Remote wireless heat supply valve control system based on Internet of Things

Abstract

The invention discloses a remote wireless heat supply valve control system based on Internet of Things, which relates to heat supply system application and comprises an intelligent communication management module, a real-time environment sensing module, a self-adaptive frequency distribution and spectrum management module, an equipment control and execution module and a system monitoring and maintenance module. According to the remote wireless heat supply valve control system based on the Internet of Things, the problem that control instruction transmission of a heat supply system is unstable due to wireless interference in a complex environment is effectively solved through the self-adaptive multi-band dynamic switching and intelligent interference recognition technology; the system not only improves the reliability and stability of heat supply control data transmission, but also optimizes heat energy distribution and resource utilization, reduces the operation cost of the system, and obviously enhances the competitiveness and adaptability of the system in the fields of central heating, commercial mansions and large residential areas.

Description

Technical Field

[0001] The present invention relates to a heating system application, in particular to a remote wireless heating valve control system based on the Internet of Things. Background Art

[0002] At present, wireless heating valve control systems based on the Internet of Things are widely used in central heating systems, commercial buildings or large residential areas to automatically control the operation of hot water boilers, heating distribution systems or central heating equipment; however, due to the complex pipe network structure, metal heat exchange equipment and other heat source interference in the heating system, wireless signal interference is easily caused, resulting in unstable or failed transmission of remote control commands; this will not only affect the normal operation of the heating system, but may also lead to equipment failure, uneven distribution of heat energy or waste of resources. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a remote wireless heating valve control system based on the Internet of Things. By adopting a hybrid communication protocol technology that integrates adaptive multi-band dynamic switching and intelligent interference identification, the problem of unstable control signal transmission caused by complex pipe network structure and equipment interference in centralized heating systems is solved. The control of heating valves is precise, heat medium is efficiently transmitted, and the overall stable operation of the system is achieved, thereby improving heating efficiency and reducing operating costs.

[0004] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote wireless heating valve control system based on the Internet of Things, including an intelligent communication management module, a real-time environment perception module, an adaptive frequency allocation and spectrum management module, an equipment control and execution module, and a system monitoring and maintenance module, wherein the modules exchange data and work together through a wired or wireless communication interface. The system includes: The intelligent communication management module is used to implement a hybrid communication protocol that integrates adaptive multi-band dynamic switching and intelligent interference identification to maintain communication stability and ensure the real-time and accuracy of heating equipment control data; when high interference is detected in the current communication frequency band, it automatically switches to a low-interference frequency band to maintain communication stability and ensure real-time and accurate transmission of heating equipment control data; The real-time environment perception module is used to collect and analyze system operation environment data. The system operation environment data refers to a comprehensive data set consisting of temperature, humidity, vibration, light, pressure and flow of the heating medium, as well as wireless signal strength, signal-to-noise ratio, and bit error rate. It is used to monitor the environmental status in real time, predict interference, and guide the system to adaptively adjust the communication and heating control strategies; in addition to monitoring the environmental temperature, humidity, vibration and light parameters, it also collects key parameters in the heating system (such as temperature, pressure and flow); based on the output of the interference prediction model, the real-time environment perception module adjusts the communication strategy in advance to avoid potential interference and ensure the stable transmission of heating control instructions; The adaptive frequency allocation and spectrum management module is used to work in coordination between different control nodes to avoid frequency band conflicts and congestion, thereby ensuring efficient transmission of heating system control instructions; The adaptive frequency allocation and spectrum management module includes a dynamic frequency selection algorithm and an adaptive modulation and encoder. It intelligently selects the current optimal communication frequency band through real-time evaluation of the 2.4GHz, 5GHz, and 900MHz wireless frequency bands, and triggers a switching mechanism when the channel quality decreases. The device control and execution module is used to accurately control the start and stop of the wireless heating valve according to the received control instructions; The equipment control and execution module is equipped with a wireless control interface, a heating valve drive unit and a feedback sensor; the heating valve drive unit drives the heating valve according to the control instruction to realize the flow regulation of the heat medium (such as hot water or steam); the feedback sensor monitors the working status of the heating valve in real time, and feeds back the status information to the intelligent communication management module to realize closed-loop control; The system monitoring and maintenance module is used to monitor the operating status of the entire heating control system in real time, and centrally display the operating status of each module, communication quality, and working conditions of the heating equipment; It also provides remote maintenance, fault detection and alarm, data storage and log management functions, making it easy for technicians to configure, upgrade and diagnose faults through the remote maintenance interface.

[0005] Preferably, the intelligent communication management module includes: a multi-band wireless communication interface configured to support at least two different wireless frequency bands, including 2.4 GHz, 5 GHz, and 900 MHz, and to dynamically switch between said frequency bands; Intelligent interference identification unit, which uses machine learning algorithms to analyze the wireless communication environment in real time and identify the type and source of interference; The protocol adaptation engine dynamically adjusts communication parameters, including modulation mode, transmission rate and transmission power, according to the output of the intelligent interference identification unit.

[0006] Preferably, the real-time environment perception module includes: A multi-sensor integrated device, configured to include a temperature sensor, a humidity sensor, a vibration sensor, and a light sensor, for comprehensively monitoring the operating status of the thermal medium in the heating system; A data acquisition and processing circuit continuously acquires data from the sensor and transmits it to a data processing center in real time; The environmental data analyzer establishes an interference prediction model through machine learning algorithms, predicts future interference conditions based on historical and real-time environmental data, and outputs interference prediction results.

