Pull cord displacement sensor and control method thereof

Abstract

The application 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: before control starts, an encoder automatically identifies an environment and requirements through a built-in intelligent algorithm, and optimal parameters are configured; in operation, signals are dynamically enhanced and intelligently filtered and optimized to eliminate noise; internal and external data are fused in real time, and a motion trend is predicted by using machine learning; a self-learning algorithm continuously optimizes encoding correction and adapts to environmental changes; based on data prediction, encoding instructions are intelligently generated and verified; according to real-time feedback, a control strategy is dynamically adjusted to realize adaptive optimization and adapt to changing working environments and task requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of encoder control methods, particularly to a pull rope displacement sensor and its control method. BACKGROUND

[0002] The encoder is one of the core components of the pull rope displacement sensor, used to convert the rotational motion of the inner wheel into a signal, thereby realizing the measurement of displacement. The accuracy and stability of the encoder directly affect the overall performance of the sensor. In the pull rope displacement sensor, the encoder is coaxially arranged with the inner wheel of the pull rope device. When the pull rope is stretched and retracted under the action of the external mechanism, the inner wheel rotates, synchronously driving the encoder to rotate, and the displacement of the external mechanism is calculated by the number of rotations of the encoder.

[0003] Although the encoder plays an important role in the pull rope displacement sensor, the existing control technology still has some limitations. For example, the resolution of the encoder may limit the measurement accuracy of the sensor, especially in situations that require extremely high precision measurement. In addition, the response speed and stability of the encoder may be constrained by the control algorithm, especially in high-speed motion or vibration environments, the performance of the encoder may decline.

[0004] In order to overcome the above limitations and improve the performance of the pull rope 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

[0005] In order to improve the performance of the pull rope displacement sensor, the present application provides a pull rope displacement sensor and its control method.

[0006] In the first aspect, the present application provides a control method for a pull rope displacement sensor, comprising the following steps:

[0007] Before the control starts, the encoder automatically identifies its working environment and application requirements through the built-in intelligent algorithm, and automatically configures the best working mode and parameters;

[0008] The signal generated by the encoder when rotating is optimized through dynamic signal enhancement technology, and at the same time, intelligent filtering algorithm is adopted to eliminate noise interference;

[0009] The position and speed data generated by the encoder are fused with external sensor data in real time, and intelligent prediction is carried out through machine learning algorithm to predict the motion trend in advance;

[0010] During the operation of the encoder, the encoding correction strategy is continuously optimized through self-learning algorithm, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes;

[0011] Based on real-time data and prediction results, intelligent generation of coding instructions, while conducting security verification;

[0012] According to real-time feedback information, dynamically adjust the control strategy, realize self-adaptive optimization, to adapt to the changing working environment and task requirements.

[0013] Optionally, before the control starts, the encoder automatically identifies its working environment and application requirements through the built-in intelligent algorithm, and automatically configures the best working mode and parameters, including the following steps:

[0014] The encoder collects environmental data through built-in sensors, and the intelligent algorithm analyzes application requirements to provide a basis for adaptive configuration;

[0015] According to the results of environmental perception and demand identification, match the best working mode and parameters from the parameter library for preliminary optimization;

[0016] During the initialization process, real-time feedback information is collected to dynamically adjust the parameters to ensure the best configuration;

[0017] Confirm the adaptive configuration result, and the encoder automatically enters the working state.

[0018] Optionally, the electrical signal generated by the encoder during rotation is optimized through dynamic signal enhancement technology, and intelligent filtering algorithm is used to eliminate noise interference, including the following steps:

[0019] Real-time evaluation of signal quality, optimization of signal strength through dynamic signal enhancement technology;

[0020] Analyze noise characteristics, intelligently select the most suitable filtering algorithm to eliminate specific types of noise;

[0021] According to the real-time changes of signals and noise, adaptively adjust the filtering parameters to ensure the filtering effect;

[0022] Verify the purity of the signal, and further optimize the signal processing strategy according to the verification result to ensure the purity and stability of the signal.

[0023] Optionally, the position and speed data generated by the encoder are fused with external sensor data in real time, and intelligent prediction is performed through machine learning algorithm to predict the movement trend in advance, including the following steps:

[0024] Collect data from the encoder and external sensors for preprocessing;

[0025] Intelligently select data fusion algorithms according to data types and application requirements to achieve effective data fusion;

[0026] Based on the fused data, construct a real-time prediction model, and train through machine learning algorithm to improve prediction accuracy;

[0027] Verify the accuracy of the prediction results and apply the prediction results to the control strategy.

