Internet of Things equipment data management method, equipment and medium

Abstract

The invention discloses an Internet of Things equipment data management method, equipment and a medium, and the method comprises the steps: determining the metadata of the equipment, and carrying out the registration according to the metadata; configuring a parameter template of the equipment according to the registered metadata so as to determine the attribute of the equipment; determining interface information of the equipment, and configuring an interface of the equipment according to the interface information; and pulling the equipment data through the interface, and carrying out standardization processing on the equipment data through the parameter template. According to the method, data formats, field naming and data coding standards of different Internet of Things devices are unified, and semantic ambiguity is eliminated. Implementers only need to input equipment information and equipment parameter classification information, multi-dimensional API interfaces (equipment / parameters / user dimensions) can be provided, and real-time streaming (WebSocket) and batch data (CSV) output are supported.

Description

Technical Field

[0001] This application relates to the technical field of the Internet of Things, and particularly to a method, device, and medium for managing data of Internet of Things devices. Background Art

[0002] With the development of the Internet, computers, and the Internet of Things in various aspects, device management has become a fundamental and important module of the system. Internet of Things management can help us configure, monitor, and maintain the growing number of connected devices in the Internet of Things. As more and more Internet of Things devices have network connection capabilities, our demand for managing data of Internet of Things devices is also increasing. The data models of current Internet of Things device data management solutions are not unified, resulting in uneven data quality and scattered data management. Summary of the Invention

[0003] To solve the above problems, this application proposes a method for managing data of Internet of Things devices, including: determining the metadata of the device and registering according to the metadata; configuring the parameter template of the device according to the registered metadata to determine the attributes of the device; determining the interface information of the device and configuring the interface of the device according to the interface information; pulling device data through the interface and performing standardized processing on the device data through the parameter template.

[0004] In one example, the method further includes: establishing a device information database and storing device identification, manufacturer, model, and communication protocol type through the device information database; defining a standardized data dictionary to map the corresponding relationship between the original fields of different devices and the fields of the unified data model through the standardized data dictionary.

[0005] In one example, the method further includes: determining a pre-set physical quantity classification system and classifying device data into the physical quantity classification system according to the parameter template; determining a pre-set unit automatic conversion mechanism and performing intelligent conversion between metric and imperial units according to the unit automatic conversion mechanism.

[0006] In one example, the method further includes: detecting the device data to determine whether there are outliers, missing values, and duplicate data in the device data; if there are outliers, missing values, and duplicate data, performing interpolation compensation and de-duplication marking on the device data.

[0007] In one example, the method further includes: extracting historical data of the device according to a pre-set cycle time and performing regional statistical analysis on the geographical location of the device according to the historical data.

[0008] In one example, the method further includes: receiving operations of business personnel through a predetermined visual configuration interface, and determining a data output template through the operations; receiving a user's subscription request, determining the real-time data stream of the device according to the subscription request, and periodically aggregating the real-time data stream to produce a report, and sending the report to the user.

[0009] In one example, the method further includes: the metadata includes device number, device name, device type, region, location, device status, and operation information.

[0010] In one example, the method further includes: the attributes include attribute number, attribute name, attribute category, data type, attribute specification, unit, creation time, and operation information.

[0011] On the other hand, the present application also proposes an IoT device data management device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the IoT device data management device can execute: determining the metadata of the device and registering it according to the metadata; configuring the parameter template of the device according to the registered metadata to determine the properties of the device; determining the interface information of the device and configuring the interface of the device according to the interface information; pulling device data through the interface, and standardizing the device data through the parameter template.

[0012] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: determine the metadata of the device and register it according to the metadata; configure the parameter template of the device according to the registered metadata to determine the properties of the device; determine the interface information of the device and configure the interface of the device according to the interface information; pull the device data through the interface and standardize the device data through the parameter template.

[0013] This application centrally stores device basic information and a standardized data dictionary, effectively solving the problem of mapping multi-source heterogeneous data, and improving data interoperability and consistency. It adopts a physical quantity classification system and a unit automatic conversion mechanism to ensure the semantic unity of cross-device data, eliminate the impact of unit differences on analysis, and enhance data comparability. This application supports multi-dimensional interface configuration, enables plug-and-play access to devices, and with the visual configuration interface, business personnel can independently define data output templates, improving service response speed. This application unifies the data formats, field names, and data coding standards of different IoT devices, eliminating semantic ambiguities. Implementers only need to enter device information and device parameter classification information. This application provides multi-dimensional API interfaces (device / parameter / user dimensions) and supports real-time stream (WebSocket) and batch data (CSV) output. Description of the Drawings

[0014] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0015] Figure 1 is a schematic flowchart of a method for managing IoT device data in an embodiment of the present application;

[0016] Figure 2 is a schematic diagram of an IoT device data management device in an embodiment of the present application. Detailed Embodiments

[0017] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The following will detail the technical solutions provided by each embodiment of the present application in conjunction with the drawings.

