CN117118849B

CN117118849B - Gateway system of Internet of things and implementation method

Info

Publication number: CN117118849B
Application number: CN202311276146.4A
Authority: CN
Inventors: 刘建刚; 姚晓蒙; 经磊
Original assignee: Jiangsu Shoujie Intelligent Equipment Co ltd
Current assignee: Jiangsu Shoujie Intelligent Equipment Co ltd
Priority date: 2023-09-29
Filing date: 2023-09-29
Publication date: 2024-02-20
Anticipated expiration: 2043-09-29
Also published as: CN117118849A

Abstract

The invention relates to the technical field of digital communication, in particular to an Internet of things gateway system and an implementation method. The method comprises the following steps: acquiring equipment parameters of marking equipment in a database; performing simulation operation on the equipment parameters, and removing abnormal values to generate safety simulation equipment data; establishing a network topology based on the safety simulation equipment data, and encrypting by utilizing a multiple encryption technology to obtain an encrypted network topology graph; carrying out data bandwidth path analysis on the encrypted network topological graph to generate bandwidth path data; performing optimal bandwidth path calculation on the bandwidth path data to obtain optimal bandwidth path data; gateway connection is carried out on gateway equipment by utilizing the optimal bandwidth path data, and gateway connection path data is generated; and carrying out iterative monitoring and gateway address setting on the gateway connection path data to generate gateway address data. The gateway method of the Internet of things is used for precisely connecting the Internet of things equipment, so that the performance of the controlled equipment is high-efficiency and rapid.

Description

Gateway system of Internet of things and implementation method

Technical Field

The invention relates to the technical field of digital communication, in particular to an Internet of things gateway system and an implementation method.

Background

The internet of things is to connect various physical devices through the internet to realize data exchange and communication between the devices, and in an internet of things system, an internet of things gateway plays an important role of connecting the devices and the internet and is responsible for tasks such as data collection, processing and forwarding. However, the traditional internet of things gateway implementation method only performs common connection on internet of things equipment, does not consider optimization of a connection line to improve equipment efficiency and reduce delay, and does not guarantee safety of the connection line.

Disclosure of Invention

Based on the above, the invention provides an internet of things gateway system and an implementation method thereof, so as to solve at least one of the above technical problems.

In order to achieve the above purpose, a method for implementing an internet of things gateway includes the following steps:

step S1: marking the Internet of things equipment according to a preset Internet of things equipment category, and generating marking equipment; acquiring equipment parameters of marking equipment in a database; performing simulation operation on the equipment parameters, and removing abnormal values to generate safety simulation equipment data;

step S2: establishing a network topology of the minimum communication distance between the devices based on the safety simulation device data, and generating a device network topology graph; encrypting the data of the equipment network topology graph by utilizing a multiple encryption technology to obtain an encrypted network topology graph;

Step S3: carrying out data bandwidth path analysis on the encrypted network topological graph to generate bandwidth path data; performing optimal bandwidth path calculation on the bandwidth path data to obtain optimal bandwidth path data; gateway connection is carried out on gateway equipment by utilizing the optimal bandwidth path data, and gateway connection path data is generated;

step S4: and carrying out iterative monitoring and gateway address setting on the gateway connection path data to generate gateway address data.

The method and the device can classify and classify the devices by marking the devices of the Internet of things, which is beneficial to managing and organizing a large number of devices and provides a basis for subsequent data processing and analysis. Acquiring device parameters of the tag device in the database may provide detailed device information including sensor data, device status, hardware specifications, etc., which are critical to subsequent simulation runs and data processing. By performing simulation operation on the device parameters, virtual device data can be generated, which is helpful for simulating the behavior and performance of real devices, and a data set which can be used for testing and analysis is provided, during the simulation operation, the quality and the reliability of the data can be improved by detecting and eliminating abnormal values, and the interference on subsequent analysis and decision can be reduced by eliminating the abnormal values, so that the used data is ensured to be accurate and reliable. Based on the safety simulation device data, the minimum communication distance between the devices, namely the physical connection or communication path between the devices, can be determined, and the connection relation and the topology structure between the devices in the Internet of things can be clearly known by establishing a device network topology diagram. The data encryption is carried out on the device network topology graph by utilizing the multiple encryption technology, so that the security and privacy protection of the network can be enhanced, the encrypted network topology graph can prevent unauthorized access and malicious attack, and the confidentiality and the integrity of topology information are ensured. The data bandwidth path analysis of the encrypted network topology graph can determine the communication path and bandwidth utilization condition among the devices, which is helpful for knowing the bottleneck and bottleneck nodes of data transmission in the network and provides a basis for subsequent optimization and decision. By performing the best bandwidth path computation on the bandwidth path data, the best path for the data transmission can be determined, which helps to optimize network performance, reduce delay and congestion for the data transmission, and improve efficiency and reliability of the data transmission. The gateway equipment can be connected by utilizing the optimal bandwidth path data, and the gateway and the Internet of things equipment can be effectively connected, so that the interconnection and the intercommunication of the Internet of things equipment can be realized, reliable gateway service can be provided, and the generated gateway connection path data can provide reference for subsequent monitoring and management. The method has the advantages that the stability and the reliability of gateway connection can be ensured by carrying out iterative monitoring on the gateway connection path data, potential problems can be found and solved in time by monitoring indexes such as network connection state, data transmission condition and the like, and the normal operation of the Internet of things system is ensured. The gateway address setting of the gateway connection path data can assign a unique identifier or address to the gateway device, which helps to distinguish and identify different gateway devices and ensures correct routing and transfer of data in the internet of things. Therefore, the gateway realization method of the Internet of things only carries out gateway connection on the Internet of things equipment through analyzing the communication distance between the equipment and the bandwidth path of the connecting line, so that the efficiency of operating the equipment by a user is improved, and the data delay is reduced; the connection circuit is encrypted by utilizing multiple encryption technology, so that the circuit safety is ensured, and the hacking is prevented.

