CN116521782A - Data asset map acquisition method, device, equipment, medium and product - Google Patents

Data asset map acquisition method, device, equipment, medium and product Download PDF

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Publication number
CN116521782A
CN116521782A CN202310490428.8A CN202310490428A CN116521782A CN 116521782 A CN116521782 A CN 116521782A CN 202310490428 A CN202310490428 A CN 202310490428A CN 116521782 A CN116521782 A CN 116521782A
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dpoi
dpois
data
data asset
coordinate system
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李特
陈文�
李晓明
万姝蓓
朱丹
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China Mobile Communications Group Co Ltd
China Mobile Information Technology Co Ltd
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China Mobile Communications Group Co Ltd
China Mobile Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/211—Schema design and management

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application provides a data asset map acquisition method, a device, equipment, a medium and a product, and relates to the technical field of data processing, wherein the method comprises the following steps: correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user; determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs; and constructing a data asset map according to the multi-dimensional coordinate system, wherein the data asset map is used for visualizing the association relation among the DPOIs. Through the steps, the related multidimensional data information can be checked through a certain DPOI so as to increase the information quantity contained in the data asset map.

Description

Data asset map acquisition method, device, equipment, medium and product
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, a medium, and a product for acquiring a data asset map.
Background
The data asset map is a panoramic view of enterprise data, can perform data asset catalog management and metadata management, provides an integrated platform for sharing and opening data, and can enable a user to quickly know the distribution condition of data assets from the data asset map.
In general, a data asset map lays out data points by using a force guiding algorithm, forms a stable coordinate relationship by balancing resultant forces among the data points, and then performs visual acquisition on the data in a classified manner by using a data tiling mode.
The prior art generally utilizes the data blood relationship to generate the data asset map, but the mode only can express the two-dimensional relationship of the data, has poor presentation capability on complex data association relationship, and has less information quantity capable of being embodied.
Disclosure of Invention
The data asset map acquisition method, device, equipment, medium and product can increase the information quantity contained in the data asset map.
In a first aspect, an embodiment of the present application provides a data asset map obtaining method, including:
correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user;
Determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs;
and constructing a data asset map according to the multi-dimensional coordinate system, wherein the data asset map is used for visualizing the association relation among the DPOIs.
In a second aspect, the present application provides a data asset map acquisition device, the device comprising:
the first acquisition module is used for correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user;
the determining module is used for determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs;
and the construction module is used for constructing a data asset map according to the multi-dimensional coordinate system, and the data asset map is used for visualizing the association relation among the DPOIs.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
The processor when executing the computer program instructions implements a data asset map acquisition method as in any one of the embodiments of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement a data asset map acquisition method as in any one of the embodiments of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, the instructions in which, when executed by a processor of an electronic device, cause the electronic device to perform a data asset map acquisition method implementing an embodiment of any one of the first aspects described above.
The data asset map acquisition method, device, equipment, medium and product provided by the embodiment of the application, wherein the method comprises the following steps: correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user; determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs; and constructing a data asset map according to the multi-dimensional coordinate system, wherein the data asset map is used for visualizing the association relation among the DPOIs. Through the steps, the multidimensional data information of the user is associated, so that the data information points DPOI contain corresponding multidimensional data information, and after a data asset map is formed, the related multidimensional data information can be checked through a certain DPOI so as to increase the information quantity contained in the data asset map.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a flow chart of a method for data asset map acquisition according to one embodiment of the present application;
FIG. 2 is another flow diagram of a data asset map acquisition method provided by one embodiment of the present application;
FIG. 3 is an exemplary diagram of a user DPOI provided in one embodiment of the present application;
FIG. 4 is a schematic diagram of a combined algorithm usage flow provided in one embodiment of the present application;
FIG. 5 is an exemplary diagram of a quadtree provided in accordance with one embodiment of the present application;
FIG. 6 is a schematic diagram of a data asset map rendering process provided by one embodiment of the present application;
FIG. 7 is a schematic diagram of a data asset map presentation flow provided by one embodiment of the present application;
FIG. 8 is a schematic diagram of a data asset map acquisition device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
In order to solve the problems in the prior art, the embodiment of the application provides a data asset map acquisition method, a device, equipment, a medium and a product. The data asset map acquisition method provided by the embodiment of the application is first described below.
Fig. 1 is a flow chart of a data asset map obtaining method according to an embodiment of the present application. As shown in fig. 1, the method specifically may include the following steps:
step 101, associating multidimensional data information of a user to form a plurality of data information points DPOI, wherein the multidimensional data information comprises basic information and behavior information of the user.