[0007] Preferably, the environmental data analyzer establishes an interference prediction model through a machine learning algorithm, predicts future interference conditions based on historical and real-time environmental data, and outputs the interference prediction result, including: Collect environmental sensor data accumulated during system operation as historical environmental data, and collect wireless communication performance indicators in the corresponding time period as communication performance data. Historical environmental data include temperature, humidity, vibration, and light parameters, and communication performance data include signal strength, bit error rate, and transmission rate. Deploy a variety of environmental sensors at key nodes of the control system to monitor environmental parameters in real time, set data collection frequency (such as every second, every minute) to capture the dynamic characteristics of environmental changes, and transmit the real-time collected environmental data to the data processing center through wired or wireless communication interfaces to obtain real-time environmental data; clean, standardize and normalize the obtained data, and then synchronize and align the data, extract features, and identify and remove outliers in sensor data during data preprocessing, such as extreme values ​​caused by sensor failures, and use interpolation to fill missing data points to ensure data integrity; convert data from different sensors to a unified scale (such as zero mean unit variance) to eliminate the impact of dimensional differences, and scale the data to a specific range (such as 0 to 1) to improve the training efficiency and stability of machine learning algorithms; Calculate the mean, variance, maximum, minimum, kurtosis, and skewness of environmental parameters to capture the central tendency and dispersion of data and obtain statistical characteristics; extract the frequency characteristics of vibration and noise signals through Fourier transform or wavelet transform, identify periodic interference patterns, and clarify frequency domain characteristics; analyze trends, seasonality, and mutation points in time series data to capture the dynamic characteristics of environmental changes and obtain time series characteristics; evaluate the correlation between each feature and wireless communication performance indicators, screen out features with correlation coefficients greater than 0.8, and apply principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce feature dimensions, retain main information, and reduce model complexity. At the same time, use decision trees and random forests to evaluate the importance of features and screen out key features; The preprocessed data set is divided into a training set and a test set according to the proportion (such as 70% training set and 30% test set) for model training and evaluation. At the same time, the K-fold cross-validation method is used to ensure the stability and generalization ability of the model on different data subsets. For supervised learning algorithms, supervised learning algorithms suitable for interference prediction are selected, including support vector machines, random forests, gradient boosting trees, and neural networks. For unsupervised learning algorithms, cluster analysis is used to discover potential patterns and associations between environmental changes and communication performance when necessary; the selected machine learning model is trained using the training set data. The machine learning model includes support vector machine (SVM), random forest, gradient boosting tree, and deep neural network to build an interference prediction model. At the same time, decision trees, clustering algorithms, principal component analysis (PCA) and linear discriminant analysis (LDA) can be combined for feature screening and dimensionality reduction to learn the relationship between environmental parameters and communication performance. The hyperparameters of the model are optimized through grid search, random search or Bayesian optimization. The accuracy, precision, recall rate and F1 score of the model are evaluated using the test set to select the model with the best performance; Continuously receive real-time data from environmental sensors and perform the same preprocessing and feature extraction steps as during training. Based on real-time data, construct feature vectors consistent with the training model to ensure the consistency and accuracy of the prediction. Input the feature vectors extracted in real time into the trained interference prediction model to predict the interference situation. The model outputs the prediction results based on the input features, and outputs the interference intensity, type, and communication performance indicators that may be affected (such as signal attenuation and bit error rate increase) in the current or future period. Based on the model prediction results, the specific impact of environmental changes on wireless communications is evaluated, such as the expected decrease in signal strength or increase in bit error rate; the predicted interference conditions are classified into three risk levels: low, medium, and high; based on the impact assessment results, corresponding communication strategy adjustment suggestions are put forward, such as switching communication frequency bands, adjusting transmission power, and changing modulation methods. When high-risk interference conditions are detected, early warning or alarm mechanisms are triggered to notify system administrators or automatically take emergency measures; The decision suggestions are passed to the intelligent communication management module, and corresponding communication strategy adjustments are executed to optimize communication performance and stability. Through the feedback mechanism, the adjusted communication effect is continuously monitored to further optimize the interference prediction model and decision-making strategy to enhance the overall anti-interference capability of the system.

[0008] Preferably, the adaptive frequency allocation and spectrum management module includes: Dynamic frequency selection algorithm, based on the output of the real-time environment perception module, intelligently selects the optimal communication frequency band; Adaptive modulation and encoding dynamically adjusts the modulation method and coding strategy according to the current communication environment.

[0009] Preferably, the process of the dynamic frequency selection algorithm intelligently selecting the optimal communication frequency band based on the output of the real-time environment perception module includes: Perform a comprehensive scan of the wireless frequency bands supported by the system, including 2.4GHz, 5GHz and 900MHz, detect the signal strength, interference level and channel utilization of each frequency band, obtain the current channel status of the frequency band, and conduct real-time evaluation of the current channel status of each frequency band, including measuring the signal-to-noise ratio (SNR), signal strength (RSSI) and the number of interference sources, obtain the communication quality indicators of each frequency band, and preset the priority of each frequency band according to the system design requirements and application scenarios, including 2.4GHz taking precedence over 5GHz, 5GHz taking precedence over 900MHz, formulate a frequency band selection strategy, determine the frequency band selection rules under different interference and channel utilization conditions, so as to optimize the overall communication performance and spectrum utilization efficiency, set a scoring mechanism, and assign a comprehensive score to each frequency band. The allocation process includes: high signal strength scores high, high signal-to-noise ratio scores high, low interference level scores high, and low channel utilization scores high; conduct a comprehensive evaluation of the frequency bands according to the preset priorities and scoring mechanisms, and select the frequency band with the highest score and meeting the priority requirements as the current optimal communication frequency band; Determine whether the communication quality of the currently used frequency band is lower than the preset threshold (such as the signal-to-noise ratio is lower than the set noise threshold or the interference level exceeds the interference threshold). When it is detected that the communication quality of the current frequency band is poor, the frequency band switching process is triggered. According to the selected optimal frequency band, the communication protocol parameters, such as frequency, bandwidth and modulation method, are adjusted to adapt to the characteristics of the new frequency band. The RF front end of the multi-band wireless communication interface is controlled to switch to the selected frequency band, including adjusting the antenna, filter and power amplifier to ensure signal synchronization and data transmission coordination during the switching process to avoid data loss or communication interruption.

