CN113204592B - Data processing method, system and device in Internet of things scene and storage medium - Google Patents
Data processing method, system and device in Internet of things scene and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application discloses a data processing method, system, device and storage medium in an Internet of things scene, and relates to the technical field of Internet of things. The method comprises the following steps: receiving a first operation instruction through a database access interface; the first operation instruction comprises at least one operation parameter; acquiring a target application scene; determining a target time sequence database according to the target application scene; mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction; issuing a second operation instruction to the target time sequence database, wherein the second operation instruction is used for indicating the target time sequence database to execute data processing operation corresponding to at least one operation parameter; by the method, a second operation instruction is issued to the target time sequence database, and the second operation instruction is used for indicating the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
Description
Technical Field
The embodiment of the application relates to the technical field of the Internet of things, in particular to a data processing method, a system, a device and a storage medium in the scene of the Internet of things.
Background
Along with the large-scale popularization of the internet of things technology, the use scene of the internet of things technology is gradually increased, and different requirements on databases are met in different application scenes.
In the related art, when a database is accessed, a corresponding database is often deployed aiming at a specific use scene so as to meet the requirements of different use scenes on the database.
However, in the multi-terminal scenario of the internet of things, the database needs to be deployed at both the cloud end and the edge device end, and the specific database deployment cannot meet the requirements of the multi-terminal application scenario, i.e., cannot directly and effectively support the multi-terminal scenario of the internet of things.
Disclosure of Invention
The embodiment of the application provides a data processing method, a system, a device and a storage medium in an Internet of things scene, which can be used for deploying different time sequence databases according to different application scenes in the Internet of things multi-terminal scene, reducing connection operations required by an upper application when accessing the different time sequence databases, and improving the access efficiency of the upper application to the time sequence databases. The technical scheme is as follows:
in one aspect, a data processing method in an internet of things scene is provided, the method comprising:
Receiving a first operation instruction through the database access interface; the first operation instruction comprises at least one operation parameter;
acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the data operation instruction;
determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases;
mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction;
and issuing the second operation instruction to the target time sequence database, wherein the second operation instruction is used for indicating the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
In another aspect, a database management system is provided, where the database management system includes a management device and at least two time sequence databases; the data management system is externally provided with a database access interface;
the management device is used for receiving a data operation instruction of an upper layer application through the database access interface; the first operation instruction comprises at least one operation parameter;
Acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the data operation instruction; determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases; mapping the attribute of the operation parameter into the attribute corresponding to the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction; issuing the second operation instruction to the target time sequence database;
the target time sequence database is used for executing the data processing operation corresponding to the at least one operation parameter.
On the other hand, a data processing device in an internet of things scene is provided, the device is applied to management equipment in a database management system, the database management system further comprises at least two time sequence databases, the database management system is externally provided with a database access interface, and the device comprises:
the first operation instruction receiving module is used for receiving a first operation instruction through the database access interface; the first operation instruction comprises at least one operation parameter;
The target application scene acquisition module is used for acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the data operation instruction;
the target time sequence database determining module is used for determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases;
the second operation instruction acquisition module is used for mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database to acquire a second operation instruction;
and the second operation instruction issuing module is used for issuing the second operation instruction to the target time sequence database, and the second operation instruction is used for indicating the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
In one possible implementation manner, the target application scenario acquisition module includes:
a deployment mode acquisition sub-module for acquiring the deployment mode of the upper layer application;
and the target application scene determining submodule is used for determining the target application scene based on the deployment mode of the upper layer application.
In one possible implementation, the attributes of the operating parameters include organization, site type, site, equipment, time, and corresponding values.
In one possible implementation manner, the second operation instruction obtaining module includes:
the first mapping sub-module is used for mapping the organization into a database in the target time sequence database based on the mapping relation corresponding to the target time sequence database;
the second mapping submodule is used for mapping the measuring point type into a data table in the database;
a third mapping sub-module, configured to map the measurement points to metrics in the database;
a fourth mapping sub-module, configured to map the device to a tag in the database;
a fifth mapping sub-module for mapping the time to a timestamp in the database;
a sixth mapping sub-module, configured to map the corresponding numerical value to a field value in the database;
and the second operation instruction acquisition sub-module is used for acquiring the second operation instruction based on the database, the data table, the measurement, the label, the timestamp and the field value.
In one possible implementation, the first operation instruction includes: a data addition instruction, a data deletion instruction, a data search instruction, and a data modification instruction.
In one possible implementation, the at least two timing databases are heterogeneous timing databases.
