Energy storage block stacking positioning method and system

Abstract

The present invention provides a method and system for stacking and positioning energy storage blocks, and relates to the technical field of stacking and positioning energy storage blocks. The method comprises: scanning a train flatbed car to identify the boundary of each flatbed car; determining the boundary points of a predetermined stacking area according to the boundary of each flatbed car; determining the adjustment range of the clamp position and angle according to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, including the coordinates of the clamp in three-dimensional space and the rotation angle of the clamp; initializing a population, in which each individual in the population represents a set of candidate solutions for the clamp position and angle. The present invention realizes the precise stacking of energy storage blocks in the train flatbed car through intelligent positioning and adjustment, improves the operating efficiency and accuracy, and ensures the safe resetting of the lifting equipment, providing convenience for continuous operation.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage block stacking and positioning, and in particular to an energy storage block stacking and positioning method and system. Background Art

[0002] Traditional palletizing methods, especially for palletizing energy storage blocks in flatbed train compartments, rely on manual operations or simple mechanized processes. However, these methods are not only inefficient, but also pose many safety hazards. Especially in the face of complex and changing compartment environments and energy storage block shapes, manual palletizing often cannot guarantee the stability and accuracy of palletizing, and is prone to problems such as unstable palletizing and misalignment.

[0003] Although automatic palletizing technology has been widely used in recent years, there are still many challenges in the automatic palletizing of energy storage blocks in train flatbed carriages. Due to the limited space in the carriage, the irregular shape of the energy storage blocks, and the vibration and impact during transportation, some automatic palletizing systems have difficulty accurately identifying the carriage boundaries and the location of the energy storage blocks, resulting in poor palletizing results and even serious safety accidents such as heavy objects falling and personal injuries. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for positioning energy storage block stacking, which realizes precise control of the position and angle of the lifting fixture, thereby effectively improving the stability and safety of energy storage block stacking.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for stacking and positioning energy storage blocks is provided, the method comprising:

[0007] Scanning the train flatbed cars to identify the boundaries of each flatbed car;

[0008] Determine the boundary points of the predetermined stacking area based on the boundaries of each flatbed carriage;

[0009] According to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, determine the adjustment range of the fixture position and angle, including the coordinates of the fixture in three-dimensional space and the rotation angle of the fixture; initialize a population, in this scenario, each individual in the population represents a set of candidate solutions for the fixture position and angle; during initialization, randomly generate a certain number of individuals within a determined parameter range to form an initial population; for each individual in the population, select the corresponding random individual and generate a mutant individual; cross-operate the mutant individual with the target individual to generate a test individual; evaluate the fitness of the test individual until the preset number of iterations is reached to obtain the final solution, where the final solution represents the final position and angle of the lifting fixture;

[0010] According to the final position and angle of the lifting fixture, the energy storage block is clamped. After successful clamping, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area.

[0011] After reaching the predetermined stacking area, the position and angle of the fixture are fine-tuned again according to the boundary points of the predetermined stacking area;

[0012] After completing the palletizing operation, the lifting equipment automatically returns to its initial state, and all fixtures are reset to their original positions, ready for the next operation.

[0013] Furthermore, the train flatbed cars are scanned to identify the boundaries of each flatbed car, including:

[0014] Using a camera to shoot the train flatbed carriage to obtain train flatbed carriage image data;

[0015] Transmit the train flatbed carriage image data to the image recognition system in real time;

[0016] In the image recognition system, the received original image is preprocessed to obtain a preprocessed image;

[0017] Scanning the pre-processed image to identify potential boundaries of the flatbed compartment;

[0018] The potential boundary of the flatbed car is modified to obtain the final boundary of the flatbed car.

[0019] Furthermore, the pre-processed image is scanned to identify potential boundaries of the flatbed carriage, including:

[0020] Use the Sobel operator to calculate the horizontal and vertical gradients of the image, and calculate the gradient magnitude and direction based on the horizontal and vertical gradients; traverse each pixel of the image and compare its gradient magnitude with the adjacent pixels along the gradient direction. If the gradient magnitude of the current pixel is not the local maximum, set its value to 0;

[0021] Setting a first threshold and a second threshold, marking pixels with a gradient magnitude higher than the first threshold as determined edges, and marking pixels with a gradient magnitude lower than the second threshold as non-edges;

[0022] The pixels marked as determined edges are output in the form of a binary image, where the edge pixel value is 1 and the non-edge pixel value is 0, so as to obtain the potential boundary of the flatbed carriage.