[0010] Preferably, the adaptive modulation and encoder dynamically adjusts the modulation mode and coding strategy according to the current communication environment, and the process is as follows: the adaptive modulation and encoder continuously monitors the status of the current communication channel, including signal strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER) and interference level, obtains environmental data provided by the real-time environmental perception module, such as temperature, humidity, vibration and light, and evaluates the potential impact of environmental data on the communication channel; preset modulation schemes, including dual-phase keying, four-phase keying, 16-order orthogonal amplitude modulation and 64-order orthogonal amplitude modulation, evaluate the anti-interference ability, data transmission rate and energy efficiency of each modulation mode under current conditions based on the current channel status and environmental data, and select the modulation mode that can provide the best balance under the current communication environment, for example, under high signal-to-noise ratio and low interference conditions, select a high-order modulation mode to increase the data transmission rate; under low signal-to-noise ratio or high interference conditions, select a low-order modulation mode to enhance anti-interference. Interference capability; preset coding schemes, including convolutional coding, low-density parity-check coding and Turbo coding, and evaluate the impact of different coding rates on data integrity and transmission efficiency according to the current communication environment. Among them, high coding rates are suitable for high-interference environments to improve data reliability, and low coding rates are suitable for low-interference environments to improve transmission rates. Select coding strategies and coding rates suitable for current channel conditions to ensure the accuracy and efficiency of data transmission and determine the optimal coding strategy; perform parameter configuration and implementation, and adjust communication protocol parameters such as carrier frequency, phase offset and amplitude modulation according to the selected modulation method to match the new modulation scheme to complete the modulation method configuration; configure the corresponding encoder and decoder parameters according to the selected coding strategy and coding rate to ensure the correct encoding and decoding of data at the transmission and receiving ends to complete the coding strategy configuration; ensure that the RF front end and communication processing unit support the switching of the selected modulation method and coding strategy, and coordinate hardware configuration with software control.

[0011] Preferably, the device control and execution module includes: Wireless control interface, receiving control instructions from the intelligent communication management module; The heating valve driving unit drives the heating valve according to the control instruction to realize the flow control of liquid or gas; Feedback sensor, which monitors the status of the heating valve (open / closed) and feeds the status information back to the system; The local processor processes control instructions and feedback data, and makes local decisions and optimizations; The feedback sensor feeds back the real-time status of the heating valve to the intelligent communication management module through the local processor to ensure the accurate execution of the control instructions.

[0012] Preferably, the system monitoring and maintenance module is configured with a remote monitoring platform, a fault detection and alarm system, a data storage and log management device, and a remote maintenance interface. The remote monitoring platform is used to centrally display the operating status, communication quality, and equipment status of each module of the system; the fault detection and alarm system is used to detect system abnormalities in real time and issue alarms in a timely manner, the data storage and log management device records system operating data and event logs, and the remote maintenance interface allows technicians to configure, upgrade, and maintain the system through the network.

[0013] Preferably, the system implements the control process through the following steps: S1: The intelligent communication management module selects the optimal communication frequency band and adjusts the communication parameters according to the current wireless environment; S2: The real-time environment perception module continuously monitors environmental changes and predicts potential interference; S3: Adaptive frequency allocation and spectrum management module optimizes spectrum resource allocation based on environmental perception results; S4: The equipment control and execution module receives and executes the control instruction to adjust the state of the heating valve; S5: The system monitoring and maintenance module monitors the system operation status in real time, performs fault detection and remote maintenance; The above steps ensure the efficient, stable and reliable operation of the system in a complex environment through the collaborative work between modules.

[0014] (III) Beneficial effects The present invention provides a remote wireless heating valve control system based on the Internet of Things. It has the following beneficial effects: The present invention adopts a remote wireless heating valve control system based on the Internet of Things, which effectively solves the problem of unstable control command transmission of the heating system in a complex environment due to wireless interference through adaptive multi-band dynamic switching and intelligent interference identification technology; the system not only improves the reliability and stability of heating control data transmission, but also optimizes heat energy distribution and resource utilization, reduces system operating costs, and significantly enhances the system's competitiveness and adaptability in the fields of centralized heating, commercial buildings and large residential areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall framework of the present invention; Figure 2 This is a control flow diagram of the remote wireless heating valve control system. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 and Figure 2 The present invention provides a technical solution: a remote wireless heating valve control system based on the Internet of Things, including an intelligent communication management module, a real-time environment perception module, an adaptive frequency allocation and spectrum management module, an equipment control and execution module, and a system monitoring and maintenance module, wherein the modules exchange data and work together through a wired or wireless communication interface. The system includes: The intelligent communication management module is used to implement a hybrid communication protocol that integrates adaptive multi-band dynamic switching and intelligent interference identification; when the intelligent communication management module detects high interference in the current communication frequency band, it automatically switches to another low-interference frequency band to maintain communication stability; the real-time environment perception module is used to collect and analyze system operating environment data, and based on the output of the interference prediction model, adjust the communication strategy in advance to avoid potential interference; the adaptive frequency allocation and spectrum management module is used to work together between different control nodes to avoid frequency band conflicts and congestion, optimize the utilization of spectrum resources, and ensure efficient digital information transmission; the equipment control and execution module is used to accurately control the opening and closing of the wireless heating valve according to the received control instructions; the system monitoring and maintenance module is used to monitor the operating status of the entire heating control system in real time, and provide remote maintenance and fault diagnosis functions.

