Remote control method and system for wireless sensing equipment

Abstract

The invention relates to the technical field of remote control of sensing equipment, in particular to a remote control method and system of wireless sensing equipment. The method comprises the following steps: collecting real-time network monitoring parameters of wireless sensing equipment; performing transient signal fluctuation identification and multi-time-point load state evolution on the real-time network monitoring parameters, and constructing a multi-time-point network load state curve; performing sliding time window segmentation on the multi-time-point network load state curve, and performing future time period state transition prediction so as to construct a future time period network state trend chart; acquiring a remote control instruction of the wireless sensing equipment; and performing dynamic instruction transmission rate adjustment on the remote control instruction of the wireless sensing equipment according to the network state trend chart in the future time period to obtain a dynamic transmission rate adjustment strategy. According to the invention, the remote control stability and performance of the wireless sensing equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote control of sensor equipment, and in particular to a remote control method and system for wireless sensor equipment. Background Art

[0002] With the rapid development of wireless sensor technology, the application of wireless sensor devices in various industries has gradually increased, especially in automation and intelligent systems. Wireless sensor devices are widely used in smart homes, industrial control, environmental monitoring, agricultural management and other fields. They can collect environmental data in real time, monitor equipment status, and perform remote control tasks. However, with the continuous expansion of application scenarios, wireless sensor devices often face a series of problems such as signal attenuation, spectrum interference, network fluctuations, etc. in actual operation. These problems may affect the normal operation of wireless devices and even cause communication interruption or inaccurate execution of control instructions.

[0003] Traditional remote control methods for wireless sensor devices mainly rely on fixed transmission frequency bands and static control strategies. This method is prone to signal interference and network congestion in complex wireless environments, resulting in delays or loss of remote control commands. In addition, existing methods usually lack real-time monitoring and intelligent adjustment of network status and signal environment, and cannot effectively respond to dynamically changing environmental factors, resulting in unstable control performance of wireless devices. Especially in large-scale deployment of wireless sensor devices, a single control strategy cannot meet the changing requirements of different devices and different tasks. Therefore, a more intelligent, flexible and efficient remote control method for wireless sensor devices is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a remote control method and system for wireless sensor equipment to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a remote control method for a wireless sensor device, comprising the following steps: Step S1: collecting real-time network monitoring parameters of wireless sensor devices; performing transient signal fluctuation recognition and multi-time point load state evolution on the real-time network monitoring parameters, and constructing a multi-time point network load state curve; Step S2: Segment the network load state curve at multiple time points into sliding time windows, and predict the state transition in future time periods, so as to construct a network state trend graph in future time periods; Step S3: obtaining a remote control instruction for a wireless sensor device; dynamically adjusting the transmission rate of the remote control instruction for the wireless sensor device according to a trend diagram of a network status in a future period to obtain a dynamic transmission rate adjustment strategy; Step S4: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device, and perform spectrum distribution analysis of interference sources to construct an interference spectrum scene diagram of the wireless sensor device; Step S5: Adaptively adjust the transmission frequency band according to the interference spectrum scene diagram, so as to obtain the optimal instruction transmission frequency band; Step S6: Perform multi-objective collaborative control optimization based on the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, so as to build an intelligent control engine to perform remote control operations of wireless sensor devices.

[0006] The present invention collects network monitoring parameters (such as delay, bandwidth, packet loss rate, etc.) of wireless sensor devices in real time, so that the system can grasp the network status in the first time, which enables the system to quickly respond to network fluctuations and improve the stability of the equipment. By identifying signal fluctuations, the system can quickly detect abnormal fluctuations or emergencies in the network, and take countermeasures (such as load balancing or resource adjustment) in advance to avoid equipment failure due to sudden network problems. The constructed multi-time point network load state curve can help analyze and understand the evolution law of network load, provide data support for subsequent network optimization, enable the device to predict and respond to future load changes, and ensure transmission quality. The generated load curve provides a data basis for subsequent time period prediction and dynamic adjustment, ensuring the long-term stability and reliability of the network status. Through sliding time window segmentation, the system can continuously update the real-time changes of the network status, capture the instantaneous changes of the network status, and ensure that the prediction results are more timely. This operation effectively eliminates the impact of short-term network fluctuations on the prediction. With the help of historical data and time series analysis, the system can accurately predict the network load, signal quality and other states in the future period, providing a basis for the system to prepare for response in advance. This predictive ability enables wireless sensor devices to avoid overload and crash in the future high load or network bottleneck period. The network status trend chart for the future period not only helps the dynamic adjustment of the control strategy, but also provides guidance for decisions such as equipment bandwidth planning and transmission rate adjustment, thereby improving the accuracy of remote control. According to the prediction of the future network status, the system can adjust the transmission rate of remote control instructions in real time, so that signal transmission can ensure quality while avoiding resource waste. For example, when the network load is low, the system can increase the transmission rate to speed up task completion; when the network load is high, the transmission rate can be reduced to avoid affecting other key tasks. Dynamic adjustment of the transmission rate can improve the communication efficiency of wireless sensor devices while ensuring data quality, avoid excessive data congestion, reduce latency, and ensure real-time execution of control instructions. The system can adapt to a variety of network environments (including high load, low bandwidth, etc.), so that the remote control of wireless sensor devices can run stably under various network conditions. Through full-band wireless signal scanning, the system can fully identify the signal frequency band usage in the current environment and provide data support for the effective use of spectrum resources. Analysis of the spectrum distribution of interference sources helps to identify signal sources that may interfere with wireless devices, such as communication interference from other devices, environmental noise, etc. By identifying interference sources in advance, the system can avoid frequency band conflicts and improve the robustness of the system. Through interference source analysis and spectrum situation evaluation, the system can automatically select the best frequency band for signal transmission to avoid frequency band conflicts or interference, which not only improves the stability of transmission, but also avoids unnecessary spectrum waste. Adaptive frequency band adjustment can ensure that wireless sensor devices can still operate stably in an environment with large interference, reduce signal loss, and ensure the accurate transmission of remote control commands.By selecting the optimal frequency band, the impact of other wireless devices or interference sources on the signal can be reduced, and the overall performance of the wireless network can be improved. By combining multiple optimization strategies such as dynamic transmission rate adjustment and adaptive frequency band adjustment, the system can perform multi-objective collaborative control to ensure the maximization of network transmission performance while avoiding the negative effects caused by excessive adjustment. Based on the optimization strategy of the previous steps, the constructed intelligent control engine can automatically make decisions and dynamically adjust the working parameters of wireless sensor devices (such as transmission rate, transmission frequency band, power, etc.) according to the real-time network status, environmental changes and equipment requirements to ensure the effective execution of control instructions. The intelligent control engine can make immediate adjustments when the network status changes, ensuring that the device can adapt to different environmental conditions and automatically adjust the transmission strategy, thereby improving the system's adaptability.

[0007] In this specification, a remote control system of a wireless sensor device is provided, which is used to execute the remote control method of the wireless sensor device as described above, including: The load state evolution module is used to collect real-time network monitoring parameters of wireless sensor devices; perform transient signal fluctuation identification and multi-time point load state evolution on the real-time network monitoring parameters, and construct a multi-time point network load state curve; The network trend module is used to segment the network load state curve at multiple time points into sliding time windows and predict the state transition in future time periods, thereby constructing a network state trend graph for future time periods. The transmission rate adjustment module is used to obtain the remote control instructions of the wireless sensor device; dynamically adjust the transmission rate of the remote control instructions of the wireless sensor device according to the network status trend diagram of the future period to obtain a dynamic transmission rate adjustment strategy; The interference source spectrum distribution module is used to scan the wireless signals of the entire frequency band in the area of ​​the wireless sensor device and perform interference source spectrum distribution analysis to construct an interference spectrum scene diagram of the wireless sensor device; An adaptive frequency band adjustment module, used to perform adaptive transmission frequency band adjustment according to the interference spectrum scene diagram, so as to obtain an optimal instruction transmission frequency band; The collaborative control module is used to perform multi-objective collaborative control optimization based on the optimal command transmission frequency band and dynamic transmission rate adjustment strategy, thereby building an intelligent control engine to perform remote control operations of wireless sensor equipment.

[0008] Through real-time monitoring and fluctuation identification, the system of the present invention can accurately evaluate the current network status and timely capture potential load peaks, congestion or signal attenuation. By constructing a multi-time point network load state curve, the system can better understand the law of network state changes, thereby providing a more reliable data basis for subsequent prediction and adjustment. By predicting the network load trend, potential network bottlenecks or congestion periods can be identified in advance, helping the system to actively make adjustments to avoid the impact of network fluctuations on remote control operations. According to the future network status trend chart, the network performance of the future period can be accurately predicted, thereby providing a basis for dynamically adjusting the transmission strategy. According to the changes in the network status, the transmission rate is dynamically adjusted to ensure that the instructions are carried out at the most appropriate transmission rate to avoid transmission delays or failures caused by network congestion or signal attenuation. By predicting the future network status and adjusting the rate accordingly, the system can be optimized according to the actual network load condition, thereby improving the response speed and transmission efficiency of the device. By comprehensively scanning the spectrum and analyzing the characteristics of the interference source, the system can accurately identify the interference source and its impact range, thereby reducing the signal quality degradation caused by external interference. Through accurate interference source identification, the system can avoid the impact of interference sources, optimize the use of spectrum resources, and ensure the stability and efficiency of signal transmission. By automatically adjusting the transmission frequency band, frequency conflicts with interference sources can be avoided, the impact of interference on transmission quality can be reduced, and the signal can be transmitted within the optimal frequency band. Adaptive frequency band adjustment enhances the system's ability to adapt to changes in different environments, especially in complex wireless environments or when there is a lot of interference, it can flexibly adjust the frequency band and optimize the transmission path. By integrating multiple optimization strategies, the collaborative control module can intelligently select the optimal control parameters (such as frequency band, transmission rate, power, etc.) in a variety of network environments to ensure efficient and stable remote control operations of the device. The module can simultaneously handle the optimization requirements of multiple targets (frequency band, rate, interference, etc.), achieve cross-dimensional control optimization, and improve the performance and stability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of the steps of a remote control method for a wireless sensor device according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0010] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0011] The example of this application provides a method and system. The execution subjects of the method and system include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of audio and image management system, information management system, and cloud data management system.

