Data management method, device, electronic device and readable storage medium

Abstract

The present invention relates to a data governance method, device, electronic device and readable storage medium, belonging to the technical field of data standardization governance, wherein the data governance method comprises: obtaining first non-standard data for preprocessing to obtain second non-standard data; formulating data standard classification for the second non-standard data based on a classification algorithm, and constructing a standard data system; projecting the standard data in the standard data system to a high-dimensional space to obtain a number of reference points; then obtaining third non-standard data for preprocessing to obtain fourth non-standard data; projecting the fourth non-standard data to a high-dimensional space to obtain a number of points to be classified; selecting a number of reference points with the closest Euclidean distance to the points to be classified; determining the category of the points to be classified based on the several reference points, and completing data governance. The method effectively reduces the impact of labor costs and subjective factors on governance results.

Description

Technical Field

[0001] The present invention relates to the field of data standardization management, and in particular to a data management method, device, electronic device and readable storage medium. Background Art

[0002] Data standardization governance refers to the behavior of unifying the description of certain phenomena, processes or objects within a specific population in order to reduce communication costs and improve the efficiency of knowledge and experience reuse.

[0003] Data standardization governance usually faces the following types of work: 1. Development of data classification standards: Traditional data classification standards are usually developed using the "expert experience" method. For example, in the contract list of the engineering industry, business experts will first divide the classification dimensions of data at all levels. For example, the first-level categories will be divided according to labor, materials, and machinery; 2. Construction of a standard data system: According to the above standard classification rules, each data point is classified and collected. The traditional construction process relies on manual labor; for example, business personnel are required to classify C35 ready-mixed concrete into the ready-mixed concrete category; 3. Mapping historical non-standard data with the current standardized data system: Before the construction of the standardized data system, there is already a large amount of related information associated with non-standard data, such as the price information of a certain list; these information associated with non-standard data are of guiding significance to subsequent related work based on the standard data system, and need to be extracted and established with the standard data system. This process is called data asset activation; the traditional processing method of this process also relies on manual labor.

[0004] The traditional methods of data governance mentioned above all rely on manual work, which has the following problems: 1. The cost of business execution is high. For example, for the extraction of historical data information, each upgrade of the standard data system means that a large amount of historical standard data has become non-standard data. It is necessary to re-establish the association relationship between all historical non-standard data according to the current version of the standard data. When the kth version is changed, the amount of data to be processed is ∑Ci (where Ci is the amount of data generated under the i-th version, i=0 to k-1, and C0 is the non-standard data before the establishment of the first version of the standard data system). Under the summation mechanism, the amount of data can easily reach millions or even tens of millions. It is very costly to manually establish the mapping relationship between new and old data; 2. Personal subjective factors have a great influence. The establishment of classification standards, the classification and collection of data points, and the establishment of the association relationship between non-standard data and standard data all rely on the personal experience and judgment of the actual executors, which can easily lead to problems such as unreasonable and incomplete classification dimensions, incorrect data point classification, and incorrect non-standard data mapping. Summary of the invention

[0005] In view of this, it is necessary to provide a data governance method, device, electronic device and readable storage medium to solve the problems of excessive reliance on manual labor and resulting in high costs and susceptibility to subjective factors in the existing technology.

[0006] In order to solve the above problems, the present invention provides a data governance method, comprising:

[0007] Acquire the first non-standard data for preprocessing to obtain second non-standard data;

[0008] Formulate a data standard classification for the second non-standard data based on a classification algorithm, and build a standard data system;

[0009] Projecting the standard data in the standard data system into a high-dimensional space to obtain a number of reference points;

[0010] Then, the third non-standard data is obtained for preprocessing to obtain fourth non-standard data;

[0011] Projecting the fourth non-standard data into the high-dimensional space to obtain a plurality of points to be classified;

[0012] Selecting the reference points whose Euclidean distances to the points to be classified are the closest;

[0013] The category of the point to be classified is determined based on the several reference points to complete data governance.

[0014] In a possible implementation, the obtaining of the first non-standard data and preprocessing to obtain the second non-standard data includes:

[0015] Acquire first non-standard data;

[0016] Filtering the first non-standard data according to a preset standard data format to obtain second non-standard data;

[0017] The obtaining of the third non-standard data and preprocessing to obtain the fourth non-standard data includes:

[0018] Obtain third non-standard data;

[0019] The third non-standard data is filtered according to a preset standard data format to obtain fourth non-standard data.

