponding to each rule and the service type to which the rule belongs.
Here, it should be noted that the above-mentioned modules correspond to steps S1 to S4 in embodiment 1, and the above-mentioned modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method described in embodiment 1. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly embodied as a hardware processor executing or executed with a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (8)
1. A method for checking quality of service data, comprising:
acquiring a data quality rule set of a service data set to be checked;
constructing a multi-layer tree structure diagram according to rules in the data quality rule set and service types to which the rules belong;
in the multi-layer tree structure diagram, sub-node local weights are distributed according to the number of rules and service types, sub-node global weights are obtained according to father-level node global weights and sub-node local weights, and sub-node scores are obtained according to father-level node total scores and sub-node local weights;
performing quality check on the service data set to be checked according to each rule and the sub-node global weight and the sub-node score corresponding to the service type to which the rule belongs;
constructing a four-layer tree structure diagram according to the first-level service classification, the second-level service classification, specific rule information and root nodes of the rules in the data quality rule set; each layer of nodes of the multi-layer tree structure chart comprises a scoring node number, a scoring node name, a scoring node local weight, a scoring node global weight and a scoring node total score; the partial weight of the scoring node is the weight ratio of the scoring node to the node of the node level; the global weight of the scoring node is the weight ratio of the node to the whole scoring model, and specifically comprises the following steps: the first layer node is the root node of the scoring model: the node name of the root node is the name of the scoring model; the local weight of the node is set to be 1, the global weight of the node is set to be 1, and the total score of the node is the total score of the model;
the second-layer node is a first-level traffic classification of all rules in the rule set package: the node name is the name of the service classification, the node local weight is a weight value which is evenly distributed according to the number of the first-level service classification, the node global weight is the global weight of the father-level node multiplied by the local weight value of the node, and the node total score is the total score of the father-level node multiplied by the local weight value of the node;
the third level node is a second level traffic classification for all rules in the rule set package: the node name is the name of the service class, the node local weight is a weight value which is evenly distributed according to the number of the two-level service classes of the node level, the node global weight is the global weight of the father level node multiplied by the local weight value of the node, and the node total score is the total score of the father level node multiplied by the local weight value of the node;
the fourth layer node is all specific rules in the rule set package: the node name is a rule name, the node local weight is a weight value which is evenly distributed according to the number of rules of the node level, the node global weight is the global weight of a father level node multiplied by the local weight value of the node, and the node total score is the score obtained by multiplying the total score of the father level node by the local weight value of the node;
the quality checking method further comprises the following steps: and adjusting the score ratio of the corresponding node according to the requirement, and if the global weight of the parent node and/or the total score of the parent node are adjusted, dynamically distributing the global weight of the child node and the score of the child node of each item of child node under the parent level again according to the change of the parent level ratio.
2. The method of claim 1, wherein the local weight of the child node is a weight ratio of the node level with the child node.
3. The method for checking quality of service data according to claim 1, wherein the global weight of the child node is a weight ratio of the child node to the whole four-layer tree structure.
4. The traffic data quality check method according to claim 1, wherein the child node global weight is a parent node global weight multiplied by a child node local weight.
5. The method of claim 1, wherein the child node score is a parent node total score multiplied by a child node local weight.
6. A service data quality check system, comprising:
the data acquisition module is configured to acquire a data quality rule set of a business data set to be checked;
the model construction module is configured to construct a multi-layer tree structure diagram according to rules in the data quality rule set and the service type to which the rules belong;
the weight distribution module is configured to distribute sub-node local weights according to the number of rules and service types in the multi-layer tree structure diagram, obtain sub-node global weights according to the parent node global weights and the sub-node local weights, and obtain sub-node scores according to the parent node total scores and the sub-node local weights;
the quality checking module is configured to perform quality checking on the service data set to be checked according to each rule and the sub-node global weight and the sub-node score corresponding to the service type to which the rule belongs;
constructing a four-layer tree structure diagram according to the first-level service classification, the second-level service classification, specific rule information and root nodes of the rules in the data quality rule set; each layer of nodes of the multi-layer tree structure chart comprises a scoring node number, a scoring node name, a scoring node local weight, a scoring node global weight and a scoring node total score; the partial weight of the scoring node is the weight ratio of the scoring node to the node of the node level; the global weight of the scoring node is the weight ratio of the node to the whole scoring model, and specifically comprises the following steps: the first layer node is the root node of the scoring model: the node name of the root node is the name of the scoring model; the local weight of the node is set to be 1, the global weight of the node is set to be 1, and the total score of the node is the total score of the model;
the second-layer node is a first-level traffic classification of all rules in the rule set package: the node name is the name of the service classification, the node local weight is a weight value which is evenly distributed according to the number of the first-level service classification, the node global weight is the global weight of the father-level node multiplied by the local weight value of the node, and the node total score is the total score of the father-level node multiplied by the local weight value of the node;
the third level node is a second level traffic classification for all rules in the rule set package: the node name is the name of the service class, the node local weight is a weight value which is evenly distributed according to the number of the two-level service classes of the node level, the node global weight is the global weight of the father level node multiplied by the local weight value of the node, and the node total score is the total score of the father level node multiplied by the local weight value of the node;
the fourth layer node is all specific rules in the rule set package: the node name is a rule name, the node local weight is a weight value which is evenly distributed according to the number of rules of the node level, the node global weight is the global weight of a father level node multiplied by the local weight value of the node, and the node total score is the score obtained by multiplying the total score of the father level node by the local weight value of the node;
further comprises: and adjusting the score ratio of the corresponding node according to the requirement, and if the global weight of the parent node and/or the total score of the parent node are adjusted, dynamically distributing the global weight of the child node and the score of the child node of each item of child node under the parent level again according to the change of the parent level ratio.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of any one of claims 1-5.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any of claims 1-5.
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Country or region after: China Address after: No. 1237 Yingxiu Road, High tech Zone, Jinan City, Shandong Province, 250101 Applicant after: Zhongyang Health Technology Group Co.,Ltd. Address before: 12 / F, building 1, Aosheng building, 1166 Xinluo street, hi tech Zone, Jinan City, Shandong Province Applicant before: SHANDONG MSUNHEALTH TECHNOLOGY GROUP Co.,Ltd. Country or region before: China |
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