[0012] See also Figures 1 to 4 The present invention provides a remote control method for a wireless sensor device, the remote control method for a wireless sensor device comprising the following steps: Step S1: collecting real-time network monitoring parameters of wireless sensor devices; performing transient signal fluctuation recognition and multi-time point load state evolution on the real-time network monitoring parameters, and constructing a multi-time point network load state curve; Step S2: Segment the network load state curve at multiple time points into sliding time windows, and predict the state transition in future time periods, so as to construct a network state trend graph in future time periods; Step S3: obtaining a remote control instruction for a wireless sensor device; dynamically adjusting the transmission rate of the remote control instruction for the wireless sensor device according to a trend diagram of a network status in a future period to obtain a dynamic transmission rate adjustment strategy; Step S4: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device, and perform spectrum distribution analysis of interference sources to construct an interference spectrum scene diagram of the wireless sensor device; Step S5: Adaptively adjust the transmission frequency band according to the interference spectrum scene diagram, so as to obtain the optimal instruction transmission frequency band; Step S6: Perform multi-objective collaborative control optimization based on the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, so as to build an intelligent control engine to perform remote control operations of wireless sensor devices.

[0013] The present invention collects network monitoring parameters (such as delay, bandwidth, packet loss rate, etc.) of wireless sensor devices in real time, so that the system can grasp the network status in the first time, which enables the system to quickly respond to network fluctuations and improve the stability of the equipment. By identifying signal fluctuations, the system can quickly detect abnormal fluctuations or emergencies in the network, and take countermeasures (such as load balancing or resource adjustment) in advance to avoid equipment failure due to sudden network problems. The constructed multi-time point network load state curve can help analyze and understand the evolution law of network load, provide data support for subsequent network optimization, enable the device to predict and respond to future load changes, and ensure transmission quality. The generated load curve provides a data basis for subsequent time period prediction and dynamic adjustment, ensuring the long-term stability and reliability of the network status. Through sliding time window segmentation, the system can continuously update the real-time changes of the network status, capture the instantaneous changes of the network status, and ensure that the prediction results are more timely. This operation effectively eliminates the impact of short-term network fluctuations on the prediction. With the help of historical data and time series analysis, the system can accurately predict the network load, signal quality and other states in the future period, providing a basis for the system to prepare for response in advance. This predictive ability enables wireless sensor devices to avoid overload and crash in the future high load or network bottleneck period. The network status trend chart for the future period not only helps the dynamic adjustment of the control strategy, but also provides guidance for decisions such as equipment bandwidth planning and transmission rate adjustment, thereby improving the accuracy of remote control. According to the prediction of the future network status, the system can adjust the transmission rate of remote control instructions in real time, so that signal transmission can ensure quality while avoiding resource waste. For example, when the network load is low, the system can increase the transmission rate to speed up task completion; when the network load is high, the transmission rate can be reduced to avoid affecting other key tasks. Dynamic adjustment of the transmission rate can improve the communication efficiency of wireless sensor devices while ensuring data quality, avoid excessive data congestion, reduce latency, and ensure real-time execution of control instructions. The system can adapt to a variety of network environments (including high load, low bandwidth, etc.), so that the remote control of wireless sensor devices can run stably under various network conditions. Through full-band wireless signal scanning, the system can fully identify the signal frequency band usage in the current environment and provide data support for the effective use of spectrum resources. Analysis of the spectrum distribution of interference sources helps to identify signal sources that may interfere with wireless devices, such as communication interference from other devices, environmental noise, etc. By identifying interference sources in advance, the system can avoid frequency band conflicts and improve the robustness of the system. Through interference source analysis and spectrum situation evaluation, the system can automatically select the best frequency band for signal transmission to avoid frequency band conflicts or interference, which not only improves the stability of transmission, but also avoids unnecessary spectrum waste. Adaptive frequency band adjustment can ensure that wireless sensor devices can still operate stably in an environment with large interference, reduce signal loss, and ensure the accurate transmission of remote control commands.By selecting the optimal frequency band, the impact of other wireless devices or interference sources on the signal can be reduced, and the overall performance of the wireless network can be improved. By combining multiple optimization strategies such as dynamic transmission rate adjustment and adaptive frequency band adjustment, the system can perform multi-objective collaborative control to ensure the maximization of network transmission performance while avoiding the negative effects caused by excessive adjustment. Based on the optimization strategy of the previous steps, the constructed intelligent control engine can automatically make decisions and dynamically adjust the working parameters of wireless sensor devices (such as transmission rate, transmission frequency band, power, etc.) according to the real-time network status, environmental changes and equipment requirements to ensure the effective execution of control instructions. The intelligent control engine can make immediate adjustments when the network status changes, ensuring that the device can adapt to different environmental conditions and automatically adjust the transmission strategy, thereby improving the system's adaptability.

[0014] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a remote control method for a wireless sensor device of the present invention. In this example, the steps of the remote control method for a wireless sensor device include: Step S1: collecting real-time network monitoring parameters of wireless sensor devices; performing transient signal fluctuation recognition and multi-time point load state evolution on the real-time network monitoring parameters, and constructing a multi-time point network load state curve; In this embodiment, a suitable wireless sensor device is selected to ensure that it has a real-time network monitoring function. Common monitoring parameters include signal strength (RSSI), data transmission rate, packet loss rate, delay, number of access users, etc. Determine the required monitoring frequency, such as collecting data once per second, to ensure that changes in network status can be captured in a timely manner. For highly dynamic environments, a higher sampling frequency (such as once every 100 milliseconds) may be required. Configure the network monitoring module of the wireless sensor device to ensure that it can stably collect and record the selected monitoring parameters. Parameters can be set through the management interface or API of the device. Set the data storage method to store the collected data in a local database or remote server for subsequent analysis. Ensure that the format of data storage is unified for subsequent processing. Start the real-time network monitoring function of the wireless sensor device and start data collection. During the collection process, the performance of the device should be monitored to ensure the continuity and accuracy of data collection. Check the status of data collection regularly and record any abnormal conditions (such as device restart, network interruption, etc.) so that the impact of these factors on the data can be considered in subsequent analysis. Select a suitable transient signal fluctuation recognition algorithm. Common methods include threshold-based detection, sliding window method, and wavelet transform. These methods can effectively identify transient events with large changes in signal strength. Set the parameters for fluctuation identification, such as setting the threshold for signal strength change (such as signal strength changes exceeding 5 dBm are considered fluctuations), and the size of the detection window (such as 10 seconds). Perform transient fluctuation analysis on the real-time collected data to identify drastic changes in signal strength. Automated detection can be achieved through programming, and fluctuation events can be recorded, with their occurrence time and change amplitude marked. For each identified transient fluctuation, record the relevant network monitoring parameters (such as delay, packet loss rate, etc.) to analyze their impact on the network load status. Use the collected real-time network monitoring parameters to construct a multi-time point network load status curve. The time series analysis method can be used to summarize the load status (such as average load, maximum load, etc.) at different time points. Visualize the load status data at each time point and generate a network load status curve to intuitively display the changing trend of the network load.

[0015] Step S2: Segment the network load state curve at multiple time points into sliding time windows, and predict the state transition in future time periods, so as to construct a network state trend graph in future time periods; In this embodiment, the size and step length of the sliding time window are determined. The window size should be selected according to the characteristics of the network load change, for example, set to 10 minutes (600 seconds) and the step length to 1 minute (60 seconds) so as to capture subtle changes in the network state. Select a suitable segmentation algorithm. Usually, a simple sliding window algorithm can be used to move on the load state curve through a fixed-length window to gradually extract the load data of each time period. Input the multi-time point network load state curve into the sliding window algorithm to gradually extract the load state data in each window. For each window, record its start and end time, as well as parameters such as the average load, maximum load and minimum load in the window. The load data of each window can be stored in a structured database table, ensuring that each record includes a timestamp, load statistics in the window and other related parameters for subsequent analysis. Select a suitable state transition prediction model, such as a time series prediction model (such as ARIMA) or a machine learning model (such as random forest, support vector machine, etc.), which can predict future states based on historical data. Determine the input features of the model, such as the average load in the window, fluctuation amplitude and other influencing factors (such as the number of access users, network environment, etc.). Using the selected prediction model, train the load data in each sliding window to generate the status prediction for the future period. The parameters of the model can be optimized by cross-validation and other methods to ensure the accuracy of the prediction. Generate network load prediction data for the future period, record the predicted value and its confidence interval for each period, so as to evaluate the reliability of the prediction. Integrate the prediction results into the network status trend chart for the future period, which should include the historical load status curve and the load status predicted in the future. Line charts or area charts can be used to display the data to intuitively show the load change trend. For trend charts, ensure that the legend and axis labels are clear so that users can understand the network load changes and prediction results in different time periods. Store the constructed network status trend chart for the future period and related prediction data in the database, and ensure that each record includes the prediction time, prediction value and other parameters for subsequent query and analysis. Regularly analyze the accuracy of the prediction results, compare the actual network load with the prediction results, and adjust the parameters of the prediction model to improve the accuracy of future predictions and provide data support for network optimization.

