WO2023020447A1 - Data processing method, data query method, device, and storage medium - Google Patents

Abstract

The present application discloses a data processing method, a data query method, a device, and a storage medium. The data processing method comprises: performing mapping conversion on field data to obtain a data model consistent with a preset unified data model framework.

Description

Data processing method, data query method, device and storage medium

Cross References to Related Applications

This application is based on a Chinese patent application with application number 202110944647.X and a filing date of August 17, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present application relates to the technical field of computers, and in particular to a data processing method, a data query method, a device, and a storage medium.

Background technique

In the field of communication, telecom management functions are divided into configuration management functions, performance management functions, fault management functions, and security management functions, etc., and different functional areas solve problems in different dimensions of network management. With the development of digitization, data analysis and management in different functional areas are mainly analyzed and decided through the corresponding data model, but the data model configured in each functional area is set to solve the specific needs of this field, so different Functional areas are often separated "information islands", without mutual connection, it is easy to cause data model frameworks between different functional areas to be unable to use each other or standards are inconsistent, resulting in information errors or information stacking.

Contents of the invention

Embodiments of the present application provide a data processing method, a data query method, a device, and a storage medium.

In the first aspect, the embodiment of the present application provides a data processing method, including:

Obtain domain data for multiple different target domains;

The domain data is mapped and converted to obtain a data model consistent with the preset unified data model framework.

In the second aspect, the embodiment of the present application provides a data query method, including:

Obtain the data model of the data processing method as described in the first aspect;

Obtain domain data meeting the preset query conditions from the data model according to the preset query conditions.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor, and when the processor executes the program, it realizes:

The data processing method described in the first aspect, or the data query method described in the second aspect.

In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used for:

Execute the data processing method described in the first aspect, or the data query method described in the second aspect.

Additional features and advantages of the application will be set forth in the description which follows, and, in part, will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Description of drawings

The accompanying drawings are used to provide a further understanding of the technical solution of the present application, and constitute a part of the specification, and are used together with the embodiments of the present application to explain the technical solution of the present application, and do not constitute a limitation to the technical solution of the present application.

Fig. 1 is a schematic flow chart of a data processing method provided by an embodiment of the present application;

FIG. 2 is an environmental framework diagram of a data processing method provided by another embodiment of the present application;

Fig. 3 is a schematic flow chart of a data processing method provided by another embodiment of the present application;

FIG. 4 is a schematic flowchart of a data processing method provided by another embodiment of the present application;

Fig. 5 is a schematic flow chart of a data processing method provided by an embodiment of the present application;

Fig. 6 is a system schematic diagram of a data query method provided by an embodiment of the present application;

Fig. 7 is a system logic diagram of a data processing method and a data query method provided by an embodiment of the present application;

FIG. 8 is a schematic flowchart of a data processing method provided by another embodiment of the present application;

Fig. 9 is an interaction diagram of a data query method provided by an embodiment of the present application;

Fig. 10 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application. In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.

It should be noted that although the functional modules are divided in the schematic diagram of the device, and the logical sequence is shown in the flowchart, in some cases, it can be executed in a different order than the module division in the device or the flowchart in the flowchart. steps shown or described. The terms "first", "second", "third", and "fourth" in the description and claims and the above drawings are used to distinguish similar objects, and not necessarily used to describe a specific order or sequence order.

Telecommunication Management Network (TMN) is one of the supporting systems for the operation of modern telecommunication networks. It is a general term for the software and hardware systems and organizational systems established to maintain the normal operation and service of the telecommunication network and effectively manage it. The telecom management network mainly includes network management system, maintenance monitoring system, etc.

Telecom management functions are divided into configuration management functions, performance management functions, fault management functions, security management functions, etc. Different functional areas solve problems in different dimensions of network management. According to this division, the subsystems are well isolated, and the functions are highly cohesive . Due to the diversity of a large number of devices and services, the network becomes complex. In order to reduce the complexity of network management and control, different autonomous mechanisms have been introduced in the industry. For example, the use of AI for intelligent analysis and decision-making enables telecom management systems and networks to be managed autonomously without human intervention, so as to reduce operating expenses and improve service experience.

