Mobile phone remote environment controller jogging control method, system and storage medium

Abstract

The application relates to the technical field of human-computer interaction and sensor control, and discloses a mobile phone remote environment controller point control method, a system and a storage medium. The method comprises the following steps: collecting original posture data through a mobile phone sensor and carrying out interference filtering, and extracting posture fluctuation characteristics; combining time distribution to determine the continuity characteristics to determine the dynamic adjustment intention; matching the posture characteristics with a preset mapping relationship to obtain an initial instruction prototype; optimizing the instruction and evaluating the response delay to obtain an executable point control signal; fusing and checking the signal with the feedback of an environment controller to generate a stable execution sequence; compressing the sequence and transmitting the sequence to the controller to receive an execution receipt; judging the execution result according to the receipt, and if the execution result does not meet the standard, the signal is corrected and the controller state and the mapping relationship are updated. The application effectively improves the accuracy of the mobile phone remote environment controller point control.

Description

Technical Field

[0001] This application relates to the field of human-computer interaction and sensor control technology, and in particular to a mobile phone remote environmental controller inching control method, system and storage medium. Background Technology

[0002] When remotely controlling devices with a mobile phone, the common practice is to tap virtual buttons on the screen. This method is inconvenient when continuous tapping is required (such as adjusting the camera angle or motor travel), as users find it difficult to experience the precise tactile feedback of physical buttons. Existing technologies attempt to replace buttons by recognizing the phone's posture, but users inevitably experience shaking when holding the phone, resulting in a large amount of interference in the data collected by the sensors. The system often cannot distinguish between intentional tapping operations and unintentional shaking, easily leading to false triggers or failure to move when it should, making it difficult to guarantee the accuracy of tapping control.

[0003] Besides inaccurate posture recognition, remote control also faces the problem of response latency. Jog control requires timely response to each action, but network transmission and device processing both take time. If the system doesn't consider latency and simply maps posture changes into commands, users will feel sluggish movements, and the operation sequence is prone to becoming disordered. More problematic is that the mobile device often has no way of knowing whether the device has actually performed the command. Without an effective feedback verification and correction mechanism, if a command fails to execute correctly due to latency or interference, the user can only rely on intuition to perform the operation again, making the entire control process neither reliable nor intelligent.

[0004] This application aims to solve the above problems by accurately identifying the intention of inching through effective interference filtering and continuous judgment, optimizing the timing of command issuance by combining delay evaluation, and using device feedback for fusion verification and correction, thereby improving the accuracy and reliability of remote inching control via mobile phone. Summary of the Invention

[0005] This application provides a method, system, and storage medium for inching control of a mobile remote environment controller, which improves the accuracy of inching control of the mobile remote environment controller.

[0006] Firstly, this application provides a method for jog control of a mobile remote environment controller, including: S1. Collect raw attitude perception data set containing three-axis acceleration data and three-axis gyroscope data through mobile phone sensors, perform interference filtering on the raw attitude perception data set, extract angular frequency parameters and amplitude parameters, and obtain attitude fluctuation characteristics. S2. Based on the attitude fluctuation characteristics and combined with the timestamp sequence distribution, use the continuity detection logic to determine whether there are continuous characteristics in the attitude fluctuation characteristics. If so, the continuous characteristics are determined as the intention of dynamic adjustment. S3. Based on the dynamic adjustment intention, and combined with the pattern comparison benchmark and intention triggering condition, the attitude fluctuation characteristics are matched with the preset mapping relationship to obtain the initial jog control command prototype. S4. Based on the initial jog control command prototype, and combined with dynamic threshold matching rules and response delay evaluation, the initial jog control command prototype is optimized to obtain an optimized jog control command. If the actual response time of the optimized jog control command is lower than a preset time threshold, then an executable jog control signal is determined. S5. The executable jog control signal and the equipment feedback data of the environmental controller are weighted and fused, and a feedback consistency check is performed to obtain a stable jog execution sequence. S6. Generate a compressed transmission packet according to the stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, send the compressed transmission packet to the target environment controller to obtain an execution receipt. S7. Based on the execution receipt, determine whether the execution result meets the preset execution standard. If not, use the instruction correction logic to correct the jog control signal, use the corrected jog control signal, combine the state update frequency, update the operating state of the environment controller, adjust the preset mapping relationship, and obtain the jog response rule.

[0007] Secondly, this application provides a mobile phone remote environment controller jog control system, comprising: The data perception filtering module is used to collect raw attitude perception data set containing triaxial acceleration data and triaxial gyroscope data through the mobile phone sensor, perform interference filtering on the raw attitude perception data set, extract angular frequency parameters and amplitude parameters, and obtain attitude fluctuation characteristics. The intention persistence recognition module is used to determine whether there is a persistence feature in the attitude fluctuation feature based on the attitude fluctuation feature and the timestamp sequence distribution, and if so, the persistence feature is determined as a dynamic adjustment intention. The pattern mapping and matching module is used to match the attitude fluctuation characteristics with the preset mapping relationship based on the dynamic adjustment intention, combined with the pattern comparison benchmark and the intention triggering condition, to obtain the initial jog control command prototype. The instruction optimization and evaluation module is used to optimize the initial jog control instruction prototype based on the initial jog control instruction prototype, combined with dynamic threshold matching rules and response delay evaluation, to obtain the optimized jog control instruction. If the actual response time of the optimized jog control instruction is lower than a preset time threshold, then an executable jog control signal is determined. The signal fusion and verification module is used to perform weighted data fusion of the executable jog control signal and the equipment feedback data of the environmental controller, and to perform feedback consistency verification to obtain a stable jog execution sequence. The instruction compression and transmission module is used to generate a compressed transmission packet based on the stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, the compressed transmission packet is sent to the target environment controller to obtain an execution receipt. The feedback iteration optimization module is used to determine whether the execution result meets the preset execution standard based on the execution receipt. If not, it uses the instruction correction logic to correct the jog control signal, and uses the corrected jog control signal, combined with the state update frequency, to update the operating state of the environmental controller, adjust the preset mapping relationship, and obtain the jog response rule.

[0008] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned mobile phone remote environment controller jog control method.

[0009] Compared with the prior art, the beneficial effects of this application are at least as follows: 1. By filtering interference from the raw posture data collected by the mobile phone sensors, the angular frequency and amplitude parameters are extracted as posture fluctuation features. Combined with the timestamp sequence distribution, a continuous detection logic is used to judge the continuous features. This can effectively distinguish between intentional user tapping operations and unintentional interference such as hand shaking. Compared with the solution of directly mapping the raw posture data, it reduces false triggers at the source, makes tapping control more in line with the user's true intention, and improves the accuracy of tapping intention recognition.

[0010] 2. Based on the initial prototype of the jog control command, the timing of command execution is further optimized by combining dynamic threshold matching rules and response delay evaluation. Only when the actual response time is lower than the preset threshold is the jog control signal determined as executable. The delay caused by network transmission and device processing is fully considered, avoiding operation sluggishness or command accumulation caused by delay, ensuring the responsiveness and continuity of jog control, and optimizing the real-time response of jog commands.