[0016] Step S3: obtaining a remote control instruction for a wireless sensor device; dynamically adjusting the transmission rate of the remote control instruction for the wireless sensor device according to a trend diagram of a network status in a future period to obtain a dynamic transmission rate adjustment strategy; In this embodiment, the source of the remote control instructions of the wireless sensor device is determined, and the instructions can usually be obtained through the user interface (UI), API interface or automated script. Ensure that the format of the instructions is unified, such as using JSON or XML format, for subsequent processing. Set the frequency of instruction acquisition, for example, check whether there are new instructions every 10 seconds to ensure that user needs and network status changes can be responded to in a timely manner. Configure the remote control instruction processing system to ensure that it can receive, parse and store instructions. A processing module can be designed, which is responsible for the verification, execution and feedback of instructions.

[0017] Set the priority rules for instructions so that important control instructions can be processed under high load conditions. For example, set a higher priority for emergency instructions to ensure that they can be executed first. Start the remote control instruction management system to monitor the reception status of instructions in real time to ensure that the system can still operate stably under high load conditions. Record the reception time, content and source of each instruction for subsequent review and analysis. For each acquired instruction, perform basic verification to ensure that its format is correct and check whether the instruction is within the allowable range (for example, whether the instruction content is valid and whether the parameters are reasonable) Adjust the dynamic instruction transmission rate according to the network status trend chart for the future period. According to the network status trend chart for the future period constructed in the previous step, determine the dynamic transmission rate adjustment strategy under different load conditions. Different transmission rates can be set according to the predicted load status, such as setting a higher transmission rate (such as 100 kbps) when the load is low and reducing the transmission rate (such as 20 kbps) when the load is high. Set the threshold and parameters for adjustment, such as automatically reducing the transmission rate when the load exceeds a certain value (such as 70%) to prevent network congestion and data loss. After obtaining the remote control command, analyze the network status trend chart for the future period in real time, and determine the transmission rate of the command based on the current and predicted network load status. The dynamic adjustment strategy should take into account the importance and real-time nature of the command, and give priority to ensuring the transmission quality of key commands. Record the timestamp, original rate, and new rate of each transmission rate adjustment to facilitate subsequent analysis of the effectiveness of the adjustment strategy. Store the results of the dynamic transmission rate adjustment in the database, ensuring that each record includes the basis for adjustment, transmission rate, command content, and its execution status for subsequent query and analysis. Regularly evaluate the effectiveness of the dynamic transmission rate adjustment strategy, analyze the command transmission success rate and delay under different load conditions, and adjust the policy parameters to improve the transmission efficiency and response speed of the network.

[0018] Step S4: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device, and perform spectrum distribution analysis of interference sources to construct an interference spectrum scene diagram of the wireless sensor device; In this embodiment, a suitable wireless signal scanning device is selected to ensure that it supports full-band scanning, usually including 2.4 GHz and 5 GHz bands. The device should have high sensitivity and a fast scanning rate to capture weak signals. Configure scanning parameters, including scanning frequency band range (such as 2.400 GHz to 2.483 GHz and 5.150 GHz to 5.825 GHz), sampling frequency (such as collecting 1000 data points per second) and continuous scanning time (such as 5 minutes) to ensure that the signal in the target area can be fully covered. Start the wireless signal scanning device and perform full-band wireless signal scanning in the target area. The device should perform multiple scans at different locations to ensure the comprehensiveness and accuracy of the signal data. During the scanning process, the signal strength, signal quality, number of interference sources and their locations of each frequency band are recorded in real time. Use indicators such as received signal strength indication (RSSI) and signal-to-noise ratio (SNR) to evaluate signal quality. The collected signal data is organized into a structured database format, including information such as frequency band, signal strength, signal quality and timestamp for subsequent analysis. Analyze the preliminary data, identify the frequency bands where the signal strength exceeds a certain threshold (such as -85 dBm), and mark the potential interference source areas. Select a suitable interference source spectrum distribution analysis method, usually using power spectral density (PSD) analysis or spectrum plotting. Obtain spectrum information through Fourier transform (FFT) and other technologies to help identify the characteristics of the interference source. Determine analysis parameters such as spectrum resolution (such as 1 MHz) and calculation window size (such as 512 sampling points) to ensure the accuracy of spectrum analysis. Perform interference source spectrum distribution analysis on the sorted signal data and generate a spectrum diagram to show the signal strength of each frequency band and the distribution of interference sources. Heat maps or 3D spectrum diagrams can be used to intuitively display data. Record the characteristics of each interference source, including frequency, signal strength and its possible source (such as equipment type, distance, etc.), to provide a basis for subsequent interference management. Integrate the analysis results into an interference spectrum scenario diagram, which should include the signal strength of each frequency band, the location of the interference source and its type. A GIS system or data visualization tool can be used to generate an interference spectrum scenario diagram to ensure intuitive display of information. Ensure that the legend, coordinate axes and annotations of the scene graph are clear, so that users can understand the interference characteristics of different frequency bands and their impact range. Store the constructed interference spectrum scene graph and related analysis data in the database, and ensure that each record includes spectrum characteristics, interference source information and analysis basis for subsequent query and analysis. Regularly update the interference spectrum scene graph, and adjust the interference source management strategy based on new scanning data and analysis results to improve the robustness and stability of the wireless sensor network.

[0019] Step S5: Adaptively adjust the transmission frequency band according to the interference spectrum scene diagram, so as to obtain the optimal instruction transmission frequency band; In this embodiment, the constructed interference spectrum scenario diagram is comprehensively analyzed to identify the signal strength and interference of each frequency band. Focus on those frequency bands with high signal strength and fewer interference sources. Generally, frequency bands with signal strength higher than -80 dBm and interference strength lower than -90 dBm are considered to be suitable candidate frequency bands. Formulate evaluation criteria and select appropriate thresholds to quickly screen out available frequency bands. For example, set the frequency band with higher signal quality and less interference as the optimal transmission frequency band. According to the analysis results, each candidate frequency band is evaluated, including parameters such as signal strength, interference source distribution, and transmission rate. The transmission quality of each frequency band can be quantified by calculating the signal-to-noise ratio (SNR). The higher the signal-to-noise ratio, the better the transmission quality. Record the evaluation results of each frequency band, including the frequency band number, signal strength, interference strength, and SNR value. Store this information in a database for subsequent selection of the optimal frequency band. According to the evaluation results, set an adaptive transmission frequency band adjustment strategy. A dynamic frequency band selection algorithm can be used to adjust the transmission frequency band based on real-time interference conditions and signal quality. Automatically switch to a frequency band with a stronger signal during high interference periods to ensure the continuity and stability of transmission. Set the threshold for frequency band adjustment, for example, when the signal strength of the current frequency band is lower than -85 dBm, automatically switch to the next candidate frequency band. In actual operation, select the optimal transmission frequency band in real time according to the current network status and interference situation. Automatic frequency band switching can be programmed to ensure seamless transmission. For example, when the interference of the current frequency band is detected to increase, the system automatically switches to an alternative frequency band with better signal quality. Record the time, original frequency band, new frequency band and its signal quality of each frequency band adjustment for subsequent analysis of the effectiveness of the adjustment strategy. Monitor the adjusted transmission frequency band in real time and evaluate its transmission performance, including delay, packet loss rate and transmission success rate. Ensure that the newly selected frequency band can maintain good signal quality and stability in actual applications. Establish a feedback mechanism to continuously optimize the frequency band adjustment strategy based on real-time monitoring data. For example, if the signal quality of the new frequency band is lower than the preset value, consider re-evaluating the frequency band selection. Store the results of the frequency band adjustment and the monitoring data in the database for subsequent analysis and optimization. Regularly analyze the usage of different frequency bands, identify the best performing frequency bands, and feed this information back into the adaptive transmission frequency band adjustment strategy. Based on long-term usage data, continuously iterate and optimize the adaptive transmission frequency band adjustment strategy to ensure that the optimal transmission frequency band can always be selected under different environments and conditions.

[0020] Step S6: Perform multi-objective collaborative control optimization based on the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, so as to build an intelligent control engine to perform remote control operations of wireless sensor devices.