[0028] Optionally, the encoder continuously optimizes the encoding correction strategy and automatically adjusts the encoding parameters during operation through a self-learning algorithm to adapt to different working conditions and environmental changes, including the following steps:

[0029] Real-time detection of encoding errors, analysis of error sources, and provision of correction strategies;

[0030] Intelligent selection of self-learning algorithms based on error analysis results and automatic adjustment of encoding parameters;

[0031] During operation, dynamically optimize the encoding parameters based on real-time feedback information;

[0032] Evaluate the correction effect and iteratively optimize based on the evaluation results.

[0033] Optionally, based on real-time data and prediction results, intelligently generate encoding instructions and perform safety verification, including the following steps:

[0034] Analyze real-time data and intelligently generate encoding instructions to ensure correctness and timeliness;

[0035] Evaluate the safety risks of the instructions to ensure that the instructions do not cause potential harm to the system or environment;

[0036] Perform safety verification to confirm the safety and effectiveness of the instructions and avoid potential control risks;

[0037] Execute the encoding instructions and collect execution feedback to ensure correct execution and system stability.

[0038] Optionally, dynamically adjust the control strategy based on real-time feedback information to achieve adaptive optimization to adapt to changing working environments and task requirements, including the following steps:

[0039] Collect real-time feedback information, analyze control effects, and provide basis for strategy adjustment;

[0040] Evaluate the effectiveness of the current control strategy and optimize based on the analysis results to improve control efficiency;

[0041] Based on real-time feedback and optimization results, automatically generate a control strategy that adapts to the current environment to achieve adaptive optimization;

[0042] Verify the effectiveness of the control strategy and iteratively optimize based on the verification results to ensure continuous improvement and adaptability of the control strategy.

[0043] In a second aspect, the present application provides a pull rope displacement sensor, and the control method of the pull rope displacement sensor is applied to the pull rope displacement sensor, and the following technical scheme is adopted:

[0044] The encoder is mounted on the side wall of the pull rope device, and the inner wheel of the pull rope device is coaxially arranged with the shaft of the encoder.

[0045] In a third aspect, the present application provides an electronic device, and the following technical scheme is adopted:

[0046] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the control method of the pull rope displacement sensor according to any one of the above when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer storage medium, and the following technical scheme is adopted:

[0048] A computer storage medium includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the control method of the pull rope displacement sensor according to any one of the above when executing the computer program.

[0049] In summary, the present application has the following beneficial technical effects:

[0050] 1. By means of intelligent initialization, dynamic signal enhancement and intelligent filtering, and self-learning coding correction, the measurement accuracy of the sensor and the reliability of signal processing are significantly improved, and the stable performance in various complex environments is ensured;

[0051] 2. Real-time data fusion and intelligent prediction technology, combined with dynamic control strategy adjustment and adaptive optimization, enable the sensor to intelligently predict motion trends, adaptively adjust control strategies, achieve more accurate control and higher flexibility, and adapt to changing working environments. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 FIG. 1 is a schematic diagram of the overall structure of a pull rope displacement sensor according to an embodiment of the present application.

[0053] Figure 2 FIG. 2 is an exploded schematic diagram of a pull rope displacement sensor according to an embodiment of the present application.

[0054] Figure 3 FIG. 4 is a flowchart of a control method of a pull rope displacement sensor according to an embodiment of the present application.

[0055] Figure 4is a flow chart of the sub-step of step S1 in the embodiment of the application.

[0056] Figure 5 is a flow chart of the sub-step of step S2 in the embodiment of the application.

[0057] Figure 6 is a flow chart of the sub-step of step S3 in the embodiment of the application.

[0058] Figure 7 is a flow chart of the sub-step of step S4 in the embodiment of the application.

[0059] Figure 8 is a flow chart of the sub-step of step S5 in the embodiment of the application.

[0060] Figure 9 is a flow chart of the sub-step of step S6 in the embodiment of the application.

[0061] Reference Signs List: 1, encoder; 2, pull rope device; 21, shell; 22, drum; 23, pull rope; 24, spring mechanism. DETAILED DESCRIPTION

[0062] The following will be described in detail in combination with the accompanying Figures 1-9 The application is further described in detail.

