Data governance method and apparatus, electronic device, computer storage medium

Abstract

The embodiment of the present disclosure provides a data governance method, and the specific implementation scheme is as follows: based on the obtained practice case, a reference model of the governance practice case is constructed; based on the reference model, a practice list including at least one practice item is generated; based on the pre-collected best practice item and the data analysis time window, the practice items in the practice list are combined to obtain at least one practice item combination; and based on the at least one practice item combination, the generation result of the target practice is determined. Through the embodiment, the efficiency of data governance is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer, and in particular, to a data governance method and device, electronic equipment and computer readable storage medium. BACKGROUND

[0002] With the continuous sinking of public security service to the grassroots, inefficient human work obviously cannot meet the safety management needs of the grassroots society. The management department of the grassroots society is increasingly collecting grassroots society big data to improve the effect of public security service. However, the grassroots society big data comes from various sources, and if there is no reasonable governance of these social big data, it may cause low running efficiency of big data algorithm system, invalid decision support and other problems. SUMMARY

[0003] Embodiments described herein provide a data governance method, a data governance device, an electronic equipment and a computer readable storage medium storing a computer program.

[0004] According to a first aspect of the present disclosure, a data governance method is provided. The method comprises: constructing a reference model of governance practice cases based on acquired practice cases; generating a practice list comprising at least one practice item based on the reference model; combining the practice items in the practice list based on pre-collected best practice items and a data analysis time window to obtain at least one practice item combination; and determining a generation result of a target practice based on the at least one practice item combination.

[0005] In some embodiments of the present disclosure, the above-mentioned data governance method further comprises: performing a simplification process on the practice items in the practice list based on the effect data of the practice cases and an evaluation algorithm.

[0006] In some embodiments of the present disclosure, the above-mentioned constructing a reference model of governance practice cases based on acquired practice cases comprises: performing an integrated process on the acquired practice cases to obtain at least one case item representing unity; screening to obtain a best case item based on the project indicators of the case items; dividing the best case item by domain type, source data storage mode and blood relationship; connecting the divided best case item by using a label; creating a dictionary library for the connected best case item to obtain the reference model of the governance practice cases.

[0007] In some embodiments of the present disclosure, the above-mentioned constructing a reference model of governance practice cases based on acquired practice cases further comprises: removing the practice cases with risks in the acquired practice cases through a data supervision mechanism, a data perception mechanism and a data sharing mechanism.

[0008] In some embodiments of the present disclosure, the combining the practice items in the practice list based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination comprises: determining a maximum number of best practice items in the practice list based on the pre-collected best practice items; combining the practice items in the practice list according to the maximum number to obtain combined practice items; determining execution relationships of the practice items in the combined practice items based on the data analysis time window of each best practice item; and determining the at least one practice item combination based on the combined practice items and the execution relationships of the practice items.

[0009] In some embodiments of the present disclosure, the determining the generation result of the target practice based on the at least one practice item combination comprises: taking each practice item combination as a practice, respectively executing the practice items in each practice item combination based on the execution relationships of the practice items in each practice item combination to obtain expected completion times of the practice items in different practices; sorting all practice item combinations based on the expected completion times of all practice items in each practice item combination; obtaining the target practice based on the sorting result of all practice item combinations; and drawing a basic relationship graph of the practice items in the target practice based on the arrangement order of the practice items in the target practice and the expected completion times of the practice items, and taking the basic relationship graph as the generation result of the target practice.

[0010] According to a second aspect of the present disclosure, a data governance device is provided. The device comprises: a construction unit configured to construct a reference model of a governance practice case based on an acquired practice case; a generation unit configured to generate a practice list comprising at least one practice item based on the reference model; a combination unit configured to combine the practice items in the practice list based on pre-collected best practice items and a data analysis time window to obtain at least one practice item combination; and a determination unit configured to determine a generation result of a target practice based on the at least one practice item combination.

[0011] In some embodiments of the present disclosure, the device further comprises: a simplification unit configured to perform a simplification processing on the practice items in the practice list based on effect data of the practice case and an evaluation algorithm.

[0012] In some embodiments of the present disclosure, the construction unit is further configured to: perform an integration processing on the acquired practice case to obtain at least one case item representing a unity; filter best case items based on item indicators of the case items; divide the best case items according to domain types, source data storage manners and blood relationship; connect the divided best case items by using labels; create a dictionary library for the connected best case items to obtain the reference model of the governance practice case.

[0013] In some embodiments of the present disclosure, the construction unit is further configured to remove risky practice cases in the obtained practice cases through a data supervision mechanism, a data awareness mechanism, and a data sharing mechanism.

[0014] In some embodiments of the present disclosure, the combination unit is further configured to determine a maximum number of best practice items in the practice list based on the pre-collected best practice items, combine the practice items in the practice list according to the maximum number to obtain combined practice items, determine an execution relationship of each practice item in the combined practice items based on a data analysis time window of each best practice item, and determine at least one practice item combination based on the combined practice items and the execution relationship of each practice item.

