CN116991952A - Method, device, equipment and medium for analyzing blood edges of water affair data - Google Patents
Method, device, equipment and medium for analyzing blood edges of water affair data Download PDFInfo
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Abstract
The application provides a blood-edge analysis method, a device, equipment and a medium for water affair data, wherein the method comprises the following steps: the method comprises the steps of combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information and storing the related information into a database; based on the database, establishing a metadata map by taking a basic data entity and a business application data entity as nodes, and establishing a blood relationship of water service data; performing cluster analysis on all added data nodes, dividing the data nodes into different major classes and minor classes, and marking all data nodes of each minor class in a metadata map; and carrying out path tracking from source data to target data through a metadata map, and realizing data blood margin analysis. The application can realize the blood-margin analysis of water service data of various data sources, and the water service department can more deeply understand the water pollution condition and the characteristics of pollution sources, and the storage, transportation, utilization and other conditions of water resources, thereby making more effective management and control measures.
Description
Technical Field
The application relates to the technical field of analysis of water affair data, in particular to a blood margin analysis method, a device, equipment and a medium of water affair data.
Background
The construction of intelligent water affairs is to strengthen town water source protection and construction and water supply facility transformation and construction, ensure town water supply safety. The whole water supply process from monitoring the tap water in the water source area to the tap water is monitored and managed in real time by utilizing an information technology means, a reasonable information public system is formulated, and the safety of water for residents is guaranteed; and (5) constructing a monitoring system of the whole-process intelligent water service system.
The data source of the water service data mainly comprises basic data and business application data, and also comprises other data related to the water service, wherein the basic data mainly refers to hydrologic water resource data, water supply network data, drainage network and auxiliary facility data, town river channel data, city water conservancy facility data and the like; the business application data mainly refer to water environment data, water ecology data, water engineering data, water supply and drainage water conservation data, pollution source data and the like. Other data related to water affairs refer to other data related to water affairs of urban population, society, economy, etc.
At present, the collection capability of water affair data is stronger and stronger in China. However, many local water affair data acquisition still face the problem such as data source dispersion, acquisition means singleness, data quality is uneven. The storage mode of water affair data also faces different degrees of problems. On one hand, some places also rely on traditional files and paper records, so that query and comparison analysis are not convenient enough. On the other hand, due to huge data volume, the management and maintenance cost of the database is high, and the situations of data accumulation, improper storage and the like with different degrees exist in some places, so that the data islanding phenomenon is serious, and the water service data is imperfect and untimely; therefore, water affair data sharing still has the problems that data is regional and department boundaries are difficult to cross, granularity and range of data opening are to be enhanced, and water affair data blood-edge analysis is obviously particularly important.
The existing data blood-edge analysis method mainly comprises the following steps:
1. storing the metadata in a centralized repository and tracking the flow and processing history of the data through a metadata management tool;
2. constructing a data blood-edge relationship by tracking movement and processing history of data in real time in a data processing process;
3. the movement and processing history of the data is tracked by recording log information generated during the processing of the data.
The method has the following defects: the collection and transmission of the water affair data often go through a plurality of links from a plurality of departments or systems, each link possibly relates to a plurality of departments or participants, the data sources are scattered, different data sources possibly use different data formats and storage modes, the data blood edge analysis needs to evaluate the quality and consistency of the data, but the data may have the problems of errors, inconsistencies, deletions and the like, and the problems can affect the reliability and accuracy of the data blood edge analysis; in existing water service data processing systems, different data sources may have different metadata formats and access interfaces, and these formats and interfaces may also be different over time and version, thereby affecting the reading of the data sources; different data processing engines and processes may generate different data blood edge paths, lack a unified data standard and data tracking mechanism, and therefore cannot deeply trace and track data sources.
In this case, a careful water data blood-line tracking mechanism needs to be established to enable the source of each data point and each operation in the process to be traced back in depth.
Thus, support for different types of data processing engines and process flows needs to be considered.
Disclosure of Invention
The application aims to solve the technical problems by providing a method, a device, equipment and a medium for analyzing the blood edges of water service data, which can realize the blood edge analysis of the water service data of various data sources, and the water service department can more deeply understand the water pollution condition and the characteristics of pollution sources, the storage, the transportation, the utilization and other conditions of water resources, thereby making more effective management and control measures.