[0063] Reference will be made to Figure 1 and Figure 2 , the embodiment of the application discloses a pull rope displacement sensor, comprising an encoder 1 and a pull rope 23 device 2, the encoder 1 is installed on the side wall of the pull rope 23 device 2, in the embodiment, the encoder 1 adopts a Kuber encoder 1, the pull rope 23 device 2 is composed of a shell 21, a drum 22, a pull rope 23 and a spring mechanism 24, the drum 22 is rotatably borne in the shell 21, one end of the pull rope 23 is fixed with the drum 22, and the other end of the pull rope 23 extends out of the outside of the shell 21. Meanwhile, the spring mechanism 24 is fixedly connected with the pull rope 23, provides the force for retracting the pull rope 23, and ensures that the pull rope 23 can be automatically retracted into the shell 21 after measurement is completed. In addition, the drum 22 is connected with the encoder 1, and the rotary displacement of the drum 22 is directly converted into the electrical signal output of the encoder 1.

[0064] Reference will be made to Figure 3 , the embodiment further provides a control method of the pull rope displacement sensor, which is applied to the pull rope displacement sensor and comprises the following steps.

[0065] S1: before control starts, the encoder 1 automatically identifies the working environment and application demand thereof through a built-in intelligent algorithm, and automatically configures the best working mode and parameters.

[0066] Reference will be made to Figure 4 , step S1 further comprises the following sub-steps.

[0067] S11: The encoder 1 collects environmental data through built-in sensors, and intelligent algorithms analyze application requirements to provide a basis for adaptive configuration.

[0068] S12: Based on the results of environmental perception and demand identification, the best working mode and parameters are matched from the parameter library for preliminary optimization.

[0069] S13: During the initialization process, real-time feedback information is collected, and parameters are dynamically adjusted to ensure optimal configuration.

[0070] S14: The adaptive configuration result is confirmed, and the encoder 1 automatically enters the working state.

[0071] Correspondingly, for steps S11-S14, the present embodiment performs the following examples.

[0072] First, assume that a pull rope displacement sensor is deployed on an outdoor construction site to monitor the displacement of heavy machinery. The environmental sensors (such as temperature and humidity sensors) built into the encoder 1 collect current environmental data, and intelligent algorithms analyze these data and the usage history of the sensors to identify the current application requirements (such as high precision and anti-interference capability). 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 increasing anti-interference settings, to lay the foundation for adaptive configuration.

[0073] Based on the environmental data and application requirements of the previous step, the intelligent algorithm selects the best working mode suitable for the outdoor construction site environment from the parameter library. For example, the "high-precision anti-interference mode" is selected, and the resolution, sampling frequency, and anti-interference filter parameters of the encoder 1 are adjusted to optimize the performance of the sensor in the current environment.

[0074] Further, during the initial start of the sensor, the encoder 1 monitors the stability and accuracy of its output signal in real time, while collecting feedback information from external devices (such as signal quality reports from the data acquisition system). If the signal is found to be disturbed or insufficient in accuracy, the encoder 1 will dynamically adjust parameters such as increasing filter strength and adjusting sampling frequency to ensure the stability and accuracy of the output signal, reaching the optimal configuration state.

[0075] After the adaptive configuration of the above steps, the encoder 1 confirms that all parameters have been adjusted to the best state, meeting the current environmental and application requirements. At this time, the encoder 1 automatically enters a stable working state, starts to accurately and stably measure the displacement of heavy machinery, and continuously monitors environmental changes and device status, preparing for further adaptive adjustment if necessary.

[0076] Looking back Figure 3 , step S1 also includes the following steps.

[0077] S2: The electrical signal generated by the encoder 1 during rotation is optimized through dynamic signal enhancement techniques, while intelligent filtering algorithms are used to eliminate noise interference.

[0078] Referring to Figure 5 , step S2 corresponds to the following sub-steps.

[0079] S21: Real-time evaluation of signal quality, optimization of signal strength through dynamic signal enhancement techniques.

[0080] S22: Analysis of noise characteristics, intelligent selection of the most suitable filtering algorithm to eliminate specific types of noise.

[0081] S23: Adaptive adjustment of filtering parameters according to real-time changes in signal and noise, ensuring filtering effect.

[0082] S24: Verification of signal purity, further optimization of signal processing strategy according to verification results, ensuring signal purity and stability.

