Forklift truck electric cylinder lifting control method based on deviation correction

Abstract

The present application relates to the technical field of electric cylinder control, and proposes a fork type mobile robot forklift electric cylinder lifting control method based on deviation correction, which comprises: generating an initial driving control signal according to a target lifting position and issuing it to a driving unit. When the equipment is lifted, the position, speed and torque multi-dimensional operation parameters are collected, the time stamp is aligned to form a synchronous operation parameter sequence, and the sequence parameters are extracted to obtain deviation characteristic information. The historical deviation data and the deviation characteristic information are fused to calculate the correction weight factor, and the factor is used to dynamically adjust the proportional gain and the integral time coefficient to generate a dynamic correction parameter set. The parameter set is superimposed on the initial driving control signal to obtain a corrected driving control signal, the multi-dimensional operation parameters are continuously collected and updated using the signal, and the dynamic correction parameter set is iteratively optimized in a closed loop to finally output the target driving control signal. The present application can improve the efficiency of the fork type mobile robot forklift electric cylinder lifting control.

Description

Technical Field

[0001] This invention relates to the field of electric cylinder control technology, and in particular to a lifting control method for electric cylinders of forklift mobile robots based on deviation correction. Background Technology

[0002] Currently, the lifting control of electric cylinders in forklift mobile robots mostly adopts a closed-loop control scheme with fixed parameters. Drive signals are generated based on preset control parameters to drive the electric cylinder to perform lifting actions, achieving basic position control functions under ideal working conditions. However, due to factors such as load weight fluctuations and nonlinear changes in mechanical transmission resistance with lifting height in actual operating scenarios, the fixed-parameter control mode cannot dynamically adapt to changes in working conditions. This easily leads to problems such as lag in lifting position tracking and large fluctuations in running speed, directly reducing the positioning accuracy and operational stability of the electric cylinder lifting, making it difficult to meet the application requirements of high-precision warehouse stacking operations.

[0003] While some existing improvement schemes introduce real-time deviation feedback correction logic, they only adjust control parameters once based on instantaneous operational deviations, without combining historical operational deviation data of the electric cylinder for cumulative analysis. This makes it impossible to effectively identify the inherent error characteristics of the system and the long-term trend of deviation changes. Furthermore, the existing correction mechanism lacks a complete closed-loop iterative optimization chain. The corrected control parameters cannot be continuously iterated and corrected as the electric cylinder's operating status is updated, resulting in insufficient adaptability and effectiveness of deviation correction. Consequently, the overall efficiency and stability of the electric cylinder lifting control cannot be continuously improved, making it unsuitable for complex and ever-changing actual operating scenarios. Therefore, improving the efficiency of electric cylinder lifting control for forklift mobile robots has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a lifting control method for electric cylinders of forklift mobile robots based on deviation correction, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a forklift lifting control method for a forklift mobile robot based on deviation correction, comprising:

[0006] A. Based on the target lifting position parameters of the forklift electric cylinder, generate the initial drive control signal of the forklift electric cylinder, and send the initial drive control signal to the drive unit of the forklift electric cylinder;

[0007] B. During the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are collected to obtain the multi-dimensional operating parameters of the forklift electric cylinder, and the multi-dimensional operating parameters are timestamped to obtain the synchronous operating parameter sequence of the forklift electric cylinder.

[0008] C. Extract deviation features from the parameters in the synchronous operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder;

[0009] D. The historical deviation data of the forklift electric cylinder is weighted and fused with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder;

[0010] E. Based on the correction weighting factor, the proportional gain coefficient and integral time coefficient of the forklift electric cylinder are dynamically adjusted to obtain the dynamic correction parameter set of the forklift electric cylinder;

[0011] F. The dynamic correction parameter set is correlated and superimposed with the initial drive control signal to obtain the correction drive control signal of the forklift electric cylinder;

[0012] G. Apply the corrected drive control signal to continuously collect the updated multi-dimensional operating parameters of the forklift electric cylinder, and perform closed-loop iterative optimization on the dynamic correction parameter set based on the updated multi-dimensional operating parameters to obtain the target drive control signal of the forklift electric cylinder.

[0013] In a preferred embodiment, generating the initial drive control signal for the forklift electric cylinder based on the target lifting position parameters of the forklift electric cylinder includes:

[0014] Obtain the target lifting position parameters of the forklift electric cylinder and perform a validity check on the target lifting position parameters to obtain the position setting value of the forklift electric cylinder;

[0015] Based on the position setting value, the preset drive control parameter library is searched to obtain the initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude of the forklift electric cylinder.

[0016] The position setting value, the initial proportional gain coefficient, the initial integral time coefficient, the initial drive current amplitude, and the initial drive voltage amplitude are encapsulated into the initial drive control signal of the forklift electric cylinder.

[0017] In a preferred embodiment, during the lifting process, the real-time position feedback signal, real-time operating speed data, and real-time output torque data of the forklift electric cylinder are collected to obtain multi-dimensional operating parameters of the forklift electric cylinder. These multi-dimensional operating parameters are then timestamped to obtain a sequence of synchronized operating parameters for the forklift electric cylinder, including:

[0018] During the sampling period of the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are received;

[0019] The data formats of the real-time position feedback signal, the real-time running speed data, and the real-time output torque data are unified to obtain the initial multi-dimensional data frame of the forklift electric cylinder;

[0020] The data in the initial multidimensional data frame are appended with the time label of the current sampling time to obtain the multidimensional operating parameters of the forklift electric cylinder;

[0021] Using a preset time interval as a reference window, the data frames in the multi-dimensional operating parameters are traversed, and data frames with the same reference window are merged into the synchronization data of the forklift electric cylinder.

[0022] The synchronization data is corrected in the time domain to obtain the synchronization operation parameter sequence of the forklift electric cylinder.

[0023] In a preferred embodiment, the step of extracting deviation features from the parameters in the synchronized operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder includes:

[0024] The synchronous operation parameter sequence is analyzed in multiple dimensions to obtain the real-time position feedback signal and real-time output torque data of the forklift electric cylinder;

[0025] The instantaneous position deviation value of the forklift electric cylinder is obtained by performing differential analysis on the real-time position feedback signal and the position set value of the forklift electric cylinder.

