Internet of Things equipment online identification method and device

Abstract

The invention provides an Internet of Things equipment online identification method and device, and the method comprises the steps: obtaining equipment features sent by at least two data sources of target equipment, and obtaining the target equipment does not depend on the single heartbeat data fed back by the target equipment, but obtaining the equipment feature data of at least two data sources related to the target equipment. The data dimension is enhanced, and the accuracy of the subsequent online state is improved to a certain extent; further, analyzing the equipment feature data to obtain equipment attributes and equipment operation data of the target equipment; and determining the online state of the target equipment according to the equipment attribute and the equipment operation data, thereby further improving the accuracy of the online state.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to an online identification method and apparatus for Internet of Things (IoT) devices. Background Technology

[0002] With the development of smart technology, various types of IoT devices are being used in all aspects of production and daily life. Online status assessment of IoT devices plays a crucial role in their application. Currently, related technologies typically determine online status solely based on the device's heartbeat signal.

[0003] However, relying solely on heartbeat signals to determine whether IoT devices are online is limited to a single data source, and may lead to inaccurate judgments if heartbeat signals are unavailable or delayed. Summary of the Invention

[0004] This disclosure proposes an online identification method and apparatus for Internet of Things (IoT) devices.

[0005] The first aspect of this disclosure provides a method for online identification of Internet of Things (IoT) devices, the method comprising:

[0006] Obtain device characteristic data from at least two data sources for the target device;

[0007] The device feature data is parsed to obtain the device attributes and device operation data of the target device;

[0008] The online status of the target device is determined based on the device attributes and the device operation data.

[0009] In this embodiment of the disclosure, parsing the device feature data to obtain the device attributes and device operation data of the target device includes:

[0010] Based on the data source of the device feature data, determine the first target parsing method corresponding to the data source;

[0011] The device feature data is parsed based on the target parsing method to obtain the data type of the device feature data;

[0012] The device feature data is parsed according to the second target parsing method corresponding to the data type to obtain the device attributes and the device operation data.

[0013] In this embodiment of the disclosure, determining the online status of the target device based on the device attributes and the device operating data includes:

[0014] Within a preset policy set, select the target policy that corresponds to the device attribute;

[0015] Based on the data type of the device operation data, an initial online status corresponding to each data type is generated;

[0016] The initial online state is adjusted based on the target strategy to obtain the online state of the target device.

[0017] In this embodiment of the disclosure, the step of filtering target policies corresponding to the device attributes in a preset policy set includes:

[0018] In the preset policy set, match the initial policy set corresponding to the device attribute;

[0019] The target strategy is determined based on the matching degree of each strategy in the initial strategy set.

[0020] In this embodiment of the disclosure, determining the target policy based on the priority of each policy in the initial policy set includes:

[0021] For any initial strategy, calculate the matching parameters between the initial strategy and the device attributes; the matching parameters characterize the degree of matching between each strategy in the initial strategy set.

[0022] Based on the magnitude of the matching parameter, the initial strategy with the largest matching parameter is selected as the target strategy from the initial strategy set.

[0023] In this embodiment of the disclosure, the data types of the device operation data include gateway connection status data, usage status data, and heartbeat data. The step of generating an initial online status corresponding to each of the data types based on the data types of the device operation data includes:

[0024] If the gateway connection status is not connected, the first initial online status is offline; if the gateway connection status is connected, the first initial status is online.

[0025] If the usage status data is the same as the operating status data of the target device, then the second initial state is online; if the usage status data is different from the operating status data of the target device, then the second initial state is offline.

[0026] If the heartbeat data indicates a timeout connection, the third initial state is an offline state; if the heartbeat data indicates a connection that has not timed out, the third initial state is an online state.

[0027] The first initial state, the second initial state, and the third initial state are taken as the initial online state.

[0028] In this embodiment of the disclosure, the target strategy includes weights, the weights representing the degree of influence, and the step of adjusting the initial online state based on the target strategy to obtain the online state of the target device includes:

[0029] The initial online state is weighted and summed with the corresponding weights to obtain the state parameter value;

[0030] If the value of the status parameter is greater than or equal to a preset threshold, the status parameter represents an offline state; if the value of the status parameter is less than the preset threshold, the status parameter represents an online state.

