Pull rope displacement sensor and control method thereof

Abstract

The invention relates to the field of encoder control methods, in particular to a control method of a pull rope displacement sensor, which comprises the following steps that: before control is started, an encoder automatically identifies environments and requirements through a built-in intelligent algorithm, and configures optimal parameters; during operation, the signals are subjected to dynamic enhancement and intelligent filtering optimization, and noise is eliminated; fusing internal and external data in real time, and predicting a motion trend by utilizing machine learning; the self-learning algorithm continuously optimizes coding correction and adapts to environmental changes; a coding instruction is intelligently generated and verified based on data prediction; and according to real-time feedback, a control strategy is dynamically adjusted, self-adaptive optimization is realized, and changing working environments and task requirements are adapted.

Description

Technical Field

[0001] The present application relates to the field of encoder control methods, and in particular to a rope displacement sensor and a control method thereof. Background Art

[0002] The encoder is one of the core components of the cable displacement sensor, which is used to convert the rotational motion of the inner wheel into a signal to measure the displacement. The accuracy and stability of the encoder directly affect the overall performance of the sensor. In the cable displacement sensor, the encoder is coaxially arranged with the inner wheel of the cable device. When the cable is extended and retracted under the action of the external mechanism, the inner wheel rotates accordingly, and the encoder is driven to rotate synchronously. The displacement of the external mechanism is calculated by the number of rotations of the encoder.

[0003] Although encoders play an important role in rope-type displacement sensors, existing control technologies still have some limitations. For example, the resolution of the encoder may limit the measurement accuracy of the sensor, especially in situations where extremely high-precision measurements are required. In addition, the response speed and stability of the encoder may also be restricted by the control algorithm, especially in high-speed motion or vibration environments, where the performance of the encoder may degrade.

[0004] In order to overcome the above limitations and improve the performance of the cable displacement sensor, the control method of the encoder becomes the key. The existing control method may not effectively solve the problems of accuracy, response speed and stability. Summary of the invention

[0005] In order to improve the performance of a draw-wire displacement sensor, the present application provides a draw-wire displacement sensor and a control method thereof.

[0006] In a first aspect, the present application provides a control method for a pull-wire displacement sensor, comprising the following steps: Before control begins, the encoder automatically identifies its working environment and application requirements through built-in intelligent algorithms, and automatically configures the best working mode and parameters; The signal generated by the encoder during rotation is optimized through dynamic signal enhancement technology, and intelligent filtering algorithms are used to eliminate noise interference; The position and speed data generated by the encoder are integrated with the external sensor data in real time, and intelligent prediction is performed through machine learning algorithms to predict movement trends in advance. During operation, the encoder continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts the encoding parameters to adapt to different working conditions and environmental changes; Based on real-time data and prediction results, intelligently generate coding instructions and perform security verification at the same time; Based on real-time feedback information, the control strategy is dynamically adjusted to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements.

[0007] Optionally, before the control starts, the encoder automatically identifies its working environment and application requirements through a built-in intelligent algorithm and automatically configures the best working mode and parameters, including the following steps: The encoder collects environmental data through built-in sensors, and intelligent algorithms analyze application requirements, providing a basis for adaptive configuration; According to the results of environmental perception and demand identification, the best working mode and parameters are matched from the parameter library for preliminary optimization; During the initialization process, feedback information is collected in real time and parameters are adjusted dynamically to ensure the best configuration; Confirm the adaptive configuration result and the encoder automatically enters the working state.

[0008] Optionally, the electrical signal generated by the encoder during rotation is optimized by a dynamic signal enhancement technique, and an intelligent filtering algorithm is used to eliminate noise interference, including the following steps: Evaluate signal quality in real time and optimize signal strength through dynamic signal enhancement technology; Analyze noise characteristics and intelligently select the most appropriate filtering algorithm to eliminate specific types of noise; Adaptively adjust the filtering parameters according to the real-time changes of signals and noise to ensure the filtering effect; Verify signal purity and further optimize signal processing strategies based on the verification results to ensure signal purity and stability.

