Device Linkage Control Method and System for Intelligent Internet of Things

Abstract

The present invention relates to the field of Internet of Things control technology, and discloses a device linkage control method and system for an intelligent Internet of Things, including: collecting Internet of Things terminal data, converting it into a standardized spatio-temporal matrix, performing feature decoupling processing to obtain a high-dimensional feature vector; querying device information to calculate the energy transfer efficiency and communication delay sensitivity of the device by using the high-dimensional feature vector, so as to construct a dynamic knowledge graph of Internet of Things devices; injecting fault signals into the dynamic knowledge graph, analyzing the abnormal propagation path of the device and the anti-interference ability of the device, performing a risk assessment on the Internet of Things device to obtain the device risk degree; constructing an initial linkage strategy for the Internet of Things device, identifying the feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies; performing a directed acyclic graph decomposition on the subset of feasible strategies, dynamically allocating resources to the decomposed task nodes, and executing the target task. The present invention can improve the coordination of device linkage control of an intelligent Internet of Things.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things control, and provides a method and system for device linkage control of an intelligent Internet of Things. Background Art

[0002] In today's era of rapid digital and intelligent development, the device linkage control of the intelligent Internet of Things is becoming increasingly widespread and crucial in various fields. In many scenarios such as industrial production, smart home, and intelligent transportation, through the interconnection and collaborative control between devices, an efficient and intelligent operation mode is realized, which has become the core demand for improving productivity, optimizing the quality of life, and improving traffic management. The device linkage control of the intelligent Internet of Things can integrate scattered devices into an organic whole, and automatically execute various complex tasks according to preset rules and real-time data interaction, greatly improving the automation degree and response speed of the system, and thus significantly enhancing the overall efficiency.

[0003] Currently, the realization of device linkage control of the intelligent Internet of Things mainly relies on traditional network architectures and control algorithms. Usually, a wired or wireless network is built to connect various devices such as sensors and actuators, and with the help of a centralized or distributed control system, the devices are linked according to pre-set logic and instructions. For example, in a smart home, smart appliances are connected through a gateway, and users can issue instructions on the mobile phone side to achieve device linkage. However, with the rapid growth of the number of Internet of Things devices and the increasing complexity of application scenarios, the traditional methods have exposed many drawbacks. On the one hand, when facing the access of a large number of devices, the traditional network architecture is prone to problems such as network congestion and excessive latency, seriously affecting the real-time performance and stability of linkage control; on the other hand, fixed control algorithms are difficult to adapt to the dynamically changing environment and diverse user needs, resulting in the inability of the device linkage control of the intelligent Internet of Things to flexibly and accurately achieve efficient cooperation between devices. Summary of the Invention

[0004] The present invention provides a method and system for device linkage control of an intelligent Internet of Things, and its main purpose is to improve the coordination of device linkage control of the intelligent Internet of Things.

[0005] To achieve the above object, the method for device linkage control of the intelligent Internet of Things provided by the present invention includes:

[0006] Collect Internet of Things terminal data, convert the Internet of Things terminal data into a standardized spatio-temporal matrix, perform feature decoupling processing on the standardized spatio-temporal matrix, and obtain a high-dimensional feature vector;

[0007] Query the device information of the Internet of Things device corresponding to the Internet of Things terminal. Based on the device information, use the high-dimensional feature vector to calculate the energy transfer efficiency and communication delay sensitivity of the Internet of Things device, obtain device status association information, and construct a dynamic knowledge graph of the Internet of Things device based on the device status association information;

[0008] Inject fault signals into the dynamic knowledge graph to obtain fault simulation information. Based on the fault simulation information, analyze the abnormal propagation path and device anti-interference ability of the Internet of Things device. Based on the abnormal propagation path and the device anti-interference ability, conduct a risk assessment on the Internet of Things device to obtain the device risk level;

[0009] Query the current energy consumption and task information of the Internet of Things terminal. Based on the device risk level, the current energy consumption, and the task information, construct an initial linkage strategy for the Internet of Things device, perform digital twinning on the Internet of Things device to obtain a simulated Internet of Things device, and use the simulated Internet of Things device to identify the feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies;

[0010] Decompose the subset of feasible strategies into a directed acyclic graph to obtain decomposed task nodes. Obtain the resource information of the Internet of Things terminal to construct a resource matching matrix for the Internet of Things device, and use the resource matching matrix to perform dynamic resource allocation on the decomposed task nodes to obtain target tasks, and execute the target tasks to achieve the linkage control of the Internet of Things device.

[0011] Optionally, the conversion of the Internet of Things terminal data into a standardized spatio-temporal matrix includes:

[0012] Perform multi-modal data fusion on the Internet of Things terminal data to obtain fused data;

[0013] Perform spatio-temporal mapping on the fused data to obtain mapped data;

[0014] Perform spatio-temporal calibration on the mapped data to obtain calibrated data;

[0015] Use a preset spatio-temporal resolution to resample the calibrated data to obtain target data;

[0016] Construct a spatio-temporal matrix of the target data to obtain a standardized spatio-temporal matrix.

[0017] Optionally, the feature decoupling process of the standardized spatio-temporal matrix to obtain a high-dimensional feature vector includes:

[0018] Perform complex domain conversion on the standardized spatio-temporal matrix to obtain a conversion matrix;

[0019] Extract the matrix features of the conversion matrix, perform independent component analysis on the matrix features, and obtain the decomposed features;

[0020] Extract the device attribute features and environmental attribute features related to the Internet of Things from the decomposed features to obtain refined features;

[0021] Perform wavelet decomposition on the refined features to obtain wavelet decomposition features;

[0022] Perform feature fusion on the wavelet decomposition features to obtain fusion features;

[0023] Calculate the mutual information gain of the fusion features;

[0024] Based on the mutual information gain, perform feature screening on the fusion features to obtain a high-dimensional feature vector.