Among them, AI intelligent analysis and decision-making systems often need to use data from different functional fields for joint analysis and decision-making, but each functional field in related technologies has its own different data model. designed for specific needs. Domains are often separated from each other as "information islands". If they are not connected with each other, it is easy to cause the data model frameworks between various domains to be incompatible or have inconsistent standards, resulting in information errors or stacking of information.

Based on this, the embodiment of the present application provides a data processing method, a data query method, a device, and a storage medium, and establishes a general-purpose, standard-based data model, so that the model frameworks between various functional fields can be used with each other or the standards are consistent , which in turn facilitates the joint analysis and decision-making of the AI intelligent analysis system based on data from different functional areas.

In the first aspect, referring to FIG. 1, the embodiment of the present application discloses a data processing method, including:

S100. Obtain domain data of multiple different target domains;

S200. Mapping and transforming the domain data to obtain a data model consistent with the preset unified data model framework.

Since each target field has its own data model, and the data model is only for a certain field, it is designed according to the specific needs of the field, and the data model of each target field is not unified. Therefore, the domain data is mapped and transformed according to the framework of the preset unified data model to obtain a general, standard-based data model, and the data model is consistent with the framework of the preset unified data model. By establishing a general-purpose and standard-based data model, the sharing of data information in different functional areas is facilitated, and the relationship between data is established, thereby improving the accuracy of the AI intelligent analysis system for analyzing data.

In some embodiments, the domains of interest include: the domain of configuration management, the domain of performance management, the domain of fault management, the domain of security management, and the domain of log management. Since telecom management functions are divided into configuration management functions, performance management functions, fault management functions, security management functions, and log management fields, different functions solve problems in different dimensions of network management. Therefore, the target areas collected include configuration management, performance management, fault management, security management, and log management, in order to obtain domain data of various functional areas in telecommunication management, so as to establish a general, standard-based data model, and facilitate Data interaction between different target areas. In addition, the target field also includes other management fields in telecommunications management, so as to construct the same data model as the unified data model framework based on the field data of different target fields, and facilitate the sharing of data information in different management fields in the telecommunications management field .

Obtain domain data in the fields of configuration management, performance management, fault management, security management, and log management, and then map and transform the domain data in different target areas with the preset unified data model as the model framework to obtain a unified model framework, Standard data model, so that the data between various fields can interact, and the AI intelligent analysis system can analyze the data of each target field more easily.

In some embodiments, performing mapping transformation on domain data includes:

The domain data is mapped and transformed according to a preset data mapping model, and the preset data mapping model includes a mapping relationship between domain data and a preset unified data model.

By using the preset data mapping model to map and transform the domain data, the domain data can be converted into a data model that is consistent with the preset unified data model framework, so as to facilitate the data communication between different target domains and improve the ability of AI. The analysis system analyzes the accuracy of the data.

Referring to Figure 2, the data processing method of this application solves the problems faced by network element management, and the environment framework corresponding to the data processing method is shown in Figure 2, the data processing method is executed by the data processing subsystem, and the data processing subsystem can be deployed On the network element side, it can also be deployed on the corresponding network element management layer, and collect data from the target field corresponding to the network element or the network element management layer through the data processing subsystem, so as to unify the field data through the management units of different target fields. Standardize the data model, and then submit the unified data model to the data query subsystem. The data can be directly queried through the data query subsystem without further processing, so that the intelligent analysis and decision-making system can be directly used for data analysis. The intelligent analysis and decision-making system has the following analysis functions: quality analysis, capacity analysis, abnormal trend analysis, log data analysis, performance data analysis, signaling analysis, and alarm data analysis, so that the unified and standard data model can be directly used for corresponding data analysis.