[0021] In this embodiment, the overall architecture of the intelligent control engine is designed to ensure that it can integrate the optimal instruction transmission frequency band and dynamic transmission rate adjustment strategy. The control engine needs to have real-time data processing capabilities, decision-making capabilities and instruction execution capabilities. Determine the input and output of the engine, including the received remote control instructions, the current network status, the optimal transmission frequency band and the dynamic transmission rate. A well-designed data stream can ensure the efficient transmission of information in the system. Set a multi-objective collaborative control strategy according to the actual needs of the wireless sensor device. The goals include ensuring the timely transmission of instructions, minimizing transmission delays, optimizing network resource utilization, and improving the success rate of instruction execution. Set a weight for each goal. For example, the weight of timely transmission of instructions can be set to 0.5, the weight of transmission delay can be set to 0.3, and the weight of resource utilization can be set to 0.2. This weighting method can help the intelligent control engine balance the priority of each goal when making decisions. Start the real-time monitoring module of the intelligent control engine to continuously monitor the network status and the working status of the wireless sensor device. Regularly collect data such as the signal strength, interference, transmission rate and device operation status of the network. Use data analysis algorithms (such as moving average or weighted average) to process real-time data and extract key features to provide a basis for subsequent decision-making. Dynamically generate instruction execution strategies based on real-time monitoring data and multi-objective collaborative control strategies. In the decision-making process, consider the current network conditions, instruction content and priority. For example, when the network load is high, it may be necessary to reduce the transmission rate to ensure the reliability of the instruction. Design a decision algorithm to select the optimal execution strategy based on the current state and target weight. For example, if the signal strength of the current frequency band is lower than -85 dBm, the system will automatically switch to the preset optimal frequency band and adjust the transmission rate accordingly. Once the instruction execution strategy is generated, immediately send the control instruction to the wireless sensor device through the optimal transmission frequency band. The device should have the ability to receive and parse instructions and perform corresponding operations according to the instructions. Record the results of each instruction transmission, including execution time, transmission rate, success or failure, etc., for subsequent analysis and optimization. Establish a feedback mechanism to monitor the execution effect of the instruction in real time, including the success rate of execution, delay time and user feedback. Dynamically adjust the decision parameters and strategies of the intelligent control engine based on the feedback information. Regularly analyze historical data of command execution to identify potential problems and room for improvement, so as to continuously optimize the performance and decision-making ability of the intelligent control engine. Through long-term data monitoring and analysis, evaluate the overall performance of the intelligent control engine, including the success rate of command transmission, network resource utilization efficiency and user satisfaction. According to the evaluation results, adjust the weights and parameters of the multi-objective collaborative control strategy to ensure that the intelligent control engine always maintains the best performance under different environments and conditions.

[0022] In this embodiment, refer to Figure 2, is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Collecting real-time network monitoring parameters of wireless sensor devices; Step S12: performing transient signal fluctuation identification on the real-time network monitoring parameters to extract the current network transient network fluctuation characteristics; Step S13: Calculating the signal transmission delay according to the real-time network monitoring parameters to obtain a signal transmission delay value; Step S14: performing data packet loss rate statistics on the real-time network monitoring parameters to obtain a network data packet loss rate; Step S15: Perform network load state deduction on the current network transient network fluctuation characteristics, signal transmission delay value and network data packet loss rate, so as to generate real-time network load state characteristics; Step S16: Perform multi-time point load state evolution on the real-time network load state characteristics to construct a multi-time point network load state curve.

[0023] In this embodiment, a suitable wireless sensor device (such as a Wi-Fi router, a network monitor or an IoT device) is selected to collect network monitoring parameters in real time. These devices should support monitoring of multiple indicators, such as signal strength, data transmission rate, number of connections and delay. The sampling frequency of the device is configured and set to collect once per second to ensure that enough real-time data is obtained to analyze the network status. For example, the sampling period can be set to 1 second, and parameters such as signal strength, transmission rate and connection status are recorded each time. The collected parameters are transmitted to the central processing unit or database via Wi-Fi or wired network. A reliable transmission protocol (such as MQTT or HTTP) is selected to ensure the integrity and timeliness of the data during transmission. The real-time monitoring data is stored in a structured manner in the database, ensuring that each record includes a timestamp, a device ID and the corresponding network monitoring parameters for subsequent analysis. The collected network monitoring parameters are monitored in real time to ensure the accuracy and stability of the data. A reasonable range can be set (such as the signal strength should be maintained between -70dBm and -30dBm) to check whether the data is abnormal in real time. The working status of the device is verified regularly to ensure the normal operation of the wireless sensor device and to deal with possible faults or data loss problems in a timely manner. Select a suitable signal fluctuation recognition algorithm, such as fast Fourier transform (FFT) or wavelet transform, to analyze transient signal fluctuations in real-time network monitoring parameters. These algorithms can effectively separate the frequency components of the signal and identify fluctuation characteristics. Determine the parameter settings of the algorithm. For example, the window size of FFT can be set to 256 data points to capture signal changes in a short period of time. Preprocess the collected network monitoring parameters to eliminate noise and unnecessary interference. Filters (such as low-pass filters) can be used to smooth the signal to improve the accuracy of fluctuation recognition. Apply the selected fluctuation recognition algorithm to analyze the processed data and extract transient signal fluctuation characteristics, such as fluctuation amplitude, frequency, and duration. Select a suitable signal delay calculation method, usually using RTT (Round Trip Time) or one-way delay measurement method. RTT estimates the delay by sending data packets and measuring the round-trip time. Determine the time window for measurement, such as performing delay calculation once every minute to ensure that enough data is obtained for analysis. In the collected real-time monitoring parameters, send data packets to the target device (such as a server or other network node) at regular intervals and record the timestamp of each data packet sent. After receiving the response data packet, record the timestamp of the response and calculate the difference between the sending time and the receiving time to obtain the delay value of a single data packet. Store the calculated signal transmission delay value in the database, ensuring that each record includes the timestamp, delay value, and related parameters for subsequent analysis. Regularly analyze the change trend of the delay value, identify abnormal delay situations, and evaluate the impact on network performance.The standard packet loss rate calculation method is adopted, packet loss rate = (number of lost packets / total number of sent packets) × 100%. This method can accurately reflect the reliability and stability of the network. Set the frequency of packet sending, for example, 10 packets per second, to obtain enough data for packet loss rate calculation. During real-time monitoring, continue to send packets and record the sending time and receiving status of each packet. Set a timeout threshold (such as 1 second). If no response is received within this time, it is considered as packet loss. Count the total number of packets sent and the number of lost packets within the set time window (such as every minute) for subsequent packet loss rate calculation. Store the statistical packet loss rate in the database, ensuring that each record contains the timestamp, packet loss rate and related parameters for subsequent analysis and report generation. Design a network load state deduction model, combine the transient network fluctuation characteristics, signal transmission delay value and data packet loss rate, and establish a multi-dimensional evaluation index. Set model parameters, such as setting the weights of delay and packet loss rate to 0.5 and 0.5 respectively, to reflect their impact on network load. Determine the input and output structure of the model. The input includes real-time network monitoring parameters, and the output is the network load status characteristics, such as "normal", "high load" or "overload". By analyzing the transient fluctuation characteristics, delay value and packet loss rate, the inference model is used to calculate the current network load status. The weighted average method or comprehensive evaluation method can be used to comprehensively evaluate various indicators. According to the calculation results, the network load status characteristics are updated in real time to ensure that managers can understand the current load of the network in a timely manner. During the real-time monitoring process, the network load status characteristics are recorded regularly, such as once a minute, to ensure that sufficient time series data can be collected for analysis. Set a time window of multiple time points, such as the load status data of the past 24 hours, to observe the change trend and periodicity of the load status. Evolution analysis is performed on the collected multi-time point network load status characteristics to identify the change pattern and trend of the load status. Time series analysis methods, such as the autoregressive moving average (ARIMA) model, can be used to predict the future network load status. By analyzing historical data, high-load periods and low-load periods can be identified so that managers can reasonably configure network resources. A load status curve is constructed based on the load status data at multiple time points to show the changes in network load in different time periods.

[0024] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: performing sliding time window segmentation on the network load state curves at multiple time points to obtain load state curves of multiple time windows; Step S22: Calculate the load peak values ​​of the load state curves of multiple time windows one by one to generate the load peak value of each time window; Step S23: performing network congestion situation assessment on the load state curves of multiple time windows to obtain the network congestion situation of the multiple time windows; Step S24: mining the time series state change trend according to the load peak of each time window and the network congestion situation of multiple time windows to generate time series state change trend characteristics; Step S25: predict the state transition in the future period based on the time series state change trend characteristics, so as to construct a network state trend diagram in the future period.