Specifically, each user contains multidimensional information such as: basic information such as user name, user age, user identification card number, etc., and behavior information such as user bank card number, user transaction information, etc. And (3) associating the multidimensional data information of the single user to obtain a data information point (Data Point ofInformation, DPOI), namely associating the multidimensional information of each user with a point on a physical position, wherein the point is the DPOI, and each DPOI comprises the following information: user basic information, data asset names, data asset contents, data asset codes, asset topics, data layering and domain division, data types, data structures, storage modes, storage positions, blood relationship, association relationship, data period, data granularity, data dimension, data index, data heat, data importance level and other information corresponding to a user. The data contained in the DPOI can be obtained from a database, and can be supplemented in a system input or manual input mode. For ease of explanation, the first DPOI will be described below as an example.
After the multidimensional data information is processed into the DPOI at the physical location, the multidimensional data information of the user corresponding to the first DPOI may be obtained according to the first DPOI, thereby increasing the amount of information contained in the data asset map.
Step 102, determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs.
Specifically, according to multidimensional data information carried in the first DPOI, a weight value of the first DPOI is calculated according to the webpage ranking algorithm and required information in a mode of matching the force guiding algorithm and the webpage ranking algorithm, the weight value is added when attractive force and repulsive force of the first DPOI are calculated, resultant force is formed through the attractive force and repulsive force, and the resultant force is converted into displacement of the first DPOI through the force guiding algorithm.
And moving the first DPOI according to the displacement obtained in the mode, and similarly, moving other DPOIs until all the DPOIs do not need to be moved any more, determining the relative positions among the DPOIs, and constructing a multidimensional coordinate system. The multi-dimensional coordinate system constructed here includes the coordinate positions of the DPOIs and the relative positions between the DPOIs, and in fact, the establishment of the multi-dimensional coordinate system is a process of selecting a display range, that is, establishing a virtual coordinate system, so that the DPOIs are displayed within a reasonable range.
And step 103, constructing a data asset map according to the multi-dimensional coordinate system, wherein the data asset map is used for visualizing the association relation among the plurality of data information points.
Specifically, the DPOI to be displayed and the data asset information corresponding to the DPOI are rendered at the client side by combining the obtained multidimensional coordinate system, and are displayed. When a user needs to enlarge the data asset map, clicking a first DPOI on the map, selecting and enlarging, and readjusting the displayed coordinate range by taking the first DPOI as a center to reacquire the DPOI needing to be displayed in the coordinate range; the reduced data asset map is similar to the enlarged data asset map and will not be described in detail herein.
In the above embodiment, the multidimensional data information of the user is associated, so that the data information points DPOI include the corresponding multidimensional data information, after the data asset map is formed, the map can be enlarged or reduced to view the first DPOI, and the relevant multidimensional data information is viewed through the first DPOI, so as to increase the information amount included in the data asset map.
In another embodiment of the present application, the determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system includes:
Obtaining a resultant force of a first DPOI according to a preset calculation expression, wherein the first DPOI is any one DPOI in the plurality of DPOIs;
determining the displacement of the first DPOI according to the resultant force of the first DPOI;
and adjusting the position of the first DPOI according to the displacement of the first DPOI to obtain the multidimensional coordinate system.
In particular, it is advantageous toCalculating the displacement of the first DPOI by a force-directed algorithm, which is a algorithm that calculates the attractive force F for each node d Repulsive force F r And forming resultant force by the distance, moving the position of the node by the resultant force, and calculating a new energy value according to the new position of the node after one execution, wherein the smaller the energy value is, the more stable the whole network is. Generally, the smaller the energy value, the clearer the configuration display of the network map, so when the energy value reaches the minimum value, the configuration state of the network map is the result that we want.
Under the calculation frame of the force guiding algorithm, the initial positions of the DPOIs are randomly distributed, and the repulsive force F between every two DPOIs in each iteration local area is calculated r The resulting unit displacement (based on coulomb's law
Calculation of k r Representing repulsive force coefficients, q1 and q2 representing a first DPOI and a second DPOI, the second DPOI being a DPOI different from the first DPOI among other DPOIs, setting the values of the first DPOI and the second DPOI to be 1 in the embodiment, r representing the distance between the first DPOI and the second DPOI, and calculating the attractive force F of each edge in each iteration d For the unit displacement generated by the two-terminal DPOI (based on Hooke's law
F d ＝k d *r
Calculation of k d Representing the gravitational coefficient, it is generally considered that each DPOI has a gravitational force F to the other DPOI d And r is equal, so that r is 1), thereby obtaining the unit displacement of resultant force, repeating the process to calculate the unit displacement of all the DPOI nodes, and continuously iterating the calculation until the positions of all the nodes are close to stable.
In this embodiment, the first DPOI is moved by using the force-directed algorithm until the first DPOI does not need to be moved any more, and similarly, other DPOIs are moved until all DPOIs reach a relatively stationary state, so that each DPOI finally reaches an equilibrium state.
In yet another embodiment of the present application, the obtaining the resultant force of the first DPOI according to the preset calculation expression includes:
calculating a weight value of the first DPOI based on the association relation between the first DPOI and other DPOIs;
calculating repulsive force and attractive force of the first DPOI based on the weight value of the first DPOI;
and combining the repulsive force and the attractive force of the first DPOI to obtain the resultant force of the first DPOI.
