Electric drum control method and control system

Abstract

The present invention relates to the technical field of automatic control of electric rollers, and discloses an electric roller control method and control system. Within a first preset time period, a first starting point and a second starting point are established for the operating strokes of a target electric roller and a target conveyor belt, respectively, and a PID control coefficient is adjusted by gradient to drive the target electric roller to perform a transportation operation; M groups of stable transportation parameters are obtained at fixed time intervals, and each group of stable transportation parameters corresponds to the relative position of the target electric roller and the target conveyor belt; a stable transportation model is obtained by training based on the PID empirical coefficients, stable transportation parameters and the relative positions of the stable transportation parameters; and a stable transportation decision and a bumpy transportation decision are executed for the target electric roller, including: dynamically adjusting the PID empirical coefficient of the target electric roller in response to the stacking state of the transported goods to achieve stable transportation or bumpy flattening of the transported goods.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of electric rollers, and more particularly to a control method and control system for electric rollers. Background Art

[0002] Electric rollers automatically transport materials by directly attaching a conveyor belt to the outer ring of the roller. Due to their compact structure and easy maintenance, they are widely used in express delivery lines. However, in semi-open production environments, debris, dust, and other foreign matter can easily become embedded in the contact surface between the roller and the conveyor belt, causing friction characteristics to fluctuate non-constantly with travel. Traditional control strategies that rely on fixed PID parameters struggle to compensate for these friction disturbances in real time, easily leading to unstable express delivery and making it difficult for barcode scanners to accurately locate and identify express goods. Summary of the Invention

[0003] The present invention provides a motorized roller control method and a control system to solve the technical problems raised in the background art.

[0004] The present invention provides a method for controlling a motorized drum, comprising:

[0005] Step 1: within a first preset time period, establish a first starting point and a second starting point for the operating stroke of the target electric roller and the target conveyor belt, respectively, and adjust the PID control coefficient by gradient to drive the target electric roller to perform the transport operation;

[0006] Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt;

[0007] Step 2: training a stable transport model based on the PID empirical coefficients, the stable transport parameters, and the relative positions of the stable transport parameters; wherein the stable transport model is used to determine the predicted transport stability at any time within a second preset time period;

[0008] Step 3: Execute a stable transportation decision and a bumpy transportation decision for the target electric drum, including:

[0009] In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods;

[0010] Step 4: synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; if the execution results do not meet expectations, update the stable transportation model.

[0011] Furthermore, the transport stability parameters include: the target operating stroke displacement distance of the electric roller and the target operating stroke displacement distance of the conveyor belt.

[0012] Furthermore, relative positions include:

[0013] Based on the first starting point, each position of the target electric drum in the working stroke is encoded as follows: ,in, Indicates the first starting point, Indicates the operating stroke length of the target electric drum;

[0014] The relative position relative to the target conveyor belt includes:

[0015] Based on the second starting point, the target conveyor belt is coded at each position in the working stroke as follows: ,in, Indicates the second starting point, Indicates the operating stroke length of the target conveyor belt;

[0016] For the mth group of stable transport parameters; 1≤m≤M, m is a positive integer;

[0017] Get the starting position and ending position of the target electric drum's operating stroke at the mth moment ;in, ;

[0018] Get the starting position and ending position of the target conveyor belt's working stroke at the mth moment ;in, ;

[0019] Determine the relative position of the mth group of stable transport parameters as and .

[0020] Furthermore, based on the stable transport parameters and relative positions, a stable transport model is trained, including:

[0021] Calculating the transport stability includes: taking the difference between the operating stroke displacement distance of the target electric drum and the operating stroke displacement distance of the target conveyor belt in the rth group of stable transport parameters as the transport stability;

[0022] The normalized transport stability is used as the sample label;

[0023] The relative position corresponding to the rth group of stable transport parameters is constructed as a position vector;

[0024] The PID control coefficients corresponding to the rth group of stable transport parameters are constructed as an operation vector;

[0025] The position vector and the running vector are spliced ​​and normalized to obtain the sample data;

[0026] Wherein, 1≤r≤M, r≠m, and r is a positive integer;

[0027] The position vector represents the position vector constructed by the relative position corresponding to the stable transport parameter;

[0028] The operation matrix represents the operation vector constructed by the PID control coefficients corresponding to the stable transportation parameters;

[0029] A stable transportation model is obtained by training based on sample data and sample labels;

[0030] Among them, the hidden layer of the stable transportation model is constructed based on a one-dimensional convolutional neural network, and the hyperparameters of the hidden layer are updated according to the mean square error loss function.