In another aspect, a computer device is provided, the computer device including a processor and a memory, where the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement a data processing method in an internet of things scenario as described in the above aspect.
In another aspect, there is provided a computer readable storage medium having at least one program code stored therein, the program code being loaded and executed by a processor to implement a data processing method in an internet of things scenario as described in the above aspect.
In another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data processing method in the internet of things scenario provided in the above various alternative implementations.
The technical scheme that this application provided can include following beneficial effect:
the database management system is additionally arranged, a first operation instruction issued by an upper layer application is received through a database access interface, a target time sequence database acted by the first operation instruction is determined by management equipment based on a use scene of the upper layer application, and operation parameters in the first operation instruction are mapped into corresponding attributes in the target time sequence database, so that the first operation instruction is converted into a second operation instruction which can directly act on the target time sequence database, and data in the target time sequence database are correspondingly processed. Therefore, on the premise of realizing the deployment of the time sequence databases corresponding to the multi-terminal application scene of the Internet of things, the connection operation required by the upper application when accessing different time sequence databases is reduced, and the access efficiency of the upper application to the time sequence databases is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 illustrates a schematic diagram of an implementation environment of the present application provided by an exemplary embodiment of the present application;
fig. 2 shows a flowchart of a data processing method in an internet of things scenario according to an exemplary embodiment of the present application;
fig. 3 shows a flowchart of a data processing method in an internet of things scenario according to an exemplary embodiment of the present application;
FIG. 4 illustrates a schematic diagram of an Influx mapping process shown in an exemplary embodiment of the present application;
FIG. 5 illustrates a schematic diagram of an OpenTSDB mapping process shown in an exemplary embodiment of the present application;
FIG. 6 illustrates an architecture diagram of a higher-level application access timing database as illustrated in an exemplary embodiment of the present application;
FIG. 7 illustrates a schematic diagram of a database management system as shown in an exemplary embodiment of the present application;
FIG. 8 is a block diagram of a data processing apparatus in an Internet of things scenario according to an exemplary embodiment of the present application;
FIG. 9 is a block diagram of a computer device shown in accordance with an exemplary embodiment;
fig. 10 is a block diagram of a computer device, according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
It should be understood that references herein to "a number" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
For ease of understanding, several terms referred to in this application are explained below.
1) Internet of things (Internet of Things IoT)
The internet of things is a network which is based on information carriers such as the Internet, a traditional telecommunication network and the like and enables all common objects capable of playing independent functions to realize interconnection and intercommunication. The application fields mainly comprise transportation and logistics, industrial manufacturing, health medical treatment, intelligent environments (families, offices, factories) and the like, and have very broad market prospects.
2) Time series database (Time Series Database TSDB)
The time sequence database is called as time sequence database, is specialized database for storing and managing time sequence data, and provides high-performance read-write and calculation-requesting distributed operation database service for the time sequence data, wherein the time sequence data is data with time labels (changing according to the sequence of time, namely time sequence).
The time sequence database is suitable for the monitoring of the equipment of the Internet of things and the monitoring scene of the Internet, and the time sequence data is mainly data acquired and generated by various real-time monitoring, checking and analyzing equipment of the power industry, the chemical industry, the operation and maintenance technology, the big data acquisition and the like. Typical characteristics of these industrial data are: the system has the advantages of high generation frequency (multiple data can be generated in one second at each monitoring point), serious dependence on acquisition time (each data requires corresponding unique time), and large measurement point multiple information quantity (the conventional real-time monitoring system has thousands of monitoring points, each monitoring point generates data in one second, and each day generates data quantity of tens of GB).
Fig. 1 shows a schematic diagram of an implementation environment of the present application provided in an exemplary embodiment of the present application, where the implementation environment includes an IoT device 110, a timing database 120, and an operation terminal 130, as shown in fig. 1;
IoT devices 110 are devices with a data collection function, and the collected data is time series data, in one possible implementation manner, ioT devices 110 may be implemented as new energy devices provided with sensors such as wind speed detectors, temperature and humidity detectors, photovoltaic sensors, and the like, such as wind generators, photovoltaic panels, and the like, as shown in fig. 1, multiple IoT devices 110 collect time series data of different data types, and send the collected time series data to time series database 120, where the time series database stores the devices collected by IoT devices.
In one possible application scenario, ioT device 110 sends timing data to timing database 120 through a gateway device.
In one possible implementation, different IoT devices 110 correspond to different timing databases 120.
The IoT device 110 and the timing database 120 are connected by a communication network. Optionally, the communication network is a wired network or a wireless network.