[0015] In some embodiments of the present disclosure, the determination unit is further configured to execute each practice item in each practice item combination respectively based on the execution relationship of the practice items in each practice item combination to obtain an expected completion time of each practice item in different practices, sort all practice item combinations based on the expected completion time of all practice items in each practice item combination, obtain a target practice based on a sorting result of all practice item combinations, draw a basic relationship graph of the practice items in the target practice based on a permutation order of the practice items in the target practice and the expected completion time of each practice item, and take the basic relationship graph as a generation result of the target practice.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, comprising at least one processor and at least one memory storing a computer program; wherein when the computer program is executed by the at least one processor, the device performs the steps of the method according to the first aspect of the present disclosure.

[0017] According to a fourth aspect of the present disclosure, a computer-readable storage medium storing a computer program is provided, wherein the computer program, when executed by a processor, implements the steps of the method according to the first aspect of the present disclosure.

[0018] The data governance method provided by the disclosure first constructs a reference model of governance practice cases based on the obtained practice cases, secondly generates a practice list including at least one practice item based on the reference model, thirdly combines the practice items in the practice list based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination, and finally determines the generation result of the target practice based on the at least one practice item combination. Thus, the obtained practice cases are combined together through the constructed reference model, so that the reference model can include the practice items of at least one item, providing a reliable basis for the generation of the target practice; the practice list including at least one practice item is generated based on the reference model, ensuring the generation effect of the target practice; and the practice items in the practice list are combined based on the pre-collected information, improving the reliability of the generation of the target practice. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the disclosure, the drawings of the embodiments will be briefly described below. It should be known that the drawings described below only relate to some embodiments of the disclosure, rather than limit the disclosure, wherein:

[0020] Figure 1 is a flowchart of one embodiment of the data governance method according to the disclosure;

[0021] Figure 2 is a flowchart of another embodiment of the data governance method according to the disclosure;

[0022] Figure 3 is a structural schematic diagram of one embodiment of the data governance device according to the disclosure;

[0023] Figure 4 is a block diagram of an electronic device for implementing the data governance method of the embodiments of the disclosure. DETAILED DESCRIPTION

[0024] In order to make the objects, technical solutions and advantages of the embodiments of the disclosure more clear, the technical solutions of the embodiments of the disclosure will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the disclosure, rather than all the embodiments. Based on the described embodiments of the disclosure, all other embodiments obtained by those skilled in the art without creative labor also belong to the scope of protection of the disclosure.

[0025] In order to solve the problem of low data governance efficiency in the prior art, the disclosure provides a simple and efficient data governance method,

[0026] Referring to Figure 1which shows a flow 100 of one embodiment of the data governance method according to the present disclosure, which comprises the following steps:

[0027] Step 101, based on the acquired practice cases, a reference model of the governance practice cases is constructed.

[0028] In this embodiment, the acquired practice cases are extracted from the massive multi-source social data governance, and are the practice schemes that have achieved remarkable results in data governance and can be applied to numerous grassroots social data.

[0029] In this embodiment, the practice cases are described using models or languages in terms of the process and details of the response to the big data governance problem, and the practice cases are the best practices that have been deeply understood, all of whose constituent elements are known, and can be directly analyzed and applied by the execution subject on which the data governance method runs.

[0030] In the field of big data governance decision-making, the core problem hindering the application of the case-driven method is the unclear understanding of the elements of the governance practice cases in this field, including the practice item composition of the practice cases, the scenario element and its characteristic analysis, and the index system of the practice effect. Therefore, the above problems can be effectively solved by constructing the reference model of the governance practice cases. In this embodiment, the reference model of the governance practice cases relies on the structured reference model, and the case-driven method is the core. The mapping relationship between the complex problem scenario and the structured best practice is established. The reference model is also a system for analyzing and processing the acquired practice cases in terms of the scene, the practice item of the practice cases (the constituent elements of the practice cases), and the effect of the practice cases. Through the reference model, practice items with various association relationships can be obtained.

[0031] From the perspective of the construction of the knowledge base of grassroots social big data governance in various places, the best practice scheme is mainly presented in the form of text reports, log documents, and interview reports. The text records in these channels are not regulated, which makes the best practice lack a unified representation and a corresponding expression model support, hindering the analysis of the best practice. At the same time, the best practice that has not been integrated and expressed is not conducive to the understanding, transmission, and application of the analysis subject, and may lead to the failure of best practice management. Finally, these best practices do not take into account the risks behind governance, including decision-making and leadership risks, data risks, technical risks, and data platform operation risks.

[0032] In order to extract the best practice in various fields such as emergency management, medical services, and social education, any one of the following three types of construction methods can be used to construct the reference model: framework modeling, ontology modeling, and knowledge element representation modeling. These three types of construction methods are conventional technologies, and will not be described here.

[0033] Step 102, generating a practice list including at least one practice item based on the reference model.