[0023] Furthermore, the fitness of an individual The calculation formula is:

[0024] ;

[0025] in, Indicates the straight-line distance between the fixture position and the target position; Indicates the difference between the fixture rotation angle and the target angle; Indicates the actual minimum distance between the fixture and surrounding obstacles; Indicates the set safety distance threshold; Indicates the time required for fixture adjustment; Indicates the energy consumption required for fixture adjustment; , , , , and represents weight; Represents a positive number.

[0026] Furthermore, according to the final position and angle of the lifting fixture, the energy storage block is clamped, including:

[0027] Compare the final position and angle of the lifting fixture with the current position and posture of the energy storage block to obtain a comparison result;

[0028] According to the comparison results, the final position and angle data of the lifting fixture are converted into specific control instructions, including the extension length, rotation angle and translation coordinates of the fixture;

[0029] Send the converted control instructions to the lifting fixture to instruct it to make automatic adjustments;

[0030] After receiving the control command, the lifting fixture automatically performs telescopic, rotating and translational actions according to the control command to achieve the clamping position of the energy storage block;

[0031] Use the camera to capture the relative position and posture of the lifting fixture and the energy storage block in real time, and monitor the accuracy of clamping based on the real-time image. If it is confirmed that the lifting fixture and the energy storage block are aligned and the posture matches, a clamping instruction is sent to the lifting fixture, and the lifting fixture executes the clamping action to grasp the energy storage block.

[0032] Furthermore, after the clamping is successful, the hoisting equipment moves the clamp and the energy storage block to the predetermined stacking area, including:

[0033] Use the positioning sensor on the lifting equipment to obtain the current three-dimensional coordinates and posture information of the lifting fixture;

[0034] Extracting the coordinates of the center point of the predetermined stacking area from the determined boundary point data of the stacking area in the flatbed carriage of the train;

[0035] Calculate the path from the current lifting fixture position to the predetermined palletizing area while avoiding obstacles;

[0036] Convert the path data from the current hoisting fixture position to the predetermined stacking area into a movement instruction sequence executed by the hoisting device, and send the movement instruction to the hoisting device, including the rotation angle, speed and acceleration parameters of each joint;

[0037] After receiving the moving instruction, the lifting equipment starts to move step by step according to the instruction sequence. During the movement, the sensors on the lifting equipment monitor the position, posture and environmental information of the lifting fixture and energy storage block in real time, so that the lifting fixture and energy storage block can reach the determined predetermined stacking area.

[0038] Further, using The algorithm calculates the path from the current lifting fixture position to the predetermined palletizing area while avoiding obstacles, including:

[0039] Define the starting point and target point, where the starting point represents the current position of the lifting fixture and the target point represents the center point of the predetermined stacking area;

[0040] Add the starting point to the open list and create a closed list, which is initially empty;

[0041] Define a g value and an h value for each node, where the g value represents the actual cost from the starting point to the current node, and the h value represents the heuristic estimated cost from the current node to the target point;

[0042] Define an f value, which is equal to the sum of the g value and the h value, to evaluate the priority of the node;

[0043] When the open list is not empty, repeat the following steps:

[0044] Select the node corresponding to the f value from the open list as the current node;

[0045] Move the current node from the open list to the closed list. If the current node is the target node, the path has been found.

[0046] Check every reachable node around the current node. For each reachable node:

[0047] If the reachable node is not accessible, it is ignored; if the reachable node is already in the closed list, it is ignored;

[0048] Calculate the g value from the starting point through the current node to the reachable node; if the g value from the starting point through the current node to the reachable node is not in the open list, add the g value from the starting point through the current node to the reachable node to the open list, and set the current node as the parent node of the reachable node;

[0049] Starting from the target node, trace back to the starting node along the parent node of each node to form a path. The node sequence on the path is the point that the lifting fixture needs to move through;

[0050] The calculated path is converted into a sequence of movement instructions executed by the lifting equipment, and the movement instructions are sent to the lifting equipment to instruct it to move the lifting fixture and energy storage block to the predetermined stacking area according to the path.

[0051] An energy storage block stacking positioning system, comprising:

[0052] The acquisition module is used to scan the flatbed cars of the train to identify the boundaries of each flatbed car; determine the boundary points of the predetermined stacking area according to the boundaries of each flatbed car; determine the adjustment range of the clamp position and angle according to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, including the coordinates of the clamp in three-dimensional space and the rotation angle of the clamp; initialize a population, in which each individual in the population represents a set of candidate solutions for the clamp position and angle; during initialization, a certain number of individuals are randomly generated within a determined parameter range to form an initial population; for each individual in the population, a corresponding random individual is selected and a mutant individual is generated; the mutant individual is cross-operated with the target individual to generate a test individual; the fitness of the test individual is evaluated until a preset number of iterations is reached to obtain a final solution, wherein the final solution represents the final position and angle of the lifting fixture;

[0053] The processing module is used to clamp the energy storage block according to the final position and angle of the lifting fixture. After successful clamping, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area; after reaching the predetermined stacking area, the position and angle of the fixture are fine-tuned again according to the boundary point of the predetermined stacking area; after completing the stacking operation, the lifting equipment automatically returns to the initial state, and all fixtures are reset to their original positions to prepare for the next operation.