Alternatively, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the Internet, but may be any network including, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks. In some embodiments, data exchanged over the network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible Markup Language, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure socket layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet protocol security (Internet Protocol Security, IPsec), and the like. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above. The application is not limited herein.
The operation terminal 130 is a device with functions of data query, addition, deletion and modification, in one possible implementation manner, the operation terminal 130 sends a data operation instruction including operation data parameters to each time sequence database 120 through the time sequence database access interface 121 provided outside each time sequence database 120, each time sequence database 120 determines target data in the time sequence database according to the operation parameters in the data operation instruction, and performs corresponding processing on the data, for example, the operation terminal 130 sends the data query instruction to the time sequence database 120, the time sequence database 120 acquires the target data based on the query parameters in the data query instruction, and feeds the target data back to the operation terminal 130, and the operation terminal 130 displays the received target data, for example, in a chart form. The operation terminal 130 may be implemented as a personal computer, a smart phone, a tablet computer, or the like.
In the interaction between the time sequence databases and the operation terminal shown in fig. 1, the operation terminal can achieve the purpose of data interaction with the time sequence databases by accessing the time sequence database access interfaces respectively corresponding to the time sequence databases, and in the process, the operation terminal needs to establish unnecessary connection operation when accessing different time sequence databases, so that the access efficiency is lower. In the embodiment of the application, by setting the database management system, when the operation terminal accesses the time sequence databases in the multi-terminal scene of the Internet of things, the corresponding databases can be accessed through one database access interface based on different application scenes, so that the connection operation of the operation terminal to different time sequence databases when the operation terminal accesses the data is reduced.
Fig. 2 shows a flowchart of a data processing method in an internet of things scenario provided by an exemplary embodiment of the present application, where the method may be performed by a computer device, and the computer device may be implemented as a management device in the data management system, where the data management system further includes at least two time sequence databases, and the database management system is externally provided with a database access interface, as shown in fig. 2, and the data processing method in the internet of things scenario includes the following steps:
step 210, receiving a first operation instruction through a database access interface; the first operation instruction includes at least one operation parameter.
In the embodiment of the application, a database access interface is defined, the database access interface is an interface which is externally arranged in a database management system and is used for receiving an operation instruction sent by an operation terminal, based on the database access interface, in a multi-terminal scene of the Internet of things, an upper layer application only needs to access the database access interface and issue a corresponding operation instruction, and can realize corresponding data operation.
Step 220, obtaining a target application scene; the target application scenario is a usage scenario of an upper layer application that issues data operation instructions.
Step 230, determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in at least two time sequence databases.
In one possible implementation manner, at least two time sequence databases included in the database management system are different time sequence databases, for example, the at least two time sequence databases may include a supporting distributed time sequence database, such as LinDB, influxDB, openTSDB, etc., so as to meet the requirement of the Cloud end (Cloud end) machine under the condition of more resources; a time-series database supporting a single machine or supporting HA (High Availability ) may also be included to meet usage scenarios due to machine resource limitations, such as only a single machine or two machines; or at least two time sequence databases comprise time sequence databases with strong query capability so as to meet the requirement of analysis scenes by utilizing the efficient query performance of the time sequence databases; the time sequence database with high-efficiency compression capability can be further included, so that the high-efficiency compression performance of the time sequence database is utilized, more storage cost is saved, and massive data scenes are met.
Step 240, mapping the attribute of the operation parameter to the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database, and obtaining the second operation instruction.
Since the different time sequence databases are each set with its own internal concept (corresponding attribute in the time sequence database) for describing the data storage mode, these concepts are usually different from the attribute of the operation parameter in the internet of things device, so, in order to establish the correspondence between the attribute of the operation parameter and the attribute in the time sequence database, a mapping relationship corresponding to the time sequence database is set in the database management system to correspond the attribute of the operation parameter to the concept in the time sequence database, for example, the device type (AI, DI, PI) in the operation parameter is mapped to the data table in the database, which is any one of the time sequence databases, so as to obtain the second operation instruction directly corresponding to the time sequence database.
Step 250, issuing a second operation instruction to the target time sequence database, where the second operation instruction is used to instruct the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
In summary, in the data processing method in the internet of things scenario provided in the embodiment of the present application, by adding the database management system, the database access interface therein receives the first operation instruction issued by the upper layer application, and the management device therein determines the target time sequence database acted by the first operation instruction based on the usage scenario of the upper layer application, maps the operation parameters in the first operation instruction to the corresponding attributes in the target time sequence database, thereby converting the first operation instruction into the second operation instruction capable of directly acting on the target time sequence database, and performs corresponding processing on the data in the target time sequence database. Therefore, on the premise of realizing the deployment of the time sequence databases corresponding to the multi-terminal application scene of the Internet of things, the connection operation required by the upper application when accessing different time sequence databases is reduced, and the access efficiency of the upper application to the time sequence databases is improved.