[0009] Optionally, the position and speed data generated by the encoder are integrated with the external sensor data in real time, and intelligent prediction is performed through a machine learning algorithm to predict the motion trend in advance, including the following steps: Collect data from encoders and external sensors and perform pre-processing; Intelligently select data fusion algorithms based on data types and application requirements to achieve effective data fusion; Based on the fused data, a real-time prediction model is built and trained through machine learning algorithms to improve prediction accuracy; Verify the accuracy of the prediction results and apply them to the control strategy.

[0010] Optionally, during operation, the encoder continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts the encoding parameters to adapt to different working conditions and environmental changes, including the following steps: Real-time detection of coding errors, analysis of error sources, and providing a basis for correction strategies; According to the error analysis results, the self-learning algorithm is intelligently selected to automatically adjust the encoding parameters; During operation, the encoding parameters are dynamically optimized based on real-time feedback information; Evaluate the correction effect and perform iterative optimization based on the evaluation results.

[0011] Optionally, the intelligent generation of coding instructions based on real-time data and prediction results and the simultaneous security verification include the following steps: Analyze real-time data and intelligently generate coded instructions to ensure the correctness and timeliness of instructions; Assess the safety risks of instructions to ensure that the instructions do not cause potential harm to the system or environment; Conduct security verification to confirm the safety and effectiveness of instructions and avoid potential control risks; Execute coded instructions and collect execution feedback to ensure correct execution of instructions and system stability.

[0012] Optionally, dynamically adjusting the control strategy according to the real-time feedback information to achieve adaptive optimization to adapt to the changing working environment and task requirements includes the following steps: Collect real-time feedback information, analyze control effects, and provide a basis for strategy adjustments; Evaluate the effectiveness of the current control strategy, optimize it based on the analysis results, and improve control efficiency; Based on real-time feedback and optimization results, control strategies that adapt to the current environment are automatically generated to achieve adaptive optimization; Verify the effectiveness of the control strategy and perform iterative optimization based on the verification results to ensure continuous improvement and adaptability of the control strategy.

[0013] In a second aspect, the present application provides a draw-wire displacement sensor, and the control method of the draw-wire displacement sensor is applied to the draw-wire displacement sensor, and the following technical solution is adopted: The utility model comprises an encoder and a rope-pulling device, wherein the encoder is installed on the side wall of the rope-pulling device, and the inner wheel of the rope-pulling device is coaxially arranged with the axis of the encoder.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method for a pull-wire displacement sensor as described in any one of the above items are implemented.

[0015] In a fourth aspect, the present application provides a computer storage medium, which adopts the following technical solution: A computer storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method of a pull-wire displacement sensor as described in any one of the above items are implemented.

[0016] In summary, this application includes the following beneficial technical effects: 1. Through intelligent initialization, dynamic signal enhancement and intelligent filtering, as well as self-learning coding correction and other steps, the measurement accuracy of the sensor and the reliability of signal processing are significantly improved, ensuring stable performance in various complex environments; 2. Real-time data fusion and intelligent prediction technology, combined with dynamic control strategy adjustment and adaptive optimization, enable the sensor to intelligently predict movement trends, adaptively adjust control strategies, achieve more precise control and higher flexibility, and adapt to the ever-changing working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a diagram showing the overall structure of a pull-wire displacement sensor in an embodiment of the present application.

[0018] Figure 2 It is an exploded schematic diagram of a pull-wire displacement sensor in an embodiment of the present application.

[0019] Figure 3 It is a flow chart of a control method of a pull-wire displacement sensor in an embodiment of the present application.

[0020] Figure 4 It is a flowchart of the sub-steps of step S1 in the embodiment of the present application.

[0021] Figure 5 It is a flowchart of the sub-steps of step S2 in the embodiment of the present application.

[0022] Figure 6 It is a flowchart of the sub-steps of step S3 in the embodiment of the present application.

[0023] Figure 7 It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0024] Figure 8 It is a flowchart of the sub-steps of step S5 in the embodiment of the present application.