[0034] In this embodiment, the practice list is a list related to the target practice. For the practice demand of the data governance scene of the target practice (target project), a practice item conforming to the data governance scene of the target practice can be selected from the reference model.

[0035] In this embodiment, the target practice can be a collection of multiple data governance scenes, for example, the target practice belongs to the grassroots life big data governance scene, which can include the data governance scenes of urban rail transit, public security management, traffic safety management, life safety management, and production safety management. For a known practice case of a data governance scene in the target practice, an attribute similarity algorithm can be used to generate a practice list for the data governance scene of the target practice. For example, for the data governance scene of urban rail transit data, an attribute similarity algorithm is used to extract practice cases related to the data attributes of urban rail transit data in the reference model, and the practice cases are decomposed to generate a practice list including at least one practice item.

[0036] In this embodiment, for a large practice project, all work in the practice project is decomposed into the smallest unit, and the decomposed unit is a practice item.

[0037] In this embodiment, when generating the practice list, attention can be paid to the matching of the target scene of the target practice and the scene of the best practice in the reference model, and the integration of practice items in the similar scene best practice. The effect evaluation of the best practice is based on social grassroots multi-source data, and an effect evaluation index system and evaluation method of the best practice are constructed for typical big data quality problems, which provides decision basis for effect comparison and selection of the best practice.

[0038] Step 103, combining the practice items in the practice list based on the pre-collected best practice items and the data analysis time window, to obtain at least one practice item combination.

[0039] In this embodiment, the pre-collected best practice item is the smallest unit obtained after the reference model decomposes the practice case. For practice cases in different scenes, the practice case can be composed of a limited number of best practice items. Further, when each best practice item is executed, each best practice item has a corresponding data analysis time window, and the running time of the best practice item can be determined through the data analysis time window.

[0040] In this embodiment, the combination of best practice items can be perfected from the aspects of practice item constitution integrity and data analysis efficiency. The practice item constitution integrity means that when the practice items are integrated in the practice list, the number of practice items is maximized, and the negative effects caused by the missing practice items are avoided. From the aspect of data analysis efficiency, it is meant that in the public security scenario, the data analysis and decision support have a time window, and the implementation time of the practice items should be considered to complete the best practice with the highest efficiency.

[0041] The method for obtaining the practice item combination provided by the present disclosure mainly considers the practice item integrity and the analysis efficiency of the practice items from the actual data governance perspective. The better the practice item integrity, the more data categories are included, and the analysis is more comprehensive. In the process of analyzing these massive data, it is considered whether the data analysis can be completed within a reasonable and effective time. If the time cost is too high, the feasibility of the scheme is lower.

[0042] In step 104, the generation result of the target practice is determined based on the at least one practice item combination.

[0043] In this embodiment, for each practice item combination in the at least one practice item combination, the basic relationship graph of the practice items in the best practice can be drawn in combination with the arrangement order relationship between the practice items, which is used as the generation result of the best practice.

[0044] Optionally, the practice result estimation of each practice item combination can be performed to obtain the estimation results of all practice item combinations. The estimation results of all practice item combinations of the at least one practice item combination are sorted, and the practice item combination with the best estimation result in the sequence is selected as the target practice. The practice item combination corresponding to the target practice and the estimation result are used as the generation result of the target practice.

[0045] The data governance method provided by the present disclosure can be used for the urban public security scenario, and can combine the theories of management science, computer science, software engineering and other disciplines to carry out exploratory work. From the perspectives of organization, regulation, technology and other multi-layer theories, the analysis method system of grassroots social big data governance is explored, including the scene analysis method, the best practice analysis method and the driving approach analysis method of big data governance. The basic needs of grassroots social big data governance analysis can be met. By deploying these methods and knowledge resources, it is expected to promote the solution and governance driving of big data governance problems in the public security scenario, and to provide effective support for the urban grassroots government to carry out and improve the big data governance analysis.

[0046] The data governance method provided by the disclosure first constructs a reference model of governance practice cases based on the obtained practice cases; secondly, generates a practice list including at least one practice item based on the reference model; thirdly, combines the practice items in the practice list based on the pre-collected best practice items and the data analysis time window, to obtain at least one practice item combination; and finally, determines the generation result of the target practice based on the at least one practice item combination. Thus, the obtained practice cases are combined together through the constructed reference model, so that the reference model can include at least one practice item of the practice item, thereby providing a reliable basis for the generation of the target practice; the practice list including at least one practice item is generated based on the reference model, thereby ensuring the generation effect of the target practice; and the practice items in the practice list are combined based on the pre-collected information, thereby improving the reliability of the generation of the target practice.

[0047] In order to reduce the data amount of generating the practice list, in another embodiment of the disclosure, referring to Figure 2 which shows a flow 200 of one embodiment of the data governance method according to the disclosure, which includes the following steps:

[0048] Step 201, constructing a reference model of governance practice cases based on the obtained practice cases.