Fig. 3 shows a flowchart of a data processing method in an internet of things scenario provided by an exemplary embodiment of the present application, where the method may be performed by a computer device, and the computer device may be implemented as a management device in the data management system, where the data management system further includes at least two time sequence databases, and the database management system is externally provided with a database access interface, as shown in fig. 3, and the data processing method in the internet of things scenario includes the following steps:
step 310, receiving a first operation instruction through a database access interface; the first operation instruction includes at least one operation parameter.
In one possible implementation, the first operation instruction includes: a data addition instruction, a data deletion instruction, a data search instruction, and a data modification instruction.
Wherein the data increment instruction is used for indicating increment specified data in the target time sequence database; the data deleting instruction is used for indicating to delete the specified data in the target time sequence database; the data searching instruction is used for acquiring appointed data in the target time sequence database; the data modification instruction is used for indicating to modify specified data in the target time sequence database.
In one possible implementation, the operating parameter has a corresponding attribute to indicate the source of the operating parameter and a corresponding operating value.
In one possible implementation, the attributes of the operating parameters include organization, site type, site, equipment, time, and corresponding values.
In the data adding instruction, the data deleting instruction, the data searching instruction and the data modifying instruction, the attribute of the operation parameters of the data adding instruction and the data modifying instruction needs to contain at least one of organization, equipment, measuring points and time and corresponding numerical values; the data deleting instruction and the data searching instruction need to include at least one of organization, equipment, measuring points and time. Taking a data increasing instruction as an example, organizing into an a wind field, wherein equipment is a fan with the number wtg001 in the a wind field, and a measuring point is a wind speed checking device with the number windspeed on the fan, and the time is 2020-10-13: 56:35, the wind speed detected by the wind speed detection device is 5m/s, namely the corresponding value is 5m/s. When data is written, the upper layer application provides a certain organization, a certain device and a certain measuring point for the database access interface, and the data can be written in a certain time value; when the data is inquired, a certain organization, a certain device, a certain measuring point and a certain time period are provided, and the corresponding value can be inquired.
Step 320, obtain the deployment mode of the upper layer application.
In one possible implementation, the deployment of the upper layer application may be one of a proprietary deployment, a public deployment, and a hybrid deployment. The privatized deployment coverage equipment is less in number, and the related application scenes are less; the quantity of equipment covered by public deployment and mixed deployment is more, and the related application scenes are more.
Step 330, determining a target application scenario based on the deployment mode of the upper layer application.
In one possible implementation manner, a corresponding relationship between a deployment manner of an upper-layer application and an application scene is set in the database management system, for example, the upper-layer application is deployed in a deployment manner a, and then the application scene of the upper-layer application is determined to be scene a; and the upper layer application is deployed in a deployment mode B, so that an application scene of the upper layer application is determined to be a scene B and the like. The corresponding relation between the deployment mode of the upper layer application and the application scene can be set based on the actual requirement.
In one possible implementation manner, the data management system may also establish a corresponding relationship between the application scenario and the data management requirement according to the actual requirement of the user on the time sequence database, for example, if the requirement of the upper layer application on data analysis is higher, determining that the application scenario of the upper layer application is a data analysis scenario; and if the requirement of the upper layer application on data compression is high, determining that the application scene of the upper layer application is a massive data scene and the like. The correspondence between the data management demand application scenarios may also be set based on actual demands.
Step 340, determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in at least two time sequence databases.
In one possible implementation manner, a correspondence between an application scenario and a time sequence database is preset in the data management system, that is, after an application scenario (target application scenario) of a current upper-layer application is determined, the time sequence database (target time sequence database) corresponding to the application scenario is determined according to the correspondence, so that data obtained under the application scenario is stored in the corresponding time sequence database, and when an operation instruction from the upper-layer application is received, the data in the corresponding time sequence database is processed correspondingly.
And 350, mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database, and obtaining a second operation instruction.
Because specific concepts are respectively corresponding to different time sequence databases, the data in the target time sequence database cannot be directly processed based on the attribute of the operation parameter in the first operation instruction, so that a mapping relation between the attribute of the operation parameter in the first operation instruction and the corresponding attribute (concept) in the target time sequence database is established, the attribute of the operation parameter in the first operation instruction and the corresponding attribute in the target time sequence database are corresponding based on the mapping relation, and a second operation instruction with the concept in the target time sequence database is generated to process the data in the target time sequence database.
