Remote control method and device based on low-power-consumption Hall sensor

Abstract

The invention relates to the technical field of remote control, in particular to a remote control method and device based on a low-power-consumption Hall sensor. The method comprises the following steps: acquiring remote control equipment type data; performing Hall sensor deployment based on the remote control equipment type data to generate Hall sensor deployment data; performing sensor working magnetic field direction calibration on the Hall sensor deployment data to generate Hall sensor deployment correction data; performing real-time signal acquisition on the Hall sensor deployment correction data to obtain a standard remote control equipment magnetic field detection signal; performing lightweight signal decoding on the standard remote control equipment magnetic field detection signal to generate remote control equipment lightweight decoding data; and performing remote control mode identification on the remote control equipment lightweight decoding data to generate remote control intention data. By optimizing Hall sensor deployment, signal decoding, response delay and multi-device cooperative control, the precision and efficiency of remote control are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote control, and in particular to a remote control method and device based on a low-power Hall sensor. Background Art

[0002] Early remote control methods mainly relied on infrared or radio frequency (RF) technology, which were limited by energy consumption, communication distance and anti-interference ability. However, with the maturity of low-power sensor technology, Hall sensors have gradually replaced traditional sensors and become the core components of long-distance, low-power and efficient remote control systems. In this process, the development of low-power Hall sensors has evolved from a single magnetic field detection to an integrated multifunctional sensing system. For example, the application of integrated circuit (IC) technology has made Hall sensors more miniaturized, low-power, and have higher accuracy and response speed. In addition, remote control systems based on Hall sensors have gradually realized intelligence, digitization and remote control. Through wireless communication modules and embedded microcontrollers, convenient control of household appliances, industrial equipment, etc. can be achieved. However, the current traditional Hall sensor signal decoding has problems such as excessive redundant information and slow processing speed, especially when the sensor signal contains complex operating modes. At the same time, due to the delay in the remote transmission of signal packets and the command response time during the transmission process, the control of the traditional system has the problem of untimely response, which leads to low accuracy and efficiency of remote control. Summary of the invention

[0003] Based on this, it is necessary to provide a remote control method and device based on a low-power Hall sensor to solve at least one of the above technical problems.

[0004] To achieve the above object, a remote control method based on a low-power Hall sensor is provided, the method comprising the following steps: Step S1: acquiring remote control device type data; performing Hall sensor deployment based on the remote control device type data to generate Hall sensor deployment data; performing sensor working magnetic field direction calibration on the Hall sensor deployment data to generate Hall sensor deployment correction data; performing real-time signal acquisition on the Hall sensor deployment correction data to obtain a standard remote control device magnetic field detection signal; Step S2: performing lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoded data of the remote control device; performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; Step S3: remotely transmit the optimized decoded control signal data with a signal packet to generate decoded control signal packet transmission data; perform instruction reception response analysis on the decoded control signal packet transmission data to generate receiving device instruction execution data and receiving device instruction execution response time; optimize the response delay of the receiving device instruction execution data through the receiving device instruction execution response time to generate a response optimization instruction; Step S4: Evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, adaptively adjust the multi-device collaborative control task allocation data until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0005] The present invention obtains the remote control device type data and accurately deploys the Hall sensor according to the device characteristics, thereby ensuring that the Hall sensor can adapt to the working environment of different remote control devices. After calibrating the working magnetic field direction of the sensor, the standard remote control device magnetic field detection signal can be accurately collected, thereby improving the accuracy and reliability of the signal. Lightweight signal decoding reduces the data burden in the transmission process and improves the decoding efficiency; through remote control mode recognition, the control intention of the remote control device can be accurately captured, and the system's responsiveness to remote control operations can be further improved. Signal decoding optimization based on control intention reduces redundant information and improves the decoding accuracy and speed of the signal. The optimized decoded control signal package can be transmitted more effectively in remote transmission, reducing signal loss and interference. By analyzing and optimizing the instruction execution response time of the receiving device, the response delay is significantly reduced, the real-time performance and stability of the system are improved, and timely feedback of the remote control operation is ensured. By evaluating the remote control completion rate, the execution effect of the remote control task can be monitored in real time to ensure that the control task is successfully completed. The adaptive adjustment of multi-device collaborative control tasks can dynamically optimize task allocation according to actual conditions, improve the flexibility and scalability of the system, and ensure that the remote control task can still meet the preset standards in a complex environment. Therefore, the present invention improves the accuracy and efficiency of remote control by optimizing Hall sensor deployment, signal decoding, response delay and multi-device collaborative control.

[0006] Preferably, step S1 comprises the following steps: Step S11: Obtain remote control device type data; Step S12: selecting a Hall sensor type based on the remote control device type data to obtain the Hall sensor type data of the remote control device; deploying the Hall sensor according to the Hall sensor type data of the remote control device to generate Hall sensor deployment data; Step S13: sensing the magnetic field of the remote control device according to the Hall sensor deployment data to generate the magnetic field sensing data of the remote control device; calibrating the working magnetic field direction of the Hall sensor deployment data according to the magnetic field sensing data of the remote control device to generate the Hall sensor deployment correction data; Step S14: perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the original magnetic field detection signal of the remote control device; perform signal preprocessing on the original magnetic field detection signal of the remote control device to generate a standard remote control device magnetic field detection signal, wherein the signal preprocessing includes signal denoising, signal enhancement and signal normalization.

[0007] The present invention can ensure that the selected sensor is suitable for a specific application scenario by selecting a suitable Hall sensor type according to the type of remote control device, thereby improving detection accuracy and stability. By calibrating the working magnetic field direction of the Hall sensor, the sensor's ability to perceive the magnetic field of the remote control device can be maximized, the error in magnetic field perception can be reduced, and the accuracy of the data can be improved. By preprocessing the original magnetic field detection signal, including denoising, enhancement and normalization, noise interference can be effectively removed, and the characteristics of the signal can be enhanced, so that the signal for subsequent analysis and application is clearer, and more reliable magnetic field data can be provided. The real-time signal acquisition function can provide real-time feedback on the magnetic field state of the remote control device, provide timely feedback information, is suitable for applications in dynamic environments, and improves the performance of the remote control device in complex environments. By acquiring and analyzing the remote control device type data, the system can flexibly adapt to different types of remote control devices, and achieve unified and efficient management and operation in diversified products.

[0008] Preferably, calibrating the sensor working magnetic field direction for the Hall sensor deployment data according to the magnetic field sensing data of the remote control device includes: Confirm the sensor installation coordinates of the Hall sensor deployment data to obtain the sensor installation coordinate data; perform installation angle analysis on the Hall sensor deployment data based on the sensor installation coordinate data to generate the sensor installation angle data; Extracting a single Hall sensor sensing data from the remote control device magnetic field sensing data to obtain Hall sensor sensing data, wherein the Hall sensor sensing data extraction includes magnetic field intensity extraction, magnetic field direction extraction, and magnetic field change extraction; based on the Hall sensor sensing data, the lateral magnetic field orientation is aggregated and compared to generate lateral magnetic field orientation aggregated data; The installation orientation of the Hall sensor deployment data is adjusted according to the lateral magnetic field orientation aggregation data to generate the Hall sensor deployment correction data.

[0009] The present invention can ensure the accuracy of the sensor's installation position by confirming the installation coordinates of the Hall sensor deployment data (the "sensor installation coordinate data" mentioned in the step), thereby improving the sensor's perception accuracy and reliability of the magnetic field. By analyzing the sensor's installation angle (the "sensor installation angle data" in step S12), it can be ensured that the sensor's installation direction meets the design requirements, thereby ensuring that the sensor's response angle to the magnetic field is correct and reducing measurement errors caused by improper installation. By extracting the sensing data of a single Hall sensor, including the extraction of magnetic field strength, magnetic field direction, and magnetic field changes (step S13), more detailed and accurate magnetic field information can be obtained. This fine data provides a reliable basis for subsequent analysis and calibration. Based on the Hall sensor sensing data, the lateral magnetic field orientation aggregation comparison (the "lateral magnetic field orientation aggregation data" in step S13) can effectively compare the data of different sensors, analyze the changes in the magnetic field distribution, ensure the consistency of the sensing data between each sensor, and reduce interference and errors. Through the aggregate comparison of the lateral magnetic field orientation, the installation orientation of the Hall sensor deployment is further adjusted (the "Hall sensor deployment correction data" in step S14) to ensure that the magnetic field sensing direction of the sensor is consistent with the actual magnetic field direction, thereby improving the overall measurement accuracy. The implementation of the entire calibration process can eliminate errors caused by installation errors, magnetic field interference or inaccurate sensor positioning, improve the stability and robustness of the remote control device's magnetic field sensing system, and enable it to maintain high accuracy in complex and dynamic environments.

[0010] Preferably, step S2 comprises the following steps: Step S21: extracting magnetic field multi-dimensional features from the magnetic field detection signal of the standard remote control device to obtain multi-dimensional feature data of the magnetic field of the remote control device, wherein the multi-dimensional feature data of the magnetic field of the remote control device includes magnetic field intensity feature data, magnetic field change rate feature data and magnetic field timestamp data; Step S22: performing spatial information association on the magnetic field intensity characteristic data, the magnetic field change rate characteristic data and the magnetic field timestamp data based on the sensor installation coordinate data to generate remote control device magnetic field spatial association data; performing lightweight signal decoding on the standard remote control device magnetic field detection signal according to the remote control device magnetic field spatial association data to generate remote control device lightweight decoding data; Step S23: performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data.