Referring to FIG. 3, in some embodiments, obtaining domain data of multiple different target domains includes but is not limited to the following steps:

S110. Acquire domain data of multiple different target domains according to a preset data collection method.

Among them, the preset data collection method is customized by the user, so that the user can set the corresponding preset data collection method according to different target fields, so as to obtain the field data of different target fields through the preset data collection method, so that according to each target The data collection method of the field to determine the corresponding preset data collection method.

Wherein, with reference to Fig. 4, according to preset data collection mode, the field data of multiple different target fields is obtained, including at least one of the following:

S111. Monitor data states of multiple different target fields according to preset subscription events, and collect field data of target fields whose data states are data change states;

S112. Collect domain data of multiple different target domains according to a preset time interval cycle.

Since different target fields can accept different data collection methods, a corresponding preset data collection method is set according to each target field, so as to obtain domain data corresponding to the target field through the preset data collection method.

Among them, if the preset collection method is the preset subscription event, then detect the data status of the corresponding target domain according to the preset subscription event, and if the data status is the data change status, collect the domain data of the target domain corresponding to the data change status . Therefore, the corresponding domain data is obtained according to the data change of the target domain, so that the corresponding data model is updated in real time according to the data change. If the preset collection method is a timing collection method, multiple domain data of different target domains are collected according to the preset time interval period, so as to update the domain data of the target domain through the preset time interval period, thereby updating the corresponding domain data at the preset time interval period. The data model of the target domain. Therefore, the preset data collection mode of each target field is set according to user-defined to correspond to the field data of the target field, thereby improving the flexibility of field data collection.

For example, if the target domain is the configuration management domain, the default collection method set for the configuration management domain is the preset subscription event. According to when a user creates an object or modifies an object, the data in the configuration management domain is monitored according to the preset subscription event. If the state is the data change state, the domain data in the configuration management domain is collected, and then a data model consistent with the preset unified data model is reconstructed based on the newly acquired domain data, thus realizing automatic acquisition of domain data and automatic update of the data model. If the target domain is the performance management domain, and the preset data collection mode corresponding to the performance management domain is a timing collection mode, the domain data of the performance management domain is acquired according to a preset time interval period. Wherein, the preset data collection method in the performance management field is set at the network element management end as a timing collection method, so as to regularly collect field data in the performance management field, and then regularly update the corresponding data model in the performance management field. Therefore, set corresponding preset data collection methods according to different target fields to realize automatic acquisition of field data, and then automatically update the data model corresponding to the target field.

Referring to Figure 5, in some embodiments, domain data is mapped and converted to obtain a data model consistent with the preset unified data model framework, including but not limited to the following steps:

S210. Extract the data content corresponding to the attribute parameters of the preset unified data model in the domain data;

S220. Establish a mapping relationship between data content and attribute parameters according to a preset data mapping model to obtain a data model.

According to the preset data mapping model, the domain data is mapped and transformed with the preset unified data model, and the preset unified data model needs to be determined first, because the preset unified data model also includes attribute parameters and the data content corresponding to the attribute parameters. Therefore, the preset unified data model is the attribute parameters and the data content corresponding to the attribute parameters, and the data content includes: unique data identifier and data business content. Therefore, according to the attribute parameters of the preset unified data model, the corresponding data content in the domain data is obtained, and then the mapping relationship between the data content and the attribute parameters is established through the preset data mapping model to construct the data model corresponding to the target domain. Therefore, domain data in different target domains are mapped and transformed with the same preset unified data model to obtain a common and standard-based data model, so as to facilitate data interaction between different target domains, and facilitate the AI intelligent analysis system to analyze Data analysis is performed on domain data of different target fields, making data analysis easier.

Among them, the attribute parameters include any of the following: data identification, data valid time, objects described by the data, data sources, and data associations.