In one possible implementation, the above procedure is implemented as:
mapping the organization into a database in the target time sequence database based on the mapping relation corresponding to the target time sequence database;
mapping the measuring point type into a data table in a database;
mapping the measuring points into metrics in a database;
mapping the device to a tag in a database;
mapping the time to a timestamp in a database;
mapping the corresponding numerical value into a field value in a database;
based on the database, the data table, the metrics, the tags, the time stamps, and the field values, a second operation instruction is obtained.
In one possible implementation, the naming of the Database, data table, metric, tag, timeStamp, and Field value in different time series databases is different, for example, in InfluxDB, which contains Database, report Policy, measurement, timeStamp, tag, field value, etc., the organization can be mapped to Database, different Measurement point types to report Policy, measurement point to Measurement, device to a Tag, and the corresponding value to a Field. For another example, openTSDB (Open Time Series Database, development time series database) contains concepts such as Namespace, table, metric, timeStamp, tag, value, data point, etc., and can map an organization to Namespace, map different measurement point types to Table, map a measurement point to Metric, map a device to a Tag, convert a corresponding Value to a Value, etc.
In one possible implementation, the database management system indicates physical isolation of data between different organizations. The physical isolation refers to that data of different organizations are isolated on hardware equipment by using different CPUs, memories, magnetic disks and the like, so that the data of each organization are not interfered with each other.
In one possible implementation manner, the data types of different measuring points can be different, the data of different measuring point types can be reserved for different data duration, wherein the data types of the measuring points and the reserved duration of the data of different measuring point types can be set by a user based on actual requirements.
Based on the above mapping process, the above objective may be achieved in a time-series Database through different attributes, and in the case of Influx, fig. 4 shows a schematic diagram of the Influx mapping process shown in an exemplary embodiment of the present application, and in the case of fig. 4, a first operation instruction is taken as a data addition instruction, where the first operation instruction includes an AI type Measurement point 410 and a DI type Measurement point 420, where data corresponding to the AI type Measurement point 410 is kept for a first duration, for example, 3 months, data corresponding to the DI type Measurement point 420 is kept for a second duration, for example, 6 months, and in the mapping process, for the purpose of achieving physical isolation of data of different organizations, different Measurement points may have different Measurement point types, and different Measurement point types keep different data durations, and the management device marks different Measurement points through different Database (databases), different organizations, marks different Measurement points through different Measurement points, marks through different reflection Policy types, and marks, so as to implement the purpose of distinguishing the corresponding Measurement points from different Measurement points into the data fields of the corresponding to the information fields (file).
For example, taking OpenTSDB (Open Time Series Database, develop a time sequence database) as an example, fig. 5 shows a schematic diagram of OpenTSDB mapping processing shown in an exemplary embodiment of the present application, as shown in fig. 5, taking a first operation instruction as a data addition instruction as an example, where the first operation instruction includes an AI type measurement point 510 and a DI type measurement point 520, where data corresponding to the AI type measurement point 510 is kept for a first period, for example, 3 months, data corresponding to the DI type measurement point 520 is kept for a second period, for example, 6 months, and when mapping processing is performed, for the purpose of realizing physical isolation of data of different organizations, different measurement points may have different measurement point types, different measurement point types keep different data durations, and the management device marks the different organizations through different namespaces, marks the different measurement points through different Metric, marks the different measurement points through different Table, marks the measurement point types of the different measurement points, thereby implementing distinction of data of the different measurement points, and simultaneously adds the corresponding data into the Value (Value) in the data base in the OpenTSDB.
In one possible implementation manner, when the attribute of at least one operation parameter in the first operation instruction is mapped to the corresponding attribute in the target time sequence database, the mapping relationship can be established based on the characteristic of the time sequence database engine so as to meet the requirement in the scene of the internet of things, for example, the above-described mapping of the equipment to the Tag can make full use of the characteristic that the Tag of the InfluxDB has an index, and when different equipment is queried, the function of fast index searching is utilized, so that the query efficiency is improved.
