Host system and method for quickly building internet of things platform

Abstract

The invention discloses a host system and method for rapidly building an Internet of Things platform, and the method comprises the following steps: the system employs a standardized process and an automatic script, and achieves one-key deployment, simplified configuration and integration. Hardware is designed in a modularized mode, and users can flexibly combine according to requirements. The system integrates various communication technologies, and seamless communication switching is realized through a dynamic protocol converter and an automatic configuration system. An operation system and an application management module based on artificial intelligence are pre-installed, a machine learning algorithm is utilized, the system performance is automatically optimized according to user behaviors, and the installation and configuration process is simplified. Multi-level dynamic security protection measures are adopted, including intrusion detection based on behavior analysis, adaptive encryption and a distributed authentication system, and security threats are monitored and responded in real time. The system is integrated with an edge micro data center, and distributed data processing and real-time analysis decision are carried out through edge nodes. The design is reasonable, the operation is simple and convenient, and the building speed and efficiency of the Internet of Things platform are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a host system and method for realizing rapid construction of an Internet of Things platform. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, the application of IoT platforms in various fields has gradually become popular. However, there are many problems in the construction and maintenance of existing IoT platforms, which limits their application and promotion. The construction process of existing IoT platforms usually involves complex hardware configuration, software installation, and system integration. These steps are cumbersome and time-consuming, requiring professionals to perform a lot of manual configuration and debugging. The lack of standardized deployment processes and automated tools leads to long construction time, low efficiency, and difficulty in quickly responding to market demand.

[0003] The hardware design of traditional IoT platforms is usually fixed, lacking modularity and flexibility, and cannot be adjusted and expanded according to specific needs. Once the hardware devices are selected, the functions and performance of the system are limited, making it difficult to adapt to changing application scenarios and needs. Existing IoT platforms usually only support a single or limited communication technology, resulting in poor compatibility with different types of IoT devices. For example, some platforms may only support Wi-Fi or Ethernet connections, but not Zigbee or cellular networks. This limitation limits the application scope of IoT systems and makes it difficult to achieve interconnection between various devices.

[0004] In terms of operating system and application management, the system performance optimization and application management process of existing IoT platforms usually rely on manual configuration, and it is difficult to automatically optimize according to actual usage. System performance and user experience are difficult to guarantee, and frequent manual intervention is required to install, update and manage applications, which increases the complexity of management and maintenance. IoT platforms face complex and ever-changing network security threats, and existing technologies are often insufficient in security protection. There is a lack of multi-level dynamic security protection measures, and it is impossible to monitor, identify and respond to potential security threats in real time. Data transmission is vulnerable to attacks, and the system is vulnerable to unauthorized access and data leakage.

[0005] Existing IoT platforms mainly rely on cloud computing for data processing and analysis, which results in high data transmission latency, and the real-time and response speeds are difficult to meet the actual application requirements. In some key application scenarios, such as smart transportation and industrial automation, real-time data processing and rapid response are essential, and existing technologies are difficult to meet these requirements. Existing IoT platforms lack automated tools and intelligent functions for system management and maintenance, and require a lot of manual intervention. Tasks such as system monitoring, fault detection, and recovery are complex and time-consuming, increasing operating costs and management difficulties.

[0006] Therefore, how to provide a host system and method for quickly building an Internet of Things platform is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] One object of the present invention is to propose a host system and method for realizing rapid construction of an Internet of Things platform. The present invention decomposes the process of building an Internet of Things platform into predefined steps by providing standardized processes and automated scripts, simplifies configuration and integration, and adopts modular hardware design, so that users can flexibly select and combine according to needs to realize rapid construction and expansion of the system. The present invention integrates multiple communication technologies, and realizes seamless switching and compatibility of wired and wireless communications through dynamic protocol converters and automatic configuration systems. An artificial intelligence-based operating system and application management module are pre-installed, and machine learning algorithms are used to automatically optimize system performance according to user behavior and usage patterns, simplify the system installation and configuration process, and provide intelligent application management and automatic update functions. Multi-level dynamic security protection measures are adopted to monitor, identify and respond to potential security threats in real time to ensure the security of data and systems. An integrated edge micro data center performs distributed data processing and real-time analysis and decision-making through edge nodes to improve the response speed and processing efficiency of the system. The present invention has the advantages of fast construction, flexible expansion, strong communication compatibility, high security, real-time data processing and high efficiency.

[0008] According to an embodiment of the present invention, a host system and method for realizing rapid construction of an Internet of Things platform include the following steps:

[0009] S1. Provide standardized processes to break down the IoT platform building process into predefined steps, simplify configuration and integration, including using predefined templates and automated scripts to achieve one-click deployment and reduce manual intervention;

[0010] S2. Modular hardware design is adopted. The hardware modules include a central processing unit, a memory unit, a storage unit, a communication module and an interface module. Users can flexibly select and combine hardware modules according to their needs to achieve construction and expansion;

[0011] S3, integrates multiple communication technologies, supports Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks through dynamic protocol converters and automatic configuration systems, realizes seamless switching and compatibility of wired and wireless communication technologies, and simplifies communication configuration;

[0012] S4. Pre-installed AI-based operating system and application management module, which can use machine learning algorithms to automatically optimize system performance based on user behavior and usage patterns, simplify system installation and configuration processes, and provide intelligent application management and automatic update functions to achieve deployment and management;

[0013] S5. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels;

[0014] S6. Integrate a computing architecture based on edge micro data centers, deploy small data centers near IoT devices, perform distributed data processing and storage through edge micro data centers, and perform real-time data analysis and decision-making on edge nodes.