The domain data is extracted according to the attribute parameters of the preset unified data model, and the data model to be constructed mainly describes the data from the multiple dimensions of data validity time, data source, data association relationship, and the object described by the data. Therefore, the corresponding data content in the domain data is extracted according to the data identification, data validity time, data described object, data source and data association relationship of the preset unified data model, that is, the data identification and data validity time of the corresponding target field are obtained. , the object described by the data, the data source and the association relationship of the data, and then according to the preset data mapping model, establish the mapping relationship between the data identification, the valid time of the data, the object described by the data, the data source, the association relationship of the data and the corresponding attribute parameters In order to obtain a data model, describe data from multiple dimensions such as data valid time, data described objects, data sources, and data associations, so as to obtain a data model with the same model framework.

In some embodiments, the data valid time includes: data generation time, data change time, and data death time.

Since the data model describes the data from multiple dimensions, and describes the data from the effective time of the data. Data valid time includes data generation time, data change time, and data death time. Among them, since the domain data of different target domains all have data generation time, but there is no data change time for no data change, and no data extinction time for data that has not died. Therefore, the data generation time is mandatory, and the data change time and data death time are optional. The data change time and data death time are determined according to whether there is data change and data death in the domain data.

For example, if the target domain is the configuration management domain, obtain the domain data of the configuration management domain, and the domain data of the configuration management domain is configuration data, then according to the create event in the configuration data, determine the data generation time according to the create event; if the configuration data If there is an update event, the data change time is determined according to the update event; if there is a delete event in the configuration data, the data death time is determined according to the delete event. Wherein, if there are multiple data extinction times, the corresponding data extinction times are all updated from multiple data whose data extinction times are empty, so as to update the data extinction time of the configuration data.

For example, if the target domain is the performance management domain, and the domain data in the performance management domain is performance data, the data generation time and data death time are determined according to the performance data start and end time, and the data generation time is the data start time, and the data death time is the data end time Time, so the data generation time and data death time can be determined according to the start and end time of performance data.

In some embodiments, the object described by the data includes: the type of the object described by the data, and the instance of the object described by the data.

Among them, it is not only necessary to describe the data from the effective time of the data, but also to describe the data from the dimension of the object described by the data. Therefore, it is determined that the data is described from the object type described by the data, and the object instance described by the data.

For example, if the target domain is the configuration management domain, the domain data of the configuration management domain is configuration data, and if the object type described by the configuration data is configuration MOC, the object instance described by the configuration data is the configuration object instance DN, so as to derive from the configuration MOC and The configuration object instance DN identifies the object described by the data in the configuration management domain. If the target domain is the performance management domain, then the domain data in the performance management domain is performance data, then the object type described by the performance data is the performance measurement object MOC, and the object instance described by the performance data is the performance measurement object instance. Therefore, it is more comprehensive to describe the performance data through the object class described by the data and the object instance described by the data.

In some embodiments, the data source includes: the data source field, the data format of the data source field, and the original field data.

Among them, the data is described by the data source, and the data of each target field is described more comprehensively and clearly through the data source field, the data format of the data source field, and the original field data.

For example, if the target domain is the configuration management domain, the data source domain corresponding to the configuration data does not exist, and the data format of the data source domain corresponding to the configuration data also does not exist. If the original domain data of the configuration data is configuration original data. Therefore, the configuration data is described through the data source domain, the data format of the data source domain, and the original domain data, so that the data model corresponding to the configuration data is unified with the data model framework of other target domains.

In some embodiments, the association relationship of data includes: the association relationship between data and other objects in this field, and the association relationship between data and objects in other fields.

Describe data from the relationship between data and other objects in this field, and the relationship between data and objects in other fields, and describe data more clearly and comprehensively.

For example, if the target domain is the configuration management domain, and there is no association relationship between configuration data and other objects in this domain, the association relationship between configuration data and other domain objects is the dependent object DN. If the target domain is the performance management domain, the relationship between performance data and other domain objects is a dependent measurement object, and the relationship between performance data and other domain objects does not exist. Therefore, it is more clear and comprehensive to describe data through the relationship between data and other objects in this field, and the relationship between data and objects in other fields, so as to obtain a general and unified data model.