In a first aspect, the present application provides a method for analyzing blood clots, comprising:
s1, combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information and storing the related information into a database; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure;
determining a field type;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities;
extracting the association information of the basic data, the business application data and other data related to water affairs;
establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints;
s2, mapping association relations among the basic data, the business application data and other data related to water affairs into a metadata map, and establishing a blood-cause relation of water affair data through a field mapping relation between an initial data source node and a target data node;
marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
s3, performing cluster analysis on all the added data nodes, and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
s4, carrying out path tracking from source data to target data through a metadata map to realize data blood-edge analysis, wherein the method comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
In a second aspect, the present application provides a blood-vessel analysis device for water-service data, which is characterized in that: comprising the following steps:
the database module is used for combing the table structure of the basic data and the business application data of the water business data through the metadata map, obtaining related information and storing the related information into a database; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure;
determining a field type;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities;
extracting the association information of the basic data, the business application data and other data related to water affairs;
establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints;
the blood-edge relation establishing module is used for mapping the association relation among the basic data, the business application data and other data related to water business into a metadata map, and establishing the blood-edge relation of the water business data through a field mapping relation between an initial data source node and a target data node; marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
the clustering module is used for carrying out cluster analysis on all the added data nodes and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
the data blood edge analysis module is used for tracking a path from source data to target data through a metadata map, and realizing data blood edge analysis, and comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
In a third aspect, the application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of the first aspect.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages: the method comprises the steps of combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information, storing the related information into a database, mapping the association relation among the basic data, the business application data and other data related to the water service into the metadata map based on the database, namely, recording the blood-vessel information of the water service data through a visual interface of the metadata map, and further knowing the source, transmission path and use condition of water resources, so that related personnel can know the storage condition, the transmission condition, the use condition and the like of the water resources, perfecting a water resource management system, finding problems and contradictions of the water resources in time, and rapidly coping with scheduling management problems. Through the combination of blood edge analysis and cluster analysis, the use condition of water resources can be deeply known, and the water resource utilization condition and the existing problems are analyzed, so that water resource utilization optimization measures are provided in a targeted manner. The water resource utilization condition is tracked in time, the water resource utilization strategy is optimized, saving measures are taken, and the like, so that the water resource utilization efficiency is improved, the water resource waste is reduced, and a high-quality water resource development pattern is formed.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
The application will be further described with reference to examples of embodiments with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method according to a first embodiment of the application;
FIG. 2 is a schematic diagram of a device according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a medium in a fourth embodiment of the present application.
Detailed Description
The embodiment of the application can realize the blood-edge analysis of the water service data of various data sources by providing the blood-edge analysis method, the device, the equipment and the medium of the service data, and the water service department can more deeply understand the water pollution condition and the characteristics of the pollution source, and the storage, the transportation, the utilization and other conditions of the water resource, thereby making more effective management and control measures.
The technical scheme in the embodiment of the application has the following overall thought: the method comprises the steps of combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information, storing the related information into a database, mapping the association relation among the basic data, the business application data and other data related to the water service into the metadata map based on the database, namely, recording the blood-vessel information of the water service data through a visual interface of the metadata map, and further knowing the source, transmission path and use condition of water resources, so that related personnel can know the storage condition, the transmission condition, the use condition and the like of the water resources, perfecting a water resource management system, finding problems and contradictions of the water resources in time, and rapidly coping with scheduling management problems. Through the combination of blood edge analysis and cluster analysis, the use condition of water resources can be deeply known, and the water resource utilization condition and the existing problems are analyzed, so that water resource utilization optimization measures are provided in a targeted manner. The water resource utilization condition is tracked in time, the water resource utilization strategy is optimized, saving measures are taken, and the like, so that the water resource utilization efficiency is improved, the water resource waste is reduced, and a high-quality water resource development pattern is formed.