[0015] Optionally, the S3 specifically includes:

[0016] S31. Integrate multiple communication technologies in the IoT platform and achieve seamless switching and compatibility between wired communication and wireless communication through a dynamic protocol converter. The wired communication includes Ethernet and optical fiber communication, and the wireless communication includes Wi-Fi, Bluetooth, Zigbee and cellular network.

[0017] S32, the dynamic protocol converter automatically selects and switches the appropriate communication protocol according to the current communication environment and requirements, and realizes seamless switching of communication technology;

[0018] S33, the automatic configuration system automatically configures communication parameters, including bandwidth, latency, frequency, and signal strength, by detecting the network environment and device status, and combines machine learning algorithms to adjust and optimize parameters in real time;

[0019] S34, automatic configuration of communication parameters, detecting bandwidth B, delay L, frequency F and signal strength S in the current communication environment, and according to the detected parameters, the dynamic protocol converter selects the optimal communication protocol and adjusts the relevant communication parameters through the automatic configuration system;

[0020] S35, the dynamic protocol converter and the automatic configuration system work together. When a change in the communication environment is detected, the dynamic protocol converter automatically selects and switches to the communication protocol that best suits the current environment. The automatic configuration system automatically adjusts the communication parameters according to the new communication protocol to achieve seamless communication switching.

[0021] Optionally, the S4 specifically includes:

[0022] S41. Pre-install an AI-based operating system and application management module, which can automatically optimize system performance based on user behavior and usage patterns using an adaptive large-scale parallel optimization algorithm combined with a dynamic evolution strategy, simplify the system installation and configuration process, and provide intelligent application management and automatic update functions, and achieve one-click installation and configuration by using automated scripts and pre-configured templates;

[0023] S42, the operating system collects user behavior data and system usage pattern data, including application usage frequency, resource usage, and system response time, and uses adaptive large-scale parallel optimization algorithms and dynamic evolution strategies to perform data analysis and pattern recognition to generate a system optimization model;

[0024] S43. Automatically adjust system configuration parameters, including memory allocation, processor scheduling, and network bandwidth allocation, based on the optimization model to achieve system performance optimization:

[0025]

[0026] Among them, O t is the current optimization parameter, α is the learning rate, is the gradient of performance improvement, β is the adaptive coefficient, is the gradient of parallel optimization, γ is the dynamic evolution factor, E t is the mutation value generated by the evolution strategy, δ is the complex optimization factor, H(O t ) is a high-dimensional nonlinear optimization function;

[0027] S44. Based on the adaptive large-scale parallel optimization algorithm and dynamic evolution strategy, an adaptive weight adjustment mechanism is introduced to dynamically adjust the weights of each gradient and factor during the optimization process, making the optimization more flexible and efficient:

[0028]

[0029] Among them, W new is the new weight, W old is the original weight, η is the adjustment factor, ΔW is the weight change, and λ is the smoothing factor;

[0030] S45. Pre-install commonly used applications and tools to reduce the number of manual installation and configuration steps for users. Use intelligent application management functions to achieve automatic installation, configuration and update of applications, and provide automatic fault detection and recovery mechanisms;

[0031] S46. Monitor the version and status of the application in real time, obtain the latest application update information, use the automatic update function to automatically download and install application updates during system idle time periods, and use the backup and recovery mechanism during the application update process.

[0032] Optionally, the S5 specifically includes:

[0033] S51. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels;

[0034] S52. Collect the operation behavior data of IoT devices, including the access records, operation logs and network traffic of the devices, analyze the behavior data using the improved isolation forest algorithm, and establish a normal behavior model:

[0035]

[0036] Where IF(x) is the behavior score, E(h(x)) is the average path length of the isolated data point x, c(n) is the normalization factor, and n is the number of samples. The improved isolation forest algorithm optimizes the accuracy and efficiency of anomaly detection by dynamically adjusting the parameter θ and the number of trees t:

[0037] θ=θinitial+η·Δθ;

[0038] Among them, θ initial is the initial parameter, η is the learning rate, and Δθ is the parameter change; the current behavior data is monitored in real time, compared with the normal behavior model, and abnormal behavior is identified. When IF(x) exceeds the preset threshold, an intrusion alarm is triggered;

[0039] S53. Based on the security requirements of inter-device communication, dynamically select the appropriate encryption algorithm and key length, monitor the communication data flow and device performance in real time, and adaptively adjust the encryption algorithm and key length:

[0040]

[0041] Among them, k new is the new key length, k old is the original key length, μ is the adjustment factor, Δk is the key change, and λ is the smoothing factor;

[0042] S54. Deploy a distributed authentication node in the IoT platform, so that the distributed authentication node generates an authentication token:

[0043] T = H (D + S + t);

[0044] Among them, T is the authentication token, H is the hash function, D is the device identifier, S is the device key, and t is the timestamp; the authentication node verifies the device's authentication token in real time and adjusts the system's access control policy based on the authentication result:

[0045]

[0046] Where V is the verification result, 1 indicates verification passed, and 0 indicates verification failed.