[0054] The above solution of the present invention includes at least the following beneficial effects:

[0055] By scanning the train flatbed cars and identifying their boundaries, the boundary points of the intended stacking area can be accurately determined, thereby improving the positioning accuracy of the energy storage block stacking. This helps ensure that the energy storage blocks are accurately placed in the intended location, reducing errors and unnecessary adjustments. Determining the adjustment range of the fixture position and angle based on the physical limitations of the lifting fixture and the characteristics of the intended stacking area can ensure that the fixture can be adjusted efficiently and safely within the feasible operating space. By initializing the population and using a genetic algorithm to find the final fixture position and angle, the efficiency and accuracy of fixture adjustment can be further improved.

[0056] Determining the final position and angle of the lifting fixture through genetic algorithms can reduce the number of trial and error and adjustments, thereby shortening the operation time and improving the efficiency of palletizing operations. In addition, the use of optimized path planning methods can also reduce collisions and unnecessary detours during the movement of lifting equipment, further improving operational efficiency. It can adapt to energy storage blocks of different shapes and sizes and different palletizing requirements. By adjusting the parameters and fitness functions in the genetic algorithm, it can easily adapt to different scenarios and requirements, improving the flexibility and versatility of the system. During the clamping and movement of energy storage blocks by lifting equipment, real-time monitoring and fine-tuning of the position and angle of the fixture can ensure the safety of the operation. Timely detection and correction of deviations can reduce the risk of accidents and ensure the safety of operators and equipment. The combination of image processing, genetic algorithms and real-time monitoring technologies has realized the automated operation of energy storage block palletizing. This helps to reduce manual intervention, reduce labor intensity, and improve production efficiency and consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a method for stacking and positioning energy storage blocks provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0059] like Figure 1 As shown, an embodiment of the present invention provides a method for stacking and positioning energy storage blocks, the method comprising the following steps:

[0060] Step 11, scanning the train flatbed carriages to identify the boundaries of each flatbed carriage;

[0061] Step 12, determining the boundary points of the predetermined stacking area according to the boundaries of each flatbed carriage;

[0062] Step 13, according to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, determine the adjustment range of the fixture position and angle, including the coordinates of the fixture in three-dimensional space and the rotation angle of the fixture; initialize a population, in this scenario, each individual in the population represents a set of candidate solutions for the fixture position and angle; during initialization, randomly generate a certain number of individuals within a determined parameter range to form an initial population; for each individual in the population, select a corresponding random individual and generate a mutant individual; cross-operate the mutant individual with the target individual to generate a test individual; evaluate the fitness of the test individual until a preset number of iterations is reached to obtain a final solution, wherein the final solution represents the final position and angle of the lifting fixture;

[0063] Step 14, according to the final position and angle of the lifting fixture, the energy storage block is clamped. After the clamping is successful, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area;

[0064] Step 15, after reaching the predetermined stacking area, fine-tune the position and angle of the fixture again according to the boundary point of the predetermined stacking area;

[0065] Step 16: After the palletizing operation is completed, the lifting equipment automatically returns to its initial state, and all fixtures are reset to their original positions, ready for the next operation.

[0066] In an embodiment of the present invention, by accurately identifying the boundaries of each flatbed carriage, the boundary points of the predetermined stacking area can be accurately determined, which ensures that the energy storage blocks are placed in the correct and safe position, avoids stacking errors caused by unclear boundaries, and determines the adjustment range in combination with the physical limitations of the lifting fixture and the characteristics of the stacking area, ensuring the practical feasibility of the positioning process. By initializing the population and using a genetic algorithm, the position and angle of the fixture can be quickly found, greatly improving the positioning efficiency and accuracy. This method can flexibly adapt to energy storage blocks of different shapes and sizes and different stacking scenarios. By adjusting the optimization parameters, it can easily cope with various complex situations, enhancing the versatility and practicality of the system. The clamping operation is performed according to the final position and angle of the fixture obtained after optimization, which reduces the trial and error time and improves the operating efficiency. At the same time, accurate positioning also improves the safety of the operation process and reduces the risks caused by misoperation.