[0083] Correspondingly, for steps S21-S24, the following examples are provided by the embodiments of the present application.

[0084] Suppose in an industrial automation production line, a pull rope displacement sensor is used to monitor the precise displacement of a robotic arm. The sensor output signal may be affected by noise such as electromagnetic interference, mechanical vibration, etc. The real-time signal quality evaluation system continuously monitors the signal-to-noise ratio (SNR) of the signal, and once the signal quality is detected to be degraded, such as the SNR being lower than the preset threshold, the system will automatically start dynamic signal enhancement techniques, such as increasing the gain of the amplifier or adjusting the parameters of the pre-filter, to enhance the signal strength and ensure the clarity and reliability of the signal.

[0085] 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 suitable filtering algorithm. For example, for Gaussian white noise, Kalman filtering can be used; for periodic noise, a band-stop filter can be used; for impulse noise, median filtering can be used. This intelligent selection ensures the best match between the filtering algorithm and the noise type, effectively eliminating the noise.

[0086] In a dynamic environment, the characteristics of the noise may change over time. Adaptive filtering technology can automatically adjust the parameters of the filter, such as the cutoff frequency, gain, etc., according to the real-time changes in signal and noise. For example, when a change in the frequency of periodic noise is detected, the adaptive filter will automatically adjust its cutoff frequency to maintain effective suppression of the noise while minimizing the impact on the signal.

[0087] In the final stage of signal processing, the system verifies the purity of the signal, i.e., checks whether there is 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 according to the verification result. 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, ensuring the purity and stability of the signal.

[0088] Looking back Figure 3 , after step S2, the following steps are included.

[0089] S3: Real-time fusion of position and velocity data generated by encoder 1 and external sensor data, intelligent prediction through machine learning algorithm, and early prediction of motion trend.

[0090] Referring to Figure 6 , step S3 corresponds to the following steps.

[0091] S31: Collect data from encoder 1 and external sensors, and perform preprocessing.

[0092] S32: According to the type of data and application requirements, intelligently select data fusion algorithm to realize effective fusion of data.

[0093] S33: Based on the fused data, construct a real-time prediction model, train through a machine learning algorithm, and improve the prediction accuracy.

[0094] S34: Verify the accuracy of the prediction result, and apply the prediction result to the control strategy.

[0095] Correspondingly, the following examples are given for steps S31-S34.

[0096] Specifically, when the pull rope displacement sensor is working, encoder 1 will continuously collect displacement data, and at the same time, 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, removing outliers, filling missing data, and converting data to a unified format to prepare for subsequent data fusion and analysis.

[0097] 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), Kalman filtering or particle filtering algorithms can be used to fuse displacement data with temperature data to correct the error of displacement measurement. Data fusion ensures that the displacement information output by the sensor is more accurate and reliable.

[0098] On the basis of fused data, a real-time prediction model is constructed for predicting 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 the model based on historical displacement data and environmental data, predicting displacement changes under specific environmental conditions. Through continuous training and optimization, the prediction model can continuously improve prediction accuracy, providing more accurate prediction results for real-time control strategies.

[0099] The displacement prediction results generated by the prediction model need to be verified to ensure their accuracy. This is usually done by comparing them with 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, improving production efficiency and safety.

[0100] Review Figure 3 After step S3, the following steps are included.

[0101] S4: During the operation of the encoder 1, the self-learning algorithm is used to continuously optimize the encoding correction strategy, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes.

[0102] Referring to Figure 7 Step S4 corresponds to the following sub-steps.

[0103] S41: Real-time detection of encoding error, analysis of error source, and provision of basis for correction strategy.

[0104] S42: According to the error analysis results, the self-learning algorithm is intelligently selected, and the encoding parameters are automatically adjusted.

[0105] S43: During operation, the encoding parameters are dynamically optimized according to real-time feedback information.

[0106] S44: Evaluate the correction effect and perform iterative optimization based on the evaluation results.

[0107] Correspondingly, for steps S41-S44, the following examples are provided.

[0108] Suppose the displacement sensor detects the difference between the displacement data output by the encoder 1 and the actual displacement, i.e., the encoding error, while working. Through the built-in self-checking system, the source of the error is analyzed, such as mechanical wear, environmental changes (temperature, humidity), electronic noise, etc. For example, if it is detected that the encoding error changes with temperature, it indicates that temperature is one of the main sources of error. These analysis results provide a basis for subsequent correction strategies, ensuring that the correction measures can be targeted at specific problems.