[0025] Fig. 9 It is a flowchart of the sub-steps of step S6 in the embodiment of the present application.

[0026] Explanation of the reference numerals: 1. Encoder; 2. Rope pulling device; 21. Housing; 22. Drum wheel; 23. Rope pulling; 24. Spring mechanism. DETAILED DESCRIPTION

[0027] The following is combined with Figure 1-Figure 9 This application is described in further detail.

[0028] Reference Figure 1 and Figure 2The embodiment of the present application discloses a drawstring displacement sensor, including an encoder 1 and a drawstring 23 device 2. The encoder 1 is mounted on the side wall of the drawstring 23 device 2. In the present embodiment, the encoder 1 adopts a Kubler encoder 1. The drawstring 23 device 2 consists of a housing 21, a drum 22, a drawstring 23 and a spring mechanism 24. The drum 22 is rotatably carried inside the housing 21. One end of the drawstring 23 is fixed to the drum 22, and the other end of the drawstring 23 extends out of the housing 21. At the same time, the spring mechanism 24 is fixedly connected to the drawstring 23 to provide a force for the drawstring 23 to retract, ensuring that the drawstring 23 can automatically retract into the housing 21 after the measurement is completed. In addition, the drum 22 is connected to the encoder 1, and the rotational displacement of the drum 22 is directly converted into an electrical signal output of the encoder 1.

[0029] Reference Figure 3 This embodiment also provides a control method for a draw-wire displacement sensor, which is applied to the draw-wire displacement sensor, and includes the following steps: S1: Before control starts, encoder 1 automatically identifies its working environment and application requirements through built-in intelligent algorithms, and automatically configures the best working mode and parameters.

[0030] Reference Figure 4 , step S1 also includes the following sub-steps.

[0031] S11: Encoder 1 collects environmental data through built-in sensors, and intelligent algorithms analyze application requirements to provide a basis for adaptive configuration.

[0032] S12: According to the results of environmental perception and demand identification, the best working mode and parameters are matched from the parameter library for preliminary optimization.

[0033] S13: During the initialization process, feedback information is collected in real time and parameters are adjusted dynamically to ensure the optimal configuration.

[0034] S14: Confirm the adaptive configuration result, and encoder 1 automatically enters the working state.

[0035] Correspondingly, for steps S11-S14, the embodiment of the present application provides the following examples.

[0036] First, assume that the pull-wire displacement sensor is deployed outdoors at a construction site to monitor the displacement of heavy machinery. The built-in environmental sensors (such as temperature and humidity sensors) of encoder 1 collect current environmental data, and the intelligent algorithm analyzes this data and the sensor's usage history to identify current application requirements (such as high precision and anti-interference ability). Based on this information, the algorithm matches the best working mode and parameters from the parameter library, such as selecting a high-precision mode and adding anti-interference settings, laying the foundation for adaptive configuration.

[0037] Based on the environmental data and application requirements in the previous step, the intelligent algorithm selects the best working mode suitable for the outdoor construction site environment from the parameter library. For example, select "high-precision anti-interference mode" and adjust the resolution, sampling frequency and anti-interference filter parameters of encoder 1 to optimize the performance of the sensor in the current environment.

[0038] Furthermore, at the initial start-up of the sensor, encoder 1 monitors the stability and accuracy of its output signal in real time, and collects feedback information from external devices (such as signal quality reports from data acquisition systems). If it is found that the signal is interfered with or the accuracy is insufficient, encoder 1 will dynamically adjust parameters, such as increasing the filtering strength and adjusting the sampling frequency, to ensure the stability and accuracy of the output signal and achieve the best configuration state.

[0039] After the adaptive configuration in the above steps, encoder 1 confirms that all parameters have been adjusted to the optimal state to meet the current environment and application requirements. At this time, encoder 1 automatically enters a stable working state and begins to accurately and stably measure the displacement of heavy machinery, while continuously monitoring environmental changes and equipment status, and preparing to make further adaptive adjustments when necessary.

[0040] Replay Figure 3 , the following steps are also included after step S1.