[0031] Furthermore, the stacking state includes:

[0032] If the stack height is greater than or equal to the preset height threshold, the stack status is abnormal; otherwise, the stack status is normal.

[0033] Furthermore, within the second preset time period, the PID empirical coefficient of the target electric drum is dynamically adjusted to achieve stable transportation of the transported goods, including:

[0034] Step 31, obtaining the starting position of the operating stroke of the target electric drum and the starting position of the operating stroke of the target conveyor belt at the kth moment in the second preset time period;

[0035] Step 32: Initialize and generate S control schemes that meet the constraints; wherein the sth control scheme includes the predicted PID control coefficient at the kth moment, the predicted end position of the target motorized drum's operating stroke at the kth moment, and the predicted end position of the target conveyor belt's operating stroke at the kth moment;

[0036] Where, 1≤s≤S, s is a positive integer;

[0037] Constraints include:

[0038] The PID control coefficients are all within the corresponding control coefficient adjustment range;

[0039] The difference between the predicted end position and the starting position of the operating stroke of the target electric drum is less than or equal to the first preset displacement difference;

[0040] The difference between the predicted end position and the starting position of the operating stroke of the target conveyor belt is less than or equal to the second preset displacement difference;

[0041] Step 33: Based on the starting position and predicted ending position of the target motorized drum's operating stroke at the k-th moment, and the starting position and predicted ending position of the target conveyor belt's operating stroke at the k-th moment, respectively generate a position vector and a reference transport stability of the s-th control scheme;

[0042] Step 34, generating an operation vector of the sth control scheme based on the predicted PID control coefficient;

[0043] Step 35: concatenate the position vector and the running vector and perform normalization processing to use them as input parameters of the stable transportation model to obtain the predicted transportation stability output by the stable transportation model;

[0044] Step 36: If the difference between the predicted transport stability and the reference transport stability is less than or equal to the preset difference threshold, retain the sth control scheme; otherwise, delete the sth control scheme;

[0045] Step 37, repeat steps 34 to 36 until U control solutions are obtained, and proceed to step 38 or step 39;

[0046] Step 38 : Select the control scheme with the minimum predicted transportation stability among the U control schemes as the control scheme at the kth moment to achieve stable transportation of the transported goods.

[0047] Furthermore, the PID empirical coefficient of the target electric drum is dynamically adjusted to achieve bump-flattening of transported goods, including:

[0048] Step 39: Select the control scheme with the greatest predicted transport stability among the U control schemes as the control scheme at the kth moment to achieve bump-smoothing of the transported goods.

[0049] Furthermore, the stable transportation model is updated, including:

[0050] Synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; wherein the execution results include whether they meet expectations or do not meet expectations;

[0051] Count the cumulative number of times that do not meet expectations. If the cumulative number reaches the preset cumulative number, a full update of the stable transportation model will be triggered;

[0052] The full update includes:

[0053] Collect time series sample data and sample labels at fixed time intervals;

[0054] Load the sliding time window to extract sample data and sample labels; the end of the sliding time window corresponds to the moment when the full update is triggered;

[0055] Based on the sample data and sample labels extracted from the sliding time window, a stable transportation model is obtained by fully updating.

[0056] In a second aspect, a motorized drum control system is provided, which is applied to the motorized drum control method, and includes:

[0057] An operation driving module is used to establish a first starting point and a second starting point for the operation stroke of the target electric roller and the target conveyor belt respectively within a first preset time period, and to drive the target electric roller to perform a transportation operation by adjusting a PID control coefficient by gradient.