[0047] Optionally, the S52 specifically includes:

[0048] S521. Collect operational behavior data of IoT devices, including device access records, operation logs, and network traffic;

[0049] S522, using an improved isolation forest algorithm to analyze the behavior data and establish a normal behavior model;

[0050] S523. Initialize the number of trees in the forest t and the maximum height of each tree h max ,Each tree is trained by recursively partitioning the data by randomly selecting a dimension and a random split point on that dimension until the maximum height is reached or only one data point remains in the node;

[0051] S524, introduce an adaptive tree height adjustment mechanism to dynamically adjust the maximum tree height h according to the characteristics of the data set max :

[0052] hmax,new=hmax,old+α·sin(Δh);

[0053] Among them, h max,new is the new maximum height, h max,old is the original maximum height, α is the adjustment factor, and Δh is the height change;

[0054] S525, using weighted path length calculation, assigning different weights to path lengths in different trees:

[0055]

[0056] Among them, E w (h(x)) is the weighted average path length, w i is the weight of the i-th tree, h i (x) is the path length in the i-th tree;

[0057] S526, dynamically adjusting the number of trees t and adaptively adjusting the parameter θ:

[0058] θnew=θold×exp(η·Δθ);

[0059] Among them, θ new is the new parameter, θ old is the original parameter, η is the adjustment factor, and Δθ is the parameter change;

[0060] S527. Monitor the current behavior data in real time, compare it with the normal behavior model, identify abnormal behavior, and trigger an intrusion alarm when IF(x) exceeds a preset threshold.

[0061] Optionally, the S6 specifically includes:

[0062] S61. Deploy edge micro data centers near IoT devices and configure small data centers, including computing nodes and storage nodes, through standardized deployment processes to achieve distributed data processing and storage.

[0063] S62. Quickly initialize computing resources and storage resources of edge nodes through predefined configuration templates and automation tools, and deploy edge computing nodes and data storage nodes;

[0064] S63, using edge nodes to preprocess and filter data to generate preprocessed data;

[0065] S64, using the preprocessed data to perform real-time data analysis and decision-making on the edge node, using an improved hierarchical Bayesian model, combined with Bayesian optimization and dynamic model selection technology, to generate edge node analysis results;

[0066] S65, transmitting the analysis results of the edge node to the central data center, combining the analysis results of the central data center, performing comprehensive analysis and decision-making, and generating comprehensive analysis results;

[0067] S66. Based on the comprehensive analysis results and real-time data analysis results, the edge micro data center dynamically adjusts computing resources and data transmission strategies to optimize system performance and response speed;

[0068] S67. Establish a data synchronization mechanism between the edge node and the central data center so that the real-time analysis results of the edge node can be transmitted to the central data center in a timely manner. The data synchronization mechanism dynamically adjusts the synchronization frequency and bandwidth allocation according to the network status.

[0069] S68. Use automated tools and scripts to achieve rapid deployment and configuration of the system, use configuration templates to optimize the construction speed, and provide detailed logs and reports;

[0070] S69. Provide a unified interface and standardized API to simplify the access and management of IoT devices, use pre-processed data and analysis results to optimize the scalability and compatibility of the IoT platform, and support plug-and-play functions to automatically identify and configure new access devices to further optimize the system's deployment capabilities.

[0071] Optionally, the system includes the following modules:

[0072] Central processing unit: high-performance multi-core processor, supporting parallel computing and multi-tasking, running stably under high load;

[0073] Memory unit: large-capacity, high-speed memory modules provide ample storage space and data caching capabilities;

[0074] Storage unit: supports a variety of storage media, provides high-speed reading and writing capabilities and large-capacity storage space to meet different data storage needs;

[0075] Communication module: Integrates multiple communication technology modules, including Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks, to communicate efficiently with various IoT devices;

[0076] Interface module: provides multiple interfaces, supports the connection of multiple external devices, and has strong scalability and compatibility;

[0077] Power module: Stable power management system, supports multiple power input modes, and operates stably in different environments;

[0078] Modular design: The system adopts a modular design, and each hardware module can be flexibly selected and combined according to needs, which facilitates system construction and expansion.

[0079] The beneficial effects of the present invention are:

[0080] The present invention decomposes the Internet of Things platform construction process into predefined steps by providing standardized processes and automated scripts, simplifies configuration and integration, realizes one-click deployment, significantly reduces manual intervention, improves construction efficiency, greatly shortens system construction time, and quickly responds to market demand. Modular hardware design is adopted, and each hardware module includes a central processing unit, a memory unit, a storage unit, a communication module, and an interface module. Users can flexibly select and combine according to their needs to achieve rapid construction and expansion of the system, enhance the flexibility and adaptability of the system, and solve the defects of fixed hardware design and lack of flexibility in the prior art. By integrating multiple communication technologies, using dynamic protocol converters and automatic configuration systems, supporting Ethernet, optical fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks, and realizing seamless switching and compatibility of wired and wireless communications, the compatibility and applicability of the system with different types of Internet of Things devices are greatly improved, and the problem of poor communication compatibility in the prior art is solved.

[0081] The present invention pre-installs an artificial intelligence-based operating system and application management module, and uses an adaptive large-scale parallel optimization algorithm combined with a dynamic evolution strategy to automatically optimize system performance according to user behavior and usage patterns, simplifying the system installation and configuration process, and providing intelligent application management and automatic update functions. This innovation reduces the user's manual configuration and management work, and improves the user experience and performance of the system. In terms of security, multi-level dynamic security protection measures are adopted, including an intrusion detection system based on behavioral analysis, an adaptive encryption module, and a distributed authentication system, which can monitor, identify and respond to potential security threats in real time to ensure the security of data and systems. By using an improved isolation forest algorithm to analyze behavioral data and dynamically adjust security policies, the system's security protection capabilities are significantly improved, solving the problem of insufficient security in the prior art.