Specifically, a web page ranking algorithm is introduced to calculate the weight value of the first DPOI, i.e
Wherein PR represents a weight value, Y i Represents a third DPOI, S (u) represents a set of DPOIs, n i The number of edges representing the DPOI, N representing the total number of all DPOIs, the alpha coefficient can be customized, i representing the current iteration number, i.e. the number of single DPOIs moving.
The calculated weight value is matched with a force guiding algorithm to be calculated, and the calculation mode is shown in the above description, and is not repeated herein, so that the displacement of the first DPOI is obtained, and the first DPOI is moved until the first DPOI is not moved.
In this embodiment, if one DPOI is associated with multiple DPOIs, the DPOI is defined as an important node, and the PR value is also higher by iterative calculation of the web page ranking algorithm. Calculating repulsive force F by using force-guiding algorithm r And attraction force F d And weight values are added in the method, so that the DPOI layout is re-optimized, and the efficiency and quality of the layout can be balanced and the coordinate distribution of the large-scale DPOI in the data asset map can be optimized through the self-adaptive calculation step strategy of the webpage ranking algorithm, so that the data loading and layout effects are better.
In a further embodiment of the present application, the repulsive force F of the first DPOI r The determination is made according to the following expression:
wherein k is r Represent the repulsive force coefficient, q 1 、q 2 Respectively representing a preset value of the first DPOI and a preset value of a second DPOI, wherein the second DPOI is any one DPOI except the first DPOI in the plurality of DPOIs, r represents a distance between the first DPOI and the second DPOI, PR (i) represents a weight value of the first DPOI, and PR (j) represents a weight value of the second DPOI;
Attraction force F of the first DPOI d The determination is made according to the following expression:
F d ＝k d *(r+PR(i))
wherein k is d Representing the coefficient of gravity.
Specifically, repulsive force F is calculated r When the two DPOIs are added with the weight values respectively, then the products are obtained, and then other operations are carried out; calculate the attraction force F d And summing the weight value of the first DPOI and the distance value between the two DPOIs, and performing other operations.
In the present embodiment, the weight value of each DPOI is added to the attractive force F by optimizing the computational expression in the force-steering algorithm d And repulsive force F r In the calculation of (2), the importance degree of each DPOI can be effectively embodied, so that the efficiency and quality of layout are balanced.
In yet another embodiment of the present application, the adjusting the position of the first DPOI according to the displacement of the first DPOI, to obtain the multi-dimensional coordinate system, includes:
when the displacement of the first DPOI is smaller than or equal to a preset first preset threshold value, adjusting the position of the first DPOI according to the displacement of the first DPOI;
recording the number of times of the first DPOI movement to obtain a first iteration number;
according to the first iteration times, the first preset threshold value is adjusted, wherein the first iteration times and the first preset threshold value are in a negative correlation;
And under the condition that the displacement of the first DPOI is larger than the first preset threshold value, obtaining a multidimensional coordinate system according to the plurality of DPOIs of which the relative positions are determined.
Specifically, in the force-directed algorithm, a great amount of time and calculation space are wasted due to the fact that the iteration times are too large, but the iteration times are too small, the algorithm does not converge, and the DPOI step-by-step is easy to fail to achieve the expected purpose. Therefore, when the force guiding algorithm is used for calculation, a first preset threshold value is set, and when the displacement of the first DPOI calculated by the force guiding algorithm is smaller than or equal to the preset first preset threshold value, the position of the first DPOI is moved according to the displacement of the first DPOI, the times of the first DPOI movement are recorded, the times of the first DPOI movement are marked as first iteration times, and the times of other DPOI movements are respectively recorded and marked as different iteration times. The first preset threshold is adjusted in real time according to the iteration times, and the first preset threshold and all the iteration times are in negative correlation, for example: when the first iteration number is 1, the first preset threshold value is 5, and when the first iteration number is 2, the first preset threshold value is 4; and when the second iteration number is 1, the first preset threshold is 5, and when the second iteration number is 3, the first preset threshold is 3, wherein the second iteration number is the number of times of movement of the second DPOI.
And under the condition that the displacement of the first DPOI is larger than a first preset threshold value, the first DPOI is not moved any more, and similarly, under the condition that the displacement of other DPOIs is larger than the corresponding first preset threshold value, all the DPOIs are not moved any more, and all the DPOIs reach a relatively stable state.
In this embodiment, the maximum displacement of the DPOI is limited each time, and the maximum displacement is gradually reduced, so that the iteration number is reduced, and an approximately stable DPOI position is obtained through iteration at a faster speed, so that the DPOI is changed from unordered distribution to ordered distribution, and meanwhile, the calculation resources required in the iteration process are also reduced.