[0049] Step 202, generating a practice list including at least one practice item based on the reference model.

[0050] Step 203, performing a simplification processing on the practice items in the practice list based on the effect data of the practice cases and the evaluation algorithm.

[0051] In this embodiment, the evaluation algorithm can adopt an attribute similarity algorithm, by comparing the effect data of the practice cases of one data governance scene in the reference model with the effect data of another data governance scene in terms of attribute similarity, obtaining the effect data of the another data governance scene with higher similarity, and adding the data items corresponding to the effect data of the another data governance scene to the practice list, to achieve the purpose of performing a simplification processing on the practice items in the practice list.

[0052] For example, the target practice belongs to the grassroots life big data governance scene. In order to obtain the practice list corresponding to the target practice, the attribute similarity algorithm is used to select the practice cases of the data governance scene of the urban rail transit data (the existing mature case scene), and the practice list corresponding to the grassroots life big data governance scene is generated. At this time, the preliminary practice list is not comprehensive, and the data source only comes from the urban rail transit data, which is not applicable to complex social grassroots data. For the grassroots life big data governance scene, since the data can also include public security management, traffic safety management, life safety management, and safety management data governance scenes, the preliminary practice list can be simplified by combining the practice effect data of the practice cases of the urban rail transit data and the effect evaluation method, and the best practice list is obtained.

[0053] In step 204, based on the pre-collected best practice items and the data analysis time window, the practice items in the practice list are combined to obtain at least one practice item combination.

[0054] In step 205, based on the at least one practice item combination, the generation result of the target practice is determined.

[0055] It should be understood that the operations and features in steps 201-202, 204-205 above correspond to the operations and features in steps 101-104, respectively, and therefore the description of the operations and features in steps 101-104 above also applies to steps 201-202, 204-205, which will not be repeated here.

[0056] The data governance method provided in this embodiment performs simplification processing on the practice items in the practice list based on the effect of the practice cases and the evaluation algorithm, reduces the data amount of the practice list, and improves the obtaining rate of the target time.

[0057] In some optional implementations of this embodiment, the above constructing the reference model of the governance practice case based on the obtained practice cases includes: performing integrated processing on the obtained practice cases to obtain at least one case item representing a unified; based on the project index of the case item, the best case item is obtained by screening; the best case item is divided by field type, source data storage mode, and blood relationship; the best case item after division is connected by using a label; a dictionary library is created for the connected best case item to obtain the reference model of the governance practice case.

[0058] In this embodiment, the obtained practice cases are large, each practice case is project information obtained by practicing in different scenes, and each practice case can correspond to one or more case items.

[0059] In the optional implementation, the project index can include: project scale, project demand.

[0060] (1) Project scale screening

[0061] In the face of different demand and different scale of project, the overall size is the key part of the work scope, which is mainly determined by the current resources, manpower and time cost, etc. The project scale includes the budget of the project, the core technology level of the company, the deliverable index of the project, human resources, project team members and all stakeholders of the project. Before the project is implemented, the feasibility of the project is fully evaluated, and the work scope is determined, and the project manager, etc. In this embodiment, based on the project index of the case project, the case project is divided into different levels, and the case project with complete project index is selected as the best case project.

[0062] (2) Determine the type of data domain

[0063] The statistical result of any index is obtained by processing the original data through multiple layers. The original data may belong to a certain field or may belong to multiple fields. For example, the basic information of the population may include personal basic information, work information, family income information and wealth information, etc. The traffic information includes various vehicle information, pedestrian information and road occupancy rate information. Therefore, in this embodiment, the domain type of the original data needs to be classified according to the completion index of the project.

[0064] (3) Source data storage mode

[0065] The social grassroots data comes from various fields, and the storage data is different, including relational database, non-relational database, text format, etc. The model to be constructed needs to support as many data source types as possible. In order to ensure the integrity of the original data, the same database type should be selected as much as possible for storage in the data synchronization process, and at the same time, the multiple data sources should be ensured in the same field.

[0066] (4) Blood relationship

[0067] In practical application, there are mainly two ways to draw the blood relationship graph, namely data table level and field level. The two kinds of blood relationship graph in the process of drawing should be selected according to the complexity and quantity of data. Generally speaking, the complexity and quantity of field are far higher than that of data table. In addition to analyzing the blood path based on which the data is generated, analyzing the data node and understanding the value of the data is also a key problem. Therefore, in the process of analyzing the blood relationship of data, the source and correlation of the whole data need to be systematically analyzed, sorted out, and divided and drawn through foreign key mapping and blood relationship graph.

[0068] (5) Tag association

[0069] The biggest reason why the blood relationship of data cannot be formed naturally is that the growth process of data to a large extent embodies the characteristics of internal generalization, and the production process of data is often accompanied by range spreading, which brings great trouble to data processing. Therefore, the concept of reverse processing is generated. This processing method greatly reduces the difficulty of data coming out. Reverse processing is to connect and track data by establishing intelligent tag relationship, so it is very important to ensure the quality and reuse of tags. Tracking tags can be tracked by the "tag-association" method, and certain induction and summary can be formed, and then the adhesion point is found through the visualization analysis of blood relationship, and finally the tags are processed in layers. The processing of tags must start from its effectiveness, and analyze its quality, usage rate, hit rate and other indicators.