[0058] Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt;

[0059] a data acquisition module for PID empirical coefficients, stable transport parameters, and relative positions of the stable transport parameters to train a stable transport model; wherein the stable transport model is used to determine the predicted transport stability at any time within a second preset time period;

[0060] Automatic control module for executing stable and bumpy transport decisions for the target drum motor, including:

[0061] In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods;

[0062] The automatic update module synchronously obtains the execution results of the stable transportation decision or the bumpy transportation decision; if the execution result does not meet the expectations, the stable transportation model is updated.

[0063] The beneficial effects of the present invention are: reducing the shaking and slipping of materials during transportation through the "stable transportation" mode, and leveling uneven stacking through the "bumpy flattening" mode. The synergistic effect of the two significantly shortens the transportation cycle, reduces downtime and material loss, thereby maximizing overall transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a module diagram of a motorized drum control method of the present invention;

[0065] Figure 2 It is a flow chart of a motorized drum control system of the present invention. DETAILED DESCRIPTION

[0066] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0067] like Figure 1 and Figure 2 As shown, a method for controlling an electric drum includes:

[0068] Step 1: within a first preset time period, establish a first starting point and a second starting point for the operating stroke of the target electric roller and the target conveyor belt, respectively, and adjust the PID control coefficient by gradient to drive the target electric roller to perform the transport operation;

[0069] Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt;

[0070] Step 2: training a stable transport model based on the PID empirical coefficients, the stable transport parameters, and the relative positions of the stable transport parameters; wherein the stable transport model is used to determine the predicted transport stability at any time within a second preset time period;

[0071] Step 3: Execute a stable transportation decision and a bumpy transportation decision for the target electric drum, including:

[0072] In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods;

[0073] Step 4: synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; if the execution results do not meet expectations, update the stable transportation model.

[0074] In one embodiment of the present invention, the adjustment range of the PID control coefficient is confirmed by an expert, and the gradient adjustment is performed based on the adjustment range of the PID control coefficient confirmed by the expert.

[0075] Specifically, in the control system of the electric roller, the PID control coefficient (proportional P, integral I, differential D) is the core parameter of the PID controller, and the PID controller is the key tool to achieve precise speed regulation and stable transportation of the electric roller.

[0076] Specifically, the stable transportation decision corresponds to the stable transportation of the transported goods, and the bumpy transportation decision corresponds to the bumpy flattening of the transported goods.

[0077] For example, consider an assembly line transporting small express packages, driven by electric rollers and transported by conveyor belts. If a height measurement device (such as an infrared sensor) determines that the stacked packages have exceeded the height limit, a bumpy transport strategy is initiated to create sliding friction on the packages, flattening them for easier scanning by the barcode scanner and preventing missed scans or inspections. Once the height of the packages falls within the limit, a stable transport strategy is initiated to stabilize the package's position and facilitate positioning and scanning by the barcode scanner.

[0078] In one embodiment of the present invention, the transport stability parameter includes: a target operating stroke displacement distance of the electric drum and a target operating stroke displacement distance of the conveyor belt.

[0079] In detail, the displacement distance is obtained by measuring, for example:

[0080] An RDIF tag is placed on the surface of the conveyor belt, and a distance measuring device is placed on the other side. When the conveyor belt is in operation, the distance measuring device measures the displacement of the conveyor belt. Simultaneously, when the distance measuring device senses the RDIF tag, it resets to zero. Similarly, the measurement of the electric drum is also carried out.

[0081] In one embodiment of the present invention, the relative position includes:

[0082] Based on the first starting point, each position of the target electric drum in the working stroke is encoded as follows: ,in, Indicates the first starting point, Indicates the operating stroke length of the target electric drum;

[0083] The relative position relative to the target conveyor belt includes:

[0084] Based on the second starting point, the target conveyor belt is coded at each position in the working stroke as follows: ,in, Indicates the second starting point, Indicates the operating stroke length of the target conveyor belt;

[0085] For the mth group of stable transport parameters; 1≤m≤M, m is a positive integer;

[0086] Get the starting position and ending position of the target electric drum's operating stroke at the mth moment ;in, ;

[0087] Get the starting position and ending position of the target conveyor belt's working stroke at the mth moment ;in, ;

[0088] Determine the relative position of the mth group of stable transport parameters as and .