[0011] The present invention includes magnetic field intensity, magnetic field change rate, and magnetic field timestamp data through multi-dimensional feature data extraction (step S21). These multi-dimensional features can fully reflect the spatiotemporal change characteristics of magnetic field signals, providing rich information resources for subsequent analysis. By extracting these features, the state and changes of the remote control device in space can be captured more accurately. This feature extraction can effectively extract valuable features from the original magnetic field signal, help better understand the dynamic changes of the remote control device, and enhance the system's recognition and response capabilities to remote control signals. By associating multi-dimensional features based on sensor installation coordinate data, spatial correlation data of the remote control device magnetic field is generated. This step ensures the spatial consistency of the magnetic field data, so that the position and action of the remote control device can be accurately reflected by the signal. This process optimizes the understanding of spatial information, improves the ability to recognize the device's actions in three-dimensional space, and provides stronger support for subsequent signal decoding and remote control mode recognition. Lightweight signal decoding (step S22) can extract core information from the standard remote control device magnetic field detection signal and generate a simplified data set. This process reduces the complexity of data processing by removing redundant information, so that the system can process signals more efficiently and improve response speed. Through signal decoding optimization (step S23), after the remote control operation intention is identified, the signal decoding is further optimized to generate more accurate control signal data, which not only improves the control accuracy, but also enables the remote control device to respond to the user's control intention more quickly and accurately. By analyzing the lightweight decoded data, the control mode of the remote control can be identified and the control intention data can be extracted. This process provides the remote control device with the ability to "understand" the user's operation and can identify the actions that the user wants to achieve, such as changing the device position, turning on / off functions, etc. By optimizing the signal decoding process (optimized decoded control signal data), this method can significantly improve the response accuracy of the remote control device to instructions, reduce errors, and improve the stability and reliability of device control.

[0012] Preferably, performing lightweight signal decoding on the standard remote control device magnetic field detection signal according to the remote control device magnetic field spatial correlation data comprises: Perform fast Fourier transform on the magnetic field detection signal of the standard remote control device according to the spatial correlation data of the magnetic field of the remote control device to generate the frequency domain data of the magnetic field of the remote control device; perform signal morphology analysis on the frequency domain data of the magnetic field of the remote control device to generate a decoding feature data set; A dictionary is constructed for the magnetic field detection signal of the standard remote control device by decoding the feature data set to generate a signal representation dictionary; based on the signal representation dictionary, the decoded feature data set is signal sparsified to generate sparse remote control device signal data, where the signal sparsification formula is as follows:

[0013] In the formula, Represented as a decoding feature dataset, Represented as a matrix of basis vectors in the dictionary, Represented as a sparse coefficient vector, It is represented as reconstruction error; Based on the L1 norm, the sparse remote control device signal data is minimized to obtain a sparse coefficient matrix, where the formula for minimizing the target calculation is as follows:

[0014] In the formula, Represented as a sparse coefficient matrix, Denoted as the regularization parameter; The sparse coefficient matrix is ​​used to optimize the signal decoding of the lightweight decoding data of the remote control device to generate optimized decoding control signal data.

[0015] The present invention can effectively extract the frequency characteristics of the signal by converting the magnetic field signal of the remote control device from the time domain to the frequency domain, so that the subsequent analysis and decoding process can focus on more meaningful frequency information and reduce the noise interference in the time domain signal. Through the morphological analysis of the magnetic field frequency domain data, a decoding feature data set is generated, and these features can help better understand the structure of the signal, thereby improving the decoding accuracy and efficiency. By establishing a signal representation dictionary through the decoding feature data set, the magnetic field signal of the remote control device can be effectively expressed as a linear combination of basis vectors in the dictionary. This representation method can more efficiently capture the essential characteristics of the signal and reduce redundant data. By representing the signal data through the sparse coefficient vector α, the unimportant parts of the signal can be eliminated and only the key information is retained. The sparse processing reduces the complexity of the signal representation, making the subsequent calculation more efficient and fast. By introducing the regularization parameter λ, further optimization of the signal sparsification is achieved. L1 norm minimization ensures that the obtained sparse coefficient matrix can best represent the essence of the signal, while avoiding overfitting and improving the generalization ability of the model. By optimizing the decoding of the remote control device's signal based on a sparse coefficient matrix, optimized decoding control signal data is generated. This optimization process ensures a higher degree of signal restoration and reduces decoding errors, thereby providing a more accurate remote control operation control signal. The optimized decoding control signal enables the remote control device to respond to the user's operation commands more accurately, improving the device's response speed and accuracy. By adopting a lightweight decoding scheme, combined with sparse representation and L1 norm minimization, the computational efficiency of the entire system has been significantly improved. This process not only speeds up the signal processing process, but also maintains high precision in signal restoration, thereby ensuring the stability and efficiency of the system in complex environments.

[0016] Preferably, step S23 includes the following steps: Step S231: extracting dynamic change features from the remote control device lightweight decoded data to obtain remote control device lightweight decoded dynamic change feature data; dividing the remote control device lightweight decoded dynamic change feature data into data sets to generate a model training set and a model test set; Step S232: training the model training set according to the recurrent neural network algorithm to generate a remote control mode recognition pre-model; optimizing and iterating the remote control mode recognition pre-model based on the model test set to generate a remote control mode recognition model; importing the remote control device lightweight decoding data into the remote control mode recognition model for label mapping to generate operation label mapping data; Step S233: classify the operation label mapping data into operation categories to obtain remote control control intention data; perform signal modulation on the remote control device lightweight decoding data based on the remote control control intention data to generate optimized decoding control signal data.

[0017] The present invention can capture the regularity and volatility of signal changes over time by extracting dynamic change features from lightweight decoded data of remote control devices. This process helps to extract meaningful time series features from remote control signals and provide a high-quality data basis for subsequent remote control mode recognition. The extracted feature data is divided into a model training set and a model test set, which ensures that the model can effectively learn the rules during the training process, and can evaluate the generalization ability of the model during the test process, avoid overfitting, and improve the reliability of the model. RNN is suitable for processing time series data. By training the model training set, the time series pattern in the remote control device signal can be effectively identified. Since the remote control device signal often has time series and time dependence, RNN can capture the long-term dependence in the signal and improve the accuracy of pattern recognition. Through optimization iteration based on the test set, the accuracy and robustness of the model are further improved. This process ensures that the model can not only perform well on the training set, but also effectively identify new signals during actual use, reducing the risk of overfitting. The trained and optimized remote control mode recognition model can efficiently decode the remote control device signal and identify the potential remote control mode in the signal. Through model recognition, operation label mapping data is generated, so that the system can identify the user's specific operation intentions, such as control commands such as switches and position adjustments. By classifying the operation label mapping data, the different types of operations received by the remote control device can be accurately identified. This step ensures that the remote control device can distinguish the user's specific control targets, such as adjusting the volume, changing the position, or executing other commands. The signal is modulated according to the remote control control intention data to optimize the control signal generation process. In this process, the system adjusts the signal according to the identified control intention, thereby generating a more accurate control signal to ensure that the remote control device can execute the user's commands in a timely and accurate manner. Finally, the optimized decoded control signal data is generated. Through this signal, the remote control device can execute the user's instructions with higher accuracy and faster response speed. Signal modulation optimization enables the control signal to work stably and effectively in complex environments.

[0018] Preferably, step S3 comprises the following steps: Step S31: encapsulating the optimized decoded control signal data into a data packet to generate a decoded control signal packet; performing signal packet remote transmission on the decoded control signal packet based on a low-power communication protocol to generate decoded control signal packet transmission data; Step S32: performing receiving device instruction execution on the decoded control signal packet transmission data to obtain receiving device instruction execution data; performing instruction response time analysis on the receiving device instruction execution data to generate the receiving device instruction execution response time; Step S33: Optimize the response delay of the receiving device instruction execution data by the receiving device instruction execution response time, and generate a response optimization instruction.

[0019] The present invention encapsulates the optimized decoded control signal data to generate a decoded control signal packet. Data packet encapsulation not only provides a standardized structure for signal transmission, but also ensures that the signal maintains integrity and consistency during transmission. This process helps to effectively transmit the control signal from the transmitting end to the receiving end. By using a low-power communication protocol for remote transmission, the decoded control signal packet can reduce power consumption while ensuring transmission stability, which is particularly important for remote control devices in battery-driven or long-term operation scenarios, and can extend the service life of the device and reduce energy consumption. By receiving and executing the transmitted data packets, the receiving device can respond to the remote control signal in a timely manner and execute the user's instructions. This process is the core of the remote control operation and ensures that the user's control intention can be accurately understood and executed by the device. The response time analysis of the instruction execution data of the receiving device can evaluate the time required for the device to receive and execute the remote control signal. This analysis helps identify potential delay bottlenecks and provides data basis for subsequent response delay optimization. By analyzing the instruction execution response time of the receiving device and optimizing the response delay of the device, this process adjusts the execution strategy of the device based on real-time response data, reduces the delay from receiving to executing the instruction, and enables the device to respond to the user's control command faster. By optimizing the response delay, the device can respond in a shorter time after receiving the command, improving the user experience. The increase in response speed not only reduces the waiting time for operation, but also makes the remote control device more agile, especially in scenarios that require quick response, such as game control, smart home device operation, etc., providing a smoother operation experience. Through low-power signal transmission and delay optimization, the device can find the best balance between energy efficiency and response time, ensuring that while saving energy, it can still maintain high-precision and stable control performance. This makes the remote control device more suitable for long-term operation and fast response application scenarios, such as automation equipment, smart home, remote monitoring, etc.

[0020] Preferably, step S33 includes the following steps: Step S331: identifying response influencing factors of the received device instruction execution data by receiving the device instruction execution response time, and generating response influencing factor identification data, wherein the response influencing factor identification includes network delay identification, device performance identification and environmental interference identification; Step S332: performing a minimum delay optimization target setting on the instruction execution response time of the receiving device according to the response influencing factor identification data, and obtaining the minimum delay optimization target setting data; performing a network path optimization on the instruction execution data of the receiving device based on the minimum delay optimization target setting data, and generating the network path optimization data; Step S333: Utilize the network path optimization data to optimize the response delay of the receiving device instruction execution data and generate a response optimization instruction.

[0021] The present invention improves the overall performance of the system through comprehensive analysis and optimization of the response time, especially in response time-sensitive applications, the device can complete the command execution more quickly. The optimized delay performance enhances the instant response capability of the system and provides a better user experience. By optimizing the response delay and enhancing the real-time performance of the system, the user can more intuitively feel the fluency of device control, especially when multiple devices work together, the optimized control instructions can achieve more efficient collaboration. By reducing the delay of network transmission and improving the efficiency of command response, unnecessary energy consumption can be reduced. The optimized network path and device response mechanism not only improve the system performance, but also reduce the energy waste of the device during the response delay process. While optimizing the response time, the stability of the system is also improved. Especially in a complex network environment, the response delay optimization can effectively avoid the response time fluctuation caused by network instability or device performance limitations, ensuring that the device can run stably under various circumstances. The optimized response delay significantly reduces the reaction time of the device after receiving the command, improving the fluency and real-time sense of the user experience. When using the remote control device, the user will more intuitively feel the efficiency and agility of the system. The effect of response optimization is not limited to speed improvement, but also reflected in the improvement of precise control. By reducing latency and optimizing network paths, users can control devices more accurately and avoid erroneous operations caused by latency.