[0109] Based on the analysis of error sources, the intelligent algorithm selects 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 the encoder 1. For example, if temperature is the main error source, the algorithm will adjust the gain, offset and other parameters of the encoder 1 in real time according to the temperature change, to compensate for the influence of temperature change on displacement measurement. This self-learning mechanism enables the encoder 1 to adapt to environmental changes and improve measurement accuracy.

[0110] Further, during the operation of the sensor, the system continuously collects real-time feedback information, such as encoding error, environmental parameter change, etc., for dynamic optimization of encoding parameters. For example, if it is detected in operation that the error gradually increases due to mechanical wear, the system will automatically adjust the compensation parameters of the encoder 1, such as increasing the strength of the filter, to reduce the influence of the error. This dynamic optimization ensures that the encoder 1 can continuously provide accurate displacement data in a changing environment.

[0111] Finally, after the implementation of the correction strategy, the system evaluates the correction effect, i.e. whether the corrected encoding error is within an acceptable range. If the evaluation result shows that the error is still too high, the system will perform iterative optimization according to the evaluation result, adjusting the parameters of the self-learning algorithm or selecting a more effective correction strategy. For example, if it is found that the current self-learning algorithm is not effective in handling a particular type of error, the system may try to use a different algorithm or increase the complexity of the algorithm to improve the correction effect.

[0112] Review Figure 3 , after step S4, the following steps are further included.

[0113] S5: Based on real-time data and prediction results, intelligently generate encoding instructions while performing security verification.

[0114] Referring to Figure 8 , step S5 corresponds to the following sub-steps.

[0115] S51: Analyze real-time data, intelligently generate encoding instructions, and ensure the correctness and timeliness of the instructions.

[0116] S52: Evaluate the safety risks of the instructions to ensure that the instructions will not cause potential harm to the system or environment.

[0117] S53: Perform security verification to confirm the safety and effectiveness of the instructions and avoid potential control risks.

[0118] S54: Execute the encoding instructions and collect execution feedback to ensure correct execution of the instructions and system stability.

[0119] Correspondingly, for steps S51-S54, the embodiments of the present application are illustrated as follows.

[0120] Specifically, when the displacement sensor monitors the displacement data in real time, the system will analyze these data and intelligently generate corresponding coding 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 coding instructions according to the real-time displacement data and the preset displacement target, instructing the encoder 1 how to adjust the displacement, ensuring that the robot arm can accurately reach the target position. At the same time, the system will ensure the timeliness of the instructions, that is, immediately generate and send instructions after data analysis, to achieve fast response.

[0121] Further, after generating the coding 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 instructions require the robot arm to make a large displacement in a short time, the system will assess 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.

[0122] In addition, before the instructions are executed, the system will conduct a safety verification to confirm the safety and effectiveness of the instructions. 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 operation procedures. If the verification result shows that the instructions have potential risks, the system will prevent the execution of the instructions and require the instructions to be regenerated or adjusted to avoid potential control risks.

[0123] Finally, after confirming the safety and effectiveness of the instructions, the system will execute the coding instructions while collecting feedback information during the execution process, such as actual displacement data, execution time, system state, etc. These feedback information is used to monitor the execution of the instructions to ensure that the instructions are correctly executed, while monitoring the system state to ensure the stable operation of the system. For example, if an abnormality is detected during the execution process, such as a large displacement deviation or system response delay, the system will immediately take measures, such as adjusting the instructions or suspending the execution, to avoid potential system failures.

[0124] Looking back Figure 3 , after step S5, the following steps are included.

[0125] S6: dynamically adjust the control strategy according to real-time feedback information to achieve adaptive optimization to adapt to the changing working environment and task requirements.

[0126] Correspondingly, referring to Figure 9 , step S6 corresponds to the following steps.

[0127] S61: collect real-time feedback information, analyze the control effect, and provide basis for strategy adjustment.

[0128] S62: Evaluate the effectiveness of the current control strategy, optimize based on analysis results, improve control efficiency.

[0129] S63: Based on real-time feedback and optimization results, automatically generate a control strategy that adapts to the current environment, achieving adaptive optimization.

[0130] S64: Verify the effectiveness of the control strategy, and based on the verification results, iteratively optimize to ensure continuous improvement and adaptability of the control strategy.