[0041] S2: The electrical signal generated by encoder 1 during rotation is optimized through dynamic signal enhancement technology, and an intelligent filtering algorithm is used to eliminate noise interference.

[0042] Reference Figure 5 , step S2 correspondingly includes the following sub-steps.

[0043] S21: Evaluates signal quality in real time and optimizes signal strength through dynamic signal enhancement technology.

[0044] S22: Analyze noise characteristics and intelligently select the most appropriate filtering algorithm to eliminate specific types of noise.

[0045] S23: Adaptively adjust the filtering parameters according to the real-time changes of the signal and noise to ensure the filtering effect.

[0046] S24: Verify signal purity and further optimize signal processing strategy based on the verification results to ensure signal purity and stability.

[0047] Correspondingly, for steps S21-S24, the embodiment of the present application provides the following examples.

[0048] Assume that in an industrial automation production line, a pull-wire displacement sensor is used to monitor the precise displacement of a robotic arm. The signal output by the sensor may be affected by noise such as electromagnetic interference and mechanical vibration. The system that evaluates signal quality in real time continuously monitors the signal-to-noise ratio (SNR) of the signal. Once a signal quality degradation is detected, such as when the SNR is below a preset threshold, the system will automatically initiate dynamic signal enhancement technology, such as increasing the gain of the amplifier or adjusting the parameters of the pre-filter, to enhance the signal strength and ensure signal clarity and reliability.

[0049] In the above scenario, the system needs to identify the type of noise, such as Gaussian white noise, periodic noise, or impulse noise. By analyzing the spectral characteristics of the noise, the system can intelligently select the most appropriate filtering algorithm. For example, for Gaussian white noise, Kalman filtering can be used; for periodic noise, a bandstop filter can be used; for impulse noise, a median filter can be used. This intelligent selection ensures the best match between the filtering algorithm and the noise type, thereby effectively eliminating the noise.

[0050] In a dynamic environment, the characteristics of noise may change over time. Adaptive filtering technology can automatically adjust the filter parameters, such as the filter's cutoff frequency, gain, etc., according to the real-time changes of the signal and noise. For example, when the frequency of periodic noise is detected to change, the adaptive filter will automatically adjust its cutoff frequency to maintain effective suppression of the noise while minimizing the impact on the signal.

[0051] At the final stage of signal processing, the system verifies the purity of the signal, that is, checks whether there is any residual noise or distortion in the signal. If the signal purity does not meet the preset standard, the system will further optimize the signal processing strategy based on the verification results. For example, if residual low-frequency noise is found in the signal, the system may increase the order of the low-pass filter or adjust its parameters to further eliminate noise and ensure the purity and stability of the signal.

[0052] Replay Figure 3 , after step S2, the following steps are also included.

[0053] S3: The position and speed data generated by encoder 1 are integrated with the external sensor data in real time, and intelligent prediction is performed through machine learning algorithms to predict the movement trend in advance.

[0054] Reference Figure 6 , step S3 correspondingly includes the following steps.

[0055] S31: Collect data from encoder 1 and external sensors and perform preprocessing.

[0056] S32: Intelligently select data fusion algorithms based on data types and application requirements to achieve effective data fusion.

[0057] S33: Based on the fused data, a real-time prediction model is built and trained through machine learning algorithms to improve prediction accuracy.

[0058] S34: Verify the accuracy of the prediction results and apply the prediction results to the control strategy.

[0059] Correspondingly, steps S31-S34 are described below with examples.

[0060] Specifically, when the pull-wire displacement sensor is working, encoder 1 will continuously collect displacement data, and external sensors (such as temperature, humidity, and vibration sensors) will also collect environmental data. The preprocessing stage includes data cleaning, format conversion, and standardization to ensure data quality. For example, outliers are removed, missing data is filled, and data is converted into a unified format to prepare for subsequent data fusion and analysis.