Example 1
As shown in fig. 1, the present embodiment provides a blood-margin analysis method for water affair data, which includes:
s1, combing the table structure of basic data and business application data of water service data, obtaining related information and storing the related information in a database; the basic data comprise hydrologic water resource data, water supply network data, drainage network and auxiliary facility data, town river data and city water conservancy facility data; the business application data comprise water environment data, water ecological data, water engineering data, water supply and drainage water saving data and pollution source data; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure; the basic data entity comprises a hydrologic water resource data entity, a water supply network data entity, a drainage network and auxiliary facility data entity, a town river data entity and an urban water conservancy facility data entity; the business application data entity comprises a water environment data entity, a water ecological data entity, a water engineering data entity, a water supply and drainage data entity and a pollution source data entity.
Determining a field type, such as text type, number type, etc.;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities; for example:
the entity attribute of the hydrologic water resource data entity is data reflecting the chemical composition, physical characteristics, biological conditions and the like of the water body, such as dissolved oxygen, pH value, turbidity, pollutant concentration, water quality index value and the like of the water. Drainage network and ancillary facility data such as water quality check point data entity: the water quality detection point comprises the position, name, type, monitoring parameters and other attributes of the water quality detection point;
pollution source data entities are exemplified by pollution discharge event entities, whose physical attributes include: evidence collection, place of occurrence, time of occurrence, reporting person, type of occurrence, etc.;
urban water conservancy facility data entities, such as water service organization entities, whose physical attributes include: name, organization type, location, etc.
Extracting the association information of the basic data, the business application data and other data related to water affairs; the other data related to water affairs comprise urban population data, social data and economic data related to water affairs; and establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints.
For example, in pollution traceability analysis, water environment data is associated with pollution source data; the method comprises the steps of monitoring the water quality index of a water body, monitoring the discharge condition of a pollution source, carrying out pollution tracing and sewage disposal event qualitative, carrying out event law enforcement by a relevant detection mechanism and a qualitative mechanism, wherein attribute information of the event law enforcement comprises a law enforcement bill, a law enforcement mechanism, a law enforcement process video and the like, and the attribute information of the event law enforcement carried out by the relevant detection mechanism and the qualitative mechanism is also related information.
S2, based on the database, establishing a metadata map by taking a basic data entity and a business application data entity as nodes, mapping association relations among the basic data, the business application data and other data related to water affairs into the metadata map, and establishing a blood-edge relation of the water affair data through a field mapping relation between an initial data source node and a target data node; marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
for the water quality detection station and the water source site, the entity corresponding to the node can be a detection point, a sampling point or a water body, and the related attribute can comprise water quality parameters (such as COD, BOD, and the like), sampling date, sampling depth, sampling position, water body type, and the like; commonalities may also be found among other different data sources and mapped to corresponding entities and attributes in the metadata map. Establishing a blood relationship of hydrologic data through a field mapping relationship between a starting data source node and a target data node; and marking and associating metadata nodes such as data tables, fields, data types and the like to establish the relation among different nodes, so as to trace the source and conversion process of the water service data.
S3, performing cluster analysis on all the added data nodes, and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
after cluster analysis, some data nodes are classified into different subclasses, but in the practical application situation, all the data nodes in the different subclasses can be combined according to a certain rule by changing the demarcation range, so that different analysis requirements can be met.
For example, after the aggregate analysis, all the drainage data are divided into a large class, and after the aggregate analysis is performed again according to different characteristics of the drainage data, such as pollutant concentration, emission, flow, water quality and the like, the drainage data in the large class are divided into different subclasses so as to further classify and compare the drainage data, so that the drainage condition can be quantitatively and qualitatively described, and the reference is provided for environmental protection work; through cluster analysis, the water department can know the water pollution condition and the characteristics of pollution sources more deeply, formulate more effective control measures and improve the water quality monitoring efficiency and quality.
S4, carrying out path tracking from source data to target data through a metadata map to realize data blood-edge analysis, wherein the method comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
In step S4, a data checksum data verification is further added to ensure that the data is not modified or lost during the processing.
In an actual application scene, the data condition can be analyzed by drawing a distribution scatter diagram on a sample, for example, in a water service scene, the storage condition, the conveying condition, the use condition and the like of water resources can be known for blood-source analysis of the data, a water resource management system is perfected, and the management efficiency and the management level are improved. Through blood edge analysis and cluster analysis, the use condition of the water resource can be deeply known, and the water resource utilization condition and the existing problems are analyzed, so that water resource utilization optimization measures are provided in a targeted manner. The water resource utilization condition is tracked in time, the water resource utilization strategy is optimized, saving measures are taken, and the like, so that the water resource utilization efficiency is improved, the water resource waste is reduced, and a high-quality water resource development pattern is formed.