[0025] Optionally, the calculation of the mutual information gain of the fusion features includes:

[0026] Use the following formula to calculate the mutual information gain of the fusion features:

[0027] ;

[0028] ;

[0029] ;

[0030] Where, represents the mutual information gain, X represents the feature set of the fusion features, represents the i-th feature of the fusion features, Y represents the corresponding target variable, (Y) represents the entropy of Y, represents the conditional entropy of Y under the condition of knowing the feature , represents the probability that the target variable Y takes the value y, represents the probability that the feature takes the value x, represents under x, Y the conditional probability of y.

[0031] Optionally, based on the device information, using the high-dimensional feature vector, calculating the energy transfer efficiency and communication delay sensitivity of the Internet of Things device to obtain device status correlation information includes:

[0032] Query the device transmission power and device reception power in the device information;

[0033] Based on the device transmission power and the device reception power, calculate the energy transfer efficiency of the Internet of Things device using the following formula:

[0034] ;

[0035] where E represents the energy transfer efficiency, represents the device reception power of the j-th device in the Internet of Things device, represents the device transmission power of the i-th device in the Internet of Things device;

[0036] Query the communication delay in the device information, analyze the communication load of the Internet of Things device using the high-dimensional feature vector, and based on the communication delay and the communication load, calculate the communication delay sensitivity of the Internet of Things device using the following formula:

[0037] ;

[0038] where, represents the communication delay sensitivity represents the communication delay from Internet of Things device i to Internet of Things device j, represents the communication load between Internet of Things device i and j;

[0039] Based on the energy transfer efficiency and the communication delay sensitivity, identify the device status association information of the Internet of Things device.

[0040] Optionally, constructing the dynamic knowledge graph of the Internet of Things device based on the device status association information includes:

[0041] Extract the entity information, relationship information, and attribute information in the device status association information to obtain knowledge data;

[0042] Perform entity alignment on the knowledge data to obtain entity alignment data;

[0043] Perform knowledge merging on the entity alignment data to obtain knowledge merging data;

[0044] Use the knowledge merging data to construct the dynamic knowledge graph of the Internet of Things device.

[0045] Optionally, analyzing the abnormal propagation path and device anti-interference ability of the Internet of Things device based on the fault simulation information includes:

[0046] Use the dynamic knowledge graph of the Internet of Things device to construct the propagation model of the Internet of Things device;

[0047] Set the propagation attributes of the propagation model to obtain the target propagation model;

[0048] Perform time series analysis on the fault simulation information to obtain the abnormal propagation path;

[0049] Query the performance index data and recovery time of the IoT device during fault simulation;

[0050] Perform index quantization processing on the performance index data and the recovery time to obtain quantization indexes;

[0051] Analyze the anti-interference ability of the IoT device using the quantization indexes.

[0052] Optionally, constructing the initial linkage strategy of the IoT device based on the device risk degree, the current energy consumption, and the task information includes:

[0053] Identify the risk level of the device risk degree;

[0054] Based on the risk level, construct a risk mitigation strategy for the IoT device;

[0055] Identify the risk type of the IoT device;

[0056] Based on the risk type, construct a risk prevention and control strategy for the IoT device;

[0057] Based on the current energy consumption, construct a resource allocation strategy for the networked device;

[0058] According to the task information, construct a collaborative operation strategy for the IoT device;

[0059] Based on the risk mitigation strategy, the risk prevention and control strategy, the resource allocation strategy, and the collaborative operation strategy, construct the initial linkage strategy of the IoT device.

[0060] Optionally, the directed acyclic graph decomposition of the feasible strategy subset to obtain decomposed task nodes includes:

[0061] Perform element decomposition on the feasible strategy subset to obtain task node elements;

[0062] Analyze the task execution relationship of the task node elements;

[0063] Based on the task execution relationship, construct a directed acyclic graph of the feasible strategy subset;

[0064] Perform node merging optimization on the directed acyclic graph to obtain an optimized directed acyclic graph;

[0065] Extract the element nodes of the optimized directed acyclic graph to obtain decomposed task nodes.

[0066] To solve the above problems, the present invention also provides a device linkage control system for an intelligent Internet of Things, and the system includes:

[0067] A data processing module, configured to collect Internet of Things terminal data, convert the Internet of Things terminal data into a standardized spatio-temporal matrix, perform feature decoupling processing on the standardized spatio-temporal matrix, and obtain a high-dimensional feature vector;

[0068] A dynamic knowledge graph construction module, configured to query device information of the Internet of Things device corresponding to the Internet of Things terminal, and based on the device information, use the high-dimensional feature vector to calculate the energy transfer efficiency and communication delay sensitivity of the Internet of Things device, obtain device status association information, and based on the device status association information, construct a dynamic knowledge graph of the Internet of Things device;

[0069] A device anti-interference analysis module, configured to inject a fault signal into the dynamic knowledge graph to obtain fault simulation information, analyze the abnormal propagation path and device anti-interference ability of the Internet of Things device based on the fault simulation information, and perform a risk assessment on the Internet of Things device based on the abnormal propagation path and the device anti-interference ability to obtain a device risk degree;

[0070] A control strategy analysis module, configured to query the current energy consumption and task information of the Internet of Things terminal, construct an initial linkage strategy for the Internet of Things device based on the device risk degree, the current energy consumption and the task information, perform digital twin on the Internet of Things device to obtain a simulated Internet of Things device, and use the simulated Internet of Things device to identify feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies;

[0071] An intelligent linkage control module, configured to decompose the subset of feasible strategies into directed acyclic graph decomposition task nodes, obtain resource information of the Internet of Things terminal, construct a resource matching matrix for the Internet of Things device, perform dynamic resource allocation on the decomposition task nodes using the resource matching matrix to obtain a target task, and execute the target task to achieve linkage control of the Internet of Things device.