Data generation time, data change time, data death time, object type described by data, object instance described by data, data source field, data format of data source field, original field data, data The relationship with other data content in this field, and the relationship between data and data content in other fields Obtain the data content corresponding to the field data, and then describe the data in different target fields according to the data content to obtain the corresponding data model to build different goals A unified and common data model in the field solves data problems in different target fields, especially the generation and death time of basic data in the fields of configuration management and performance management, as well as the time correlation between basic data and runtime objects, so as to facilitate AI intelligence. The analysis system analyzes the data more efficiently and accurately.

Among them, the specific framework of the preset unified data model refers to Table 1, that is, data identification, data generation time, data change time, data death time, object type described by data, object instance described by data, data source field, The data format of the data source field, the original field data, the relationship between the data and other data content in this field, and the relationship between the data and the data content in other fields.

Table 1

Therefore, by obtaining domain data from different target domains, processing them into a unified data model, and sending the data model to the data query subsystem to provide a unified query function, it solves the need for domain data in different target domains to be duplicated by the AI intelligent analysis system. The problem of processing makes the AI intelligent analysis system more efficient and accurate in the analysis of domain data in different target areas, and realizes the data interaction between different target areas.

For example, according to the preset unified data model, data generation time, data change time, data death time, object type described by data, object instance described by data, data source field, data format of data source field, original field data, The relationship between data and other data content in this field, and the relationship between data and data content in other fields Obtain the data content corresponding to the domain data, and build a data model with the same model framework as the preset unified data model according to the data content. If the target domain is the configuration management domain, construct the data model corresponding to Table 1 with the domain data of the configuration management domain, and refer to Table 2 for the data model corresponding to the configuration management domain. If the target domain is the performance management domain, refer to Table 3 for the data model corresponding to the performance management domain.

Table 2

table 3

Therefore, the data model of the same model framework is constructed according to the domain data of different target domains to facilitate data interaction between different target domains.

In the second aspect, referring to FIG. 6, the embodiment of the present application also discloses a data query method, including:

S300. Acquire the data model of the data processing method in the first aspect;

S400. Obtain domain data meeting the preset query conditions from the data model according to the preset query conditions.

Since the data models corresponding to different target fields are obtained according to the data processing method in the first aspect, and the data model is a model of a unified model framework, the field data corresponding to the target field can be quickly found according to the preset query conditions, making data query more efficient. Efficient and accurate.

In some embodiments, the preset query conditions include any one or more of the following: data identification, preset data valid time interval, objects described by the data, data sources, and data associations.

If the user only needs to query the field data that meets the time interval requirements, then directly enter the preset valid data interval to search for the corresponding field data, and the obtained field data can be from multiple target fields, so that the user can find the target field that meets the needs .

Wherein, the data identifier is the unique identifier of the domain data. Therefore, according to the data identifier input by the user corresponding to the domain data of the target domain corresponding to the output data identifier, the data search is made more accurate and faster. The preset data effective time interval is the interval between the data generation time and the data death time. The domain data that meets the preset data effective time interval can be obtained through the preset data effective time interval, so the acquired domain data includes domain data of several target domains. , that is, collect domain data from the time dimension where the data generation time and data death time are within the preset data valid interval, making domain data search more flexible. If the input default query condition is the object described by the data, the corresponding data field will be obtained directly according to the object described by the data, and the field corresponding to the data source will be obtained according to the data source, and the association with the data will be obtained according to the association relationship of the data The domain data corresponding to the relationship. Therefore, since the data model is mapped and transformed according to the preset unified data model, the corresponding domain data can be quickly obtained according to the preset query conditions input by the user, making domain data query faster and more accurate.

Referring to Figure 2 and Figure 7, the data query method is executed by the data query subsystem, and the data query subsystem can be deployed on the network element side, or can be deployed on the network element management layer, so as to directly obtain the data processing subsystem through the data query subsystem Output a unified data model, making data query more convenient.