Based on the same inventive concept, the application also provides a device corresponding to the method in the first embodiment, and the details of the second embodiment are shown.
Example two
As shown in fig. 2, in this embodiment, there is provided a blood-margin analysis device for water affair data, including:
the database module is used for combing the table structure of the basic data and the business application data of the water business data through the metadata map, obtaining related information and storing the related information into a database; the basic data comprise hydrologic water resource data, water supply network data, drainage network and auxiliary facility data, town river data and city water conservancy facility data; the business application data comprise water environment data, water ecological data, water engineering data, water supply and drainage water saving data and pollution source data; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure; the basic data entity comprises a hydrologic water resource data entity, a water supply network data entity, a drainage network and auxiliary facility data entity, a town river data entity and an urban water conservancy facility data entity; the business application data entity comprises a water environment data entity, a water ecological data entity, a water engineering data entity, a water supply and drainage data entity and a pollution source data entity.
Determining a field type, such as text type, number type, etc.;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities; for example:
the entity attribute of the hydrologic water resource data entity is data reflecting the chemical composition, physical characteristics, biological conditions and the like of the water body, such as dissolved oxygen, pH value, turbidity, pollutant concentration, water quality index value and the like of the water. Drainage network and ancillary facility data such as water quality check point data entity: the water quality detection point comprises the position, name, type, monitoring parameters and other attributes of the water quality detection point;
pollution source data entities are exemplified by pollution discharge event entities, whose physical attributes include: evidence collection, place of occurrence, time of occurrence, reporting person, type of occurrence, etc.;
urban water conservancy facility data entities, such as water service organization entities, whose physical attributes include: name, organization type, location, etc.
Extracting the association information of the basic data, the business application data and other data related to water affairs; the other data related to water affairs comprise urban population data, social data and economic data related to water affairs; and establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints.
For example, in pollution traceability analysis, water environment data is associated with pollution source data; the method comprises the steps of monitoring the water quality index of a water body, monitoring the discharge condition of a pollution source, carrying out pollution tracing and sewage disposal event qualitative, carrying out event law enforcement by a relevant detection mechanism and a qualitative mechanism, wherein attribute information of the event law enforcement comprises a law enforcement bill, a law enforcement mechanism, a law enforcement process video and the like, and the attribute information of the event law enforcement carried out by the relevant detection mechanism and the qualitative mechanism is also related information.
The blood-edge relation establishing module is used for establishing a metadata map by taking a basic data entity and a business application data entity as nodes based on the database, mapping the association relation among the basic data, the business application data and other data related to water affairs into the metadata map, and establishing the blood-edge relation of the water affair data through a field mapping relation between an initial data source node and a target data node; marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
for the water quality detection station and the water source site, the entity corresponding to the node can be a detection point, a sampling point or a water body, and the related attribute can comprise water quality parameters (such as COD, BOD, and the like), sampling date, sampling depth, sampling position, water body type, and the like; commonalities may also be found among other different data sources and mapped to corresponding entities and attributes in the metadata map. Establishing a blood relationship of hydrologic data through a field mapping relationship between a starting data source node and a target data node; and marking and associating metadata nodes such as data tables, fields, data types and the like to establish the relation among different nodes, so as to trace the source and conversion process of the water service data.
The clustering module is used for carrying out cluster analysis on all the added data nodes and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
after cluster analysis, some data nodes are classified into different subclasses, but in the practical application situation, all the data nodes in the different subclasses can be combined according to a certain rule by changing the demarcation range, so that different analysis requirements can be met.
For example, after the aggregate analysis, all the drainage data are divided into a large class, and after the aggregate analysis is performed again according to different characteristics of the drainage data, such as pollutant concentration, emission, flow, water quality and the like, the drainage data in the large class are divided into different subclasses so as to further classify and compare the drainage data, so that the drainage condition can be quantitatively and qualitatively described, and the reference is provided for environmental protection work; through cluster analysis, the water department can know the water pollution condition and the characteristics of pollution sources more deeply, formulate more effective control measures and improve the water quality monitoring efficiency and quality.