[0020] In a possible implementation manner, filtering the first non-standard data according to a preset standard data format includes:

[0021] Deleting data in the first non-standard data whose format is different from the standard data format;

[0022] The filtering the third non-standard data according to a preset standard data format includes:

[0023] Delete data in the third non-standard data whose format is different from the standard data format.

[0024] In a possible implementation, formulating a data standard classification for the second non-standard data based on a classification algorithm and constructing a standard data system includes:

[0025] Constructing a classification tree for the second non-standard data based on a hierarchical classification algorithm;

[0026] The classification tree includes the data standard classification and the standard data system.

[0027] In a possible implementation, constructing a classification tree for the second non-standard data based on a hierarchical classification algorithm includes:

[0028] Taking each data point in the second non-standard data as a separate cluster to form an initial hierarchical structure;

[0029] At each step of the hierarchy, two clusters with the highest similarity are selected, merged to obtain a new cluster, and the hierarchy is updated until all the data points are in the same cluster and / or a preset clustering threshold is reached.

[0030] In a possible implementation, selecting two clusters with the highest similarity at each step of the hierarchy and merging them to obtain a new cluster includes:

[0031] calculating the Euclidean distances between different clusters in the hierarchy;

[0032] At each step of the hierarchy, two clusters with the closest Euclidean distance are selected and merged to obtain a new cluster.

[0033] In a possible implementation manner, determining the category of the to-be-classified point based on the plurality of reference points includes:

[0034] Counting the categories of the plurality of reference points;

[0035] The category with the largest statistical number is taken as the category of the point to be classified.

[0036] The present invention also provides a data management device, comprising:

[0037] A data acquisition module, used to acquire first non-standard data and perform preprocessing to obtain second non-standard data, and further acquire third non-standard data and perform preprocessing to obtain fourth non-standard data;

[0038] A classification module, used to formulate a data standard classification for the second non-standard data based on a classification algorithm, and to construct a standard data system;

[0039] A projection module, used to project the standard data in the standard data system into a high-dimensional space to obtain reference points, and to project the fourth non-standard data into the high-dimensional space to obtain points to be classified;

[0040] The data management module is used to select a number of reference points that are closest to the to-be-classified point in terms of Euclidean distance, and determine the category of the to-be-classified point based on the reference points to complete data management. The present invention also provides an electronic device, comprising:

[0041] Memory, used to store programs;

[0042] A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the data governance method described in any one of the above method items.

[0043] The present invention also provides a readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the data governance method described in any one of the above-mentioned method items.

[0044] The beneficial effects of the present invention are as follows: the present invention provides a data governance method, which realizes standardized processing of non-standard data through a classification algorithm. In the process of building a standardized data system, there is no need for deep human involvement. It only needs to hand over the construction process of the classification algorithm to the data governance staff, and adjust the main resource dependence from dependence on people to dependence on computing resources. Business experts only need to evaluate the results, which greatly liberates manpower. In addition, by handing over data governance to the algorithm, its governance cycle can be compressed to the monthly level, and the labor cost can be converted into computer hardware cost, thereby bringing about a significant improvement in time and economic benefits and reducing labor costs. In addition, the computer algorithm is a fully automatic process in the process of executing governance, and there will be no influence of subjective factors when conducting data governance, thus avoiding the problem of unreasonable standard data system or failure to achieve full coverage due to limited experience and knowledge reserves of business experts. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A method flow chart of an embodiment of a data governance method provided by the present invention;

[0046] Figure 2 for Figure 1 A method flow chart of step S101 in an embodiment;

[0047] Figure 3 for Figure 1 A method flow chart of an embodiment of step S104;

[0048] Figure 4 for Figure 1 A method flow chart of an embodiment of step S102;

[0049] Figure 5 for Figure 4 A method flow chart of an embodiment of step S302;

[0050] Figure 6 for Figure 1 A method flow chart of an embodiment of step S106;

[0051] Figure 7 A schematic diagram of the structure of an embodiment of a data management device provided by the present invention;

[0052] Figure 8 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0054] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0055] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] A specific embodiment of the present invention discloses a data governance method, including:

[0058] In order to solve the above problems, the present invention provides a data governance method, such as Figure 1 As shown, including:

[0059] Step S101: Acquire first non-standard data and perform preprocessing to obtain second non-standard data;

[0060] Step S102: formulating a data standard classification for the second non-standard data based on a classification algorithm, and constructing a standard data system;

[0061] Step S103: Projecting the standard data in the standard data system into a high-dimensional space to obtain a reference point;

[0062] Step S104: acquiring the third non-standard data and preprocessing it to obtain fourth non-standard data, and projecting the fourth non-standard data into a high-dimensional space to obtain points to be classified;

[0063] Step S105: Select several reference points whose Euclidean distances to the points to be classified are the closest;

[0064] Step S106: Determine the category of the point to be classified based on the reference point to complete data governance.