To sum up, referring to FIG. 7, the data processing method in the first aspect is executed by the data processing subsystem, the data query method in the second aspect is executed by the data query subsystem, and the data processing subsystem can be deployed near the network elements in the target field. The end can also be deployed on the network element management end of the target field. It is responsible for collecting domain data from the network element or the corresponding field of the network element management end, and then establishes a corresponding data model with the preset unified data model for the domain data through the preset data mapping model. , and then submit the data model to the data query subsystem in a unified manner. The data query subsystem is responsible for persisting data to provide data query services, querying and analyzing data in different dimensions such as data time, object, source, and data identification.

Among them, the data processing subsystem includes: domain data receiving service and data processing service, and the data query subsystem includes: unified data receiving service, data publishing service and data query service. The domain data receiving service sets the corresponding preset data collection method to obtain domain data, and then uses the data processing service to map and convert the acquired domain data according to the preset data mapping model and the preset unified data model to obtain a unified data model. And send the unified data model to the unified data receiving service, and the data publishing service will publish the data model, then the data query service will send the preset query conditions entered by the user to the data center, and the data center will obtain the unified data according to the preset query conditions. The data accepts the corresponding data model in the service, so as to obtain the domain data corresponding to the data model, making the domain data query of different target domains faster and more accurate.

Specifically, if the preset data collection method is a preset subscription event, monitor the data status of multiple different target fields according to the preset subscription event, and collect the data status of the domain data of the target field whose data status is the data change status; if the preset data collection If the method is a timing collection method, the field data of multiple different target fields are collected according to the preset time interval cycle. Then according to the preset unified data model, the data generation time, data change time, data death time, object type described by the data, object instance described by the data, data source field, data format of the data source field, original field data, data The association relationship with other data content in this field, and the association relationship between data and data content in other fields Obtain the data content corresponding to the field data, and then establish the data generation time, data change time, data death time, and data location through the preset data mapping model. The type of object described, the object instance described by the data, the field of data source, the data format of the field of data source, the original field data, the relationship between data and other data content in this field, the relationship between data and data content in other fields and data Content mapping relationship to obtain a unified and common data model. Then send the data model to the data query subsystem, and determine the corresponding domain data according to the data identifier input by the user, the preset data valid time interval, the object described by the data, the data source, and the relationship between the data, making domain data query faster And accurate.

Specifically, the flow of the data processing method is shown in Figure 8. When starting to construct a unified data model for domain data in different target domains, set the data collection mode of the target domain according to the user to obtain the preset data collection mode, and then according to The preset data collection method is the preset subscription event or timing collection method to collect domain data, and then convert the domain data mapping into a data model according to the preset data mapping model according to the preset unified data model, so as to obtain a unified model framework with Table 1 Then store the unified data model in the local database, and then publish the unified data model, so that subsequent users can quickly query domain data according to the data model.

The interaction diagram of the data query method refers to Figure 9. After a unified and standard data model is built, the intelligent analysis and decision-making system can send query data instructions to the network element management layer, and the network element management layer can query the corresponding target field according to the preset query conditions The data model is used to match the corresponding domain data according to the preset query conditions to realize the rapid query of the domain data, and quickly return the corresponding domain data obtained from the query to the intelligent analysis and decision-making system, so as to realize the rapid query of data, and the intelligent analysis and decision-making system Domain data with a unified data model can be used directly.

In the third aspect, referring to FIG. 10 , other embodiments of the present application also disclose an electronic device including: a memory 200, a processor 100, and a computer program stored in the memory 200 and operable on the processor 100, and the processor 100 executes The program implements: the data processing method of the first aspect, or the data query method of the second aspect.