[0082] The present invention integrates a computing architecture based on edge micro data centers, deploys small data centers near IoT devices, performs distributed data processing and storage through edge micro data centers, and performs real-time data analysis and decision-making on edge nodes. The improved hierarchical Bayesian model, combined with Bayesian optimization and dynamic model selection technology, improves the accuracy and real-time performance of data analysis, reduces data transmission delays, improves the response speed and processing efficiency of the system, meets the high real-time data processing requirements of intelligent transportation and industrial automation, and makes up for the shortcomings of existing technologies in data processing real-time performance and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0084] Figure 1 A flowchart of a host system and method for quickly building an Internet of Things platform proposed by the present invention;

[0085] Figure 2 The following is a flowchart of the dynamic protocol converter and automatic configuration system in the present invention. DETAILED DESCRIPTION

[0086] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0087] refer to Figure 1-2 , a host system and method for realizing rapid construction of an Internet of Things platform, comprising the following steps:

[0088] S1. Provide standardized processes to break down the IoT platform building process into predefined steps, simplify configuration and integration, including using predefined templates and automated scripts to achieve one-click deployment and reduce manual intervention;

[0089] S2. Modular hardware design is adopted. The hardware modules include a central processing unit, a memory unit, a storage unit, a communication module and an interface module. Users can flexibly select and combine hardware modules according to their needs to achieve construction and expansion;

[0090] S3, integrates multiple communication technologies, supports Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks through dynamic protocol converters and automatic configuration systems, realizes seamless switching and compatibility of wired and wireless communication technologies, and simplifies communication configuration;

[0091] S4. Pre-installed AI-based operating system and application management module, which can use machine learning algorithms to automatically optimize system performance based on user behavior and usage patterns, simplify system installation and configuration processes, and provide intelligent application management and automatic update functions to achieve deployment and management;

[0092] S5. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels;

[0093] S6. Integrate a computing architecture based on edge micro data centers, deploy small data centers near IoT devices, perform distributed data processing and storage through edge micro data centers, and perform real-time data analysis and decision-making on edge nodes.

[0094] In this implementation, S3 specifically includes:

[0095] S31. Integrate multiple communication technologies in the IoT platform and achieve seamless switching and compatibility between wired communication and wireless communication through a dynamic protocol converter. The wired communication includes Ethernet and optical fiber communication, and the wireless communication includes Wi-Fi, Bluetooth, Zigbee and cellular network.

[0096] S32, the dynamic protocol converter automatically selects and switches the appropriate communication protocol according to the current communication environment and requirements, and realizes seamless switching of communication technology;

[0097] S33, the automatic configuration system automatically configures communication parameters, including bandwidth, latency, frequency, and signal strength, by detecting the network environment and device status, and combines machine learning algorithms to adjust and optimize parameters in real time;

[0098] S34, automatic configuration of communication parameters, detecting bandwidth B, delay L, frequency F and signal strength S in the current communication environment, and according to the detected parameters, the dynamic protocol converter selects the optimal communication protocol and adjusts the relevant communication parameters through the automatic configuration system;

[0099] S35, the dynamic protocol converter and the automatic configuration system work together. When a change in the communication environment is detected, the dynamic protocol converter automatically selects and switches to the communication protocol that best suits the current environment. The automatic configuration system automatically adjusts the communication parameters according to the new communication protocol to achieve seamless communication switching.

[0100] In this implementation, S4 specifically includes:

[0101] S41. Pre-install an AI-based operating system and application management module, which can automatically optimize system performance based on user behavior and usage patterns using an adaptive large-scale parallel optimization algorithm combined with a dynamic evolution strategy, simplify the system installation and configuration process, and provide intelligent application management and automatic update functions, and achieve one-click installation and configuration by using automated scripts and pre-configured templates;

[0102] S42, the operating system collects user behavior data and system usage pattern data, including application usage frequency, resource usage, and system response time, and uses adaptive large-scale parallel optimization algorithms and dynamic evolution strategies to perform data analysis and pattern recognition to generate a system optimization model;

[0103] S43. Automatically adjust system configuration parameters, including memory allocation, processor scheduling, and network bandwidth allocation, based on the optimization model to achieve system performance optimization:

[0104]

[0105] Among them, O t is the current optimization parameter, α is the learning rate, is the gradient of performance improvement, β is the adaptive coefficient, is the gradient of parallel optimization, γ is the dynamic evolution factor, E t is the mutation value generated by the evolution strategy, δ is the complex optimization factor, H(O t ) is a high-dimensional nonlinear optimization function;

[0106] S44. Based on the adaptive large-scale parallel optimization algorithm and dynamic evolution strategy, an adaptive weight adjustment mechanism is introduced to dynamically adjust the weights of each gradient and factor during the optimization process, making the optimization more flexible and efficient:

[0107]

[0108] Among them, Wnew is the new weight, W old is the original weight, η is the adjustment factor, ΔW is the weight change, and λ is the smoothing factor;

[0109] S45. Pre-install commonly used applications and tools to reduce the number of manual installation and configuration steps for users. Use intelligent application management functions to achieve automatic installation, configuration and update of applications, and provide automatic fault detection and recovery mechanisms;

[0110] S46. Monitor the version and status of the application in real time, obtain the latest application update information, use the automatic update function to automatically download and install application updates during system idle time periods, and use the backup and recovery mechanism during the application update process.