[0019] As Figure 1 shown, to solve the above problems, a method for managing IoT device data provided by an embodiment of the present application includes:

[0020] S101. Determine the metadata of the device and register according to the metadata.

[0021] Device metadata registration is the primary link in the standardized management of Internet of Things device data. Its core lies in establishing a unique and complete digital identity profile for each device. This process requires accurately determining the key attribute information of the device, including device number, name, type, affiliated region, location block, status, and operation records, etc., to form a structured metadata set. By constructing a device information database, these metadata are persistently stored, and a standardized data dictionary is defined to establish the mapping relationship between the original fields of different devices and the fields of the unified data model, effectively solving the problem of heterogeneous data integration. Metadata include device number, device name, device type, affiliated region, location block, device status, and operation information.

[0022] Metadata registration adopts a combination of automated tools and manual verification to ensure the integrity and accuracy of core information such as device identification, manufacturer, model, and communication protocol type. The system will perform validity verification on the metadata, automatically filter duplicate or conflicting information, and guide users to complete missing fields through a visual interface. The system refers to a system that implements a solution for the standardized management of Internet of Things device data, providing a function of entering device and parameter information in the configuration process and persisting the entered data. The registered metadata will serve as the data foundation for the full life cycle management of the device, supporting subsequent parameter configuration, interface docking, data pulling, and standardized processing, etc., significantly improving the device access efficiency and management standardization, and laying a solid foundation for building a unified Internet of Things data asset catalog.

[0023] S102. Configure the parameter template of the device according to the metadata after registration to determine the attributes of the device.

[0024] Based on the registered device metadata, the system enters the parameter template configuration stage. Its core goal is to establish a standardized device attribute definition system. This process classifies device parameters into preset categories such as temperature and humidity through a physical quantity classification system, and defines a complete set of attributes including attribute number, name, classification, data type, specification, unit, and creation time. The system supports an automatic unit conversion mechanism, which can intelligently handle the conversion between metric and imperial units to ensure the consistency of cross-device data representation.

[0025] The configuration process uses a visual template editor. Business personnel can flexibly adjust the parameter template structure according to information such as device type and affiliated region in the device metadata. For example, for agricultural sensor devices, exclusive parameters such as soil humidity and light intensity can be configured; for industrial devices, key indicators such as vibration frequency and pressure threshold can be defined. The system will perform conflict detection on the configuration results, automatically prompt problems such as duplicate attributes or unit mismatches, and record the template change history through the version control function to ensure the traceability of the configuration process.

[0026] The parameter template with the configuration completed is deeply associated with the device metadata, forming the core data layer of the device digital twin, providing a unified data semantic framework for subsequent data standardization processing, quality verification, and multi-dimensional analysis, and significantly improving the integration efficiency and application value of Internet of Things device data.

[0027] S103. Determine the interface information of the device, and configure the interface of the device according to the interface information.

[0028] Device interface configuration is an important part of the Internet of Things data standardization management. Its core lies in establishing a seamless communication channel between the device and the data platform. This process needs to accurately determine the communication protocol type of the device (such as MQTT, HTTP, CoAP, etc.), the data transmission method (real-time stream or batch transmission), and the interface specification (including IP address, port number, authentication information, etc.). Based on the interface information, the system completes the network parameter binding, protocol adaptation, and security authentication settings between the device side and the data platform through an automated configuration tool or manual intervention.

[0029] The configuration process supports a visual protocol mapping table. Technicians can quickly match the corresponding parser and data converter according to the communication protocol type in the device metadata. For example, for industrial devices using the Modbus protocol, the system automatically loads the Modbus RTU / TCP parsing module; for sensors transmitting in JSON format, the corresponding JSON parser is configured. After the configuration is completed, the system will perform a connectivity test and data integrity verification on the interface, automatically retry failed connections, and generate a configuration log to ensure the stability and reliability of data transmission.

[0030] Through the standardized interface configuration process, the system can flexibly support the access of multiple types and multiple protocols of Internet of Things devices, eliminate device communication barriers, lay a solid foundation for subsequent data standardization processing, quality monitoring, and multi-dimensional analysis, and significantly improve the access efficiency of Internet of Things devices and the data fusion ability.

[0031] S104. Pull device data through the interface, and perform standardization processing on the device data through the parameter template.

[0032] Based on the configured standardized device interface, the system regularly or real-time pulls the original device data. This process supports multiple communication protocols, such as MQTT, HTTP, and data transmission modes, such as real-time stream, batch file. The pulled original data then enters the standardization processing engine, and the engine automatically processes the original data according to the physical quantity classification system, unit conversion rules, and data cleaning strategies defined in the parameter template.