[0025] In this embodiment, the size and step length of the sliding time window are determined. A fixed time period (such as 5 minutes or 10 minutes) is usually selected as the window size, and the step length can be set to half of the window size to achieve smooth segmentation. For example, if the window size is set to 10 minutes, the step length is set to 5 minutes. When selecting the window size, the characteristics of network load fluctuations should be considered to ensure that the window can capture the changing trend of the load while avoiding the influence of data noise caused by excessive segmentation. According to the set time window parameters, the multi-time point network load state curve is subjected to sliding segmentation. A loop algorithm can be used to traverse the entire load state data set, and the load state data that meets the window size is extracted each time. The data of each time window is stored in a structured data set or array to ensure that the start and end time and load state data of each window can be recorded. Verify the data integrity of each time window to ensure that the load state data in each window are valid data. The minimum number of data points can be set, for example, a window must contain at least 30 data points. Select a suitable load peak calculation method, usually using the maximum value method, and directly extract the maximum load value from the data of each time window as the load peak. Set the threshold for peak calculation, for example, the load peak must exceed a certain benchmark value (such as 80%) to ensure the validity of the calculation result. For each divided time window, traverse the load status data in it and use the set peak calculation method to extract the maximum load value. You can find the maximum value by traversing the data array of each window using built-in functions or custom algorithms. Store the calculated load peak and the corresponding time window information in a new data structure, ensuring that each record contains the start time of the time window and the corresponding load peak. Store the load peak of each time window in the database for subsequent analysis and query. Design a reasonable data structure to ensure that the peak data of each time window can be quickly accessed and counted. Regularly analyze the trend of load peaks, identify peak hours and potential network congestion risks, and provide data support for subsequent network optimization. Determine the indicators for network congestion assessment, which usually include load peak, average load, packet loss rate, and latency. Each indicator should have a corresponding threshold to determine whether the network is in a congested state. Set the classification criteria for congestion status. For example, a load peak exceeding 90% and a packet loss rate exceeding 5% can be marked as "severe congestion", and a load peak between 80%-90% and an increase in latency can be marked as "mild congestion". Comprehensively evaluate the load status data of each time window. You can use the weighted scoring method to weight the scores of each indicator to obtain a comprehensive congestion score. For example, the load peak accounts for 60%, the packet loss rate accounts for 30%, and the latency accounts for 10%. According to the comprehensive congestion score and the set standards, mark the network congestion situation of each time window. This judgment can be achieved using simple conditional statements. Integrate the load peak and network congestion situation of each time window to form a multidimensional time series data set.Ensure that each record includes the time window, load peak, congestion situation and its related parameters. Set the time range for data analysis, such as the data of the past 72 hours, to capture the changing trend of network load. Select appropriate time series trend mining methods, such as moving average, exponential smoothing or trend line analysis, to analyze the evolution of load peak and congestion situation. Determine the parameter settings for trend mining, such as the moving average window size can be set to 3 time windows to smooth short-term fluctuations and highlight long-term trends. Apply the selected trend mining method to analyze the integrated time series data to identify the changing patterns of load peak and congestion situation. Features such as rising, falling and periodic changes can be extracted. Generate time series state change trend features based on the analysis results to ensure that the features can reflect the dynamic changes of network load and potential congestion risks. Select an appropriate prediction model to predict the future state based on the historical time series state change trend features. Commonly used models include autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) network or SARIMA model. Determine the parameter settings of the model, such as the number of autoregressions, the number of differences, and the number of moving averages of the ARIMA model, which can be adjusted according to the characteristics of the historical data. Use the selected prediction model to train the integrated time series state change trend characteristics to ensure that the model can capture the laws in the historical data. Perform state change predictions for future time periods and generate future network state trend data. For example, predict the load peak and network congestion situation in the next 24 hours. Store the prediction results in the database, ensuring that each record includes the prediction time, the predicted network state characteristics and their related parameters for subsequent analysis and report generation. Use visualization tools to generate network state trend charts for future time periods, showing the comparison between the prediction results and historical data, to help managers grasp the changing trend of future network loads.

[0026] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Obtaining remote control instructions for wireless sensor equipment; Step S32: Calculate the quality data volume of the remote control instruction of the wireless sensor device to obtain the control instruction data volume; Step S33: analyzing the instruction transmission bandwidth requirement according to the control instruction data volume, thereby obtaining instruction transmission bandwidth requirement data; Step S34: Calculating the transmission bandwidth performance deviation of the instruction transmission bandwidth demand data based on the network status trend diagram for the future period, thereby obtaining the instruction transmission bandwidth performance deviation value; Step S35: dynamically adjusting the instruction transmission rate according to the instruction transmission bandwidth performance deviation value to obtain a dynamic transmission rate adjustment strategy.

[0027] In this embodiment, the source of the remote control instruction is determined, which usually includes a control center, a user terminal or an automation system. Ensure that these sources can generate and send control instructions to the wireless sensor device. Configure the instruction format to ensure that the instruction can be correctly parsed during the transmission process. Standard protocols (such as MQTT or HTTP) can be used to format the instructions to ensure that the instructions contain necessary parameters such as device ID, command type and execution time. Use a reliable communication protocol to ensure timely transmission and reception of instructions. For example, a TCP / IP protocol stack can be used to ensure the integrity and order of data transmission. Configure the network connection of the device to ensure that the wireless sensor device can communicate stably with the control center, which may include the configuration of Wi-Fi, Bluetooth or other wireless communication technologies. The wireless sensor device should be set to listen for instructions from the control center. An asynchronous programming model can be used to implement this function to ensure that the device can respond to the received instructions in real time. After receiving the control instruction, perform preliminary parsing and verification to ensure the integrity and validity of the instruction. Record the timestamp and source of each received instruction for subsequent auditing and analysis. Select a method for calculating the amount of control instruction data, which can usually be calculated based on the number of bytes of the instruction. The number of bytes of each control instruction can be determined by analyzing its structure, including command field, parameter field and check field. Set the standard of instruction validity, for example, the number of bytes of the instruction must be within a certain range (such as 50 bytes to 500 bytes) to ensure the rationality of data volume calculation. After receiving each control instruction, record its byte number immediately. This can be achieved through the built-in function of the programming language to directly obtain the byte length of the string or data packet. Accumulate the number of bytes of each instruction to generate the total data volume of the control instruction. This process can be set to be counted once every hour for subsequent analysis. Store the calculated control instruction data volume in the database, ensuring that each record contains the timestamp, instruction content and data volume for subsequent query and analysis. Determine the calculation model for instruction transmission bandwidth requirements, usually using the formula: bandwidth requirement (bps) = control instruction data volume (bytes) × 8 / transmission time (seconds), which can convert the number of bytes into bit rate. Set the transmission time, for example, it can be set to 1 second, so as to calculate the bandwidth required per second. Based on the recorded control instruction data volume, apply the set bandwidth requirement calculation model to calculate the bandwidth requirement data in each time period. Ensure that the calculation results are updated in real time and reflect the current network status. Summarize the bandwidth requirements of each time period and generate a historical data report of bandwidth requirements for subsequent analysis. Use the established future network status trend chart to analyze network performance indicators for future time periods, including available bandwidth, latency, and packet loss rate. Set the time range of the future time period, such as predicting the network performance status for the next 24 hours, so as to compare it with the current command transmission bandwidth requirements.The calculation of the transmission bandwidth performance deviation value can usually be expressed by the following formula: Performance Deviation (%) = ((Future Available Bandwidth - Current Bandwidth Demand) / Current Bandwidth Demand) × 100%. This calculation can intuitively reflect the adaptability of the future network status to the current demand. The data in the calculation process should be updated in real time to ensure that the deviation calculation can reflect the dynamic changes of the network. The calculated bandwidth performance deviation value is stored in the database, and each record is ensured to contain a timestamp, deviation value, and related performance indicators for subsequent query and analysis. The trend of the performance deviation value is regularly analyzed to identify potential network bottlenecks and improvement measures, and provide data support for subsequent dynamic adjustments. According to the bandwidth performance deviation value, the dynamic adjustment strategy of the instruction transmission rate is set. For example, when the deviation value is greater than 10%, the instruction transmission rate can be reduced; when the deviation value is less than -10%, the transmission rate can be increased. The adjustment range is set. For example, the amplitude of each adjustment of the transmission rate can be set to 10% or 20% of the current rate to ensure the flexibility and adaptability of the adjustment. After receiving the bandwidth performance deviation value, the instruction transmission rate is immediately adjusted according to the set dynamic adjustment strategy. Automatic adjustments can be made through programming to ensure the real-time nature of the adjustment process. Record the timestamp of each rate adjustment and the new rate after adjustment for subsequent auditing and optimization. Record the adjusted dynamic transmission rate in the database, ensuring that each record includes the timestamp, old rate, new rate, and adjustment basis for subsequent analysis. Regularly evaluate the effectiveness of the dynamic adjustment strategy, analyze the adjusted transmission performance and user feedback, and continuously optimize the adjustment strategy to improve network performance and user experience.

[0028] In this embodiment, step S4 includes the following steps: Step S41: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device to extract the environmental wireless spectrum signal; Step S42: Identify signal interference sources in the area of ​​the ambient wireless spectrum signal, and mark multiple regional signal interference sources; Step S43: performing interference source type analysis on multiple regional signal interference sources to obtain type data of each interference source; Step S44: performing interference source spectrum distribution analysis according to the type data of each interference source to construct an interference spectrum scenario diagram of the wireless sensor device.