[0061] Based on the type of displacement data and environmental data, the intelligent algorithm will select the most suitable data fusion method. For example, if the displacement data is affected by environmental factors (such as temperature changes), algorithms such as Kalman filtering or particle filtering can be used to fuse the displacement data with the temperature data to correct the error in displacement measurement. Data fusion ensures that the displacement information output by the sensor is more accurate and reliable.

[0062] Based on the fused data, a real-time prediction model is constructed to predict the future state or displacement trend of the displacement sensor. For example, machine learning algorithms such as support vector machines (SVM), neural networks, or random forests can be used to train models based on historical displacement data and environmental data to predict displacement changes under specific environmental conditions. Through continuous training and optimization, the prediction model can continuously improve the prediction accuracy and provide more accurate prediction results for real-time control strategies.

[0063] The displacement prediction results generated by the prediction model need to be verified to ensure their accuracy. This is usually done by comparing with the actual displacement data. If the prediction results are highly consistent with the actual data, it means that the model prediction effect is good. Based on the verified prediction results, they can be applied to the control strategy. For example, when it is predicted that the displacement will exceed the safety threshold, the motion trajectory of the robot arm can be adjusted in advance to avoid collision or overload, thereby improving production efficiency and safety.

[0064] Replay Figure 3 , after step S3, the following steps are also included.

[0065] S4: During operation, encoder 1 continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts encoding parameters to adapt to different working conditions and environmental changes.

[0066] Reference Figure 7, step S4 correspondingly includes the following sub-steps.

[0067] S41: Detect coding errors in real time, analyze the sources of errors, and provide a basis for correction strategies.

[0068] S42: According to the error analysis results, intelligently select a self-learning algorithm and automatically adjust the encoding parameters.

[0069] S43: During the operation, the encoding parameters are dynamically optimized according to the real-time feedback information.

[0070] S44: Evaluate the correction effect and perform iterative optimization based on the evaluation results.

[0071] Correspondingly, for steps S41-S44, the embodiment of the present application provides the following examples.

[0072] Assume that when the cable displacement sensor is working, the difference between the displacement data output by encoder 1 and the actual displacement, i.e., the encoding error, is monitored in real time. Through the built-in self-checking system, the sources of the error are analyzed, such as mechanical wear, environmental changes (temperature, humidity), electronic noise, etc. For example, if the encoding error is detected to change with temperature, this indicates that temperature is one of the main sources of error. These analysis results provide a basis for subsequent correction strategies to ensure that the correction measures can be targeted at specific problems.

[0073] Based on the analysis of the error source, the intelligent algorithm will select the most suitable self-learning algorithm, such as online learning neural network, support vector regression (SVR) or adaptive filtering algorithm, to automatically adjust the parameters of encoder 1. For example, if temperature is the main error source, the algorithm will adjust the gain, offset and other parameters of encoder 1 in real time according to the temperature change to compensate for the impact of temperature change on displacement measurement. This self-learning mechanism enables encoder 1 to adapt to environmental changes and improve measurement accuracy.

[0074] Furthermore, during the operation of the sensor, the system will continue to collect real-time feedback information, such as encoding errors, changes in environmental parameters, etc., for dynamic optimization of encoding parameters. For example, if the error caused by mechanical wear is detected to be gradually increasing during operation, the system will automatically adjust the compensation parameters of encoder 1, such as increasing the strength of the filter, to reduce the impact of the error. This dynamic optimization ensures that encoder 1 can continue to provide accurate displacement data in a changing environment.

[0075] Finally, after the correction strategy is implemented, the system will evaluate the correction effect, that is, whether the corrected encoding error is within an acceptable range. If the evaluation results show that the error is still high, the system will iteratively optimize according to the evaluation results, adjust the parameters of the self-learning algorithm or select a more effective correction strategy. For example, if it is found that the current self-learning algorithm is not effective in handling a specific type of error, the system may try to use a different algorithm or increase the complexity of the algorithm to improve the correction effect.

[0076] Replay Figure 3 , after step S4, the following steps are also included.

[0077] S5: Based on real-time data and prediction results, intelligently generate coding instructions and perform security verification at the same time.

[0078] Reference Figure 8 , step S5 correspondingly includes the following sub-steps.