[0111] In this implementation manner, S5 specifically includes:

[0112] S51. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels;

[0113] S52. Collect the operation behavior data of IoT devices, including the access records, operation logs and network traffic of the devices, analyze the behavior data using the improved isolation forest algorithm, and establish a normal behavior model:

[0114]

[0115] Where IF(x) is the behavior score, E(h(x)) is the average path length of the isolated data point x, c(n) is the normalization factor, and n is the number of samples. The improved isolation forest algorithm optimizes the accuracy and efficiency of anomaly detection by dynamically adjusting the parameter θ and the number of trees t:

[0116] θ=θinitial+η·Δθ;

[0117] Among them, θ initial is the initial parameter, η is the learning rate, and Δθ is the parameter change; the current behavior data is monitored in real time, compared with the normal behavior model, and abnormal behavior is identified. When IF(x) exceeds the preset threshold, an intrusion alarm is triggered;

[0118] S53. Based on the security requirements of inter-device communication, dynamically select the appropriate encryption algorithm and key length, monitor the communication data flow and device performance in real time, and adaptively adjust the encryption algorithm and key length:

[0119]

[0120] Among them, k new is the new key length, k old is the original key length, μ is the adjustment factor, Δk is the key change, and λ is the smoothing factor;

[0121] S54. Deploy a distributed authentication node in the IoT platform, so that the distributed authentication node generates an authentication token:

[0122] T = H (D + S + t);

[0123] Among them, T is the authentication token, H is the hash function, D is the device identifier, S is the device key, and t is the timestamp; the authentication node verifies the device's authentication token in real time and adjusts the system's access control policy based on the authentication result:

[0124]

[0125] Where V is the verification result, 1 indicates verification passed, and 0 indicates verification failed.

[0126] In this implementation manner, the S52 specifically includes:

[0127] S521. Collect operational behavior data of IoT devices, including device access records, operation logs, and network traffic;

[0128] S522, using an improved isolation forest algorithm to analyze the behavior data and establish a normal behavior model;

[0129] S523. Initialize the number of trees in the forest t and the maximum height of each tree h max ,Each tree is trained by recursively partitioning the data by randomly selecting a dimension and a random split point on that dimension until the maximum height is reached or only one data point remains in the node;

[0130] S524, introduce an adaptive tree height adjustment mechanism to dynamically adjust the maximum tree height h according to the characteristics of the data set max :

[0131] hmax,new=hmax,old+α·sin(Δh);

[0132] Among them, h max,new is the new maximum height, h max,old is the original maximum height, α is the adjustment factor, and Δh is the height change;

[0133] S525, using weighted path length calculation, assigning different weights to path lengths in different trees:

[0134]

[0135] Among them, E w (h(x)) is the weighted average path length, w i is the weight of the i-th tree, h i (x) is the path length in the i-th tree;

[0136] S526, dynamically adjusting the number of trees t and adaptively adjusting the parameter θ:

[0137] θnew=θold×exp(η·Δθ);

[0138] Among them, θ new is the new parameter, θ old is the original parameter, η is the adjustment factor, and Δθ is the parameter change;

[0139] S527. Monitor the current behavior data in real time, compare it with the normal behavior model, identify abnormal behavior, and trigger an intrusion alarm when IF(x) exceeds a preset threshold.

[0140] In this implementation manner, S6 specifically includes:

[0141] S61. Deploy edge micro data centers near IoT devices and configure small data centers, including computing nodes and storage nodes, through standardized deployment processes to achieve distributed data processing and storage.

[0142] S62. Quickly initialize computing resources and storage resources of edge nodes through predefined configuration templates and automation tools, and deploy edge computing nodes and data storage nodes;

[0143] S63, using edge nodes to preprocess and filter data to generate preprocessed data;

[0144] S64, using the preprocessed data to perform real-time data analysis and decision-making on the edge node, using an improved hierarchical Bayesian model, combined with Bayesian optimization and dynamic model selection technology, to generate edge node analysis results;

[0145] S65, transmitting the analysis results of the edge node to the central data center, combining the analysis results of the central data center, performing comprehensive analysis and decision-making, and generating comprehensive analysis results;

[0146] S66. Based on the comprehensive analysis results and real-time data analysis results, the edge micro data center dynamically adjusts computing resources and data transmission strategies to optimize system performance and response speed;

[0147] S67. Establish a data synchronization mechanism between the edge node and the central data center so that the real-time analysis results of the edge node can be transmitted to the central data center in a timely manner. The data synchronization mechanism dynamically adjusts the synchronization frequency and bandwidth allocation according to the network status.

[0148] S68. Use automated tools and scripts to achieve rapid deployment and configuration of the system, use configuration templates to optimize the construction speed, and provide detailed logs and reports;

[0149] S69. Provide a unified interface and standardized API to simplify the access and management of IoT devices, use pre-processed data and analysis results to optimize the scalability and compatibility of the IoT platform, and support plug-and-play functions to automatically identify and configure new access devices to further optimize the system's deployment capabilities.

[0150] In this embodiment, the system includes the following modules:

[0151] Central processing unit: high-performance multi-core processor, supporting parallel computing and multi-tasking, running stably under high load;

[0152] Memory unit: large-capacity, high-speed memory modules provide ample storage space and data caching capabilities;

[0153] Storage unit: supports a variety of storage media, provides high-speed reading and writing capabilities and large-capacity storage space to meet different data storage needs;

[0154] Communication module: Integrates multiple communication technology modules, including Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks, to communicate efficiently with various IoT devices;

[0155] Interface module: provides multiple interfaces, supports the connection of multiple external devices, and has strong scalability and compatibility;

[0156] Power module: Stable power management system, supports multiple power input modes, and operates stably in different environments;

[0157] Modular design: The system adopts a modular design, and each hardware module can be flexibly selected and combined according to needs, which facilitates system construction and expansion.