[0011] 3. By weightedly fusing the optimized executable jog control signal with the equipment feedback data returned by the environmental controller and performing feedback consistency verification, a stable jog execution sequence is generated only when the deviation between the fused parameters and the current operating state is within the tolerance range, forming a closed-loop verification mechanism. This solves the problem of "issuing and then not caring, execution result unknown" in the traditional solution and enhances the stability of jog execution.

[0012] 4. After receiving the execution receipt, the system determines whether the execution result meets the preset standard. If it does not, the system uses the instruction correction logic to correct the jog control signal. At the same time, the system updates the operating status of the environmental controller and adjusts the preset mapping relationship to obtain the updated jog response rules. This allows the system to continuously optimize its behavior mapping and control logic based on the actual execution deviation. Over long-term use, the system gradually adapts to the user's operating habits and the current network environment, achieving adaptive iterative optimization of the control logic. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the mobile remote environment controller jog control method of this application; Figure 2 This is a diagram of the mixed signal of handheld shaking and jogging operation in this application; Figure 3 This is a duration diagram of each fluctuation segment in this application; Figure 4 This is a schematic diagram of the mobile phone remote environment controller jog control system of this application. Detailed Implementation

[0015] This application provides a method, system, and storage medium for inching control of a mobile phone remote environment controller. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the mobile phone remote environment controller jog control method in this application includes: Step S1: Collect raw attitude perception data set containing three-axis acceleration data and three-axis gyroscope data through the mobile phone sensor, filter the raw attitude perception data set for interference, extract the angular frequency parameters and amplitude parameters, and obtain the attitude fluctuation characteristics.

[0017] In one specific embodiment, the process of performing step S1 may specifically include the following steps: The phone's built-in microelectromechanical inertial measurement unit (MEMS) simultaneously collects three-axis acceleration data and three-axis gyroscope data, which are then combined into a raw attitude perception data set. The raw attitude perception data set is input into a low-pass filter with a preset cutoff frequency to filter out high-frequency noise components caused by hand shake and environmental vibration, so as to obtain an effective attitude data stream. Angular velocity values ​​are extracted from the three-axis gyroscope data in the effective attitude data stream to obtain the angular frequency parameters of attitude changes; Vector magnitude calculation is performed on the three-axis acceleration data in the effective attitude data stream to extract the amplitude parameters of attitude changes; By synchronously associating the angular frequency parameter and the amplitude parameter according to the timestamp, attitude fluctuation characteristics are obtained.

[0018] Specifically, the phone's built-in microelectromechanical inertial measurement unit (MEMS) simultaneously acquires three-axis acceleration data and three-axis gyroscope data. The three-axis acceleration data reflects the phone's linear motion in three orthogonal directions in space, while the three-axis gyroscope data reflects the phone's angular velocity around three orthogonal axes. Combining these two types of data at the same sampling time yields the raw attitude perception data set. Since interference from hand shake and environmental vibrations typically manifests as high-frequency noise components, while intentional user gestures correspond to attitude changes concentrated in the lower frequency band, the raw attitude perception data set is input into a low-pass filter with a preset cutoff frequency. The cutoff frequency of this low-pass filter can be set according to the frequency range of natural arm shaking, for example, 5 Hz. Signal components below this frequency are retained, while components above this frequency are attenuated, thereby filtering out high-frequency noise components generated by hand shake and environmental vibrations, and outputting an effective attitude data stream. For example, when a user holds a mobile phone to perform a tap operation, the frequency of involuntary hand shaking is usually between 8 and 12 Hz, while the frequency of intentional phone shaking is generally between 2 and 4 Hz. After the above low-pass filtering process, the high-frequency shaking is effectively suppressed, and the retained data more realistically reflects the user's tap operation intention.

[0019] Based on this, the angular velocity values ​​output by the three-axis gyroscope data in the effective attitude data stream are directly extracted as angular frequency parameters to characterize the rate of attitude change. The unit of this parameter is radians per second (rad / s), and the value range is usually between 0 and 20 rad / s. The higher the value, the faster the user swings the phone. It can also be converted to Hertz (Hz), and the conversion relationship is frequency (Hz) = angular frequency (rad / s) / 2π.

[0020] Simultaneously, vector magnitude calculation is performed on the three-axis acceleration data in the effective attitude data stream. This involves calculating the square root of the sum of the squares of the three axial acceleration components to obtain the amplitude of the resultant acceleration, which serves as the amplitude parameter. This amplitude parameter eliminates the influence of the phone's orientation on the acceleration reading and reflects the degree of attitude change during user operation. For example, when the user rapidly shakes the phone, the amplitude parameter shows a significant spike, while when the user slowly rotates the phone, the amplitude parameter changes more gradually.

[0021] The obtained angular frequency and amplitude parameters are synchronously associated according to their respective timestamps to form a joint description of the attitude change at each sampling moment, i.e., the attitude fluctuation feature. This attitude fluctuation feature exists in the form of a time series, with each frame of data containing information in three dimensions: timestamp, angular frequency, and amplitude, providing structured input data for subsequent determination of whether the attitude fluctuation is persistent.

[0022] Through the above interference filtering and feature extraction processing, noise components unrelated to user operation in the original sensor data are effectively removed. At the same time, the core features related to the jog operation are extracted in a parameterized form, enabling the system to perform intent recognition based on clean and effective features. This reduces the probability of false triggering caused by hand tremors or environmental vibrations from the source, laying a reliable data foundation for accurately recognizing the user's dynamic adjustment intent.

[0023] Step S2: Based on the attitude fluctuation characteristics and the timestamp sequence distribution, use the continuity detection logic to determine whether there are persistent characteristics in the attitude fluctuation characteristics. If so, the persistent characteristics are determined as the intention to dynamically adjust.

[0024] In one specific embodiment, the process of performing step S2 may specifically include the following steps: Obtain the timestamp sequence distribution corresponding to the attitude fluctuation features, and arrange the feature values ​​of each sampling point in the attitude fluctuation features in time sequence according to the timestamp sequence distribution to obtain the attitude fluctuation time series array. A continuous detection logic is used to perform a sliding window scan on the attitude fluctuation time series array, and the correlation coefficient of the feature values ​​within adjacent windows is calculated. When the correlation coefficient exceeds the preset correlation threshold for N consecutive sliding windows, the attitude fluctuation feature is determined to have a persistent feature, where N is a positive integer; The duration of the jog operation and the duration of the hand shake are calculated based on the attitude fluctuation time series array. When the duration of the jog operation is higher than the first preset duration, the duration of the hand shake is lower than the second preset duration, and the first preset duration is greater than the second preset duration, the continuous feature is determined as a dynamic adjustment intention.

[0025] Specifically, the attitude fluctuation features exist in the form of a time series, with each sampling point carrying corresponding timestamp information. The distribution of these timestamps is obtained, and the feature values ​​of each sampling point in the attitude fluctuation features, including angular frequency and amplitude parameters, are arranged in chronological order according to the timestamps, forming an attitude fluctuation time series array. Each element of this array contains three dimensions of information: timestamp, angular frequency value, and amplitude, completely recording the user's attitude change trajectory over a continuous time period. To determine whether the user's attitude changes are continuous, a continuity detection logic is used to process the attitude fluctuation time series array.