[0022] Preferably, step S4 comprises the following steps: Step S41: performing device communication interconnection on the receiving device instruction execution data based on the response optimization instruction to generate multi-device collaborative control interconnection data; performing task coordination and resource allocation on the multi-device collaborative control interconnection data to generate multi-device collaborative control task allocation data; Step S42: performing execution status feedback on the multi-device collaborative control task allocation data to generate execution status feedback data; performing remote control completion rate evaluation on the execution status feedback data to generate the remote control completion rate; Step S43: Compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is lower than the preset remote control standard completion rate, the multi-device collaborative control task allocation data is adaptively adjusted until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0023] The present invention can realize collaborative operation between multiple devices by interconnecting devices through communication based on response optimization instructions. By creating multi-device collaborative control interconnection data, each device can share data and coordinate actions, thereby improving the overall efficiency of the system. Task coordination and resource allocation are performed on the multi-device collaborative control interconnection data to ensure that the devices can reasonably allocate the resources required for executing tasks according to their respective characteristics and the needs of the current tasks. For example, in a complex automated production line or smart home system, different devices (such as robots, sensors, controllers, etc.) can reasonably divide the work and cooperate according to the task requirements to avoid resource waste or repeated execution of tasks. By realizing the effective collaboration of multiple devices, the system can complete complex tasks more efficiently, avoid conflicts or resource redundancy between devices, thereby improving overall work efficiency and collaboration capabilities. By performing execution status feedback on the multi-device collaborative control task allocation data, the status of each device during the task execution process can be monitored in real time to understand whether there is a task execution deviation or problem. Based on the execution status feedback data, the remote control completion rate is evaluated to understand the degree and quality of task completion. This indicator can help determine whether the current collaborative control has achieved the expected goal and whether there is an incomplete or delayed execution problem. For example, if a device fails to complete a task on time when multiple devices are working together to perform a task, the system can adjust the task allocation in time through the feedback mechanism to ensure that the task is completed smoothly. Real-time execution status feedback and remote control completion rate evaluation can effectively monitor the progress and quality of task execution, ensure that the task is carried out as expected, and provide data support for subsequent optimization. The remote control completion rate is compared with the preset remote control standard completion rate to ensure that the execution quality of the task meets the set standard. When the remote control completion rate is lower than the standard, the system will adaptively adjust the multi-device collaborative control task allocation data until the remote control completion rate reaches or exceeds the preset standard. This adaptive adjustment mechanism can dynamically optimize task allocation, ensure that the task can be completed smoothly, and improve the flexibility of the system. For example, if a device fails to complete a task on time due to a fault or insufficient resources, the system can automatically adjust the task allocation and reallocate it to other devices to avoid task stagnation or delay.

[0024] In this specification, a remote control device based on a low-power Hall sensor is provided, which is used to execute the above-mentioned remote control method based on a low-power Hall sensor. The remote control device based on a low-power Hall sensor includes: The sensor deployment module is used to obtain the remote control device type data; deploy the Hall sensor based on the remote control device type data to generate the Hall sensor deployment data; calibrate the sensor working magnetic field direction on the Hall sensor deployment data to generate the Hall sensor deployment correction data; perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the standard remote control device magnetic field detection signal; The decoding optimization module is used to perform lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoding data of the remote control device; perform remote control mode recognition on the lightweight decoding data of the remote control device to generate remote control control intention data; perform signal decoding optimization on the lightweight decoding data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; The response optimization module is used to perform signal packet remote transmission of the optimized decoded control signal data to generate decoded control signal packet transmission data; perform command reception response analysis on the decoded control signal packet transmission data to generate receiving device command execution data and receiving device command execution response time; perform response delay optimization on the receiving device command execution data through the receiving device command execution response time to generate response optimization instructions; The control comparison module is used to evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, the multi-device collaborative control task allocation data is adaptively adjusted until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0025] The beneficial effect of the present invention is that by acquiring the remote control device type data and deploying the Hall sensor based on its characteristics, the accurate deployment of the sensor is ensured, and the accuracy and stability of signal acquisition are improved. Through the calibration of the working magnetic field direction and real-time signal acquisition, the magnetic field detection signal of the standard remote control device is generated, which effectively reduces the error and interference in the signal acquisition process, thereby improving the reliability and accuracy of the remote control control system. By lightweight decoding and remote control mode recognition of the magnetic field detection signal of the standard remote control device, data redundancy is effectively reduced, and the data processing speed and efficiency are improved. The decoding optimization based on the control intention not only improves the decoding accuracy, but also makes the transmission of the control signal more efficient, reduces unnecessary decoding burden, and optimizes the responsiveness of the system. By remotely transmitting the optimized decoded control signal package, combined with the instruction execution response time analysis, the response delay of the receiving device is optimized, thereby greatly reducing the delay in the signal transmission process, and improving the real-time response capability of the remote control system, which ensures the timeliness and efficiency of the device control, especially in the real-time control task, reducing the operation inaccuracy caused by the delay. By evaluating the remote control completion rate and comparing it with the preset standard, the completion of the control task is monitored in real time. By adaptively adjusting the allocation of multi-device collaborative control tasks, the system is ensured to continue to meet the remote control standards in a complex multi-device environment, which not only improves the adaptability and flexibility of the system, but also ensures the efficiency and stability when working together between devices, thereby ensuring the smooth execution of remote control operations. Therefore, the present invention improves the accuracy and efficiency of remote control by optimizing Hall sensor deployment, signal decoding, response delay and multi-device collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the steps of a remote control method based on a low-power Hall sensor; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0029] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0030] To achieve this, please refer to Figures 1 to 3 , a remote control method based on a low power consumption Hall sensor, the method comprising the following steps: Step S1: acquiring remote control device type data; performing Hall sensor deployment based on the remote control device type data to generate Hall sensor deployment data; performing sensor working magnetic field direction calibration on the Hall sensor deployment data to generate Hall sensor deployment correction data; performing real-time signal acquisition on the Hall sensor deployment correction data to obtain a standard remote control device magnetic field detection signal; Step S2: performing lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoded data of the remote control device; performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; Step S3: remotely transmit the optimized decoded control signal data with a signal packet to generate decoded control signal packet transmission data; perform instruction reception response analysis on the decoded control signal packet transmission data to generate receiving device instruction execution data and receiving device instruction execution response time; optimize the response delay of the receiving device instruction execution data through the receiving device instruction execution response time to generate a response optimization instruction; Step S4: Evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, adaptively adjust the multi-device collaborative control task allocation data until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0031] The present invention obtains the remote control device type data and accurately deploys the Hall sensor according to the device characteristics, thereby ensuring that the Hall sensor can adapt to the working environment of different remote control devices. After calibrating the working magnetic field direction of the sensor, the standard remote control device magnetic field detection signal can be accurately collected, thereby improving the accuracy and reliability of the signal. Lightweight signal decoding reduces the data burden in the transmission process and improves the decoding efficiency; through remote control mode recognition, the control intention of the remote control device can be accurately captured, and the system's responsiveness to remote control operations can be further improved. Signal decoding optimization based on control intention reduces redundant information and improves the decoding accuracy and speed of the signal. The optimized decoded control signal package can be transmitted more effectively in remote transmission, reducing signal loss and interference. By analyzing and optimizing the instruction execution response time of the receiving device, the response delay is significantly reduced, the real-time performance and stability of the system are improved, and timely feedback of the remote control operation is ensured. By evaluating the remote control completion rate, the execution effect of the remote control task can be monitored in real time to ensure that the control task is successfully completed. The adaptive adjustment of multi-device collaborative control tasks can dynamically optimize task allocation according to actual conditions, improve the flexibility and scalability of the system, and ensure that the remote control task can still meet the preset standards in a complex environment. Therefore, the present invention improves the accuracy and efficiency of remote control by optimizing Hall sensor deployment, signal decoding, response delay and multi-device collaborative control.

[0032] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow of a remote control method based on a low-power Hall sensor of the present invention. In this example, the remote control method based on a low-power Hall sensor includes the following steps: Step S1: acquiring remote control device type data; performing Hall sensor deployment based on the remote control device type data to generate Hall sensor deployment data; performing sensor working magnetic field direction calibration on the Hall sensor deployment data to generate Hall sensor deployment correction data; performing real-time signal acquisition on the Hall sensor deployment correction data to obtain a standard remote control device magnetic field detection signal; In an embodiment of the present invention, the device type information is obtained through the hardware identification of the remote control device (such as model, manufacturer, built-in sensor type). Hardware identification can obtain the device ID, version number, etc. through the firmware or driver of the device. In the application layer software of the remote control device, the basic information of the device is obtained through the API interface or communication protocol (such as Bluetooth, Wi-Fi). According to the obtained device identification data, the specific type of the remote control device (such as home remote control, industrial control remote control, etc.) is analyzed, and this information helps to determine the subsequent sensor deployment and configuration. According to the acquired remote control device type data, a suitable Hall sensor model is selected. Different types of remote control devices require different types of Hall sensors (such as sensing range, sensitivity, installation method, etc.). When selecting a Hall sensor, the spatial layout of the device, the task requirements (such as measurement accuracy), and the degree of magnetic field interference in the working environment should be considered. According to the type and design of the remote control device, determine the installation location of the Hall sensor (for example, on the housing of the remote control device, near the button, in the control module area, etc.). Use magnetic fixing, screw fixing or gluing to install it to ensure the stability and reliability of the sensor. According to the interface type of the device, select a suitable connection method (such as analog signal interface, digital signal interface or wireless communication). After deployment is complete, record the specific installation location, installation angle, sensor type, working parameters and other information of the Hall sensor, and generate Hall sensor deployment data for use in subsequent steps. Determine the installation coordinates of each Hall sensor for subsequent magnetic field direction calibration. Obtain the spatial coordinates of the sensor by calculation or using the sensor's built-in positioning system (such as optical sensor, laser scanner, etc.). Use the device's built-in positioning data or external positioning system (such as GPS, IMU) to record the precise coordinates of the Hall sensor in space. Use the sensor's installation coordinates and the device's reference coordinate system (such as device housing, axis, etc.) to calculate the installation angle of each Hall sensor. Perform angle calibration by accurately measuring the sensor's installation angle and comparing it with the device's design parameters. Compare the data of multiple Hall sensors horizontally to analyze the magnetic field direction and strength measured by different sensors. Perform azimuth aggregation based on the magnetic field direction data, that is, aggregate the data of multiple sensors into unified magnetic field direction data and compare it with the design parameters. Correct the working direction of the Hall sensor based on the calibrated angle data and magnetic field direction data, generate Hall sensor deployment correction data, and ensure that the measurement data of each sensor is accurate. Start the Hall sensor for real-time data collection and obtain the sensor's sensing data of the magnetic field in the working state. The signal collected by the sensor can be an analog signal (converted to a digital signal through ADC) or a digital signal (output data directly through a digital interface). Perform data preprocessing on the collected raw signal to remove noise, enhance the signal, and perform filtering. Apply filters (such as low-pass filters and high-pass filters) to remove unnecessary noise and maintain valid signals.According to the characteristics of the magnetic field, the signal is enhanced to improve the signal-to-noise ratio. The signal is adjusted to the standard range to ensure that it will not be affected by the range in subsequent processing. After signal preprocessing, the standard remote control device magnetic field detection signal is obtained. These signals will be used as input data for subsequent operations, such as remote control mode recognition and signal decoding.