[0031] In this embodiment of the disclosure, the method further includes:

[0032] The online status is sent to the user terminal of the target device.

[0033] In this embodiment of the disclosure, parsing the device feature data according to the second target parsing method corresponding to the data type to obtain the device attributes and the device operation data includes:

[0034] Determine the second target parsing method corresponding to the data type;

[0035] Based on the second target parsing method, the device feature data is parsed to obtain initial device attributes and initial device operation data;

[0036] The initial device attributes and the initial device operation data are preprocessed respectively to obtain the device attributes and the device operation data.

[0037] A second aspect of this disclosure provides an online identification device for Internet of Things (IoT) devices, the device comprising:

[0038] The acquisition module is used to acquire device characteristic data sent by at least two data sources for the target device.

[0039] The parsing module is used to parse the device feature data to obtain the device attributes and device operation data of the target device;

[0040] The determination module is used to determine the online status of the target device based on the device attributes and the device operation data.

[0041] An embodiment of the third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor running the computer program to implement the method described in the first aspect above.

[0042] An embodiment of the fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method described in the first aspect above.

[0043] The technical solutions provided in this disclosure have at least the following technical effects or advantages:

[0044] The system acquires device characteristics sent from at least two data sources for the target device. Since acquiring the target device does not rely on a single heartbeat data feedback from the target device, but rather on acquiring device characteristic data from at least two data sources related to the target device, the data dimensionality is enhanced, which improves the accuracy of subsequent online status to a certain extent. Furthermore, the device characteristic data is parsed to obtain the device attributes and device operation data of the target device. Based on the device attributes and the device operation data, the online status of the target device is determined, which further improves the accuracy of online status.

[0045] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description or may be learned by practice of this disclosure. Attached Figure Description

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this disclosure. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0047] In the attached diagram:

[0048] Figure 1 A flowchart illustrating an online identification method for IoT devices according to an embodiment of this disclosure is shown.

[0049] Figure 2 A schematic diagram of an online identification method for Internet of Things (IoT) devices provided in an embodiment of this disclosure is shown;

[0050] Figure 3 This diagram illustrates the structure of an online identification device for Internet of Things (IoT) devices according to an embodiment of the present disclosure.

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure is shown;

[0052] Figure 5 A schematic diagram of a storage medium provided according to an embodiment of the present disclosure is shown. Detailed Implementation

[0053] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0054] It should be noted that, unless otherwise stated, the technical or scientific terms used in this disclosure shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure pertains.

[0055] This disclosure proposes an online identification method for Internet of Things (IoT) devices, such as... Figure 1 This disclosure illustrates an online identification method for IoT devices according to an embodiment of the present disclosure. The online identification method for IoT devices according to this embodiment may include the following steps:

[0056] In step S11, device feature data sent by at least two data sources for the target device is obtained.

[0057] For example, the target device is an IoT device whose online status needs to be determined, such as monitoring equipment, lighting equipment, or control equipment in an industrial park. The device characteristic data sent from at least two data sources can be device characteristic data from the target device; it can also be device characteristic data from a gateway connected to the target device; if the target device has a corresponding management platform, and that management platform also has corresponding device characteristic data, then the management platform can also be used as a data source for the target device.

[0058] The data sent from the target device can include basic operating status, such as online / offline status, power status, and fault codes; real-time sensor data, such as physical quantities like temperature, humidity, and light intensity; device operating modes, such as energy-saving mode, normal mode, and maintenance mode; unique device identifiers, such as MAC address, serial number, and device ID; and memory usage.

[0059] Data from the gateway can include network connection status, such as the connectivity, signal strength, and retransmission rate of the link between the device and the gateway; communication protocol interaction records, such as the handshake success rate of Modbus, MQTT, and CoAP protocols; device heartbeat timestamps cached on the gateway side, used to determine whether the device has timed out and failed to respond; and gateway configuration information, such as device binding relationships, access control lists, and port mapping status.