Step 360, issuing a second operation instruction to the target time sequence database, where the second operation instruction is used to instruct the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
In one possible implementation manner, at least two databases included in the database management system are heterogeneous time sequence databases, and in the connection process, the management device can realize access to the time sequence databases through protocols of crossing heterogeneous engines through Http/thread and the like. Fig. 6 illustrates an architecture diagram of an access time sequence database of an upper layer application according to an exemplary embodiment of the present application, as shown in fig. 6, the upper layer application 610 issues a first operation instruction to a database management system, where the first operation instruction includes information such as an organization, a measurement point, a device timestamp, and the like, the database management system receives the first operation instruction through an externally configured database access interface, the management device 620 maps an attribute of at least one operation parameter in the first operation instruction to a concept in the first time sequence database 630 based on a usage scenario of the upper layer application taking an edge device application scenario as an example, generates a second operation instruction, and sends the second operation instruction to the first time sequence database 630 based on a protocol such as Http/tlift that can span a heterogeneous engine, so as to implement access to the first time sequence database 630, and performs a data processing operation corresponding to the at least one operation parameter in the first time sequence database 630 based on the second operation instruction.
It should be noted that, after determining the usage scenario of the upper layer application, the corresponding timing database of the upper layer application is uniquely determined, that is, only one link in fig. 6 is passed.
In summary, in the data processing method in the internet of things scenario provided in the embodiment of the present application, by adding the database management system, the database access interface therein receives the first operation instruction issued by the upper layer application, and the management device therein determines the target time sequence database acted by the first operation instruction based on the usage scenario of the upper layer application, maps the operation parameters in the first operation instruction to the corresponding attributes in the target time sequence database, thereby converting the first operation instruction into the second operation instruction capable of directly acting on the target time sequence database, and performs corresponding processing on the data in the target time sequence database. Therefore, on the premise of realizing the deployment of the time sequence databases corresponding to the multi-terminal application scene of the Internet of things, the connection operation required by the upper application when accessing different time sequence databases is reduced, and the access efficiency of the upper application to the time sequence databases is improved.
FIG. 7 is a schematic diagram of a database management system according to an exemplary embodiment of the present application, wherein the database management system 700 includes a management device 710 and at least two timing databases 720 as shown in FIG. 7; the data management system is externally provided with a database access interface;
The management device 710 is configured to receive a data operation instruction of an upper layer application through a database access interface; the first operation instruction comprises at least one operation parameter;
acquiring a target application scene; the target application scene is a use scene of an upper layer application for issuing a data operation instruction; determining a target time sequence database 721 according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in at least two time sequence databases 720; mapping the attribute of the operation parameter into the attribute corresponding to the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction; issuing a second operation instruction to the target time sequence database;
a target timing database 721 for performing data processing operations corresponding to the at least one operating parameter.
In summary, in the data processing method in the internet of things scenario provided in the embodiment of the present application, by adding the database management system, the database access interface therein receives the first operation instruction issued by the upper layer application, and the management device therein determines the target time sequence database acted by the first operation instruction based on the usage scenario of the upper layer application, maps the operation parameters in the first operation instruction to the corresponding attributes in the target time sequence database, thereby converting the first operation instruction into the second operation instruction capable of directly acting on the target time sequence database, and performs corresponding processing on the data in the target time sequence database. Therefore, on the premise of realizing the deployment of the time sequence databases corresponding to the multi-terminal application scene of the Internet of things, the connection operation required by the upper application when accessing different time sequence databases is reduced, and the access efficiency of the upper application to the time sequence databases is improved.
Fig. 8 is a block diagram of a data processing apparatus in an internet of things scenario provided in an exemplary embodiment of the present application, where the apparatus may be applied to a computer device, and the computer device may be implemented as a management device in the foregoing data management system, where the data management system further includes at least two time sequence databases, and the database management system is externally provided with a database access interface, as shown in fig. 8, where the data processing apparatus in the internet of things scenario includes:
a first operation instruction receiving module 810, configured to receive a first operation instruction through a database access interface; the first operation instruction comprises at least one operation parameter;
a target application scene acquisition module 820, configured to acquire a target application scene; the target application scene is a use scene of an upper layer application for issuing a data operation instruction;
the target time sequence database determining module 830 is configured to determine a target time sequence database according to a target application scenario, where the target time sequence database is a time sequence database corresponding to the target application scenario in at least two time sequence databases;
the second operation instruction obtaining module 840 is configured to map the attribute of the operation parameter to the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database, so as to obtain a second operation instruction;
And a second operation instruction issuing module 850, configured to issue a second operation instruction to the target time sequence database, where the second operation instruction is used to instruct the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
In one possible implementation, the target application scenario acquisition module 820 includes:
the deployment mode acquisition sub-module is used for acquiring the deployment mode of the upper layer application;
the target application scene determining sub-module is used for determining a target application scene based on a deployment mode of the upper layer application.
In one possible implementation, the attributes of the operating parameters include organization, site type, site, equipment, time, and corresponding values.