[0070] (6) Establish a new dictionary library

[0071] The final demand of different case projects is different, and the interface of each data is different in the intervention process. Many original dictionary libraries of data are no longer applicable and incomplete. In order to better handle the logical relationship between data and show better results, we need to establish a new dictionary library according to the current business needs of target practice. The more rich the content of the word library is, the more fine the word atomization degree is, and the higher the accuracy of the word will be. The word library established based on word blood relationship analysis is more conducive to the improvement of data analysis effectiveness.

[0072] Dictionary library is a library that explains specific words in different fields and departments. For example, some databases use a certain code to represent some specific information. For example, there are many departments in a unit, and the name of each department is very long. At this time, each department has a unique code, which can save storage space. Since the reference model puts multiple fields of data together, and the original dictionary library of data is for the original data at the beginning, after the integration of multi-source data, creating a new dictionary library can provide a reliable basis for the generation of subsequent target practice.

[0073] The reference model provided by the optional implementation mode is a gene structure representation platform, which can meet the needs of knowledge completeness, universality and complexity expression. The gene structure representation characterizes the best practice as a whole organic system, and expresses the composition of the best practice in the form of gene structure, which can identify the best practice elements and their correlation from a more linked and systematic perspective. Especially when the target practice is complex, the systematic composition cognition is of great help. In view of the foregoing advantages of the gene structure representation, the present disclosure proposes necessary improvement measures based on the gene structure, combined with the massive data status of the grass-roots society: optimizing the decision and leadership mechanism to ensure the organization foundation; ensuring data quality and optimizing the quality assurance system; improving the technology integration capability and perfecting the governance platform.

[0074] The reference model for constructing governance practice cases provided by the optional implementation mode first integrates the practice cases to make all the practice cases uniform, thereby improving the uniformity of the data; secondly, based on the project indicators of the case projects, the best cases are screened to provide a reliable data basis for the construction of the reference model; the case projects are divided by domain type, source data storage mode and blood relationship, which can describe the reference model from multiple levels; the best case projects after division are connected by tags, so that the data at each level of the reference model is effectively fused together; a dictionary library is created for the connected best case projects, thereby improving the effectiveness of data analysis of the reference model.

[0075] Optionally, based on different governance scenarios, only one or more of the above integration, screening, domain type division, source data storage mode, blood relationship division and dictionary library creation can exist. Specifically, the above constructing a reference model of governance practice cases based on the obtained practice cases can include: integrating the obtained practice cases to obtain at least one case project with uniform representation; dividing the at least one case project by domain type, source data storage mode and blood relationship; connecting the best case projects after division by tags; creating a dictionary library for the connected best case projects to obtain the reference model of the governance practice cases.

[0076] In order to improve the security of the data in the obtained reference model, in some optional implementation modes of the present embodiment, the above constructing a reference model of governance practice cases based on the obtained practice cases further includes: removing the practice cases with risks in the obtained practice cases through a data supervision mechanism, a data perception mechanism and a data sharing mechanism.

[0077] In this embodiment, the practice cases with risks refer to any one or more of the following: a practice case with a quality risk of data collection, a practice case with a decision risk and an operation risk, a practice case with an integration and operation risk of data exchange, and a practice case with an interaction risk of data supply.

[0078] In this embodiment, the data supervision mechanism is an optimized decision and leadership mechanism, which can exclude practice cases with a decision risk and an operation risk when making decisions on practice cases by improving data collection organizations and making each department in the organization perform its own function.

[0079] In this embodiment, the data perception mechanism is a mechanism for optimizing the quantity and quality of data governance, which can exclude practice cases with a quality risk of data collection when collecting practice cases by improving the quality of data governance.

[0080] In this embodiment, the data sharing mechanism is a mechanism for enhancing data integration and operation capability and sharing the results of data governance, which can exclude practice cases with an integration and operation risk of data exchange by improving the data sharing mechanism.

[0081] Optionally, the interaction risk of data supply can also be removed by a data improvement mechanism, wherein the data improvement mechanism refers to improving a technical support system platform of data governance to ensure that the technical support system platform contains all-round and multi-aspect technical support and improve technical level.

[0082] The method for constructing a reference model of governance practice cases provided by the optional implementation manner removes practice cases with risks in obtained practice cases through a data supervision mechanism, a data perception mechanism, and a data sharing mechanism, thereby providing a reliable technical basis for cleaning practice cases.

[0083] In some embodiments of the present disclosure, the combining of practice items in the practice list based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination includes: determining a maximum number of best practice items in the practice list based on the pre-collected best practice items; combining the practice items in the practice list according to the maximum number to obtain combined practice items; determining an execution relationship of each practice item in the combined practice items based on the data analysis time window of each best practice item; and determining at least one practice item combination based on the combined practice items and the execution relationship of each practice item.