Preferably, step S1 comprises the steps of:

step S11: marking the Internet of things equipment according to a preset Internet of things equipment category, and generating marking equipment;

step S12: acquiring equipment parameters of marking equipment in a database;

step S13: modeling data based on the marking equipment, and performing simulation operation by using equipment parameters to generate simulated equipment data;

step S14: performing simulation equipment data anomaly detection calculation on simulation equipment data by using a simulation equipment anomaly detection mathematical formula to generate anomaly detection data of the simulation equipment data;

step S15: performing abnormality judgment on the abnormality detection data by using a preset abnormality threshold of the simulation equipment, and marking the simulation equipment data corresponding to the abnormality detection data as abnormality simulation equipment data when the abnormality detection data is larger than the abnormality threshold of the simulation equipment; when the abnormality detection data is not greater than the abnormality threshold of the simulation equipment, marking the simulation equipment data corresponding to the abnormality detection data as safety simulation equipment data;

step S16: and eliminating the abnormal simulation equipment data.

According to the method, the Internet of things equipment is marked, and can be classified and categorized according to the preset equipment category, so that a large number of equipment can be managed and organized, and a foundation is provided for subsequent data processing and analysis. Acquiring device parameters of the tag device in the database may provide detailed device information including sensor data, device status, hardware specifications, etc., which are critical to subsequent simulation runs and data processing. By data modeling and simulation running of the tag device, virtual device data can be generated that helps simulate the behavior and performance of a real device and provides a data set that can be used for testing and analysis. By calculating the simulated equipment data by applying an anomaly detection mathematical formula, an anomaly mode or an anomaly value in the data can be identified, which is helpful for finding out the anomaly condition of equipment behaviors and provides a basis for early warning and fault diagnosis. By setting the abnormal threshold value of the simulation equipment, the abnormal detection data can be judged, the data exceeding the threshold value is marked as abnormal simulation equipment data, and the data not exceeding the threshold value is marked as safety simulation equipment data, so that the equipment data can be further distinguished and classified, and a basis is provided for subsequent processing and analysis. The abnormal simulation equipment data are removed, the data set can be purified, and the accuracy and the credibility of the data are improved, so that the interference of the abnormal data on subsequent analysis and decision making is avoided, and the reliability of the used data is ensured.

Preferably, the mathematical formula for detecting abnormality of the analog device in step S14 is as follows:

；

where P represents abnormality detection data of analog device data, x represents device energy data generated from the analog device data, a represents mean value data of the device energy data, b represents fluctuation degree of the device energy data, T represents cycle length of the analog device data, k represents frequency attenuation rate of use of the analog device data, and τ represents an abnormality adjustment value of the abnormality detection data.

The invention utilizes a mathematical formula for detecting abnormality of simulation equipment, which fully considers the interaction relation among equipment energy data x, average value data a, fluctuation degree b, period length T, using frequency attenuation rate k and functions of the equipment energy data generated according to the simulation equipment data to form a functional relation:

that is to say,performing exponential transformation on device energy data generated from the analog device data using an exponential function,adjusting the distribution form and the deviation state of the equipment energy data, calculating the centralized trend of the equipment energy data according to the mean value data of the equipment energy data, and calculating the change amplitude of the equipment energy data according to the fluctuation degree of the equipment energy data, so as to preliminarily detect the abnormality of the data; mapping and converting the device energy data by logarithmic function, compressing it to [0, 1 ] ]This helps to determine if the device energy data is outside of normal range; periodic anomaly detection is performed on the period length of the analog device data and the frequency decay rate of use of the analog device data using a sinusoidal function, which facilitates detection of periodic anomaly patterns or frequency anomalies. The application of the exponential function and the sine function can transform and adjust the equipment energy data, introduce nonlinear and periodic characteristics, and enhance the recognition capability of different types of abnormal modes. Different types of abnormal patterns may manifest as abrupt changes in energy levels, periodic oscillations, or other non-conventional patterns that can be more accurately captured by introducing non-linear and periodic transformations. The method has the advantages that through integrating parameters such as the mean value, fluctuation degree, cycle length, frequency attenuation rate and the like of the energy data of the equipment, and applying an exponential function and a sine function to perform data transformation, the detection capability of the data abnormality of the simulation equipment is improved, the abnormality mode is easier to detect, and the recognition and early warning capability of the behavior abnormality of the equipment of the Internet of things are improved. And the function relation is adjusted and corrected by utilizing the anomaly adjustment value tau of the anomaly detection data, so that the error influence caused by the anomaly data or error items is reduced, the anomaly detection data P of the analog equipment data is generated more accurately, and the accuracy and the reliability of the analog equipment data anomaly detection calculation of the analog equipment data are improved. Meanwhile, the adjustment value in the formula can be adjusted according to actual conditions and is applied to different analog equipment data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S2 comprises the steps of:

step S21: establishing a network topology between devices based on the safety simulation device data, and generating an initial network topology graph;

step S22: performing minimum communication distance calculation and node optimization on the initial network topology by using a minimum spanning tree algorithm to generate a device network topology;

step S23: performing node hash calculation on the equipment network topological graph by using a hash algorithm to generate a node hash abstract;

step S24: and encrypting the data of the node hash abstract by using asymmetric encryption to obtain an encrypted network topological graph.