[0072] Compared with the problems described in the background art, in the embodiments of the present invention, first, by collecting Internet of Things (IoT) terminal data, including multi-modal sensor data such as temperature and pressure, a comprehensive and rich data foundation is provided, and the feature decoupling process is performed on the connected terminal data to improve the data quality, thereby avoiding the misleading of subsequent analysis by interference factors and making the analysis results more reliable; further, the present invention helps users deeply understand the operating status and performance bottlenecks of devices through steps of device status analysis and knowledge graph construction, provides a basis for subsequent optimization and management, and can clearly display the overall architecture and operation dynamics of IoT devices, thereby helping users quickly understand the mutual relationships and impacts between devices; the present invention can understand the fault chain reaction and potential problems in a virtual environment through fault simulation and risk assessment of IoT devices, so as to prevent faults in advance and reduce the overall risk level; further, the present invention reduces the time and cost of actual strategy attempts and improves the success rate of device linkage control by constructing an initial control strategy for IoT devices and performing simulation verification; furthermore, the present invention ensures that tasks are executed in a suitable resource environment by decomposing tasks and allocating resources for the initial linkage strategy, improves the task execution efficiency and quality, and implements device linkage control after the task resource configuration is completed, thereby improving the coordination of device linkage control in intelligent IoT. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic flowchart of a method for device linkage control of an intelligent Internet of Things provided by an embodiment of the present invention;

[0074] Figure 2 It is a schematic diagram of a module for implementing the method for device linkage control of the intelligent Internet of Things provided by an embodiment of the present invention.

[0075] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0077] The embodiments of the present application provide a method for device linkage control of an intelligent Internet of Things. The execution subject of the method for device linkage control of the intelligent Internet of Things includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for device linkage control of the intelligent Internet of Things can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0078] Embodiment 1:

[0079] Referring to Figure 1 as shown in the figure, it is a schematic flowchart of a device linkage control method for an intelligent Internet of Things provided by an embodiment of the present invention. In this embodiment, the device linkage control method for the intelligent Internet of Things includes:

[0080] S1. Collect Internet of Things terminal data, convert the Internet of Things terminal data into a standardized spatio-temporal matrix, and perform feature decoupling processing on the standardized spatio-temporal matrix to obtain a high-dimensional feature vector.

[0081] In the embodiment of the present invention, collecting the Internet of Things terminal data can provide a comprehensive and rich data basis for subsequent data analysis and processing.

[0082] Among them, the Internet of Things terminal data refers to data of multi-modal sensors such as temperature, pressure, motion trajectory, energy consumption, and communication signal strength, which can be obtained through edge computing nodes deployed on the Internet of Things terminals.

[0083] Furthermore, in the embodiment of the present invention, converting the Internet of Things terminal data into a standardized spatio-temporal matrix can eliminate the spatio-temporal deviation between devices, enabling data from different devices to be compared and analyzed under the same spatio-temporal reference.

[0084] Among them, the standardized spatio-temporal matrix refers to a matrix used to represent the distribution and characteristics of data in the Internet of Things field in the time and space dimensions.

[0085] As an embodiment of the present invention, converting the Internet of Things terminal data into a standardized spatio-temporal matrix includes: performing multi-modal data fusion on the Internet of Things terminal data to obtain fusion data, performing spatio-temporal mapping on the fusion data to obtain mapped data, performing spatio-temporal calibration on the mapped data to obtain calibrated data, resampling the calibrated data using a preset spatio-temporal resolution to obtain target data, and constructing a spatio-temporal matrix of the target data to obtain a standardized spatio-temporal matrix.

[0086] Among them, the preset spatio-temporal resolution refers to the fineness of sampling data in the time and space dimensions during the process of converting Internet of Things terminal data into a standardized spatio-temporal matrix.

[0087] Optionally, the fusion data can be obtained by fusing the IoT terminal data from different types of sensors such as temperature, humidity, and location using a multi-modal fusion model based on deep learning. The mapping data can be obtained by mapping the fusion data using a coordinate transformation algorithm. The calibration data can be obtained by performing time calibration on the mapping data using high-precision atomic clock synchronization technology and then performing space calibration on the time-calibrated mapping data using ultrasonic ranging technology. The target data can be obtained by resampling using linear interpolation according to a preset spatio-temporal resolution. The standardized spatio-temporal matrix can be obtained by constructing a matrix of all zeros and then filling the corresponding values of the target data into the all-zero matrix with the time of the target data as the row index and the spatial position as the column index.

[0088] Furthermore, in the embodiment of the present invention, by performing feature decoupling processing on the standardized spatio-temporal matrix, the high-dimensional feature vector can remove data noise to improve data quality, thereby avoiding the misleading of subsequent analysis by interference factors and making the analysis results more reliable.

[0089] As an embodiment of the present invention, the process of performing feature decoupling processing on the standardized spatio-temporal matrix to obtain a high-dimensional feature vector includes: performing complex domain conversion on the standardized spatio-temporal matrix to obtain a conversion matrix, extracting the matrix features of the conversion matrix, performing independent component analysis on the matrix features to obtain decomposition features, extracting the device attribute features and environmental attribute features related to the Internet of Things from the decomposition features to obtain refined features, performing wavelet decomposition on the refined features to obtain wavelet decomposition features, performing feature fusion on the wavelet decomposition features to obtain fusion features, calculating the mutual information gain of the fusion features, and performing feature screening on the fusion features based on the mutual information gain to obtain a high-dimensional feature vector.

[0090] Among them, the device attribute features refer to the features that can reflect the inherent characteristics of the IoT device itself, such as device model, hardware parameters, etc. The environmental attribute features refer to the features that reflect the external environmental conditions where the device is located, such as temperature, humidity, etc. The mutual information gain refers to the degree of reduction in the uncertainty of the target information after knowing a certain feature. For example, in the IoT scenario, it is the correlation measure between the fusion features and the target information such as the device state (normal / fault, etc.). If a certain feature is highly correlated with device failure, after knowing the value of this feature, the uncertainty of the device failure state is greatly reduced, and the mutual information gain of this feature is high, which can be used to judge the value of the feature for analyzing the target information. The high-dimensional feature vector refers to the key features selected from the original standardized spatio-temporal matrix after a series of complex processes.