[0026] By performing a discreteness analysis on the instantaneous position deviation value, the speed fluctuation amplitude of the forklift electric cylinder can be obtained;

[0027] The torque change rate of the forklift electric cylinder is obtained by numerically differentiating the real-time output torque data.

[0028] Based on the timestamps in the synchronous operation parameter sequence, the deviation characteristic information of the forklift electric cylinder is obtained by associating the instantaneous position deviation value, the speed fluctuation amplitude, and the torque change rate.

[0029] In a preferred embodiment, the step of weightedly fusing the historical deviation data of the forklift electric cylinder with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder includes:

[0030] The historical deviation feature vector of the forklift electric cylinder is retrieved, and the historical deviation feature vector is arranged according to the time sequence to obtain the historical deviation data sequence of the forklift electric cylinder.

[0031] The current instantaneous deviation value, current speed fluctuation amplitude, and current torque change rate are extracted from the deviation feature information to obtain the current deviation feature vector of the forklift electric cylinder;

[0032] The historical deviation data sequence is cumulatively integrated to obtain the total historical deviation contribution of the historical deviation data sequence.

[0033] The correction weight factor of the forklift electric cylinder is obtained by fusing the current deviation feature vector with the total historical deviation contribution value.

[0034] In a preferred embodiment, the formula for calculating the correction weighting factor is:

[0035] ;

[0036] in, This represents the correction weighting factor. This represents the overall magnitude of the current deviation eigenvector. This represents the total contribution of the historical deviation. This indicates the preset adjustment coefficient, and , This represents the deviation ratio between the current deviation feature vector and the mean vector of the historical deviation data sequence.

[0037] In a preferred embodiment, the dynamic adjustment of the proportional gain coefficient and integral time coefficient of the forklift electric cylinder based on the correction weighting factor to obtain the dynamic correction parameter set of the forklift electric cylinder includes:

[0038] Obtain the initial proportional gain coefficient and initial integral time coefficient of the forklift electric cylinder;

[0039] The numerical range of the correction weight factor is determined to obtain the current correction level of the forklift electric cylinder;

[0040] Based on the current correction level, retrieve the corresponding gain adjustment reference value and integral adjustment reference value from the preset correction level parameter correspondence;

[0041] The sum of the gain adjustment reference value and the initial proportional gain coefficient is used as the dynamic proportional gain coefficient of the forklift electric cylinder.

[0042] The dynamic integral time coefficient of the forklift electric cylinder is obtained by superimposing the integral adjustment reference value and the initial integral time coefficient.

[0043] By integrating the dynamic proportional gain coefficient and the dynamic integral time coefficient, the dynamic correction parameter set of the forklift electric cylinder is obtained.

[0044] In a preferred embodiment, the step of associating and superimposing the dynamic correction parameter set with the initial drive control signal to obtain the corrected drive control signal for the forklift electric cylinder includes:

[0045] The initial drive control signal is decapsulated to obtain the initial drive control parameter set of the forklift electric cylinder;

[0046] The dynamic correction parameter set is analyzed to obtain the dynamic proportional gain coefficient and dynamic integral time coefficient of the forklift electric cylinder;

[0047] The corrected drive control signal for the forklift electric cylinder is obtained by replacing the initial proportional gain coefficient and the initial integral time coefficient in the initial drive control parameter set with the dynamic proportional gain coefficient and the dynamic integral time coefficient.

[0048] In a preferred embodiment, the application of the corrected drive control signal to continuously collect updated multi-dimensional operating parameters of the forklift electric cylinder includes:

[0049] The correction drive control signal is sent to the drive unit of the forklift electric cylinder to drive the forklift electric cylinder to perform the corrected lifting action;

[0050] During the execution of the corrected lifting action, the updated position feedback signal, updated running speed data, and updated output torque data of the forklift electric cylinder are collected at preset sampling time intervals.

[0051] The updated position feedback signal, the updated running speed data, and the updated output torque data are timestamped, and the tagged data are combined and encoded to obtain the updated multidimensional operating parameters of the forklift electric cylinder.

[0052] In a preferred embodiment, the step of performing closed-loop iterative optimization of the dynamic correction parameter set based on the updated multidimensional operating parameters to obtain the target drive control signal for the forklift electric cylinder includes:

[0053] The updated multidimensional operating parameters are analyzed for deviation to obtain the updated deviation feature vector of the forklift electric cylinder.

[0054] When the instantaneous position deviation value in the updated deviation feature vector is continuously less than the preset allowable deviation threshold, the iterative optimization of the dynamic correction parameter set is stopped, and the updated deviation feature vector is correlated and compared with the dynamic correction parameter set to obtain the updated correction weight factor of the forklift electric cylinder.

[0055] Based on the updated correction weight factor, an updated dynamic correction parameter set for the forklift electric cylinder is generated;

[0056] The updated dynamic correction parameter set is correlated and superimposed with the initial drive control parameters in the initial drive control signal to obtain the target drive control signal for the forklift electric cylinder.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. This invention improves the accuracy and operational stability of forklift electric cylinder lifting control by collecting multi-dimensional operating parameters and synchronizing timestamps, extracting multi-dimensional deviation features to generate correction weight factors, and dynamically adjusting the proportional gain coefficient and integral time coefficient to achieve precise correction of the drive control signal, reduce position deviation during the lifting process, smooth speed fluctuations and torque mutations, and ensure stable execution of the lifting action.

[0059] 2. This invention improves the response efficiency and adaptability of the electric cylinder lifting control of forklifts by combining historical deviation data and real-time deviation information to complete weighted fusion calculations. It relies on a closed-loop iterative optimization mechanism to continuously update correction parameters and drive control signals, so that the control logic can dynamically adapt to the electric cylinder's operating state, optimize the execution efficiency of the overall control process, and enhance the operational reliability and working condition adaptability of the electric cylinder lifting process. Attached Figure Description

[0060] Figure 1 A flowchart illustrating a method for lifting a forklift using an electric cylinder based on deviation correction for an embodiment of the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] This application provides a method for lifting control of a forklift's electric cylinder based on deviation correction. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0064] Reference Figure 1The diagram shown is a flowchart illustrating a forklift lifting control method for a forklift mobile robot based on deviation correction, according to an embodiment of the present invention. In this embodiment, the forklift lifting control method for a forklift mobile robot based on deviation correction includes:

[0065] A. Based on the target lifting position parameters of the forklift electric cylinder, generate the initial drive control signal of the forklift electric cylinder, and send the initial drive control signal to the drive unit of the forklift electric cylinder;

[0066] In this embodiment of the invention, generating the initial drive control signal for the forklift electric cylinder based on the target lifting position parameters of the forklift electric cylinder includes:

[0067] Obtain the target lifting position parameters of the forklift electric cylinder and perform a validity check on the target lifting position parameters to obtain the position setting value of the forklift electric cylinder;

[0068] Based on the position setting value, the preset drive control parameter library is searched to obtain the initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude of the forklift electric cylinder.