[0084] In this embodiment, the pre-collected best practice items are practice items in cases that have been implemented through the idea of best practices, and the determined maximum number of best practice items can be the number of best practice items that can be accommodated by practice cases in different data governance scenarios.

[0085] In this embodiment, the data analysis time window of the best practice item is time information of the best practice item in the specific practice process, for example, the data analysis time window of the best practice item includes: the starting time point and the ending time point of the best practice item, and the data analysis time window can also include: the execution time of the key technology in the best practice item, etc. Specifically, the data analysis time window of the best practice item can be obtained by referring to the model, the execution time of each best practice item can be specifically analyzed through the data analysis time window of the best practice item, and the execution relationship of each best practice item can be obtained by sorting the execution time.

[0086] In this optional implementation, starting from the best practices collected from the diversity problem scenarios of big data governance, the effective fields in the practice list can be removed, the effective field metadata can be standardized, the content label can be processed, and a plurality of practice items that can extract the value of massive data can be integrated to achieve the efficient big data governance scenario response effect of collecting less data field data and spending less time and labor cost.

[0087] In this embodiment, the practice item combination structure is structured as the practice item and the execution relationship, which can ensure the depth cognition and transmission of the target practice, and still provide decision support when the analysis subject lacks field experience.

[0088] The method for determining the practice item combination provided in this optional implementation determines the maximum number of best practice items under different data governance scenarios through the pre-collected best practice items, combines the practice items in the practice list according to the maximum number, obtains the combined practice items, and guarantees the completeness of the combined practice items; and determines the execution relationship of each practice item based on the data analysis time window of each best practice item, which guarantees the rationality of the practice item combination in the practice item combination.

[0089] Optionally, for the initial stage of data governance, when no best practice items are collected for various data governance scenarios, the number of practice items in each data governance scenario can be set in advance for each data governance scenario, and then the practice items in the practice list are combined based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination. The combination includes: in the current data governance scenario, based on the pre-determined best practice items in the current data governance scenario, determining the maximum number of best practice items in the practice list, such as matching the best practice items in the current data scenario with the practice items in the practice list, and accumulating the data of the best practice items that match successfully until the maximum data is reached; combining the practice items in the practice list according to the maximum number to obtain the combined practice items; determining the execution relationship of each practice item in the combined practice items based on the data analysis time window of each best practice item; and determining at least one practice item combination based on the combined practice items and the execution relationship of each practice item.

[0090] In some embodiments of the present disclosure, the generation result of the target practice based on at least one practice item combination includes:

[0091] Each practice item combination is regarded as a practice, and each practice item is executed based on the execution relationship of the practice items in each practice item combination to obtain the expected completion time of each practice item in different practices; all practice items in each practice item combination are sorted based on the expected completion time of each practice item; the target practice is obtained based on the sorting result of all practice item combinations; and the basic relationship graph of the practice items in the target practice is drawn based on the arrangement order of the practice items in the target practice and the expected completion time of each practice item, and the basic relationship graph is taken as the generation result of the target practice.

[0092] In this optional implementation, the expected completion time is the estimated time for running each practice item, and through the sorting of multiple estimated times, all practice items in each practice item combination can be sorted to obtain the sorting result of the practice item combination.

[0093] In this optional implementation, when the practice item combination is determined, the execution order of each practice item in the practice item combination is determined accordingly, and the completion time of each practice item in various practices is estimated based on the time requirement of the data governance scenario to obtain the expected completion time of the practice item.

[0094] In this optional implementation, the quantitative effect evaluation index system and evaluation method of the best practice are established, which makes the effect evaluation more objective and further improves the reliability of the generated best practice.

[0095] The method for determining the generation result of the target practice provided by the optional implementation combines the arrangement order relationship between the practice items and the expected completion time of each practice item, draws a basic relationship graph of the practice items in the best practice, and uses the basic relationship graph as a result of the generation of the best practice, thereby improving the understanding and cognition of the best practice by the execution subject and ensuring the reliability of the generation of the target practice.

[0096] With reference to the method for determining the generation result of the target practice provided by the optional implementation, Figure 3 , as an implementation of the method shown in the above Figure 1 , the present application provides a data governance device, which corresponds to the method embodiment shown in Figure 1 , and can be applied to various electronic devices.

[0097] As shown in Figure 3 , the data governance device 300 of the embodiment can include a construction unit 301, a generation unit 302, a combination unit 303, and a determination unit 304. The construction unit 301 can be configured to construct a reference model of the governance practice case based on the acquired practice case. The generation unit 302 can be configured to generate a practice list including at least one practice item based on the reference model. The combination unit 303 can be configured to combine the practice items in the practice list based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination. The determination unit 304 can be configured to determine the generation result of the target practice based on the at least one practice item combination.