[0029] In this embodiment, a suitable wireless signal scanning device is selected, such as a full-band spectrum analyzer or a software defined radio (SDR) device, which should be able to cover the required frequency bands (such as 2.4 GHz and 5 GHz bands) to capture all wireless signals in the environment. Configure the scanning parameters of the device, such as setting the scanning frequency range (such as from 100 MHz to 6GHz) and the scanning time, to ensure that the signal acquisition of the full frequency band can be completed within a reasonable time. It is recommended to set the scanning time of each frequency band to 1 second. Start the wireless signal scanning and record the parameters such as the signal strength, frequency and modulation mode of each frequency band. The spectrum analysis software can be used to monitor the scanning process in real time to ensure the accuracy and integrity of the data. During the scanning process, check the status of the device regularly to ensure the stability of signal reception. The status check can be set every 10 minutes, and the operation data of the device can be recorded. Select a suitable signal interference source identification algorithm, such as a threshold-based detection method or a machine learning algorithm (such as a clustering algorithm), to analyze the extracted wireless spectrum signal data. Set the threshold for interference source identification, for example, when the signal strength is higher than a certain value (such as -70 dBm), it can be regarded as an interference source to improve the accuracy of identification. Apply the selected algorithm to analyze the ambient wireless spectrum signals and identify areas with abnormal signal strength. The signal strength and frequency in the scan data can be clustered to automatically mark potential interference sources. For each marked interference source, record its location (coordinates), signal strength, frequency and other information for subsequent analysis. Store the identified signal interference source information in the database, ensuring that each record includes the type, location and signal characteristics of the interference source for subsequent query and analysis. Use visualization tools to display the distribution of interference sources, such as heat maps or scatter plots, to help network managers intuitively understand the distribution characteristics of interference sources. Determine the classification criteria for interference sources, which usually include wireless device types (such as Wi-Fi routers, Bluetooth devices, microwave ovens, etc.) and interference levels (such as high, medium, and low). These criteria should be adjusted according to the actual application scenario. Set the parameters required for classification, such as frequency range, signal strength, and modulation mode, to more accurately identify and classify interference sources. Analyze each identified signal interference source and mark the interference source type according to the set classification criteria. This process can be achieved by matching the characteristics of known devices. Record the type information of each interference source, including device type, frequency range, and interference level, for subsequent spectrum distribution analysis. Store the type data of the interference source in the database, and ensure that each record includes information such as the ID, type, signal characteristics, and location of the interference source for subsequent query and analysis. Regularly verify the accuracy of the interference source classification, and use a sampling inspection method to manually confirm some interference sources to ensure the reliability of the classification results. Select an appropriate spectrum distribution analysis method, such as histogram analysis or spectrum drawing method, to visualize the distribution of each interference source in the spectrum.Determine the parameters for data analysis, such as frequency segments and signal strength ranges, to better display the spectrum characteristics of the interference sources. Apply analysis methods to perform spectrum distribution analysis on the type data of each interference source, and generate distribution maps of interference sources in different frequency bands. This process can be achieved by counting the number of interference sources and signal strength in each frequency band. Present the analysis results in a graphical way to show the proportion and impact of different types of interference sources in the spectrum, helping users understand the nature and intensity of interference.

[0030] In this embodiment, the specific steps of step S44 are: Calculate the signal power spectrum density according to the type data of each interference source to generate a power spectrum density value of each interference source; Calculating the signal strength of interference sources for multiple regional signal interference sources to obtain the signal strength of multiple interference sources; Calculating the signal frequency ranges of multiple regional signal interference sources to generate the interference source signal frequency range; The power spectrum density value of each interference source, the signal strength of multiple interference sources and the frequency range of the interference source signal are mined to obtain the signal spectrum modulation characteristics of each interference source; Calculate the relative positions of multiple regional signal interference sources and extract the regional position coordinates of each interference source; Perform interference source spatial distribution analysis according to the regional location coordinates of each interference source to generate an interference source spatial distribution map; According to the signal frequency modulation characteristics of each interference source, the spatial spectrum scene distribution is fitted to the interference source spatial distribution map to construct the interference spectrum scene map of the wireless sensor device.

[0031] In this embodiment, a suitable power spectral density (PSD) calculation method is selected, and Fourier transform (FFT) or Welch method is usually used. The Welch method can effectively reduce the influence of noise and provide a more stable PSD estimate by segmenting and overlapping weighting the signal. Determine the analysis parameters, such as setting the length of each signal segment (such as 1024 points) and the overlap rate (such as 50%) to ensure sufficient frequency resolution and accuracy. Perform FFT processing on the signal of each interference source to extract its frequency domain characteristics. The calculated spectrum data needs to be power normalized, usually using the formula: PSD(f) = |FFT(x)|² / N, where N is the length of the signal segment. Associate the calculated power spectral density value with the type data of the interference source, and record the power spectral density value and its frequency range of each interference source. Store the power spectral density value of each interference source in a database, ensuring that each record includes the interference source ID, power spectral density value and related frequency information for subsequent analysis. Regularly analyze the changing trend of the power spectral density value, identify the interference source with higher power, and evaluate its potential impact on the wireless sensor device. Select a signal strength calculation method, usually measured by received signal strength indication (RSSI). RSSI refers to the signal strength received from the interference source, usually expressed in dBm. Determine the time window for measurement, such as recording the signal strength every 1 second to capture instantaneous changes in signal strength. During the scan, record the RSSI value of each interference source in real time. These values ​​can be obtained through the built-in measurement function of the wireless sensor device and the data is sent to the database in real time. Take multiple measurements for each interference source and calculate its average signal strength to reduce the impact of random noise on the measurement results. Store the signal strength data of each interference source in the database, ensuring that each record includes the interference source ID, signal strength value and its timestamp for subsequent query and analysis. Select a frequency range calculation method, usually determined by analyzing the spectrum data of each interference source. The frequency range can be represented by the difference between the maximum frequency and the minimum frequency. Determine the analysis parameters, such as setting frequency segments (such as every 1 MHz) to more accurately identify the frequency range. Analyze the spectrum data of each interference source to find the frequency bands in its spectrum where the signal strength is higher than the set threshold (such as -90 dBm), so as to determine the effective frequency range. Record the lowest and highest frequencies of each interference source and calculate its frequency range. Ensure that the calculation results are combined with the power spectrum density data to form a complete frequency feature of the interference source. Select a suitable modulation feature mining method, such as identifying the spectrum characteristics of the interference source by analyzing the modulation type of the signal (such as AM, FM, PSK, etc.). Feature extraction algorithms such as short-time Fourier transform (STFT) or a combination of time domain and frequency domain methods can be used. Determine the window length and overlap rate of feature extraction to obtain high-resolution time-frequency representation.Analyze the power spectrum density value and signal strength of each interference source to identify the modulation mode and characteristics of the signal. This can be achieved by analyzing the signal waveform, spectrum distribution and its changes. Combine the extracted modulation characteristics with the type data of the interference source and record the modulation characteristic information of each interference source.

[0032] The signal spectrum modulation feature data of each interference source is stored in the database, ensuring that each record includes the interference source ID, modulation features and related parameters for subsequent query and analysis. The modulation features of the interference source are analyzed regularly to identify the interference source with a greater impact on the wireless sensor equipment, providing a basis for subsequent spectrum management. Select a suitable relative position calculation method, usually using triangulation or signal strength positioning technology. By measuring the RSSI value of the interference source and combining it with the known receiver position, the relative coordinates of the interference source can be estimated. Determine the benchmark for position calculation, such as selecting several receivers with known positions and ensuring that they are distributed widely enough to improve positioning accuracy. After obtaining the signal strength data of each interference source, apply the position calculation algorithm to estimate the relative position of each interference source based on the received signal strength. The weighted average method or the least squares method can be used to optimize the position calculation. Record the relative position coordinates (such as x, y, and z coordinates) of each interference source to ensure the accuracy and reliability of the position data. Select a suitable spatial distribution analysis method, usually using point density analysis or heat map analysis. By analyzing the relative position coordinates of the interference source, the spatial distribution characteristics of the interference source can be intuitively displayed. Determine the analysis parameters, such as setting the resolution and color mapping of the heat map, to better display the distribution of interference sources. Input the regional location coordinates of each interference source into the spatial distribution analysis algorithm to generate a spatial distribution map of the interference source. This can be achieved by calculating the density of interference sources in each area. Use visualization tools to display the spatial distribution of interference sources, such as heat maps or scatter plots, to help users understand the distribution characteristics and impact range of interference sources. Store the generated spatial distribution map of interference sources in the database, ensuring that the graphical data is combined with the location information of the interference sources for subsequent query and analysis. Use visualization tools to generate a spatial distribution map of interference sources to help network managers intuitively understand the spatial characteristics of interference sources and their potential impact on wireless networks.

[0033] Select an appropriate scene distribution fitting method, usually using an interpolation algorithm or a machine learning model (such as regression analysis) to fit the spatial spectrum distribution of the interference source. By modeling the signal frequency modulation characteristics of the interference source, an interference spectrum scene map can be generated. Determine the fitting parameters, such as the complexity of the fitting model and the selection of training data, to ensure the accuracy and effectiveness of the fitting results. Combine the signal frequency modulation characteristics of each interference source with its spatial location information, and apply the fitting algorithm to generate an interference spectrum scene distribution map. Identify the spectrum characteristics and changes of the interference source by analyzing the sample data. Record the generated scene map data, including spectrum characteristics, spatial distribution, and time information, for subsequent analysis.

[0034] In this embodiment, step S5 includes the following steps: Step S51: Calculate the available transmission frequency for the remote control instruction of the wireless sensor device and identify the available transmission frequency range; Step S52: performing a global frequency conflict analysis on the available transmission frequency range according to the interference spectrum scenario diagram of the wireless sensor device to obtain a frequency conflict range; Step S53: Adaptively adjust the transmission frequency band according to the frequency conflict range, thereby obtaining multiple adaptive transmission frequency bands; Step S54: performing transmission simulation on multiple adaptive transmission frequency bands one by one, and extracting transmission simulation data of each frequency band; Step S55: Select the optimal frequency band according to the transmission simulation data of each frequency band, so as to obtain the optimal instruction transmission frequency band.