[0018] The intelligent communication management module includes: The intelligent communication management module contains a multi-band wireless communication interface, supports different wireless frequency bands of 2.4GHz, 5GHz, and 900MHz, and uses adaptive multi-band dynamic switching technology to achieve anti-interference transmission; the intelligent communication management module is equipped with an intelligent interference identification unit, which uses machine learning algorithms to analyze the wireless communication environment in real time and identify the type and source of interference; the protocol adaptation engine dynamically adjusts the communication parameters (including modulation mode, transmission rate and transmission power) according to the interference identification results to ensure the stability of heating control data transmission.

[0019] It should be further explained that, in the specific implementation process, the protocol adaptation engine dynamically adjusts the communication parameters according to the output of the intelligent interference identification unit, including the process of modulation mode, transmission rate and transmission power, including: receiving interference data from the intelligent interference identification unit, the interference data including interference intensity, interference type and interference source location information, wherein the receiving frequency and timestamp are synchronized to ensure the real-time and accuracy of the interference data, analyzing the received interference data, evaluating the interference degree of the current communication link and its impact on the signal strength and bit error rate in the communication performance, determining the nature and source of the interference, identifying whether it is co-frequency interference, cross-frequency interference or pulse interference, and obtaining the interference assessment result; according to the interference assessment result, through a preset decision algorithm, determining the communication parameters that need to be adjusted, setting the adjustment priority, and determining the adjustment order, the adjustment priority includes adjusting the modulation mode first, and then adjusting the transmission rate and the transmission power; for modulation mode adjustment, switching the modulation scheme from BPSK (biphase shift keying) to QPSK (quadruple phase shift keying) to improve the anti-interference capability.

[0020] Adaptive modulation is applied to dynamically select the optimal modulation method according to the current channel conditions. For transmission rate adjustment, the data transmission rate is dynamically adjusted to reduce the rate in a high-interference environment to reduce the bit error rate, or to increase the rate in a low-interference environment to increase data throughput. Adaptive coding technology is used to adjust the coding method according to changes in the transmission rate to ensure data integrity. For transmit power adjustment, the transmit power is increased to enhance the signal coverage, or reduced to reduce interference to other devices according to the distance and interference conditions. Power adaptive adjustment is implemented to balance communication quality and energy consumption.

[0021] Monitor the adjusted communication performance, which includes signal strength, bit error rate, and transmission stability. Execute the feedback mechanism to feed back the adjusted communication performance data to the protocol adaptation engine, evaluate the adjustment effect, record the adjustment process and effect, and accumulate historical data for subsequent analysis and model optimization. Use machine learning algorithms to analyze historical adjustment data, optimize parameter adjustment strategies, improve the accuracy and response speed of future adjustments, and continuously optimize to ensure that the protocol adaptation engine can cope with the dynamically changing wireless environment and maintain the efficient operation of the communication system.

[0022] It should be further explained that, in the specific implementation process, the multi-band wireless communication interface is configured to support at least two different wireless frequency bands, including 2.4GHz, 5GHz, and 900MHz, and the process for dynamically switching between frequency bands includes: the multi-band wireless communication interface continuously scans and detects the currently supported wireless frequency bands, including the channel status of 2.4GHz, 5GHz and 900MHz, including signal strength, interference level and channel utilization, and monitors the environmental changes of each frequency band in real time. When a new interference source appears, the availability and communication quality of each frequency band are evaluated, and an initial frequency band selection strategy is formulated according to the preset frequency band priority: 2.4GHz takes precedence over 5GHz, and 5GHz takes precedence over 900MHz. According to the real-time monitoring results, the communication quality and interference status of each frequency band are dynamically evaluated, and the frequency band selection strategy is adjusted to optimize the communication performance. When it is detected that the interference level of the currently used frequency band exceeds the preset interference threshold, the frequency band switching mechanism is triggered, and based on the real-time monitoring data and the frequency band selection strategy, the backup frequency band with the least current interference and the best communication quality is selected for switching to ensure The backup frequency band is confirmed to have sufficient signal strength and low interference level when switching to ensure communication stability after switching. According to the selected backup frequency band, the communication protocol parameters, including frequency, bandwidth, and modulation mode, are adjusted to match the new frequency band characteristics. The RF front end of the multi-band wireless communication interface is controlled to switch to the selected backup frequency band, including adjusting the antenna, filter, and power amplifier to ensure signal synchronization and data transmission coordination during the switching process to avoid data loss or communication interruption. After switching to the backup frequency band, the communication quality is immediately verified to obtain the switching result, including signal strength, bit error rate, and transmission rate to ensure successful switching. The switching result is fed back to the intelligent communication management module for updating the frequency band selection strategy and optimizing future switching decisions. When the communication quality of the backup frequency band is restored to a high level, the system can automatically switch back to the primary frequency band with a higher priority to optimize resource utilization. During the frequency band switching process, a notification is sent to the system administrator or user to inform the switching status and the current communication frequency band. When the frequency band switching fails or the backup frequency band cannot provide stable communication, an alarm mechanism is triggered to prompt that manual intervention or system adjustment is required.