According to the invention, the network topology between the devices is established based on the safety simulation device data, so that the connection relation and the topology structure between the devices can be determined, the layout and the communication path of the devices in the Internet of things can be known, and a foundation is provided for subsequent network optimization and management. The minimum communication distance of the initial network topology graph is calculated through the minimum spanning tree algorithm, the communication distance between the devices can be optimized, the communication path of the optimized device network topology graph can be shortened, delay and loss of data transmission are reduced, communication efficiency and reliability are improved, the node optimization can adjust the position and connection relation of the device nodes according to the characteristics, transmission requirements, topological structure and other factors of the devices, and network performance and resource utilization are further optimized. By performing hash computation on the device network topology graph, a node hash digest can be generated, which is a digital fingerprint of the topology graph, used to verify the integrity of the topology graph and prevent unauthorized modification, which helps to protect the data integrity of the network topology and prevent the topology graph from being tampered or manipulated. By encrypting the node hash abstract through the application of an asymmetric encryption algorithm, the data security of the network topology graph can be improved, unauthorized access and tampering can be prevented by encrypting the network topology graph, confidentiality and integrity of the topology graph are ensured, and the security of the Internet of things system is enhanced.

Preferably, step S22 comprises the steps of:

step S221: calculating the communication distance of the safety simulation equipment data by using a simulation equipment communication distance optimization algorithm to generate equipment communication distance;

step S222: and optimizing and adjusting the network node with the minimum communication distance to the initial network topology by using the minimum spanning tree algorithm and the equipment communication distance to generate the equipment network topology.

The invention can calculate the communication distance between the devices by simulating the device communication distance optimization algorithm, which is helpful for determining the communication path and the distance relation between the devices, provides a basis for the subsequent network optimization and node adjustment, calculates the communication distance between the devices, can optimize the communication path, reduces the communication delay and the packet loss rate, and improves the communication performance and the data transmission efficiency. The node optimization adjustment can be performed on the initial network topology diagram through the minimum spanning tree algorithm and the equipment communication distance, the optimized equipment network topology diagram can shorten the communication path, delay and loss of data transmission are reduced, communication efficiency and reliability are improved, the connection relation between the equipment can be optimized through the minimum spanning tree algorithm, the communication path between the nodes in the network is shortest, economy and efficiency are improved, and the method is favorable for utilizing available network resources to the greatest extent and improving the resource utilization efficiency of the Internet of things system.

Preferably, the analog device communication distance optimization algorithm in step S221 is as follows:

；

where K represents a device communication distance of the safety simulation device data, c represents a signal frequency of the safety simulation device data in space, d represents a transmission signal strength of the safety simulation device data, f represents a path loss between device communications, g represents a noise power between device communications, m represents a reception power of a receiving device at the time of device communications, ω represents a wavelength of a propagation signal, θ represents an included angle between devices, and μ represents an abnormal adjustment value of the device communication distance.

The invention utilizes an analog device communication distance optimization algorithm which fully considers the interaction relation among signal frequency c of safety analog device data in space, transmitting signal strength d of safety analog device data, path loss f between device communication, noise power g between device communication, receiving power m of receiving device during device communication, wavelength omega of a propagation signal, included angle theta between devices and functions to form a functional relation:

that is to say,the communication distance refers to the maximum distance that reliable communication can be performed between devices, and the communication distance between the devices can be adjusted to be in an optimal range by calculating various parameters and variables related to the data of the safety simulation devices so as to improve the reliability and efficiency of communication; the path loss between the device communication refers to the situation that signals are weakened due to signal attenuation, scattering, impedance matching and the like in the propagation process, the path loss between the device communication is considered, the weakening situation of the signals in the propagation process can be accurately reflected, the communication distance between the devices can be estimated more accurately by considering the path loss factor, and the problem of excessive attenuation or insufficient signals is avoided; the noise power between the device communication refers to the power loss caused by random noise existing in the communication system, and by considering the noise power factor, the communication distance between the devices can be more accurately estimated, and the anti-interference capability and the communication quality of the communication system are improved. The position and the orientation of the equipment are comprehensively considered in calculation through the included angle between the wavelength of the propagation signal and the equipment, and the communication distance between the equipment can be optimized through considering the included angle relation factor between the wavelength of the propagation signal and the equipment, so that the problems of blocking of signal propagation or multipath propagation are avoided. Through the parameters and the calculation formula in the optimization algorithm, the communication distance between the devices can be accurately acquired, the influence of noise and path loss is reduced, the result reaches an expected value, the signal transmission quality is improved, the network performance is improved, the abnormal condition correction capability is provided, the communication efficiency, the reliability and the robustness of the Internet of things system are improved, and better user experience and application support are provided.

Preferably, step S3 comprises the steps of:

step S31: extracting historical bandwidth data of the simulated equipment data to generate equipment historical bandwidth data;

step S32: performing feature extraction processing on the historical bandwidth data of the equipment by using a convolutional neural network algorithm to generate the bandwidth feature data of the equipment;

step S33: performing bandwidth path analysis based on the encrypted network topological graph and the device bandwidth characteristic data to generate bandwidth path data;

step S34: performing optimal bandwidth path calculation on the bandwidth path data by utilizing a shortest path algorithm to obtain optimal bandwidth path data;

step S35: and carrying out gateway connection on the gateway equipment by utilizing the optimal bandwidth path data to generate gateway connection path data.

The invention simulates the device data to extract the historical bandwidth data, and can acquire the bandwidth use condition of the device in the past period, which is helpful for knowing the bandwidth requirement and use mode of the device and provides a basis for the subsequent analysis and optimization of the bandwidth path. The characteristic extraction is carried out on the historical bandwidth data of the device through the convolutional neural network algorithm, key characteristic information can be extracted from the data, the capturing of important characteristics such as bandwidth use modes, periodic changes and trends of the device is facilitated, and more accurate input data is provided for subsequent bandwidth path analysis and optimal bandwidth path calculation. By combining the encrypted network topology map with the device bandwidth feature data and performing bandwidth path analysis, the bandwidth requirements and transmission paths between devices can be determined, which is helpful for understanding the bottleneck and optimization potential in the network and provides a basis for subsequent optimal bandwidth path calculation. And calculating the bandwidth path data by applying a shortest path algorithm, so that the optimal bandwidth path between the devices can be determined, the data transmission can be more efficient, stable and reliable by the optimal bandwidth path, the delay and congestion of the data transmission are reduced, and the overall system performance and user experience are improved. By using the optimal bandwidth path data to carry out gateway connection on gateway equipment, effective connection and centralized management on the Internet of things equipment can be realized, which is helpful for optimizing a network architecture, improving the transmission efficiency and reliability of data and providing a foundation for subsequent monitoring and management.