[0035] In this embodiment, the available frequency range of the wireless sensor device is determined, which usually includes 2.4 GHz and 5 GHz frequency bands. According to the hardware specifications of the device, the supported frequency range and channels are confirmed. The boundaries of the frequency range are set, for example, 2.400 GHz to 2.483 GHz is the 2.4 GHz frequency band, and 5.150 GHz to 5.825 GHz is the 5 GHz frequency band, to ensure that all possible transmission frequencies are covered. Using the interference spectrum scene graph data obtained in the previous step, the frequency bands in the spectrum with signal strength below a certain threshold (such as -90 dBm) are analyzed to identify available transmission frequencies. The signal strength of each frequency band is recorded, and the frequency range with low signal strength and less interference is screened out to ensure that the selected frequency can ensure the stability of data transmission. The determined available transmission frequency range is stored in the database, ensuring that the record includes the starting frequency, the ending frequency and the corresponding signal strength of the frequency range. Select a suitable frequency conflict analysis method, such as threshold-based conflict detection or spectrum occupancy analysis. Usually, a conflict threshold (such as -80 dBm) can be set to determine whether the frequency band is occupied by an interference source. Determine the analysis parameters and set the calculation window for frequency conflicts (e.g., 10 minutes) to more accurately reflect the frequency status. Compare the available frequency range with the interference spectrum scenario diagram to identify the frequency bands with conflicts. By comparing the signal strength, determine whether there is strong signal interference in the available frequency range. Record the conflict status of each frequency band and mark the conflict range for subsequent frequency band adjustment. Set the adaptive frequency band adjustment strategy based on the results of the frequency conflict analysis. When a frequency band is found to have a conflict, automatically select the next available frequency band for adjustment. Set the adjustment amplitude and frequency, for example, each adjustment can select the upper and lower adjacent frequency bands to avoid frequency conflicts as much as possible. After identifying the frequency conflict, automatically execute the adjustment strategy and switch to the available frequency band. Automatic frequency band switching can be implemented through programming to ensure the continuity of command transmission. Record the timestamp, original frequency band, and new frequency band of each frequency band adjustment for subsequent auditing and optimization. Store the information of the adaptive transmission frequency band in the database to ensure that each record includes the historical information of the frequency band adjustment and the currently used frequency band. Regularly analyze the effect of the frequency band adjustment, evaluate the transmission performance and stability after adjustment, and provide data support for subsequent frequency band selection. Set the environmental conditions for transmission simulation, including signal strength, number and type of interference sources, etc. These environmental parameters should be similar to the actual wireless transmission conditions to ensure the authenticity of the simulation data. Determine simulation parameters, such as the transmission duration of each frequency band (such as 5 minutes), so as to collect enough data for analysis. Perform transmission simulation for each adaptive transmission frequency band. Send control instructions through wireless sensor devices and record parameters such as transmission delay, packet loss rate and signal quality of each frequency band in real time. After each simulation, collect simulation data, including information such as transmission success rate, average delay and signal strength.Store the transmission simulation data of each frequency band in the database, and ensure that each record includes the frequency band, transmission simulation data and its time information for subsequent statistics and analysis. Determine the criteria for selecting the optimal frequency band, which usually include the lowest transmission delay, the lowest packet loss rate and the highest transmission success rate. A comprehensive scoring system can be set to weight these parameters. Set weights, such as 40% for transmission delay, 30% for packet loss rate and 30% for transmission success rate, to more comprehensively evaluate the performance of the frequency band. Apply the set selection criteria to analyze the transmission simulation data of each frequency band and calculate the comprehensive score of each frequency band. Based on the scoring results, select the frequency band with the best performance as the optimal frequency band. Record the selection results of the optimal frequency band, including the score and its source data, for subsequent audit and optimization. Store the results of the optimal frequency band selection in the database, and ensure that each record includes the optimal frequency band information and its related performance indicators for subsequent query and analysis. Generate regular reports to show the transmission performance of different frequency bands and the basis for the selection of the optimal frequency band, to help managers understand the status of wireless transmission and optimization strategies.

[0036] In this embodiment, step S6 includes the following steps: Step S61: sensing the dynamic change of the spectrum of the interference spectrum scene graph of the wireless sensor device to generate the dynamic change characteristics of the spectrum in the area; Step S62: Calculate the real-time signal interference intensity based on the dynamic change characteristics of the spectrum in the area, thereby generating regional real-time signal interference intensity data; Step S63: Optimizing the transmission power of the remote control command of the wireless sensor device according to the regional real-time signal interference strength data, thereby generating the optimized transmission power of the command; Step S64: performing multi-objective collaborative control optimization according to the instruction transmission optimization power, the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, thereby constructing an intelligent control engine; Step S65: Execute remote control operations of the wireless sensor device based on the intelligent control engine.

[0037] In this embodiment, a suitable spectrum dynamic change perception method is selected, and time-frequency analysis technology is usually used, such as short-time Fourier transform (STFT) or wavelet transform. These methods can capture the changes in the spectrum over time and identify the dynamic characteristics of the spectrum. Determine analysis parameters, such as window size (for example, 256 sampling points) and overlap rate (such as 50%), to ensure that high-resolution data can be obtained in both the time domain and the frequency domain. Monitor the interference spectrum scene diagram of the wireless sensor device in real time, collect spectrum data and perform time-frequency analysis. Record the spectrum characteristics at different time points, including information such as frequency distribution, signal strength and interference source location. By analyzing the changes in spectrum data, the dynamic change characteristics of the spectrum in the area are extracted, such as instantaneous changes in frequency, fluctuations in signal strength, etc. The method for determining the calculation of signal interference strength is usually measured using received signal strength indication (RSSI). RSSI is the received signal strength, usually in dBm. Set the calculation period of interference strength, for example, once every 5 seconds, to ensure real-time and accuracy. Use the dynamic change characteristics of the spectrum to monitor the signal interference strength in the area in real time. Record the RSSI value of each signal source and calculate the overall interference strength in the area. Summarize the signal strength data of each interference source, calculate the average interference strength in the area, and set a threshold to identify changes in interference strength (such as above -75 dBm). Store the calculated regional real-time signal interference strength data in the database, ensuring that each record includes the timestamp, interference strength and related parameters for subsequent query and analysis. Determine the model for transmission power optimization, usually adjusting the transmission power based on the interference strength and the required received signal strength (RSSI). Set the target received signal strength, such as the target RSSI should be greater than -80 dBm, to ensure reliable transmission of instructions. Set the parameters for power optimization, such as the adjusted power range (such as 1 mW to 100 mW), so that adjustments can be made within an acceptable range. Adjust the transmission power of the wireless sensor device based on the real-time signal interference strength data. Calculate the required power to ensure that the signal can still reach the target RSSI under the influence of interference strength. Record the timestamp and new power value of each power adjustment to evaluate the effect of power optimization later. Design a multi-objective collaborative control optimization model, and take the instruction transmission optimization power, the optimal instruction transmission frequency band and the dynamic transmission rate as optimization targets. Set the weight of each goal to ensure that the model can balance the relationship between each goal. Determine the input parameters, including the current interference strength, signal quality, and network status, so that they can be dynamically adjusted during the optimization process. Based on the above design, build an intelligent control engine to achieve dynamic control of wireless sensor devices. Through real-time monitoring and feedback mechanisms, automatically adjust the transmission power, frequency band, and rate to achieve optimal transmission performance. Implement the adaptive mechanism of the control engine, which can automatically adjust the control strategy according to environmental changes to ensure good communication quality under different conditions.Store the status information and control decisions of the intelligent control engine in the database, and ensure that each record includes the decision basis, adjustment parameters and their effects for subsequent analysis and optimization. Regularly analyze the performance of the control engine and evaluate its performance in different environments to provide data support for further improvement. Design the remote control operation execution process of wireless sensor equipment, including instruction reception, processing, execution and feedback mechanism. Ensure that each link can operate efficiently and respond in a timely manner. Formulate the time window and priority strategy for job execution to facilitate reasonable scheduling in a multi-task environment. Receive and process remote control instructions in real time based on the intelligent control engine. Execute instructions and monitor the execution status based on the optimized transmission power, frequency band and rate. Record the execution results of each control operation, including success or failure, execution time and related parameters, for subsequent feedback and analysis. Store the execution results of remote control operations in the database, and ensure that each record includes the operation ID, execution status and feedback information for subsequent query and analysis.

[0038] In this embodiment, a remote control system of a wireless sensor device is provided, which is used to execute the remote control method of the wireless sensor device as described above, including: The load state evolution module is used to collect real-time network monitoring parameters of wireless sensor devices; perform transient signal fluctuation identification and multi-time point load state evolution on the real-time network monitoring parameters, and construct a multi-time point network load state curve; The network trend module is used to segment the network load state curve at multiple time points into sliding time windows and predict the state transition in future time periods, thereby constructing a network state trend graph for future time periods. The transmission rate adjustment module is used to obtain the remote control instructions of the wireless sensor device; dynamically adjust the transmission rate of the remote control instructions of the wireless sensor device according to the network status trend diagram of the future period to obtain a dynamic transmission rate adjustment strategy; The interference source spectrum distribution module is used to scan the wireless signals of the entire frequency band in the area of ​​the wireless sensor device and perform interference source spectrum distribution analysis to construct an interference spectrum scene diagram of the wireless sensor device; An adaptive frequency band adjustment module, used to perform adaptive transmission frequency band adjustment according to the interference spectrum scene diagram, so as to obtain an optimal instruction transmission frequency band; The collaborative control module is used to perform multi-objective collaborative control optimization based on the optimal command transmission frequency band and dynamic transmission rate adjustment strategy, thereby building an intelligent control engine to perform remote control operations of wireless sensor equipment.