[0089] In one embodiment of the present invention, the target operating stroke of the electric drum is the circumference of the outer side of the electric drum, and the target operating stroke of the conveyor belt is the length of the conveyor belt.

[0090] In one embodiment of the present invention, data from the distance measuring device is acquired at fixed time intervals as the starting position and the ending position at corresponding moments.

[0091] In one embodiment of the present invention, the working stroke represents the running direction and running path of the electric drum, and the running direction and running path of the conveyor belt.

[0092] In one embodiment of the present invention, a stable transport model is obtained by training based on stable transport parameters and relative positions, including:

[0093] Calculating the transport stability includes: taking the difference between the operating stroke displacement distance of the target electric drum and the operating stroke displacement distance of the target conveyor belt in the rth group of stable transport parameters as the transport stability;

[0094] The normalized transport stability is used as the sample label;

[0095] The relative position corresponding to the rth group of stable transport parameters is constructed as a position vector;

[0096] The PID control coefficients corresponding to the rth group of stable transport parameters are constructed as an operation vector;

[0097] The position vector and the running vector are spliced ​​and normalized to obtain the sample data;

[0098] Wherein, 1≤r≤M, r≠m, and r is a positive integer;

[0099] The position vector represents the position vector constructed by the relative position corresponding to the stable transport parameter;

[0100] The operation matrix represents the operation vector constructed by the PID control coefficients corresponding to the stable transportation parameters;

[0101] A stable transportation model is obtained by training based on sample data and sample labels;

[0102] Among them, the hidden layer of the stable transportation model is constructed based on a one-dimensional convolutional neural network, and the hyperparameters of the hidden layer are updated according to the mean square error loss function.

[0103] Specifically, the smaller the transport stability (tending to 0), the better the displacement synchronization between the roller and the conveyor belt, and the less likely the express package is to slip or jitter due to speed difference; conversely, the greater the transport stability, the higher the risk of slipping of the express package.

[0104] For example, in the rth data set, the roller displacement is 0.5 meters and the conveyor belt displacement is 0.6 meters, so the transport stability is 0.1 meters. In this case, the package may move backward due to the faster conveyor belt, resulting in a deviation in the code scanning positioning.

[0105] Specifically, in order to eliminate the dimensional differences in transport stability under different working conditions, it is necessary to normalize it (normalize the maximum and minimum values) to a dimensionless value in the range of 0 to 1.

[0106] Relative position is to normalize the absolute position of the roller and the conveyor belt to a dimensionless value between 0 and 1. The position vector is a four-dimensional vector. In a certain set of data, the roller runs from 0.2 to 0.5 and the conveyor belt runs from 0.3 to 0.6. The position vector is The operating vector is a combination of the three PID control parameters and is a three-dimensional vector. The position vector and the operating vector are combined into an input that the model can process.

[0107] The core of the stable transport model is to learn the mapping between sample data and sample labels using a one-dimensional convolutional neural network. A typical one-dimensional convolutional neural network architecture includes the following: The input layer receives a seven-dimensional normalized feature vector. Convolutional layer 1 uses 32 filters with a kernel size of 3 (covering three continuous features) and a ReLU activation function to extract local features. Convolutional layer 2 uses 64 filters with a kernel size of 3 and a ReLU activation function to further extract features. A global average pooling layer compresses the convolutional layer output into a one-dimensional vector, reducing computational complexity. The fully connected layer outputs a single neuron, predicting the normalized transport stability. The mean squared error (MSE) loss function is used to calculate the deviation between the predicted value and the true value. The weights of the convolutional and fully connected layers are updated using the backpropagation algorithm to minimize the loss and ensure that the model output is as close to the sample label as possible.

[0108] In one embodiment of the present invention, the stacking state includes:

[0109] If the stack height is greater than or equal to the preset height threshold, the stack status is abnormal; otherwise, the stack status is normal.