[0098] In some embodiments of the present disclosure, the device 300 further includes a simplification unit (not shown in the figure), which is configured to perform a simplification process on the practice items in the practice list based on the effect data of the practice case and the evaluation algorithm.

[0099] In some embodiments of the present disclosure, the construction unit 301 is further configured to: perform an integration process on the acquired practice case to obtain at least one case item representing a unity; filter the best case item based on the project index of the case item; divide the best case item in terms of domain type, source data storage mode, and blood relationship; use a label to connect the divided best case item; create a dictionary library for the connected best case item to obtain the reference model of the governance practice case.

[0100] In some embodiments of the present disclosure, the construction unit 301 is further configured to remove the practice case with risks in the acquired practice case through a data supervision mechanism, a data perception mechanism, and a data sharing mechanism.

[0101] In some embodiments of the present disclosure, the combination unit 303 is further configured to determine a maximum number of best practice items in the practice list based on the pre-collected best practice items, combine the practice items in the practice list according to the maximum number to obtain combined practice items, determine an execution relationship of each practice item in the combined practice items based on the data analysis time window of each best practice item, and determine at least one practice item combination based on the combined practice items and the execution relationship of each practice item.

[0102] In some embodiments of the present disclosure, the determination unit 304 is further configured to execute each practice item combination as one practice based on the execution relationship of the practice items in each practice item combination, obtain an expected completion time of each practice item in different practices, sort all practice item combinations based on the expected completion time of all practice items in each practice item combination, obtain a target practice based on the sorting result of all practice item combinations, and draw a basic relationship graph of the practice items in the target practice based on the arrangement order between the practice items in the target practice and the expected completion time of each practice item, and take the basic relationship graph as a generation result of the target practice.

[0103] The data governance apparatus provided by the embodiment first constructs a reference model of the governance practice case based on the acquired practice case by the construction unit 301, secondly generates a practice list including at least one practice item based on the reference model by the generation unit 302, thirdly combines the practice items in the practice list based on the pre-collected best practice items and the data analysis time window to obtain at least one practice item combination by the combination unit 303, and finally determines a generation result of a target practice based on the at least one practice item combination by the determination unit 304. Thus, the acquired practice cases are combined together through the constructed reference model, so that the reference model can include at least one practice item, thereby providing a reliable foundation for the generation of the target practice, the practice list including at least one practice item is generated based on the reference model, thereby ensuring the generation effect of the target practice, and the practice items in the practice list are combined based on the pre-collected information, thereby improving the reliability of the generation of the target practice.

[0104] Figure 4 A schematic block diagram of an electronic device 400 illustrating a data governance method according to an embodiment of the present disclosure is shown. As shown, the electronic device 400 can include a processor 401 and a memory 402 storing a computer program. When the computer program is executed by the processor 401, the electronic device 400 can perform the steps of the method as shown in Figure 4 or Figure 1 In one example, the electronic device 400 can be a computer device or a cloud computing node. Figure 2

[0105] ​In embodiments of the present disclosure, the processor 401 has at least one, and the processor 401 can be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a processor based on a multi-core processor architecture, and the like. The memory 402 has at least one, and the memory 402 can be any type of memory implemented using data storage technology, including but not limited to random access memory, read only memory, semiconductor-based memory, flash memory, disk storage, and the like.

[0106] In addition, in embodiments of the present disclosure, the electronic device 400 can also include an input device 403, such as a microphone, a keyboard, a mouse, and the like, for inputting a pre-acquired practice case. In addition, the electronic device 400 can also include an output device 404, such as a loudspeaker, a display, and the like, for outputting the generation result of the target practice.

[0107] The data governance method provided by the embodiments of the present disclosure can be applied to any electronic device with display function, for example, electronic paper, mobile phone, tablet computer, television, notebook computer, digital photo frame, wearable device or navigator, and the like.

[0108] In other embodiments of the present disclosure, a computer readable storage medium storing a computer program is also provided, wherein the computer program can implement the steps of the method as shown in Figures 1 to 2 when executed by a processor.

[0109] The data governance method provided by the embodiments, first, based on the acquired practice case, a reference model for governing the practice case is constructed; second, based on the reference model, a practice list including at least one practice item is generated; third, based on the pre-collected best practice item and the data analysis time window, the practice items in the practice list are combined to obtain at least one practice item combination; finally, based on the at least one practice item combination, the generation result of the target practice is determined. Thus, by combining the acquired practice cases through the constructed reference model, the reference model can include at least one practice item, providing a reliable foundation for the generation of the target practice; based on the reference model, a practice list including at least one practice item is generated, ensuring the generation effect of the target practice; based on the pre-collected information, the practice items in the practice list are combined, improving the reliability of the target practice generation.

[0110] The diagrams of the flowcharts and block diagrams in the drawings show the architecture, functionality, and operation of possible implementations of apparatuses and methods according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a segment, or a portion of code which comprises one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowcharts, and combinations thereof, can be implemented by dedicated hardware-based systems which perform the specified functions or acts, or can be implemented by a combination of dedicated hardware and computer instructions.