Preferably, step S32 comprises the steps of:

step S321: carrying out data division on the historical bandwidth data of the equipment on a time sequence to respectively generate a historical bandwidth training set and a historical bandwidth testing set;

step S322: establishing a mapping relation between historical bandwidth data and bandwidth characteristics by using a convolutional neural network algorithm so as to generate an initial bandwidth characteristic prediction model;

step S323: performing model training on the initial bandwidth characteristic prediction model by using a bandwidth training set to generate a bandwidth characteristic prediction model;

step S324: and transmitting the bandwidth test set to a bandwidth characteristic prediction model to extract bandwidth data characteristics and generate equipment bandwidth characteristic data.

The invention divides the historical bandwidth data of the equipment into the training set and the testing set, can ensure the independence and the accuracy of the training and the evaluation process of the model, the training set is used for establishing the prediction model, and the testing set is used for verifying the generalization capability and the performance of the model. By means of a convolutional neural network algorithm, a mapping relation between historical bandwidth data and bandwidth characteristics is established, so that key characteristics are extracted from the historical bandwidth data, and the key characteristics are converted into bandwidth characteristic data which can be used for prediction. The bandwidth training set is used for training the initial bandwidth characteristic prediction model, so that the model can learn the mode and rule in the historical bandwidth data, and the model training aims at adjusting the parameters and weights of the model through an optimization algorithm so as to fit the training data to the greatest extent and realize accurate prediction of unknown data. By transmitting the bandwidth test set into the bandwidth feature prediction model, bandwidth feature data can be extracted from the test data, which reflects the bandwidth usage patterns, trends and variations of the device, which can be used for subsequent bandwidth path analysis and optimal bandwidth path calculation.

Preferably, step S4 comprises the steps of:

step S41: performing running condition average value calculation on the gateway connection path data to generate average gateway connection data;

step S42: the network monitoring equipment is utilized to collect and process the running status of the network connection path data in real time, and real-time gateway connection data are generated;

step S43: designing a safe data interval of gateway connection based on the average gateway connection data to generate a gateway safe connection interval;

step S44: carrying out gateway connection safety real-time comparison processing on the real-time gateway connection data and the gateway safety connection interval, eliminating the gateway connection path data when the real-time gateway connection data is not in the gateway safety connection interval, and returning to the step S33 to generate iterative network connection path data; when the real-time gateway connection data is in the gateway security connection interval, step S45 is performed;

step S45: and setting gateway address according to the network connection path data to generate gateway address data.

According to the invention, the average performance index of gateway connection can be obtained by calculating the running condition mean value of the gateway connection path data, which is helpful for evaluating the running condition of the whole Internet of things system, knowing the average quality and stability of network connection and providing a basis for subsequent safe data interval design and real-time comparison. The network connection path data is collected and processed in real time through the network monitoring equipment, so that the real-time running condition of the network connection can be obtained, the performance, the availability and the safety of the network connection can be monitored in real time, and abnormal conditions can be found and processed in time. By analyzing and designing based on the mean gateway connection data, a secure data interval of the gateway connection can be determined, the secure data interval being a set data range within which the gateway connection is considered secure. This helps establish security criteria and thresholds to determine if a network connection is abnormal or unsafe. The security of the gateway connection can be verified by comparing the real-time gateway connection data with the gateway security connection interval, and when the real-time gateway connection data is in the gateway security connection interval, the gateway connection is safe; when the real-time gateway connection data is not in the gateway safety connection interval, the fact that the gateway connection is abnormal or at risk is indicated, the potential safety problem can be detected and processed in time, and the safety and reliability of the network connection of the Internet of things system are guaranteed. According to the network connection path data, the address setting of the gateway equipment is determined, and through reasonable gateway address setting, the management and control of network connection can be realized, the correct transmission and routing of data are ensured, and the reliability and performance of the Internet of things system are improved.

In this specification, there is provided an internet of things gateway system, including:

and a data acquisition module: the method comprises the steps of marking Internet of things equipment according to a preset Internet of things equipment category, and generating marking equipment; acquiring equipment parameters of marking equipment in a database; performing simulation operation on the equipment parameters, and removing abnormal values to generate safety simulation equipment data;

the network topology diagram construction module: establishing a network topology of the minimum communication distance between the devices based on the safety simulation device data, and generating a device network topology graph; encrypting the data of the equipment network topology graph by utilizing a multiple encryption technology to obtain an encrypted network topology graph;

gateway connection module: the method comprises the steps of performing data bandwidth path analysis on an encrypted network topological graph to generate bandwidth path data; performing optimal bandwidth path calculation on the bandwidth path data to obtain optimal bandwidth path data; gateway connection is carried out on gateway equipment by utilizing the optimal bandwidth path data, and gateway connection path data is generated;

gateway address generation module: and carrying out iterative monitoring and gateway address setting on the gateway connection path data to generate gateway address data.