In yet another embodiment of the present application, the determining the relative positions of the DPOIs according to the multidimensional data information, and constructing a multidimensional coordinate system, includes:
selecting a first region according to the plurality of DPOIs, wherein the first region comprises the plurality of DPOIs;
dividing the first region into a plurality of second regions on average under the condition that the number of DPOIs in the first region is larger than a second preset threshold value;
dividing each second region into a plurality of third regions on average under the condition that the number of DPOIs in any second region is larger than the second preset threshold value;
And under the condition that the number of DPOIs in each third region is smaller than the second preset threshold value, calculating first displacement of each second region and second displacement of each third region based on a force guiding algorithm, moving the second region according to the first displacement, and moving the third region according to the second displacement to obtain a multidimensional coordinate system.
Specifically, the second preset threshold is set, and the first area is selected, and for convenience of description, the first area is selected as a square area, so that the first area is a smallest square area including all DPOIs to be processed. Under the condition that the number of DPOIs contained in the first area is larger than a second preset threshold value, the first area is divided evenly to obtain 4 second areas, and the second areas are square; and under the condition that the number of DPOIs contained in any one second region is larger than a second preset threshold value, dividing the second region evenly to obtain 4 third regions, wherein the third regions are square, dividing the first region in such a way until the number of DPOIs contained in all m-th regions is smaller than or equal to the second preset threshold value, and assuming that the number of DPOIs contained in all third regions is smaller than or equal to the third preset threshold value for convenience of description.
According to a force guiding algorithm, calculating the displacement of each DPOI in the third area, moving each DPOI in the third area according to the calculated displacement of each DPOI in the third area until the DPOI is not moved, calculating the displacement of each third area, wherein the calculating mode is not described in detail, moving the corresponding third area according to the calculated displacement of the third area until all third areas are not moved, calculating the displacement of each second area, and moving the corresponding second area according to the calculated displacement of the second area until all second areas are not moved, and thus, completing the re-layout of all DPOIs to obtain a data asset map.
In this embodiment, the areas are divided for the DPOIs in the above manner, and the divided areas are rearranged, where the time complexity is from O (n 2 ) The method is reduced to O (n), so that the calculated amount and the calculated time can be effectively reduced, and the reaction speed is increased.
In yet another embodiment of the present application, after said obtaining a data asset map from said multi-dimensional coordinate system, comprising:
obtaining a plurality of pictures according to the data asset map, wherein the pictures are pictures in different area ranges of the data asset map;
Acquiring a first input of a user to the data asset map;
and responding to the first input, displaying a first picture, wherein the first picture is a picture in the plurality of pictures, and the first picture is determined according to the first input.
Specifically, according to the multidimensional data information in step 101, the association relationship between the DPOIs can be obtained, and the weight value of each DPOI can be calculated according to the association relationship between the DPOIs, where the calculation mode is in the prior art and is not described herein. Setting the DPOI level according to the weight value among the DPOIs, wherein the DPOIs with the same weight value are the same DPOI level, and the weight value and the DPOI level are in a negative correlation relationship, namely the larger the weight value of the first DPOI is, the smaller the DPOI level is.
Because the number of DPOIs displayed in the selection range is limited, DPOIs with smaller DPOIs are preferentially displayed, in order to facilitate description, when other DPOIs need to be checked, the data asset map can be further enlarged, after the user inputs the data asset map, an enlargement instruction is received, so that the DPOIs with 1 added to the DPOIs displayed, namely the displayed DPOIs with 3 added to the DPOIs are displayed, and the DPOIs with less than or equal to 3 are displayed, namely the DPOIs with 1, 2 and 3 simultaneously displayed, but because of the limited number of DPOIs, the display range is reselected according to the position clicked by the user on the map as the center, the DPOIs with 1, 2 and 3 added to the DPOIs in the range are displayed, and the user can select the dimension data needed to be checked according to the needs. Similarly, when a zoom-out instruction is received, the DPOI level is reduced by 1, that is, the displayed DPOI level is 1, and then the DPOI with the DPOI level of 1 is displayed.
Dividing the data asset map into a plurality of pictures according to the DPOI level, numbering the pictures, searching the pre-cached pictures according to the first input of the user when the user uses the data asset map, and splicing and displaying the pictures according to the numbers.
In the implementation, when the client accesses the data asset map, the DPOI range displayed as required can be directly called for the pictures cached by the server, and the pictures are spliced and displayed, so that the calculation pressure of the client for graphic rendering is reduced.
Fig. 2 is another flow chart of a data asset map obtaining method according to an embodiment of the present application, which is applied to display of user data assets, and the visual display of a general data asset map generally adopts a data tiling manner to perform panoramic or local display on metadata. When the data volume is large, the calculation and network resources consumed by the data asset map display can be multiplied, so that the data asset map browsing can be very slow, and the user experience is poor.
The existing scheme is a mode of carrying out data asset map display by utilizing the data blood relationship, only two-dimensional relationship of data can be expressed, the display capability of complex data association relationship is poor, the information quantity which can be embodied is also less, and certain limitation is brought to services provided by data managers and developers.