The data blood edge analysis module is used for tracking a path from source data to target data through a metadata map, and realizing data blood edge analysis, and comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
Further, the apparatus of this embodiment further includes a data verification module and a merging module:
and the data verification module is used for carrying out data verification and data verification on the data in the process of processing the data by the data blood edge analysis module so as to ensure that the data is not modified or lost in the processing process.
And the merging module is used for merging all the data nodes in different subclasses according to a certain rule under the condition of need.
In an actual application scene, the data condition can be analyzed by drawing a distribution scatter diagram on a sample, for example, in a water service scene, the storage condition, the conveying condition, the use condition and the like of water resources can be known for blood-source analysis of the data, a water resource management system is perfected, and the management efficiency and the management level are improved. Through blood edge analysis and cluster analysis, the use condition of the water resource can be deeply known, and the water resource utilization condition and the existing problems are analyzed, so that water resource utilization optimization measures are provided in a targeted manner. The water resource utilization condition is tracked in time, the water resource utilization strategy is optimized, saving measures are taken, and the like, so that the water resource utilization efficiency is improved, the water resource waste is reduced, and a high-quality water resource development pattern is formed.
Since the device described in the second embodiment of the present application is a device for implementing the method described in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a person skilled in the art can understand the specific structure and the deformation of the device, and thus the detailed description thereof is omitted herein. All devices used in the method according to the first embodiment of the present application are within the scope of the present application.
Based on the same inventive concept, the application provides an electronic device embodiment corresponding to the first embodiment, and the details of the third embodiment are shown in the specification.
Example III
The present embodiment provides an electronic device, as shown in fig. 3, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where any implementation of the first embodiment may be implemented when the processor executes the computer program.
Since the electronic device described in this embodiment is a device for implementing the method in the first embodiment of the present application, those skilled in the art will be able to understand the specific implementation of the electronic device and various modifications thereof based on the method described in the first embodiment of the present application, so how the electronic device implements the method in the embodiment of the present application will not be described in detail herein. The apparatus used to implement the methods of embodiments of the present application will be within the scope of the intended protection of the present application.
Based on the same inventive concept, the application provides a storage medium corresponding to the first embodiment, and the detail of the fourth embodiment is shown in the specification.
Example IV
The present embodiment provides a computer readable storage medium, as shown in fig. 4, on which a computer program is stored, which when executed by a processor, can implement any implementation of the first embodiment.
The technical scheme provided by the embodiment of the application has at least the following technical effects or advantages: the method comprises the steps of combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information, storing the related information into a database, mapping the association relation among the basic data, the business application data and other data related to the water service into the metadata map based on the database, namely, recording the blood-vessel information of the water service data through a visual interface of the metadata map, and further knowing the source, transmission path and use condition of water resources, so that related personnel can know the storage condition, the transmission condition, the use condition and the like of the water resources, perfecting a water resource management system, finding problems and contradictions of the water resources in time, and rapidly coping with scheduling management problems. Through the combination of blood edge analysis and cluster analysis, the use condition of water resources can be deeply known, and the water resource utilization condition and the existing problems are analyzed, so that water resource utilization optimization measures are provided in a targeted manner. The water resource utilization condition is tracked in time, the water resource utilization strategy is optimized, saving measures are taken, and the like, so that the water resource utilization efficiency is improved, the water resource waste is reduced, and a high-quality water resource development pattern is formed.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While specific embodiments of the application have been described above, it will be appreciated by those skilled in the art that the specific embodiments described are illustrative only and not intended to limit the scope of the application, and that equivalent modifications and variations of the application in light of the spirit of the application will be covered by the claims of the present application.
Claims (10)
1. A blood margin analysis method for water affair data is characterized in that: comprising the following steps:
s1, combing the table structure of basic data and business application data of water service data through a metadata map, obtaining related information and storing the related information into a database; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure;
determining a field type;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities;
extracting the association information of the basic data, the business application data and other data related to water affairs;
establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints;
s2, based on the database, establishing a metadata map by taking a basic data entity and a business application data entity as nodes, mapping association relations among the basic data, the business application data and other data related to water affairs into the metadata map, and establishing a blood-edge relation of the water affair data through a field mapping relation between an initial data source node and a target data node;
marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
s3, performing cluster analysis on all the added data nodes, and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
s4, carrying out path tracking from source data to target data through a metadata map to realize data blood-edge analysis, wherein the method comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
2. The method for blood-based analysis of water-borne data according to claim 1, wherein: in the step S4, a data checksum data verification is further added to ensure that the data is not modified or lost during the processing.