The method has the advantages that the network performance of the Internet of things system can be optimized by performing simulation operation, communication distance optimization, bandwidth path analysis and optimal bandwidth path calculation on the equipment parameters, the optimized network has shorter communication distance, higher bandwidth utilization rate and lower delay, the efficiency and the reliability of data transmission are improved, and the performance of the whole system is enhanced. Through multiple encryption technology, node hash calculation, gateway connection safety real-time comparison, the data safety of the Internet of things system can be protected, measures such as encryption of network topology diagrams, encryption of transmission data, safety verification of gateway connection and the like are effectively prevented from unauthorized access and data tampering, and information safety and privacy protection of the Internet of things system are guaranteed. Through the network topology establishment of the minimum communication distance between the devices, node optimization adjustment and iterative monitoring of the gateway connection path, the network topology structure of the Internet of things system can be optimized, the optimized network topology has higher efficiency and stability, the energy consumption can be reduced, the network congestion can be reduced, and the communication quality between the devices can be improved. Through feature extraction of historical bandwidth data, bandwidth path analysis and monitoring processing of real-time gateway connection data, effective management of bandwidth resources can be achieved, bandwidth resources can be reasonably allocated according to analysis and prediction of bandwidth feature data, bandwidth paths are optimized, and bandwidth utilization rate and network transmission efficiency are improved. By simulating the steps of abnormality detection of equipment data, safety comparison of network connection paths, iterative monitoring and the like, abnormal conditions in network connection can be diagnosed and processed in time, the system has fault tolerance, abnormal equipment and abnormal connection can be automatically removed, the fault propagation range is reduced, and the stability and reliability of the system are improved.

Drawings

Fig. 1 is a schematic flow chart of steps of an implementation method of an internet of things gateway according to the present invention;

FIG. 2 is a detailed flowchart illustrating the implementation of step S1 in FIG. 1;

FIG. 3 is a detailed flowchart illustrating the implementation of step S2 in FIG. 1;

FIG. 4 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 4, the present invention provides a method for implementing an internet of things gateway, comprising the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, the step flow diagram of an implementation method of an internet of things gateway of the present invention is provided, and in the embodiment, the implementation method of the internet of things gateway includes the following steps:

in the embodiment of the invention, the category of the equipment of the internet of things is an intelligent home system, and each equipment is marked to generate marking equipment, such as a temperature sensor, a humidity sensor, an intelligent lamp, an intelligent socket, an intelligent door lock and the like. The device parameters of the marking device are obtained from the database, for example, the power and brightness adjusting range of the intelligent lamp, the current and voltage information of the intelligent socket, the switching state of the intelligent door lock, the verification mode and other device parameters are obtained from the database. The device parameters are simulated and run, and abnormal values are removed to generate safety simulation device data, for example, for an intelligent socket, the fluctuation of current of the intelligent socket can be simulated, and abnormal values are removed.

in the embodiment of the invention, based on the safety simulation equipment data, network topology establishment of the minimum communication distance between the equipment is carried out, a plurality of intelligent equipment such as intelligent lamps, intelligent sockets and intelligent door locks are assumed, and the minimum communication distance between the intelligent equipment is determined according to the communication requirements and the distance limitation between the intelligent lamps, the intelligent sockets and the intelligent door locks so as to establish a network topology diagram of the equipment. In order to protect the safety of communication data between devices, the network topology of the devices is encrypted by utilizing a multiple encryption technology, and the encryption technology can carry out confidentiality processing on the network topology, so that only authorized entities can obtain and read information in the network topology, for example, nodes, connections and communication paths in the network topology are encrypted by using a symmetric encryption algorithm and a key management scheme.

In the embodiment of the invention, the data bandwidth path analysis is carried out on the encrypted network topological graph, the feasible paths and the corresponding bandwidths among different devices are determined by analyzing the connection and the bandwidth information among all the devices in the encrypted network topological graph, for example, the bandwidth path analysis from the intelligent lamp A to the intelligent socket B is considered, the connection relation between the lamp A and the socket B in the encrypted network topological graph and the bandwidth limitation among the lamp A and the socket B are analyzed, the feasible paths are found, and the corresponding bandwidths are calculated. And (3) carrying out optimal bandwidth path calculation on the bandwidth path data, and calculating an optimal bandwidth path through an algorithm by taking the bandwidth requirement, transmission delay and optimization target between devices into consideration on the basis of the bandwidth path analysis, wherein the optimal bandwidth path can be calculated by using a shortest path algorithm or other optimization algorithms according to the bandwidth requirement and the optimization target between the intelligent lamp A and the socket B so as to ensure the efficiency and the quality of data transmission. And gateway connection is carried out on the gateway equipment by utilizing the optimal bandwidth path data, and the gateway connection path data is generated to connect the gateway equipment to the optimal bandwidth path so as to realize centralized management and transmission of the data.

In the embodiment of the invention, the gateway connection path data is subjected to iterative monitoring, the stability and reliability of the gateway connection path data are ensured by monitoring the running condition and performance index of the gateway connection path data, for example, indexes such as delay, packet loss rate, bandwidth utilization rate and the like of data transmission can be monitored, and abnormality detection and judgment are carried out according to a set threshold value, and if abnormal conditions are found, if the delay is too high or the packet loss rate exceeds the threshold value, corresponding processing such as removing an abnormal path or carrying out path adjustment is needed. According to the monitoring result and the gateway connection path data, a unique address is allocated to the gateway equipment so as to identify and manage the gateway equipment, wherein the address can be a logical address or a physical address in a network, the specific setting method depends on the requirements of system design and implementation, for example, an IP address or a MAC address can be allocated to the gateway equipment so as to enable the gateway equipment to have a unique identification in the network.

Preferably, step S1 comprises the steps of:

Step S12: acquiring equipment parameters of marking equipment in a database;

step S16: and eliminating the abnormal simulation equipment data.

As an example of the present invention, referring to fig. 2, a detailed implementation step flow diagram of step S1 in fig. 1 is shown, where step S1 includes:

in the embodiment of the invention, different types of equipment, such as a temperature sensor, a humidity sensor and an illumination sensor, are marked in the category of the equipment of the Internet of things, and the marked equipment is used as a data source in the system of the Internet of things.