[0033] Step S2: performing lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoded data of the remote control device; performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; In an embodiment of the present invention, the standard remote control device magnetic field detection signal is converted into a frequency domain signal by using a fast Fourier transform (FFT). FFT decomposes the signal into different frequency components, which helps to extract periodic information in the signal and reduce the computational complexity in the decoding process. The frequency domain data of the remote control device magnetic field generated by FFT includes the amplitude and phase information of different frequency components. The signal morphology analysis is performed on the frequency domain data after FFT conversion to extract features that are helpful for decoding. These features include frequency distribution, amplitude change trend, phase change, etc. By analyzing the signal in the time domain and frequency domain, the periodicity, repetitive pattern and signal change law of the signal are determined. A dictionary learning method is used to build a dictionary for the decoding feature data set. The dictionary consists of a set of basis vectors used to represent the main components of the signal. The decoding feature data set is matched with the dictionary, and the signal is sparsely processed by sparse representation technology. The goal of sparseness is to find the core components of the signal and represent them with fewer coefficients. The specific calculation formula is: ;in, To decode the feature dataset, is the dictionary matrix, is a sparse coefficient vector, is the reconstruction error. Based on the L1 norm, the sparse signal data is minimized to obtain the sparse coefficient matrix: ,in It is expressed as a regularization parameter, which is used to control the sparsity. The sparse coefficient matrix is ​​used to reconstruct and decode the magnetic field detection signal of the standard remote control device to generate lightweight decoded data of the remote control device. These data contain the control signal information of the remote control device, but after sparse representation and optimization, they are easy to transmit and process quickly. The lightweight decoded data of the remote control device generated from step S2 represents the control signal of the remote control device, which can contain information such as the mode, key events, and direction control of the remote control device. Dynamic change feature extraction is performed on the lightweight decoded data of the remote control device to extract the change law and time series characteristics of the signal. Features include the rate of change of the signal, the timing pattern, and periodicity. The data set of the dynamically changing feature data is divided into a model training set and a model test set. The training set is trained using deep learning algorithms such as recurrent neural networks (RNN) or long short-term memory networks (LSTM) to learn the operation mode of the remote control device. These models can process sequence data and identify dynamic changes in time, and are suitable for remote control mode recognition. During the training process, the network automatically extracts the time series characteristics of the remote control operation based on the input lightweight decoded data, and learns the relationship between different modes. The model is evaluated on the model test set, and the recognition model is iteratively optimized through back propagation and optimization algorithms. The accuracy of remote control mode recognition is improved by continuously adjusting network parameters. The trained remote control mode recognition model is used to predict the lightweight decoded data of the remote control device to generate remote control control intention data, which represents the user's control intention, such as key operation, direction adjustment or mode switching. Based on the remote control control intention data, the lightweight decoded data of the remote control device is modulated. According to the control intention, the parameters of the decoded signal are adjusted, such as changing the signal frequency, amplitude, etc., so as to more accurately perform user operations. The modulated signal is combined with the original decoded data to generate optimized decoded control signal data. The optimized control signal is more precise and can achieve faster response time and higher control accuracy.

[0034] Step S3: remotely transmit the optimized decoded control signal data with a signal packet to generate decoded control signal packet transmission data; perform instruction reception response analysis on the decoded control signal packet transmission data to generate receiving device instruction execution data and receiving device instruction execution response time; optimize the response delay of the receiving device instruction execution data through the receiving device instruction execution response time to generate a response optimization instruction; In an embodiment of the present invention, the optimized decoded control signal data (including various instructions, parameters, configurations, etc.) is encapsulated into a standard data packet format. This format should have efficient compression and encryption capabilities to reduce delays and security risks during transmission. Design a data packet format containing header information (such as packet header, target device ID, timestamp, data length, etc.), data body (actual control signal data) and tail information (such as checksum, integrity verification information). Use a suitable compression algorithm (such as LZ4 or Zstd) and encryption algorithm (such as AES) to process the data packet to ensure transmission efficiency and data security. The signal packet is remotely transmitted to the receiving device via the Internet, wireless network or dedicated communication link. Use a reliable transmission protocol (such as TCP / IP, QUIC) to ensure the accurate arrival of the data packet. Select the most suitable protocol according to the application scenario. If high reliability and low packet loss rate are required, TCP protocol is a better choice; if lower latency is required, QUIC is more suitable. Bandwidth control is performed according to the actual network conditions to ensure that high-priority data packets can be transmitted first and avoid delays caused by network congestion. After receiving the signal packet, the receiving device first performs data parsing to extract the decoded control signal and instruction content. And record the processing status, timestamp and other information of the receiving device. Extract the instruction part (such as control commands, execution parameters, etc.) in the data packet, and allocate appropriate processing resources according to the processing capabilities and characteristics of the receiving device. Record the time interval from the receiving device receiving the instruction to the start of execution, that is, the "instruction execution response time". The analysis of this period helps to evaluate system performance and response efficiency. Conduct in-depth analysis of the instruction execution response time of the receiving device to identify delay bottlenecks. The main analysis factors include network delay, receiving device processing capability, instruction complexity, etc. Through logging and real-time monitoring data, find out the main factors affecting the response time. For example, when the network delay is high, the data transmission path can be optimized or the bandwidth can be increased; when the receiving device processing capability is insufficient, the device resource allocation can be optimized. Use machine learning models (such as linear regression, decision trees, etc.) to predict future response delays based on historical response time data and optimize them. If multiple instructions have similar execution requirements, batch processing can be used to reduce the response time of each instruction. Adjust the priority of instruction execution according to the importance and urgency of the instruction. High-priority instructions can be processed first to reduce delays. According to the results of network analysis, the transmission path is adjusted, the optimal network transmission protocol is selected, and the network transmission time is reduced. According to the optimization analysis results, response optimization instructions are generated to adjust the signal packet transmission and receiving device processing strategy in the system, or directly adjust the processing capacity and network settings of the receiving device. According to the real-time response delay analysis, the system automatically or manually intervenes to dynamically optimize the instructions. The optimized instructions will be fed back to the receiving device, and the device will adjust the execution process according to the new optimization strategy to reduce the subsequent response delay.

[0035] Step S4: Evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, adaptively adjust the multi-device collaborative control task allocation data until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0036] In the embodiment of the present invention, the control completion rate (CCR) is defined as the ratio of the receiving device to successfully execute and respond to instructions within a certain period of time. The specific formula is: CCR = (number of successfully executed instructions ÷ total number of instructions) × 100%. The number of successfully executed instructions refers to the number of instructions that the receiving device successfully executes and produces the expected effect, and the total number of instructions refers to the total number of instructions received within the evaluation time window. The system monitors the execution process of the receiving device in real time and records the execution status of each instruction, including successful execution, failed execution, timeout execution, etc. Combined with the execution data after the response optimization instruction, the execution time, response time and execution result of each instruction are evaluated to calculate the remote control completion rate of each device. Based on the above data, the execution of instructions of all receiving devices is comprehensively evaluated, and the remote control completion rate of the system is finally obtained. When designing the system, a target remote control completion rate is set according to actual needs and system performance requirements. For example, the preset standard can be 95%, which means that the system expects that more than 95% of the instructions can be successfully executed. The preset standard can be dynamically adjusted according to the specific application scenario and device performance of the system. Compare the actual calculated remote control completion rate with the preset standard completion rate to evaluate the remote control capability of the current system. If the remote control completion rate is lower than the preset standard, it means that the system has failed to achieve the expected control capability and needs further optimization. When the remote control completion rate is lower than the preset standard, the system needs to dynamically adjust the multi-device collaborative control tasks to improve the overall completion rate. Adaptive adjustment can be performed in the following ways: Reassign control tasks to devices with lighter loads to avoid execution failure or delay of some devices due to overload. Dynamically assign tasks by calculating the load capacity of each device (such as response time, task execution success rate, etc.). Adjust the priority of instructions according to the execution capability of the device and the urgency of the task. Prioritize key tasks to devices with strong execution capabilities to ensure the successful completion of key tasks. Optimize resource scheduling according to the real-time status of the device (such as battery power, processing power, network bandwidth, etc.) to ensure that each device performs tasks in its best state. Assign multiple tasks to different devices to reduce the burden of a single device and improve the remote control completion rate of the entire system. By coordinating the workflows of multiple devices, reduce conflicts and duplication of work between devices and improve the overall collaborative control efficiency. The system can set up a feedback mechanism to adjust the task allocation strategy in real time according to the adjusted task allocation and remote control completion rate. The system continuously monitors the remote control completion rate to ensure the effect of the adjusted task allocation and resource scheduling. If the remote control completion rate is lower than the preset standard, the adjustment will continue; if it reaches or exceeds the preset standard, the system will enter the normal remote control mode. When the remote control completion rate reaches or exceeds the preset standard, the adaptive adjustment will stop, confirming that the system has been optimized to the best state, and start the remote control operation.After ensuring that the remote control completion rate is greater than or equal to the preset standard, the system will start to execute the remote control task. At this time, the device performs control operations according to the predetermined task, and the system continues to monitor the execution status to ensure efficient and stable control.