[0111] The singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a" or "the" element is

[0112] Further aspects and scope of adaptations will become apparent from the description provided herein. It should be understood that the various aspects of the present disclosure can be implemented alone or in combination with one or more other aspects. It should also be understood that the description and specific examples herein are intended to be illustrative only and are not intended to limit the scope of the present disclosure.

[0113] The foregoing detailed description of a number of embodiments of the present disclosure has been presented for purposes of illustration and description. It is apparent to those skilled in the art, however, that various modifications and changes can be made without departing from the spirit and scope of the present disclosure. The scope of the disclosure is defined by the appended claims.

Claims

1. A data governance method, the method comprising: Based on the acquired practical cases, a reference model for governance practice cases is constructed; Based on the reference model, a practice list including at least one practice item is generated; the practice item is a constituent element of the practice case, and the practice list is a list related to the target practice, i.e., the target project. Based on the practice requirements of the data governance scenario in which the target practice is located, practice items that conform to the data governance scenario of the target practice are selected from the reference model. Based on the pre-collected best practice items and the data analysis time window, the practice items in the practice list are combined to obtain at least one combination of practice items; Based on the combination of at least one practice item, the generation result of the target practice is determined; The reference model for constructing governance practice cases based on the acquired practice cases includes: The acquired practical cases are processed in an integrated manner to obtain at least one case project with a unified representation; Based on the project metrics of the aforementioned case projects, the best case projects were selected. The best case projects are categorized by domain type, source data storage method, and lineage. Tags are used to link the best-case projects after the segmentation; A dictionary of best practice cases is created for the linked projects, providing a reference model for governance practices. The step of generating a practice list that includes at least one practice item based on the reference model includes: generating the practice list for a known data governance scenario in the target practice using an attribute similarity algorithm.

2. The method according to claim 1, further comprising: Based on the effectiveness data and evaluation algorithm of the aforementioned practice cases, the practice items in the practice list are simplified.

3. The method according to claim 1, wherein, The reference model for constructing governance practice cases based on the acquired practice cases also includes: Risky practice cases are removed from the acquired practice cases through data supervision mechanisms, data awareness mechanisms, and data sharing mechanisms.

4. The method according to claim 1, wherein, The combination of practice items in the practice list based on pre-collected best practice items and data analysis time windows to obtain at least one combination of practice items includes: Based on the pre-collected best practice items, determine the maximum number of best practice items in the practice list; The practice items in the practice list are combined according to the maximum number to obtain the combined practice items; Based on the data analysis time window of each best practice item, determine the execution relationship of each practice item in the combined practice items; Based on the combined practice items and the execution relationships of each practice item, at least one combination of practice items is determined.

5. The method according to claim 1, wherein, The determination of the generation result of the target practice based on the combination of the at least one practice item includes: Each practice item is combined into a single practice. Based on the execution relationships of the practice items in the combination, each practice item is executed separately to obtain the expected completion time of each practice item in different practices. All practice item combinations are sorted based on the expected completion time of all practice items in each combination. Based on the ranking results of all practice combinations, the target practice is obtained; Based on the order of practice items in the target practice and the expected completion time of each practice item, a basic relationship graph of practice items in the target practice is drawn, and the basic relationship graph is used as the result of generating the target practice.

6. A data governance apparatus, the apparatus comprising: The building blocks are configured to construct reference models of governance practice cases based on the acquired practice cases; The generation unit is configured to generate a practice list including at least one practice item based on the reference model; the practice item is a constituent element of a practice case, and the practice list is a list related to the target practice, i.e., the target project. Based on the practice requirements of the data governance scenario in which the target practice is located, practice items that conform to the data governance scenario of the target practice are selected from the reference model. The combination unit is configured to combine the practice items in the practice list based on pre-collected best practice items and data analysis time windows to obtain at least one practice item combination; The determining unit is configured to determine the generation result of the target practice based on the combination of the at least one practice item; The reference model for constructing governance practice cases based on the acquired practice cases includes: The acquired practical cases are processed in an integrated manner to obtain at least one case project with a unified representation; Based on the project metrics of the aforementioned case projects, the best case projects were selected. The best case projects are categorized by domain type, source data storage method, and lineage. Tags are used to link the best-case projects after the segmentation; A dictionary of best practice cases is created for the linked projects, providing a reference model for governance practices. The step of generating a practice list that includes at least one practice item based on the reference model includes: generating the practice list for a known data governance scenario in the target practice using an attribute similarity algorithm.

7. The apparatus according to claim 6, further comprising: The simplification unit is configured to simplify the practice items in the practice list based on the effect data and evaluation algorithm of the practice cases.

8. An electronic device, comprising: At least one processor; as well as At least one memory storing a computer program; When the computer program is executed by the at least one processor, the electronic device performs the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, wherein, The computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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