Step S12: acquiring equipment parameters of marking equipment in a database;

in the embodiment of the invention, the equipment parameters of the marking equipment are obtained from a database, for example, for a temperature sensor, parameters such as the measurement range, the resolution, the sampling frequency and the like are obtained.

in the embodiment of the invention, the data modeling is performed based on the marking equipment, the equipment parameters are utilized for simulation operation, the three-dimensional modeling technology is used for simulating the behavior of the equipment, for example, a three-dimensional model of a temperature sensor can be established, and the simulated temperature data is generated by inputting the environmental parameters and the equipment characteristics.

in the embodiment of the invention, the mathematical formula for detecting the abnormality of the simulation equipment is utilized to carry out the calculation for detecting the abnormality of the simulation equipment data. We use specific mathematical formulas or algorithms to detect anomalies in the simulated device data, for example statistical methods or machine learning algorithms can be used to identify anomalies in the temperature sensor data.

in the embodiment of the invention, the abnormality judgment is carried out on the abnormality detection data by utilizing the preset abnormality threshold value of the simulation equipment, and the corresponding simulation equipment data is marked as abnormal simulation equipment data according to the preset threshold value, if the abnormality threshold value of the simulation equipment is 0.8 and the abnormality detection data exceeds the threshold value when the abnormality detection data is 0.9; otherwise, it is marked as security analog device data.

Step S16: and eliminating the abnormal simulation equipment data.

In the embodiment of the invention, the abnormal simulation equipment data is removed to ensure that the simulation equipment data used in the subsequent steps is reliable and accurate, and the removal of the abnormal simulation equipment data is beneficial to improving the data quality and reliability of the Internet of things system.

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That is to say,performing exponential transformation on the equipment energy data generated according to the analog equipment data by using an exponential function, adjusting the distribution form and the deviation of the equipment energy data, calculating the centralized trend of the equipment energy data according to the mean value data of the equipment energy data, and calculating the variation amplitude of the equipment energy data according to the fluctuation degree of the equipment energy data, so as to primarily detect the abnormality of the data; mapping and converting the device energy data by logarithmic function, compressing it to [0, 1 ]]This helps to determine if the device energy data is outside of normal range; periodic anomaly detection is performed on the period length of the analog device data and the frequency decay rate of use of the analog device data using a sinusoidal function, which facilitates detection of periodic anomaly patterns or frequency anomalies. The application of the exponential function and the sine function can transform and adjust the equipment energy data, introduce nonlinear and periodic characteristics, and enhance the recognition capability of different types of abnormal modes. Different types of abnormal patterns may manifest as abrupt changes in energy levels, periodic oscillations, or other non-conventional patterns that can be more accurately captured by introducing non-linear and periodic transformations. The method has the advantages that through integrating parameters such as the mean value, fluctuation degree, cycle length, frequency attenuation rate and the like of the energy data of the equipment, and applying an exponential function and a sine function to perform data transformation, the detection capability of the data abnormality of the simulation equipment is improved, the abnormality mode is easier to detect, and the recognition and early warning capability of the behavior abnormality of the equipment of the Internet of things are improved. And the function relation is adjusted and corrected by utilizing the anomaly adjustment value tau of the anomaly detection data, so that the error influence caused by the anomaly data or error items is reduced, the anomaly detection data P of the analog equipment data is generated more accurately, and the accuracy and the reliability of the analog equipment data anomaly detection calculation of the analog equipment data are improved. Meanwhile, the adjustment value in the formula can be adjusted according to actual conditions and is applied to different analog equipment data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S2 comprises the steps of:

As an example of the present invention, referring to fig. 3, a detailed implementation step flow diagram of step S2 in fig. 1 is shown, where step S2 includes:

in the embodiment of the invention, an initial network topology diagram between devices is constructed according to the position and connection relation of safety simulation device data, for example, a plurality of sensor devices such as a temperature sensor A, a humidity sensor B and an illumination sensor C are arranged, and according to the arrangement and connection mode of the sensor devices in a physical space, the initial network topology diagram can be established to represent the connection relation between the sensor devices.

in the embodiment of the invention, the minimum communication distance between the devices in the initial network topology graph is calculated by using the minimum spanning tree algorithm, node optimization is carried out, the minimum spanning tree algorithm helps us find a tree containing all the device nodes, the total distance of connection in the tree is minimum, the network topology is optimized by using the minimum spanning tree algorithm, and the minimum communication distance between the devices is ensured, so that the communication efficiency and quality are improved.

in the embodiment of the invention, each node in the equipment network topological graph is calculated by using a hash algorithm to generate the node hash abstract, and the hash algorithm converts the node information in the equipment network topological graph into the abstract with fixed length so as to facilitate subsequent verification and comparison, thus ensuring the integrity and consistency of the network topology and preventing unauthorized modification.

In the embodiment of the invention, an asymmetric encryption algorithm, such as RSA, is used for encrypting the node hash abstract, asymmetric encryption uses a combination of a public key and a private key, wherein the public key is used for encrypting data, the private key is used for decrypting data, an encrypted network topology is obtained after the node hash abstract is encrypted, and the private key is transmitted to a user, so that the user has the authority of consulting the network topology, and confidentiality and security of topology information are ensured.