The electronic device may be a mobile terminal device or a non-mobile terminal device. Mobile terminal devices can be mobile phones, tablet computers, notebook computers, handheld computers, vehicle-mounted terminal devices, wearable devices, super mobile personal computers, netbooks, personal digital assistants, CPE, UFI (wireless hotspot equipment), etc.; non-mobile terminal devices can be It is a personal computer, a television, a teller machine or a self-service machine, etc.; the implementation plan of this application is not specifically limited.

The memory 200 can be an external memory or an internal memory, and the external memory is an external memory card, such as a Micro SD card. The external memory card communicates with the processor through the external memory interface to realize the data storage function. Such as saving music, video and other files in the external memory card. Internal memory may be used to store computer-executable program code, including instructions.

The processor 100 may include one or more processing units, for example: the processor 100 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU), etc. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

In a fourth aspect, a computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to: execute the data processing method according to the first aspect, or execute the data query method according to the second aspect.

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The embodiment of the present application includes: the data model is obtained by mapping and transforming the field data of different target fields, and the data model is mapped and transformed according to the preset unified data model, which facilitates the sharing of data information in different target fields to establish relationship, thereby improving the accuracy of the data analyzed by the intelligent analysis system.

Those skilled in the art can understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware and an appropriate combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit . Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes both volatile and nonvolatile media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. permanent, removable and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

The above is a specific description of the preferred implementation of the application, but the application is not limited to the above-mentioned implementation, and those skilled in the art can also make various equivalent deformations or replacements without violating the spirit of the application. Equivalent modifications or replacements are all within the scope defined by the claims of the present application.

Claims (15)

A data processing method, comprising: Obtain domain data for multiple different target domains; The domain data is mapped and converted to obtain a data model consistent with the preset unified data model framework. The data processing method according to claim 1, wherein said mapping and transforming said domain data comprises: The domain data is mapped and converted according to a preset data mapping model, and the preset data mapping model includes a mapping relationship between the domain data and the preset unified data model. The data processing method according to claim 1, wherein said acquiring domain data of a plurality of different target domains comprises: The field data of a plurality of different target fields are acquired according to a preset data collection method. The data processing method according to claim 3, wherein said acquiring the domain data of a plurality of different target domains according to a preset data collection method comprises at least one of the following: monitoring the data status of multiple different target domains according to preset subscription events, and collecting the domain data of the target domain whose data status is a data change status; The domain data of the multiple different target domains are collected according to a preset time interval cycle. The data processing method according to claim 1, wherein said mapping and transforming said domain data to obtain a data model consistent with said preset unified data model framework includes: Extracting the data content corresponding to the attribute parameters of the preset unified data model in the domain data; A mapping relationship between the data content and the attribute parameters is established according to a preset data mapping model to obtain the data model. The data processing method according to claim 5, wherein the attribute parameters include any of the following: data identification, data valid time, objects described by the data, data sources, and data associations. The data processing method according to claim 6, wherein the data valid time includes: data generation time, data change time, and data death time. The data processing method according to claim 6, wherein the object described by the data includes: the type of the object described by the data, and the instance of the object described by the data. The data processing method according to claim 6, wherein the data source includes: the data source field, the data format of the data source field, and the original field data. The data processing method according to claim 6, wherein the data association relationship includes: the association relationship between the data and other data content in the field, and the association relationship between the data and the data content in other fields. The data processing method according to any one of claims 1 to 10, wherein the target field includes: the field of configuration management, the field of performance management, the field of fault management, the field of security management, and the field of log management. Data query methods, including: Obtaining the data model of the data processing method according to any one of claims 1 to 11; Obtain domain data meeting the preset query conditions from the data model according to the preset query conditions. The data query method according to claim 12, comprising: The preset query conditions include any one or more of the following: data identification, preset data valid time interval, objects described by the data, data sources, and data associations. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, it realizes: The data processing method according to any one of claims 1 to 11, or the data query method according to any one of claims 12 to 13. A computer-readable storage medium storing computer-executable instructions for: Executing the data processing method according to any one of claims 1 to 11, or the data query method according to any one of claims 12 to 13.

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