[0079] S51: Analyze real-time data and intelligently generate coded instructions to ensure the correctness and timeliness of the instructions.

[0080] S52: Evaluate the safety risks of instructions to ensure that they do not cause potential harm to the system or environment.

[0081] S53: Perform security verification to confirm the safety and effectiveness of the instructions and avoid potential control risks.

[0082] S54: Execute coded instructions and collect execution feedback to ensure correct execution of instructions and system stability.

[0083] Correspondingly, for steps S51-S54, the embodiment of the present application provides the following examples.

[0084] Specifically, when the pull-wire displacement sensor monitors displacement data in real time, the system will parse the data and intelligently generate corresponding coded instructions. For example, if the sensor detects that the robot arm needs to move from the current position to the target position, the system will intelligently generate accurate coded instructions based on the real-time displacement data and the preset displacement target, instructing encoder 1 how to adjust the displacement to ensure that the robot arm can reach the target position accurately. At the same time, the system will ensure the timeliness of the instructions, that is, generate and send instructions immediately after the data is parsed to achieve a quick response.

[0085] Furthermore, after generating the coded instructions, the system will conduct a safety risk assessment to check whether the instructions may cause potential harm to the system or the environment. For example, if the instruction requires the robot arm to move a large amount in a short period of time, the system will evaluate whether this may cause the robot arm to overload or collide with surrounding objects. If potential risks are detected, the system will adjust the instructions, such as reducing the displacement amplitude or increasing the displacement time, to ensure the safety of the operation.

[0086] In addition, before the instruction is executed, the system will perform safety verification to confirm the safety and effectiveness of the instruction. For example, the system will simulate the results of the instruction execution to check whether it will cause the robot arm to exceed the safety range or violate the operating procedures. If the verification result shows that the instruction has potential risks, the system will prevent the execution of the instruction and require the instruction to be regenerated or adjusted to avoid potential control risks.

[0087] Finally, after confirming that the instruction is safe and effective, the system will execute the encoded instruction and collect feedback information during the execution process, such as actual displacement data, execution time, system status, etc. This feedback information is used to monitor the execution of the instruction to ensure that the instruction is executed correctly, and to monitor the system status to ensure stable operation of the system. For example, if an abnormality is detected during the execution process, such as excessive displacement deviation or system response delay, the system will immediately take measures, such as adjusting the instruction or suspending execution, to avoid potential system failures.

[0088] Replay Figure 3 , after step S5, the following steps are also included.

[0089] S6: Dynamically adjust the control strategy based on real-time feedback information to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements.

[0090] Corresponding, refer to Fig. 9 , step S6 correspondingly includes the following steps.

[0091] S61: Collect real-time feedback information, analyze control effects, and provide a basis for strategy adjustments.

[0092] S62: Evaluate the effectiveness of the current control strategy, optimize it based on the analysis results, and improve control efficiency.

[0093] S63: Based on real-time feedback and optimization results, automatically generate control strategies that adapt to the current environment to achieve adaptive optimization.

[0094] S64: Verify the effectiveness of the control strategy and perform iterative optimization based on the verification results to ensure continuous improvement and adaptability of the control strategy.

[0095] Correspondingly, for steps S61-S64, the embodiment of the present application provides the following examples.

[0096] Specifically, when the cable displacement sensor is working, it will continuously collect real-time feedback information, such as displacement data, response time for executing instructions, system status, etc. The system will analyze this feedback information to evaluate the effectiveness of the current control strategy. For example, if the sensor detects that the accuracy of displacement measurement decreases under certain environmental conditions (such as temperature changes or increased load), this indicates that the current control strategy may need to be adjusted to adapt to environmental changes. These analysis results provide a basis for strategy adjustments to ensure that the control strategy can be optimized for specific problems.