[0026] The continuity detection logic is implemented using a sliding window scanning method: a fixed-length sliding window is set, for example, the number of sampling points corresponding to 0.5 seconds. Starting from the beginning of the time series array, the window slides forward one step at a time, calculating the correlation coefficient of feature values ​​within adjacent windows sequentially. The correlation coefficient is calculated as follows: for two adjacent windows A and B, the angular frequency value sequences of all sampling points in window A and window B are extracted from the attitude fluctuation time series array, respectively. These two sequences are used as the two sets of input variables for the Pearson correlation coefficient formula to calculate an angular frequency correlation coefficient. Similarly, the amplitude sequences of all sampling points in window A and window B are extracted to calculate an amplitude correlation coefficient. The angular frequency correlation coefficient and the amplitude correlation coefficient are then weighted and summed according to preset weights, for example, the angular frequency weight is set to 0.5 and the amplitude weight is set to 0.5, to obtain the comprehensive correlation coefficient of adjacent windows.

[0027] The correlation coefficient can be calculated using the Pearson correlation coefficient formula. This coefficient ranges from -1 to +1. The closer the value is to +1, the more consistent the trend of posture fluctuation characteristics within adjacent windows, indicating better continuity in posture changes. When the calculated comprehensive correlation coefficient exceeds a preset correlation threshold for N consecutive sliding windows (for example, if the preset correlation threshold is set to 0.7 and N is set to 3, meaning the comprehensive correlation coefficient is greater than 0.7 for three consecutive sliding windows), the posture fluctuation characteristic is considered to have a continuous characteristic. For example, when a user intentionally performs a tap operation, their hand will continuously and rhythmically shake the phone, and the posture change trend within adjacent time periods is relatively consistent, resulting in a high correlation coefficient. However, when a user only unconsciously shakes the phone, the posture changes within adjacent time periods are often chaotic, resulting in a low correlation coefficient and greater fluctuations.

[0028] Based on the determination that the attitude fluctuation characteristics are persistent, it is necessary to further distinguish whether this persistence originates from intentional jogging or hand tremors. Therefore, the duration of the jogging operation and the duration of the hand tremors are calculated based on the attitude fluctuation time series array.

[0029] The duration of a jog operation refers to the time elapsed from when the user intentionally shakes the phone until the jog operation is completed. The specific calculation method is as follows: An amplitude trigger threshold is set, which can be determined based on the statistical characteristics of amplitudes in historical static states or set to a fixed value based on experience. Then, the amplitude parameters in the attitude fluctuation time series array are scanned. When the amplitude continuously exceeds the trigger threshold, the start time of the time period is recorded; when the amplitude continuously falls below the trigger threshold, the end time of the time period is recorded. The difference between the start and end times of the time period is the duration of the jog operation. If this duration is greater than a first preset duration, the time period is marked as a valid jog operation segment.

[0030] The duration of hand tremor refers to the length of time that the periodic micro-vibrations caused by involuntary contractions of hand muscles last. The specific calculation method is as follows: bandpass filtering is applied to the angular frequency parameters in the posture fluctuation time series array to retain the frequency signal components that match the characteristics of human hand tremor; then, an angular frequency fluctuation threshold is set. When the filtered angular frequency signal continuously exceeds this threshold, the start and end times of that period are recorded, and the difference is the duration of hand tremor.

[0031] If the duration of a jog operation segment is longer than the first preset duration, and within a preset time window before and after the jog operation segment (e.g., 0.5 seconds before and after), the duration of all hand-held shaking segments is shorter than the second preset duration, then the persistence feature is determined to be a dynamic adjustment intention.

[0032] Through the above continuous detection and duration comparison processing, the system can accurately identify whether the user has the intention to make dynamic adjustments from continuous posture fluctuations, avoiding misjudging brief unconscious shaking as a valid operation, while also not missing conscious jogging movements that last for a long time.

[0033] refer to Figure 2 The figure shows a mixed signal diagram of hand-shaking and jogging operations. As shown, the horizontal axis represents time, and the vertical axis represents signal amplitude. The light red filled area represents the hand-shaking signal, which is characterized by high frequency, low amplitude, and short duration. The light green filled area represents the intentional jogging operation signal, which is characterized by low frequency, high amplitude, and long duration. The blue solid line represents the mixed signal waveform. It can be seen that there are differences in waveform shape and duration between hand-shaking and jogging operations.

[0034] refer to Figure 3 The figure shows the duration of each fluctuation segment. The horizontal axis represents the fluctuation segment number, and the vertical axis represents the duration. The red bars correspond to the hand-shaking segment, whose duration is less than the second preset duration (red dashed line); the green bars correspond to the jogging operation segment, whose duration is more than the first preset duration (red solid line). It can be seen that the duration of the shaking segment is significantly shorter than that of the jogging segment, and there is a clear threshold boundary between the two. This proves that the duration comparison can effectively distinguish between conscious jogging operation and hand-shaking.

[0035] Step S3: Based on the dynamic adjustment intention, and combined with the pattern comparison benchmark and intention triggering conditions, the attitude fluctuation characteristics are matched with the preset mapping relationship to obtain the initial jog control command prototype.

[0036] In one specific embodiment, the process of performing step S3 may specifically include the following steps: Based on the dynamic adjustment intention, the pattern comparison benchmark and intention triggering conditions pre-stored locally on the mobile terminal are retrieved, and the posture fluctuation features are compared with the pattern comparison benchmark to calculate the feature similarity and obtain the feature matching degree parameter. Determine whether the feature matching degree parameter meets the preset trigger threshold range in the intention triggering condition. If so, match the attitude fluctuation feature with the preset mapping relationship accordingly. Based on the matching results, the corresponding control command type and initial value of control parameters are extracted from the preset mapping relationship and combined to generate the initial jog control command prototype.

[0037] Specifically, based on the dynamic adjustment intent, the pattern comparison benchmark and intent triggering conditions pre-stored locally on the mobile terminal are retrieved. The pattern comparison benchmark is a pre-established feature template library containing standard attitude fluctuation features corresponding to various typical jog operation modes, such as the angular frequency and amplitude parameter ranges for fast jog modes, the parameter range for slow jog modes, and the parameter change patterns for continuous jog modes. The intent triggering conditions are a set of threshold rules used to determine the validity of feature matching, including the lower limit threshold for feature matching required for triggering. The attitude fluctuation features obtained in step S1 are compared with each standard template in the pattern comparison benchmark to calculate feature similarity parameters. Feature similarity calculation can use a cosine similarity algorithm or an Euclidean distance algorithm. Taking cosine similarity as an example, the angular frequency parameter sequence and amplitude parameter sequence in the attitude fluctuation features are vectorized with their corresponding sequences in the templates, and the cosine value of the angle between the two vectors is calculated. The closer this value is to +1, the more consistent the changing trends of the two vectors. The angular frequency similarity and amplitude similarity are weighted and fused according to preset weights, for example, the angular frequency weight is set to 0.6 and the amplitude weight is set to 0.4, and finally a comprehensive feature matching parameter is obtained.