[0067] After reaching the predetermined palletizing area, the position and angle of the fixture are fine-tuned according to the boundary points to further ensure the accuracy of palletizing. This refined adjustment method helps to improve the quality of palletizing and meet the requirements of high-precision palletizing. After the palletizing operation is completed, the hoisting equipment can automatically return to the initial state and the fixture is reset to the original position. This automated reset mechanism not only saves manual operation time, but also makes quick preparations for the next operation, improving the consistency and efficiency of the overall workflow.

[0068] In a preferred embodiment of the present invention, the above step 11, scanning the train flatbed cars to identify the boundaries of each flatbed car, may include:

[0069] Step 111, using a camera to photograph the train flatbed carriage to obtain train flatbed carriage image data;

[0070] Step 112, transmitting the train flatbed carriage image data to the image recognition system in real time;

[0071] Step 113, in the image recognition system, preprocessing the received original image to obtain a preprocessed image;

[0072] Step 114, scanning the pre-processed image to identify potential boundaries of the flatbed carriage;

[0073] Step 115, correcting the potential boundary of the flatbed carriage to obtain the final boundary of the flatbed carriage.

[0074] In an embodiment of the present invention, an industrial-grade camera with a high frame rate and high resolution is selected to ensure that clear images can still be captured when the train passes at high speed. Considering the vibration and environmental factors during the operation of the train, a stable installation point is selected, such as using a shock-proof bracket to install it on an elevated structure or next to the track. Adjust the angle of the camera to ensure that the field of view can fully cover the width of the flatbed car while avoiding excessive background interference. Using sensors or photoelectric switches on the track, when a train is detected approaching, the camera is automatically triggered to shoot. Image recognition technology can also be used to trigger shooting by monitoring changes in the video stream, such as detecting the appearance of moving objects or specific shapes. Set the camera to continuous shooting mode to ensure continuous image capture during the passage of the train, and use high-speed continuous shooting function to capture more details and reduce motion blur.

[0075] Consider using exposure control and image stabilization techniques to further optimize image quality.

[0076] Assess the on-site environment and select appropriate wired (such as direct fiber connection) or wireless (such as dedicated Wi-Fi network, 4G / 5G mobile network) transmission methods to ensure the stability and high bandwidth of the transmission link to support the transmission of real-time high-definition video streams. Apply efficient video compression algorithms, such as H.264 or H.265, to reduce the amount of transmitted data without losing too much image quality, encode image data before sending to ensure data integrity and security, set a buffer at the receiving end to smooth data transmission delays caused by network fluctuations, and monitor data transmission status in real time, including key indicators such as transmission rate and packet loss rate, to ensure data real-time and reliability.

[0077] Select appropriate filtering algorithms based on the image characteristics, such as Gaussian filtering for removing Gaussian noise and median filtering for removing noise. Adjust the filter parameters, set appropriate thresholds, convert edge detection results into binary images, dynamically adjust thresholds based on local characteristics of the image, use dilation operations to connect broken edges, use erosion operations to remove isolated noise points, use Hough transform to detect straight line segments in the image, and perform straight line fitting on the detected edges. For broken boundaries caused by occlusion or uneven lighting, contour tracking-based algorithms can be used to connect them. Verify the corrected boundaries based on the actual size and geometric features of the flatbed car.

[0078] Consider a railway freight station where trains frequently pass through with flatbed cars. To automate the unloading and palletizing process, the boundaries of each flatbed car are accurately identified as the train passes by, and a row of fixtures (e.g., 5) are guided for precise positioning. An industrial-grade camera with a high frame rate and high resolution is selected and mounted firmly on an elevated structure using a shock-proof mount to ensure clear images of the train's flatbed cars. The camera's angle is carefully adjusted to ensure that its field of view fully covers the width of the five flatbed cars. Using sensors on the trackside, the camera is automatically triggered to take continuous shots when a train approaches. At the same time, image recognition technology is used to assist in triggering, ensuring that no passing train is missed. The camera is set to high-speed continuous shooting mode to capture as many details as possible and reduce motion blur.

[0079] Considering the on-site environment, the image data was transmitted to the processing center in real time through direct fiber connection. In order to ensure the real-time and reliability of the data, the H.265 video compression algorithm was applied, and the image data was encoded before sending. A buffer was set at the receiving end to smooth out possible network fluctuations. After the image data arrived at the processing center, it was first filtered by Gaussian and median to remove noise, and then converted into a binary image by setting a suitable threshold. The Canny algorithm was used for edge detection to accurately identify the boundaries of the flatbed carriage. The detected edges were fitted with a straight line through the Hough transform to correct possible distortions. For broken boundaries, an algorithm based on contour tracking was used to connect them. Finally, the boundaries were verified according to the actual size and geometric features of the flatbed carriage, realizing an efficient and accurate automated unloading and stacking process.