In one possible implementation, the second operation instruction acquisition module 840 includes:
the first mapping sub-module is used for mapping the organization into a database in the target time sequence database based on the mapping relation corresponding to the target time sequence database;
the second mapping submodule is used for mapping the measuring point type into a data table in the database;
a third mapping sub-module, configured to map the measurement points to metrics in the database;
a fourth mapping sub-module, configured to map the device to a tag in the database;
A fifth mapping sub-module for mapping the time to a time stamp in the database;
a sixth mapping sub-module, configured to map the corresponding numerical value to a record value in the database;
and the second operation instruction acquisition sub-module is used for acquiring a second operation instruction based on the database, the tag, the data table, the timestamp and the recorded value.
In one possible implementation, the first operation instruction includes: a data addition instruction, a data deletion instruction, a data search instruction, and a data modification instruction.
In one possible implementation, at least two of the timing databases are heterogeneous timing databases.
In summary, in the data processing method in the internet of things scenario provided in the embodiment of the present application, by adding the database management system, the database access interface therein receives the first operation instruction issued by the upper layer application, and the management device therein determines the target time sequence database acted by the first operation instruction based on the usage scenario of the upper layer application, maps the operation parameters in the first operation instruction to the corresponding attributes in the target time sequence database, thereby converting the first operation instruction into the second operation instruction capable of directly acting on the target time sequence database, and performs corresponding processing on the data in the target time sequence database. Therefore, on the premise of realizing the deployment of the time sequence databases corresponding to the multi-terminal application scene of the Internet of things, the connection operation required by the upper application when accessing different time sequence databases is reduced, and the access efficiency of the upper application to the time sequence databases is improved.
Fig. 9 is a block diagram of a computer device 900, shown in accordance with an exemplary embodiment. The computer device may be implemented as a management device in the above-described aspects of the present application. The computer apparatus 900 includes a central processing unit (Central Processing Unit, CPU) 901, a system Memory 904 including a random access Memory (Random Access Memory, RAM) 902 and a Read-Only Memory (ROM) 903, and a system bus 905 connecting the system Memory 904 and the central processing unit 901. The computer device 900 also includes a basic Input/Output system (I/O) 906, which helps to transfer information between various devices within the computer, and a mass storage device 907, for storing an operating system 913, application programs 914, and other program modules 915.
The basic input/output system 906 includes a display 908 for displaying information and an input device 909, such as a mouse, keyboard, etc., for user input of information. Wherein the display 908 and the input device 909 are connected to the central processing unit 901 via an input output controller 910 connected to the system bus 905. The basic input/output system 906 can also include an input/output controller 910 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input-output controller 910 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 907 is connected to the central processing unit 901 through a mass storage controller (not shown) connected to the system bus 905. The mass storage device 907 and its associated computer-readable media provide non-volatile storage for the computer device 900. That is, the mass storage device 907 may include a computer readable medium (not shown) such as a hard disk or a compact disk-Only (CD-ROM) drive.
The computer readable medium may include computer storage media and communication media without loss of generality. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, erasable programmable read-Only register (Erasable Programmable Read Only Memory, EPROM), electrically erasable programmable read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, digital versatile disks (Digital versatile disc, DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer storage medium is not limited to the one described above. The system memory 904 and mass storage device 907 described above may be collectively referred to as memory.
According to various embodiments of the present application, the computer device 900 may also operate by being connected to a remote computer on a network, such as the Internet. I.e., the computer device 900 may be connected to the network 912 through a network interface unit 911 coupled to the system bus 905, or other types of networks or remote computer systems (not shown) may be coupled using the network interface unit 911.
The memory further includes one or more programs stored in the memory, and the central processor 901 implements all or part of the steps of the method shown in fig. 2 or fig. 3 by executing the one or more programs.
Fig. 10 is a block diagram of a computer device 1000, shown in accordance with an exemplary embodiment. The computer device 1000 may be implemented as the above-described operation terminal, such as a smart phone, a tablet computer, or a desktop computer. The computer device 1000 may also be referred to by other names of user devices, portable terminals, laptop terminals, desktop terminals, and the like.
In general, the computer device 1000 includes: a processor 1001 and a memory 1002.
The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 1001 may also include a main processor, which is a processor for processing data in an awake state, also referred to as a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1001 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 1001 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 1002 may include one or more computer-readable storage media, which may be non-transitory. Memory 1002 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 1002 is used to store at least one instruction for execution by processor 1001 to implement the methods provided by the method embodiments in the present application.
In some embodiments, the computer device 1000 may further optionally include: a peripheral interface 1003, and at least one peripheral. The processor 1001, the memory 1002, and the peripheral interface 1003 may be connected by a bus or signal line. The various peripheral devices may be connected to the peripheral device interface 1003 via a bus, signal wire, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 1004, a display 1005, a camera assembly 1006, audio circuitry 1007, a positioning assembly 1008, and a power supply 1009.