[0097] Furthermore, based on the analysis of real-time feedback information, the system will evaluate the effectiveness of the current control strategy, such as the accuracy, response speed, and energy consumption of the displacement control. If the analysis results show that the control efficiency is lower than expected, the system will optimize according to the analysis results. For example, if it is found that the accuracy of the displacement control decreases under high load conditions, the system may adjust the gain parameters of encoder 1, or introduce more complex control algorithms, such as fuzzy control or adaptive control, to improve the control accuracy and response speed under high load conditions.

[0098] In addition, after collecting real-time feedback information and optimizing it, the system automatically generates a control strategy that adapts to the current environment. For example, if the real-time feedback information shows that the ambient temperature has increased, the system will automatically generate a new control strategy, such as adjusting the temperature compensation parameters of encoder 1, or enabling an additional cooling system to ensure the accuracy and stability of displacement measurement in a high-temperature environment. This adaptive optimization mechanism enables the cable-drawn displacement sensor to automatically adjust the control strategy according to environmental changes, improving control efficiency and adaptability.

[0099] Furthermore, after implementing a new control strategy, the system will verify the effectiveness of the strategy, that is, evaluate whether the accuracy, response speed, and stability of the displacement control meet expectations. If the verification results show that the control strategy is still insufficient, the system will iteratively optimize according to the verification results, adjust the control parameters, or introduce more advanced control algorithms. For example, if it is found that the control strategy is not effective under certain environmental conditions, the system may try to use machine learning algorithms, such as deep learning or reinforcement learning, to improve the adaptability and robustness of the control strategy.

[0100] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a control method of a pull-wire displacement sensor is implemented: S1: Before control starts, encoder 1 automatically identifies its working environment and application requirements through built-in intelligent algorithms, and automatically configures the best working mode and parameters.

[0101] S2: The signal generated by encoder 1 during rotation is optimized through dynamic signal enhancement technology, and an intelligent filtering algorithm is used to eliminate noise interference.

[0102] S3: The position and speed data generated by encoder 1 are integrated with the external sensor data in real time, and intelligent prediction is performed through machine learning algorithms to predict the movement trend in advance.

[0103] S4: During operation, encoder 1 continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts encoding parameters to adapt to different working conditions and environmental changes.

[0104] S5: Based on real-time data and prediction results, intelligently generate coding instructions and perform security verification at the same time.

[0105] S6: Dynamically adjust the control strategy based on real-time feedback information to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements.

[0106] When the computer program is executed by a processor, it can implement any control method of the pull-wire displacement sensor in the above method embodiments.

[0107] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Before control starts, encoder 1 automatically identifies its working environment and application requirements through built-in intelligent algorithms, and automatically configures the best working mode and parameters.

[0108] S2: The electrical signal generated by encoder 1 during rotation is optimized through dynamic signal enhancement technology, and an intelligent filtering algorithm is used to eliminate noise interference.

[0109] S3: The position and speed data generated by encoder 1 are integrated with the external sensor data in real time, and intelligent prediction is performed through machine learning algorithms to predict the movement trend in advance.

[0110] S4: During operation, encoder 1 continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts encoding parameters to adapt to different working conditions and environmental changes.

[0111] S5: Based on real-time data and prediction results, intelligently generate coding instructions and perform security verification at the same time.

[0112] S6: Dynamically adjust the control strategy based on real-time feedback information to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements.

[0113] When the computer program is executed by a processor, it can implement any control method of the pull-wire displacement sensor in the above method embodiments.

[0114] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), programmable ROM (EPROM), erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0115] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0116] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A control method for a pull-wire displacement sensor, characterized in that: The following steps are involved: Before control begins, the encoder (1) automatically identifies its working environment and application requirements through a built-in intelligent algorithm and automatically configures the best working mode and parameters; The signal generated by the encoder (1) during rotation is optimized by dynamic signal enhancement technology, and an intelligent filtering algorithm is used to eliminate noise interference; The position and speed data generated by the encoder (1) are integrated with the external sensor data in real time, and intelligent prediction is performed through machine learning algorithms to predict the movement trend in advance; During operation, the encoder (1) continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts the encoding parameters to adapt to different working conditions and environmental changes; Based on real-time data and prediction results, intelligently generate coding instructions and perform security verification at the same time; Based on real-time feedback information, the control strategy is dynamically adjusted to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements.

2. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: Before the control starts, the encoder (1) automatically identifies its working environment and application requirements through a built-in intelligent algorithm and automatically configures the best working mode and parameters, including the following steps: The encoder (1) collects environmental data through built-in sensors, and the intelligent algorithm analyzes application requirements to provide a basis for adaptive configuration; According to the results of environmental perception and demand identification, the best working mode and parameters are matched from the parameter library for preliminary optimization; During the initialization process, feedback information is collected in real time and parameters are adjusted dynamically to ensure the best configuration; After confirming the adaptive configuration result, the encoder (1) automatically enters the working state.

3. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: The signal generated by the encoder (1) during rotation is optimized by a dynamic signal enhancement technology, and noise interference is eliminated by an intelligent filtering algorithm, comprising the following steps: Evaluate signal quality in real time and optimize signal strength through dynamic signal enhancement technology; Analyze noise characteristics and intelligently select the most appropriate filtering algorithm to eliminate specific types of noise; Adaptively adjust the filtering parameters according to the real-time changes of signals and noise to ensure the filtering effect; Verify signal purity and further optimize signal processing strategies based on the verification results to ensure signal purity and stability.

4. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: The method of fusing the position and speed data generated by the encoder (1) with the external sensor data in real time, performing intelligent prediction through a machine learning algorithm, and predicting the motion trend in advance comprises the following steps: Collect data from encoders (1) and external sensors and perform pre-processing; Intelligently select data fusion algorithms based on data types and application requirements to achieve effective data fusion; Based on the fused data, a real-time prediction model is built and trained through machine learning algorithms to improve prediction accuracy; Verify the accuracy of the prediction results and apply them to the control strategy.

5. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: During operation, the encoder (1) continuously optimizes the encoding correction strategy through a self-learning algorithm and automatically adjusts the encoding parameters to adapt to different working conditions and environmental changes, including the following steps: Real-time detection of coding errors, analysis of error sources, and providing a basis for correction strategies; According to the error analysis results, the self-learning algorithm is intelligently selected to automatically adjust the encoding parameters; During operation, the encoding parameters are dynamically optimized based on real-time feedback information; Evaluate the correction effect and perform iterative optimization based on the evaluation results.

6. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: The method of intelligently generating coding instructions based on real-time data and prediction results and performing security verification at the same time includes the following steps: Analyze real-time data and intelligently generate coded instructions to ensure the correctness and timeliness of instructions; Assess the safety risks of instructions to ensure that they do not cause potential harm to the system or environment; Conduct security verification to confirm the safety and effectiveness of instructions and avoid potential control risks; Execute coded instructions and collect execution feedback to ensure correct execution of instructions and system stability.

7. The control method of a pull-wire displacement sensor according to claim 1, characterized in that: The method of dynamically adjusting the control strategy according to the real-time feedback information to achieve adaptive optimization to adapt to the ever-changing working environment and task requirements includes the following steps: Collect real-time feedback information, analyze control effects, and provide a basis for strategy adjustments; Evaluate the effectiveness of the current control strategy, optimize it based on the analysis results, and improve control efficiency; Based on real-time feedback and optimization results, control strategies that adapt to the current environment are automatically generated to achieve adaptive optimization; Verify the effectiveness of the control strategy and perform iterative optimization based on the verification results to ensure continuous improvement and adaptability of the control strategy.

8. A draw-wire displacement sensor, wherein the control method of a draw-wire displacement sensor according to any one of claims 1 to 7 is applied to the draw-wire displacement sensor, characterized in that: The invention comprises an encoder (1) and a pull rope (23) device (2), wherein the encoder (1) is mounted on the side wall of the pull rope (23) device (2), and the inner wheel of the pull rope (23) device (2) is coaxially arranged with the axis of the encoder (1).

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the control method of a pull-wire displacement sensor as described in any one of claims 1 to 7 are implemented.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method of a rope displacement sensor according to any one of claims 1 to 7 are implemented.

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