[0065] It should be noted that the application scenario of the present invention is the cost list of the engineering industry. In this scenario, the standard data is the cost list of the national standard or enterprise standard, and the non-standard data refers to the business data generated in the newly occurring business instance. For example, the process of building a standard data system is roughly as follows:

[0066] Set up a standard classification for animals. There are three baskets under this classification, namely cats, dogs, and sheep. Each basket contains 1,000 pictures of cats, dogs, and sheep. These pictures are the standard data points in the standard data system. When a new picture is obtained later, this picture is a non-standard data point. The so-called data governance is to determine whether the picture is a cat, dog, or sheep, and hang the picture in the corresponding basket.

[0067] It is understandable that, in a specific implementation process, the first non-standard data may come from historical data stored in a local database, and the third non-standard data may come from current business data newly added to the local database.

[0068] It should also be noted that in order to ensure that the standard data system finally constructed has a good degree of credibility, the quantity is also a hard indicator when obtaining non-standard data, and it must be no less than 10 million.

[0069] During implementation, since data is more easily linearly separable in high-dimensional space than in low-dimensional space, in steps S103-106, standard data and non-standard data need to be projected into high-dimensional space for data management. During projection, RBF kernel function can be used for projection.

[0070] Compared with the prior art, the data governance method provided by the present invention realizes standardized processing of non-standard data through classification algorithms. In the process of building a standardized data system, there is no need for deep human participation. It only needs to hand over the construction process of the classification algorithm to the data governance staff, and adjusts the main resource dependence from dependence on people to dependence on computing resources. Business experts only need to evaluate the results, which greatly liberates manpower. In addition, by handing over data governance to algorithms, its governance cycle can be compressed to the monthly level, and the labor cost can be converted into computer hardware cost, thereby bringing about a significant improvement in time and economic benefits and reducing labor costs. In addition, the computer algorithm is a fully automatic process in the process of executing governance, and there will be no influence of subjective factors when conducting data governance, thus avoiding the problem of unreasonable standard data system or failure to achieve full coverage due to limited experience and knowledge reserves of business experts.

[0071] In a possible implementation, in order to facilitate the establishment of a standard data system in the subsequent process, such as Figure 2 As shown, step S101 includes:

[0072] Step S201: Acquire first non-standard data;

[0073] Step S202: filtering the first non-standard data according to a preset standard data format to obtain second non-standard data.

[0074] like Figure 3 As shown, step S104 includes:

[0075] Step S301: Acquire third non-standard data;

[0076] Step S302: filtering the third non-standard data according to a preset standard data format to obtain fourth non-standard data.

[0077] It should be noted that, in the present embodiment, the second non-standard data and the fourth non-standard data are both non-standard data in a standard format, that is, the data structure meets the requirements of the standard data format, but the data value does not meet the requirements. For example, if a certain data is not standardized, the structure of the instance data generated is different (for example, the attribute column and the hierarchical structure of the data are different). At this time, the standard data structure should be extracted first (for example, which dimensions should be used to describe the characteristics of the animal, such as coat color, outline, and number of legs), and then the data in the non-standard data that does not meet this general data structure is filtered; after filtering, the structure of the remaining data is standard, but the value is non-standard (for example, although the remaining data all describe coat color information, some data are described as yellow, some data are described as orange, and some data are described in RGB). These data are "non-standard data in a standard format" and the available values ​​of the data need to be further standardized. How to further standardize the available values ​​of the data is not specifically limited in the present embodiment.

[0078] During implementation, in step S102, natural language processing technology (NLP) can be used to filter the two sets of non-standard data according to a preset standard data format, further reducing manpower consumption and avoiding the problem of incomplete filtering due to human negligence.

[0079] In a possible implementation, step S202 includes:

[0080] Delete the data in the first non-standard data whose format is different from the standard data format.

[0081] Step S302 includes:

[0082] Delete the data in the third non-standard data whose format is different from the standard data format.

[0083] In a possible implementation, step S102 includes:

[0084] A classification tree is constructed for the second non-standard data based on a hierarchical classification algorithm, and the classification tree includes data standard classification and a standard data system.

[0085] In one possible implementation, Figure 4 As shown, step S102 includes:

[0086] Step S401: taking each data point in the second non-standard data as a separate cluster to form an initial hierarchical structure;

[0087] Step S402: at each step of the hierarchy, two clusters with the highest similarity are selected, merged to obtain a new cluster, and the hierarchy is updated until all data points are in the same cluster and / or a preset clustering threshold is reached.