[0080] Through high-resolution cameras and advanced image recognition technology, the recognition accuracy of flatbed car boundaries can be improved, reducing misjudgments and missed detections. Automated scanning and identification of flatbed car boundaries can significantly reduce manual intervention, improve work efficiency, and reduce labor costs. Accurate boundary recognition helps ensure the precise positioning of automated equipment such as fixtures during the palletizing process, avoiding collisions or misoperations, thereby improving operational safety. Through real-time data transmission and image preprocessing technology, the impact of environmental factors such as vibration and lighting changes during train operation on image quality can be effectively addressed, enhancing the robustness and stability of the system. Accurate flatbed car boundary recognition helps achieve more sophisticated logistics management, including cargo tracking, inventory control, etc., thereby improving the efficiency and reliability of the entire logistics system.

[0081] In another preferred embodiment of the present invention, the above step 114, scanning the pre-processed image to identify the potential boundary of the flatbed carriage, may include:

[0082] Step 1141, using the Sobel operator to calculate the horizontal and vertical gradients of the image, and calculating the gradient magnitude and direction according to the horizontal and vertical gradients; traversing each pixel of the image, and comparing its gradient magnitude with the adjacent pixels along the gradient direction, if the gradient magnitude of the current pixel is not a local maximum, then setting its value to 0;

[0083] Step 1142, setting a first threshold and a second threshold, marking pixels with a gradient magnitude higher than the first threshold as determined edges, and marking pixels with a gradient magnitude lower than the second threshold as non-edges;

[0084] Step 1143, output the pixels marked as determined edges in the form of a binary image, wherein the edge pixel value is 1 and the non-edge pixel value is 0, so as to obtain the potential boundary of the flatbed carriage.

[0085] In the embodiment of the present invention, the Sobel operator has two 3×3 convolution kernels, one for detecting edges in the horizontal direction. , and another one for detecting edges in the vertical direction The horizontal Sobel operator is as follows: (used to detect vertical edges, i.e. calculate horizontal gradients).

[0086] The vertical Sobel operator is usually in the form of: (Used to detect horizontal edges, i.e. calculate vertical gradients) Convolve the horizontal Sobel operator with the preprocessed image to obtain the horizontal gradient of each pixel. . Convolve the vertical Sobel operator with the preprocessed image to obtain the vertical gradient of each pixel. For each pixel in the image, according to its horizontal gradient and vertical gradient , using the Pythagorean theorem to calculate the gradient magnitude , which indicates the strength of the edge. At the same time, the gradient direction is calculated , this angle represents the direction of the edge, relative to the horizontal axis. After the above steps, the gradient magnitude and direction of each pixel are obtained.

[0087] Traverse each pixel of the image and compare its gradient magnitude with the gradient magnitude of the adjacent pixels along the gradient direction. If the gradient magnitude of the current pixel is not the local maximum (i.e., it is not the largest in its gradient direction), set its value to 0 to suppress non-edge pixels and highlight the true edge. Set two thresholds: the first threshold (high threshold) and the second threshold (low threshold), which are used to distinguish strong edges from weak edges, as well as non-edges. Pixels with gradient magnitudes higher than the first threshold are marked as definite edges (strong edges), which are likely to be true edge points, and pixels with gradient magnitudes lower than the second threshold are marked as non-edges, which are considered not to be edge points. For pixels with gradient magnitudes between the first threshold and the second threshold (weak edges), further processing is required to determine whether they belong to true edges. Starting from the pixels marked as definite edges, try to connect these edge points to form continuous edge lines. For weak edge pixels, if they are connected to definite edges or are confirmed as part of the edge under certain conditions (such as by hysteresis thresholding), they are also included in the final edge image. The pixels marked as definite edges and partial weak edges are output in the form of a binary image. In this binary image, the value of the edge pixel is 1 and the value of the non-edge pixel is 0. The binary image thus obtained represents the potential boundary of the flatbed carriage.

[0088] The Sobel operator can accurately locate the area with drastic grayscale changes in the image, that is, the edge position, by calculating the horizontal and vertical gradients of the image. Compared with other edge detection operators, the Sobel operator has a certain inhibitory effect on the noise in the image. It can effectively remove the pseudo edges caused by noise by calculating the gradient amplitude and direction, and combining non-maximum suppression and double threshold processing, thereby improving the reliability of edge detection. The Sobel operator uses convolution operations and can use the genetic algorithm in the image processing library for efficient calculation. In addition, the convolution kernel of the Sobel operator is small (3×3), which makes the calculation process relatively simple and fast, and is suitable for real-time or large-scale image processing tasks. By setting different first thresholds and second thresholds, the sensitivity and accuracy of edge detection can be flexibly adjusted. According to actual needs, the integrity of the edge and the degree of detail retention can be weighed to obtain the best edge detection results.

[0089] Outputting the pixels marked as determined edges in the form of a binary image simplifies the complexity of subsequent processing. This binary representation facilitates further processing steps such as morphological operations, edge tracking, contour extraction, etc., and helps to achieve more advanced image analysis and understanding functions.

[0090] In a preferred embodiment of the present invention, the above step 12, determining the boundary points of the predetermined stacking area according to the boundary of each flatbed carriage, may include:

[0091] From the identified flatbed car boundary, extract the key points that constitute the boundary, which are the intersections or endpoints of the boundary segments. Based on the extracted boundary points, calculate the size (such as length, width) and position (such as the coordinates of the car in the image) of the flatbed car. Determine the principle of the stacking area. For example, the stacking area may need to avoid the edge of the car by a certain distance to ensure the stability and safety of the cargo stacking. Based on the car size, position and stacking area principles, calculate the boundary points of the predetermined stacking area. These points will define the scope and shape of the stacking area. For example, if the stacking area is rectangular and needs to be a certain distance from the edge of the car, the four corner points of the stacking area can be determined by offsetting them equidistantly inside the car boundary. Verify the calculated stacking area boundary points to ensure that they meet the requirements of the actual application, for example, check whether the boundary points are located inside the car and do not exceed the actual load range of the car. Output and record the finalized stacking area boundary points in an appropriate data structure (such as a coordinate list).

[0092] In a preferred embodiment of the present invention, the above step 13 determines the adjustment range of the clamp position and angle, including the coordinates of the clamp in three-dimensional space and the rotation angle of the clamp, according to the physical limitations of the lifting clamp and the characteristics of the predetermined stacking area; initializes a population, in which each individual in the population represents a set of candidate solutions for the clamp position and angle; during initialization, a certain number of individuals are randomly generated within a determined parameter range to form an initial population; for each individual in the population, a corresponding random individual is selected and a variant individual is generated; the variant individual is cross-operated with the target individual to generate a test individual; and the fitness of the test individual is evaluated until a preset number of iterations is reached to obtain a final solution, wherein the final solution represents the final position and angle of the lifting clamp, and may include:

[0093] According to the physical limitations of the lifting fixture and the characteristics of the predetermined palletizing area, determine the coordinate range of the fixture in three-dimensional space ( , , ). Determine the rotation angle range of the fixture, which includes yaw, pitch and roll angles. Define a data structure of an individual (candidate solution), including the three-dimensional coordinates and rotation angles of the fixture. Set the number of individuals in the population, and randomly generate each individual in the initial population within the defined parameter range. Define a fitness function to evaluate the pros and cons of each individual (i.e., each set of fixture positions and angles). This function should consider factors such as the efficiency and safety of the lifting operation. Perform fitness evaluation on each individual in the initial population, and select individuals from the current population based on the fitness of the individual using selection methods such as roulette and tournaments. For the selected individual pairs (target individuals and random individuals), perform crossover operations (such as single-point crossover, multi-point crossover, etc.) to generate new test individuals, and perform mutation operations on the test individuals to increase the diversity of the population. Mutation can randomly introduce small changes in the fixture position or angle. Perform fitness evaluation on the test individuals generated by crossover and mutation, compare the test individuals with the original individuals, and decide whether to replace the original individuals with the test individuals based on the fitness values, thereby updating the population. The evolution process is repeated until the preset number of iterations is reached. After the iteration, the individual with the highest fitness in the population is the final position and angle of the lifting fixture, and the final solution is output, including the optimal position and angle of the fixture.

[0094] In a preferred embodiment of the present invention, the fitness of an individual The calculation formula is:

[0095] ;

[0096] in, Indicates the straight-line distance between the fixture position and the target position; Indicates the difference between the fixture rotation angle and the target angle; Indicates the actual minimum distance between the fixture and surrounding obstacles; Indicates the set safety distance threshold; Indicates the time required for fixture adjustment; Indicates the energy consumption required for fixture adjustment; , , , , and represents weight; Represents a positive number.

[0097] In the embodiment of the present invention, the weight is set , , , , and . Set a positive number To avoid division by zero. Set the safety distance threshold . Based on the coordinates of the fixture position and the target position, the straight-line distance is calculated using the Euclidean distance formula . Based on the current rotation angle of the fixture and the target angle, calculate the difference between the two. Detect obstacles around the fixture and calculate the distance between the fixture and each obstacle, and find the minimum value from these distances as Estimate the time required for adjustment based on the current position and angle of the fixture, as well as the target position and angle Similarly, estimate the energy consumption required to adjust , the straight-line distance, angle difference, minimum obstacle distance, adjustment time and the inverse of energy consumption are weighted summed and used in the calculation process To avoid division by zero, calculate the fitness .

[0098] Taking into account multiple objectives such as fixture position, angle, distance to obstacles, adjustment time and energy consumption, the optimization process is more comprehensive and a solution can be found. , , , , and You can weigh different optimization goals according to actual needs. For example, if you pay more attention to the adjustment time, you can increase The value of and The ratio of the distance between the fixture and the obstacle is used to evaluate the safe distance between the fixture and the obstacle, which helps to avoid unsafe solutions that are too close to the obstacle during the optimization process, thereby improving the safety of the operation. and , while ensuring the quality of operation, we can choose more efficient and energy-saving adjustment solutions as much as possible, thereby improving the efficiency of the overall operation. Since the fitness function is clear and explainable, each step in the optimization process can be intuitively reflected by the fitness value, which is convenient for analyzing and visualizing the optimization process.

[0099] In a preferred embodiment of the present invention, the above step 14, performing the clamping operation of the energy storage block according to the final position and angle of the lifting fixture, may include:

[0100] Step 144, comparing the final position and angle of the lifting fixture with the current position and posture of the energy storage block to obtain a comparison result;

[0101] Step 145, according to the comparison result, convert the final position and angle data of the lifting fixture into specific control instructions, including the extension length, rotation angle and translation coordinates of the fixture;

[0102] Step 146, sending the converted control instruction to the lifting fixture to instruct it to perform automatic adjustment;

[0103] Step 147, after receiving the control instruction, the lifting fixture automatically performs extension, rotation and translation actions according to the control instruction to achieve the clamping position of the energy storage block;

[0104] Step 148, use a camera to capture the relative position and posture of the lifting fixture and the energy storage block in real time, and monitor the accuracy of clamping based on the real-time image. If it is confirmed that the lifting fixture and the energy storage block are aligned and the postures match, a clamping instruction is sent to the lifting fixture, and the lifting fixture executes the clamping action to grasp the energy storage block.

[0105] In an embodiment of the present invention, the final position and angle data of the lifting fixture are obtained, which is the result obtained by the previous genetic algorithm. At the same time, the position and posture information of the current energy storage block is obtained, which is achieved by a sensor, and the position and angle data of the lifting fixture are compared with the position and posture of the energy storage block, and the difference between the two is calculated, including the distance difference and the angle difference. According to the comparison results, the specific parameters that need to be adjusted for the lifting fixture are calculated, including the telescopic length, the rotation angle and the translation coordinate. The calculation of these parameters should be based on the actual difference between the lifting fixture and the energy storage block, and take into account the kinematic characteristics of the fixture and the limitations of the operating space. The calculated parameters are converted into a control instruction format that the lifting fixture can recognize and execute, which involves mapping the parameter value to the corresponding input of the fixture control system to ensure the accuracy and compatibility of the instruction, and the converted control instruction is sent to the control system of the lifting fixture through the communication interface, which is achieved by wired or wireless means, depending on the configuration and communication requirements of the system. When the lifting fixture receives the control instruction and starts the adjustment process, the camera is used to capture the relative position and posture of the fixture and the energy storage block in real time. Through image processing and analysis technology, the alignment between the fixture and the energy storage block is continuously monitored. If it is found that the fixture deviates from the expected trajectory or fails to accurately align with the energy storage block during the adjustment process, necessary adjustments can be made based on real-time feedback data. This may include correcting control instructions, adjusting the motion parameters of the fixture, or taking other corrective measures.

[0106] When it is confirmed that the lifting fixture has been accurately aligned with the energy storage block and the postures match, prepare to perform the clamping action. This usually involves ensuring that the clamping components of the fixture are in the correct position and state so that they can firmly grasp the energy storage block, sending a clamping instruction to the lifting fixture, triggering the clamping mechanism of the fixture to move, and the instruction should clearly specify parameters such as the clamping force, speed and duration to ensure the safety and effectiveness of the clamping process. After receiving the clamping instruction, the lifting fixture performs the clamping action according to the preset parameters, which includes the clamping components of the fixture moving toward the energy storage block, contacting and applying clamping force, and finally firmly grasping the energy storage block.

[0107] After the gripping action is completed, a camera or other sensor is used to verify whether the energy storage block has been successfully grasped. This can be achieved by checking the relative position, posture, and clamping force between the fixture and the energy storage block.

[0108] If the verification result shows that the energy storage block has been successfully clamped, the entire operation process is ended. At this time, the control system of the clamp can be turned off, the communication connection can be disconnected, and preparations can be made for subsequent handling or processing tasks. If the clamping fails or there are problems, the above steps need to be repeated for adjustment and clamping operations.