Preferably, step S22 comprises the steps of:

In the embodiment of the invention, the communication distance between the safety simulation device data is calculated by using a simulation device communication distance optimization algorithm, the algorithm considers the factors such as the position, the signal strength and the transmission medium among the devices, and the optimal communication distance among the devices is calculated according to a specific optimization criterion, for example, for a wireless sensor network, the factors such as the signal attenuation, the interference and the propagation path among the devices can be considered, so that the communication distance is optimized and the communication quality is improved. On the basis of an initial network topology diagram, network nodes are optimally adjusted by using a minimum spanning tree algorithm and equipment communication distance information to realize network connection with minimum communication distance, the minimum spanning tree algorithm can help us find a tree containing all equipment nodes, so that the total distance of connection in the tree is minimum, and the connection mode and the node position in the network topology diagram can be adjusted by taking the equipment communication distance as an edge weight to optimize the communication distance between the equipment.

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that is to say,the communication distance refers to the maximum distance that reliable communication can be performed between devices, and the communication distance between the devices can be adjusted to be in an optimal range by calculating various parameters and variables related to the data of the safety simulation devices so as to improve the reliability and efficiency of communication; path loss between device communications The method is characterized in that the conditions of weakening signals due to signal attenuation, scattering, impedance matching and the like in the propagation process are considered, the path loss between equipment communication is considered, the weakening conditions of the signals in the propagation process can be accurately reflected, the communication distance between the equipment can be estimated more accurately by considering the path loss factors, and the problem of excessive attenuation or insufficient signals is avoided; the noise power between the device communication refers to the power loss caused by random noise existing in the communication system, and by considering the noise power factor, the communication distance between the devices can be more accurately estimated, and the anti-interference capability and the communication quality of the communication system are improved. The position and the orientation of the equipment are comprehensively considered in calculation through the included angle between the wavelength of the propagation signal and the equipment, and the communication distance between the equipment can be optimized through considering the included angle relation factor between the wavelength of the propagation signal and the equipment, so that the problems of blocking of signal propagation or multipath propagation are avoided. Through the parameters and the calculation formula in the optimization algorithm, the communication distance between the devices can be accurately acquired, the influence of noise and path loss is reduced, the result reaches an expected value, the signal transmission quality is improved, the network performance is improved, the abnormal condition correction capability is provided, the communication efficiency, the reliability and the robustness of the Internet of things system are improved, and better user experience and application support are provided.

Preferably, step S3 comprises the steps of:

As an example of the present invention, referring to fig. 4, a detailed implementation step flow diagram of step S3 in fig. 1 is shown, where step S3 includes:

in the embodiment of the invention, historical bandwidth data is extracted from analog device data, which involves analyzing and processing the analog device data to obtain the bandwidth usage of the device in the past period of time, for example, for a sensor device, we can extract the data transmission amount in each time segment, so as to obtain the historical bandwidth data.

in the embodiment of the invention, the characteristic extraction processing is carried out on the historical bandwidth data of the equipment by utilizing a convolutional neural network algorithm. The convolution neural network algorithm can automatically learn key features in the data, extract features which are significant to bandwidth path analysis, and generate bandwidth feature data of the device by carrying out operations such as convolution, pooling, full connection and the like on historical bandwidth data, wherein the features can better describe the bandwidth use mode of the device.

in the embodiment of the invention, the bandwidth path analysis is carried out on the basis of the encrypted network topological graph and the bandwidth characteristic data of the equipment, and the available bandwidth paths among different equipment can be determined by analyzing the network topological structure and the bandwidth characteristic data of the equipment, wherein the bandwidth path data describe the communication paths among the equipment and the available bandwidths thereof, thereby providing important information for subsequent network optimization and resource allocation.

in the embodiment of the invention, the bandwidth path data is calculated by utilizing a shortest path algorithm, such as Dijkstra algorithm or Floyd-Warshall algorithm, so as to find the optimal bandwidth path, and the shortest path algorithm considers the distance between devices and bandwidth limitation to determine the optimal communication path, so that the data can be transmitted at the optimal speed and quality.

In the embodiment of the invention, the connection mode of the gateway equipment is determined according to the optimal bandwidth path data so as to realize optimal network connection, and the gateway connection path data describing the connection path between the equipment and the gateway is generated by connecting the equipment to the proper gateway, and the path data is helpful for optimizing network transmission and providing efficient data flow.

Preferably, step S32 comprises the steps of:

In the embodiment of the invention, the historical bandwidth data of the equipment is divided according to the time sequence and is divided into the historical bandwidth training set and the historical bandwidth testing set, the data which can be used for training the bandwidth characteristic prediction model by dividing the data set is from the historical record, and the testing set is used for evaluating the performance of the model on new data, so that the model can be ensured to accurately predict the bandwidth characteristics of unknown data. The mapping relation between the historical bandwidth data and the bandwidth characteristics is established by using a convolution neural network algorithm, the convolution neural network can automatically learn the spatial and temporal characteristics in the data, extract useful characteristic information from the historical bandwidth data, and an initial bandwidth characteristic prediction model is established by carrying out convolution, pooling and full connection operation on the historical bandwidth data. The initial bandwidth characteristic prediction model is trained by utilizing the bandwidth training set, historical bandwidth data is input into the model and compared with real bandwidth characteristics, parameters and weights of the model are adjusted so that the model can predict the bandwidth characteristics more accurately, and the bandwidth characteristic prediction model is generated by the iterative optimization training process and can predict the bandwidth characteristics of the equipment according to the historical data. The bandwidth test set is used for transmitting data into the bandwidth characteristic prediction model, the bandwidth data characteristic extraction is carried out through the model, the model converts the input bandwidth data into corresponding bandwidth characteristics, such as bandwidth utilization rate, bandwidth stability and the like, and the characteristic data can better describe the bandwidth characteristics of the equipment and provide valuable information for subsequent network optimization and bandwidth path analysis.