[0158] Embodiment 1:

[0159] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a smart city construction project. The municipal government plans to quickly build an Internet of Things platform to monitor and manage various aspects of the city, including transportation, environment, energy, and public safety. However, the construction process of the traditional Internet of Things platform is complicated and time-consuming, requiring professionals to perform a lot of manual configuration and debugging, which is difficult to meet the needs of rapid deployment. In addition, the hardware design of the existing Internet of Things platform is fixed, the communication technology has poor compatibility, and the real-time and security of data processing are insufficient, which have also become the main obstacles in the implementation of smart city projects.

[0160] To solve these problems, the municipal government decided to adopt the host system and implementation method for quickly building an IoT platform proposed in the present invention. First, the municipal government decomposed the IoT platform building process into predefined steps through the provided standardized processes and automated scripts, simplified configuration and integration, and achieved one-click deployment. By using predefined templates and automated tools, the infrastructure was built in just one day, greatly shortening the two weeks required by traditional methods.

[0161] With modular hardware design, the municipal government flexibly selected central processing units, memory units, storage units, communication modules, and interface modules according to demand, realizing the rapid construction and expansion of the system. For example, in order to achieve real-time monitoring of traffic, a high-performance multi-core processor and a large-capacity memory module were selected to ensure the stable operation of the system under high load. In order to support different sensors and devices, a variety of communication technology modules are integrated, including Ethernet, optical fiber, Wi-Fi, Bluetooth, Zigbee, and cellular networks, realizing seamless switching and compatibility of wired and wireless communications. In terms of operating system and application management, an artificial intelligence-based operating system and application management module are pre-installed, using adaptive large-scale parallel optimization algorithms combined with dynamic evolution strategies to automatically optimize system performance according to user behavior and usage patterns. The system can intelligently manage and update applications, reducing the manual operation and maintenance workload of users. Through automated scripts and pre-configured templates, one-click installation and configuration are achieved, and the entire process takes only a few hours, while traditional methods may take several days.

[0162] In order to ensure the security of data and systems, the municipal government has adopted multi-level dynamic security protection measures, including an intrusion detection system based on behavioral analysis, an adaptive encryption module, and a distributed authentication system, which can monitor, identify, and respond to potential security threats in real time. The improved isolation forest algorithm is used to analyze behavioral data and dynamically adjust security policies, effectively preventing the risk of data leakage and system attacks. The computing architecture based on edge micro data centers is integrated, and small data centers are deployed near IoT devices. Distributed data processing and storage are performed through edge micro data centers, and real-time data analysis and decision-making are performed on edge nodes. The use of an improved hierarchical Bayesian model, combined with Bayesian optimization and dynamic model selection technology, improves the accuracy and real-time performance of data analysis. Traffic monitoring data can be pre-processed at edge nodes, reducing data transmission delays and realizing real-time traffic flow monitoring and abnormal situation warnings.

[0163] During the project implementation, by comparing the construction time, system performance and security of the traditional method and the method of the present invention, the traditional method usually takes 2 weeks to complete the construction of the Internet of Things platform, while the method of the present invention only takes 1 day, and the construction efficiency is increased by nearly 14 times. In the process of building the urban traffic monitoring system, after adopting the method of the present invention, the system's real-time processing capability of traffic data during peak hours has increased by 30%, and the delay has been reduced from the original 500 milliseconds to 100 milliseconds, greatly improving the response speed of traffic monitoring. Through the application of multi-level dynamic security protection measures, the system has successfully prevented 50 potential security threats in the past 3 months, and no data leakage incidents have occurred. In contrast, the Internet of Things platform of the traditional method has suffered at least 5 data leaks in the same period of time, and the security has been significantly improved.

[0164] Specifically, in actual applications, the municipal government conducted detailed testing and evaluation of the monitoring of the intelligent transportation system. Through the deployment of edge micro data centers, traffic data can be pre-processed and analyzed locally, and only key data needs to be transmitted to the central data center, which greatly reduces the amount of data transmission and latency. During peak hours, the amount of traffic data that can be processed per second has increased from 500 in traditional methods to 2,000, a four-fold increase. The system can monitor traffic flow at each intersection in real time, analyze abnormal conditions and automatically adjust traffic signals, reducing urban traffic congestion.

[0165] Table 1 Comparison of project implementation effects

[0166]

[0167]

[0168] It can be seen from the above table data that in the process of building the Internet of Things platform, the method of the present invention significantly shortens the construction time. The traditional method usually takes 14 days to complete, while the method of the present invention only takes 1 day. This improvement has increased the efficiency of building the Internet of Things platform by 14 times and can quickly respond to market demand. By integrating multiple communication technologies and adopting modular hardware design, the method of the present invention has significantly improved the system performance. In terms of traffic data processing capacity during peak hours, the traditional method can only process 500 data per second, while the method of the present invention can process 2000 data per second, and the performance has been improved by 4 times. At the same time, the data processing delay is reduced from 500 milliseconds in the traditional method to 100 milliseconds, and the response speed is significantly improved. Through multi-level dynamic security protection measures, including an intrusion detection system based on behavioral analysis, an adaptive encryption module, and a distributed authentication system, 50 potential security threats have been successfully blocked in the past 3 months, while the traditional method has failed to block any security threats in the same time. In addition, the method of the present invention has not had any data leakage incidents in the past 3 months, while the traditional method has had 5 data leakages, which significantly improves the security of the system.

[0169] The method of the present invention has significant improvements in setup time, system performance and security: the setup time is reduced from 14 days to 1 day, and the efficiency is improved by 14 times; the data processing delay is reduced from 500 milliseconds to 100 milliseconds, and the response speed is improved; the peak data processing capacity is increased from 500 per second to 2000 per second, and the performance is improved by 4 times; the security threat prevention is increased from 0 times to 50 times, and the security is significantly improved; the data leakage incidents are reduced from 5 times to 0 times, and the system security is guaranteed.

[0170] In summary, the present invention significantly improves the efficiency of building the Internet of Things platform and the system performance through standardized, modular and intelligent design, solves many problems in the prior art, has the advantages of rapid construction, flexible expansion, strong communication compatibility, high security and high real-time data processing, and has broad application prospects and practical value. The municipal government applied the present invention in the smart city construction project and successfully realized the construction of an efficient, safe and intelligent Internet of Things platform, achieving remarkable results and good social benefits.

[0171] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A host system and method for realizing rapid construction of an Internet of Things platform, characterized in that: The steps include: S1. Provide standardized processes to break down the IoT platform building process into predefined steps, simplify configuration and integration, including using predefined templates and automated scripts to achieve one-click deployment and reduce manual intervention; S2. Modular hardware design is adopted. The hardware modules include a central processing unit, a memory unit, a storage unit, a communication module and an interface module. Users can flexibly select and combine hardware modules according to their needs to achieve construction and expansion; S3, integrates multiple communication technologies, supports Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks through dynamic protocol converters and automatic configuration systems, realizes seamless switching and compatibility of wired and wireless communication technologies, and simplifies communication configuration; S4. Pre-installed AI-based operating system and application management module, which can use machine learning algorithms to automatically optimize system performance based on user behavior and usage patterns, simplify system installation and configuration processes, and provide intelligent application management and automatic update functions to achieve deployment and management; S5. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels; S6. Integrate a computing architecture based on edge micro data centers, deploy small data centers near IoT devices, perform distributed data processing and storage through edge micro data centers, and perform real-time data analysis and decision-making on edge nodes.

2. The method for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The S3 specifically includes: S31. Integrate multiple communication technologies in the IoT platform and achieve seamless switching and compatibility between wired communication and wireless communication through a dynamic protocol converter. The wired communication includes Ethernet and optical fiber communication, and the wireless communication includes Wi-Fi, Bluetooth, Zigbee and cellular network. S32, the dynamic protocol converter automatically selects and switches the appropriate communication protocol according to the current communication environment and requirements, and realizes seamless switching of communication technology; S33, the automatic configuration system automatically configures communication parameters, including bandwidth, latency, frequency, and signal strength, by detecting the network environment and device status, and combines machine learning algorithms to adjust and optimize parameters in real time; S34, automatic configuration of communication parameters, detecting bandwidth B, delay L, frequency F and signal strength S in the current communication environment, and according to the detected parameters, the dynamic protocol converter selects the optimal communication protocol and adjusts the relevant communication parameters through the automatic configuration system; S35, the dynamic protocol converter and the automatic configuration system work together. When a change in the communication environment is detected, the dynamic protocol converter automatically selects and switches to the communication protocol that best suits the current environment. The automatic configuration system automatically adjusts the communication parameters according to the new communication protocol to achieve seamless communication switching.

3. The method for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The S4 specifically includes: S41. Pre-install an AI-based operating system and application management module, which can automatically optimize system performance based on user behavior and usage patterns using an adaptive large-scale parallel optimization algorithm combined with a dynamic evolution strategy, simplify the system installation and configuration process, and provide intelligent application management and automatic update functions, and achieve one-click installation and configuration by using automated scripts and pre-configured templates; S42, the operating system collects user behavior data and system usage pattern data, including application usage frequency, resource usage, and system response time, and uses adaptive large-scale parallel optimization algorithms and dynamic evolution strategies to perform data analysis and pattern recognition to generate a system optimization model; S43. Automatically adjust system configuration parameters, including memory allocation, processor scheduling, and network bandwidth allocation, based on the optimization model to achieve system performance optimization: Among them, O t is the current optimization parameter, α is the learning rate, is the gradient of performance improvement, β is the adaptive coefficient, is the gradient of parallel optimization, γ is the dynamic evolution factor, E t is the mutation value generated by the evolution strategy, δ is the complex optimization factor, H(O t ) is a high-dimensional nonlinear optimization function; S44. Based on the adaptive large-scale parallel optimization algorithm and dynamic evolution strategy, an adaptive weight adjustment mechanism is introduced to dynamically adjust the weights of each gradient and factor during the optimization process, making the optimization more flexible and efficient: Among them, W new is the new weight, W old is the original weight, η is the adjustment factor, ΔW is the weight change, and λ is the smoothing factor; S45. Pre-install commonly used applications and tools to reduce the number of manual installation and configuration steps for users. Use intelligent application management functions to achieve automatic installation, configuration and update of applications, and provide automatic fault detection and recovery mechanisms; S46. Monitor the version and status of the application in real time, obtain the latest application update information, use the automatic update function to automatically download and install application updates during system idle time periods, and use the backup and recovery mechanism during the application update process.