Aiming at scenes under the conditions of massive data and complex demands, in the embodiment, technical improvement is mainly carried out on the aspects of data asset display efficiency, interaction experience and the like. The following design is specifically made:
the multi-dimensional data asset information is packaged, and then a multi-dimensional coordinate system is built by combining a binding force guiding algorithm, a webpage ranking algorithm, a simulated annealing algorithm and a rapid multi-pole sub-algorithm, so that high-efficiency, high-quality and multi-dimensional display of the data asset map is realized, and the display effect, response efficiency and interactive experience of the data asset map can be obviously improved.
The design of the application has three parts: and constructing a multidimensional coordinate system and a data asset map visual display by using a multidimensional data information fusion packaging and combination algorithm.
A first part: and (5) multi-dimensional data information fusion packaging. Referring to fig. 3, a data information point (hereinafter referred to as a DPOI) is an information element of a data asset in a data asset map, and one DPOI generally represents one data asset. The content of the DPOI can be obtained through a metadata management system (or a data asset management system), and can also be supplemented through a system input or manual input mode. The information that the DPOI needs to encapsulate mainly comprises: data asset name, data asset content, data asset encoding, asset theme (including multi-level theme), data layering and domain division, data type, data structure, storage mode, storage location, blood relationship, association relationship, data period, data granularity, data dimension, data index, data heat, data importance level, etc. The DPOI information may be stored in a relational database, a graph database, or the like.
A second part: the combination algorithm builds a multi-dimensional coordinate system. Referring to fig. 4, according to the data asset information carried in each DPOI, a force guiding algorithm (FR algorithm) is applied, and a webpage ranking algorithm (PageRank algorithm), a simulated annealing algorithm and a fast multipole sub-algorithm (FMM algorithm) are used for improving and optimizing performance, so that the DPOI is distributed to reasonable coordinate points in a map canvas in the modes of fastest response, lowest cost, optimal layout and the like.
The force guiding algorithm is to calculate the attractive force, repulsive force and distance of each DPOI to form a resultant force, and then move the position of the DPOI by the resultant force to finally reach a balanced state of the positions of all nodes. Under the calculation framework of the FR algorithm, the initial DPOI positions are randomly distributed, unit displacement generated by repulsive force between every two DPOIs in each iteration local area is calculated (based on Coulomb law calculation), unit displacement generated by attractive force of each side to the DPOIs at two ends is calculated (based on Hooke's law calculation) so as to obtain resultant unit displacement, the process is repeated to calculate the unit displacement of all the DPOI nodes, and iterative calculation is continued until all the node positions are close to be stable.
When the coordinate position is calculated, the quality of the coordinate layout still has a lot of defects, the content layout lacks key points and layers, and particularly when large-scale data are processed, a layout result with high quality is difficult to quickly construct, so that the PageRank algorithm can be introduced to optimize the FR, and the quality of the layout is improved.
The weight value of each DPOI is calculated by introducing a PageRank algorithm, the layout quality is optimized, the calculation expression is not described in detail, if one node is associated with a plurality of nodes, the node is defined as a highlight node, the PR value is higher by PageRank iterative calculation, when the repulsive force and attractive force are calculated by using FR, the PR value is brought, specifically, when the repulsive force is calculated, the product of PR of two nodes is added, and when the attractive force is calculated, the PR value of the node is added, so that the re-optimization of the layout of the DPOI node is realized, the efficiency and the quality of the layout can be balanced by the self-adaptive calculation step strategy of PageRank, the coordinate distribution of the large-scale DPOI in a data asset map is more optimized, and the data loading and layout effect is better. The PageRank algorithm is introduced to calculate the weight of each node, the importance of the node is analyzed, the optimization of the node layout is realized, the quality of the layout is improved, and meanwhile, the data information points can be displayed more intuitively and more with a sense of primary and secondary.
Because the iteration times are externally designated parameters in the force-directed algorithm, the iteration times reaching convergence are different for undirected graphs with different topological structures, if the iteration times are too large, the corresponding calculation time consumption is increased along with the increase, some redundant iteration processes are easy to exist, and if the iteration times are too small, the algorithm is not converged at the moment, the DPOI node distribution is easy to be unbalanced, and the effect is not ideal, so that the condition of algorithm iteration termination is controlled by introducing a simulated annealing algorithm, and the high efficiency and the controllability of iteration are ensured.
And introducing a simulated annealing algorithm, limiting the maximum displacement of the DPOI each time, gradually reducing the maximum displacement threshold, reducing the iteration times, and finally obtaining an approximately stable DPOI position by iteration at a higher speed. In this way, all DPOI positions under interaction forces are balanced with minimal iteration overhead and the final coordinates no longer need to be adjusted. At this time, the coordinate distribution of the DPOI on the data asset map is changed from unordered distribution state to ordered, and the coordinate of the DPOI on the map can be finally determined.
The dimension combination of the preset data asset can be calculated through the FR algorithm and the simulated annealing combination algorithm, and a coordinate system under the specified dimension can be formed. By calculating the combination of the full-volume dimensions, a multi-dimensional coordinate system under the full-volume data asset map can be formed.