Preferably, step S4 comprises the steps of:

In the embodiment of the invention, the gateway connection path data is counted and analyzed, the average value of each index is calculated, the indexes can comprise network delay, packet loss rate, bandwidth utilization rate and the like, the indexes are used for evaluating the overall running condition of gateway connection, and the overall understanding of the network connection path data can be obtained by calculating the average value, so that a foundation is provided for the subsequent design of the safety data interval. The running state of the gateway connection path data is acquired in real time by the aid of the network monitoring equipment, the monitoring equipment can collect information such as network delay, packet loss rate, bandwidth use condition and the like, the data are processed and analyzed in real time, the current running state of the gateway connection can be obtained through the data acquired in real time, and latest information is provided for subsequent safe data interval design. Based on the mean gateway connection data obtained by previous calculation, a safety data interval is designed, for example, the designed safety data interval is 0.75 times to 1.25 times of the mean gateway connection data, the interval defines the normal range of network connection, connection beyond the range is considered abnormal, and the safety data interval can be flexibly adjusted according to actual requirements and service requirements so as to ensure the stability and safety of the network connection. Comparing the gateway connection data acquired in real time with the gateway safety connection interval designed before in real time, if the real-time data is not in the safety connection interval, namely exceeds the normal range, regarding the real-time data as abnormal data, rejecting the gateway connection path data, returning to the step S33 after rejecting the abnormal data, carrying out the step S33 and the subsequent steps again, and carrying out iteration to generate more reliable network connection path data; if the real-time data is within the secure connection interval, i.e. within the normal range, step S45 is continued. According to the network connection path data which is screened and eliminated from abnormal data, the gateway address is set, the gateway is connected to proper equipment or network nodes, so that the reliability and the high efficiency of network connection are ensured, through setting the correct gateway address, a user can add the gateway address to control the equipment of the Internet of things, and the flow and the exchange of the data in the Internet of things are ensured.

The method has the beneficial effects that the network performance of the Internet of things system can be optimized by performing steps of simulation operation, communication distance optimization, bandwidth path analysis, optimal bandwidth path calculation and the like on the equipment parameters, the optimized network has shorter communication distance, higher bandwidth utilization rate and lower delay, the efficiency and the reliability of data transmission are improved, and the performance of the whole system is enhanced. Through multiple encryption technology, node hash calculation, gateway connection safety real-time comparison and the like, the data safety of the Internet of things system can be protected, measures such as encryption of a network topological graph, encryption of transmission data, safety verification of gateway connection and the like are effectively prevented from unauthorized access and data tampering, and information safety and privacy protection of the Internet of things system are guaranteed. Through the steps of network topology establishment, node optimization adjustment, iterative monitoring of gateway connection paths and the like of the minimum communication distance between the devices, the network topology structure of the Internet of things system can be optimized, the optimized network topology has higher efficiency and stability, the energy consumption can be reduced, the network congestion can be reduced, and the communication quality between the devices can be improved. Through the steps of characteristic extraction of historical bandwidth data, bandwidth path analysis, monitoring processing of real-time gateway connection data and the like, effective management of bandwidth resources can be achieved, bandwidth resources can be reasonably allocated according to analysis and prediction of bandwidth characteristic data, bandwidth paths are optimized, and bandwidth utilization rate and network transmission efficiency are improved. By simulating the steps of abnormality detection of equipment data, safety comparison of network connection paths, iterative monitoring and the like, abnormal conditions in network connection can be diagnosed and processed in time, the system has fault tolerance, abnormal equipment and abnormal connection can be automatically removed, the fault propagation range is reduced, and the stability and reliability of the system are improved.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for realizing the gateway of the Internet of things is characterized by comprising the following steps of:

step S1: marking the Internet of things equipment according to a preset Internet of things equipment category, and generating marking equipment; acquiring equipment parameters of marking equipment in a database; performing simulation operation on the equipment parameters, and removing abnormal values to generate safety simulation equipment data; wherein, step S1 comprises the following steps:

step S12: acquiring equipment parameters of marking equipment in a database;

step S14: performing simulation equipment data anomaly detection calculation on simulation equipment data by using a simulation equipment anomaly detection mathematical formula to generate anomaly detection data of the simulation equipment data; the mathematical formula for detecting the abnormality of the simulation equipment is as follows:

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wherein P represents abnormality detection data of analog device data, x represents device energy data generated from the analog device data, a represents mean value data of the device energy data, b represents fluctuation degree of the device energy data, T represents cycle length of the analog device data, k represents frequency attenuation rate of use of the analog device data, and τ represents an abnormality adjustment value of the abnormality detection data;

Step S16: removing abnormal simulation equipment data;

step S2: establishing a network topology of the minimum communication distance between the devices based on the safety simulation device data, and generating a device network topology graph; encrypting the data of the equipment network topology graph by utilizing a multiple encryption technology to obtain an encrypted network topology graph; wherein, step S2 includes the following steps:

step S24: performing data encryption on the node hash abstract by utilizing asymmetric encryption to obtain an encrypted network topological graph;

step S3: carrying out data bandwidth path analysis on the encrypted network topological graph to generate bandwidth path data; performing optimal bandwidth path calculation on the bandwidth path data to obtain optimal bandwidth path data; gateway connection is carried out on gateway equipment by utilizing the optimal bandwidth path data, and gateway connection path data is generated; wherein, step S3 includes the following steps:

step S35: gateway connection is carried out on gateway equipment by utilizing the optimal bandwidth path data, and gateway connection path data is generated;

step S4: performing iterative monitoring and gateway address setting on the gateway connection path data to generate gateway address data; wherein, step S4 includes the following steps:

2. The method for implementing the gateway of the internet of things according to claim 1, wherein the step S22 includes the steps of:

3. The method according to claim 2, wherein the analog device communication distance optimization algorithm in step S221 is as follows:

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4. The method for implementing the gateway of the internet of things according to claim 1, wherein the step S32 includes the steps of:

5. The gateway system of the internet of things, which is used for executing the gateway implementation method of the internet of things according to claim 1, and comprises: