Modularized extensible Internet of Things multi-scene dynamic regulation and control system and regulation and control method

Abstract

The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and a regulation and control method, and belongs to the field of intelligent regulation and control. The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and method. The system comprises a data acquisition unit, a cloud analysis unit and a regulation and control execution unit. According to the invention, the problem of single scene in the prior art is solved, the efficiency and reliability of data transmission are ensured by collecting multi-scene data of the Internet of Things in real time and automatically selecting the optimal communication protocol and link according to the network condition, cloud analysis is combined, and according to the preset scene feature model, the data is subjected to multi-scene data transmission. According to the invention, the scene type can be automatically identified and the corresponding regulation and control rule can be called, the accurate and real-time regulation and control of the Internet of Things equipment can be realized according to the regulation and control rule, the operation efficiency of the equipment and the overall performance of the system can be effectively improved, the regulation and control process can be evaluated and analyzed through feedback information, the regulation and control rule can be automatically adjusted, and a continuously optimized closed-loop control system can be formed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and specifically to a modular and scalable Internet of Things multi-scenario dynamic control system and control method. Background Art

[0002] With the rapid development of the Internet of Things (IoT), it has been widely applied in a variety of fields, including smart homes, smart industry, smart agriculture, and smart healthcare. However, the complexity and diversity of the IoT also present new challenges. Traditional IoT systems are typically designed for a single scenario and lack flexibility and scalability. When the application scenario changes or new functionality is required, large-scale modifications to the entire system are often required, which is costly, time-consuming, and labor-intensive. Therefore, this approach does not meet existing needs. To address this, we propose a modular and scalable IoT multi-scenario dynamic control system and control method. Summary of the Invention

[0003] The purpose of the present invention is to provide a modular and scalable Internet of Things multi-scenario dynamic control system and control method. By real-time collection of Internet of Things multi-scenario data and combining it with cloud analysis, it can automatically identify the scene type and call the corresponding control rules, thereby realizing accurate and real-time control of Internet of Things devices, effectively improving the equipment operation efficiency and overall system performance, and solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a modular and scalable multi-scenario dynamic control system for the Internet of Things, comprising a data acquisition unit, a cloud analysis unit, and a control execution unit; The data acquisition unit is configured to collect various types of data in different scenarios of the Internet of Things in real time, including but not limited to environmental data, device operation data, and user behavior data; The cloud analysis unit is configured to match various types of data collected in real time by the data acquisition unit according to a preset scene feature model, automatically identify the current IoT scene type and obtain corresponding control rules; The control execution unit is configured to perform real-time control on the IoT device according to the scenario type and corresponding control rules determined by the cloud analysis unit.

[0005] Furthermore, the data acquisition unit includes: The data acquisition module is configured to consist of multiple different types of sensor units and is deployed at various monitoring points in a distributed architecture to collect various types of data in different IoT scenarios in real time; The data processing module is configured to clean, filter and convert the collected raw data, remove noise data, invalid data and duplicate data, and convert them into a unified standard format; The data transmission module is configured to transmit processed data of various types to the cloud analysis unit, supports multiple communication protocols, and automatically selects the optimal communication protocol and link according to the network conditions.

[0006] Furthermore, the data transmission module includes: The network monitoring module is configured to monitor key network indicators in real time, including network latency and packet loss rate, to assess the current network status. Specifically: The network latency is determined by periodically sending a probe signal and receiving feedback, and calculating the round-trip time of the signal. Count the number of successful and failed transmissions within a certain period of time and calculate the packet loss rate; a communication selection module configured to automatically select an optimal communication protocol and link based on the evaluation results provided by the network monitoring module; The data packaging module is configured to package and encapsulate the processed data and transmit it according to the communication protocol and link selected by the selected communication selection module. At the same time, the size, transmission interval and transmission parameters of the data packet are dynamically adjusted according to the network conditions. Specifically: Set transmission parameters, including transmission rate, transmission power, and number of retransmissions, according to network conditions and communication protocol requirements; Dynamically adjust the packet size based on network conditions. In networks with sufficient bandwidth, the packet size will be increased appropriately. In environments with limited bandwidth or network congestion, the packet size will be reduced. According to the network status and data priority, the transmission interval of the data packet is dynamically adjusted. For data with high real-time requirements, the transmission interval is shortened, and for data with low real-time requirements, the transmission interval is appropriately extended.

[0007] Furthermore, the cloud analysis unit includes: A model library construction module is configured to build a model library and store a variety of pre-defined scenario feature models. Each model corresponds to a specific IoT scenario type and contains the typical characteristics, correlations, and change patterns of various types of data in that scenario. At the same time, the model library is extensible and can be used to add new scenario feature models or update and optimize existing models. Among them, a machine learning algorithm is used to build a scene feature model through learning and training of a large amount of historical data; A scene matching module is configured to extract key features from various data collected in real time by the data acquisition unit, match the extracted features with the scene feature model in the model library, and determine the current IoT scene type; The rule acquisition module is configured to store control rules corresponding to various IoT scenario types in advance. The control rules define the specific strategies and operating methods for controlling IoT devices in specific scenarios. According to the scenario type determined by the scenario matching module, the corresponding control rules are obtained and transmitted to the control execution unit.

[0008] Furthermore, the scene matching module performs the following process: A matching algorithm is used to calculate the similarity or matching degree between the feature and each scene feature model, and the closest scene type is selected as the recognition result; The confidence level of the recognition result is evaluated. If the confidence level is lower than a preset threshold range, data is received again from the data acquisition unit to determine the IoT scenario type.

[0009] Furthermore, the control execution unit includes: A rule parsing module is configured to receive and parse the control rules from the cloud analysis unit, extract the specific operation requirements and parameters in the control rules, and convert them into control signals that the IoT device can understand; The control execution module is configured to perform actual control operations on IoT devices based on the control signals generated by the rule parsing module; The control feedback module is configured to send feedback information to the cloud analysis unit after executing the control operation to confirm the execution result of the control operation. The feedback information includes the status change of the equipment and the success or failure of the operation. Based on the feedback information, the control process is evaluated and analyzed through association rule mining and cluster analysis to discover potential problems and optimization directions in the control process, and the control rules are automatically adjusted according to the evaluation results.

[0010] Furthermore, the control execution module has the ability to process multiple tasks in parallel, and can control multiple IoT devices at the same time, and reasonably allocate resources according to the priority and real-time status of different IoT devices.

[0011] Furthermore, after obtaining the corresponding control rules, it also includes: Determine, according to the control rules, multiple IoT devices to be controlled and the control parameters of each IoT device to be controlled; Generate a control cloud scenario based on the control parameters of each IoT device to be controlled, and determine standard control behavior feature data of each IoT device to be controlled in the control cloud scenario; Determine a standard operating curve for each IoT device to be regulated based on the standard regulation behavior characteristic data, and determine operating parameters for each IoT device to be regulated according to the standard operating curve; Determine the control decision index under the control rule based on the operating parameters, and determine the correction behavior parameter under the control decision index through the decision fitness behavior analysis function; Determine the parameter adjustment frequency and parameter adjustment range of each IoT device to be regulated based on the correction behavior parameters; Determine a parameter adjustment time difference for each IoT device to be regulated based on the parameter adjustment frequency and the parameter adjustment determination range, and generate parameter adjustment time series data for each IoT device to be regulated based on the parameter adjustment time difference; Obtain parameter adjustment state sequence data corresponding to the parameter adjustment time series data, and generate a parameter scheduling strategy for each IoT device to be regulated based on the parameter adjustment state sequence data and the parameter adjustment time series data; Determine the dynamic response target of each IoT device to be regulated through a parameter scheduling strategy, and determine the dynamic control parameters for each IoT device to be regulated based on the dynamic response target; Through dynamic control parameters, dynamic matching is performed according to the real-time changes in user behavior data, and each IoT device to be regulated is controlled dynamically.

[0012] Furthermore, in the process of real-time control of IoT devices, it also includes: Determine the control parameters of each IoT device to be regulated according to the regulation rules, and determine the current working mode of each IoT device to be regulated according to the control parameters; Determine the service capability index of each IoT device to be regulated based on the current working mode; The peak value of the control response for each IoT device to be controlled is calculated based on the service capability index of each IoT device to be controlled:

[0013] in, It is expressed as the peak value of the control response for the i-th IoT device to be controlled, It is expressed as the average value of the control response of the ith IoT device to be controlled, It is expressed as the control performance fluctuation factor of the i-th IoT device to be controlled, It is represented as the control response delay factor under the control rule, b is represented as the control response accuracy factor under the control rule, k is represented as the control balance factor, Expressed as the historical response effect factor of the control rule, It is represented as the service capability index of the ith IoT device to be regulated, It is represented as the scheduling weight of the i-th IoT device to be regulated; The network delay parameter of each IoT device to be regulated is set according to the regulation response peak value of each IoT device to be regulated.

[0014] A modular and scalable multi-scenario dynamic control method for the Internet of Things is applicable to a modular and scalable multi-scenario dynamic control system for the Internet of Things, comprising the following steps: By collecting various types of data from different IoT scenarios in real time and performing cleaning, screening, and format conversion, it automatically selects the optimal communication protocol and link for data transmission based on network conditions; Match the collected data according to the preset scene feature model and automatically identify the current IoT scene type; According to the identified scenario type, the corresponding control rules are retrieved from the rule configuration module; Real-time control of IoT devices based on control rules, while monitoring the operating status and feedback information of the devices; The control process is evaluated and analyzed based on the feedback information, and the control rules are automatically adjusted according to the evaluation results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention ensures the efficiency and reliability of data transmission by collecting multi-scenario data of the Internet of Things in real time and automatically selecting the optimal communication protocol and link according to the network status, adapting to complex network environments, combining cloud analysis, and automatically identifying the scene type and calling the corresponding control rules according to the preset scene feature model of the data. It can realize accurate and real-time control of the Internet of Things devices according to the control rules, effectively improving the operating efficiency of the equipment and the overall performance of the system, evaluating and analyzing the control process through feedback information, and automatically adjusting the control rules to form a continuously optimized closed-loop control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural diagram of the modular and scalable multi-scenario dynamic control system of the Internet of Things of the present invention; Figure 2 This is a flowchart of the modular and scalable multi-scenario dynamic control method of the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] To solve the technical problem that existing IoT systems are usually designed for a single scenario, lack flexibility and scalability, and often require large-scale transformation of the entire system when the application scenario changes or new functions need to be expanded, which is costly, time-consuming and labor-intensive, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: A modular and scalable multi-scenario dynamic control system for the Internet of Things, including a data acquisition unit, a cloud analysis unit, and a control execution unit; The data acquisition unit is configured to collect various types of data in different scenarios of the Internet of Things in real time, including but not limited to environmental data, device operation data, and user behavior data; The cloud analysis unit is configured to match various types of data collected in real time by the data acquisition unit according to a preset scene feature model, automatically identify the current IoT scene type and obtain corresponding control rules; The control execution unit is configured to perform real-time control on the IoT device according to the scenario type and corresponding control rules determined by the cloud analysis unit.

[0019] The technical effect of the above technical solution is: through the real-time collection of multi-dimensional data (environment, device operation, user behavior, etc.) by the data acquisition unit, and combined with the preset scene feature model of the cloud analysis unit, the system can quickly and accurately identify the current IoT scene type, so that it can call the corresponding control rules according to the identified IoT scene type, so that the control execution unit can perform real-time control of the IoT device according to the control rules, thereby realizing accurate and real-time control of the IoT device, effectively improving the equipment operation efficiency and overall system performance.

[0020] The data acquisition unit includes: The data acquisition module is configured to consist of multiple different types of sensor units and is deployed at various monitoring points in a distributed architecture to collect various types of data in different IoT scenarios in real time; The data processing module is configured to clean, filter and convert the collected raw data, remove noise data, invalid data and duplicate data, and convert them into a unified standard format; The data transmission module is configured to transmit processed data of various types to the cloud analysis unit, supports multiple communication protocols, and automatically selects the optimal communication protocol and link according to the network conditions.

[0021] The technical effects of the above technical solution are as follows: the data acquisition module is composed of a variety of different types of sensors, which can collect various types of data in different scenarios of the Internet of Things in real time. Through multi-dimensional data collection methods, it ensures that the system can fully perceive various information in the Internet of Things scenarios, and the sensors are deployed at various monitoring points in a distributed architecture, which can cover a wide range of areas and scenarios, and realize all-round monitoring of the Internet of Things environment. The data processing module can obtain high-quality data through cleaning and screening, and provide more accurate input for the cloud analysis unit, thereby improving the accuracy of scene recognition and the rationality of control rules. The processed data is converted into a unified standard format, which can ensure the consistency and compatibility of data during transmission and analysis. The data transmission module supports multiple communication protocols (such as Wi-Fi, 4G / 5G, LoRa, NB-IoT, etc.) and automatically selects the optimal communication protocol and link according to the network conditions. Through the dynamic selection mechanism, it can flexibly adjust the transmission strategy according to different network environments to ensure the efficiency and reliability of data transmission.

[0022] Data transmission module, including: The network monitoring module is configured to monitor key network indicators in real time, including network latency and packet loss rate, to assess the current network status. Specifically: The network latency is determined by periodically sending a probe signal and receiving feedback, and calculating the round-trip time of the signal. Count the number of successful and failed transmissions within a certain period of time and calculate the packet loss rate; a communication selection module configured to automatically select an optimal communication protocol and link based on the evaluation results provided by the network monitoring module; When the Wi-Fi network signal strength is high and the bandwidth is sufficient, Wi-Fi is preferred for data transmission to achieve fast and stable data upload; When the Wi-Fi signal is weak and a mobile data network (e.g., 4G, 5G) is available, it automatically switches to the mobile data network for transmission; In remote areas or environments with poor network coverage, wide area network protocols such as LoRa or NB-IoT can be selected for data transmission to ensure that data can be reliably transmitted to the cloud; The data packaging module is configured to package and encapsulate the processed data and transmit it according to the communication protocol and link selected by the selected communication selection module. At the same time, the size, transmission interval and transmission parameters of the data packet are dynamically adjusted according to the network conditions. Specifically: Set transmission parameters, including transmission rate, transmission power, and number of retransmissions, according to network conditions and communication protocol requirements; Dynamically adjust the packet size based on network conditions. In networks with sufficient bandwidth, the packet size will be increased appropriately. In environments with limited bandwidth or network congestion, the packet size will be reduced. According to the network status and data priority, the transmission interval of the data packet is dynamically adjusted. For data with high real-time requirements, the transmission interval is shortened, and for data with low real-time requirements, the transmission interval is appropriately extended.

[0023] The technical effects of the above technical solution are as follows: the network monitoring module can monitor the key indicators of the network (such as delay and packet loss rate) in real time, can promptly discover network problems, and provide an accurate basis for link selection and data packaging. The communication selection module can automatically select the optimal communication protocol and link according to the evaluation results of the network monitoring module, ensuring the reliable transmission of data in different network environments, thereby reducing the risk of data loss and transmission failure. The data packaging module can dynamically adjust the size, transmission interval and transmission parameters of the data packet according to the network conditions, so as to effectively utilize network resources, thereby improving the overall efficiency of data transmission. The data transmission module ensures stable operation in various network environments by real-time monitoring of network conditions, intelligent link selection and dynamic adjustment of transmission parameters, so as to effectively respond to emergencies such as network failures and signal interference, and ensure the continuous operation of the system.

[0024] Cloud analysis unit, including: A model library construction module is configured to build a model library and store a variety of pre-defined scenario feature models. Each model corresponds to a specific IoT scenario type and contains the typical characteristics, correlations, and change patterns of various types of data in that scenario. At the same time, the model library is extensible and can be used to add new scenario feature models or update and optimize existing models. Among them, a machine learning algorithm is used to build a scene feature model through learning and training of a large amount of historical data; A scene matching module is configured to extract key features from various data collected in real time by the data acquisition unit, match the extracted features with the scene feature model in the model library, and determine the current IoT scene type; The rule acquisition module is configured to store control rules corresponding to various IoT scenario types in advance. The control rules define the specific strategies and operating methods for controlling IoT devices in specific scenarios. According to the scenario type determined by the scenario matching module, the corresponding control rules are obtained and transmitted to the control execution unit.

[0025] The technical effects of the above technical solution are as follows: by learning and training a large amount of historical data through machine learning algorithms, a variety of scene feature models are constructed. Each model describes in detail the typical characteristics, correlation relationships and change patterns of various types of data in a specific IoT scenario, so that the scene feature model can effectively capture the complexity and diversity of the scene, providing a solid foundation for accurate scene recognition. The scene matching module can extract key features from the real-time collected data, and by calculating the similarity or matching degree between the features and the model, the system can accurately identify the current IoT scene type, ensuring the accuracy and real-time nature of scene recognition, thereby providing an accurate basis for subsequent control operations. The rule acquisition module obtains the corresponding control rules according to the determined scene type and transmits them to the control execution unit, ensuring the timeliness and effectiveness of the control operation and avoiding control errors caused by rule mismatch. The cloud analysis unit significantly improves the intelligence level, adaptability and reliability of the IoT multi-scene dynamic control system through accurate scene recognition, flexible control rule management and self-learning capabilities, thereby providing users with efficient and accurate control services.

[0026] The scene matching module performs the following process: A matching algorithm is used to calculate the similarity or matching degree between the feature and each scene feature model, and the closest scene type is selected as the recognition result; The confidence level of the recognition result is evaluated. If the confidence level is lower than a preset threshold range, data is received again from the data acquisition unit to determine the IoT scenario type.

[0027] The technical effect of the above technical solution is: the scene matching module adopts an advanced matching algorithm, which can calculate the similarity or matching degree between the data features collected in real time and the preset scene feature model, so that the system can accurately identify the current IoT scene type. The scene matching module not only identifies the scene type, but also performs a confidence assessment on the recognition results, so as to effectively judge the reliability of the recognition results. When the confidence is lower than the preset threshold range, the system will automatically re-receive data from the data acquisition unit and re-perform scene recognition, thereby improving the reliability of scene recognition and reducing the possibility of misjudgment.

[0028] Control execution unit, including: A rule parsing module is configured to receive and parse the control rules from the cloud analysis unit, extract the specific operation requirements and parameters in the control rules, and convert them into control signals that the IoT device can understand; The control execution module is configured to perform actual control operations on IoT devices based on the control signals generated by the rule parsing module; Among them, the control execution module has the ability to process multiple tasks in parallel, and can control multiple IoT devices at the same time, and reasonably allocate resources according to the priority and real-time status of different IoT devices; The control feedback module is configured to send feedback information to the cloud analysis unit after executing the control operation to confirm the execution result of the control operation. The feedback information includes the status change of the equipment and the success or failure of the operation. Based on the feedback information, the control process is evaluated and analyzed through association rule mining and cluster analysis to discover potential problems and optimization directions in the control process, and the control rules are automatically adjusted according to the evaluation results.

[0029] The technical effect of the above technical solution is: the rule parsing module can extract the specific operation requirements and parameters by parsing the control rules and convert them into control signals that the Internet of Things devices can understand. The precise parsing and conversion capabilities ensure the accurate transmission of the control instructions and avoid equipment operation errors caused by unclear instructions. The control execution module can perform actual control operations on the Internet of Things devices according to the generated control signals, and the control execution module has multi-task parallel processing capabilities and can control multiple Internet of Things devices at the same time. The parallel processing mechanism significantly improves the system's response speed and control efficiency. After executing the control operation, the control feedback module uses real-time feedback and optimization mechanisms. The system can promptly discover and solve problems in the control process, thereby automatically adjusting the control rules to form a continuously optimized closed-loop control system, thereby significantly improving the system's adaptability and stability.

[0030] Specifically, this embodiment further proposes a modular and scalable multi-scenario dynamic control method for the Internet of Things, which is applicable to a modular and scalable multi-scenario dynamic control system for the Internet of Things, including the following steps: By collecting various types of data from different IoT scenarios in real time and performing cleaning, screening, and format conversion, it automatically selects the optimal communication protocol and link for data transmission based on network conditions; Match the collected data according to the preset scene feature model and automatically identify the current IoT scene type; According to the identified scenario type, the corresponding control rules are retrieved from the rule configuration module; Real-time control of IoT devices based on control rules, while monitoring the operating status and feedback information of the devices; The control process is evaluated and analyzed based on the feedback information, and the control rules are automatically adjusted according to the evaluation results.

[0031] In one embodiment, after obtaining the corresponding control rules, the method further includes: Determine, according to the control rules, multiple IoT devices to be controlled and the control parameters of each IoT device to be controlled; Generate a control cloud scenario based on the control parameters of each IoT device to be controlled, and determine standard control behavior feature data of each IoT device to be controlled in the control cloud scenario; Determine a standard operating curve for each IoT device to be regulated based on the standard regulation behavior characteristic data, and determine operating parameters for each IoT device to be regulated according to the standard operating curve; Determine the control decision index under the control rule based on the operating parameters, and determine the correction behavior parameter under the control decision index through the decision fitness behavior analysis function; Determine the parameter adjustment frequency and parameter adjustment range of each IoT device to be regulated based on the correction behavior parameters; Determine a parameter adjustment time difference for each IoT device to be regulated based on the parameter adjustment frequency and the parameter adjustment determination range, and generate parameter adjustment time series data for each IoT device to be regulated based on the parameter adjustment time difference; Obtain parameter adjustment state sequence data corresponding to the parameter adjustment time series data, and generate a parameter scheduling strategy for each IoT device to be regulated based on the parameter adjustment state sequence data and the parameter adjustment time series data; Determine the dynamic response target of each IoT device to be regulated through a parameter scheduling strategy, and determine the dynamic control parameters for each IoT device to be regulated based on the dynamic response target; Through dynamic control parameters, dynamic matching is performed according to the real-time changes in user behavior data, and each IoT device to be regulated is controlled dynamically.

[0032] In this embodiment, the standard control behavior characteristic data is represented by the device output behavior characteristic data of each IoT device to be controlled in the control scenario; In this embodiment, the control decision indicator is represented as a decision indicator for controlling each IoT device to be controlled under the control rule.

[0033] In this embodiment, the decision fitness behavior analysis function is represented as a function that performs behavior fitness analysis on the decision indicator; In this embodiment, the parameter scheduling strategy is represented as a scheduling strategy for the operating parameters of each IoT device to be regulated.

[0034] In this embodiment, the dynamic response target is represented by the dynamic working response target of each IoT device to be regulated to the parameter scheduling strategy; In this embodiment, the dynamic control parameters are represented by dynamic working control parameters of each IoT device to be regulated under the regulation rules.

[0035] The beneficial effects of the above technical solution are: by determining the dynamic working control parameters of each IoT device to be regulated under the regulation rules, dynamic control of all IoT devices to be regulated can be achieved to meet the correlation control between the IoT devices to be regulated and user behavior, ensuring the user experience and improving practicality and stability.

[0036] Furthermore, in the process of real-time control of IoT devices, it also includes: Determine the control parameters of each IoT device to be regulated according to the regulation rules, and determine the current working mode of each IoT device to be regulated according to the control parameters; Determine the service capability index of each IoT device to be regulated based on the current working mode; The peak value of the control response for each IoT device to be controlled is calculated based on the service capability index of each IoT device to be controlled:

[0037] in, It is expressed as the peak value of the control response for the i-th IoT device to be controlled, It is expressed as the average value of the control response of the ith IoT device to be controlled, It is expressed as the control performance fluctuation factor of the i-th IoT device to be controlled, It is represented as the control response delay factor under the control rule, b is represented as the control response accuracy factor under the control rule, k is represented as the control balance factor, Expressed as the historical response effect factor of the control rule, It is represented as the service capability index of the ith IoT device to be regulated, It is represented as the scheduling weight of the i-th IoT device to be regulated; The network delay parameter of each IoT device to be regulated is set according to the regulation response peak value of each IoT device to be regulated.

[0038] In this embodiment, the service capability index is represented by the performance service capability index of each IoT device to be regulated in the current working mode; In this embodiment, the control response peak value is represented by the response accuracy peak value of each IoT device to be controlled under the control rule; In this embodiment, the control performance fluctuation factor is represented by the fluctuation factor of the control performance of each IoT device to be controlled, which is determined based on the frequency and intensity of use and the years of use; In this embodiment, the control response delay factor is represented by a statistical factor of the control response delay of each IoT device to be controlled, which is determined according to the device performance and network protocol; In this embodiment, the control balancing factor is expressed as the control balancing factor for each IoT device to be controlled under the control rule; In this embodiment, the historical response effect factor is represented as a mapping factor of the historical device response under the regulation rule.

[0039] The beneficial effects of the above technical solution are: by calculating the control response peak of each IoT device to be regulated, the network delay parameters can be accurately set according to the device response status and service capability parameters of each IoT device to be regulated, thereby ensuring that each IoT device to be regulated can respond to operations quickly and accurately, thereby improving stability, work efficiency and practicality.

[0040] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0041] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A modular and scalable multi-scenario dynamic control system for the Internet of Things, characterized by: It includes data acquisition unit, cloud analysis unit and control execution unit; The data acquisition unit is configured to collect various types of data in different scenarios of the Internet of Things in real time, including but not limited to environmental data, device operation data, and user behavior data; The cloud analysis unit is configured to match various types of data collected in real time by the data acquisition unit according to a preset scene feature model, automatically identify the current IoT scene type and obtain corresponding control rules; The control execution unit is configured to perform real-time control on the IoT device according to the scenario type and corresponding control rules determined by the cloud analysis unit.

2. A modular and scalable multi-scenario dynamic control system for the Internet of Things according to claim 1, characterized in that: After obtaining the corresponding control rules, it also includes: Determine, according to the control rules, multiple IoT devices to be controlled and the control parameters of each IoT device to be controlled; Generate a control cloud scenario based on the control parameters of each IoT device to be controlled, and determine standard control behavior feature data of each IoT device to be controlled in the control cloud scenario; Determine a standard operating curve for each IoT device to be regulated based on the standard regulation behavior characteristic data, and determine operating parameters for each IoT device to be regulated based on the standard operating curve; Determine the control decision index under the control rule based on the operating parameters, and determine the correction behavior parameter under the control decision index through the decision fitness behavior analysis function; Determine the parameter adjustment frequency and parameter adjustment range of each IoT device to be regulated based on the correction behavior parameters; Determine a parameter adjustment time difference for each IoT device to be regulated based on the parameter adjustment frequency and the parameter adjustment determination range, and generate parameter adjustment time series data for each IoT device to be regulated based on the parameter adjustment time difference; Obtain parameter adjustment state sequence data corresponding to the parameter adjustment time series data, and generate a parameter scheduling strategy for each IoT device to be regulated based on the parameter adjustment state sequence data and the parameter adjustment time series data; Determine the dynamic response target of each IoT device to be regulated through a parameter scheduling strategy, and determine the dynamic control parameters for each IoT device to be regulated based on the dynamic response target; Through dynamic control parameters, dynamic matching is performed according to the real-time changes in user behavior data, and each IoT device to be regulated is controlled dynamically.

3. A modular and scalable multi-scenario dynamic control system for the Internet of Things according to claim 1, characterized in that: The process of real-time control of IoT devices also includes: Determine the control parameters of each IoT device to be regulated according to the regulation rules, and determine the current working mode of each IoT device to be regulated according to the control parameters; Determine the service capability index of each IoT device to be regulated based on the current working mode; The peak value of the control response for each IoT device to be controlled is calculated based on the service capability index of each IoT device to be controlled: in, It is expressed as the peak value of the control response for the i-th IoT device to be controlled, It is expressed as the average value of the control response of the ith IoT device to be controlled, It is expressed as the control performance fluctuation factor of the i-th IoT device to be controlled, It is represented as the control response delay factor under the control rule, b is represented as the control response accuracy factor under the control rule, k is represented as the control balance factor, Expressed as the historical response effect factor of the control rule, It is represented as the service capability index of the ith IoT device to be regulated, It is represented as the scheduling weight of the i-th IoT device to be regulated; The network delay parameter of each IoT device to be regulated is set according to the regulation response peak value of each IoT device to be regulated.

4. A modular and scalable multi-scenario dynamic control system for the Internet of Things according to claim 1, characterized in that: The data acquisition unit includes: The data acquisition module is configured to consist of multiple different types of sensor units and is deployed at various monitoring points in a distributed architecture to collect various types of data in different IoT scenarios in real time; The data processing module is configured to clean, filter and convert the collected raw data, remove noise data, invalid data and duplicate data, and convert them into a unified standard format; The data transmission module is configured to transmit processed data of various types to the cloud analysis unit, supports multiple communication protocols, and automatically selects the optimal communication protocol and link according to the network conditions.

5. A modular and scalable multi-scenario dynamic control system for the Internet of Things according to claim 4, characterized in that: The data transmission module includes: The network monitoring module is configured to monitor key network indicators in real time, including network latency and packet loss rate, to assess the current network status. Specifically: The network latency is determined by periodically sending a probe signal and receiving feedback, and calculating the round-trip time of the signal. Count the number of successful and failed transmissions within a certain period of time and calculate the packet loss rate; a communication selection module configured to automatically select an optimal communication protocol and link based on the evaluation results provided by the network monitoring module; The data packaging module is configured to package and encapsulate the processed data and transmit it according to the communication protocol and link selected by the selected communication selection module. At the same time, the size, transmission interval and transmission parameters of the data packet are dynamically adjusted according to the network conditions. Specifically: Set transmission parameters, including transmission rate, transmission power, and number of retransmissions, according to network conditions and communication protocol requirements; Dynamically adjust the packet size based on network conditions. In networks with sufficient bandwidth, the packet size will be increased appropriately. In environments with limited bandwidth or network congestion, the packet size will be reduced. According to the network status and data priority, the transmission interval of the data packet is dynamically adjusted. For data with high real-time requirements, the transmission interval is shortened, and for data with low real-time requirements, the transmission interval is appropriately extended.

6. The modular and scalable multi-scenario dynamic control system of the Internet of Things according to claim 1, characterized in that: The cloud analysis unit includes: A model library construction module is configured to build a model library and store a variety of pre-defined scenario feature models. Each model corresponds to a specific IoT scenario type and contains the typical characteristics, correlations, and change patterns of various types of data in that scenario. At the same time, the model library is extensible and can be used to add new scenario feature models or update and optimize existing models. Among them, a machine learning algorithm is used to build a scene feature model through learning and training of a large amount of historical data; A scene matching module is configured to extract key features from various data collected in real time by the data acquisition unit, match the extracted features with the scene feature model in the model library, and determine the current IoT scene type; The rule acquisition module is configured to store control rules corresponding to various IoT scenario types in advance. The control rules define the specific strategies and operating methods for controlling IoT devices in specific scenarios. According to the scenario type determined by the scenario matching module, the corresponding control rules are obtained and transmitted to the control execution unit.

7. A modular and scalable multi-scenario dynamic control system for the Internet of Things according to claim 6, characterized in that: The scene matching module performs the following process: A matching algorithm is used to calculate the similarity or matching degree between the feature and each scene feature model, and the closest scene type is selected as the recognition result; The confidence level of the recognition result is evaluated. If the confidence level is lower than a preset threshold range, data is received again from the data acquisition unit to determine the IoT scenario type.

8. The modular and scalable multi-scenario dynamic control system of the Internet of Things according to claim 1, characterized in that: The control execution unit includes: A rule parsing module is configured to receive and parse the control rules from the cloud analysis unit, extract the specific operation requirements and parameters in the control rules, and convert them into control signals that the IoT device can understand; The control execution module is configured to perform actual control operations on IoT devices based on the control signals generated by the rule parsing module; The control feedback module is configured to send feedback information to the cloud analysis unit after executing the control operation to confirm the execution result of the control operation. The feedback information includes the status change of the equipment and the success or failure of the operation. Based on the feedback information, the control process is evaluated and analyzed through association rule mining and cluster analysis to discover potential problems and optimization directions in the control process, and the control rules are automatically adjusted according to the evaluation results.

9. The modular and scalable multi-scenario dynamic control system of Internet of Things according to claim 8, characterized in that: The control execution module has the ability to process multiple tasks in parallel, and can control multiple IoT devices at the same time, and reasonably allocate resources according to the priority and real-time status of different IoT devices.

10. A modular and scalable multi-scenario dynamic control method for the Internet of Things, applicable to a modular and scalable multi-scenario dynamic control system for the Internet of Things according to any one of claims 1 to 9, characterized in that: The following steps are involved: By collecting various types of data from different IoT scenarios in real time and performing cleaning, screening, and format conversion, it automatically selects the optimal communication protocol and link for data transmission based on network conditions; Match the collected data according to the preset scene feature model and automatically identify the current IoT scene type; According to the identified scenario type, the corresponding control rules are retrieved from the rule configuration module; Real-time control of IoT devices based on control rules, while monitoring the operating status and feedback information of the devices; The control process is evaluated and analyzed based on the feedback information, and the control rules are automatically adjusted according to the evaluation results.

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