[0110] In one embodiment of the present invention, the preset height threshold is determined by express delivery industry staff.

[0111] In one embodiment of the present invention, dynamically adjusting the PID empirical coefficient of the target electric drum within the second preset time period to achieve stable transportation of the transported goods includes:

[0112] Step 31, obtaining the starting position of the operating stroke of the target electric drum and the starting position of the operating stroke of the target conveyor belt at the kth moment in the second preset time period;

[0113] Step 32: Initialize and generate S control schemes that meet the constraints; wherein the sth control scheme includes the predicted PID control coefficient at the kth moment, the predicted end position of the target motorized drum's operating stroke at the kth moment, and the predicted end position of the target conveyor belt's operating stroke at the kth moment;

[0114] Where, 1≤s≤S, s is a positive integer;

[0115] Constraints include:

[0116] The PID control coefficients are all within the corresponding control coefficient adjustment range;

[0117] The difference between the predicted end position and the starting position of the operating stroke of the target electric drum is less than or equal to the first preset displacement difference;

[0118] The difference between the predicted end position and the starting position of the operating stroke of the target conveyor belt is less than or equal to the second preset displacement difference;

[0119] Step 33: Based on the starting position and predicted ending position of the target motorized drum's operating stroke at the k-th moment, and the starting position and predicted ending position of the target conveyor belt's operating stroke at the k-th moment, respectively generate a position vector and a reference transport stability of the s-th control scheme;

[0120] Step 34, generating an operation vector of the sth control scheme based on the predicted PID control coefficient;

[0121] Step 35: concatenate the position vector and the running vector and perform normalization processing to use them as input parameters of the stable transportation model to obtain the predicted transportation stability output by the stable transportation model;

[0122] Step 36: If the difference between the predicted transport stability and the reference transport stability is less than or equal to the preset difference threshold, retain the sth control scheme; otherwise, delete the sth control scheme;

[0123] Step 37, repeat steps 34 to 36 until U control solutions are obtained, and proceed to step 38 or step 39;

[0124] Step 38 : Select the control scheme with the minimum predicted transportation stability among the U control schemes as the control scheme at the kth moment to achieve stable transportation of the transported goods.

[0125] In one embodiment of the present invention, dynamically adjusting the PID empirical coefficient of the target electric drum to achieve bump-flattening of transported goods includes:

[0126] Step 39: Select the control scheme with the greatest predicted transport stability among the U control schemes as the control scheme at the kth moment to achieve bump-smoothing of the transported goods.

[0127] In one embodiment of the present invention, U<S, and U is a positive integer.

[0128] In one embodiment of the present invention, determining all U executable solutions at the kth moment based on an optimization algorithm specifically includes:

[0129] First, S control schemes that meet the constraints are randomly generated, including: PID control coefficients and the displacement distances of the electric drum and the conveyor belt.

[0130] If the difference between the transport stability corresponding to the random control scheme and the transport stability output by the model is small (less than or equal to the threshold), then the scheme is feasible. Otherwise, the random scheme is not feasible.

[0131] Then, until U feasible control solutions are obtained, the stacking state at the corresponding moment is responded to. If the stacking state is too high, the control solution with the highest transport stability is selected for execution. Otherwise, the control solution with the lowest transport stability is selected for execution, corresponding to the bump-flattening decision and the stable transport decision, respectively.

[0132] In one embodiment of the present invention, updating the stable transportation model includes:

[0133] Synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; wherein the execution results include whether they meet expectations or do not meet expectations;

[0134] Count the cumulative number of times that do not meet expectations. If the cumulative number reaches the preset cumulative number, a full update of the stable transportation model will be triggered;

[0135] The full update includes:

[0136] Collect time series sample data and sample labels at fixed time intervals;

[0137] Load the sliding time window to extract sample data and sample labels; the end of the sliding time window corresponds to the moment when the full update is triggered;

[0138] Based on the sample data and sample labels extracted from the sliding time window, a stable transportation model is obtained by fully updating.