3. The method for blood-based analysis of water-borne data according to claim 1, wherein: in step S3, all the data nodes in different subclasses are combined according to a certain rule.
4. The method for blood-based analysis of water-borne data according to claim 1, wherein:
the basic data comprise hydrologic water resource data, water supply network data, drainage network and auxiliary facility data, town river data and city water conservancy facility data;
the business application data comprise water environment data, water ecological data, water engineering data, water supply and drainage water saving data and pollution source data;
the other data related to water affairs comprise urban population data, social data and economic data related to water affairs;
the basic data entity comprises a hydrologic water resource data entity, a water supply network data entity, a drainage network and auxiliary facility data entity, a town river data entity and an urban water conservancy facility data entity;
the business application data entity comprises a water environment data entity, a water ecological data entity, a water engineering data entity, a water supply and drainage data entity and a pollution source data entity.
5. The blood margin analysis device of water affair data is characterized in that: comprising the following steps:
the database module is used for combing the table structure of the basic data and the business application data of the water business data through the metadata map, obtaining related information and storing the related information into a database; the obtaining related information includes:
extracting the relation between the basic data entity and the business application data entity of the table structure;
determining a field type;
establishing each water service data table according to the relation and the field type, and determining the relation among the water service data tables;
defining entity attributes, fields and constraints between basic data entities and business application data entities;
extracting the association information of the basic data, the business application data and other data related to water affairs;
establishing association relations and entity attribute information among the basic data, the business application data and other data related to water affairs according to the association information and the entity attributes, fields and constraints;
the blood-edge relation establishing module is used for establishing a metadata map by taking a basic data entity and a business application data entity as nodes based on the database, mapping the association relation among the basic data, the business application data and other data related to water affairs into the metadata map, and establishing the blood-edge relation of the water affair data through a field mapping relation between an initial data source node and a target data node; marking and associating metadata nodes in the metadata map so as to establish the relation among different metadata nodes and trace the source and conversion process of water service data;
the clustering module is used for carrying out cluster analysis on all the added data nodes and dividing the data nodes into different major classes; performing clustering analysis again on all the data nodes in each major class to obtain each minor class in each major class; marking all data nodes of each subclass in a metadata map;
the data blood edge analysis module is used for tracking a path from source data to target data through a metadata map, and realizing data blood edge analysis, and comprises the following steps:
tracking a flow path of the water service data between the source system and the target system;
deeply analyzing the storage, transportation and utilization conditions of water resources;
inquiring the position information of a specific hydrological site and the basin to which the specific hydrological site belongs;
and carrying out correlation analysis on the water quality detection condition of a specific hydrologic station and the basin to which the specific hydrologic station belongs.
6. The water service data blood edge analysis device according to claim 5, wherein: further comprises:
and the data verification module is used for carrying out data verification and data verification on the data in the process of processing the data by the data blood edge analysis module so as to ensure that the data is not modified or lost in the processing process.
7. The water service data blood edge analysis device according to claim 5, wherein:
and the data nodes in different subclasses are combined according to a certain rule if necessary.
8. The water service data blood edge analysis device according to claim 5, wherein:
the basic data comprise hydrologic water resource data, water supply network data, drainage network and auxiliary facility data, town river data and city water conservancy facility data;
the business application data comprise water environment data, water ecological data, water engineering data, water supply and drainage water saving data and pollution source data;
the other data related to water affairs comprise urban population data, social data and economic data related to water affairs;
the basic data entity comprises a hydrologic water resource data entity, a water supply network data entity, a drainage network and auxiliary facility data entity, a town river data entity and an urban water conservancy facility data entity;
the business application data entity comprises a water environment data entity, a water ecological data entity, a water engineering data entity, a water supply and drainage data entity and a pollution source data entity.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when the program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 4.
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| CN117354070A (en) * | 2023-12-06 | 2024-01-05 | 南京勤德智能科技有限公司 | Intelligent water service data terminal safety protection system and method based on Internet of things |
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