However, due to the adoption of the combined calculation mode of the FR+ simulated annealing algorithm, the time complexity of the whole coordinate construction is O (n x n), when the order of magnitude of the DPOI node and the edge is relatively large, obvious calculation performance bottleneck can occur, and the problem of overlong loading of the picture layout time is caused, and at the moment, the calculation performance can be optimized by introducing a rapid multipole sub-algorithm.
Referring to fig. 5, the fast multipole sub-algorithm surrounds all DPOI nodes with a square, divides the rectangle into four small rectangles, if the number of nodes in the small rectangle is higher than a set threshold, the small rectangle is continuously subdivided into four small rectangles, and so on until the number of nodes in each rectangle is lower than the set threshold, thus constructing a multi-layer quadtree, converting the point correspondence of n by layering the DPOI nodes into the relation between a small number of groups, for any rectangle, finding the nearest neighbor node and the interaction area thereof, starting from the sub-node of the quadtree until tracing to the root node, calculating multipole expansion at each node, starting from the root node, obtaining the resultant force of the whole area at each sub-node, then considering the subdivided small rectangle contained in the larger rectangle, calculating the resultant force of the interaction area of the small rectangle for the small rectangle, and for the resultant force of the sub-node and the nearest neighbor node, directly hardening. Thus avoiding the complexity of calculating the resultant force between any two DPOIs, and letting the DPThe time complexity of the OI resultant calculation is defined by O (n 2 ) Down to O (n). By introducing a fast multipole sub-algorithm, the construction efficiency of the multidimensional coordinate system can be greatly optimized, so that the realization of the data asset map and the visualization is better supported. In fig. 5, S represents the largest square, A, B, C, D represents the small rectangle divided by the square S, A1, A2, A3, A4 represent the sub-rectangle divided by the rectangle a, B1, B2, B3, B4 represent the sub-rectangle divided by the rectangle B, C1, C2, C3, C4 represent the sub-rectangle divided by the rectangle C, and D1, D2, D3, D4 represent the sub-rectangle divided by the rectangle D, and the division of the sub-rectangles is continued in this manner.
Third section: the data asset map is visually presented. Referring to fig. 6, the data asset map is displayed, the DPOI to be displayed is screened through the DPOI level, the coordinate range, the dimension information and the like, and is rendered at the client side in combination with the coordinate system and the data asset information to be displayed, and is displayed.
When the user needs to enlarge the data asset map, the DPOI level is +1, and meanwhile, the coordinate range to be displayed is adjusted by taking the map click point as the center, so that the DPOI to be displayed is obtained again. Otherwise, when the data asset map needs to be reduced, the level-1 is displayed, and the coordinate range is adjusted to obtain the DPOI needing to be displayed. According to the level control (or the importance degree of the data asset) of the data information point (DPOI), different data asset information can be displayed on the data asset map at different levels in a scaling mode, and the situation that too much display information is loaded at one time to cause too much pressure of a client can be avoided; and more important data asset information can be displayed at a low level, so that the user experience is obviously improved.
Referring to fig. 7, for a data asset map with a large data volume, after a coordinate system is generated in advance, a level and dimension combination can be displayed separately, the full DPOI is rendered, and a picture of the data asset map is generated and cut into small pictures. The server side numbers the cut small pictures and loads the cut small pictures into a server cache. When the client accesses the data asset map, the DPOI range displayed as required can be directly called for the pictures cached by the server, and the pictures are spliced and displayed, so that the calculation pressure of the client for graphic rendering is reduced.
In summary, the processing steps in the data asset map system are as follows:
1. each DPOI information is acquired from an external system and loaded into a relational database or graph database.
2. By utilizing the loaded DPOI information, the coordinate distribution of the data asset in the data asset map is efficiently calculated and optimized through the combination of force guiding, pageRank, simulated annealing and fast multipole algorithm, and a multidimensional coordinate system can be calculated through different dimensions.
3. The client selects the dimension information to be focused, and realizes the rapid visual display and operation of the data asset map through a pre-calculated coordinate system. By caching the data asset map pictures, the calculation pressure of the client side for rendering the data asset map in a large-data-volume scene can be reduced.
Fig. 8 shows a schematic structural diagram of a data asset map acquisition device 800 according to an embodiment of the present application, and for convenience of explanation, only a portion relevant to the embodiment of the present application is shown.
Referring to fig. 8, a data asset map acquisition device 800 includes:
a first obtaining module 801, configured to correlate multi-dimensional data information of a user to form a plurality of data information points DPOI, where the multi-dimensional data information includes basic information and behavior information of the user;
A determining module 802, configured to determine the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and construct a multidimensional coordinate system, where the multidimensional coordinate system includes the coordinate position of each DPOI and the relative positional relationship between the DPOIs;
a construction module 803, configured to construct a data asset map according to the multi-dimensional coordinate system, where the data asset map is used to visualize association relationships between the plurality of data information points.