[0038] After obtaining the feature matching degree parameter, it is determined whether the parameter meets the preset trigger threshold range in the intent triggering conditions. The trigger threshold range includes a lower limit threshold for matching degree, which is usually set to a lower limit threshold, such as 0.75. When the feature matching degree parameter is greater than or equal to this lower limit threshold, it is considered that the current attitude fluctuation feature matches a certain standard template in the pattern comparison benchmark. If the trigger threshold range is met, the current attitude fluctuation feature is matched with the preset mapping relationship. The preset mapping relationship is a mapping table that associates attitude fluctuation features with control commands. This mapping table records the control command type and initial value of control parameters corresponding to each standard template. For example, the control command type corresponding to the fast jog template is "step jog", and the initial value of the control parameter is a single step amount of 0.5 degrees; the control command type corresponding to the slow jog template is "continuous jog", and the initial value of the control parameter is a movement speed of 5 degrees per second. Based on the feature matching result, the corresponding control command type and initial value of control parameters are extracted from the preset mapping relationship, and the two are combined to generate the initial jog control command prototype. This instruction prototype is a raw instruction that has not yet undergone latency optimization and response evaluation. It consists of two parts: an instruction type identifier and parameter values, such as "Instruction type: step-like jog, parameter: step amount 0.5 degrees, execution direction: forward". For example, when a user quickly swings their phone to the left and the similarity between the posture fluctuation feature and the rapid jog template reaches 0.82, the system matches the operation as "leftward step-like jog" and generates the corresponding initial instruction prototype for subsequent optimization.

[0039] Through the above pattern comparison and mapping matching process, the user's continuous and rhythmic posture fluctuations are transformed into control command prototypes with clear semantics. This achieves an accurate conversion from physical actions to control intentions, providing structured input data for subsequent command optimization and response evaluation. It ensures that the source of the jog control commands has a reasonable intention basis and avoids control confusion caused by arbitrary mapping.

[0040] Step S4: Based on the initial jog control command prototype, and combined with dynamic threshold matching rules and response delay evaluation, optimize the initial jog control command prototype to obtain the optimized jog control command. If the actual response time of the optimized jog control command is lower than the preset time threshold, then the executable jog control signal is determined.

[0041] In one specific embodiment, the process of performing step S4 may specifically include the following steps: Based on the initial jog control command prototype, real-time network transmission delay parameters and target environment controller terminal processing delay parameters are collected, and the total command response time evaluation value is calculated. Retrieve the pre-stored dynamic threshold matching rules, compare the total instruction response time evaluation value with the response level threshold in the dynamic threshold matching rules, and determine the target response level; Based on the target response level, the execution timing of the initial jog control command prototype is optimized and adjusted to obtain the optimized jog control command; When the actual response time of the optimized jog control command is lower than the preset time threshold, the optimized jog control command will be determined as an executable jog control signal.

[0042] Specifically, by using an initial jog control command prototype, a network status monitoring thread pre-deployed on the mobile device is invoked to send a set of lightweight probe messages to the target environment controller. The round-trip time of each probe message from sending to receiving the controller's response is recorded. The weighted moving average of multiple probe results is then used to obtain the real-time network transmission latency parameter. Simultaneously, the processing timestamp field returned by the environment controller during historical responses is parsed. This field records the time difference between the controller receiving the complete command packet and starting to execute the command. By smoothing and filtering the most recent historical data, the terminal processing latency parameter is obtained. The two latency parameters are then directly added together to obtain the total command response time evaluation value. This evaluation value represents the total time required for a command to go from being ready on the mobile device to actually starting execution by the environment controller, under the current network link quality and controller load conditions.

[0043] After obtaining the total command response time evaluation value, the dynamic threshold matching rule pre-stored in the phone's local database is retrieved. This dynamic threshold matching rule is a mapping table that associates response time ranges with response levels, defining multiple response level thresholds. Specifically, the mapping table divides the response time into several consecutive intervals, each interval corresponding to a response level. For example, the first response level corresponds to a short response time, the second response level corresponds to a moderate response time, and the third response level corresponds to a long response time. The total command response time evaluation value calculated in step S4 is compared one by one with the above-mentioned response level thresholds to determine the target response level of the current command.

[0044] Based on the determined target response level, the execution timing of the initial jog control command prototype is optimized and adjusted. The core logic of the optimization is: the lower the response level (i.e., the longer the response time), the more advanced the command needs to be issued to compensate for the latency caused by network transmission and device processing. Specifically, the system pre-stores an execution timing offset table, which records the advance issuance time offset corresponding to each response level. The longer the latency corresponding to the response level, the larger the offset value. Once the target response level is determined, the corresponding offset is retrieved from the offset table, and then the execution time field in the initial jog control command prototype is adjusted forward by this offset to obtain the optimized jog control command. For example, if the current network conditions are poor, resulting in a high estimated total response time for the command, the system will adjust the execution timing forward by a larger offset, so that the actual action time of the environmental controller is aligned as closely as possible with the user's intended operation, thereby achieving a responsive operating experience.

[0045] After completing the above optimizations, it is necessary to verify whether the optimization effect meets the requirements. After sending the optimized jog control command, the time difference between the completion of the user's gesture operation and the actual start time of the controller is calculated by receiving the execution timestamp returned by the environmental controller. This difference is the actual response time. This actual response time is compared with a preset time threshold. The preset time threshold is a pre-set boundary value used to determine whether the response meets the requirements of human-computer interaction comfort. When the actual response time is lower than the preset time threshold, it indicates that the optimized command can meet the user's responsiveness requirements. At this time, the optimized jog control command is determined as an executable jog control signal and sent to the subsequent fusion verification module. If the actual response time is not lower than the preset time threshold, the parameters in the execution timing offset table are readjusted, for example, by increasing the offset value corresponding to the current response level, and the optimization and verification process is re-executed. If the actual response time still cannot be lower than the preset time threshold after multiple adjustments, it is determined that the current network environment cannot meet the real-time requirements of jog control. The system outputs a prompt message and pauses jog control, waiting for the network condition to improve before automatically resuming, or it continues to operate in a degraded mode with reduced control precision.

[0046] Through the above dynamic threshold matching and response delay evaluation processing, the timing of command issuance can be adaptively adjusted according to the real-time changes in network and device status, effectively compensating for the unavoidable physical delay in remote control, so that users can obtain a stable, consistent and responsive inching control experience in different network environments.

[0047] Step S5: Weighted data fusion of the executable jog control signal and the equipment feedback data from the environmental controller is performed, and feedback consistency verification is performed to obtain a stable jog execution sequence.