[0037] Preferably, step S1 comprises the following steps: Step S11: Obtain remote control device type data; Step S12: selecting a Hall sensor type based on the remote control device type data to obtain the Hall sensor type data of the remote control device; deploying the Hall sensor according to the Hall sensor type data of the remote control device to generate Hall sensor deployment data; Step S13: sensing the magnetic field of the remote control device according to the Hall sensor deployment data to generate the magnetic field sensing data of the remote control device; calibrating the working magnetic field direction of the Hall sensor deployment data according to the magnetic field sensing data of the remote control device to generate the Hall sensor deployment correction data; Step S14: perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the original magnetic field detection signal of the remote control device; perform signal preprocessing on the original magnetic field detection signal of the remote control device to generate a standard remote control device magnetic field detection signal, wherein the signal preprocessing includes signal denoising, signal enhancement and signal normalization.

[0038] In an embodiment of the present invention, the remote control device is connected through the hardware interface (such as serial port, I2C, SPI, etc.) or wireless communication interface (such as Bluetooth, Wi-Fi, etc.) of the device, and these interfaces can obtain basic information of the device, such as model, hardware specifications, sensor type, etc. The type and functional parameters of the device are obtained by communicating with the device using a standard protocol (such as Device Description Protocol, DDP). For some intelligent remote control devices, the device type information is queried through the built-in storage of the device or from a cloud database. The device type is classified using a specific algorithm or rule, for example, the category of the device (such as smart home, robot, remote control car, etc.) is determined by the hardware identification code (ID) or configuration file of the device. If the device has multiple sensors and functions, it can be parsed and classified through the software configuration file. According to the device type data (for example, the working scene and use environment of the remote control device), select a suitable Hall sensor type. For example: for devices with high-precision positioning requirements, select a linear Hall sensor, which can provide a continuous magnetic field strength output. For simple magnetic field detection applications, select a digital Hall sensor, which can provide a switching signal of the magnetic field. The selection of the sensor depends on factors such as the magnetic field sensing requirements, accuracy requirements, and working environment of the device. Based on the device type data, the performance of different types of sensors is analyzed through intelligent algorithms (such as machine learning models) to select the Hall sensor that best matches the device. For example, automatic selection is performed based on the device's sensor profile and working conditions (such as temperature, humidity, and magnetic field strength). According to the design requirements of the remote control device, select a suitable location to install the Hall sensor. Common deployment locations include the surface of the remote control housing or near the control unit to ensure that the magnetic field changes around the device can be sensed. The stability and accuracy of the sensor are ensured through PCB design, welding, or installation of the sensor module inside the device. For devices with high precision requirements, multiple points will also be deployed to obtain more complete magnetic field information. Start the sensor for real-time perception, and use the Hall effect principle to detect the direction and intensity of the magnetic field where the device is located. The Hall sensor converts the magnetic field signal into a voltage signal and transmits it to the processing unit (such as a microcontroller, FPGA, etc.) for further processing. Collect and store sensor data to form preliminary magnetic field perception data, which includes the magnetic field strength and direction information at the sensor location. Analyze the magnetic field perception data collected by the remote control device to determine whether the sensor is in the correct working direction. Vector operations can be used to analyze the deviation between the output voltage of each Hall sensor and the theoretical magnetic field direction. If a deviation is found, the sensor installation position or the working direction of the sensor needs to be adjusted. The magnetic field direction of the Hall sensor is dynamically calibrated through algorithms (such as least squares method or Kalman filtering) to generate correction coefficients, which will be used to correct the sensor direction error in subsequent data processing.If the device allows, directly adjust the hardware position of the sensor or rotate the sensor to ensure that it is consistent with the magnetic field direction of the device. Based on the calibration process, generate correction data for the Hall sensor deployment, including the correction angle, direction coefficient, etc.

[0039] Preferably, calibrating the sensor working magnetic field direction for the Hall sensor deployment data according to the magnetic field sensing data of the remote control device includes: Confirm the sensor installation coordinates of the Hall sensor deployment data to obtain the sensor installation coordinate data; perform installation angle analysis on the Hall sensor deployment data based on the sensor installation coordinate data to generate the sensor installation angle data; Extracting a single Hall sensor sensing data from the remote control device magnetic field sensing data to obtain Hall sensor sensing data, wherein the Hall sensor sensing data extraction includes magnetic field intensity extraction, magnetic field direction extraction, and magnetic field change extraction; based on the Hall sensor sensing data, the lateral magnetic field orientation is aggregated and compared to generate lateral magnetic field orientation aggregated data; The installation orientation of the Hall sensor deployment data is adjusted according to the lateral magnetic field orientation aggregation data to generate the Hall sensor deployment correction data.

[0040] In an embodiment of the present invention, the installation position of the sensor is defined by using the three-dimensional coordinate system of the device (for example, based on the Cartesian coordinate system X, Y, Z). The sensor installation coordinate data is usually obtained by measuring tools (such as laser rangefinders, three-dimensional scanners, etc.) to ensure the accuracy of the position of the Hall sensor in space. If the device has multiple sensors, ensure that the relative position of each sensor is correctly represented in the coordinate system inside the device. Use the positioning system or image recognition technology (such as computer vision, AR technology, etc.) inside the device for auxiliary positioning to ensure that the installation coordinates of each sensor are consistent with the design requirements. After obtaining the installation coordinate data of the sensor, it is necessary to analyze the installation angle. The goal of the installation angle analysis is to ensure that the installation angle of each Hall sensor (the angle with the coordinate system of the device or the direction of the magnetic field) meets the requirements. According to the installation position coordinates of the sensor, the installation angle of the Hall sensor relative to the device coordinate system is calculated using the principle of spatial geometry. The angle between the sensor and the axis of the device coordinate system can usually be calculated by the vector dot product formula: ;in, is the sensor installation direction vector, is the device magnetic field direction vector, is the angle between the two. If there is a deviation in the installation angle of the sensor, it is necessary to use a mechanical device for physical adjustment, or to correct the sensor installation angle data through software. If the installation angle does not meet the requirements, it is optimized and adjusted based on software correction or sensor physical adjustment to generate accurate installation angle data. Each Hall sensor generates different induction signals, indicating the strength, direction and change of the magnetic field. These data are extracted through the following steps: The Hall sensor outputs a voltage signal proportional to the magnetic field strength. These signals can be collected and converted into digital data by ADC (analog-to-digital converter) to represent the sensed magnetic field strength. The Hall sensor can sense the direction of the magnetic field. The direction of the magnetic field can be inferred by the change of the induction signal combined with the installation direction of the sensor. The specific method is to analyze the direction of the magnetic field by using vector operations by comparing with the installation direction of the sensor. The magnetic field change refers to the change of the magnetic field strength perceived by the sensor over time. Through real-time signal acquisition, the data of magnetic field changes can be extracted and its fluctuations can be analyzed. Through data processing techniques (such as filtering, smoothing, etc.), the collected magnetic field sensing data is structured to obtain a clear set of sensing data, which contains the magnetic field strength, direction and change information of each sensor. In order to obtain comprehensive magnetic field sensing data, it is necessary to compare the data of multiple Hall sensors horizontally. By comparing the magnetic field directions sensed by the sensors at different positions of the device, the magnetic field distribution around the entire device can be obtained. The magnetic field directions of all sensors are aggregated and analyzed, especially the changes in the magnetic field between sensors in the horizontal direction (perpendicular to the main direction of the device). This step includes calculating the difference in magnetic field directions between sensors and identifying the lateral change trend of the magnetic field. By comparing the magnetic field data sensed by different sensors horizontally, the lateral magnetic field orientation aggregate data is generated, which includes the change pattern of magnetic field intensity and direction, the magnetic field deviation of the device, etc. According to the lateral magnetic field orientation aggregate data, analyze whether the installation orientation of each Hall sensor matches the overall magnetic field direction of the device. If a deviation is found, the position and angle of the Hall sensor can be adjusted. If there is a significant difference in the magnetic field direction sensed by multiple sensors, it is necessary to adjust the installation angle of the sensor or rearrange the position of the sensor. This adjustment can be compensated by hardware adjustment (such as rotating the sensor) or by software algorithm. The adjusted sensor orientation will generate new deployment correction data. The correction data includes the final installation angle, orientation and adjustment parameters of the sensor. This data can be used for subsequent magnetic field sensing calculations and device control optimization.

[0041] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: extracting magnetic field multi-dimensional features from the magnetic field detection signal of the standard remote control device to obtain multi-dimensional feature data of the magnetic field of the remote control device, wherein the multi-dimensional feature data of the magnetic field of the remote control device includes magnetic field intensity feature data, magnetic field change rate feature data and magnetic field timestamp data; Step S22: performing spatial information association on the magnetic field intensity characteristic data, the magnetic field change rate characteristic data and the magnetic field timestamp data based on the sensor installation coordinate data to generate remote control device magnetic field spatial association data; performing lightweight signal decoding on the standard remote control device magnetic field detection signal according to the remote control device magnetic field spatial association data to generate remote control device lightweight decoding data; Step S23: performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data.