[0131] Correspondingly, for steps S61-S64, the embodiments of the present application are illustrated as follows.

[0132] Specifically, the pull rope displacement sensor will continuously collect real-time feedback information such as displacement data, response time of execution instructions, system status, etc. The system will analyze these 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 change or load increase), it indicates that the current control strategy may need to be adjusted to adapt to environmental changes. These analysis results provide the basis for strategy adjustment, ensuring that the control strategy can be optimized for specific problems.

[0133] Further, based on the analysis of real-time feedback information, the system will evaluate the effectiveness of the current control strategy, such as displacement control accuracy, response speed, energy consumption, etc. If the analysis result shows that the control efficiency is lower than expected, the system will optimize according to the analysis result. For example, if it is found that the displacement control accuracy decreases under high load conditions, the system may adjust the gain parameter of the 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.

[0134] In addition, after collecting real-time feedback information and optimizing, the system will automatically generate a control strategy that adapts to the current environment. For example, if the real-time feedback information shows that the environmental temperature rises, the system will automatically generate a new control strategy, such as adjusting the temperature compensation parameter of the encoder 1, or enabling additional cooling systems, to ensure the accuracy and stability of displacement measurement in high temperature environment. This adaptive optimization mechanism enables the pull rope displacement sensor to automatically adjust the control strategy according to environmental changes, improving control efficiency and adaptability.

[0135] Further, after implementing the new control strategy, the system verifies the effectiveness of the strategy, i.e., evaluates whether the precision, response speed and stability of displacement control meet the expectations. If the verification result shows that the control strategy still has deficiencies, the system will iteratively optimize the control parameters or introduce more advanced control algorithms based on the verification result. For example, if it is found that the control strategy does not work well 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.

[0136] The embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected by a system bus. 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 external terminals through network connection. The computer program is executed by the processor to implement a control method of a pull rope displacement sensor:

[0137] S1: Before the control starts, the encoder 1 automatically identifies its working environment and application requirements through the built-in intelligent algorithm, and automatically configures the best working mode and parameters.

[0138] S2: The signal generated by the encoder 1 during rotation is optimized through dynamic signal enhancement technology, and at the same time, intelligent filtering algorithm is used to eliminate noise interference.

[0139] S3: The position and speed data generated by the encoder 1 are fused with external sensor data in real time, and intelligent prediction is performed through machine learning algorithm to predict the movement trend in advance.

[0140] S4: During the operation of the encoder 1, the encoding correction strategy is continuously optimized through self-learning algorithm, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes.

[0141] S5: Based on real-time data and prediction results, intelligent encoding instructions are generated, and safety verification is performed.

[0142] S6: According to the real-time feedback information, the control strategy is dynamically adjusted to realize adaptive optimization to adapt to the changing working environment and task requirements.

[0143] The computer program is executed by the processor to implement the control method of the pull rope displacement sensor in any of the above method embodiments.

[0144] The embodiment also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0145] S1: Before the control starts, the encoder 1 automatically identifies its working environment and application requirements through the built-in intelligent algorithm, and automatically configures the best working mode and parameters.

[0146] S2: The electrical signal generated by the encoder 1 during rotation is optimized through dynamic signal enhancement technology, and the intelligent filtering algorithm is used to eliminate noise interference.

[0147] S3: The position and speed data generated by the encoder 1 are fused with external sensor data in real time, intelligent prediction is performed through a machine learning algorithm, and motion trends are predicted in advance.

[0148] S4: During the operation of the encoder 1, the encoding correction strategy is continuously optimized through a self-learning algorithm, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes.

[0149] S5: Based on real-time data and prediction results, the encoding instructions are intelligently generated, and safety verification is performed.

[0150] S6: According to real-time feedback information, the control strategy is dynamically adjusted to realize adaptive optimization to adapt to the changing working environment and task requirements.

[0151] The computer program is executed by a processor to implement the control method of the pull rope displacement sensor in any of the above method embodiments.

[0152] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), programmable ROM (EPROM), erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified. In actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the above-described functions.

[0154] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the above-mentioned embodiments of the present application are described in detail, those skilled in the art should understand that they can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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 of a pull cord displacement sensor, characterized by, The method comprises the following steps: Before the control starts, the encoder (1) automatically identifies its working environment and application requirements through the built-in intelligent algorithm, automatically configures the best working mode and parameters; In the initial stage of sensor start, the encoder (1) monitors the stability and accuracy of its output signal in real time, and collects feedback information from external devices; The signal generated by the encoder (1) during rotation is optimized through dynamic signal enhancement technology, and intelligent filtering algorithm is used to eliminate noise interference, including: real-time evaluation of signal quality, optimization of signal strength through dynamic signal enhancement technology; analyze the characteristics of noise, intelligently select the most suitable filtering algorithm to eliminate specific types of noise; according to the real-time changes of signal and noise, adaptively adjust the filtering parameters to ensure the filtering effect; verify the purity of the signal, and further optimize the signal processing strategy according to the verification result to ensure the purity and stability of the signal; The position and speed data generated by the encoder (1) are fused with external sensor data in real time, and intelligent prediction is performed through machine learning algorithm to predict the motion trend in advance; During the operation of the encoder (1), the encoding correction strategy is continuously optimized through self-learning algorithm, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes; Based on real-time data and prediction results, intelligent encoding instructions are generated, and safety verification is performed; According to the real-time feedback information, the control strategy is dynamically adjusted to realize adaptive optimization to adapt to the changing working environment and task requirements.

2. The control method of a pull cord displacement sensor according to claim 1, characterized by, The encoder (1) automatically identifies its working environment and application requirements through the built-in intelligent algorithm before the control starts, and automatically configures the best working mode and parameters, comprising 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, real-time feedback information is collected, and parameters are dynamically adjusted to ensure optimal configuration; After confirming the adaptive configuration result, the encoder (1) automatically enters the working state.

3. The control method of a pull cord displacement sensor according to claim 1, characterized by, The position and speed data generated by the encoder (1) are fused with external sensor data in real time, and intelligent prediction is performed through machine learning algorithm to predict the motion trend in advance, comprising the following steps: Collect data from the encoder (1) and external sensors for preprocessing; According to the data type and application requirements, intelligently select data fusion algorithm to realize effective data fusion; Based on the fused data, build a real-time prediction model, train through machine learning algorithm to improve prediction accuracy; Verify the accuracy of the prediction result, and apply the prediction result to the control strategy.

4. The control method of a pull cord displacement sensor according to claim 1, characterized by, During the operation of the encoder (1), the encoding correction strategy is continuously optimized through self-learning algorithm, and the encoding parameters are automatically adjusted to adapt to different working conditions and environmental changes, comprising the following steps: Real-time detection of encoding errors, analysis of error sources to provide basis for correction strategy; According to the error analysis result, intelligently select self-learning algorithm to automatically adjust the encoding parameters; During the operation, dynamically optimize the encoding parameters according to the real-time feedback information; Evaluate the correction effect and iteratively optimize based on the evaluation results.

5. The control method of a pull cord displacement sensor according to claim 1, characterized by, The intelligent generation of coding instructions based on real-time data and prediction results, along with security verification, includes the following steps: Analyzing real-time data and intelligently generating coding instructions to ensure correctness and timeliness of instructions; Evaluating the safety risks of instructions to ensure that instructions do not cause potential harm to the system or environment; Performing security verification to confirm the safety and effectiveness of instructions and avoid potential control risks; Executing coding instructions and collecting execution feedback to ensure correct execution of instructions and system stability.

6. The control method of a pull cord displacement sensor according to claim 1, wherein The dynamic adjustment of control strategies based on real-time feedback information to achieve adaptive optimization to adapt to changing working environments and task requirements, including the following steps: Collecting real-time feedback information and analyzing control effects to provide a basis for strategy adjustment; Evaluating the effectiveness of the current control strategy and optimizing it based on the analysis results to improve control efficiency; Based on real-time feedback and optimization results, automatically generate control strategies that adapt to the current environment to achieve adaptive optimization; Verify the effectiveness of the control strategy and iteratively optimize it based on the verification results to ensure continuous improvement and adaptability of the control strategy.

7. A pull wire displacement sensor to which the control method of the pull wire displacement sensor according to any one of claims 1 to 6 is applied, characterized by The device comprises an encoder (1) and a pull rope (23) device (2), the encoder (1) is installed 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 shaft of the encoder (1).

8. An electronic device, comprising: The device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the steps of the control method of the pull rope displacement sensor according to any one of claims 1-6.

9. A computer storage medium, characterized in that The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the control method of the pull rope displacement sensor according to any one of claims 1-6.

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