[0139] In one embodiment of the present invention, the expected result for the stable transport decision is stable transport, while the expected result for the bumpy flattening decision is bumpy flattening. Both transport decisions are implemented based on transport stability. However, transport stability can change over time, potentially leading to inconsistent results. Therefore, the stable transport model needs to be retrained based on the latest data at regular intervals.

[0140] An electric roller control system, applied to the above-mentioned electric roller control method, comprises:

[0141] An operation driving module is used to establish a first starting point and a second starting point for the operation stroke of the target electric roller and the target conveyor belt respectively within a first preset time period, and to drive the target electric roller to perform a transportation operation by adjusting a PID control coefficient by gradient.

[0142] Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt;

[0143] a data acquisition module for PID empirical coefficients, stable transport parameters, and relative positions of the stable transport parameters to train a stable transport model; wherein the stable transport model is used to determine the predicted transport stability at any time within a second preset time period;

[0144] Automatic control module for executing stable and bumpy transport decisions for the target drum motor, including:

[0145] In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods;

[0146] The automatic update module synchronously obtains the execution results of the stable transportation decision or the bumpy transportation decision; if the execution result does not meet the expectations, the stable transportation model is updated.

[0147] It should be noted that the intervals and thresholds are set for ease of comparison. The threshold size depends on the amount of sample data and the cardinality set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless numerical calculations. These formulas are derived from software simulations of the most recent real-world conditions using large amounts of data. The preset parameters in these formulas are set by those skilled in the art based on actual conditions.

[0148] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for controlling an electric drum, characterized in that: include: Step 1: within a first preset time period, establish a first starting point and a second starting point for the operating stroke of the target electric roller and the target conveyor belt, respectively, and adjust the PID control coefficient by gradient to drive the target electric roller to perform the transport operation; Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt; The transport stability parameters include: the target operating stroke displacement distance of the electric drum and the target operating stroke displacement distance of the conveyor belt; The relative position includes: based on the first starting point, encoding each position of the target electric drum in the working stroke into: ,in, Indicates the first starting point, Indicates the operating stroke length of the target electric drum; The relative position relative to the target conveyor belt includes: Based on the second starting point, the target conveyor belt is coded at each position in the working stroke as follows: ,in, Indicates the second starting point, Indicates the operating stroke length of the target conveyor belt; For the mth group of stable transport parameters; 1≤m≤M, m is a positive integer; Get the starting position and ending position of the target electric drum's operating stroke at the mth moment ;in, ; Get the starting position and ending position of the target conveyor belt's working stroke at the mth moment ;in, ; Determine the relative position of the mth group of stable transport parameters as and ; Step 2: Based on the PID empirical coefficients, the stable transport parameters and the relative positions of the stable transport parameters, a stable transport model is trained, including: Calculating the transport stability includes: taking the difference between the operating stroke displacement distance of the target electric drum and the operating stroke displacement distance of the target conveyor belt in the rth group of stable transport parameters as the transport stability; The normalized transport stability is used as the sample label; The relative position corresponding to the rth group of stable transport parameters is constructed as a position vector; The PID control coefficients corresponding to the rth group of stable transport parameters are constructed as an operation vector; The position vector and the running vector are spliced ​​and normalized to obtain the sample data; Wherein, 1≤r≤M, r≠m, and r is a positive integer; The position vector represents the position vector constructed by the relative position corresponding to the stable transport parameter; The operation matrix represents the operation vector constructed by the PID control coefficients corresponding to the stable transportation parameters; A stable transportation model is obtained by training based on sample data and sample labels; The hidden layer of the stable transport model is constructed based on a one-dimensional convolutional neural network, and the hyperparameters of the hidden layer are updated according to the mean square error loss function; The stable transport model is used to determine the predicted transport stability at any time within the second preset time period; Step 3: Execute a stable transportation decision and a bumpy transportation decision for the target electric drum, including: In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods; Step 4: synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; if the execution results do not meet expectations, update the stable transportation model.

2. A method for controlling an electric drum according to claim 1, characterized in that: Stack status, including: If the stack height is greater than or equal to the preset height threshold, the stack status is abnormal; otherwise, the stack status is normal.

3. A method for controlling an electric drum according to claim 2, characterized in that: During the second preset time period, the PID empirical coefficient of the target electric drum is dynamically adjusted to achieve stable transportation of the transported goods, including: Step 31, obtaining the starting position of the operating stroke of the target electric drum and the starting position of the operating stroke of the target conveyor belt at the kth moment in the second preset time period; Step 32: Initialize and generate S control schemes that meet the constraints; wherein the sth control scheme includes the predicted PID control coefficient at the kth moment, the predicted end position of the target motorized drum's operating stroke at the kth moment, and the predicted end position of the target conveyor belt's operating stroke at the kth moment; Where, 1≤s≤S, s is a positive integer; Constraints include: The PID control coefficients are all within the corresponding control coefficient adjustment range; The difference between the predicted end position and the starting position of the operating stroke of the target electric drum is less than or equal to the first preset displacement difference; The difference between the predicted end position and the starting position of the operating stroke of the target conveyor belt is less than or equal to the second preset displacement difference; Step 33: Based on the starting position and predicted ending position of the target motorized drum's operating stroke at the k-th moment, and the starting position and predicted ending position of the target conveyor belt's operating stroke at the k-th moment, respectively generate a position vector and a reference transport stability of the s-th control scheme; Step 34, generating an operation vector of the sth control scheme based on the predicted PID control coefficient; Step 35: concatenate the position vector and the running vector and perform normalization processing to use them as input parameters of the stable transportation model to obtain the predicted transportation stability output by the stable transportation model; Step 36: If the difference between the predicted transport stability and the reference transport stability is less than or equal to the preset difference threshold, retain the sth control scheme; otherwise, delete the sth control scheme; Step 37, repeat steps 34 to 36 until U control solutions are obtained, and proceed to step 38 or step 39; Step 38 : Select the control scheme with the minimum predicted transportation stability among the U control schemes as the control scheme at the kth moment to achieve stable transportation of the transported goods.

4. A method for controlling an electric drum according to claim 3, characterized in that: Dynamically adjust the PID empirical coefficient of the target electric drum to achieve bumpy flattening of transported goods, including: Step 39: Select the control scheme with the greatest predicted transport stability among the U control schemes as the control scheme at the kth moment to achieve bump-smoothing of the transported goods.

5. A method for controlling an electric drum according to claim 4, characterized in that: Updated stable transport model, including: Synchronously obtain the execution results of the stable transportation decision or the bumpy transportation decision; wherein the execution results include whether they meet expectations or do not meet expectations; Count the cumulative number of times that do not meet expectations. If the cumulative number reaches the preset cumulative number, a full update of the stable transportation model will be triggered; The full update includes: Collect time series sample data and sample labels at fixed time intervals; Load the sliding time window to extract sample data and sample labels; the end of the sliding time window corresponds to the moment when the full update is triggered; Based on the sample data and sample labels extracted from the sliding time window, a stable transportation model is obtained by fully updating.

6. An electric roller control system, applied to an electric roller control method according to any one of claims 1 to 5, characterized in that: include: An operation driving module is used to establish a first starting point and a second starting point for the operation stroke of the target electric roller and the target conveyor belt respectively within a first preset time period, and to drive the target electric roller to perform a transportation operation by adjusting a PID control coefficient by gradient. Acquire M groups of stable transport parameters at fixed time intervals, and each group of stable transport parameters corresponds to a relative position of a target electric drum and a target conveyor belt; a data acquisition module for PID empirical coefficients, stable transport parameters, and relative positions of the stable transport parameters to train a stable transport model; wherein the stable transport model is used to determine the predicted transport stability at any time within a second preset time period; Automatic control module for executing stable and bumpy transport decisions for the target drum motor, including: In response to the stacking state of the transported goods, the PID empirical coefficient of the target electric roller is dynamically adjusted to achieve stable transportation or bumpy flattening of the transported goods; The automatic update module synchronously obtains the execution results of the stable transportation decision or the bumpy transportation decision; if the execution result does not meet the expectations, the stable transportation model is updated.

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