[0042] In an embodiment of the present invention, the magnetic field signal of the remote control device is collected in real time by using a Hall sensor to obtain the original magnetic field detection signal, which is usually expressed as a voltage output proportional to the magnetic field strength. The collected original magnetic field signal is subjected to signal denoising and filtering to remove high-frequency noise and external interference. Common filtering methods include low-pass filtering, Kalman filtering, etc. The intensity characteristics of the magnetic field are extracted from the denoised signal, and the magnetic field strength data is usually obtained by calculating the instantaneous amplitude of the signal (for example, using a square root algorithm to calculate the amplitude of the signal). According to the time series data (the magnetic field strength value corresponding to each timestamp), the rate of change of the magnetic field strength at different time points (i.e., the derivative of the magnetic field strength) is calculated. The rate of change of the magnetic field strength is obtained by differential calculation of the signal, which is usually calculated by the following formula: rate of change = (magnetic field strength at the current moment - magnetic field strength at the previous moment) ÷ time interval. Each collected magnetic field signal will correspond to a timestamp, which is used to mark the time sequence of the signal. Timestamp data is important information for analyzing the change of the magnetic field over time. The timestamp is converted into a unified format (such as UTC format) for subsequent data analysis and processing. The extracted magnetic field intensity, rate of change, and timestamp data are combined into a multidimensional feature data set, which will serve as the basis for subsequent analysis. According to the installation coordinates of each Hall sensor (including spatial position and installation angle), the magnetic field characteristics (including intensity, rate of change, and timestamp) collected by the sensor are mapped to the spatial coordinate system of the device, so that the data collected by each sensor can be associated with the physical position and orientation of the device. By establishing a three-dimensional spatial coordinate system (such as using a Cartesian coordinate system), the magnetic field data of each sensor is mapped to its specific position in the device, and then "magnetic field space association data" is obtained. This step can map the two-dimensional data of each sensor to three-dimensional space through linear transformation, rotation matrix, etc. The magnetic field data (intensity, rate of change, and timestamp) of each sensor is combined with its spatial coordinate data to form a unified magnetic field space data structure. According to the timestamp and spatial coordinates, spatiotemporal association analysis is performed to identify the relationship between the magnetic field data collected by different sensors at the same time point. The data is grouped by a clustering algorithm (such as K-means or DBSCAN) to identify the magnetic field change pattern in the same area. The processed magnetic field strength, rate of change and timestamp data are associated with the spatial coordinate data of the sensor to generate the final "remote control device magnetic field spatial association data", which can be represented by a data table or structured data format (such as JSON, XML). Based on the magnetic field spatial association data, the core features of the signal are extracted by lightweight decoding of the signal to generate "remote control device lightweight decoding data". This process mainly removes redundant information and retains key spatial and temporal information for subsequent control mode recognition. Based on lightweight decoded data (such as magnetic field strength, rate of change, timestamp, spatial coordinates, etc.), key features are selected for pattern recognition.Feature selection can use PCA (principal component analysis) and other techniques to reduce the dimension and retain the features that best represent the remote control mode. Use an existing control data set (for example, trained with historical control data) to train a machine learning model (such as support vector machine (SVM), random forest, neural network, etc.), which can identify different control modes based on the input magnetic field characteristics. For example, by analyzing the change pattern of the magnetic field characteristics, it can be identified whether the user is moving forward, backward, turning left, turning right, etc. The machine learning model will output the predicted results of the control mode (such as forward, backward, turning, etc.), which is the "remote control intention data". This data can be used to directly control the behavior of the remote control device. Convert the control intention data into an easy-to-process format, such as passing the recognition results to subsequent systems or devices for execution in formats such as JSON and XML. Based on the control intention data, optimize the signal decoding of the remote control device. By combining real-time feedback data (such as delay and error of sensor signals), adjust the decoding algorithm to make the decoding process more efficient and accurate. The accuracy of decoding can be further improved by dynamically learning and adjusting algorithm parameters. For example, according to the different control modes of the device, dynamically adjust the decoding strategy to adapt to different remote control needs. Based on the optimized signal decoding, accurate control signal data is generated and transmitted to the control system of the remote control device. This control signal data can be directly used to execute remote control commands.

[0043] Preferably, performing lightweight signal decoding on the standard remote control device magnetic field detection signal according to the remote control device magnetic field spatial correlation data comprises: Perform fast Fourier transform on the magnetic field detection signal of the standard remote control device according to the spatial correlation data of the magnetic field of the remote control device to generate the frequency domain data of the magnetic field of the remote control device; perform signal morphology analysis on the frequency domain data of the magnetic field of the remote control device to generate a decoding feature data set; A dictionary is constructed for the magnetic field detection signal of the standard remote control device by decoding the feature data set to generate a signal representation dictionary; based on the signal representation dictionary, the decoded feature data set is signal sparsified to generate sparse remote control device signal data, where the signal sparsification formula is as follows:

[0044] In the formula, Represented as a decoding feature dataset, Represented as a matrix of basis vectors in the dictionary, Represented as a sparse coefficient vector, It is represented as reconstruction error; Based on the L1 norm, the sparse remote control device signal data is minimized to obtain a sparse coefficient matrix, where the formula for minimizing the target calculation is as follows:

[0045] In the formula, Represented as a sparse coefficient matrix, Denoted as the regularization parameter; The sparse coefficient matrix is ​​used to optimize the signal decoding of the lightweight decoding data of the remote control device to generate optimized decoding control signal data.

[0046] In an embodiment of the present invention, a magnetic field detection signal of a remote control device (i.e., a standard magnetic field detection signal collected by a Hall sensor) is used as an input signal. The signal is specifically a time domain signal, which contains information about changes in the magnetic field of the remote control device. The time domain signal is converted into a frequency domain signal by fast Fourier transform (FFT). The FFT algorithm can convert a discrete time signal into its frequency components, helping to extract periodic information and frequency characteristics in the signal. Through FFT processing, the magnetic field frequency domain data of the remote control device is obtained, which includes the amplitude and phase information of each frequency component. By analyzing the frequency domain signal, the significant frequency components in the signal are extracted. The main frequency components and the noise part can be identified by spectrum analysis (for example, by calculating the power spectrum density). The frequency domain signal is filtered to remove high-frequency noise, and the main features of the signal, such as the center frequency, bandwidth, phase information, etc. of the frequency band, are extracted. The feature information (such as frequency domain features, phase features, amplitude features, etc.) obtained by the above analysis is organized into a decoded feature data set, which is used for subsequent dictionary learning and sparse processing. The goal of dictionary learning is to extract a set of basis vectors from the decoded feature data set, which can effectively represent the magnetic field detection signal of the remote control device. A dictionary is generated using a dictionary learning method based on sparse coding (such as the K-SVD algorithm). This method automatically learns a set of basis vectors that best represent the signal based on the decoded feature data set. The basis vectors in the dictionary are continuously updated in an iterative manner so that the representation of each feature data set can be approximated with the least sparse coefficients. For each feature data set X, it is represented by a dictionary D and a sparse coefficient α: X≈D⋅α, where D is a matrix composed of the learned dictionary basis vectors, and α is a sparse coefficient vector, which represents the sparse representation of the signal under the dictionary basis vector. Through dictionary learning, a dictionary containing multiple basis vectors is generated, which can efficiently represent the magnetic field signal of the remote control device. The signal can be represented by a combination of these basis vectors and sparse coefficients. Through the sparse coding method, the decoded feature data set is sparsed using the signal representation dictionary, that is, the original signal is represented as a linear combination of the dictionary basis vectors through the sparse coefficient α. The formula for signal sparsification is: In the formula, Represented as a decoding feature dataset, Represented as a matrix of basis vectors in the dictionary, Represented as a sparse coefficient vector, The optimization goal of the sparsification process is to make the representation of the signal under the dictionary basis vector as sparse as possible while minimizing the reconstruction error. This goal can be achieved through the L1 norm constraint, specifically: in, is a sparse coefficient matrix, is a regularization parameter that controls the degree of sparsity. Penalize to make the coefficients as sparse as possible, thereby reducing the redundant part of the signal. Use optimization algorithms (such as ISTA, FISTA, etc.) to iterate and obtain a sparse coefficient matrix . Using a sparse coefficient matrix Optimize the signal decoding of the remote control device. The optimized control signal should be able to more accurately reflect the operation intention of the remote control device and remove redundant information. , we can reconstruct the optimized signal: X′=D⋅A; where X′ is the optimized signal, reflecting the actual control state of the remote control device. Through the optimized signal data, a decoding control signal for controlling the remote control device is generated. This signal will be used to drive the actual operation of the remote control device.

[0047] Preferably, step S23 includes the following steps: Step S231: extracting dynamic change features from the remote control device lightweight decoded data to obtain remote control device lightweight decoded dynamic change feature data; dividing the remote control device lightweight decoded dynamic change feature data into data sets to generate a model training set and a model test set; Step S232: training the model training set according to the recurrent neural network algorithm to generate a remote control mode recognition pre-model; optimizing and iterating the remote control mode recognition pre-model based on the model test set to generate a remote control mode recognition model; importing the remote control device lightweight decoding data into the remote control mode recognition model for label mapping to generate operation label mapping data; Step S233: classify the operation label mapping data into operation categories to obtain remote control control intention data; perform signal modulation on the remote control device lightweight decoding data based on the remote control control intention data to generate optimized decoding control signal data.

[0048] In the embodiment of the present invention, the analysis is performed based on the lightweight decoded data of the remote control device. The lightweight decoded data is usually the result of the signal after frequency domain analysis and sparse processing, and contains the operation information of the remote control device. Analyze the change trend of the signal, for example, by calculating the dynamic features of the signal such as the rate of change and acceleration, and extract the change pattern of the signal in time or space. Extract the spectrum features of the signal in different time periods, which are of great significance for identifying the control mode of the remote control device. Extract the time series features of the signal, such as peak value, periodic change, mutation, etc., which helps to capture the dynamic evolution of the signal. The extracted features include dynamic changes in the time dimension (such as increase and decrease trends), spectrum features (such as frequency band changes) and periodic features, etc., which will be used as input data for subsequent model training. The extracted dynamic change feature data is divided into two parts: a training set and a test set. The training set is used to train the model, and the test set is used to evaluate the performance of the model. The commonly used division ratio is 70% training set and 30% test set, or 80% and 20%. The dynamic change feature data of the remote control device is used as input. Each data point contains information such as the time series characteristics and spectrum changes of the signal, which will be input into the RNN model. Use recurrent neural network (RNN) or its variants (such as LSTM, GRU, etc.) to process time series data. RNN can remember the past state through the loop structure, so that the model can handle the dynamic characteristics of time series signals. Use LSTM units to avoid the gradient vanishing problem in traditional RNN and capture the dependencies in long time series. GRU is a simplified version of LSTM and can learn more effectively, especially when computing resources are limited. Common loss functions include cross entropy loss (for classification tasks) or mean square error (for regression tasks). Common optimization algorithms include Adam optimizer, SGD (stochastic gradient descent), etc., which can effectively accelerate the training process. The network weights and biases are optimized through backpropagation algorithm to improve the prediction accuracy. During the training process, RNN will continuously update its weights according to the training set, and finally generate a preliminary remote control mode recognition pre-model. Use the test set data to evaluate the trained pre-model. Check the recognition performance of the model by calculating the accuracy, recall rate, F1 value and other indicators of the model. If the model performs poorly on the test set, you need to adjust the model structure (such as adding or reducing hidden layer nodes), change the learning rate, improve feature selection, or further clean the data. Optimize the model by adjusting the hyperparameters of the RNN (such as learning rate, number of hidden layer units, etc.). You can use methods such as cross-validation to select the best hyperparameters. If the model is not robust enough to new remote control device signals, you can use an incremental learning strategy to retrain or fine-tune with more training data. Through multiple optimization iterations, gradually improve the performance of the model so that it can better identify the operating mode of the remote control device. After the optimized model can achieve satisfactory accuracy on the test set, the final remote control mode recognition model can be generated.Use the trained remote control pattern recognition model to infer the lightweight decoded data of the new remote control device. By importing the input data (lightweight decoded data) into the model, the model will predict the corresponding operation label. Each remote control device operation corresponds to a label, and the model will infer its corresponding operation based on the dynamic characteristics of the input signal. For example, some change patterns represent the "forward" operation, and some spectrum features represent the "stop" operation. The output data will be the current operation intention label of the remote control device, such as "forward", "stop", "turn", etc. The operation label mapping data is further classified. According to the different types of labels (such as forward, stop, turn, etc.), the operation labels are classified. Traditional classification methods (such as support vector machines, decision trees, etc.) or fully connected layers in deep learning can be used for classification tasks, depending on the type and complexity of the operation. Generate remote control control intention data based on the classification results. Each label corresponds to a control intention, which represents the action or state currently performed by the remote control device. According to the remote control control intention data, the lightweight decoded data of the remote control device is modulated. The purpose of this step is to generate a new control signal so that the remote control device can perform corresponding operations according to the recognized control intention. Traditional modulation methods such as amplitude modulation (AM), frequency modulation (FM) or feature-based modulation methods can be used, depending on the control requirements of the remote control device. If the "forward" control intention is recognized, a corresponding signal is generated to instruct the device to move forward. Ultimately, the modulated control signal will be used as the input signal of the remote control device to make the device perform the corresponding action. These control signals will more accurately reflect the control intention.