4. The method for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The S5 specifically includes: S51. Adopt multi-level dynamic security protection measures, including intrusion detection systems based on behavioral analysis, adaptive encryption modules and distributed authentication systems, which can monitor, identify and respond to potential security threats in real time and automatically adjust security policies according to threat levels; S52. Collect the operation behavior data of IoT devices, including the access records, operation logs and network traffic of the devices, analyze the behavior data using the improved isolation forest algorithm, and establish a normal behavior model: Where IF(x) is the behavior score, E(h(x)) is the average path length of the isolated data point x, c(n) is the normalization factor, and n is the number of samples. The improved isolation forest algorithm optimizes the accuracy and efficiency of anomaly detection by dynamically adjusting the parameter θ and the number of trees t: θ=θinitial+η·Δθ; Among them, θ initial is the initial parameter, η is the learning rate, and Δθ is the parameter change; the current behavior data is monitored in real time, compared with the normal behavior model, and abnormal behavior is identified. When IF(x) exceeds the preset threshold, an intrusion alarm is triggered; S53. Based on the security requirements of inter-device communication, dynamically select the appropriate encryption algorithm and key length, monitor the communication data flow and device performance in real time, and adaptively adjust the encryption algorithm and key length: Among them, k new is the new key length, k old is the original key length, μ is the adjustment factor, Δk is the key change, and λ is the smoothing factor; S54. Deploy a distributed authentication node in the IoT platform, so that the distributed authentication node generates an authentication token: T = H (D + S + t); Among them, T is the authentication token, H is the hash function, D is the device identifier, S is the device key, and t is the timestamp; the authentication node verifies the device's authentication token in real time and adjusts the system's access control policy based on the authentication result: Where V is the verification result, 1 indicates that the verification is successful, and 0 indicates that the verification fails.

5. The method for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The S52 specifically includes: S521. Collect operational behavior data of IoT devices, including device access records, operation logs, and network traffic; S522, using an improved isolation forest algorithm to analyze the behavior data and establish a normal behavior model; S523. Initialize the number of trees in the forest t and the maximum height of each tree h max ,Each tree is trained by recursively partitioning the data by randomly selecting a dimension and a random split point on that dimension until the maximum height is reached or only one data point remains in the node; S524, introduce an adaptive tree height adjustment mechanism to dynamically adjust the maximum tree height h according to the characteristics of the data set max : hmax,new=hmax,old+α·sin(Δh); Among them, h max,new is the new maximum height, h max,old is the original maximum height, α is the adjustment factor, and Δh is the height change; S525, using weighted path length calculation, assigning different weights to path lengths in different trees: Among them, E w (h(x)) is the weighted average path length, w i is the weight of the i-th tree, h i (x) is the path length in the i-th tree; S526, by dynamically adjusting the number of trees t and adaptively adjusting the parameter θ: θnew=θold×exp(η·Δθ); Among them, θ new is the new parameter, θ old is the original parameter, η is the adjustment factor, and Δθ is the parameter change; S527. Monitor the current behavior data in real time, compare it with the normal behavior model, identify abnormal behavior, and trigger an intrusion alarm when IF(x) exceeds a preset threshold.

6. The method for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The S6 specifically includes: S61. Deploy edge micro data centers near IoT devices and configure small data centers, including computing nodes and storage nodes, through standardized deployment processes to achieve distributed data processing and storage. S62. Quickly initialize computing resources and storage resources of edge nodes through predefined configuration templates and automation tools, and deploy edge computing nodes and data storage nodes; S63, using edge nodes to preprocess and filter data to generate preprocessed data; S64, using the preprocessed data to perform real-time data analysis and decision-making on the edge node, using an improved hierarchical Bayesian model, combined with Bayesian optimization and dynamic model selection technology, to generate edge node analysis results; S65, transmitting the analysis results of the edge node to the central data center, combining the analysis results of the central data center, performing comprehensive analysis and decision-making, and generating comprehensive analysis results; S66. Based on the comprehensive analysis results and real-time data analysis results, the edge micro data center dynamically adjusts computing resources and data transmission strategies to optimize system performance and response speed; S67. Establish a data synchronization mechanism between the edge node and the central data center so that the real-time analysis results of the edge node can be transmitted to the central data center in a timely manner. The data synchronization mechanism dynamically adjusts the synchronization frequency and bandwidth allocation according to the network status. S68. Use automated tools and scripts to achieve rapid deployment and configuration of the system, use configuration templates to optimize the construction speed, and provide detailed logs and reports; S69. Provide a unified interface and standardized API to simplify the access and management of IoT devices, use pre-processed data and analysis results to optimize the scalability and compatibility of the IoT platform, and support plug-and-play functions to automatically identify and configure new access devices to further optimize the system's deployment capabilities.

7. The host system for realizing rapid construction of an Internet of Things platform according to claim 1, characterized in that: The system includes the following modules: Central processing unit: high-performance multi-core processor, supporting parallel computing and multi-tasking, running stably under high load; Memory unit: large-capacity, high-speed memory modules provide ample storage space and data caching capabilities; Storage unit: supports a variety of storage media, provides high-speed reading and writing capabilities and large-capacity storage space to meet different data storage needs; Communication module: Integrates multiple communication technology modules, including Ethernet, fiber, Wi-Fi, Bluetooth, Zigbee and cellular networks, to communicate efficiently with various IoT devices; Interface module: provides multiple interfaces, supports the connection of multiple external devices, and has strong scalability and compatibility; Power module: Stable power management system, supports multiple power input modes, and operates stably in different environments; Modular design: The system adopts a modular design, and each hardware module can be flexibly selected and combined according to needs, which facilitates system construction and expansion.

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