[0033] The standardization process includes key steps such as data mapping and transformation, e.g., mapping device private fields to unified data model fields; unit standardization, e.g., unifying Celsius and Fahrenheit into Kelvin temperature; outlier filtering, e.g., removing noise data outside the reasonable range of physical quantities; and missing value compensation, e.g., using interpolation algorithms to fill data gaps. The system automatically performs data verification, cleaning, and transformation operations through a predefined rule library and algorithm model to generate a high-quality dataset that complies with standard specifications. During the processing, the system generates a complete data processing log, recording data lineage and quality metrics, providing traceable data assets for subsequent data analysis and decision-making.

[0034] In one embodiment, to build a standardized management system for IoT devices, it is necessary to first establish a device information database. As the core data asset, this database centrally stores key metadata such as device identifiers, manufacturers, models, communication protocol types, etc. Through automated tools or manual entry, the system collects and structurally stores this information to form the data foundation for the full life cycle management of devices. On this basis, defining a standardized data dictionary becomes a key step. Through expert review and automated mapping technology, this dictionary establishes an accurate correspondence between the original fields of different devices and the fields of the unified data model. For example, map the "temperature value" field of device A to "ambient_temperature" in the standard model, and map the "humidity percentage" of device B to "relative_humidity", effectively solving the problem of heterogeneous data integration. The dictionary supports version control and dynamically updates mapping rules as new devices are connected, ensuring data semantic consistency, providing a unified data naming specification for subsequent parameter configuration, interface docking, and standardization processing, and significantly improving the efficiency and quality of IoT data fusion.

[0035] In one embodiment, to achieve standardized management of device data, the system pre-constructs a multi-dimensional physical quantity classification system, covering core dimensions such as temperature, humidity, pressure, energy consumption, etc. Under each dimension, specific physical quantity types are further divided. After device data is accessed, the system automatically classifies the data into the corresponding classification levels according to the physical quantity attributes defined in the parameter template. For example, classify the vibration frequency data of industrial equipment into the "mechanical vibration" subclass, and classify the PM2.5 value of environmental monitoring equipment into the "air quality" category.

[0036] Meanwhile, the system establishes an automatic unit conversion mechanism and builds an intelligent conversion rule library for metric and imperial units. When the original data of the device uses non-standard units, such as the imperial unit "foot", the conversion engine automatically matches the predefined conversion formula: 1 foot = 0.3048 meters, and stores the value after converting it into the standard measurement unit. This mechanism supports dynamic expansion and can automatically adapt to the unit usage habits of different countries and regions based on the regional information in the device metadata. For example, the metric system is the default in the EU, and the imperial system is compatible in the US, ensuring the measurement unity of cross-regional device data. Through the dual guarantees of physical quantity classification and unit standardization, the system effectively eliminates data heterogeneity and lays a solid foundation for building a globally consistent data analysis model.

[0037] In one embodiment, the system performs multi-level quality verification on the collected device data. First, it detects the data distribution through the statistical rule engine to identify outliers that exceed the reasonable range of physical quantities, such as a temperature sensor reporting an extreme value of -50°C. For the problem of missing values, time series interpolation algorithms, such as linear interpolation and moving average, are used to perform intelligent compensation in combination with the device type and historical data patterns in the device metadata. For duplicate data, the system uses hash verification and sequence comparison technologies to perform deduplication marking, retaining the first valid record and marking the duplicate source. During the processing, the system automatically generates quality labels to record the outlier correction method, the basis for filling missing values, and the deduplication strategy, ensuring the traceability of data lineage. This mechanism supports dynamic threshold configuration and can adjust the verification rules according to the device's region and operating environment characteristics, effectively adapting to the data quality control requirements of different industrial scenarios and providing clean and reliable data assets for upper-layer applications.

[0038] In one embodiment, the system supports automatically extracting the historical data of the device at preset intervals, such as daily / weekly / monthly, and combines the geographical location coordinates stored in the device information database to conduct spatial statistical analysis. By integrating the GIS engine, historical data such as the device's operating status and failure frequency are overlaid with geographical grids, and algorithms such as kernel density analysis and spatial clustering are used to identify hotspots and abnormal aggregation areas of device distribution. For example, if a cluster of temperature and humidity sensors in a certain urban area frequently shows data drift, the system can automatically circle this area as a high-priority area for device maintenance. The analysis results are visually presented in the form of heat maps, spatial distribution maps, etc., supporting the optimization of regional device scheduling and preventive maintenance decision-making. This mechanism supports dynamic adjustment of the analysis period and geographical grid granularity to adapt to the management requirements of IoT devices in cities of different scales and provides data-driven geospatial decision support for smart city operation and maintenance.

[0039] In one embodiment, the system provides a visual configuration interface. Through low-code operations such as dragging fields and configuring aggregation logic, business personnel can define a data output template that includes key metrics, such as device online rate and failure rate. The interface supports multi-dimensional filtering by device type, region, and time range, and presets a variety of visual components, such as line charts, bar charts, and dashboards, to meet personalized display requirements.

[0040] For the user subscription service, the system opens a subscription management module. Users can select target devices as needed, specify data dimensions, such as real-time temperature and pressure values, and the report generation period. After the subscription is triggered, the system captures the device data stream in real time and performs ETL processing, including extraction, transformation, and loading, at regular intervals to convert the raw data into a structured report, supporting output in formats such as PDF, Excel, and CSV. The report generation engine automatically embeds metadata such as data quality labels and exception marks to ensure data traceability. Finally, the customized report is pushed to the user through email or system notification channels to achieve the closed-loop delivery of data services, significantly improving the efficiency of business decision-making.

[0041] As Figure 2 shown, an embodiment of the present application also provides an Internet of Things device data management device, including:

[0042] At least one processor; and,

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions executable by the at least one processor. When the instructions are executed by the at least one processor, an Internet of Things device data management device can perform:

[0045] Determine the metadata of the device and register according to the metadata;

[0046] Configure the parameter template of the device according to the registered metadata to determine the attributes of the device;

[0047] Determine the interface information of the device and configure the interface of the device according to the interface information;

[0048] Pull device data through the interface and perform standardization processing on the device data through the parameter template.

[0049] An embodiment of the present application also provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set to:

[0050] Determine the metadata of the device and register according to the metadata;

[0051] Configure the parameter template of the device according to the metadata after registration to determine the attributes of the device;

[0052] Determine the interface information of the device and configure the interface of the device according to the interface information;

[0053] Pull device data through the interface and perform standardization processing on the device data through the parameter template.

[0054] In the 1990s, it was obvious to distinguish whether an improvement in a technology was an improvement in hardware (e.g., improvement in circuit structures such as diodes, transistors, switches, etc.) or an improvement in software (improvement in method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements in hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structures by programming the improved method flows into the hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. The designer can program by himself to "integrate" a digital system on a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL). And there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that as long as the method flow is slightly logically programmed with the above-mentioned several hardware description languages and programmed into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0055] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0056] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0057] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0058] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0059] The devices, media, and methods provided by the embodiments of this application correspond one by one. Therefore, the devices and media also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0060] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0061] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0062] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0064] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0065] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0066] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0067] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0068] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for managing data of an Internet of Things device, characterized in that, Including: Determine the metadata of the device and register according to the metadata; Configure the parameter template of the device according to the registered metadata to determine the attributes of the device; Determine the interface information of the device and configure the interface of the device according to the interface information; Pull device data through the interface and standardize the device data through the parameter template.

2. The method according to claim 1, wherein The method further includes: Establish a device information database and store device identification, manufacturer, model, and communication protocol type through the device information database; Define a standardized data dictionary to map the corresponding relationships between the original fields of different devices and the fields of the unified data model through the standardized data dictionary.

3. The method according to claim 1, characterized in that, The method further includes: Determine a pre-set physical quantity classification system and classify device data into the physical quantity classification system according to the parameter template; Determine a pre-set unit automatic conversion mechanism and perform intelligent conversion between metric and imperial units according to the unit automatic conversion mechanism.

4. The method according to claim 1, wherein The method further includes: Detect the device data to determine whether there are outliers, missing values, and duplicate data in the device data; If there are outliers, missing values, and duplicate data, perform interpolation compensation and de-duplication marking on the device data.

5. The method according to claim 1, wherein The method further includes: Extract the historical data of the device according to a pre-set cycle time and perform regional statistical analysis on the geographical location of the device according to the historical data.

6. The method according to claim 1, characterized in that, The method further includes: Receive the operations of business personnel through a pre-determined visualization configuration interface and determine a data output template through the operations; Receive a subscription request from a user, determine the real-time data stream of the device according to the subscription request, periodically summarize the real-time data stream to generate a report, and send the report to the user.

7. The method according to claim 1, wherein The method further includes: The metadata includes device number, device name, device type, affiliated region, location block, device status, and operation information.

8. The method according to claim 1, wherein The method further includes: The attributes include attribute number, attribute name, attribute classification, data type, attribute specification, unit, creation time, and operation information.

9. An Internet of Things device data management device, characterized in that, Including: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that an Internet of Things device data management device can execute: Determine the metadata of the device and register according to the metadata; Configure the parameter template of the device according to the registered metadata to determine the attributes of the device; Determine the interface information of the device and configure the interface of the device according to the interface information; Pull device data through the interface and standardize the device data through the parameter template.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are set to: Determine the metadata of the device and register according to the metadata; Configure the parameter template of the device according to the registered metadata to determine the attributes of the device; Determine the interface information of the device and configure the interface of the device according to the interface information; Pull device data through the interface and perform standardization processing on the device data through the parameter template.

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