Optionally, the determining module 802 includes:
the first acquisition submodule is used for acquiring the resultant force of a first DPOI according to a preset calculation expression, wherein the first DPOI is any one DPOI in the plurality of DPOIs;
a determining submodule, configured to determine a displacement of the first DPOI according to a resultant force of the first DPOI;
and the second acquisition sub-module is used for adjusting the position of the first DPOI according to the displacement of the first DPOI to obtain the multi-dimensional coordinate system.
Optionally, the first obtaining sub-module includes:
a first calculating unit, configured to calculate a weight value of the first DPOI based on an association relationship between the first DPOI and other DPOIs;
a second calculation unit configured to calculate repulsive force and attractive force of the first DPOI based on the weight value of the first DPOI;
A first acquisition unit, configured to calculate a difference between the repulsive force and the attractive force of the first DPOI, and obtain a resultant force of the first DPOI.
Optionally, repulsive force F of the first DPOI r The determination is made according to the following expression:
wherein k is r Represent the repulsive force coefficient, q 1 、q 2 Respectively representing a preset value of the first DPOI and a preset value of a second DPOI, wherein the second DPOI is any one DPOI except the first DPOI in the plurality of DPOIs, r represents a distance between the first DPOI and the second DPOI, PR (i) represents a weight value of the first DPOI, and PR (j) represents a weight value of the second DPOI;
attraction force F of the first DPOI d The determination is made according to the following expression:
F d ＝k d *(r+PR(i))
wherein k is d Representing the coefficient of gravity.
Optionally, the second obtaining sub-module includes:
a first adjusting unit, configured to adjust, according to the displacement of the first DPOI, the position of the first DPOI when the displacement of the first DPOI is less than or equal to a preset first preset threshold;
the second acquisition unit is used for recording the number of times of the first DPOI movement and obtaining a first iteration number;
the second adjusting unit is used for adjusting the first preset threshold according to the first iteration times, wherein the first iteration times and the first preset threshold are in a negative correlation;
And a third obtaining unit, configured to obtain a multidimensional coordinate system according to the multiple DPOIs that have determined the relative position, when the displacement of the first DPOI is greater than the first preset threshold.
Optionally, the determining module 802 includes:
a selecting sub-module, configured to select a first area according to the multiple DPOIs, where the first area includes the multiple DPOIs;
the first dividing sub-module is used for dividing the first area into a plurality of second areas on average under the condition that the number of DPOIs in the first area is larger than a second preset threshold value;
the second dividing sub-module is used for dividing each second region into a plurality of third regions on average under the condition that the number of DPOI in any second region is larger than the second preset threshold value;
a calculating sub-module, configured to calculate, based on a force steering algorithm, a first displacement of each of the second regions and a second displacement of each of the third regions, when the number of DPOI in each of the third regions is smaller than the second preset threshold;
and the third acquisition submodule is used for moving the second region according to the first displacement and moving the third region according to the second displacement to obtain a multidimensional coordinate system.
Optionally, the data asset map obtaining apparatus 800 includes:
the second acquisition module is used for acquiring a plurality of pictures according to the data asset map, wherein the pictures are pictures in different area ranges of the data asset map;
a third acquisition module for acquiring a first input of a user to the data asset map;
and the display module is used for responding to the first input and displaying a first picture, wherein the first picture is a picture in the plurality of pictures, and the first picture is determined according to the first input.
The data asset map obtaining apparatus 800 provided in the embodiment of the present application can implement each process implemented by the foregoing method embodiment, and in order to avoid repetition, a description is omitted here.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Fig. 9 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.
The device may include a processor 901 and a memory 902 in which program instructions are stored.
The steps of any of the various method embodiments described above are implemented when the processor 901 executes a program.
For example, a program may be partitioned into one or more modules/units, which are stored in the memory 902 and executed by the processor 901 to complete the present application. One or more of the modules/units may be a series of program instruction segments capable of performing specific functions to describe the execution of the program in the device.
In particular, the processor 901 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 902 may include mass storage for data or instructions. By way of example, and not limitation, the memory 902 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory 902 may include removable or non-removable (or fixed) media, where appropriate. The memory 902 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 902 is a non-volatile solid state memory.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 901 implements any one of the methods of the above embodiments by reading and executing program instructions stored in the memory 902.
In one example, the electronic device may also include a communication interface 903 and a bus 910. The processor 901, the memory 902, and the communication interface 903 are connected to each other via a bus 910 and perform communication with each other.
The communication interface 903 is mainly used to implement communication between each module, device, unit, and/or apparatus in the embodiments of the present application.
Bus 910 includes hardware, software, or both, that couple the components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 910 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In addition, in combination with the method in the above embodiment, the embodiment of the application may be implemented by providing a storage medium. The storage medium has program instructions stored thereon; the program instructions, when executed by a processor, implement any of the methods of the embodiments described above.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running a program or instructions, the processes of the above method embodiment are realized, the same technical effects can be achieved, and in order to avoid repetition, the description is omitted here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
The embodiments of the present application provide a computer program product, which is stored in a storage medium, and the program product is executed by at least one processor to implement the respective processes of the above method embodiments, and achieve the same technical effects, and are not repeated herein.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer grids such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data asset map acquisition device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data asset map acquisition device, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.