[0049] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: encapsulating the optimized decoded control signal data into a data packet to generate a decoded control signal packet; performing signal packet remote transmission on the decoded control signal packet based on a low-power communication protocol to generate decoded control signal packet transmission data; Step S32: performing receiving device instruction execution on the decoded control signal packet transmission data to obtain receiving device instruction execution data; performing instruction response time analysis on the receiving device instruction execution data to generate the receiving device instruction execution response time; Step S33: Optimize the response delay of the receiving device instruction execution data by the receiving device instruction execution response time, and generate a response optimization instruction.

[0050] In an embodiment of the present invention, header information is added to the signal packet, including the packet identifier (ID), signal type, data length, checksum, etc., to identify the type and integrity of the signal packet. The decoded control signal data itself is included, such as instructions (forward, stop, turn, etc.) and control parameters (speed, direction, etc.). Check information and terminator to ensure the integrity of the data packet and the correctness during transmission. The generated data packet structure should meet the requirements of the low-power communication protocol to ensure energy saving during transmission. The data packet after encapsulation will be a signal packet in a standard format, which can be used for subsequent remote transmission. This data packet contains operating instructions and control data, and has a simple and efficient structure. Select a suitable low-power communication protocol (such as LoRa, Zigbee, BLE (Bluetooth Low Energy), etc.), which can ensure stable data transmission without consuming a lot of energy. The power consumption is reduced by a lower data transmission rate, but the remote control signal can be effectively transmitted. Some protocols such as LoRa support long-distance, low-bandwidth wireless transmission, which is suitable for remote control. The signal packet transmission has a low delay to ensure the real-time nature of the control instructions. The encapsulated control signal packet is sent to the receiving end through a low-power protocol. During the transmission process, the device ensures that the data packet can be sent stably by setting parameters such as frequency channel and power. Error detection and retransmission mechanism are used during the transmission process to ensure that the data is not lost or damaged during remote transmission. The signal packet transmitted by the low-power protocol will be sent to the remote receiving device and decoded at the receiving end. The receiving device will receive the data packet transmitted remotely, decode the data packet first, and obtain the control signal and instruction information in it. The receiving device performs related operations according to the instructions in the decoded control signal packet. For example, through the decoded control signal, the device performs actions such as forward, stop, and turn. If the control signal contains other parameters (such as speed, angle, etc.), the receiving device will adjust its behavior according to these parameters. Execution data usually includes the result of the operation, the current state of the device, whether an error or failure occurs, etc. These data will be recorded and used for subsequent response time analysis and optimization. Record the timestamp from sending the control signal packet to the receiving device completing the execution and returning feedback. Calculate the response time of the device: the delay from the control signal to the device feedback. Considering the transmission delay, processing delay and execution feedback delay, statistical analysis methods (such as mean and variance) can be used to evaluate the response time. By analyzing the response time under different devices, environmental conditions, and communication network conditions, the response time data of the receiving device is generated. If the response time exceeds a predetermined threshold, it means that there is a delay problem in the system and further optimization is needed. The delay in the transmission process can be reduced by adjusting the communication protocol, increasing the signal frequency, optimizing the modulation method, etc. If the device processing response time is long, its processing speed can be improved by optimizing the device's hardware and software. For example, adopt a more efficient decoding algorithm, reduce unnecessary calculations, and improve the device's processing capabilities.By optimizing the feedback mechanism, the time from execution to feedback is reduced. This can be achieved by compressing feedback data, optimizing feedback signals, etc. Based on the results of delay optimization, new control instructions are generated to achieve the purpose of reducing delays and improving response speed. The response delay of the device can be reduced by adjusting the size of the control signal packet, reducing the redundant part of data transmission, or by modifying the transmission protocol, adjusting the device operation process, etc. The optimized instructions will be sent to the receiving device again to ensure that the response time is effectively optimized. If the optimization measures are effective enough, the receiving device will provide a faster response when executing the control instruction.

[0051] Preferably, step S33 includes the following steps: Step S331: identifying response influencing factors of the received device instruction execution data by receiving the device instruction execution response time, and generating response influencing factor identification data, wherein the response influencing factor identification includes network delay identification, device performance identification and environmental interference identification; Step S332: performing a minimum delay optimization target setting on the instruction execution response time of the receiving device according to the response influencing factor identification data, and obtaining the minimum delay optimization target setting data; performing a network path optimization on the instruction execution data of the receiving device based on the minimum delay optimization target setting data, and generating the network path optimization data; Step S333: Utilize the network path optimization data to optimize the response delay of the receiving device instruction execution data and generate a response optimization instruction.

[0052] In an embodiment of the present invention, by setting specific optimization goals according to influencing factors, delay minimization is ensured. Based on network delay identification data, optimization goals are set to reduce network delay. For example, the goal may be to reduce the impact of high-delay links and select shorter and more stable transmission paths. Based on device performance identification data, goals are set to improve the response speed of the receiving device. For example, the goal may be to reduce the CPU load of the device, or to optimize algorithms and task scheduling so that the device can respond to instructions faster. Based on environmental interference identification data, goals are set to reduce the impact of the environment on signal transmission. For example, the goal may be to select a clearer wireless frequency band or optimize the device placement location to avoid interference sources. Based on the above analysis, a comprehensive delay optimization goal is formulated. For example, the optimization goal is divided into three sub-goals: network delay, device performance, and environmental interference, each goal having a corresponding weight and optimization criteria. The output data set contains the optimization goals and priorities of each factor. Based on the network delay identification data, the shortest network path with the lowest delay is selected. For example, a routing protocol (such as OSPF, BGP, etc.) is used to select the optimal path, or a software defined network (SDN) technology is used to dynamically adjust the transmission path of the data packet. Distribute network traffic evenly to multiple paths through load balancing algorithms (such as the minimum number of connections, hashing algorithms, etc.) to avoid overloading certain network nodes and reduce latency. Reduce transmission latency by reducing the number of forwarding hops for data packets and avoiding network congestion nodes. Output optimized network path data, including the optimal path selected based on the latency optimization goal. This data can include network node information, optimized paths, and expected latency reduction. Perform actual latency optimization based on network path optimization data. Adjust the transmission route and frequency of signal packets based on the optimized network path data to minimize network latency. Adjust transmission parameters such as packet size, frequency, power, etc. to ensure optimal latency and reliability. Optimize the task scheduling and execution logic of the device based on device performance and environmental interference to reduce response time. Generate response optimization instructions based on the optimized network path and device adjustment data, which will be executed in the receiving device to achieve low-latency and efficient response.

[0053] Preferably, step S4 comprises the following steps: Step S41: performing device communication interconnection on the receiving device instruction execution data based on the response optimization instruction to generate multi-device collaborative control interconnection data; performing task coordination and resource allocation on the multi-device collaborative control interconnection data to generate multi-device collaborative control task allocation data; Step S42: performing execution status feedback on the multi-device collaborative control task allocation data to generate execution status feedback data; performing remote control completion rate evaluation on the execution status feedback data to generate the remote control completion rate; Step S43: Compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is lower than the preset remote control standard completion rate, the multi-device collaborative control task allocation data is adaptively adjusted until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

[0054] In the embodiment of the present invention, by adopting low-power and high-efficiency communication protocols, such as ZigBee, LoRa or protocols based on Wi-Fi and Bluetooth Mesh, each device can be interconnected and exchange instructions and data in real time within a certain physical range. Point-to-point communication between devices or a relay-based communication network structure is realized to ensure that each device can exchange data with other devices. Task scheduling algorithms (such as weighted polling scheduling algorithms, priority scheduling algorithms, etc.) are applied to reasonably allocate tasks according to the current load, task complexity, and priority of each device. According to the resource conditions of each device (such as CPU, memory, and sensor availability), reasonable resource allocation is performed on tasks to ensure efficient operation of the system. For tasks that require multiple devices to complete collaboratively, collaborative control algorithms such as distributed control and leader-follower strategies are adopted to ensure that multiple devices perform tasks collaboratively. Through device communication interconnection and task collaboration, multi-device collaborative control task allocation data is generated, including the assigned tasks, required resources, and collaboration methods of each device. Use embedded sensors or external monitoring systems in the device to capture the working status of the device in real time, such as task execution progress, resource consumption, fault information, etc. The device uploads the execution status to the central control system through the communication network to form execution status feedback data, which includes task completion, resource usage, abnormal status, etc. According to the execution progress of the device and the task allocation, the overall completion rate of the remote control task is calculated. For example, set the completion degree of each device and calculate the overall task completion degree according to the weight: in, For overall task completion, For equipment completion progress, is the total task progress, For equipment The remote control completion rate data reflects the completion of the current task. The calculated remote control completion rate is compared with the preset standard completion rate. If the current completion rate is lower than the preset standard, it means that the execution efficiency of the task is insufficient and needs to be optimized. According to the comparison results, the adaptive adjustment mechanism is executed to optimize the task allocation to ensure that the load and task allocation of each device are reasonable. For devices with slow task execution progress, more suitable tasks are reallocated or the priority is adjusted. The workload of the device is optimized through scheduling algorithms such as shortest job first (SJF) or dynamic priority scheduling. If the resource bottleneck of some devices affects the completion of the task, the resources are reallocated to ensure that the task can be completed in time. For example, the power consumption management mode, communication protocol or collaboration mode of the device is adjusted to reduce communication delay or improve the processing capacity of the device. After each adjustment, the remote control completion rate is recalculated and compared with the standard completion rate until the remote control completion rate is greater than or equal to the preset standard. Once the completion rate reaches the preset standard, the adjustment is stopped and the remote control task is executed.