[0023] The environmental data analyzer establishes an interference prediction model through a machine learning algorithm, predicts future interference situations based on historical and real-time environmental data, and outputs the interference prediction results. The process includes: collecting environmental sensor data accumulated during system operation as historical environmental data, and collecting wireless communication performance indicators in the corresponding time period as communication performance data. The historical environmental data includes temperature, humidity, vibration, and light parameters, and the communication performance data includes signal strength, bit error rate, and transmission rate. A variety of environmental sensors are deployed at key nodes of the control system to monitor environmental parameters in real time, capture the dynamic characteristics of environmental changes once a second, and transmit the real-time collected environmental data to the data processing center through wired or wireless communication interfaces to obtain real-time environmental data; the obtained data is cleaned, standardized and normalized, and then synchronized and aligned to extract features. For data preprocessing, extreme values ​​caused by sensor failures are identified and removed, and missing data points are filled by interpolation to ensure data integrity; data from different sensors are converted to a unified zero-mean unit variance scale to eliminate the impact of dimensional differences, and the data is scaled to a range of 0 to 1 to improve the training efficiency and stability of the machine learning algorithm.

[0024] Calculate the statistics of mean, variance, maximum, minimum, kurtosis and skewness of environmental parameters, capture the central tendency and dispersion of data, and obtain statistical characteristics; extract the frequency characteristics of vibration and noise signals through Fourier transform or wavelet transform, identify periodic interference patterns, and clarify frequency domain characteristics; analyze trends, seasonality and mutation points in time series data, capture the dynamic characteristics of environmental changes, and obtain time series characteristics; evaluate the correlation between each feature and wireless communication performance indicators, screen out features with correlation coefficients greater than 0.8, and apply principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce feature dimensions, retain main information, and reduce model complexity. At the same time, use decision trees and random forests to evaluate the importance of features and screen out key features.

[0025] The preprocessed data set is divided into a training set and a test set with 70% training set and 30% test set for model training and evaluation. At the same time, the K-fold cross-validation method is used to ensure the stability and generalization ability of the model on different data subsets. For supervised learning algorithms, supervised learning algorithms suitable for interference prediction are selected, including support vector machines, random forests, gradient boosting trees, and neural networks. For unsupervised learning algorithms, cluster analysis is used to discover potential patterns and associations between environmental changes and communication performance when necessary; the selected machine learning model is trained using the training set data to learn the relationship between environmental parameters and communication performance; the hyperparameters of the model are optimized through grid search, random search, or Bayesian optimization; the accuracy, precision, recall, and F1 score of the model are evaluated using the test set, and the model with the best performance is selected.

[0026] Continuously receive real-time data from environmental sensors and perform the same preprocessing and feature extraction steps as during training. Based on real-time data, build a feature vector consistent with the training model to ensure the consistency and accuracy of the prediction. Input the feature vector extracted in real time into the trained interference prediction model to predict the interference situation. The model outputs the prediction results based on the input features, and outputs the interference intensity, type and communication performance indicators that may be affected in the future: signal attenuation and increased bit error rate.

[0027] Based on the model prediction results, the specific impact of environmental changes on wireless communications is evaluated, such as the expected decrease in signal strength or increase in bit error rate; the predicted interference situations are classified into three risk levels: low, medium, and high; based on the impact assessment results, corresponding communication strategy adjustment suggestions are put forward, such as switching communication frequency bands, adjusting transmission power, and changing modulation methods. When high-risk interference situations are detected, an early warning or alarm mechanism is triggered to notify the system administrator or automatically take emergency measures; the decision suggestions are passed to the intelligent communication management module, and the corresponding communication strategy adjustments are executed to optimize communication performance and stability. Through the feedback mechanism, the adjusted communication effects are continuously monitored, and the interference prediction model and decision-making strategy are further optimized to enhance the overall anti-interference capability of the system.

[0028] The real-time monitored environmental data and communication performance data are continuously stored to form an expanding historical data set, providing a rich data foundation for model retraining; new data are labeled according to the actual communication performance, including normal, light interference, and heavy interference labels, to enrich the training data and improve the supervised learning effect of the model; the machine learning model is regularly retrained with the latest historical data to improve the adaptability and accuracy of the model to ensure that it can cope with environmental changes and emerging interference patterns, and online learning algorithms are introduced to enable the model to be updated in real time in the data stream, quickly adapt to environmental changes, maintain the real-time and accuracy of prediction capabilities, and dynamically adjust the data analysis and prediction models according to feedback from system operation, optimize feature selection, model parameters and prediction algorithms, and ensure that the interference prediction model always has the best performance; the prediction accuracy and response speed of the model are continuously monitored, and the problem of model performance degradation is discovered and resolved in a timely manner to ensure the long-term stable operation of the system.

[0029] The dynamic frequency selection algorithm intelligently selects the optimal communication frequency band based on the output of the real-time environment perception module. The process includes: comprehensively scanning the wireless frequency bands supported by the system, including 2.4GHz, 5GHz and 900MHz, detecting the signal strength, interference level and channel utilization of each frequency band, obtaining the current channel status of the frequency band, and conducting real-time evaluation of the current channel status of each frequency band, including measuring the signal-to-noise ratio (SNR), signal strength (RSSI) and the number of interference sources, obtaining the communication quality indicators of each frequency band, and presetting the priority of each frequency band according to the system design requirements and application scenarios. 2.4GHz takes precedence over 5GHz, 5GHz takes precedence over 900MHz, formulates a frequency band selection strategy, determines the frequency band selection rules under different interference and channel utilization conditions to optimize the overall communication performance and spectrum utilization efficiency, sets a scoring mechanism, and assigns a comprehensive score to each frequency band. The allocation process includes: high signal strength scores high, high signal-to-noise ratio scores high, low interference level scores high, and low channel utilization scores high; based on the preset priority and scoring mechanism, conducts a comprehensive evaluation of the frequency bands, and selects the frequency band with the highest score that meets the priority requirements as the current optimal communication frequency band.

[0030] Determine whether the signal-to-noise ratio in the communication quality of the currently used frequency band is lower than the set noise threshold. When it is detected that the communication quality of the current frequency band is poor, the frequency band switching process is triggered. According to the selected optimal frequency band, the frequency, bandwidth and modulation method in the communication protocol parameters are adjusted to adapt to the characteristics of the new frequency band. The RF front end of the multi-band wireless communication interface is controlled to switch to the selected frequency band, including adjusting the antenna, filter and power amplifier to ensure signal synchronization and data transmission coordination during the switching process to avoid data loss or communication interruption. After switching to the new frequency band, the communication quality is immediately verified, including measuring the signal strength, bit error rate and transmission rate to ensure that the switching is successful and the communication quality meets the requirements. The communication quality data after switching is fed back to the intelligent communication management module for updating the frequency band selection strategy and optimizing future switching decisions. When the communication quality in the new frequency band is restored, When the system recovers to a high level, it can automatically switch back to the main frequency band with a higher priority to optimize spectrum resource utilization and communication efficiency. It records in detail the time, reason, and communication quality indicators before and after each frequency band switching to form a frequency band switching log, analyze the historical data of frequency band switching, and use machine learning algorithms to optimize frequency band selection and switching strategies to improve the accuracy and efficiency of future switching decisions. Based on historical data and real-time monitoring results, it predicts possible interference situations in the future, formulates switching plans in advance, and reduces the risk of sudden communication interruptions. During the frequency band switching process, it sends notifications to system administrators or users to inform them of the switching status and current communication frequency band to ensure that users understand the system operation status. When the frequency band switching fails or the backup frequency band cannot provide stable communication, the alarm mechanism is triggered to prompt the need for manual intervention or system adjustment to ensure that the system responds to abnormal situations in a timely manner.

[0031] The process includes frequency band scanning and detection, frequency band priority setting and strategy formulation, optimal frequency band selection, frequency band switching decision and execution, post-switching verification and optimization, history recording and learning optimization, and user and system notifications. Through these steps, the dynamic frequency selection algorithm can intelligently select the optimal communication frequency band based on the output of the real-time environmental perception module, optimize the stability and anti-interference capability of wireless communication, and ensure the efficient operation of the remote wireless heating valve control system based on the Internet of Things in a complex wireless environment.

[0032] The adaptive modulation and encoder dynamically adjusts the modulation mode and coding strategy according to the current communication environment. The process is as follows: The adaptive modulation and encoder continuously monitors the status of the current communication channel, including key indicators: signal strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER) and interference level, obtains environmental data provided by the real-time environmental perception module, including: temperature, humidity, vibration and light, and evaluates the potential impact of environmental data on the communication channel; preset modulation schemes, including dual-phase keying, quadrature keying, 16-order orthogonal amplitude modulation and 64-order orthogonal amplitude modulation, based on the current channel status and environmental data, evaluates the anti-interference ability, data transmission rate and energy efficiency of each modulation mode under current conditions, and selects the modulation mode that can provide the best balance under the current communication environment. Under high signal-to-noise ratio and low interference conditions, a high-order modulation mode is selected to increase the data transmission rate; under low signal-to-noise ratio or high interference conditions, a low-order modulation mode is selected to enhance anti-interference. Interference capability; preset coding schemes, including convolutional coding, low-density parity check coding and Turbo coding, and evaluate the impact of different coding rates on data integrity and transmission efficiency according to the current communication environment. Among them, high coding rates are suitable for high-interference environments to improve data reliability, and low coding rates are suitable for low-interference environments to improve transmission rates. Select coding strategies and coding rates suitable for current channel conditions to ensure the accuracy and efficiency of data transmission and determine the optimal coding strategy; perform parameter configuration and implementation, and adjust the carrier frequency, phase offset or amplitude modulation in the communication protocol parameters according to the selected modulation mode to match the new modulation scheme to complete the modulation mode configuration; configure the corresponding encoder and decoder parameters according to the selected coding strategy and coding rate to ensure the correct encoding and decoding of data at the transmission and receiving ends to complete the coding strategy configuration; ensure that the RF front end and communication processing unit support the switching of the selected modulation mode and coding strategy, and coordinate hardware configuration and software control.

[0033] The process of dynamically adjusting the modulation mode and coding strategy includes communication environment evaluation, modulation mode selection, coding strategy adjustment, parameter configuration and implementation; thus, the adaptive modulation and encoder can dynamically select the most suitable modulation mode and coding strategy according to the current communication environment, and optimize the stability of wireless communication, data transmission rate and anti-interference capability.

[0034] The system monitoring and maintenance module is equipped with a remote monitoring platform, a fault detection and alarm system, a data storage and log management device, and a remote maintenance interface. The remote monitoring platform is used to centrally display the operating status, communication quality, and equipment status of each module of the system; the fault detection and alarm system is used to detect system anomalies in real time and issue alarms in a timely manner. The data storage and log management device records system operating data and event logs. The remote maintenance interface allows technicians to configure, upgrade, and maintain the system through the network.

[0035] like Figure 2 As shown, the system implements the control process through the following steps: S1: The intelligent communication management module selects the optimal communication frequency band and adjusts the communication parameters according to the current wireless environment; S2: The real-time environment perception module continuously monitors environmental changes and predicts potential interference; S3: The adaptive frequency allocation and spectrum management module optimizes the allocation of spectrum resources based on the environmental perception results; S4: The equipment control and execution module receives and executes control instructions, adjusts the state of the heating valve, and realizes precise regulation of the heat medium; S5: The system monitoring and maintenance module monitors the system operation status in real time, performs fault detection and remote maintenance; The whole process ensures the efficient, stable and safe operation of the heating system in a complex environment.

[0036] Among them, the above steps ensure the efficient, stable and reliable operation of the system in complex environments through the collaborative work between modules; the intelligent communication management module and the real-time environment perception module continuously optimize the communication strategy and spectrum management through self-learning and adaptive mechanisms to adapt to dynamically changing environmental conditions; among them, the self-learning and adaptive mechanisms include machine learning algorithms based on historical data and real-time data, which are used to continuously optimize interference identification, frequency selection and modulation strategies.

[0037] Through the implementation of the above technical solutions, the limitations of the existing anti-interference protocols in the prior art are solved, the dependence on a single frequency band is avoided, and the lack of flexibility of the fixed interference response strategy is avoided. In addition, the problem that most anti-interference protocols fail to fully utilize intelligent algorithms for interference identification and classification, resulting in inaccurate interference response strategies and affecting communication efficiency is solved. Through the implementation of the hybrid communication protocol technology that integrates adaptive multi-band dynamic switching with intelligent interference identification, the problems of unstable communication and insufficient anti-interference capabilities of the prior art in complex environments are solved, and the problem that a large number of wireless devices and metal structures that often exist in the environment cause wireless signal interference, resulting in unstable or failed transmission of remote control commands is solved; it not only improves the communication reliability and stability of the system, but also optimizes resource utilization, reduces operating costs, and significantly enhances the competitiveness and adaptability of the system in practical applications.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0039] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote wireless heating valve control system based on the Internet of Things, characterized in that: The system comprises an intelligent communication management module, a real-time environment perception module, an adaptive frequency allocation and spectrum management module, an equipment control and execution module, and a system monitoring and maintenance module, wherein the modules exchange data and work together through a wired or wireless communication interface. The system comprises: the intelligent communication management module is used to implement a hybrid communication protocol integrating adaptive multi-band dynamic switching and intelligent interference identification; it is used to automatically switch to a low-interference frequency band when high interference is detected, and dynamically adjust communication parameters; the intelligent communication management module is integrated with a multi-band wireless communication interface, supports 2.4GHz, 5GHz, and 900MHz frequency bands, and includes an intelligent interference identification unit, which uses a pre-trained neural network model to perform real-time data sampling, signal amplitude and noise ratio calculation on the received wireless signal, and analyze and quantify the interference intensity, type and source; The real-time environment perception module is used to collect and analyze system operating environment data, and based on the output of the interference prediction model, adjust the communication strategy in advance to avoid potential interference; the real-time environment perception module consists of a multi-sensor acquisition unit, which integrates temperature, humidity, vibration and light sensors to regularly collect key environmental parameters in the heating system and the operating status of the heating medium; the adaptive frequency allocation and spectrum management module is used to work together between different control nodes to avoid frequency band conflicts and congestion; the device control and execution module is used to accurately control the opening and closing of the wireless heating valve according to the received control instructions; the system monitoring and maintenance module is used to monitor the operating status of the entire heating control system in real time, and provide remote maintenance and fault diagnosis functions.

2. According to the Internet of Things-based remote wireless heating valve control system of claim 1, it is characterized by: The intelligent communication management module comprises: The protocol adaptation engine dynamically adjusts communication parameters according to the interference assessment results output by the interference identification unit, including automatic switching between BPSK, QPSK, and 16-QAM modulation modes, adjusting data transmission rate and transmission power, and thus realizing adaptive multi-band dynamic switching.

3. According to the Internet of Things-based remote wireless heating valve control system of claim 2, it is characterized in that: The real-time environment perception module also includes: A data acquisition and processing circuit continuously acquires data from the sensor and transmits it to a data processing center in real time; The environmental data analyzer establishes an interference prediction model through machine learning algorithms, predicts future interference conditions based on historical and real-time environmental data, and outputs interference prediction results.

4. According to the Internet of Things-based remote wireless heating valve control system of claim 3, it is characterized in that: The process of the environmental data analyzer establishing an interference prediction model through a machine learning algorithm, predicting future interference conditions based on historical and real-time environmental data, and outputting interference prediction results includes: Collect environmental sensor data accumulated during system operation as historical environmental data, and collect wireless communication performance indicators in the corresponding time period as communication performance data. Deploy a variety of environmental sensors at key nodes of the control system to monitor environmental parameters in real time, set data collection frequency to capture dynamic characteristics of environmental changes, and obtain real-time environmental data; clean, standardize and normalize the obtained data, and then synchronize and align the data to extract features; Calculate the mean, variance, maximum, minimum, kurtosis, and skewness of environmental parameters to capture the central tendency and dispersion of data and obtain statistical characteristics; extract the frequency characteristics of vibration and noise signals through Fourier transform or wavelet transform, identify periodic interference patterns, and clarify frequency domain characteristics; analyze trends, seasonality, and mutation points in time series data to capture the dynamic characteristics of environmental changes and obtain time series characteristics; evaluate the correlation between each feature and wireless communication performance indicators, screen out features with correlation coefficients greater than 0.8, and apply principal component analysis and linear discriminant analysis to reduce feature dimensions and retain main information. At the same time, use decision trees and random forests to evaluate the importance of features and screen out key features; Divide the preprocessed data set into training set and test set in proportion for model training and evaluation. For supervised learning algorithms, select supervised learning algorithms suitable for interference prediction. For unsupervised learning algorithms, use cluster analysis to discover potential patterns and associations between environmental changes and communication performance when necessary. Use the training set data to train the selected machine learning model to learn the relationship between environmental parameters and communication performance. Optimize the model's hyperparameters through grid search, random search or Bayesian optimization. Use the test set to evaluate the model's accuracy, precision, recall and F1 score, and select the model with the best performance. Continuously receive real-time data from environmental sensors and perform the same preprocessing and feature extraction steps as during training. Based on the real-time data, construct a feature vector consistent with the training model. Input the feature vector extracted in real time into the trained interference prediction model to predict the interference situation. The model outputs the prediction result based on the input features. Based on the model prediction results, the specific impact of environmental changes on wireless communications is evaluated; the predicted interference conditions are classified into three risk levels: low, medium, and high; according to the impact assessment results, corresponding communication strategy adjustment suggestions are put forward, and when high-risk interference conditions are detected, the early warning or alarm mechanism is triggered to notify the system administrator or automatically take emergency measures; The decision suggestions are passed to the intelligent communication management module, and corresponding communication strategy adjustments are executed to optimize communication performance and stability. Through the feedback mechanism, the adjusted communication effect is continuously monitored to further optimize the interference prediction model and decision-making strategy to enhance the overall anti-interference capability of the system.