[0088] It should be noted that in the past, when data governance was completed manually, it was necessary to first formulate data standard classifications and then establish a standard data system based on the standard classifications. In this embodiment, in the process of constructing the classification tree, the classification tree automatically completes the data standard classification. That is to say, when clustering is performed through the classification tree, formulating data standard classifications and constructing a standard data system are integrated. This method further improves the efficiency of data governance and reduces time and labor costs to a certain extent.

[0089] During implementation, in order to improve the credibility of the standard data system and adapt to the needs of different application scenarios, after the classification tree is constructed, data governance staff are required to score and fine-tune the classification numbers.

[0090] In one possible implementation, Figure 5 As shown, step S402 includes:

[0091] Step S501: Calculate the Euclidean distance between different clusters in the hierarchical structure;

[0092] Step S502: at each step of the hierarchy, two clusters with the closest Euclidean distance are selected and merged to obtain a new cluster.

[0093] It is understood that clustering algorithms are based on similarity measures between data points, usually using Euclidean distance or correlation as a measure of similarity. The goal of clustering algorithms is to minimize the differences within the group while maximizing the differences between the groups to ensure the formation of clusters with high cohesion and low coupling. Such technical principles make clustering algorithms an effective data classification tool.

[0094] In one possible implementation, Figure 6 As shown, step S106 includes:

[0095] Step S601: Count the categories of several reference points;

[0096] Step S602: taking the category with the largest statistical number as the category of the point to be classified.

[0097] The technical solution of this embodiment is described below in conjunction with a specific application scenario. This method invention is applied to a case of building a standardized checklist system for the construction industry:

[0098] (a) Application scenario 1: Establishment of a standardized data system

[0099] Step 1: Obtain sufficient non-standard lists;

[0100] Step 2: Clean the non-standard inventory data and define the standard inventory data format;

[0101] Step 3: Use NLP technology to standardize the non-standard list according to the standard list data format;

[0102] Step 4: Use clustering algorithms to build a standard list classification tree layer by layer for non-standard data in standard format;

[0103] Step 5: Score and fine-tune the constructed classification tree to meet the needs of the application scenario.

[0104] (b) Application scenario 2: Building a list price database based on the standard list system

[0105] Step 1: Define the list price data channel;

[0106] Step 2: Obtain non-standard lists and price information from each channel;

[0107] Step 3: Standardize the non-standard inventory format according to the standard inventory data format;

[0108] Step 4: Use the classification algorithm to find the corresponding standard list classification;

[0109] Step 5: Migrate the price information of the non-standard list to the standard list.

[0110] It can be understood that the data governance method provided in this embodiment can simultaneously meet the needs of the above two application scenarios based on the same business.

[0111] Accordingly, if Figure 7 As shown, the present invention also provides a data management device 70, including:

[0112] The data acquisition module 710 is used to acquire the first non-standard data and perform preprocessing to obtain the second non-standard data, and further acquire the third non-standard data and perform preprocessing to obtain the fourth non-standard data;

[0113] A classification module 720, for formulating a data standard classification for the first non-standard data based on a classification algorithm, and constructing a standard data system;

[0114] A projection module 730 is used to project the standard data in the standard data system into a high-dimensional space to obtain reference points, and to project the second non-standard data into the high-dimensional space to obtain points to be classified;

[0115] The data management module 740 is used to select a number of reference points that are closest to the points to be classified in terms of Euclidean distance, and determine the categories of the points to be classified based on the reference points to complete data management.

[0116] like Figure 8 As shown, the present invention also provides an electronic device 80. The electronic device 80 includes a processor 810, a memory 820 and a display 830. Figure 8 Only some of the components of the electronic device 80 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0117] In some embodiments, the processor 810 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes stored in the memory 820 or process data, such as the data governance method of the present invention.

[0118] In some embodiments, the processor 810 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 810 may be local or remote. In some embodiments, the processor 810 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0119] In some embodiments, the memory 820 may be an internal storage unit of the electronic device 80, such as a hard disk or memory of the electronic device 80. In other embodiments, the memory 820 may also be an external storage device of the electronic device 80, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 80.

[0120] Furthermore, the memory 820 may include both an internal storage unit of the electronic device 80 and an external storage device. The memory 820 is used to store application software installed in the electronic device 80 and various data.

[0121] In some embodiments, the display 830 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 830 is used to display information on the electronic device 80 and to display a visual user interface. The components 610-630 of the electronic device 80 communicate with each other via a system bus.