Claims

1. A remote control method based on a low-power Hall sensor, characterized in that: The following steps are involved: Step S1: Obtain remote control device type data; Perform Hall sensor deployment based on remote control device type data to generate Hall sensor deployment data; Calibrate the working magnetic field direction of the Hall sensor deployment data to generate Hall sensor deployment correction data; Perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the magnetic field detection signal of the standard remote control device; Step S2: performing lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoded data of the remote control device; performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; Step S3: remotely transmit the optimized decoding control signal data in the form of signal packets to generate decoding control signal packet transmission data; Performing command reception response analysis on the decoded control signal packet transmission data to generate receiving device command execution data and receiving device command execution response time; Optimizing the response delay of the receiving device instruction execution data by the receiving device instruction execution response time, and generating a response optimization instruction; Step S4: Evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, adaptively adjust the multi-device collaborative control task allocation data until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

2. The remote control method based on low power consumption Hall sensor according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain remote control device type data; Step S12: selecting a Hall sensor type based on the remote control device type data to obtain the Hall sensor type data of the remote control device; deploying the Hall sensor according to the Hall sensor type data of the remote control device to generate Hall sensor deployment data; Step S13: sensing the magnetic field of the remote control device according to the Hall sensor deployment data to generate the magnetic field sensing data of the remote control device; calibrating the working magnetic field direction of the Hall sensor deployment data according to the magnetic field sensing data of the remote control device to generate the Hall sensor deployment correction data; Step S14: perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the original magnetic field detection signal of the remote control device; perform signal preprocessing on the original magnetic field detection signal of the remote control device to generate a standard remote control device magnetic field detection signal, wherein the signal preprocessing includes signal denoising, signal enhancement and signal normalization.

3. The remote control method based on low power consumption Hall sensor according to claim 2, characterized in that: Calibration of the sensor working magnetic field direction based on the Hall sensor deployment data according to the magnetic field sensing data of the remote control device includes: Confirm the sensor installation coordinates of the Hall sensor deployment data to obtain the sensor installation coordinate data; perform installation angle analysis on the Hall sensor deployment data based on the sensor installation coordinate data to generate the sensor installation angle data; Extracting a single Hall sensor sensing data from the remote control device magnetic field sensing data to obtain Hall sensor sensing data, wherein the Hall sensor sensing data extraction includes magnetic field intensity extraction, magnetic field direction extraction, and magnetic field change extraction; based on the Hall sensor sensing data, the lateral magnetic field orientation is aggregated and compared to generate lateral magnetic field orientation aggregated data; The installation orientation of the Hall sensor deployment data is adjusted according to the lateral magnetic field orientation aggregation data to generate the Hall sensor deployment correction data.

4. The remote control method based on low power consumption Hall sensor according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting magnetic field multi-dimensional features from the magnetic field detection signal of the standard remote control device to obtain multi-dimensional feature data of the magnetic field of the remote control device, wherein the multi-dimensional feature data of the magnetic field of the remote control device includes magnetic field intensity feature data, magnetic field change rate feature data and magnetic field timestamp data; Step S22: performing spatial information association on the magnetic field intensity characteristic data, the magnetic field change rate characteristic data and the magnetic field timestamp data based on the sensor installation coordinate data to generate remote control device magnetic field spatial association data; performing lightweight signal decoding on the standard remote control device magnetic field detection signal according to the remote control device magnetic field spatial association data to generate remote control device lightweight decoding data; Step S23: performing remote control mode recognition on the lightweight decoded data of the remote control device to generate remote control control intention data; performing signal decoding optimization on the lightweight decoded data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data.

5. The remote control method based on low power consumption Hall sensor according to claim 4, characterized in that: Lightweight signal decoding of standard remote control device magnetic field detection signals based on remote control device magnetic field spatial correlation data includes: Perform fast Fourier transform on the magnetic field detection signal of the standard remote control device according to the spatial correlation data of the magnetic field of the remote control device to generate the frequency domain data of the magnetic field of the remote control device; perform signal morphology analysis on the frequency domain data of the magnetic field of the remote control device to generate a decoding feature data set; A dictionary is constructed for the magnetic field detection signal of the standard remote control device by decoding the feature data set to generate a signal representation dictionary; based on the signal representation dictionary, the decoded feature data set is signal sparsified to generate sparse remote control device signal data, where the signal sparsification formula is as follows: In the formula, Represented as a decoding feature dataset, Represented as a matrix of basis vectors in the dictionary, Represented as a sparse coefficient vector, It is represented as reconstruction error; Based on the L1 norm, the sparse remote control device signal data is minimized to obtain a sparse coefficient matrix, where the formula for minimizing the target calculation is as follows: In the formula, Represented as a sparse coefficient matrix, is represented as the regularization parameter; The sparse coefficient matrix is ​​used to optimize the signal decoding of the lightweight decoding data of the remote control device to generate optimized decoding control signal data.

6. The remote control method based on low power consumption Hall sensor according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: extracting dynamic change features from the remote control device lightweight decoded data to obtain remote control device lightweight decoded dynamic change feature data; dividing the remote control device lightweight decoded dynamic change feature data into data sets to generate a model training set and a model test set; Step S232: training the model training set according to the recurrent neural network algorithm to generate a remote control mode recognition pre-model; optimizing and iterating the remote control mode recognition pre-model based on the model test set to generate a remote control mode recognition model; importing the remote control device lightweight decoding data into the remote control mode recognition model for label mapping to generate operation label mapping data; Step S233: classify the operation label mapping data into operation categories to obtain remote control control intention data; perform signal modulation on the remote control device lightweight decoding data based on the remote control control intention data to generate optimized decoding control signal data.

7. The remote control method based on low power consumption Hall sensor according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: encapsulating the optimized decoded control signal data into a data packet to generate a decoded control signal packet; performing signal packet remote transmission on the decoded control signal packet based on a low-power communication protocol to generate decoded control signal packet transmission data; Step S32: performing receiving device instruction execution on the decoded control signal packet transmission data to obtain receiving device instruction execution data; performing instruction response time analysis on the receiving device instruction execution data to generate the receiving device instruction execution response time; Step S33: Optimize the response delay of the receiving device instruction execution data by the receiving device instruction execution response time, and generate a response optimization instruction.

8. The remote control method based on low power consumption Hall sensor according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: identifying response influencing factors of the received device instruction execution data by receiving the device instruction execution response time, and generating response influencing factor identification data, wherein the response influencing factor identification includes network delay identification, device performance identification and environmental interference identification; Step S332: performing a minimum delay optimization target setting on the instruction execution response time of the receiving device according to the response influencing factor identification data, and obtaining the minimum delay optimization target setting data; performing a network path optimization on the instruction execution data of the receiving device based on the minimum delay optimization target setting data, and generating the network path optimization data; Step S333: Utilize the network path optimization data to optimize the response delay of the receiving device instruction execution data and generate a response optimization instruction.

9. The remote control method based on low power consumption Hall sensor according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing device communication interconnection on the receiving device instruction execution data based on the response optimization instruction to generate multi-device collaborative control interconnection data; performing task coordination and resource allocation on the multi-device collaborative control interconnection data to generate multi-device collaborative control task allocation data; Step S42: performing execution status feedback on the multi-device collaborative control task allocation data to generate execution status feedback data; performing remote control completion rate evaluation on the execution status feedback data to generate the remote control completion rate; Step S43: Compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is lower than the preset remote control standard completion rate, the multi-device collaborative control task allocation data is adaptively adjusted until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

10. A remote control device based on a low-power Hall sensor, characterized in that: Used to execute the remote control method based on the low power consumption Hall sensor as claimed in claim 1, the remote control device based on the low power consumption Hall sensor comprises: The sensor deployment module is used to obtain the remote control device type data; deploy the Hall sensor based on the remote control device type data to generate the Hall sensor deployment data; calibrate the sensor working magnetic field direction on the Hall sensor deployment data to generate the Hall sensor deployment correction data; perform real-time signal acquisition on the Hall sensor deployment correction data to obtain the standard remote control device magnetic field detection signal; The decoding optimization module is used to perform lightweight signal decoding on the magnetic field detection signal of the standard remote control device to generate lightweight decoding data of the remote control device; perform remote control mode recognition on the lightweight decoding data of the remote control device to generate remote control control intention data; perform signal decoding optimization on the lightweight decoding data of the remote control device based on the remote control operation intention data to generate optimized decoding control signal data; The response optimization module is used to perform signal packet remote transmission of the optimized decoded control signal data to generate decoded control signal packet transmission data; perform command reception response analysis on the decoded control signal packet transmission data to generate receiving device command execution data and receiving device command execution response time; perform response delay optimization on the receiving device command execution data through the receiving device command execution response time to generate response optimization instructions; The control comparison module is used to evaluate the remote control completion rate of the receiving device instruction execution data based on the response optimization instruction to generate the remote control completion rate; compare the remote control completion rate with the preset remote control standard completion rate. When the remote control completion rate is less than the preset remote control standard completion rate, the multi-device collaborative control task allocation data is adaptively adjusted until the remote control completion rate is greater than or equal to the preset remote control standard completion rate to execute the remote control operation.

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