[0069] The position setting value, the initial proportional gain coefficient, the initial integral time coefficient, the initial drive current amplitude, and the initial drive voltage amplitude are encapsulated into the initial drive control signal of the forklift electric cylinder.

[0070] The system receives the target lifting position parameters of the forklift electric cylinder from the upper-level operation scheduling system, reads the maximum and minimum lifting heights calibrated by the electric cylinder before it leaves the factory as the legality verification benchmark, compares the value of the target lifting position parameter with the verification benchmark one by one, confirms that the value of the target lifting position parameter is within the range of the maximum and minimum lifting heights, and determines the target lifting position parameter that has passed the verification as the position setting value of the forklift electric cylinder.

[0071] A drive control parameter library was pre-constructed through five sets of calibration tests: no load, 25% rated load, 50% rated load, 75% rated load, and 100% rated load. The entire stroke of the electric cylinder was divided into 10 equidistant lifting position intervals. Under each load level, the proportional gain coefficient and integral time coefficient were adjusted for each lifting position interval to ensure that the steady-state deviation of the lifting position within the interval was less than 0.2% and the overshoot was less than 5%. The adjusted control parameters were associated with and stored with the corresponding lifting position interval and load level to form a structured drive control parameter library. The lifting position interval into which the position setpoint falls was determined. Using the lifting position interval as the retrieval index, a matching search was performed in the drive control parameter library to read all the control parameters stored under the corresponding index. From this, the initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude of the forklift electric cylinder were extracted.

[0072] According to the preset fixed serial signal frame structure, the position setting value, initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude are sequentially filled into the corresponding data bits. An agreed fixed frame header identifier is added at the beginning of the data frame, and an accumulation and check field is added at the end of the data frame. All fields are combined in the agreed order to form a complete data frame format, thus obtaining the initial drive control signal of the forklift electric cylinder.

[0073] The beneficial effects are that by verifying the legality of the target lifting position parameters, the validity of the input parameters can be guaranteed, avoiding control anomalies caused by invalid parameters. Relying on the preset drive control parameter library to quickly retrieve the appropriate initial control parameters can shorten the generation time of the initial control signal. Using a standardized encapsulation format to generate the initial drive control signal can ensure the accuracy of the drive unit's signal recognition, providing a stable and reliable initial control foundation for subsequent deviation correction and lifting control.

[0074] B. During the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are collected to obtain the multi-dimensional operating parameters of the forklift electric cylinder, and the multi-dimensional operating parameters are timestamped to obtain the synchronous operating parameter sequence of the forklift electric cylinder.

[0075] In this embodiment of the invention, during the lifting process, the real-time position feedback signal, real-time operating speed data, and real-time output torque data of the forklift electric cylinder are collected to obtain multi-dimensional operating parameters of the forklift electric cylinder. These multi-dimensional operating parameters are then timestamped to obtain a sequence of synchronized operating parameters for the forklift electric cylinder, including:

[0076] During the sampling period of the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are received;

[0077] The data formats of the real-time position feedback signal, the real-time running speed data, and the real-time output torque data are unified to obtain the initial multi-dimensional data frame of the forklift electric cylinder;

[0078] The data in the initial multidimensional data frame are appended with the time label of the current sampling time to obtain the multidimensional operating parameters of the forklift electric cylinder;

[0079] Using a preset time interval as a reference window, the data frames in the multi-dimensional operating parameters are traversed, and data frames with the same reference window are merged into the synchronization data of the forklift electric cylinder.

[0080] The synchronization data is corrected in the time domain to obtain the synchronization operation parameter sequence of the forklift electric cylinder.

[0081] After the lifting action is officially started, the data acquisition command is triggered according to the pre-set fixed sampling cycle. The real-time position feedback signal is read from the position detection element installed at the end of the electric cylinder rod, the real-time running speed data is read from the speed detection element installed on the side of the electric cylinder drive shaft, and the real-time output torque data is read from the torque detection element installed at the output transmission end of the electric cylinder. The three types of raw data are stored in the corresponding data temporary storage unit to complete the reception.

[0082] The valid numerical portions of the three types of raw data are extracted separately. Redundant fields such as status flags and transmission check bits are removed from the raw data. All valid numerical values ​​are converted into a unified digital storage format with the same bit width. The three types of numerical values ​​are then filled into the corresponding field positions of the same data frame according to the pre-agreed field arrangement order, and combined to form the initial multidimensional data frame of the forklift electric cylinder.

[0083] Read the system global clock count value corresponding to the current sampling time as a time tag, add the time tag to the reserved field in the frame header of the initial multidimensional data frame, so that each frame of data carries a unique time identifier corresponding to the sampling time. The data frame after the time tag is added is the multidimensional operating parameter of the forklift electric cylinder.

[0084] A fixed-duration time reference window is preset, and all data frames of multi-dimensional operating parameters are read sequentially according to time sequence. The reference window interval of the time tag carried by each frame is compared one by one. All data frames whose time tags fall within the same reference window interval are collected into the same data group. The data collected in each group is the synchronization data of the forklift electric cylinder.

[0085] Using the center time of each reference window as a unified standard time point, the time difference between the time tag of each data frame in the same group of synchronous data and the standard time point is calculated. The values ​​in each data frame are converted and corrected according to the proportion of the time difference to the duration of the reference window, so that the time reference of all data in the same group is unified to the standard time point. All data groups that have completed time domain correction are arranged in chronological order to obtain the synchronous operation parameter sequence of the forklift electric cylinder.