[0122] In one embodiment, when the processor 810 executes the data management program in the memory 820, the following steps may be implemented:

[0123] Acquire two sets of non-standard data and pre-process them respectively to obtain first non-standard data and second non-standard data;

[0124] Formulate data standard classification for the first non-standard data based on the classification algorithm and build a standard data system;

[0125] Project the standard data in the standard data system into the high-dimensional space to obtain the reference point;

[0126] Project the second non-standard data into the high-dimensional space to obtain points to be classified;

[0127] Select several reference points with the closest Euclidean distance to the points to be classified;

[0128] Determine the category of the points to be classified based on the reference points and complete data governance.

[0129] It should be understood that: when the processor 810 executes the data management program in the memory 820, in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0130] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 80 mentioned, and the electronic device 80 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 80 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0131] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the data method provided in the above-mentioned method embodiments can be implemented.

[0132] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0133] The data governance method, device, electronic device and storage medium provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A data governance method, characterized in that: include: Acquire the first non-standard data for preprocessing to obtain second non-standard data; Formulate a data standard classification for the second non-standard data based on a classification algorithm, and build a standard data system; Projecting the standard data in the standard data system into a high-dimensional space to obtain a number of reference points; Then, the third non-standard data is obtained for preprocessing to obtain fourth non-standard data; Projecting the fourth non-standard data into the high-dimensional space to obtain a plurality of points to be classified; Selecting the reference points whose Euclidean distances to the points to be classified are the closest; Determine the category of the point to be classified based on the several reference points to complete data governance; The step of formulating a data standard classification for the second non-standard data based on a classification algorithm and constructing a standard data system includes: Constructing a classification tree for the second non-standard data based on a hierarchical classification algorithm; The classification tree includes the data standard classification and the standard data system.

2. The data governance method according to claim 1, characterized in that: The obtaining of the first non-standard data and preprocessing to obtain the second non-standard data includes: Acquire first non-standard data; Filtering the first non-standard data according to a preset standard data format to obtain second non-standard data; The obtaining of the third non-standard data and preprocessing to obtain the fourth non-standard data includes: Obtain third non-standard data; The third non-standard data is filtered according to a preset standard data format to obtain fourth non-standard data.

3. The data governance method according to claim 2, characterized in that: The filtering of the first non-standard data according to a preset standard data format includes: Deleting data in the first non-standard data whose format is different from the standard data format; The filtering the third non-standard data according to a preset standard data format includes: Delete data in the third non-standard data whose format is different from the standard data format.

4. The data governance method according to claim 1, characterized in that: The step of constructing a classification tree for the second non-standard data based on a hierarchical classification algorithm includes: Taking each data point in the second non-standard data as a separate cluster to form an initial hierarchical structure; At each step of the hierarchy, two clusters with the highest similarity are selected, merged to obtain a new cluster, and the hierarchy is updated until all the data points are in the same cluster and / or a preset clustering threshold is reached.

5. The data governance method according to claim 4, characterized in that: At each step of the hierarchy, two clusters with the highest similarity are selected and merged to obtain new clusters, including: calculating the Euclidean distances between different clusters in the hierarchy; At each step of the hierarchy, two clusters with the closest Euclidean distance are selected and merged to obtain a new cluster.

6. The data governance method according to claim 1, characterized in that: The determining the category of the point to be classified based on the plurality of reference points comprises: Counting the categories of the plurality of reference points; The category with the largest statistical number is taken as the category of the point to be classified.

7. A data management device, characterized in that: include: A data acquisition module, used to acquire first non-standard data and perform preprocessing to obtain second non-standard data, and further acquire third non-standard data and perform preprocessing to obtain fourth non-standard data; A classification module, used to formulate a data standard classification for the second non-standard data based on a classification algorithm, and to construct a standard data system; A projection module, used to project the standard data in the standard data system into a high-dimensional space to obtain reference points, and to project the fourth non-standard data into the high-dimensional space to obtain points to be classified; A data management module is used to select a number of reference points that are closest to the point to be classified in terms of Euclidean distance, and determine the category of the point to be classified based on the reference points to complete data management; The step of formulating a data standard classification for the second non-standard data based on a classification algorithm and constructing a standard data system includes: Constructing a classification tree for the second non-standard data based on a hierarchical classification algorithm; The classification tree includes the data standard classification and the standard data system.

8. An electronic device, characterized in that: include: Memory, used to store programs; A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the data governance method described in any one of claims 1 to 6 above.

9. A readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the data governance method described in any one of claims 1 to 6 above.

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