In some embodiments, the computer device 1000 also includes one or more sensors 1010. The one or more sensors 1010 include, but are not limited to: acceleration sensor 1011, gyroscope sensor 1012, pressure sensor 1013, fingerprint sensor 1014, optical sensor 1015, and proximity sensor 1016.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is not limiting as to the computer device 1000, and may include more or fewer components than shown, or may combine certain components, or employ a different arrangement of components.
Those of skill in the art will appreciate that in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The disclosed embodiments also provide a computer device storage medium storing at least one program code loaded and executed by a processor to implement a program designed to implement all or part of the steps performed by a management device in the method shown in fig. 2 or 3 described above.
According to one aspect of the present disclosure, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium and executes the computer instructions to cause the computer device to perform all or part of the steps performed by the management device in the methods shown in fig. 2 or 3 described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. The data processing method under the scene of the Internet of things is characterized in that the method is executed by management equipment in a database management system, the database management system also comprises at least two time sequence databases, the database management system is externally provided with a database access interface, and the method comprises the following steps:
receiving a first operation instruction through the database access interface; the first operation instruction comprises at least one operation parameter;
acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the first operation instruction;
determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases;
mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction;
and issuing the second operation instruction to the target time sequence database, wherein the second operation instruction is used for indicating the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
2. The method of claim 1, wherein the obtaining the target application scenario comprises:
acquiring a deployment mode of the upper layer application;
and determining the target application scene based on the deployment mode of the upper layer application.
3. A method according to any one of claims 1 or 2, wherein the attributes of the operating parameters include organization, site type, site, equipment, time and corresponding values.
4. The method according to claim 3, wherein mapping the attribute of the operation parameter to the attribute corresponding to the target time sequence database based on the mapping relation corresponding to the target time sequence database, to obtain the second operation instruction includes:
mapping the organization into a database in the target time sequence database based on a mapping relation corresponding to the target time sequence database;
mapping the measuring point type into a data table in the database;
mapping the measurement points to metrics in the database;
mapping the device to a tag in the database;
mapping the time to a timestamp in the database;
mapping the corresponding numerical value to a field value in the database;
And obtaining the second operation instruction based on the database, the data table, the measurement, the tag, the timestamp and the field value.
5. The method of claim 1, wherein the first operation instruction comprises: a data addition instruction, a data deletion instruction, a data search instruction, and a data modification instruction.
6. The method of claim 1, wherein the at least two timing databases are heterogeneous timing databases.
7. A database management system, wherein the database management system comprises a management device and at least two time sequence databases; the database management system is externally provided with a database access interface;
the management device is used for receiving a first operation instruction of an upper layer application through the database access interface; the first operation instruction comprises at least one operation parameter;
acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the first operation instruction; determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases; mapping the attribute of the operation parameter into the attribute corresponding to the target time sequence database based on the mapping relation corresponding to the target time sequence database to obtain a second operation instruction; issuing the second operation instruction to the target time sequence database;
The target time sequence database is used for executing the data processing operation corresponding to the at least one operation parameter.
8. The data processing device in the scene of the Internet of things is characterized in that the device is applied to management equipment in a database management system, the database management system also comprises at least two time sequence databases, the database management system is externally provided with a database access interface, and the device comprises:
the first operation instruction receiving module is used for receiving a first operation instruction through the database access interface; the first operation instruction comprises at least one operation parameter;
the target application scene acquisition module is used for acquiring a target application scene; the target application scene is a use scene of an upper layer application which issues the first operation instruction;
the target time sequence database determining module is used for determining a target time sequence database according to the target application scene, wherein the target time sequence database is a time sequence database corresponding to the target application scene in the at least two time sequence databases;
the second operation instruction acquisition module is used for mapping the attribute of the operation parameter into the corresponding attribute in the target time sequence database based on the mapping relation corresponding to the target time sequence database to acquire a second operation instruction;
And the second operation instruction issuing module is used for issuing the second operation instruction to the target time sequence database, and the second operation instruction is used for indicating the target time sequence database to execute the data processing operation corresponding to the at least one operation parameter.
9. A computer device, the computer device comprising a processor and a memory; the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, which are loaded and executed by the processor to implement the data processing method in the internet of things scenario of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that at least one program code is stored in the computer readable storage medium, which is loaded and executed by a processor to implement a data processing method in the internet of things scenario according to any one of claims 1 to 7.
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