[0060] Data from the management platform can include platform registration status, such as whether the device is on the platform's whitelist and the authorization validity period; historical operation logs, such as the last login time, command issuance records, and parameter modification records; policy configuration information, such as scheduled tasks, alarm thresholds, and linkage rules; and device activity calculated by the platform, such as the frequency of data reporting per unit time and the number of abnormal events triggered.

[0061] The embodiments disclosed herein do not limit the type and number of data sources, which can be determined by those skilled in the art based on the actual situation.

[0062] In step S12, the device feature data is parsed to obtain the device attributes and device operation data of the target device.

[0063] For example, after obtaining the device feature data, it is first necessary to parse the device feature data to obtain the corresponding device attributes and device operation data. Regular expressions for each attribute field can be predefined to extract the corresponding device attributes; alternatively, a sliding window can be used to extract the time-series features corresponding to the target device and to parse events in the operation log to extract the corresponding device operation data, such as sensor values ​​and log times.

[0064] In some embodiments, since the data sources for acquiring device feature data are different, the encoding methods for the device feature data corresponding to each data source are different. Therefore, when parsing the device feature data, the encoding / decoding method of the corresponding data source can be obtained in advance so that the device feature data of the corresponding data source can be parsed according to that encoding / decoding method. Therefore, step S12 can also be implemented in the following way: determining a first target parsing method corresponding to the data source of the device feature data; parsing the device feature data based on the first target parsing method to obtain the data type of the device feature data; and parsing the device feature data according to a second target parsing method corresponding to the data type to obtain device attributes and device operation data.

[0065] For example, the data source type can be at least two of the following: the target device itself, the associated gateway, the management platform, etc. The first target parsing method is a protocol parsing rule, a data format conversion rule, or a metadata mapping rule corresponding to the data source type, which is used to convert the raw data into a unified intermediate format.

[0066] The device feature data is initially analyzed based on the first target parsing method to extract data feature identifiers. The data feature identifiers are then matched with a predefined data type classification model to determine the data type of the device feature data. The data types include device attributes and device operation data. The device operation data may include environmental parameters, working modes, resource usage, network connection status, etc. Specifically, based on the structure of the device operation data, it can also be divided into at least one of structured data, unstructured data, time-series data, and event log data.

[0067] The second target parsing method corresponding to the data type is invoked; for structured data, a rule engine is used to extract static attribute fields; for unstructured data, a natural language processing model or protocol decoder is used to extract dynamic running parameters; for time series data, a time series analysis algorithm is used to calculate the state change trend; and for event log data, pattern recognition technology is used to extract operation events and exception codes.

[0068] The device attributes and device operation data output by the second target parsing method are subjected to conflict verification and priority fusion to generate a structured device status profile. Among them, the device attributes include device identifier, firmware version and configuration parameters, and the device operation data includes real-time sensor values, communication status and operation logs.

[0069] By adopting a hierarchical parsing architecture, efficient and accurate parsing of feature data from multi-source heterogeneous devices is achieved, solving the problem of poor adaptability of traditional single parsing methods to complex data sources and improving the reliability of IoT device status management.

[0070] After parsing the device feature data from each data source, since the data formats of each data source are different, the device feature data can be parsed based on the second target parsing method to obtain the initial device attributes and initial device operation data. Then, the initial device attributes and initial device operation data are preprocessed to obtain the device attributes and device operation data.

[0071] For example, initial device attributes include device ID, model, and installation location. Initial device operational data includes sensor values, status flags, heartbeat timestamps, signal strength, the content and reception time of the most recent uplink message, and downlink command response latency. Missing values ​​in required fields of the initial device attributes can be handled, and a unified format can be implemented. Descriptions of the same attribute from multiple data sources can be deduplicated based on priority. For initial device operational data, time-series alignment and missing data repair can be performed first. Time-series alignment can be achieved by correcting timestamps; for example, out-of-order data caused by network latency can be reordered using a sliding window. Data without timestamps can be completed using reception time or associated event time. For continuous data (such as temperature and vibration), moving average filtering, wavelet denoising, or outlier removal based on statistical thresholds can be used. For discrete data (such as status flags), frequency analysis can be used to filter out transient fluctuations (e.g., repeated "online-offline" reports within 1 second are considered noise).

[0072] In step S13, the online status of the target device is determined based on the device attributes and device operation data.

[0073] For example, when determining the online status of a target device, different target devices with different attributes determine their online status in different ways.

[0074] For example, for battery-powered low-power sensor devices, a "sleep heartbeat interval" and a "wake-up window" are defined in their static attributes. In this case, the device is only considered "offline" if no uplink heartbeat frame conforming to the protocol format is received within N consecutive "sleep heartbeat intervals" and the device is not currently outside the "wake-up window". If the device is currently within the "wake-up window" and no data is received, the tolerance time is extended until the window ends before making a judgment, thereby avoiding misjudging normal power-saving behavior as disconnection.

[0075] For in-vehicle intelligent terminal devices, "speed threshold" and "offline delay after engine shutdown" are defined in their static attributes. In this case, when the instantaneous speed reported by the device remains zero and the engine shutdown signal is valid, the heartbeat cycle is allowed to be automatically extended to the duration set by "offline delay after engine shutdown". If no heartbeat is received within this extended period, it is determined to be "offline"; otherwise, it remains "online" to accommodate the periodic sleep requirements after the vehicle is turned off.

[0076] In some embodiments, determining the online status of a target device based on device attributes and device operation data includes: filtering target policies corresponding to device attributes from a preset policy set; generating initial online statuses corresponding to each data type based on the data type of the device operation data; and adjusting the initial online statuses based on the target policies to obtain the online status of the target device.

[0077] For example, a mapping relationship between device attributes and policies can first be established, where the mapping relationship can use multiple dimensions of device attributes to determine the corresponding target policy. When multiple policies match, the final policy is determined by a preset priority rule, such as business criticality > geographic location > device type.

[0078] For example, a data type feature library can be established, such as real-time streaming data, periodically reported data, and event-triggered data. The initial online status of the corresponding data can be generated based on the data's timeliness requirements. Judgment criteria for each type of data can be generated; for example, real-time data must be continuously online or offline, while log data is allowed intermittent connections, with the online status determined based on the interval.

[0079] After obtaining the initial online status corresponding to each data type in the device characteristic data, it is necessary to merge the initial online status of each data type to obtain the final online status. For example, this can be determined by the merging method set in the target strategy, which specifies the priority of each data type: real-time control commands > fault alarms > status heartbeats > log data. When a high-priority data type requires the device to be online, it directly overrides the low-priority status.

[0080] In some embodiments, filtering target policies corresponding to device attributes in a preset policy set includes: matching an initial policy set corresponding to the device attributes in the preset policy set; and determining the target policy based on the matching degree of each policy in the initial policy set.

[0081] For example, based on the device type, ID, manufacturer, model, and power consumption attributes in the device attributes, a corresponding initial strategy set is matched within a preset strategy set. The matching can satisfy fuzzy rules, thus avoiding situations where no strategy is matched if the preset strategy set does not include a strategy that perfectly corresponds to the device attributes. The initial strategy set can be a strategy that satisfies all attributes in the device attributes; alternatively, if no corresponding strategy exists for the current attribute, the attribute information at the next higher level can be used as the initial strategy set. The hierarchy of attribute information can be defined as: Device Type > ID > Manufacturer > Model. Of course, this embodiment does not limit the hierarchy of attribute information; those skilled in the art can determine it according to actual circumstances.

[0082] During matching, if the strategy corresponding to the model is empty, the strategy set corresponding to the manufacturer will be used as the initial strategy set. If there is only one strategy in the initial strategy set, this strategy is assumed to have the highest matching degree with the target device and will be selected as the target strategy.

[0083] In some embodiments, determining a target policy based on the matching degree of each policy in the initial policy set includes: for any initial policy, calculating a matching parameter between the initial policy and the device attribute; the matching parameter characterizes the matching degree of each policy in the initial policy set; and based on the magnitude of the matching parameter, selecting the initial policy with the largest matching parameter as the target policy from the initial policy set.

[0084] For example, this can be achieved as follows: each strategy is represented as a dictionary containing the conditions (key-value pairs) to be matched; the device attributes are represented as a dictionary containing information such as device type, ID, manufacturer, and model; the conditions of each strategy are traversed, and the number of conditions that match the device attributes is counted; the matching parameters of all strategies are compared, and the strategy with the highest score is selected.

[0085] Each strategy's conditions are compared item by item with the device attributes; a perfect match earns a score. When multiple strategies have the same score, the first strategy that appears can be selected by default, and the priority logic can be expanded as needed.

[0086] In some embodiments, the data types of device operation data include gateway connection status data, usage status data, and heartbeat data. Based on the data types of the device operation data, an initial online state corresponding to each data type is generated, including: if the gateway connection status is not connected, then the first initial online state is offline; if the gateway connection status is connected, then the first initial state is online; if the usage status data is the same as the target device's operation status data, then the second initial state is online; if the usage status data is different from the target device's operation status data, then the second initial state is offline; if the heartbeat data indicates a timeout connection, then the third initial state is offline; if the heartbeat data indicates a connection that has not timed out, then the third initial state is online; the first initial state, the second initial state, and the third initial state are used as the initial online state.

[0087] There are also some embodiments where the target strategy includes weights, the weights representing the degree of influence, and the initial online state is adjusted based on the target strategy to obtain the online state of the target device, including: weighting the initial online state with the corresponding weights to obtain a state parameter value; if the state parameter value is greater than or equal to a preset threshold, the state parameter represents the offline state; if the state parameter value is less than the preset threshold, the state parameter represents the online state.

[0088] The online status is sent to the user's terminal on the target device. This online status can be sent to the corresponding Kafka, API, etc., on the user's terminal to push the online status to the target device's user terminal.

[0089] like Figure 2 The following is the implementation flow of the above-mentioned online identification method for IoT devices, as shown in the figure. Figure 2As shown, after obtaining device feature data from at least two data sources, the data is parsed, and after determining the online status, a message is sent to the corresponding user terminal.

[0090] As can be seen from the above examples, the IoT device online identification method provided in this disclosure acquires device features sent by at least two data sources for the target device. Since acquiring the target device does not rely on a single heartbeat data feedback from the target device, but rather acquires device feature data from at least two data sources related to the target device, the data dimensionality is enhanced, which improves the accuracy of subsequent online status to a certain extent. Furthermore, the device feature data is parsed to obtain the device attributes and device operation data of the target device. Based on the device attributes and device operation data, the online status of the target device is determined, which further improves the accuracy of the online status.

[0091] Corresponding to the above implementation of the online identification method for IoT devices, this disclosure also provides an online identification device for IoT devices, which is used to perform the above-described... Figure 1 An illustrated embodiment of an online identification method for IoT devices. For example... Figure 3 As shown, the online identification device for IoT devices includes:

[0092] The acquisition module 301 is used to acquire device characteristic data sent by at least two data sources of the target device;

[0093] The parsing module 302 is used to parse the device feature data to obtain the device attributes and device operation data of the target device;

[0094] The determination module 303 is used to determine the online status of the target device based on the device attributes and the device operation data.

[0095] The IoT device online identification device and the IoT device online identification method provided in the above embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0096] This disclosure also provides an electronic device for performing the above-described online identification method for IoT devices. Please refer to... Figure 4 This illustrates a schematic diagram of an electronic device provided by some embodiments of the present disclosure. For example... Figure 4 As shown, the electronic device includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected via the bus 402. The memory 401 stores a computer program that can run on the processor 400. When the processor 400 runs the computer program, it executes the aforementioned functions described in this disclosure. Figure 1 The illustrated embodiment provides an online identification method for IoT devices.

[0097] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0098] Bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 401 is used to store programs, and the processor 400 executes the programs after receiving execution instructions. Figure 1 The illustrated embodiments reveal that the online identification method for IoT devices can be applied to, or implemented by, the processor 400.

[0099] The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 400 or by instructions in software form. The processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 401. The processor 400 reads the information in memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0100] The electronic devices provided in this disclosure and the online identification method for IoT devices provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0101] This disclosure also provides a computer-readable storage medium corresponding to the online identification method for IoT devices provided in the foregoing embodiments. Please refer to... Figure 5 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the online identification method for Internet of Things devices provided in any of the foregoing embodiments.

[0102] It should be noted that examples of the computer-readable storage medium may also 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 optical and magnetic storage media, which will not be elaborated here.

[0103] The computer-readable storage medium provided in the above embodiments of this disclosure and the online identification method for Internet of Things devices provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0104] It should be noted that:

[0105] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this disclosure may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0106] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this disclosure, various features of this disclosure are sometimes grouped together in a single embodiment, figure, or description thereof. However, this approach to disclosure should not be construed as reflecting a schematic diagram in which the claimed disclosure requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this disclosure.

[0107] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of this disclosure and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0108] The above description is merely a preferred embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. An online identification method for Internet of Things (IoT) devices, characterized in that, The method includes: Obtain device characteristic data from at least two data sources for the target device; The device feature data is parsed to obtain the device attributes and device operation data of the target device; The online status of the target device is determined based on the device attributes and the device operation data.

2. The method according to claim 1, characterized in that, The step of parsing the device feature data to obtain the device attributes and device operation data of the target device includes: Based on the data source of the device feature data, determine the first target parsing method corresponding to the data source; The device feature data is parsed based on the first target parsing method to obtain the data type of the device feature data; The device feature data is parsed according to the second target parsing method corresponding to the data type to obtain the device attributes and the device operation data.

3. The method according to claim 1, characterized in that, Determining the online status of the target device based on the device attributes and the device operating data includes: Within a preset policy set, select the target policy that corresponds to the device attribute; Based on the data type of the device operation data, an initial online status corresponding to each data type is generated; The initial online state is adjusted based on the target strategy to obtain the online state of the target device.

4. The method according to claim 3, characterized in that, The step of filtering target policies corresponding to the device attributes from a preset policy set includes: In the preset policy set, match the initial policy set corresponding to the device attribute; The target strategy is determined based on the matching degree of each strategy in the initial strategy set.

5. The method according to claim 4, characterized in that, The step of determining the target strategy based on the matching degree of each strategy in the initial strategy set includes: For any initial strategy, calculate the matching parameters between the initial strategy and the device attributes; the matching parameters characterize the degree of matching between each strategy in the initial strategy set. Based on the magnitude of the matching parameter, the initial strategy with the largest matching parameter is selected as the target strategy from the initial strategy set.

6. The method according to claim 3, characterized in that, The data types of the device operation data include gateway connection status data, usage status data, and heartbeat data. Generating an initial online status corresponding to each data type based on the data types of the device operation data includes: If the gateway connection status is not connected, the first initial online status is offline; if the gateway connection status is connected, the first initial status is online. If the usage status data is the same as the operating status data of the target device, then the second initial state is online; if the usage status data is different from the operating status data of the target device, then the second initial state is offline. If the heartbeat data indicates a timeout connection, the third initial state is an offline state; if the heartbeat data indicates a connection that has not timed out, the third initial state is an online state. The first initial state, the second initial state, and the third initial state are taken as the initial online state.

7. The method according to claim 6, characterized in that, The target strategy includes weights, which represent the degree of influence. Adjusting the initial online state based on the target strategy to obtain the online state of the target device includes: The initial online state is weighted and summed with the corresponding weights to obtain the state parameter value; If the value of the status parameter is greater than or equal to a preset threshold, the status parameter represents an offline state; if the value of the status parameter is less than the preset threshold, the status parameter represents an online state.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: The online status is sent to the user terminal of the target device.

9. The method according to claim 2, characterized in that, The step of parsing the device feature data according to the second target parsing method corresponding to the data type to obtain the device attributes and the device operation data includes: Determine the second target parsing method corresponding to the data type; Based on the second target parsing method, the device feature data is parsed to obtain initial device attributes and initial device operation data; The initial device attributes and the initial device operation data are preprocessed respectively to obtain the device attributes and the device operation data.

10. An online identification device for Internet of Things (IoT) devices, characterized in that, The device includes: The acquisition module is used to acquire device characteristic data sent by at least two data sources for the target device. The parsing module is used to parse the device feature data to obtain the device attributes and device operation data of the target device; The determination module is used to determine the online status of the target device based on the device attributes and the device operation data.

Images