[0091] Optionally, the conversion matrix can be obtained by applying Fourier transform technology to convert the standardized spatio-temporal matrix from the real number domain to the complex number domain. The matrix features can be obtained by using matrix analysis methods such as singular value decomposition to extract key features such as eigenvalues and eigenvectors of the conversion matrix. The decomposed features can be obtained by performing dimensionality reduction on the matrix features through principal component analysis and then removing redundant information. The refined features can be obtained by screening device attribute features (such as device model, hardware parameter-related features) and environmental attribute features (such as environmental factors related to temperature, humidity, etc.) from the dimensionality-reduced features according to the characteristics of IoT devices and the environment. The wavelet decomposition features can be obtained by decomposing the refined features at different frequency scales using wavelet transform technology. The fused features can be obtained by fusing the wavelet decomposition features in a way such as splicing or weighted summation. The high-dimensional feature vector can be obtained by screening features with a gain higher than a threshold according to a preset mutual information gain threshold, and the preset mutual information gain threshold can be set to 0.1, which needs to be set in combination with actual applications.

[0092] Further, as another optional embodiment of the present invention, calculating the mutual information gain of the fused features includes: calculating the mutual information gain of the fused features using the following formula:

[0093] ;

[0094] ;

[0095] ;

[0096] where, represents the mutual information gain, X represents the feature set of the fused features, represents the i-th feature of the fused features, Y represents the corresponding target variable, (Y) represents the entropy of Y, represents the conditional entropy of Y given the known feature , represents the probability that the target variable Y takes the value y, represents the probability that the feature takes the value x, represents that under x, Y the conditional probability of taking the value y.

[0097] It should be further noted that the core principle of the above mutual information gain calculation formula is based on information theory, aiming to quantify the contribution degree of the fused features to the information of the target variable, reflecting the dependence relationship between the features and the target variable from a statistical perspective. By calculating the mutual information gain through this formula, the features that are most explanatory for the target variable can be effectively screened out. In the scenario of Internet of Things device analysis, such as in the device fault prediction task, the fused features that are closely related to the device fault state can be accurately found. Sorting a large number of fused features according to their contribution degrees to the target variable helps to remove redundant features and highlight key features, providing strong support for building an efficient device analysis model and improving the accuracy of analysis and prediction.

[0098] S2. Query the device information of the Internet of Things device corresponding to the Internet of Things terminal. Based on the device information and using the high-dimensional feature vector, calculate the energy transfer efficiency and communication delay sensitivity of the Internet of Things device to obtain device status correlation information. Based on the device status correlation information, construct a dynamic knowledge graph of the Internet of Things device.

[0099] In the embodiment of the present invention, querying the device information of the Internet of Things device corresponding to the Internet of Things terminal can provide necessary data support for comprehensively understanding the Internet of Things device.

[0100] Among them, the device information refers to various information including the model, specifications, functional characteristics, communication protocol, etc. of the device.

[0101] Optionally, the device information can be obtained through common communication protocols of the Internet of Things, such as MQTT.

[0102] Furthermore, in the embodiment of the present invention, by calculating the energy transfer efficiency and communication delay sensitivity of the Internet of Things device based on the device information and using the high-dimensional feature vector to obtain device status correlation information, important index data for measuring the performance of the Internet-connected device can be obtained, which can further help users deeply understand the operating state and performance bottleneck of the device and provide a basis for subsequent optimization and management.

[0103] Among them, the energy transfer efficiency refers to the effective utilization degree in the process of energy input and output during the operation of the Internet of Things device, and the communication delay sensitivity refers to the sensitivity degree of the Internet of Things device to the delay in the communication process.

[0104] As an embodiment of the present invention, calculating the energy transfer efficiency and communication delay sensitivity of the Internet of Things device based on the device information and using the high-dimensional feature vector to obtain device status correlation information includes: querying the device transmit power and device receive power in the device information, and based on the device transmit power and the device receive power, using the following formula to calculate the energy transfer efficiency of the Internet of Things device:

[0105] ;

[0106] where E represents the energy transfer efficiency, represents the device receiving power of the j-th device in the Internet of Things device, represents the device transmitting power of the i-th device in the Internet of Things device;

[0107] Query the communication delay in the device information, analyze the communication load of the Internet of Things device using the high-dimensional feature vector, and calculate the communication delay sensitivity of the Internet of Things device based on the communication delay and the communication load using the following formula:

[0108] ;

[0109] where, represents the communication delay sensitivity represents the communication delay from Internet of Things device i to Internet of Things device j, represents the communication load between Internet of Things devices i and j;

[0110] Identify the device status association information of the Internet of Things device based on the energy transfer efficiency and the communication delay sensitivity.

[0111] Furthermore, in the embodiment of the present invention, by constructing the dynamic knowledge graph of the Internet of Things device based on the device status association information, the overall architecture and operation dynamics of the Internet of Things device can be clearly displayed, thereby helping users quickly understand the mutual relationships and influences between devices.

[0112] Among them, the dynamic knowledge graph refers to a structured knowledge representation form that can reflect the status and mutual relationships of Internet of Things devices in real time as time changes. It organizes information in the form of a graph, where nodes represent various entities in the Internet of Things, such as devices, environmental factors, and users, etc., and edges represent the relationships between entities, such as communication connections between devices, energy transfer relationships, and influence relationships between devices and environmental factors, etc.

[0113] As an embodiment of the present invention, constructing the dynamic knowledge graph of the Internet of Things device based on the device status association information includes: extracting entity information, relationship information, and attribute information from the device status association information to obtain knowledge data, performing entity alignment on the knowledge data to obtain entity alignment data, performing knowledge merging on the entity alignment data to obtain knowledge merging data, and using the knowledge merging data to construct the dynamic knowledge graph of the Internet of Things device.

[0114] Among them, the entity information refers to the unique identifier of the device, such as device ID, serial number, etc. The relationship information refers to the connections between IoT device entities, such as communication relationships (e.g., there is a data transmission link between device A and device B), information such as the environmental temperature change affecting the operating state of the device, etc. The attribute information refers to the characteristics used to describe entities and relationships in detail, such as the computing power, storage capacity, and communication bandwidth of the device, etc. The dynamic knowledge graph refers to a structured knowledge representation form that can reflect the state of IoT devices and their mutual relationships changing over time in real time.