Claims (10)

1. A method of data asset map acquisition, the method comprising:
correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user;
determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs;
and constructing a data asset map according to the multi-dimensional coordinate system, wherein the data asset map is used for visualizing the association relation among the DPOIs.
2. The method according to claim 1, wherein determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, comprises:
obtaining a resultant force of a first DPOI according to a preset calculation expression, wherein the first DPOI is any one DPOI in the plurality of DPOIs;
determining the displacement of the first DPOI according to the resultant force of the first DPOI;
and adjusting the position of the first DPOI according to the displacement of the first DPOI to obtain the multidimensional coordinate system.
3. The method of claim 2, wherein obtaining the resultant force of the first DPOI according to the preset calculation expression comprises:
calculating a weight value of the first DPOI based on the association relation between the first DPOI and other DPOIs;
calculating repulsive force and attractive force of the first DPOI based on the weight value of the first DPOI;
and combining the repulsive force and the attractive force of the first DPOI to obtain the resultant force of the first DPOI.
4. A method according to claim 3, characterized in that the repulsive force F of the first DPOI r The determination is made according to the following expression:
wherein k is r Represent the repulsive force coefficient, q 1 、q 2 Respectively representing a preset value of the first DPOI and a preset value of a second DPOI, wherein the second DPOI is any one DPOI except the first DPOI in the plurality of DPOIs, r represents a distance between the first DPOI and the second DPOI, PR (i) represents a weight value of the first DPOI, and PR (j) represents a weight value of the second DPOI;
attraction force F of the first DPOI d The determination is made according to the following expression:
F d ＝k d *(r+PR(i))
wherein k is d Representing the coefficient of gravity.
5. The method of claim 2, wherein said adjusting the position of the first DPOI based on the displacement of the first DPOI, obtaining the multi-dimensional coordinate system, comprises:
When the displacement of the first DPOI is smaller than or equal to a preset first preset threshold value, adjusting the position of the first DPOI according to the displacement of the first DPOI;
recording the number of times of the first DPOI movement to obtain a first iteration number;
according to the first iteration times, the first preset threshold value is adjusted, wherein the first iteration times and the first preset threshold value are in a negative correlation;
and under the condition that the displacement of the first DPOI is larger than the first preset threshold value, obtaining a multidimensional coordinate system according to the plurality of DPOIs of which the relative positions are determined.
6. The method according to claim 1, wherein determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, comprises:
selecting a first region according to the plurality of DPOIs, wherein the first region comprises the plurality of DPOIs;
dividing the first region into a plurality of second regions on average under the condition that the number of DPOIs in the first region is larger than a second preset threshold value;
dividing each second region into a plurality of third regions on average under the condition that the number of DPOIs in any second region is larger than the second preset threshold value;
And under the condition that the number of DPOIs in each third region is smaller than the second preset threshold value, calculating first displacement of each second region and second displacement of each third region based on a force guiding algorithm, moving the second region according to the first displacement, and moving the third region according to the second displacement to obtain a multidimensional coordinate system.
7. The method of claim 1, wherein after the obtaining a data asset map from the multi-dimensional coordinate system, the method further comprises:
obtaining a plurality of pictures according to the data asset map, wherein the pictures are pictures in different area ranges of the data asset map;
acquiring a first input of a user to the data asset map;
and responding to the first input, displaying a first picture, wherein the first picture is a picture in the plurality of pictures, and the first picture is determined according to the first input.
8. A data asset map acquisition device, the device comprising:
the first acquisition module is used for correlating multi-dimensional data information of a user to form a plurality of data information points DPOI, wherein the multi-dimensional data information comprises basic information and behavior information of the user;
The determining module is used for determining the relative positions of the DPOIs according to the multidimensional data information corresponding to each DPOI, and constructing a multidimensional coordinate system, wherein the multidimensional coordinate system comprises the coordinate position of each DPOI and the relative position relation among the DPOIs;
and the construction module is used for constructing a data asset map according to the multi-dimensional coordinate system, and the data asset map is used for visualizing the association relation among the DPOIs.
9. An electronic device, the device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a data asset map acquisition method as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the data asset map acquisition method according to any of claims 1-7.
CN202310490428.8A 2023-05-04 2023-05-04 Data asset map acquisition method, device, equipment, medium and product Pending CN116521782A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117150570A (en) * 2023-11-01 2023-12-01 国网浙江省电力有限公司 Asset information process safety monitoring method and system based on data resource elements

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117150570A (en) * 2023-11-01 2023-12-01 国网浙江省电力有限公司 Asset information process safety monitoring method and system based on data resource elements
CN117150570B (en) * 2023-11-01 2024-01-26 国网浙江省电力有限公司 Asset information process safety monitoring method and system based on data resource elements

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