[0086] The beneficial effects are that the synchronous acquisition and format unification of multi-source operating data can ensure the compatibility and processability of different types of data; the addition of time labels and the merging of benchmark windows can achieve time dimension matching of multi-dimensional data; and the time domain correction can eliminate the sampling time deviation of different detection elements, ensuring the time consistency of subsequent deviation feature extraction, and providing an accurate and reliable synchronous data foundation for subsequent deviation correction calculation.

[0087] C. Extract deviation features from the parameters in the synchronous operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder;

[0088] In this embodiment of the invention, the step of extracting deviation features from the parameters in the synchronized operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder includes:

[0089] The synchronous operation parameter sequence is analyzed in multiple dimensions to obtain the real-time position feedback signal and real-time output torque data of the forklift electric cylinder;

[0090] The instantaneous position deviation value of the forklift electric cylinder is obtained by performing differential analysis on the real-time position feedback signal and the position set value of the forklift electric cylinder.

[0091] By performing a discreteness analysis on the instantaneous position deviation value, the speed fluctuation amplitude of the forklift electric cylinder can be obtained;

[0092] The torque change rate of the forklift electric cylinder is obtained by numerically differentiating the real-time output torque data.

[0093] Based on the timestamps in the synchronous operation parameter sequence, the deviation characteristic information of the forklift electric cylinder is obtained by associating the instantaneous position deviation value, the speed fluctuation amplitude, and the torque change rate.

[0094] According to the pre-agreed rules for dividing the synchronous data frame fields, each set of data in the synchronous operation parameter sequence is read frame by frame. The physical quantity attribute corresponding to each field is identified. The field content corresponding to the position detection quantity is extracted as the real-time position feedback signal of the forklift electric cylinder, and the field content corresponding to the torque detection quantity is extracted as the real-time output torque data of the forklift electric cylinder.

[0095] Retrieve the pre-stored position setting value of the forklift electric cylinder, take the real-time position feedback signal value corresponding to the same timestamp and perform a difference calculation with the position setting value value, and determine the result of the calculation as the instantaneous position deviation value of the forklift electric cylinder.

[0096] An analysis sample set is formed by selecting the instantaneous position deviation values ​​corresponding to a continuous preset number of sampling times. The average value of all values ​​in the set is calculated as the benchmark reference value. The difference between each value in the set and the benchmark reference value is calculated. The absolute value of all differences is taken and the average value is calculated. This average value is the speed fluctuation amplitude of the forklift electric cylinder.

[0097] Select the real-time output torque data corresponding to two adjacent sampling times, calculate the numerical difference between the two torque data, calculate the time interval between the two sampling times, divide the torque numerical difference by the time interval length to obtain the torque change value per unit time, which is the torque change rate of the forklift electric cylinder.

[0098] Based on the timestamps carried by each set of data in the synchronous operation parameter sequence, the instantaneous position deviation value, speed fluctuation amplitude and torque change rate corresponding to the same timestamp are matched and combined one by one. The three matched feature quantities are arranged in a fixed order to form a single feature unit. The feature units at all times together constitute the deviation feature information of the forklift electric cylinder.

[0099] The beneficial effects are that target operation data can be accurately extracted through field parsing, the difference operation can directly quantify the degree of deviation of position control, the discreteness calculation can reflect the fluctuation level of position deviation, the torque change calculation per unit time can reflect the dynamic change rate of torque, and the feature matching based on timestamps can ensure the time consistency of multi-dimensional features, providing comprehensive and accurate deviation feature data support for the subsequent generation of correction weight factors.

[0100] D. The historical deviation data of the forklift electric cylinder is weighted and fused with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder;

[0101] In this embodiment of the invention, the step of weightedly fusing the historical deviation data of the forklift electric cylinder with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder includes:

[0102] The historical deviation feature vector of the forklift electric cylinder is retrieved, and the historical deviation feature vector is arranged according to the time sequence to obtain the historical deviation data sequence of the forklift electric cylinder.

[0103] The current instantaneous deviation value, current speed fluctuation amplitude, and current torque change rate are extracted from the deviation feature information to obtain the current deviation feature vector of the forklift electric cylinder;

[0104] The historical deviation data sequence is cumulatively integrated to obtain the total historical deviation contribution of the historical deviation data sequence.

[0105] The correction weight factor of the forklift electric cylinder is obtained by fusing the current deviation feature vector with the total historical deviation contribution value.

[0106] The formula for calculating the correction weighting factor is as follows:

[0107] ;

[0108] in, This represents the correction weighting factor. This represents the overall magnitude of the current deviation eigenvector. This represents the total contribution of the historical deviation. This indicates the preset adjustment coefficient, and , This represents the deviation ratio between the current deviation feature vector and the mean vector of the historical deviation data sequence.

[0109] The historical deviation feature vectors stored in the most recent 100 complete lifting operations are retrieved from the historical data storage unit of the electric cylinder control system. The sliding window length is fixed at 100 operations. According to the order of the operation sampling time corresponding to each group of historical deviation feature vectors, all historical deviation feature vectors are arranged in sequence to form a continuous and ordered data set, which is the historical deviation data sequence of the forklift electric cylinder.

[0110] The feature unit corresponding to the latest sampling time in the deviation feature information is located. The current instantaneous deviation value, the current speed fluctuation amplitude, and the current torque change rate are extracted from the feature unit. The three feature data are combined into a complete feature data unit according to the pre-agreed fixed field order. This set of feature data units is the current deviation feature vector of the forklift electric cylinder.

[0111] Read all feature values ​​in each group of historical deviation feature vectors in the historical deviation data sequence in chronological order, and sum up all the read feature values ​​one by one to obtain the cumulative total of all historical deviation feature data. This cumulative total is the total historical deviation contribution value of the historical deviation data sequence.

[0112] First, calculate the total value of all feature data within the current deviation feature vector. Then, calculate the ratio of this total value to the total value of historical deviation contribution to obtain the basic weight value. Next, adjust the basic weight value based on the degree of deviation between the current deviation feature and the average level of historical deviation. The final value obtained after adjustment is the correction weight factor of the forklift electric cylinder.

[0113] First, normalize the three dimensions of the current deviation feature vector: divide the instantaneous position deviation value by the rated stroke of the electric cylinder to obtain the normalized position deviation; divide the speed fluctuation amplitude by the rated lifting speed of the electric cylinder to obtain the normalized speed fluctuation; and divide the torque change rate by the rated torque change rate threshold of the electric cylinder to obtain the normalized torque change rate. Then, square the three normalized values, sum them, and take the square root to obtain the comprehensive amplitude, which ranges from [0, ...]. ].

[0114] The historical deviation feature vector amplitudes of the most recent 100 complete lifting operations are selected to form a sliding window sequence. The amplitudes of all normalized historical deviation feature vectors within the sliding window are summed to obtain the total historical deviation contribution value.

[0115] The preset adjustment coefficient range is [0.5, 3], with a default value of 1, which is used to adjust the steepness of the Sigmoid mapping; the more frequent the load fluctuations and the faster the deviation changes, the larger the value should be.

[0116] First, calculate the average value of each normalized dimension of all historical deviation feature vectors in the historical deviation data sequence to form a historical mean vector; calculate the L2 norm of the difference vector between the current normalized deviation feature vector and the historical mean vector, and then divide it by the L2 norm of the historical mean vector to obtain the deviation ratio.

[0117] This correction weight factor calculation process is used to integrate the current operational deviation status with the cumulative impact of historical deviations to generate the corresponding correction weight factor value.

[0118] The calculation process first calculates the proportion of the current deviation feature vector's comprehensive magnitude in the sum of its own and the historical deviation contribution values ​​to obtain the basic weight coefficient.

[0119] The calculation process then generates a gain adjustment coefficient based on the deviation ratio through nonlinear mapping. First, the product of the preset adjustment coefficient and the deviation ratio is calculated. The negative power of the product value is then raised to the natural constant to obtain the exponential result. The exponential result is then increased by one and the reciprocal is taken to obtain the mapping base value. The mapping base value is then increased by one to obtain the gain adjustment coefficient. Finally, the base weight coefficient and the gain adjustment coefficient are multiplied to obtain the final correction weight factor.

[0120] The generated correction weighting factor provides a quantitative basis for the subsequent dynamic adjustment of the proportional gain coefficient and integral time coefficient of the forklift electric cylinder, and directly determines the correction range of the control parameters.

[0121] When the overall magnitude of the current deviation eigenvector increases, the value of the basic weight coefficient also increases, and the output value of the correction weight factor increases synchronously.

[0122] As the total contribution of historical deviation increases, the value of the basic weight coefficient decreases accordingly, and the output value of the correction weight factor decreases synchronously.

[0123] When the deviation ratio between the current deviation feature vector and the mean vector of the historical deviation data sequence increases, the value of the gain adjustment coefficient increases accordingly, and the overall output value of the correction weight factor shows an upward trend.

[0124] When the preset adjustment coefficient value increases, the rate at which the gain adjustment coefficient changes with the deviation ratio also increases, and the steepness of the change in the value of the correction weight factor also increases.

[0125] The beneficial effects are that by retrieving and arranging historical deviation data in a time sequence, the historical error change pattern of the electric cylinder can be completely restored; by extracting the current deviation feature vector, the current operating deviation status can be accurately characterized; by accumulating and calculating the total contribution value of historical deviations, the overall impact of long-term operating errors can be quantified; and by fusing the current deviation with historical deviations, both real-time operating characteristics and inherent system error characteristics can be taken into account, making the obtained correction weight factor more in line with actual operating requirements, and providing a precise quantitative basis for the dynamic adjustment of subsequent control parameters.

[0126] E. Based on the correction weighting factor, the proportional gain coefficient and integral time coefficient of the forklift electric cylinder are dynamically adjusted to obtain the dynamic correction parameter set of the forklift electric cylinder;

[0127] In this embodiment of the invention, the dynamic adjustment of the proportional gain coefficient and integral time coefficient of the forklift electric cylinder based on the correction weighting factor to obtain the dynamic correction parameter set of the forklift electric cylinder includes:

[0128] Obtain the initial proportional gain coefficient and initial integral time coefficient of the forklift electric cylinder;

[0129] The numerical range of the correction weight factor is determined to obtain the current correction level of the forklift electric cylinder;

[0130] Based on the current correction level, retrieve the corresponding gain adjustment reference value and integral adjustment reference value from the preset correction level parameter correspondence;

[0131] The sum of the gain adjustment reference value and the initial proportional gain coefficient is used as the dynamic proportional gain coefficient of the forklift electric cylinder.

[0132] The dynamic integral time coefficient of the forklift electric cylinder is obtained by superimposing the integral adjustment reference value and the initial integral time coefficient.

[0133] By integrating the dynamic proportional gain coefficient and the dynamic integral time coefficient, the dynamic correction parameter set of the forklift electric cylinder is obtained.

[0134] The initial drive control signal is retrieved from the temporary storage area of ​​the control unit. The field position corresponding to the control parameter is located according to the preset frame structure of the signal. The numerical content in the corresponding field is read to obtain the initial proportional gain coefficient and initial integral time coefficient of the forklift electric cylinder.

[0135] The range of the correction weight factor [0,2] is pre-divided into four continuous numerical intervals, corresponding to four correction levels. The higher the level, the greater the degree of deviation and the stronger the correction intensity. The correspondence between the correction level parameters is pre-determined through calibration experiments. When the deviation increases, the proportional gain is increased to speed up the response speed and the integral time is increased to suppress overshoot.

[0136] A one-to-one mapping relationship is established between each correction level and its corresponding two adjustment values ​​in advance, and the relationship is stored to form a correction level parameter correspondence. The current correction level is used as the retrieval basis to perform a matching search in the correction level parameter correspondence, and the two adjustment values ​​corresponding to the level are read to obtain the gain adjustment reference value and the integral adjustment reference value respectively.

[0137] The specific correspondences are as follows: Level 1 correction (W∈[0,0.3]) has a gain adjustment reference value of 0 and an integral adjustment reference value of 0, meaning the initial parameters remain unchanged; Level 2 correction (W∈(0.3,0.8]) has a gain adjustment reference value of 10% of the initial proportional gain coefficient and an integral adjustment reference value of 5% of the initial integral time coefficient; Level 3 correction (W∈(0.8,1.5]) has a gain adjustment reference value of 25% of the initial proportional gain coefficient and an integral adjustment reference value of 15% of the initial integral time coefficient; Level 4 correction (W∈(1.5,2]) has a gain adjustment reference value of 40% of the initial proportional gain coefficient and an integral adjustment reference value of 30% of the initial integral time coefficient.

[0138] The obtained gain adjustment reference value is added to the initial proportional gain coefficient, and the result is directly determined as the dynamic proportional gain coefficient of the forklift electric cylinder.

[0139] The obtained integral adjustment reference value is added to the initial integral time coefficient, and the result is directly determined as the dynamic integral time coefficient of the forklift electric cylinder.

[0140] The dynamic proportional gain coefficient and dynamic integral time coefficient are combined into a unified parameter set according to a pre-agreed parameter arrangement order. A timestamp corresponding to the current sampling time is added to this parameter set. The combined parameter set is the dynamic correction parameter set of the forklift electric cylinder.

[0141] The beneficial effects are that extracting basic control parameters from the initial drive control signal can ensure the continuity of control logic before and after adjustment; determining the matching correction level by numerical range can realize the graded adaptation of correction intensity; relying on the preset correspondence to retrieve the adjustment reference value can ensure the standardization and reproducibility of the adjustment process; the method of superimposing adjustment values ​​based on initial parameters can realize the smooth transition of control parameters; and the integrated dynamic correction parameter set can be directly used for subsequent drive signal correction, effectively improving the degree of adaptation between control parameters and actual operating deviation state.

[0142] F. The dynamic correction parameter set is correlated and superimposed with the initial drive control signal to obtain the correction drive control signal of the forklift electric cylinder;

[0143] In this embodiment of the invention, the step of associating and superimposing the dynamic correction parameter set with the initial drive control signal to obtain the correction drive control signal for the forklift electric cylinder includes:

[0144] The initial drive control signal is decapsulated to obtain the initial drive control parameter set of the forklift electric cylinder;

[0145] The dynamic correction parameter set is analyzed to obtain the dynamic proportional gain coefficient and dynamic integral time coefficient of the forklift electric cylinder;

[0146] The corrected drive control signal for the forklift electric cylinder is obtained by replacing the initial proportional gain coefficient and the initial integral time coefficient in the initial drive control parameter set with the dynamic proportional gain coefficient and the dynamic integral time coefficient.

[0147] Read the complete data frame of the initial drive control signal. First, verify the sum field at the end of the frame to confirm that the data frame is complete and valid. Then, according to the pre-agreed frame structure field division rules, split each data bit in the data frame in sequence and extract all parameter items such as position set value, initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude. Combine all the extracted parameter items into a parameter set according to the original correspondence to obtain the initial drive control parameter set of the forklift electric cylinder.

[0148] Read the complete data content of the dynamic correction parameter set, locate the storage location corresponding to the proportional gain parameter and the integral time parameter according to the preset field arrangement order of the parameter set, read the numerical content of the corresponding location respectively, distinguish the physical meaning of the two parameters according to the preset parameter attribute identifier, and obtain the dynamic proportional gain coefficient and dynamic integral time coefficient of the forklift electric cylinder respectively.

[0149] Locate the parameter positions corresponding to the initial proportional gain coefficient and initial integral time coefficient in the initial drive control parameter set, remove the original parameter values ​​from their original positions, write the dynamic proportional gain coefficient into the parameter position corresponding to the original initial proportional gain coefficient, and write the dynamic integral time coefficient into the parameter position corresponding to the original initial integral time coefficient. Keep the original values ​​and arrangement of the remaining parameter items unchanged. Reassemble all the replaced parameters into a complete data frame according to the original frame structure of the initial drive control signal, recalculate and update the check field at the end of the frame, and obtain the corrected drive control signal for the forklift electric cylinder.

[0150] The beneficial effects are that the integrated drive signal can be decomposed into an independent and editable set of parameters through decapsulation, ensuring the accuracy and controllability of subsequent parameter replacement operations. Targeted analysis of the dynamic correction parameter set can accurately separate the two core control parameters to be updated. Updating parameters by in-situ replacement can retain the original settings of the remaining initial control parameters, avoiding unexpected changes in parameters in non-correction dimensions. The re-encapsulated correction drive control signal can be directly adapted to the signal recognition rules of the original drive unit, providing a compliant and accurate drive basis for the correction control of subsequent electric cylinder lifting actions.

[0151] G. Apply the corrected drive control signal to continuously collect the updated multi-dimensional operating parameters of the forklift electric cylinder, and perform closed-loop iterative optimization on the dynamic correction parameter set based on the updated multi-dimensional operating parameters to obtain the target drive control signal of the forklift electric cylinder.

[0152] In this embodiment of the invention, the application of the corrected drive control signal to continuously collect the updated multi-dimensional operating parameters of the forklift electric cylinder includes:

[0153] The correction drive control signal is sent to the drive unit of the forklift electric cylinder to drive the forklift electric cylinder to perform the corrected lifting action;

[0154] During the execution of the corrected lifting action, the updated position feedback signal, updated running speed data, and updated output torque data of the forklift electric cylinder are collected at preset sampling time intervals.

[0155] The updated position feedback signal, the updated running speed data, and the updated output torque data are timestamped, and the tagged data are combined and encoded to obtain the updated multidimensional operating parameters of the forklift electric cylinder.

[0156] The step of performing closed-loop iterative optimization of the dynamic correction parameter set based on the updated multidimensional operating parameters to obtain the target drive control signal for the forklift electric cylinder includes:

[0157] The updated multidimensional operating parameters are analyzed for deviation to obtain the updated deviation feature vector of the forklift electric cylinder.

[0158] When the instantaneous position deviation value in the updated deviation feature vector is continuously less than the preset allowable deviation threshold, the iterative optimization of the dynamic correction parameter set is stopped, and the updated deviation feature vector is correlated and compared with the dynamic correction parameter set to obtain the updated correction weight factor of the forklift electric cylinder.

[0159] Based on the updated correction weight factor, an updated dynamic correction parameter set for the forklift electric cylinder is generated;

[0160] The updated dynamic correction parameter set is correlated and superimposed with the initial drive control parameters in the initial drive control signal to obtain the target drive control signal for the forklift electric cylinder.

[0161] Through a fixed communication link between the control unit and the forklift electric cylinder drive unit, the encapsulated correction drive control signal is sent to the signal input interface of the drive unit according to a pre-agreed transmission protocol. After the drive unit performs integrity verification and decapsulation processing on the received signal, it outputs drive power of corresponding amplitude to the electric cylinder's power actuator according to the various control parameters in the signal, thereby driving the electric cylinder push rod to perform the corrected lifting action.

[0162] During the continuous execution of the corrected lifting action, the control system periodically triggers the acquisition command according to the preset fixed sampling time interval, and acquires the updated position feedback signal from the position detection element installed at the end of the electric cylinder rod, the updated running speed data from the speed detection element installed on the side of the electric cylinder drive shaft, and the updated output torque data from the torque detection element installed at the output transmission end of the electric cylinder, and stores the three types of acquired data into the corresponding data temporary storage area.

[0163] The system global clock value corresponding to the current sampling time is read as a time tag. This time tag is then attached to the corresponding field positions of the three types of updated operating data. According to the pre-agreed multidimensional data frame structure, the updated position feedback signal, updated operating speed data, and updated output torque data with the attached time tag are sequentially filled into the corresponding data bits. After the combination encoding is completed, a complete data frame is formed. This data frame is the updated multidimensional operating parameter of the forklift electric cylinder.

[0164] The data frames corresponding to the updated multidimensional operating parameters are parsed to extract the position feedback data and torque data. The position feedback data is then compared with the pre-stored position setting value to obtain the updated instantaneous position deviation value. The discreteness of a preset number of consecutive instantaneous position deviation values ​​is calculated to obtain the updated speed fluctuation amplitude. The torque data at adjacent sampling times are calculated to obtain the updated torque change rate. The three feature data are combined into feature units in a fixed order to obtain the updated deviation feature vector of the forklift electric cylinder.

[0165] The updated deviation feature vector is compared with the deviation correction adaptation range corresponding to the dynamic correction parameter set one by one. The matching degree between the current deviation state and the existing correction parameters is calculated. The updated correction weight factor of the forklift electric cylinder is obtained by weighted fusion calculation combined with the total historical deviation contribution value.

[0166] The updated correction weight factor is determined by a numerical range to identify the corresponding updated correction level. Based on this correction level, the corresponding gain adjustment reference value and integral adjustment reference value are retrieved from the preset correction level parameter correspondence. The gain adjustment reference value is superimposed with the initial proportional gain coefficient to obtain the updated dynamic proportional gain coefficient. The integral adjustment reference value is superimposed with the initial integral time coefficient to obtain the updated dynamic integral time coefficient. The two parameters are integrated into a parameter set according to a fixed format to obtain the updated dynamic correction parameter set of the forklift electric cylinder.

[0167] Extract the initial drive control parameters from the initial drive control signal, locate the parameter positions corresponding to the initial proportional gain coefficient and the initial integral time coefficient, and replace the initial parameters at the corresponding positions with the updated dynamic proportional gain coefficient and the updated dynamic integral time coefficient from the updated dynamic correction parameter set, while keeping the original values ​​and arrangement order of the remaining parameters unchanged. Re-encapsulate the original signal into a complete data frame according to the frame structure of the original signal to obtain the target drive control signal of the forklift electric cylinder.

[0168] During the closed-loop iterative optimization process, it is determined in real time whether the instantaneous position deviation value is less than the preset allowable deviation threshold, which is 0.1% of the rated stroke of the electric cylinder. When the instantaneous position deviation value is less than the allowable deviation threshold for three consecutive sampling periods, it is determined that the iteration has converged, the iteration is stopped, and the current drive control signal is output as the target drive control signal.

[0169] If the convergence condition is not met when the electric cylinder reaches the target lifting position, the current drive control signal is output directly and the iteration is terminated.

[0170] The beneficial effects are that sending the correction drive control signal to the drive unit can directly implement the lifting control logic after deviation correction; continuously collecting and updating multi-dimensional operating parameters can obtain the actual operating status of the electric cylinder after correction in real time; deviation analysis and weight factor calculation based on the updated operating data can verify the actual adjustment effect of the current correction parameters; and continuously updating the correction parameters and drive control signal through closed-loop iteration can enable the control logic to dynamically adapt to the operating status of the electric cylinder, gradually reduce the position deviation during the lifting process, smooth out speed fluctuations and torque mutations during operation, improve the accuracy and smoothness of electric cylinder lifting control, and ensure that the final generated target drive control signal can adapt to complex and ever-changing actual working conditions.

[0171] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0172] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A lifting control method for a forklift mobile robot based on deviation correction, characterized in that, The method includes: A. Based on the target lifting position parameters of the forklift electric cylinder, generate the initial drive control signal of the forklift electric cylinder, and send the initial drive control signal to the drive unit of the forklift electric cylinder; B. During the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are collected to obtain the multi-dimensional operating parameters of the forklift electric cylinder, and the multi-dimensional operating parameters are timestamped to obtain the synchronous operating parameter sequence of the forklift electric cylinder. C. Extract deviation features from the parameters in the synchronous operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder; D. The historical deviation data of the forklift electric cylinder is weighted and fused with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder; E. Based on the correction weighting factor, the proportional gain coefficient and integral time coefficient of the forklift electric cylinder are dynamically adjusted to obtain the dynamic correction parameter set of the forklift electric cylinder; F. The dynamic correction parameter set is correlated and superimposed with the initial drive control signal to obtain the correction drive control signal of the forklift electric cylinder; G. Apply the corrected drive control signal to continuously collect the updated multi-dimensional operating parameters of the forklift electric cylinder, and perform closed-loop iterative optimization on the dynamic correction parameter set based on the updated multi-dimensional operating parameters to obtain the target drive control signal of the forklift electric cylinder.

2. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The step of generating the initial drive control signal for the forklift electric cylinder based on the target lifting position parameters of the forklift electric cylinder includes: Obtain the target lifting position parameters of the forklift electric cylinder and perform a validity check on the target lifting position parameters to obtain the position setting value of the forklift electric cylinder; Based on the position setting value, the preset drive control parameter library is searched to obtain the initial proportional gain coefficient, initial integral time coefficient, initial drive current amplitude, and initial drive voltage amplitude of the forklift electric cylinder. The position setting value, the initial proportional gain coefficient, the initial integral time coefficient, the initial drive current amplitude, and the initial drive voltage amplitude are encapsulated into the initial drive control signal of the forklift electric cylinder.

3. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, During the lifting process, the real-time position feedback signal, real-time operating speed data, and real-time output torque data of the forklift electric cylinder are collected to obtain multi-dimensional operating parameters of the forklift electric cylinder. These multi-dimensional operating parameters are then timestamped to obtain a sequence of synchronous operating parameters for the forklift electric cylinder, including: During the sampling period of the lifting process, the real-time position feedback signal, real-time running speed data and real-time output torque data of the forklift electric cylinder are received; The data formats of the real-time position feedback signal, the real-time running speed data, and the real-time output torque data are unified to obtain the initial multi-dimensional data frame of the forklift electric cylinder; The data in the initial multidimensional data frame are appended with the time label of the current sampling time to obtain the multidimensional operating parameters of the forklift electric cylinder; Using a preset time interval as a reference window, the data frames in the multi-dimensional operating parameters are traversed, and data frames with the same reference window are merged into the synchronization data of the forklift electric cylinder. The synchronization data is corrected in the time domain to obtain the synchronization operation parameter sequence of the forklift electric cylinder.

4. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The step of extracting deviation features from the parameters in the synchronized operation parameter sequence to obtain the deviation feature information of the forklift electric cylinder includes: The synchronous operation parameter sequence is analyzed in multiple dimensions to obtain the real-time position feedback signal and real-time output torque data of the forklift electric cylinder; The instantaneous position deviation value of the forklift electric cylinder is obtained by performing differential analysis on the real-time position feedback signal and the position set value of the forklift electric cylinder. By performing a discreteness analysis on the instantaneous position deviation value, the speed fluctuation amplitude of the forklift electric cylinder can be obtained; The torque change rate of the forklift electric cylinder is obtained by numerically differentiating the real-time output torque data. Based on the timestamps in the synchronous operation parameter sequence, the deviation characteristic information of the forklift electric cylinder is obtained by associating the instantaneous position deviation value, the speed fluctuation amplitude, and the torque change rate.

5. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The step of weighting and fusing the historical deviation data of the forklift electric cylinder with the deviation feature information to obtain the correction weight factor of the forklift electric cylinder includes: The historical deviation feature vector of the forklift electric cylinder is retrieved, and the historical deviation feature vector is arranged according to the time sequence to obtain the historical deviation data sequence of the forklift electric cylinder. The current instantaneous deviation value, current speed fluctuation amplitude, and current torque change rate are extracted from the deviation feature information to obtain the current deviation feature vector of the forklift electric cylinder; The historical deviation data sequence is cumulatively integrated to obtain the total historical deviation contribution of the historical deviation data sequence. The correction weight factor of the forklift electric cylinder is obtained by fusing the current deviation feature vector with the total historical deviation contribution value.

6. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The formula for calculating the correction weighting factor is as follows: ； in, This represents the correction weighting factor. This represents the overall magnitude of the current deviation eigenvector. This represents the total contribution of the historical deviation. This indicates the preset adjustment coefficient, and , This represents the deviation ratio between the current deviation feature vector and the mean vector of the historical deviation data sequence.

7. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The dynamic adjustment of the proportional gain coefficient and integral time coefficient of the forklift electric cylinder based on the correction weight factor yields a dynamic correction parameter set for the forklift electric cylinder, including: Obtain the initial proportional gain coefficient and initial integral time coefficient of the forklift electric cylinder; The numerical range of the correction weight factor is determined to obtain the current correction level of the forklift electric cylinder; Based on the current correction level, retrieve the corresponding gain adjustment reference value and integral adjustment reference value from the preset correction level parameter correspondence; The sum of the gain adjustment reference value and the initial proportional gain coefficient is used as the dynamic proportional gain coefficient of the forklift electric cylinder. The dynamic integral time coefficient of the forklift electric cylinder is obtained by superimposing the integral adjustment reference value and the initial integral time coefficient. By integrating the dynamic proportional gain coefficient and the dynamic integral time coefficient, the dynamic correction parameter set of the forklift electric cylinder is obtained.

8. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The step of associating and superimposing the dynamic correction parameter set with the initial drive control signal to obtain the corrected drive control signal for the forklift electric cylinder includes: The initial drive control signal is decapsulated to obtain the initial drive control parameter set of the forklift electric cylinder; The dynamic correction parameter set is analyzed to obtain the dynamic proportional gain coefficient and dynamic integral time coefficient of the forklift electric cylinder; The corrected drive control signal for the forklift electric cylinder is obtained by replacing the initial proportional gain coefficient and the initial integral time coefficient in the initial drive control parameter set with the dynamic proportional gain coefficient and the dynamic integral time coefficient.

9. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 1, characterized in that, The application of the corrected drive control signal continuously collects the updated multi-dimensional operating parameters of the forklift electric cylinder, including: The correction drive control signal is sent to the drive unit of the forklift electric cylinder to drive the forklift electric cylinder to perform the corrected lifting action; During the execution of the corrected lifting action, the updated position feedback signal, updated running speed data, and updated output torque data of the forklift electric cylinder are collected at preset sampling time intervals. The updated position feedback signal, the updated running speed data, and the updated output torque data are timestamped, and the tagged data are combined and encoded to obtain the updated multidimensional operating parameters of the forklift electric cylinder.

10. The forklift lifting control method for a forklift mobile robot based on deviation correction as described in claim 9, characterized in that, The step of performing closed-loop iterative optimization of the dynamic correction parameter set based on the updated multidimensional operating parameters to obtain the target drive control signal for the forklift electric cylinder includes: The updated multidimensional operating parameters are analyzed for deviation to obtain the updated deviation feature vector of the forklift electric cylinder. When the instantaneous position deviation value in the updated deviation feature vector is continuously less than the preset allowable deviation threshold, the iterative optimization of the dynamic correction parameter set is stopped, and the updated deviation feature vector is correlated and compared with the dynamic correction parameter set to obtain the updated correction weight factor of the forklift electric cylinder. Based on the updated correction weight factor, an updated dynamic correction parameter set for the forklift electric cylinder is generated; The updated dynamic correction parameter set is correlated and superimposed with the initial drive control parameters in the initial drive control signal to obtain the target drive control signal for the forklift electric cylinder.

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