[0115] Optionally, the knowledge data uses text extraction technology in natural language processing to parse the device status association information. It is obtained by identifying device entities, relationships between devices (such as communication relationships, energy transfer relationships), and attributes of devices and relationships (such as device models, energy transfer efficiency values) from data such as energy transfer efficiency and communication delay sensitivity. The entity alignment data can adopt a method combining rule matching and machine learning, such as the cosine similarity algorithm combined with clustering technology, to compare device entities in knowledge data from different sources and unify different expressions referring to the same device. For example, "device A" and "the device numbered 001 produced by a certain factory" are identified as the same entity. The knowledge merging data can use graph database-related technologies, such as the merge operation of Neo4j. Integrate the data after entity alignment, summarize the relationships and attributes of the same entity, remove duplicate information, and fuse the scattered knowledge into a complete knowledge system. The dynamic knowledge graph can be based on the knowledge merging data and use a knowledge graph construction tool (such as Stardog). A knowledge graph reflecting the state of IoT devices and their mutual relationships is obtained by taking device entities as nodes, relationships as edges, and attributes as descriptions of nodes and edges.

[0116] S3. Inject a fault signal into the dynamic knowledge graph to obtain fault simulation information. Based on the fault simulation information, analyze the abnormal propagation path and device anti-interference ability of the IoT device. Based on the abnormal propagation path and the device anti-interference ability, conduct a risk assessment on the IoT device to obtain the device risk degree.

[0117] In the embodiment of the present invention, by injecting a fault signal into the dynamic knowledge graph to obtain fault simulation information, the chain reactions that may be caused by various faults in the IoT device can be comprehensively and systematically understood in a virtual environment, and potential problems and hidden dangers can be discovered in advance.

[0118] Among them, the fault simulation information can refer to historical fault data to analyze the patterns and rules of fault occurrence. Through data mining algorithms such as association rule mining, the frequently co-occurring fault characteristics and related device states are found. Then, in the dynamic knowledge graph, the simulation information is injected into the corresponding entities (devices, environments, etc.) and relationships according to these rules.

[0119] In an embodiment of the present invention, by analyzing the abnormal propagation path and device anti-interference ability of the IoT device based on the fault simulation information, the vulnerability and fault propagation rules of the IoT system can be deeply understood to help users focus on key devices during IoT linkage control to reduce the fault probability of the devices.

[0120] Among them, the abnormal propagation path refers to the diffusion path when, in the complex network composed of IoT devices, when a certain device fails or is in an abnormal state, this abnormality spreads to other related devices along the connections and mutual relationships between the devices. The device anti-interference ability refers to the ability to maintain its normal operation, ensure stable performance, and accurately execute tasks in the face of interference such as the aging of the device's own hardware, software vulnerabilities, electromagnetic interference, changes in environmental temperature and humidity, and network attacks.

[0121] As an embodiment of the present invention, analyzing the abnormal propagation path and device anti-interference ability of the IoT device based on the fault simulation information includes: using the dynamic knowledge graph of the IoT device to construct a propagation model of the IoT device, setting propagation attributes for the propagation model to obtain a target propagation model, performing time series analysis on the fault simulation information to obtain the abnormal propagation path, querying the performance index data and recovery time of the IoT device during fault simulation, performing index quantization processing on the performance index data and the recovery time to obtain quantization indexes, and using the quantization indexes to analyze the device anti-interference ability of the IoT device.

[0122] Among them, the propagation model refers to a graph structure-based model used to simulate the process of abnormal propagation between IoT devices. The propagation attribute refers to the characteristic description of the connection edges in the propagation model, such as propagation probability and propagation delay. The performance index data refers to various data used to measure the operating state and performance of the device during the fault simulation of the IoT device, such as data transmission rate and packet loss rate.

[0123] Optionally, the propagation model can be based on the dynamic knowledge graph of IoT devices. Using the device nodes in the graph as model nodes and the relationships between devices as connection edges, a propagation model can be constructed using graph theory algorithms such as Dijkstra's algorithm. The target propagation model can be obtained by setting propagation attributes such as propagation probability and propagation delay for the edges in the propagation model based on the fault simulation scenario. The abnormal propagation path can be determined by using the ARIMA model to analyze the propagation trajectory of the fault signal among devices over time according to the chronological order of fault occurrences and combining with the propagation rules of the target propagation model. The quantization index can be obtained by quantifying the performance index data and the recovery time using normalization techniques. The anti-interference ability of the device can be evaluated by constructing an evaluation model using a linear regression model with the quantization index as the input to analyze the degree of performance impact and recovery ability of the device when facing fault interference.

[0124] In an embodiment of the present invention, by using the abnormal propagation path and the anti-interference ability of the device, a risk assessment is performed on the IoT device to obtain the device risk degree, so as to identify devices with higher risks in the system, monitor, maintain, and upgrade these devices, and prevent the occurrence of faults in advance to reduce the overall risk level during the linkage control of system devices.

[0125] Among them, the device risk degree is a numerical index that comprehensively quantifies and evaluates the probability of a fault occurring in an IoT device in the system and the degree of fault impact.

[0126] Optionally, the device risk degree is first determined based on the abnormal propagation path to determine the scope of influence and propagation speed caused by a device fault, such as how many production line links are affected after a key device fails. Then, in combination with the anti-interference ability of the device, such as the performance fluctuation of the device in an electromagnetic interference environment. It is obtained by weighted calculation of these two factors.

[0127] S4. Query the current energy consumption and task information of the IoT terminal. Based on the device risk degree, the current energy consumption, and the task information, construct an initial linkage strategy for the IoT device, perform digital twin on the IoT device to obtain a simulated IoT device, and use the simulated IoT device to identify the feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies.

[0128] In an embodiment of the present invention, querying the current energy consumption and task information of the IoT terminal can help users evaluate the device operation cost and energy efficiency and understand whether the device is in a high-energy consumption abnormal state.

[0129] Among them, the current energy consumption refers to the energy value consumed by the Internet of Things terminal during the current operation process, which can be collected through the built-in energy consumption monitoring module or relevant sensors of the device. For example, the power consumption data recorded by the smart meter in real time. The task information refers to the details of the tasks that the Internet of Things terminal is currently executing or about to execute, such as task types (data collection, transmission, analysis, etc.), task priorities, and estimated completion times, which can be obtained by querying the device task scheduling system.

[0130] In the embodiment of the present invention, by constructing the initial linkage strategy of the Internet of Things device based on the device risk degree, the current energy consumption, and the task information, it is possible to balance device risks, energy consumption, and task requirements, strive to ensure the completion of tasks while improving the overall reliability and energy utilization efficiency of the device, reducing the system operation cost and the probability of faults, and enhancing the comprehensive performance of the Internet of Things system.

[0131] As an embodiment of the present invention, constructing the initial linkage strategy of the Internet of Things device based on the device risk degree, the current energy consumption, and the task information includes: identifying the risk level of the device risk degree, constructing a risk mitigation strategy for the Internet of Things device based on the risk level, identifying the risk type of the Internet of Things device, constructing a risk prevention and control strategy for the Internet of Things device based on the risk type, constructing a resource allocation strategy for the Internet-connected device based on the current energy consumption, constructing a collaborative operation strategy for the Internet of Things device according to the task information, and constructing the initial linkage strategy of the Internet of Things device based on the risk mitigation strategy, the risk prevention and control strategy, the resource allocation strategy, and the collaborative operation strategy.

[0132] Among them, the risk level refers to different levels divided according to the device risk degree, which is used to intuitively reflect the high and low degree of the device failure risk. The risk mitigation strategy refers to the strategy for reducing the impact of faults formulated for high-risk level devices. The risk type refers to different cause categories that cause the Internet of Things device to malfunction or operate abnormally, such as hardware failures, network failures, and environmental factors. The risk prevention and control strategy refers to the prevention and response strategies formulated for different risk types. For example, for the risk of hardware failures, a mechanism for regular hardware detection and replacement can be established, and high-quality hardware devices can be used to improve reliability. For software failures, software patches can be updated in a timely manner, and vulnerability scans and repairs can be performed. The allocation strategy refers to the strategy for reasonably allocating energy, computing resources, etc. according to the current energy consumption and task priorities of the Internet of Things device. The collaborative operation strategy refers to the strategy for coordinating each device to execute tasks in an orderly manner based on the task information of the Internet of Things device.

[0133] Optionally, the risk level can be divided by setting a risk degree threshold range for the device risk degree, such as low, medium, and high. For high-risk level devices, redundant backup technology can be used to prepare standby devices. When the risk of the main device is high, it can be switched in time to reduce the impact of failures and achieve risk mitigation. The risk type can be identified based on fault simulation information and historical data using data mining algorithms, such as hardware failures, network attacks, etc. Then, according to the risk type, for example, for network attack risks, prevention and control strategies can be constructed using technologies such as firewalls and encrypted communications. The resource allocation strategy can be constructed by analyzing the current energy consumption data. If the energy consumption is too high, dynamic voltage and frequency adjustment technology can be used to reduce the operating frequency of the device to reduce energy consumption. At the same time, energy resources are allocated according to the device priority. The collaborative operation strategy can analyze the task information, clarify the task process and dependencies, and then use task scheduling algorithms, such as priority scheduling, to arrange the order of devices to execute tasks and coordinate the collaborative operation between devices to construct it. The initial linkage strategy can use system integration technology to make each strategy cooperate with each other to form.

[0134] In the embodiment of the present invention, by performing digital twin on the Internet of Things device, the obtained simulated Internet of Things device can provide a safe and controllable virtual environment for testing and verifying the initial linkage strategy.

[0135] Optionally, the digital twin of the Internet of Things device to obtain a simulated Internet of Things device can be realized through operation of multi-physics field simulation software, such as COMSOL Multiphysics.

[0136] In the embodiment of the present invention, by using the simulated Internet of Things device to identify the feasible strategies in the initial linkage strategy, the obtained feasible strategy subset can quickly find practical and feasible solutions from many initially designed linkage strategies, reducing the time and cost of attempting strategies on actual Internet of Things devices, thereby improving the success rate of strategy implementation.

[0137] Optionally, the feasible strategy subset can use optimization algorithms such as genetic algorithms and particle swarm optimization algorithms. Encode each strategy in the initial linkage strategy as an individual in the algorithm, and use device performance, energy consumption, task completion rate, etc. as the evaluation indicators of the fitness function. Through algorithm iteration and optimization, let the simulated Internet of Things device find the feasible strategy that can make the fitness function value optimal, that is, the most in line with the actual needs, and form it in the continuous attempt.

[0138] S5. Decompose the feasible policy subset into a directed acyclic graph to obtain decomposed task nodes, acquire the resource information of the IoT terminal, construct a resource matching matrix for the IoT device, perform dynamic resource allocation on the decomposed task nodes using the resource matching matrix to obtain a target task, and execute the target task to achieve the linkage control of the IoT device.

[0139] In the embodiment of the present invention, by decomposing the feasible policy subset into a directed acyclic graph to obtain decomposed task nodes, the task dependency relationship can be clarified in the case of numerous IoT devices and complex tasks, thereby avoiding task execution conflicts and improving the execution efficiency.

[0140] As an embodiment of the present invention, decomposing the feasible policy subset into a directed acyclic graph to obtain decomposed task nodes includes: decomposing the elements of the feasible policy subset to obtain task node elements, analyzing the task execution relationship of the task node elements, constructing a directed acyclic graph of the feasible policy subset based on the task execution relationship, performing node merging optimization on the directed acyclic graph to obtain an optimized directed acyclic graph, and extracting the element nodes of the optimized directed acyclic graph to obtain decomposed task nodes.

[0141] Among them, the task execution relationship refers to a method for describing the sequence and dependency relationship of task node elements in terms of time and logic. The directed acyclic graph is a special graph structure composed of vertices (i.e., task node elements) and directed edges. In the analysis of IoT device policies, vertices represent individual task units, such as device A performing a certain operation and device B performing data processing. Directed edges reflect the task execution relationship, and their direction represents the sequence of tasks, pointing from the task node that is executed first to the task node that is executed later. For example, if device B can only start after device A completes the task, a directed edge is drawn from the task node of device A to the task node of device B.

[0142] Optionally, each policy in the feasible policy subset can be regarded as a whole by the task node element, and using text parsing technology, the policy content is split according to the task execution steps. For example, if the policy includes the collaborative work process between devices, it can be split into task nodes such as device A executing a task and device B receiving data. The directed acyclic graph can be constructed by analyzing the sequence and dependency relationship of task node elements to determine the direction of the directed edge, and then applying a graph construction algorithm with task node elements as vertices and task execution relationships as directed edges. For the optimized directed acyclic graph, multiple task node elements with the same predecessor and successor nodes in the directed acyclic graph can be merged using a graph optimization algorithm. For example, if the two consecutive tasks of device c have the same impact on subsequent tasks, they can be merged into one node to simplify the graph structure.

[0143] In the embodiment of the present invention, by obtaining the resource information of the Internet of Things terminal to construct the resource matching matrix of the Internet of Things device, the resource status of each device and the resource requirements of each task can be clearly understood, so as to achieve the precise matching of resources and tasks.

[0144] Among them, the resource matching matrix refers to a two-dimensional matrix used to intuitively present the corresponding relationship between the resource status of Internet of Things terminal devices and the task resource requirements, and can be constructed through the NumPy library in Python.

[0145] Furthermore, in the embodiment of the present invention, by using the resource matching matrix to perform dynamic resource allocation on the decomposed task nodes to obtain the target task, it can ensure that each task can be executed in the most suitable resource environment, improving the efficiency and quality of task execution.

[0146] Optionally, the target task can first clarify the resource requirements of the decomposed task nodes. Through the resource matching matrix, compare the resource status of each device, and according to the task priority and resource margin, reasonably allocate the device resources to the task nodes. Then, monitor the task execution process in real time. If the resources are insufficient or the task priority changes, re-adjust the resource allocation with the help of the matrix until the task is completed.

[0147] Even further, in the embodiment of the present invention, by executing the target task to realize the linkage control of the Internet of Things device, the previously formulated strategies, planned tasks, and allocated resources can be transformed into actual device actions, thereby realizing the efficient collaborative control of the Internet of Things device.

[0148] Embodiment 2:

[0149] As Figure 2 shown, it is the functional module diagram of the device linkage control system of the intelligent Internet of Things of the present invention.

[0150] The device linkage control system 200 of the intelligent Internet of Things of the present invention can be installed in an electronic device. According to the functions achieved, the device linkage control system of the intelligent Internet of Things can include a data processing module 201, a dynamic knowledge graph construction module 202, a device anti-interference analysis module 203, a control strategy analysis module 204, and an intelligent linkage control module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0151] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0152] The data processing module 201 is configured to collect Internet of Things (IoT) terminal data, convert the IoT terminal data into a standardized spatio-temporal matrix, perform feature decoupling processing on the standardized spatio-temporal matrix, and obtain a high-dimensional feature vector.

[0153] The dynamic knowledge graph construction module 202 is configured to query device information of the IoT device corresponding to the IoT terminal, calculate the energy transfer efficiency and communication delay sensitivity of the IoT device by using the high-dimensional feature vector based on the device information, obtain device status association information, and construct a dynamic knowledge graph of the IoT device based on the device status association information.

[0154] The device anti-interference analysis module 203 is configured to inject a fault signal into the dynamic knowledge graph to obtain fault simulation information, analyze the abnormal propagation path and device anti-interference ability of the IoT device based on the fault simulation information, and perform a risk assessment on the IoT device based on the abnormal propagation path and the device anti-interference ability to obtain a device risk degree.

[0155] The control strategy analysis module 204 is configured to query the current energy consumption and task information of the IoT terminal, construct an initial linkage strategy for the IoT device based on the device risk degree, the current energy consumption, and the task information, perform digital twin on the IoT device to obtain a simulated IoT device, and identify a feasible strategy subset in the initial linkage strategy by using the simulated IoT device.

[0156] The intelligent linkage control module 205 is configured to decompose the feasible strategy subset into directed acyclic graph decomposition task nodes, obtain resource information of the IoT terminal to construct a resource matching matrix for the IoT device, perform dynamic resource allocation on the decomposition task nodes by using the resource matching matrix to obtain a target task, and execute the target task to achieve linkage control of the IoT device.

[0157] Specifically, each module in the device linkage control system 200 of the intelligent IoT in the embodiment of the present invention adopts the same technical means as those in the Figure 1 device linkage control method of the intelligent IoT described above, and can produce the same technical effects, which will not be elaborated here.

[0158] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A device linkage control method for the intelligent Internet of Things, characterized in that: The method comprises: Collecting IoT terminal data, converting the IoT terminal data into a standardized space-time matrix, and performing feature decoupling processing on the standardized space-time matrix to obtain a high-dimensional feature vector; Query the device information of the IoT device corresponding to the IoT terminal, calculate the energy transfer efficiency and communication delay sensitivity of the IoT device based on the device information and using the high-dimensional feature vector, obtain device state association information, and construct a dynamic knowledge graph of the IoT device based on the device state association information; Injecting a fault signal into the dynamic knowledge graph to obtain fault simulation information, analyzing the abnormal propagation path and the anti-interference capability of the IoT device based on the fault simulation information, and performing a risk assessment on the IoT device based on the abnormal propagation path and the anti-interference capability of the device to obtain a device risk degree; Query the current energy consumption and task information of the IoT terminal, construct an initial linkage strategy for the IoT device based on the device risk, the current energy consumption and the task information, perform digital twinning on the IoT device to obtain a simulated IoT device, and use the simulated IoT device to identify feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies; The feasible strategy subset is decomposed into a directed acyclic graph to obtain decomposed task nodes, and resource information of the Internet of Things terminal is obtained to construct a resource matching matrix of the Internet of Things device. The resource matching matrix is ​​used to dynamically allocate resources to the decomposed task nodes to obtain a target task, and the target task is executed to achieve linkage control of the Internet of Things device.

2. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The converting the IoT terminal data into a standardized space-time matrix includes: Performing multimodal data fusion on the IoT terminal data to obtain fused data; Performing spatiotemporal mapping on the fused data to obtain mapping data; Performing time-space calibration on the mapping data to obtain calibration data; Resampling the calibration data using a preset spatiotemporal resolution to obtain target data; A space-time matrix of the target data is constructed to obtain a standardized space-time matrix.

3. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The step of performing feature decoupling processing on the standardized spatiotemporal matrix to obtain a high-dimensional feature vector includes: Performing complex domain conversion on the standardized space-time matrix to obtain a conversion matrix; Extracting matrix features of the conversion matrix, performing independent component decomposition on the matrix features, and obtaining decomposition features; Extracting device attribute features and environment attribute features related to the Internet of Things from the decomposed features to obtain refined features; Performing wavelet decomposition on the refined features to obtain wavelet decomposition features; Performing feature fusion on the wavelet decomposition features to obtain fused features; Calculating the mutual information gain of the fusion feature; The fused features are screened based on the mutual information gain to obtain a high-dimensional feature vector.

4. The device linkage control method of the intelligent Internet of Things as claimed in claim 3, characterized in that: The calculating the mutual information gain of the fusion feature includes: The mutual information gain of the fusion feature is calculated using the following formula: ； ； ； in, represents the mutual information gain, X represents the feature set of fusion features, represents the i-th feature of the fusion feature, and Y is expressed as The corresponding target variable is (Y) represents the entropy of Y, Indicates that the known features Under the condition of, the conditional entropy of Y is, represents the probability that the target variable Y takes the value y, Representation characteristics The probability of taking the value x, Indicated in Under the condition of x, Y The conditional probability of y.

5. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The method of calculating the energy transfer efficiency and communication delay sensitivity of the IoT device based on the device information and using the high-dimensional feature vector to obtain device state association information includes: Query the device transmit power and the device receive power in the device information; Based on the device transmission power and the device receiving power, the energy transfer efficiency of the IoT device is calculated using the following formula: ； Where E represents the energy transfer efficiency, represents the device receiving power of the jth device in the IoT device, represents the device transmission power of the i-th device in the IoT device; The communication delay in the device information is queried, the communication load of the IoT device is analyzed using the high-dimensional feature vector, and based on the communication delay and the communication load, the communication delay sensitivity of the IoT device is calculated using the following formula: ； in, represents the communication delay sensitivity, represents the communication delay from IoT device i to IoT device j, represents the communication load between IoT devices i and j; Based on the energy transfer efficiency and the communication delay sensitivity, device state association information of the Internet of Things device is identified.

6. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The step of constructing a dynamic knowledge graph of the IoT device based on the device state association information includes: Extracting entity information, relationship information and attribute information from the device status association information to obtain knowledge data; Performing entity alignment on the knowledge data to obtain entity alignment data; Performing knowledge merging on the entity alignment data to obtain knowledge merging data; The knowledge is combined with data to construct a dynamic knowledge graph of the IoT device.

7. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The analyzing the abnormal propagation path and the anti-interference capability of the IoT device based on the fault simulation information includes: Using the dynamic knowledge graph of the IoT device, a propagation model of the IoT device is constructed; Setting propagation attributes of the propagation model to obtain a target propagation model; Performing time series analysis on the fault simulation information to obtain an abnormal propagation path; Query the performance indicator data and recovery time of the IoT device during fault simulation; Performing indicator quantification processing on the performance indicator data and the recovery time to obtain a quantitative indicator; The quantitative index is used to analyze the device anti-interference capability of the Internet of Things device.

8. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: The constructing the initial linkage strategy of the IoT device based on the device risk, the current energy consumption and the task information includes: Identify the risk level of the equipment; Based on the risk level, build a risk mitigation strategy for IoT devices; Identify the risk type of the IoT device; Based on the risk type, construct a risk prevention and control strategy for the IoT device; Based on the current energy consumption, construct a resource allocation strategy for the networked device; Constructing a collaborative operation strategy for the IoT devices according to the task information; Based on the risk mitigation strategy, the risk prevention and control strategy, the resource allocation strategy and the collaborative operation strategy, an initial linkage strategy for the Internet of Things devices is constructed.

9. The device linkage control method of the intelligent Internet of Things according to claim 1, characterized in that: Decomposing the feasible strategy subset into a directed acyclic graph to obtain decomposed task nodes includes: Decomposing the feasible strategy subset into elements to obtain task node elements; Analyzing the task execution relationship of the task node elements; Based on the task execution relationship, construct a directed acyclic graph of the feasible strategy subset; Performing node merging optimization on the directed acyclic graph to obtain an optimized directed acyclic graph; The element nodes of the optimized directed acyclic graph are extracted to obtain decomposition task nodes.

10. The equipment linkage control system of the intelligent Internet of Things is characterized by: The system comprises: A data processing module is used to collect IoT terminal data, convert the IoT terminal data into a standardized space-time matrix, and perform feature decoupling processing on the standardized space-time matrix to obtain a high-dimensional feature vector; A dynamic knowledge graph construction module is used to query the device information of the IoT device corresponding to the IoT terminal, calculate the energy transfer efficiency and communication delay sensitivity of the IoT device based on the device information and the high-dimensional feature vector, obtain device state association information, and construct a dynamic knowledge graph of the IoT device based on the device state association information; The device anti-interference analysis module is used to inject fault signals into the dynamic knowledge graph to obtain fault simulation information, analyze the abnormal propagation path and device anti-interference capability of the IoT device based on the fault simulation information, and conduct risk assessment on the IoT device based on the abnormal propagation path and the device anti-interference capability to obtain the device risk level; A control strategy analysis module is used to query the current energy consumption and task information of the IoT terminal, construct an initial linkage strategy for the IoT device based on the device risk, the current energy consumption and the task information, perform digital twinning on the IoT device to obtain a simulated IoT device, and use the simulated IoT device to identify feasible strategies in the initial linkage strategy to obtain a subset of feasible strategies; The intelligent linkage control module is used to perform directed acyclic graph decomposition on the subset of feasible strategies to obtain decomposed task nodes, obtain resource information of the Internet of Things terminal to construct a resource matching matrix of the Internet of Things device, use the resource matching matrix to dynamically allocate resources to the decomposed task nodes, obtain the target task, and execute the target task to achieve linkage control of the Internet of Things device.

Images