[0039] Through real-time monitoring and fluctuation identification, the system of the present invention can accurately evaluate the current network status and timely capture potential load peaks, congestion or signal attenuation. By constructing a multi-time point network load state curve, the system can better understand the law of network state changes, thereby providing a more reliable data basis for subsequent prediction and adjustment. By predicting the network load trend, potential network bottlenecks or congestion periods can be identified in advance, helping the system to actively make adjustments to avoid the impact of network fluctuations on remote control operations. According to the future network status trend chart, the network performance of the future period can be accurately predicted, thereby providing a basis for dynamically adjusting the transmission strategy. According to the changes in the network status, the transmission rate is dynamically adjusted to ensure that the instructions are carried out at the most appropriate transmission rate to avoid transmission delays or failures caused by network congestion or signal attenuation. By predicting the future network status and adjusting the rate accordingly, the system can be optimized according to the actual network load condition, thereby improving the response speed and transmission efficiency of the device. By comprehensively scanning the spectrum and analyzing the characteristics of the interference source, the system can accurately identify the interference source and its impact range, thereby reducing the signal quality degradation caused by external interference. Through accurate interference source identification, the system can avoid the impact of interference sources, optimize the use of spectrum resources, and ensure the stability and efficiency of signal transmission. By automatically adjusting the transmission frequency band, frequency conflicts with interference sources can be avoided, the impact of interference on transmission quality can be reduced, and the signal can be transmitted within the optimal frequency band. Adaptive frequency band adjustment enhances the system's ability to adapt to changes in different environments, especially in complex wireless environments or when there is a lot of interference, it can flexibly adjust the frequency band and optimize the transmission path. By integrating multiple optimization strategies, the collaborative control module can intelligently select the optimal control parameters (such as frequency band, transmission rate, power, etc.) in a variety of network environments to ensure efficient and stable remote control operations of the device. The module can simultaneously handle the optimization requirements of multiple targets (frequency band, rate, interference, etc.), achieve cross-dimensional control optimization, and improve the performance and stability of the overall system.

[0040] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0041] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A remote control method for a wireless sensor device, characterized in that: The following steps are involved: Step S1: collecting real-time network monitoring parameters of wireless sensor devices; performing transient signal fluctuation recognition and multi-time point load state evolution on the real-time network monitoring parameters, and constructing a multi-time point network load state curve; Step S2: Segment the network load state curve at multiple time points into sliding time windows, and predict the state transition in future time periods, so as to construct a network state trend graph in future time periods; Step S3: obtaining a remote control instruction for a wireless sensor device; dynamically adjusting the transmission rate of the remote control instruction for the wireless sensor device according to a trend diagram of a network status in a future period to obtain a dynamic transmission rate adjustment strategy; Step S4: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device, and perform spectrum distribution analysis of interference sources to construct an interference spectrum scene diagram of the wireless sensor device; Step S5: Adaptively adjust the transmission frequency band according to the interference spectrum scene diagram, so as to obtain the optimal instruction transmission frequency band; Step S6: Perform multi-objective collaborative control optimization based on the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, so as to build an intelligent control engine to perform remote control operations of wireless sensor devices.

2. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Collecting real-time network monitoring parameters of wireless sensor devices; Step S12: performing transient signal fluctuation identification on the real-time network monitoring parameters to extract the current network transient network fluctuation characteristics; Step S13: Calculating the signal transmission delay according to the real-time network monitoring parameters to obtain a signal transmission delay value; Step S14: performing data packet loss rate statistics on the real-time network monitoring parameters to obtain a network data packet loss rate; Step S15: Perform network load state deduction on the current network transient network fluctuation characteristics, signal transmission delay value and network data packet loss rate, so as to generate real-time network load state characteristics; Step S16: Perform multi-time point load state evolution on the real-time network load state characteristics to construct a multi-time point network load state curve.

3. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing sliding time window segmentation on the network load state curves at multiple time points to obtain load state curves of multiple time windows; Step S22: Calculate the load peak values ​​of the load state curves of multiple time windows one by one to generate the load peak value of each time window; Step S23: performing network congestion situation assessment on the load state curves of multiple time windows to obtain the network congestion situation of the multiple time windows; Step S24: mining the time series state change trend according to the load peak of each time window and the network congestion situation of multiple time windows to generate time series state change trend characteristics; Step S25: predict the state transition in the future period based on the time series state change trend characteristics, so as to construct a network state trend diagram in the future period.

4. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Obtaining remote control instructions for wireless sensor equipment; Step S32: Calculate the quality data volume of the remote control instruction of the wireless sensor device to obtain the control instruction data volume; Step S33: analyzing the instruction transmission bandwidth requirement according to the control instruction data volume, thereby obtaining instruction transmission bandwidth requirement data; Step S34: Calculating the transmission bandwidth performance deviation of the instruction transmission bandwidth demand data based on the network status trend diagram for the future period, thereby obtaining the instruction transmission bandwidth performance deviation value; Step S35: dynamically adjusting the instruction transmission rate according to the instruction transmission bandwidth performance deviation value to obtain a dynamic transmission rate adjustment strategy.

5. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Scan the wireless signal of the entire frequency band in the area for the wireless sensor device to extract the environmental wireless spectrum signal; Step S42: Identify signal interference sources in the area of ​​the ambient wireless spectrum signal, and mark multiple regional signal interference sources; Step S43: performing interference source type analysis on multiple regional signal interference sources to obtain type data of each interference source; Step S44: performing interference source spectrum distribution analysis according to the type data of each interference source to construct an interference spectrum scenario diagram of the wireless sensor device.

6. The remote control method of a wireless sensor device according to claim 5, characterized in that: The specific steps of step S44 are: Calculate the signal power spectrum density according to the type data of each interference source to generate a power spectrum density value of each interference source; Calculating the signal strength of interference sources for multiple regional signal interference sources to obtain the signal strength of multiple interference sources; Calculating the signal frequency ranges of multiple regional signal interference sources to generate the interference source signal frequency range; The power spectrum density value of each interference source, the signal strength of multiple interference sources and the frequency range of the interference source signal are mined to obtain the signal spectrum modulation characteristics of each interference source; Calculate the relative positions of multiple regional signal interference sources and extract the regional position coordinates of each interference source; Perform interference source spatial distribution analysis according to the regional location coordinates of each interference source to generate an interference source spatial distribution map; According to the signal frequency modulation characteristics of each interference source, the spatial spectrum scene distribution is fitted to the interference source spatial distribution map to construct the interference spectrum scene map of the wireless sensor device.

7. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: Calculate the available transmission frequency for the remote control instruction of the wireless sensor device and identify the available transmission frequency range; Step S52: performing a global frequency conflict analysis on the available transmission frequency range according to the interference spectrum scenario diagram of the wireless sensor device to obtain a frequency conflict range; Step S53: Adaptively adjust the transmission frequency band according to the frequency conflict range, thereby obtaining multiple adaptive transmission frequency bands; Step S54: performing transmission simulation on multiple adaptive transmission frequency bands one by one, and extracting transmission simulation data of each frequency band; Step S55: Select the optimal frequency band according to the transmission simulation data of each frequency band, so as to obtain the optimal instruction transmission frequency band.

8. The remote control method of a wireless sensor device according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: sensing the dynamic change of the spectrum of the interference spectrum scene graph of the wireless sensor device to generate the dynamic change characteristics of the spectrum in the area; Step S62: Calculate the real-time signal interference intensity based on the dynamic change characteristics of the spectrum in the area, thereby generating regional real-time signal interference intensity data; Step S63: Optimizing the transmission power of the remote control command of the wireless sensor device according to the regional real-time signal interference strength data, thereby generating the optimized transmission power of the command; Step S64: performing multi-objective collaborative control optimization according to the instruction transmission optimization power, the optimal instruction transmission frequency band and the dynamic transmission rate adjustment strategy, thereby constructing an intelligent control engine; Step S65: Execute remote control operations of the wireless sensor device based on the intelligent control engine.

9. A remote control system for a wireless sensor device, characterized in that: A remote control method for executing the wireless sensor device according to claim 1, comprising: The load state evolution module is used to collect real-time network monitoring parameters of wireless sensor devices; perform transient signal fluctuation identification and multi-time point load state evolution on the real-time network monitoring parameters, and construct a multi-time point network load state curve; The network trend module is used to segment the network load state curve at multiple time points into sliding time windows and predict the state transition in future time periods, thereby constructing a network state trend graph for future time periods. The transmission rate adjustment module is used to obtain the remote control instructions of the wireless sensor device; dynamically adjust the transmission rate of the remote control instructions of the wireless sensor device according to the network status trend diagram of the future period to obtain a dynamic transmission rate adjustment strategy; The interference source spectrum distribution module is used to scan the wireless signals of the entire frequency band in the area of ​​the wireless sensor device and perform interference source spectrum distribution analysis to construct an interference spectrum scene diagram of the wireless sensor device; An adaptive frequency band adjustment module, used to perform adaptive transmission frequency band adjustment according to the interference spectrum scene diagram, so as to obtain an optimal instruction transmission frequency band; The collaborative control module is used to perform multi-objective collaborative control optimization based on the optimal command transmission frequency band and dynamic transmission rate adjustment strategy, thereby building an intelligent control engine to perform remote control operations of wireless sensor equipment.

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