5. The remote wireless heating valve control system based on the Internet of Things according to claim 4 is characterized in that: The adaptive frequency allocation and spectrum management module includes: Dynamic frequency selection algorithm, based on the output of the real-time environment perception module, intelligently selects the optimal communication frequency band; Adaptive modulation and encoding dynamically adjusts the modulation mode and coding strategy according to the current communication environment.

6. The remote wireless heating valve control system based on the Internet of Things according to claim 5 is characterized in that: The process of the dynamic frequency selection algorithm intelligently selecting the optimal communication frequency band based on the output of the real-time environment perception module includes: Perform a comprehensive scan of the wireless frequency bands supported by the system, including 2.4GHz, 5GHz and 900MHz, detect the signal strength, interference level and channel utilization of each frequency band, obtain the current channel status of the frequency band, and conduct real-time evaluation of the current channel status of each frequency band, including measuring the signal-to-noise ratio, signal strength and number of interference sources, and obtaining the communication quality indicators of each frequency band. According to the system design requirements and application scenarios, the priority of each frequency band is preset, including 2.4GHz taking precedence over 5GHz, and 5GHz taking precedence over 900MHz. A frequency band selection strategy is formulated to determine the frequency band selection rules under different interference and channel utilization conditions, and a scoring mechanism is set to assign a comprehensive score to each frequency band. The allocation process includes: a high signal strength score is high, a high signal-to-noise ratio score is high, a low interference level score is high, and a low channel utilization score is high; according to the preset priority and scoring mechanism, the frequency band is comprehensively evaluated, and the frequency band with the highest score and meeting the priority requirements is selected as the current optimal communication frequency band; Determine whether the communication quality of the currently used frequency band is lower than the preset threshold. When it is detected that the communication quality of the current frequency band is poor, trigger the frequency band switching process, adjust the communication protocol parameters according to the selected optimal frequency band, control the RF front end of the multi-band wireless communication interface, and switch to the selected frequency band.

7. The remote wireless heating valve control system based on the Internet of Things according to claim 6 is characterized in that: The adaptive modulation and encoder dynamically adjusts the modulation mode and coding strategy according to the current communication environment, and the process is as follows: The adaptive modulation and encoding module continuously monitors the status of the current communication channel, obtains the environmental data provided by the real-time environmental perception module, and evaluates the potential impact of the environmental data on the communication channel; The preset modulation schemes include bi-phase keying, quad-phase keying, 16-order quadrature amplitude modulation and 64-order quadrature amplitude modulation. Based on the current channel status and environmental data, the anti-interference ability, data transmission rate and energy efficiency of each modulation method under the current conditions are evaluated, and the modulation method that can provide the best balance under the current communication environment is selected; the preset coding schemes include convolutional coding, low-density parity check coding and Turbo coding. According to the current communication environment, the impact of different coding rates on data integrity and transmission efficiency is evaluated, and the coding strategy and coding rate suitable for the current channel conditions are selected to determine the optimal coding strategy; parameter configuration and implementation are performed, and according to the selected modulation method, the communication protocol parameters are adjusted to match the new modulation scheme to complete the modulation method configuration; according to the selected coding strategy and coding rate, the corresponding encoder and decoder parameters are configured to complete the coding strategy configuration.

8. The remote wireless heating valve control system based on the Internet of Things according to claim 7 is characterized in that: The device control and execution module includes: A wireless control interface, used to receive control instructions from an intelligent communication management module; A heating valve driving unit, used to drive the heating valve according to a control instruction to achieve flow control of liquid or gas; Feedback sensor, used to monitor the status of the heating valve and feed back the status information to the system; A local processor is used to process control instructions and feedback data, and make local decisions and optimizations; Wherein, the feedback sensor is also used to feed back the real-time status of the heating valve to the intelligent communication management module through the local processor.

9. The remote wireless heating valve control system based on the Internet of Things according to claim 8 is characterized in that: The system monitoring and maintenance module is configured with a remote monitoring platform, a fault detection and alarm system, a data storage and log management device, and a remote maintenance interface. The remote monitoring platform is used to centrally display the operating status, communication quality, and equipment status of each module of the system; the fault detection and alarm system is used to detect system anomalies in real time and issue alarms in a timely manner; the data storage and log management device records system operating data and event logs; and the remote maintenance interface allows technicians to configure, upgrade, and maintain the system through the network.

10. The remote wireless heating valve control system based on the Internet of Things according to claim 9, characterized in that: The system implements the control process through the following steps: S1: The intelligent communication management module selects the optimal communication frequency band and adjusts the communication parameters according to the current wireless environment; S2: The real-time environment perception module continuously monitors environmental changes and predicts potential interference; S3: Adaptive frequency allocation and spectrum management module optimizes spectrum resource allocation based on environmental perception results; S4: The equipment control and execution module receives and executes the control instruction to adjust the state of the heating valve; S5: The system monitoring and maintenance module monitors the system operation status in real time, performs fault detection and remote maintenance; The above steps ensure the efficient, stable and reliable operation of the system in a complex environment through the collaborative work between modules.

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