[0048] In one specific embodiment, the process of performing step S5 may specifically include the following steps: The device feedback data transmitted back from the environmental controller is received by the wireless communication module. The executable jog control signal and the device feedback data are timestamp aligned to obtain a synchronization data group. The executable jog control signals in the synchronous data group are assigned a first weighting coefficient, and the equipment feedback data is assigned a second weighting coefficient. Weighted data fusion is then performed to obtain the fused control parameters. Perform feedback consistency verification on the fusion control parameters to determine whether the deviation between the fusion control parameters and the current operating state of the environmental controller is within the preset tolerance range. If so, arrange the fusion control parameters in chronological order to generate a stable jogging execution sequence.

[0049] Specifically, the device feedback data transmitted in real time from the environmental controller is received via a wireless communication module. This device feedback data includes the environmental controller's current position information, operating speed, output power, or execution angle, as well as corresponding timestamp information. The executable jog control signal determined in step S4 is then time-stamp aligned with the aforementioned device feedback data according to their respective timestamps. The specific method for timestamp alignment is as follows: using the timestamp of the device feedback data as a reference, the sampling point of the executable jog control signal closest to that timestamp is found, and the two are paired and combined to form a set of synchronous data. Each pair of data in this synchronous data set represents the correspondence between the state that the mobile terminal expects the device to execute and the state that the device actually responds to within the same or very close time window.

[0050] After obtaining the synchronization data set, the system assigns a first weighting coefficient to the executable jog control signals in the synchronization data set and a second weighting coefficient to the device feedback data, performing weighted data fusion. The values ​​of the first and second weighting coefficients reflect the degree of trust in the control signals and feedback data during the fusion process. Typically, the first weighting coefficient is set to a value between 0.4 and 0.6, and the second weighting coefficient is set to 1 minus the first weighting coefficient, with the sum of the two being 1. For example, if the current network conditions are good and the device feedback data is relatively stable, the first weighting coefficient can be set to 0.4 and the second weighting coefficient to 0.6, making the fusion result more similar to the actual feedback state; if the device feedback data has significant noise or delay, the first weighting coefficient can be set to 0.6 and the second weighting coefficient to 0.4, making the fusion result more similar to the desired control signal. Before performing weighted data fusion, the control parameters in the executable jog control signals and the state parameters in the device feedback data need to be normalized, unifying their dimensions to the same numerical range. Normalization can be achieved using the maximum-minimum normalization method. This involves subtracting the minimum value of the parameter from the original value and then dividing by the difference between the maximum and minimum values, mapping all parameters to the range of 0 to 1. For example, the actual value of a control parameter within its theoretical range, after the above calculation, yields a normalized value between 0 and 1. The amplitude parameter and control parameter undergo the same normalization process. After normalization, the normalized control signal value is multiplied by a first weighting coefficient, and then the normalized feedback data value is multiplied by a second weighting coefficient to obtain the fused control parameter. This fused control parameter integrates the desired control intent and the actual equipment state, serving as the basis for subsequently generating the execution sequence.

[0051] After obtaining the fused control parameters, the system performs a feedback consistency check on these parameters. The purpose of the feedback consistency check is to determine whether the deviation between the fused control parameters and the current operating state of the environmental controller is within an acceptable range. Specifically, the absolute difference or relative deviation percentage between the fused control parameters and the current operating state of the environmental controller is calculated, and this deviation is compared with a preset tolerance range. The preset tolerance range is an allowable deviation interval pre-set based on the mechanical precision and response characteristics of the environmental controller. For example, for angle control, the preset tolerance range can be set to ±0.5 degrees; for speed control, it can be set to ±2%. When the calculated deviation is within the preset tolerance range, it indicates that the difference between the fused control parameters and the actual state of the equipment is within acceptable limits, and there is no conflict or abnormality. At this time, the fused control parameters are arranged in chronological order according to their timestamps to generate a stable jogging execution sequence. This stable jogging execution sequence is an ordered set of instructions, where each element contains a timestamp and the corresponding fused control parameter, representing the state target that the environmental controller needs to achieve at a specific moment. For example, when the user performs continuous jogging operations, the stable jogging execution sequence will sequentially record the target position or target speed at multiple time points, forming a smooth motion trajectory. If the deviation exceeds the preset tolerance range, it indicates that there is a significant conflict between the fusion control parameters and the actual state of the equipment. At this time, the system discards the fusion parameter and triggers an alarm or re-executes the instruction correction process until a fusion parameter that meets the consistency verification is obtained before generating the execution sequence.

[0052] Through the weighted data fusion and feedback consistency verification process described above, the system effectively integrates the control signals sent from the mobile terminal with the actual status returned by the environmental controller, eliminating the deviations or noise that may exist in a single data source. This ensures that the generated jog execution sequence not only conforms to the user's control intentions but also matches the actual operating capabilities of the equipment, thereby significantly improving the stability and reliability of jog control and avoiding execution failures or equipment damage caused by mismatch between control commands and equipment status.

[0053] Step S6: Generate a compressed transmission packet according to the stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, send the compressed transmission packet to the target environment controller and obtain the execution receipt.

[0054] In one specific embodiment, the process of performing step S6 may specifically include the following steps: Each execution instruction in the stable jogging execution sequence is encapsulated according to a preset data frame format, and the encapsulated data frame is compressed and encoded using a lossless compression algorithm to generate a compressed transmission packet. Obtain the byte length parameter of the compressed transmission packet and determine whether the byte length parameter is less than or equal to the preset length threshold; If so, the compressed transmission packet is sent to the target environment controller via the wireless communication module; The system receives an acknowledgment response from the target environment controller after successfully parsing the compressed transmission packet, and uses this acknowledgment response as the execution receipt.

[0055] Specifically, each execution instruction in the stable jogging execution sequence obtained in step S5 is encapsulated according to a preset data frame format. This preset data frame format is a predefined data structure comprising three parts: a frame header, a data payload, and a frame trailer. The frame header identifies the start position and frame type of the data frame; the data payload stores the specific content of the execution instruction, including timestamps, control parameter types, and control parameter values; and the frame trailer stores a checksum for the receiver to verify data integrity. After encapsulation, a lossless compression algorithm is used to compress and encode the encapsulated data frame, generating a compressed transmission packet. A lossless compression algorithm is one that does not lose any original information during compression and decompression; for example, Huffman coding or LZ77 compression algorithms can be used. Taking Huffman coding as an example, this algorithm counts the frequency of each character in the data frame and then assigns a variable-length code to each character based on the frequency, with higher-frequency characters receiving shorter codes, thus reducing the overall number of bits required for data storage. Through lossless compression, the number of bytes in the original data frame is effectively reduced while ensuring that the decompressed data is completely consistent with the uncompressed data, without affecting the accuracy of the control instructions.

[0056] After generating the compressed transmission packet, the system obtains the byte length parameter of the compressed transmission packet, i.e., the total number of bytes occupied by the compressed data. This byte length parameter is compared with a preset length threshold. The preset length threshold is a value pre-set based on the maximum single transmission unit of the wireless communication module and the reliability requirements of network transmission; for example, it can be set to 512 bytes or 1024 bytes. When the byte length parameter of the compressed transmission packet is less than or equal to the preset length threshold, it indicates that the data packet is suitable for single transmission under the current network conditions and will not be fragmented by the network layer due to excessive data size, thus reducing the risk of packet loss and retransmission. If the byte length parameter is greater than the preset length threshold, the system needs to split the stable point execution sequence, dividing it into multiple sub-sequences for encapsulation and compression until the compressed transmission packet corresponding to each sub-sequence meets the byte length requirement.

[0057] Once the byte length parameter meets the preset length threshold, the system sends the compressed transmission packet to the target environment controller via the wireless communication module. The wireless communication module can be a Wi-Fi module, Bluetooth module, or cellular mobile communication module, selected based on the communication protocol between the mobile phone and the environment controller. Upon receiving the compressed transmission packet, the target environment controller decompresses the data packet using the corresponding decompression algorithm to restore the original data frame. Then, it performs integrity verification on the data frame (e.g., verifying data integrity by checking the checksum at the end of the frame). If the verification passes, it executes the corresponding jogging operation according to the execution instructions in the data frame. After completing the above processing, the target environment controller generates a confirmation response message. This message includes a status code indicating successful data packet reception, a reception timestamp, a flag indicating execution readiness, and the actual operating parameters after executing the jogging instruction (e.g., actual position angle, actual operating speed, etc.). The environment controller then sends this confirmation response message back to the mobile phone via its wireless communication module. Upon receiving this confirmation response message, the mobile phone recognizes it as an execution receipt. For example, if no execution receipt is received within the predetermined timeout period of 2 seconds, or if the received receipt contains an error status code, the system determines that the transmission has failed, triggers the retransmission mechanism, and resends the compressed transmission packet until a valid execution receipt is received or the maximum number of retransmissions of 3 is reached.

[0058] Through the above compression, encapsulation, and transmission processing, the system effectively reduces the data load during wireless transmission, lowers network bandwidth usage and transmission latency, and confirms transmission reliability through the execution receipt mechanism, ensuring that the stable jogging execution sequence can be delivered completely and accurately to the target environment controller.

[0059] Step S7: Based on the execution receipt, determine whether the execution result meets the preset execution standard. If not, use the instruction correction logic to correct the jog control signal, use the corrected jog control signal, combine the state update frequency, update the operating state of the environmental controller, adjust the preset mapping relationship, and obtain the jog response rule.

[0060] In one specific embodiment, the process of performing step S7 may specifically include the following steps: The actual operating parameters of the environmental controller contained in the execution receipt are analyzed. The deviation of the actual operating parameters is compared with the preset execution standard to determine whether the execution result meets the preset execution standard. If not, the executable jog control signal is corrected using the instruction correction logic to obtain the corrected jog control signal. Based on the environmental controller's status update frequency, the corrected jog control signal is sent to the environmental controller to update its operating status. At the same time, the preset mapping relationship is adjusted based on the correction result to obtain the jog response rule.

[0061] Specifically, the actual operating parameters of the environmental controller contained in the execution feedback are analyzed. These actual operating parameters are the real state data returned by the environmental controller after executing the jog command, such as the actual reached position angle, actual operating speed, or actual output power. The deviation of these actual operating parameters is compared with the preset execution standard. The preset execution standard is a pre-set benchmark value or range used to measure whether the command execution is qualified. For example, for angle jog control, the preset execution standard can be set to the target angle ±1 degree. The specific method of deviation comparison is to calculate the absolute difference or relative deviation percentage between the actual operating parameters and the preset execution standard to obtain the deviation amount. It is then determined whether this deviation amount is within an acceptable range, that is, whether the execution result meets the preset execution standard.

[0062] If the deviation exceeds the acceptable range, it indicates that the execution result of this jog control signal has not met expectations. In this case, the system uses instruction correction logic to correct the executable jog control signal determined in step S4. This instruction correction logic adopts a cumulative error compensation mechanism based on historical deviations.

[0063] Specifically, the system maintains a historical deviation queue for each control parameter type. This queue stores the deviation amounts and corresponding control parameter values ​​of the five most recent execution results. When a correction is needed, the system retrieves the five most recent deviation records from the historical deviation queue and calculates a weighted moving average of the deviation amounts. Deviation records more recent than the current time are assigned higher weights, with weight coefficients set sequentially to 0.4, 0.25, 0.15, 0.1, and 0.1. Simultaneously, the system calculates the trend of deviation changes, i.e., the difference between the current deviation and the previous deviation.

[0064] The weighted moving average and trend are not directly input into the PID controller. Instead, they are used to determine whether to trigger PID correction and whether dynamic adjustment of the PID parameters is needed. The specific judgment logic is as follows: If the absolute value of the weighted moving average is greater than the preset deviation tolerance threshold, the PID correction process is triggered; if the absolute value of the trend is positive for three consecutive times and increases with each successive positive, indicating a diverging trend in the deviation, the proportional coefficient in the PID parameters is temporarily increased to 1.5 times the original value to enhance the suppression capability; if the absolute value of the trend is negative for three consecutive times and decreases with each successive negative, indicating a converging trend in the deviation, the proportional coefficient in the PID parameters is temporarily decreased to 0.8 times the original value to avoid overshoot.

[0065] The specific implementation of the PID correction process is as follows: The current deviation is used as the input to the PID controller, and the output of the PID controller is the adjustment amount to the original control parameters. The mathematical expression for the PID controller is: Adjustment amount Where e(t) is the current deviation; This is the proportionality coefficient, with a value of 2.0; This is the integral coefficient, with a value of 0.5; This is the differential coefficient, with a value of 0.1. Integral term. The calculation method is as follows: Take the sum of the most recent 5 deviations from the historical deviation queue, and multiply it by the sampling time interval of 0.1 seconds. Differential term. The calculation method is as follows: subtract the previous deviation from the current deviation, and divide by the sampling time interval of 0.1 seconds.

[0066] The calculated adjustment amount is added to the parameters of the original executable jog control signal to obtain the corrected jog control signal.

[0067] Upon receiving the corrected jog control signal, the system sends it to the environmental controller based on the environmental controller's status update frequency to update the controller's operating status. The status update frequency refers to the time interval within which the environmental controller can receive and process new commands. This frequency value is dynamically learned by monitoring the timestamp field in the controller's response, rather than relying on a preset fixed value. Specifically, the system counts the time intervals between sending a command and receiving a response several times recently, and takes the minimum value as the controller's fastest response cycle. This serves as the benchmark for the status update frequency, preventing controller overload or packet loss due to excessively rapid transmission.

[0068] After sending the corrected jog control signal and completing the state update, the system simultaneously adjusts the preset mapping relationship based on the correction result to obtain the jog response rule. The adjustment of the preset mapping relationship adopts a progressive update strategy based on confidence weighting. Specifically, each record in the preset mapping relationship, that is, the correspondence between a certain attitude fluctuation characteristic and the control command type and the initial value of the control parameter, is accompanied by a confidence weight, the initial value of which is set to a medium-high level, for example, 0.8. After a correction is completed, the system calculates the ratio between the current correction amount and the original initial parameter value. This ratio reflects the accuracy of the original mapping relationship. If the correction amount is small, it means that the original mapping relationship is relatively accurate, so the confidence weight of the mapping relationship is increased; if the correction amount is large, it means that the original mapping relationship has a large deviation, so its confidence weight is decreased. When updating the initial parameter value in the mapping relationship, the system does not directly replace the original value with the corrected parameter value, but performs a weighted update according to the confidence weight: the new initial parameter value is equal to the original initial parameter value multiplied by the confidence weight plus the corrected parameter value multiplied by one minus the confidence weight. Through this gradual update, the preset mapping relationship can quickly respond to changes in system error without causing drastic fluctuations due to a single abnormal deviation, thus maintaining the stability of the control logic. Simultaneously, when the confidence weight of a mapping relationship falls below a preset lower threshold multiple times consecutively, the system determines that the mapping relationship has failed, triggering a relearning mechanism to forcibly collect new attitude fluctuation features and recalibrate the mapping relationship. Finally, the mapping relationship formed after these adjustments becomes the optimized jog response rule. This rule is persistently stored locally on the phone for pattern matching in step S3 of the subsequent jog control process.

[0069] If the deviation comparison results show that the actual operating parameters meet the preset execution standards, the system does not need to make corrections, but still performs a small confidence weight increase operation on the currently used mapping relationship to enhance the reliability of the mapping relationship, and also forms a jog response rule for subsequent use.

[0070] Through the aforementioned feedback iterative optimization processes—integrating cumulative error compensation, proportional-integral-derivative control, dynamic learning of state update frequency, and confidence-weighted progressive updates—the system forms a complete intelligent closed loop from command issuance to execution verification and then to parameter correction and adaptive mapping. This mechanism not only compensates for single execution deviations but also learns error patterns from historical execution data, proactively predicting and suppressing potential future deviations. This continuously improves the control accuracy and stability of the jog control system during use, significantly enhancing the system's adaptability to changes in user habits, equipment aging, and environmental fluctuations.

[0071] It is understood that the executing entity of this application can be a mobile remote environment controller jog control system, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0072] The above describes the jog control method for the mobile phone remote environment controller in the embodiments of this application. The following describes the jog control system for the mobile phone remote environment controller in the embodiments of this application. Please refer to [link / reference]. Figure 4 One embodiment of the mobile phone remote environment controller jog control system in this application includes: The data perception filtering module is used to collect raw attitude perception data sets containing triaxial acceleration data and triaxial gyroscope data through mobile phone sensors, perform interference filtering on the raw attitude perception data sets, extract angular frequency parameters and amplitude parameters, and obtain attitude fluctuation characteristics. The intention persistence recognition module is used to determine whether there is a persistent feature in the attitude fluctuation features based on the attitude fluctuation features and the timestamp sequence distribution. If so, the persistent feature is identified as a dynamic adjustment intention. The pattern mapping and matching module is used to match the attitude fluctuation characteristics with the preset mapping relationship based on the dynamic adjustment intention, combined with the pattern comparison benchmark and the intention triggering condition, to obtain the initial jog control command prototype. The instruction optimization and evaluation module is used to optimize the initial jog control instruction prototype based on the initial jog control instruction prototype, combined with dynamic threshold matching rules and response delay evaluation, to obtain the optimized jog control instruction. If the actual response time of the optimized jog control instruction is lower than the preset time threshold, then the executable jog control signal is determined. The signal fusion and verification module is used to perform weighted data fusion of the executable jog control signal and the equipment feedback data from the environmental controller, and to perform feedback consistency verification to obtain a stable jog execution sequence. The instruction compression and transmission module is used to generate compressed transmission packets based on a stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, the compressed transmission packet is sent to the target environment controller to obtain an execution receipt. The feedback iteration optimization module is used to determine whether the execution result meets the preset execution standard based on the execution receipt. If not, it uses the instruction correction logic to correct the jog control signal, and uses the corrected jog control signal, combined with the state update frequency, to update the operating state of the environmental controller, adjust the preset mapping relationship, and obtain the jog response rule.

[0073] Through the collaborative work of the aforementioned components, the entire system constructs a complete closed loop from mobile phone posture perception to environmental controller response. The data perception filtering module filters interference from the raw triaxial acceleration and gyroscope data, extracting angular frequency and amplitude parameters that characterize the user's operational intent, providing reliable posture fluctuation features for subsequent processing. The intent continuity recognition module, combined with timestamp sequence distribution, employs continuous detection logic to effectively distinguish between intentional jogging operations and unintentional interference such as handshakes, reducing false triggers at the source. The pattern mapping and matching module, based on the identified dynamic adjustment intent, generates an initial jogging control command prototype by referring to a preset mapping relationship, achieving accurate conversion from posture features to control semantics. The command optimization and evaluation module further introduces dynamic threshold matching and response delay evaluation to optimize and adjust the command execution timing, ensuring that the actual response time meets preset requirements and avoiding operational lag caused by network or device latency. The signal fusion and verification module weightedly fuses the executable jogging control signal with the device feedback data returned by the environmental controller and performs consistency verification. Only when the deviation between the fused parameters and the current operating state is within the tolerance range is a stable jogging execution sequence generated, forming an effective closed-loop verification mechanism. The instruction compression and transmission module encapsulates and losslessly compresses the execution sequence, sending the instruction packet to the target controller while meeting the byte length threshold, thus balancing transmission efficiency and reliability. The feedback and iterative optimization module determines whether the execution result meets the standard based on the execution feedback. If deviations exist, it automatically corrects the control signal, updates the operating status of the environmental controller, and adjusts the preset mapping relationship, enabling the system to continuously optimize its control logic based on actual execution conditions. These modules cooperate and progress layer by layer, jointly achieving high accuracy, low latency response, and adaptive iterative capabilities for remote mobile control.

[0074] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the mobile phone remote environment controller jog control method.

[0075] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a mobile remote environment controller to perform all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for inching control of a mobile phone remote environmental controller, characterized in that, include: S1. Collect raw attitude perception data set containing three-axis acceleration data and three-axis gyroscope data through mobile phone sensors, perform interference filtering on the raw attitude perception data set, extract angular frequency parameters and amplitude parameters, and obtain attitude fluctuation characteristics. S2. Based on the attitude fluctuation characteristics and combined with the timestamp sequence distribution, use the continuity detection logic to determine whether there are continuous characteristics in the attitude fluctuation characteristics. If so, the continuous characteristics are determined as the intention of dynamic adjustment. S3. Based on the dynamic adjustment intention, and combined with the pattern comparison benchmark and intention triggering condition, the attitude fluctuation characteristics are matched with the preset mapping relationship to obtain the initial jog control command prototype. S4. Based on the initial jog control command prototype, and combined with dynamic threshold matching rules and response delay evaluation, the initial jog control command prototype is optimized to obtain an optimized jog control command. If the actual response time of the optimized jog control command is lower than a preset time threshold, then an executable jog control signal is determined. S5. The executable jog control signal and the equipment feedback data of the environmental controller are weighted and fused, and feedback consistency verification is performed to obtain a stable jog execution sequence. S6. Generate a compressed transmission packet according to the stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, send the compressed transmission packet to the target environment controller to obtain an execution receipt. S7. Based on the execution receipt, determine whether the execution result meets the preset execution standard. If not, use the instruction correction logic to correct the jog control signal, use the corrected jog control signal, combine the state update frequency, update the operating state of the environment controller, adjust the preset mapping relationship, and obtain the jog response rule.

2. The method according to claim 1, characterized in that, S1 includes: The phone's built-in microelectromechanical inertial measurement unit (MEMS) simultaneously collects three-axis acceleration data and three-axis gyroscope data, which are then combined into a raw attitude perception data set. The original attitude perception data set is input into a low-pass filter with a preset cutoff frequency to filter out high-frequency noise components caused by hand shake and environmental vibration, so as to obtain an effective attitude data stream. The angular velocity values ​​are extracted from the three-axis gyroscope data in the effective attitude data stream to obtain the angular frequency parameters of the attitude change; The vector magnitude of the three-axis acceleration data in the effective attitude data stream is calculated to extract the amplitude parameters of the attitude change. The angular frequency parameter and the amplitude parameter are synchronously associated according to the timestamp to obtain the attitude fluctuation characteristics.

3. The method according to claim 1, characterized in that, S2 includes: Obtain the timestamp sequence distribution corresponding to the attitude fluctuation features, and arrange the feature values ​​of each sampling point in the attitude fluctuation features in time sequence according to the timestamp sequence distribution to obtain the attitude fluctuation time sequence array. A continuous detection logic is used to perform a sliding window scan on the attitude fluctuation time series array, and the correlation coefficient of the feature values ​​in adjacent windows is calculated. When the correlation coefficient exceeds the preset correlation threshold for N consecutive sliding windows, the attitude fluctuation feature is determined to have a persistent feature, where N is a positive integer; The duration of the jog operation and the duration of the hand shake are calculated based on the attitude fluctuation timing array. When the duration of the jog operation is higher than the first preset duration, the duration of the hand shake is lower than the second preset duration, and the first preset duration is greater than the second preset duration, the persistence feature is determined as a dynamic adjustment intention.

4. The method according to claim 1, characterized in that, S3 includes: Based on the dynamic adjustment intention, the pattern comparison benchmark and intention triggering condition pre-stored locally on the mobile terminal are retrieved, and the posture fluctuation feature is compared with the pattern comparison benchmark to calculate the feature similarity and obtain the feature matching degree parameter. Determine whether the feature matching degree parameter meets the preset trigger threshold range in the intent triggering condition. If so, match the attitude fluctuation feature with the preset mapping relationship accordingly. Based on the matching results, the corresponding control command type and initial value of control parameters are extracted from the preset mapping relationship and combined to generate an initial jog control command prototype.

5. The method according to claim 1, characterized in that, S4 includes: Based on the initial jog control command prototype, real-time network transmission delay parameters and target environment controller terminal processing delay parameters are collected to calculate the total command response time evaluation value. Retrieve the pre-stored dynamic threshold matching rules, compare the total response time evaluation value of the instruction with the response level threshold in the dynamic threshold matching rules, and determine the target response level; Based on the target response level, the execution timing of the initial jog control command prototype is optimized and adjusted to obtain the optimized jog control command. When the actual response time of the optimized jog control command is lower than the preset time threshold, the optimized jog control command is determined as an executable jog control signal.

6. The method according to claim 1, characterized in that, S5 includes: The device feedback data transmitted back from the environmental controller is received by the wireless communication module. The executable jog control signal and the device feedback data are timestamp aligned to obtain a synchronization data group. The executable jog control signal in the synchronous data group is assigned a first weighting coefficient, the device feedback data is assigned a second weighting coefficient, and weighted data fusion is performed to obtain fused control parameters. A feedback consistency check is performed on the fusion control parameters to determine whether the deviation between the fusion control parameters and the current operating state of the environmental controller is within a preset tolerance range. If so, the fusion control parameters are arranged in chronological order to generate a stable jogging execution sequence.

7. The method according to claim 1, characterized in that, S6 includes: Each execution instruction in the stable jogging execution sequence is encapsulated according to a preset data frame format, and the encapsulated data frame is compressed and encoded using a lossless compression algorithm to generate a compressed transmission packet. Obtain the byte length parameter of the compressed transmission packet, and determine whether the byte length parameter is less than or equal to a preset length threshold; If so, the compressed transmission packet is sent to the target environment controller via the wireless communication module; The system receives the confirmation response information returned by the target environment controller after successfully parsing the compressed transmission packet, and identifies the confirmation response information as an execution receipt.

8. The method according to claim 1, characterized in that, S7 includes: The actual operating parameters of the environmental controller contained in the execution receipt are analyzed, and the deviation of the actual operating parameters is compared with the preset execution standard to determine whether the execution result meets the preset execution standard. If not, the executable jog control signal is corrected using instruction correction logic to obtain the corrected jog control signal. Based on the state update frequency of the environmental controller, the corrected jog control signal is sent to the environmental controller to update the operating state of the environmental controller. At the same time, the preset mapping relationship is adjusted based on the correction result to obtain the jog response rule.

9. A mobile phone remote environment controller jog control system, used to implement the method as described in any one of claims 1-8, characterized in that, include: The data perception filtering module is used to collect raw attitude perception data set containing triaxial acceleration data and triaxial gyroscope data through the mobile phone sensor, perform interference filtering on the raw attitude perception data set, extract angular frequency parameters and amplitude parameters, and obtain attitude fluctuation characteristics. The intention persistence recognition module is used to determine whether there is a persistence feature in the attitude fluctuation feature based on the attitude fluctuation feature and the timestamp sequence distribution, and if so, the persistence feature is determined as a dynamic adjustment intention. The pattern mapping and matching module is used to match the attitude fluctuation characteristics with the preset mapping relationship based on the dynamic adjustment intention, combined with the pattern comparison benchmark and the intention triggering condition, to obtain the initial jog control command prototype. The instruction optimization and evaluation module is used to optimize the initial jog control instruction prototype based on the initial jog control instruction prototype, combined with dynamic threshold matching rules and response delay evaluation, to obtain the optimized jog control instruction. If the actual response time of the optimized jog control instruction is lower than a preset time threshold, then an executable jog control signal is determined. The signal fusion and verification module is used to perform weighted data fusion of the executable jog control signal and the equipment feedback data of the environmental controller, and to perform feedback consistency verification to obtain a stable jog execution sequence. The instruction compression and transmission module is used to generate a compressed transmission packet based on the stable jogging execution sequence. If the byte length parameter of the compressed transmission packet meets the preset length threshold, the compressed transmission packet is sent to the target environment controller to obtain an execution receipt. The feedback iteration optimization module is used to determine whether the execution result meets the preset execution standard based on the execution receipt. If not, it uses the instruction correction logic to correct the jog control signal, and uses the corrected jog control signal, combined with the state update frequency, to update the operating state of the environmental controller, adjust the preset mapping relationship, and obtain the jog response rule.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the mobile phone remote environment controller jog control method as described in any one of claims 1-8.

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