Link status detection method and system for Internet of Things devices

Abstract

The present application provides a link status detection method and system for an Internet of Things (IoT) device. The method constructs an IoT link model based on a number of IoT network nodes and the connection relationships between the nodes, detects the communication status and application status of each layer of network nodes, and obtains detection result data. Furthermore, an IoT fault model is constructed based on the IoT link model to obtain the fault causal relationship between each network node and the faults between the nodes. The detection result data is then associated with the IoT fault model. This allows for rapid identification of the fault point causing an IoT anomaly based on the detection result data and the IoT fault model. In the event of an IoT anomaly, the fault point is quickly located and alarm data is generated for the fault point. This allows maintenance personnel to quickly receive an alarm, address the fault and abnormality as quickly as possible, and improve IoT operational stability.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of Internet of Things technology, and in particular, to a method and system for detecting the link status of an Internet of Things device. Background Art

[0002] As the scale of IoT devices in IoT systems expands at an exponential rate, the distribution, heterogeneity and non-collaboration of the network are gradually increasing, which makes the control and management of IoT devices increasingly difficult and the overall grasp of network performance increasingly complex.

[0003] When an abnormality occurs in the Internet of Things, it is usually only possible to rely on local network inspections to discover and eliminate local fault factors, but it is impossible to locate individual fault points through the Internet of Things system. Therefore, it is impossible to quickly deal with the abnormal state of the Internet of Things, which is not conducive to the stable operation of the Internet of Things. Summary of the Invention

[0004] In order to overcome the problems existing in the related technologies, the present application provides a link status detection method and system for Internet of Things devices to improve the power system. By detecting the communication status and application status of the nodes of the Internet of Things, the fault points that cause abnormal operation of the Internet of Things can be quickly located, thereby quickly eliminating the faults and maintaining stable operation of the Internet of Things.

[0005] According to a first aspect of an embodiment of the present application, a link status detection method for an Internet of Things device is provided, comprising the following steps:

[0006] Setting an Internet of Things link model, wherein the Internet of Things link model includes a plurality of network nodes and connection relationships between the plurality of network nodes;

[0007] According to the IoT link model, perform communication status detection and application status detection of each layer of network nodes to obtain detection result data, including:

[0008] Determining a detection path based on the IoT link model;

[0009] Initiate detection tasks according to the detection path, including communication status detection and application status detection;

[0010] Obtain detection result data based on the response results of communication status detection and application status detection;

[0011] Based on the IoT link model, construct an IoT fault model, wherein the IoT fault model includes each network node and a fault causal relationship between the network nodes;

[0012] Associating the detection result data with the IoT fault model to determine the fault point causing the IoT anomaly;

[0013] According to the preset Internet of Things alarm rules, alarm data is generated for the fault point and the alarm data is output.

[0014] According to a second aspect of an embodiment of the present application, a link status detection system for an Internet of Things device is provided, comprising:

[0015] A link module, configured to set an Internet of Things link model, wherein the Internet of Things link model includes a plurality of network nodes and connection relationships between the plurality of network nodes;

[0016] The detection module is used to detect the communication status and application status of each layer of network nodes according to the IoT link model and obtain detection result data, including:

[0017] Determining a detection path based on the IoT link model;

[0018] Initiate detection tasks according to the detection path, including communication status detection and application status detection;

[0019] Obtain detection result data based on the response results of communication status detection and application status detection;

[0020] A fault module, configured to construct an Internet of Things fault model based on the Internet of Things link model, wherein the Internet of Things fault model includes each network node and a fault causal relationship between the network nodes;

[0021] A judgment module, configured to associate the detection result data with the IoT fault model to determine the fault point causing the IoT anomaly;

[0022] The advanced precision module is used to generate alarm data for the fault point according to the preset Internet of Things alarm rules and output the alarm data.

[0023] The present invention discloses a link status detection method and system for IoT devices. By constructing an IoT link model based on a number of IoT network nodes and the connection relationships between them, the method detects the communication status and application status of each layer of network nodes to obtain detection result data. Furthermore, an IoT fault model is constructed corresponding to the IoT link model to obtain the causal relationship between each network node and the faults between them. The detection result data is then associated with the IoT fault model. This allows for rapid identification of the fault point causing IoT anomalies based on the detection result data and the IoT fault model. In the event of abnormal IoT operation, the fault point is quickly located and alarm data is generated for the fault point. This allows maintenance personnel to quickly receive alarms, address faults and abnormalities as quickly as possible, and improve IoT operational stability.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0025] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A schematic diagram of the operating environment of the link status detection method for an IoT device shown in an embodiment of the present application;

[0028] Figure 2 This is a flowchart of a method for detecting the link status of an IoT device according to one embodiment of the present application;

[0029] Figure 3 This is a schematic diagram of an Internet of Things link model created in an embodiment of the present application;

[0030] Figure 4 This is a schematic diagram of an Internet of Things fault model of a network node according to an embodiment of the present application;

[0031] Figure 5 This is a schematic diagram of the structure of a link status detection system for an Internet of Things device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0033] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0034] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0035] In the description of this application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances. The singular forms "a", "said", and "the" used in this application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. The words "if" / "if" used herein can be interpreted as "at the time of" or "when" or "in response to determination". In addition, in the description of this application, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0036] See also Figure 1 , which is a schematic diagram of the application environment of the link status detection method of the Internet of Things device shown in the embodiment of the present application. Figure 1 As shown, the link status detection method of the Internet of Things device can be applied to the field of the Internet of Things, and its application environment includes a detection client 101 and a detection server 102. The detection client 101 and the detection server 102 interact through a wired or wireless network.

[0037] Among them, the detection client 101 refers to a client program located at the detection node for status detection. It can be a detection client program for independent settings, or it can be merged with other detection programs, or it can exist in the form of a plug-in. It is used to perform communication status detection and application status detection of network nodes at each layer to obtain detection result data.

[0038] The hardware pointed to by the detection client 101 is essentially a computer device. Specifically, it can be an electronic device, a personal computer, or other types of computer devices. The detection client 101 can access the Internet through a well-known network access method and establish a data communication link with the detection server 102.

[0039] The detection server 102 is a data server that can be responsible for further connecting related operation data servers and other servers that provide related support, so as to form a logically related service cluster to provide services for related terminal devices, such as Figure 1The detection server 102 is mainly used to set the Internet of Things link model and the Internet of Things fault model, receive the detection result data of the communication status detection and application status detection of each layer of network nodes uploaded by the detection client 101, associate the detection result data with the Internet of Things fault model, and determine the fault point causing the Internet of Things anomaly.

[0040] Furthermore, a status monitor 103 may be provided, and the status monitor 103 is used to communicate data with the detection server 102 and display the status of the corresponding network node in the Internet of Things link model according to the detection result data.

[0041] Example 1

[0042] The following will be combined with the Figure 2 , a link status detection method for an Internet of Things device provided in an embodiment of the present application is introduced in detail.

[0043] See also Figure 2 The embodiment of the present application provides a link status detection method for an IoT device, which is mainly run on the detection server 102 and includes the following steps:

[0044] Step S101: Setting an Internet of Things link model, wherein the Internet of Things link model includes a plurality of network nodes of the Internet of Things and connection relationships between the plurality of network nodes;

[0045] Step S102: performing communication status detection and application status detection of each layer of network nodes according to the IoT link model to obtain detection result data;

[0046] Step S103: constructing an Internet of Things fault model based on the Internet of Things link model, wherein the Internet of Things fault model includes each network node and the fault causal relationship between the network nodes;

[0047] Step S104: Correlating the detection result data with the IoT fault model to determine the fault point causing the IoT abnormality;

[0048] Step S105: Generate alarm data for the fault point according to the preset Internet of Things alarm rules, and output the alarm data.

[0049] The present invention discloses a link status detection method for IoT devices. By constructing an IoT link model based on a number of IoT network nodes and the connection relationships between them, the method detects the communication status and application status of each layer of network nodes to obtain detection result data. Furthermore, an IoT fault model is constructed corresponding to the IoT link model to obtain the causal relationship between each network node and the faults between them. The detection result data is then associated with the IoT fault model. This allows for rapid identification of the fault point causing IoT anomalies based on the detection result data and the IoT fault model. In the event of abnormal IoT operation, the fault point is quickly located and alarm data is generated for the fault point. This allows maintenance personnel to quickly receive alarms, address faults and abnormalities as quickly as possible, and improve IoT operational stability.

[0050] In step S101, an Internet of Things link model is set, wherein the Internet of Things link model includes a plurality of network nodes of the Internet of Things and connection relationships between the plurality of network nodes.

[0051] The network nodes are those composed of IoT devices themselves, and the connection relationships are those between any two of these network nodes. IoT network nodes primarily consist of various IoT devices, also known as IoT objects. Detectable IoT objects include cameras, printers, access control systems, various smart home appliances, and their control systems.

[0052] The connection relationship between the network nodes is represented by the multi-layer connection relationship between the nodes in the Internet of Things link model.

[0053] The IoT link model is as follows Figure 3 As shown, in the IoT link model, there are several network nodes of the IoT, which are network nodes formed by IoT devices themselves and are marked as nodes. Different nodes are node1, node2, ...

[0054] The connection relationship between the plurality of network nodes is a pairwise connection relationship between IoT nodes, which is marked as link. Figure 3 As shown, the direct connection between node0 and node1 is link1, the direct connection between node1 and node3 is link3, ...

[0055] Step S102: performing communication status detection and application status detection of network nodes at each layer according to the IoT link model to obtain detection result data;

[0056] Based on the IoT link model, two detections are performed on multi-layer nodes: ping status detection and protocol status detection, and the detection result data is returned to the detection server 102.

[0057] Specifically, the detection client 101 first determines a detection path based on the IoT link model. For example, to detect the status of network node 4, the detection path starts from node 0, node 0 --- node 1 --- node 2 --- node 4.

[0058] Initiate a detection task based on the detection path, including communication status detection and application status detection. For communication status detection, initiate packet detection from the starting node to all network nodes along the detection path, obtain the response from each network node to the packet detection, and determine the communication status of each network node. For application status detection, initiate a protocol detection on the IoT device at the endpoint node, and determine the application status of the endpoint node based on the protocol response of the IoT device.

[0059] Then, based on the response results of the communication status detection and application status detection, the detection result data is obtained.

[0060] Communication status detection uses packet probing, primarily sending ping packets, initiated from node 0, to each node along the detection path. For example, a packet probing task for node 4 requires initiating packet probing for node 1, node 2, and node 4, respectively. The communication status of each network node is determined based on its response to the packet probing.

[0061] Application status detection requires initiating protocol detection of IoT objects within the network node. Note that application status detection only detects the final network node; in the example above, only node 4 is detected. The communication protocol used for initiating application status detection varies depending on the type of IoT device. The application status is determined based on the IoT device's response to the corresponding communication protocol.

[0062] The detection client 101 obtains detection result data according to the response results of the communication status detection and the application status detection, and sends the detection result to the detection server 102.

[0063] Regarding step S103: constructing an Internet of Things fault model based on the Internet of Things link model.

[0064] The Internet of Things fault model includes each network node and the fault causal relationship between the network nodes. The fault causal relationship between the network nodes corresponds to the link relationship between the network nodes. The Internet of Things fault model is composed of the fault models of each network node. The fault model of a certain network node is automatically generated corresponding to its connection relationship with other network nodes. For example: the fault model of network node node4 is as follows: Figure 4 As shown, the fault model of network node node4 includes three main branches, which correspond to different detection result data: 1. If the communication status detection is normal and the application status detection is normal, then the network node node4 is normal; 2. If the communication status detection is normal and the application status detection is abnormal, then the network of network node node4 is normal and the application status is faulty; 3. If the communication status detection is abnormal and the application status detection is abnormal, then the network of network node node4 is abnormal; it has three sub-branches respectively: 3.1. If the upstream network nodes node1 and 2 are normal, but node4 is abnormal, then it corresponds to Link1 fault; 3.2. If the upstream network node node1 is normal, but node2 and 4 are abnormal, then it corresponds to Link2 fault; 3.3. If the upstream network nodes node1 and 2 are normal, and node4 is also normal, then it corresponds to Link4 fault.

[0065] In step S104, the detection result data is associated with the Internet of Things fault model to determine the fault point causing the Internet of Things abnormality.

[0066] According to the detection result data obtained in step S102 and the Internet of Things fault model obtained in step S103, the fault point data corresponding to the current detection result data can be quickly obtained.

[0067] For step S105: according to the preset Internet of Things alarm rules, generate alarm data for the fault point and output the alarm data.

[0068] By setting the alarm rules, the timing of the alarm can be easily adjusted so as to output the alarm data most efficiently. The alarm rules can be set according to the business requirements of the timing.

[0069] In one embodiment, the alarm rule includes: if the detection result data obtained twice consecutively are abnormal, it is determined to be an alarm, wherein the abnormality of the detection result data includes communication abnormality or application abnormality.

[0070] Generally speaking, a communication anomaly alarm indicates that an IoT device has been disconnected or offline, while an application anomaly alarm indicates that an IoT device has been replaced by another device.

[0071] The step of outputting alarm data may include: if the network node has communication abnormalities for 24 consecutive hours or application abnormalities for 4 consecutive hours, the output alarm level is high; if the network node has communication abnormalities for 4 consecutive hours or application abnormalities for 1 consecutive hour, the output alarm level is medium-high; if the network node has communication abnormalities for 1 consecutive hour or there is one application abnormality, the output alarm level is medium; if the network node has only one application abnormality, the output alarm level is low.

[0072] In other words, the default alarm rule is: if two consecutive detection results are abnormal, an alarm is generated. If an IoT device is offline for 24 consecutive hours or is replaced for four consecutive hours, the alarm level is high; if an IoT device is offline for four consecutive hours or is replaced for one consecutive hour, the alarm level is medium-high; if an IoT device is offline for one consecutive hour or is detected to have been replaced once, the alarm level is medium; if an IoT device is offline only once, the alarm level is low.

[0073] In order to more intuitively display the status of the corresponding network nodes in the Internet of Things link model, the status of the corresponding network nodes can also be displayed in real time by setting a status monitor 103.

[0074] The status monitor 103 is used to communicate data with the detection server 102, obtain and display the Internet of Things link model, and display the status of the corresponding network node in the Internet of Things link model according to the detection result data.

[0075] Among them, if the status of the network node is normal, the network node is displayed in a first color; if the status of the network node is communication abnormality, the network node is displayed in a second color; if the status of the network node is application abnormality, the network node is displayed in a third color.

[0076] For example, the IoT link model in the status monitor 103 can be configured to display the status of all IoT devices, and use three colors to represent their corresponding status values: green: device status is normal; orange: device is replaced; and gray: device is offline.

[0077] In the link status detection method for IoT devices of the present application, by deploying a detection client 101, the link status of IoT devices can be detected in real time. When an IoT device goes offline due to a network link failure, it can be discovered in a timely manner and an alarm can be issued. At the same time, the detection client 101 can also detect link delays, switch port status, etc. to achieve real-time perception of link status.

[0078] The detection client 101 initiates detection of the network node corresponding to the IoT device, including packet detection and protocol detection. The detection server 102 analyzes and diagnoses the data returned by the detection, including analysis algorithms and models, where the model includes the connection model and status fault model of the IoT device.

[0079] The link model of IoT devices reflects the direct connection relationship between IoT devices and can be maintained by maintenance personnel; the state fault model is used to diagnose which end caused the fault when the state is faulty. The fault model is automatically generated by the relationship of the link model.

[0080] The status monitor 103 is used to display the status data of the network nodes, can display alarms of abnormal status, and serve as an interface for configuring alarm rules.

[0081] Example 2

[0082] As another embodiment of the present application, a link status detection system for an Internet of Things device is provided.

[0083] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of a link status detection system for an Internet of Things device in this application. The link status detection system for an Internet of Things device includes:

[0084] The link module 501 is used to set an Internet of Things link model, wherein the Internet of Things link model includes a plurality of Internet of Things network nodes and the connection relationship between the plurality of network nodes;

[0085] The detection module 502 is used to detect the communication status and application status of each layer of network nodes according to the Internet of Things link model and obtain detection result data;

[0086] A fault module 503 is configured to construct an IoT fault model based on the IoT link model, wherein the IoT fault model includes each network node and a fault causal relationship between the network nodes;

[0087] A decision module 504 is configured to associate the detection result data with the IoT fault model to determine the fault point causing the IoT anomaly;

[0088] The advanced precision module 505 is used to generate alarm data for the fault point according to the preset Internet of Things alarm rules and output the alarm data.

[0089] It should be noted that the above embodiment 2 is an apparatus embodiment of the present application, which can be used to execute the method in embodiment 1 of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the method embodiment of the present application.

[0090] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the function selected in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 function selected in a box or multiple boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 steps for the function selected in a box or multiple boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0094] The memory may include non-permanent memory 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.

[0095] 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. The 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 disc read-only memory (CD-ROM), digital versatile disc (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.

[0096] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0097] The above are merely 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 modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A link status detection method for an Internet of Things device, characterized in that: The following steps are involved: Setting an Internet of Things link model, wherein the Internet of Things link model includes a plurality of network nodes of the Internet of Things and connection relationships between the plurality of network nodes; According to the IoT link model, perform communication status detection and application status detection of each layer of network nodes to obtain detection result data, including: Determining a detection path based on the IoT link model; Initiate detection tasks according to the detection path, including communication status detection and application status detection; Obtain detection result data based on the response results of communication status detection and application status detection; Based on the IoT link model, construct an IoT fault model, wherein the IoT fault model includes each network node and a fault causal relationship between the network nodes; Associating the detection result data with the IoT fault model to determine the fault point causing the IoT anomaly; According to the preset Internet of Things alarm rules, alarm data is generated for the fault point and the alarm data is output.

2. The link status detection method of an Internet of Things device according to claim 1, characterized in that: In the step of setting the Internet of Things link model, the network nodes are network nodes composed of the Internet of Things devices themselves, and the connection relationship is a connection relationship between any two of the network nodes.

3. The link status detection method of an Internet of Things device according to claim 1, characterized in that: The step of initiating a detection task according to the detection path includes: Initiate packet detection from the starting node to all network nodes on the detection path one by one; obtain the response of each network node to the packet detection and determine the communication status of each network node; Initiate a protocol probe on the IoT device at the endpoint node; and determine the application status of the endpoint node based on the protocol response of the IoT device.

4. The link status detection method of an Internet of Things device according to claim 1, characterized in that: According to the preset IoT alarm rules, the step of generating alarm data for the fault point includes: if the detection result data obtained twice in a row are abnormal, it is judged as an alarm, wherein the abnormality of the detection result data includes communication abnormality or application abnormality.

5. The link status detection method of the Internet of Things device according to claim 4, characterized in that: The step of outputting alarm data comprises: If a network node has communication anomalies for 24 consecutive hours or application anomalies for 4 consecutive hours, the output alarm level will be high; if a network node has communication anomalies for 4 consecutive hours or application anomalies for 1 consecutive hour, the output alarm level will be medium-high; if a network node has communication anomalies for 1 consecutive hour or there is a single application anomaly, the output alarm level will be medium; if a network node has only one application anomaly, the output alarm level will be low.

6. The link status detection method of an Internet of Things device according to claim 1, characterized in that: After the step of outputting the alarm data, the method further includes: The Internet of Things link model is displayed, and the status of the corresponding network node is displayed in the Internet of Things link model according to the detection result data.

7. The link status detection method of an Internet of Things device according to claim 1, characterized in that: The step of displaying the status of the corresponding network node in the Internet of Things link model according to the detection result data includes: If the status of the network node is normal, the network node is displayed in a first color; if the status of the network node is communication abnormality, the network node is displayed in a second color; if the status of the network node is application abnormality, the network node is displayed in a third color.

8. A link status detection system for an Internet of Things device, characterized in that: include: A link module, configured to set an Internet of Things link model, wherein the Internet of Things link model includes a plurality of network nodes of the Internet of Things and connection relationships between the plurality of network nodes; The detection module is used to detect the communication status and application status of each layer of network nodes according to the IoT link model and obtain detection result data, including: Determining a detection path based on the IoT link model; Initiate a detection task according to the detection path, including communication status detection and application status detection; Obtain detection result data based on the response results of communication status detection and application status detection; A fault module, configured to construct an Internet of Things fault model based on the Internet of Things link model, wherein the Internet of Things fault model includes each network node and a fault causal relationship between the network nodes; A judgment module, configured to associate the detection result data with the IoT fault model to determine the fault point causing the IoT anomaly; The advanced precision module is used to generate alarm data for the fault point according to the preset Internet of Things alarm rules and output the alarm data.

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