[0109] By accurately comparing the final position and angle of the lifting fixture with the energy storage block and converting them into specific control instructions, it can be ensured that the fixture can be accurately moved to the predetermined position and aligned with the energy storage block in the correct posture, thereby improving the accuracy and success rate of clamping. The entire clamping process, from comparison and instruction conversion to sending control instructions and executing clamping actions, can be automated without manual intervention, improving production efficiency and consistency of operation. Using a camera to capture the relative position and posture of the lifting fixture and the energy storage block in real time, any deviation can be discovered and corrected in time to ensure the accuracy and safety of the clamping process. Since the control instructions are dynamically generated based on the comparison results, this method can adapt to energy storage blocks of different sizes, shapes and weights, as well as different clamping environments and requirements, and has strong flexibility and adaptability. During the clamping process, through precise comparison and real-time monitoring, the safe distance and posture matching between the lifting fixture and the energy storage block can be ensured to avoid collision or damage, thereby improving the safety of operation. Accurate clamping operations can reduce unnecessary repetitive movements and energy consumption, while reducing waste or damaged parts caused by improper operations, thereby helping to achieve energy conservation, emission reduction and environmental protection goals.

[0110] In a preferred embodiment of the present invention, the above step 14, after the clamping is successful, the hoisting equipment moves the clamp and the energy storage block to the determined predetermined stacking area, further comprising:

[0111] Step 149, using the positioning sensor on the lifting device to obtain the current three-dimensional coordinates and posture information of the lifting fixture;

[0112] Step 150, extracting the center point coordinates of the predetermined stacking area from the determined boundary point data of the stacking area in the flatbed carriage of the train;

[0113] Step 151, calculating a path from the current hoisting fixture position to a predetermined stacking area while avoiding obstacles;

[0114] Step 152, converting the path data from the current hoisting fixture position to the predetermined stacking area into a movement instruction sequence executed by the hoisting device, and sending the movement instruction to the hoisting device, including the rotation angle, speed and acceleration parameters of each joint;

[0115] Step 153, after receiving the moving instruction, the lifting equipment starts to move step by step according to the instruction sequence. During the movement, the sensors on the lifting equipment monitor the position, posture and environmental information of the lifting fixture and the energy storage block in real time, so that the lifting fixture and the energy storage block reach the determined predetermined stacking area.

[0116] In the embodiment of the present invention, the sensor or external perception system on the lifting equipment is used to identify and locate obstacles on the moving path of the lifting fixture, and the path planning algorithm (such as ) Calculate a collision-free path from the current position to the predetermined stacking area. Smooth and optimize the calculated path to reduce the jitter and unnecessary movements of the lifting equipment during movement, and convert the planned path data into a sequence of movement instructions that the lifting equipment can execute. This includes converting the path points into the rotation angle, speed and acceleration parameters of each joint, verifying the generated movement instruction sequence to ensure the continuity and safety of the instructions, and sending the verified movement instruction sequence to the control system of the lifting equipment through the communication interface. During the execution of the movement instruction by the lifting equipment, the position, posture and surrounding environment information of the lifting fixture and energy storage block are monitored in real time through the sensors on the equipment. If it is found that the lifting fixture and energy storage block deviate from the expected trajectory or encounter obstacles during the movement, make necessary adjustments immediately based on the real-time feedback data, such as replanning the path or correcting the movement instruction. When the lifting fixture and energy storage block arrive at the predetermined stacking area, confirm through sensor data to ensure that the fixture and energy storage block have accurately reached the target position. Once it is confirmed that it has reached the predetermined palletizing area, the moving operation is ended and preparations are made for subsequent palletizing or releasing energy storage blocks. At the same time, unnecessary sensors and systems are turned off to save energy.

[0117] By using the positioning sensor on the lifting equipment, the current three-dimensional coordinates and posture information of the lifting fixture can be accurately obtained to ensure the accuracy and controllability of the moving process. At the same time, the center point coordinates of the predetermined stacking area are extracted from the boundary point data, providing a clear target position for the movement. The optimal path is calculated under the premise of avoiding obstacles, which not only improves the movement efficiency, but also reduces the risk of collision with obstacles and ensures the safety of operation. The path data is converted into a sequence of mobile instructions executable by the lifting equipment, including the rotation angle, speed and acceleration parameters of each joint, so that the lifting equipment can move accurately and smoothly according to the preset instructions. During the movement process, the sensors on the lifting equipment monitor the position, posture and environmental information of the lifting fixture and energy storage block in real time, providing real-time visual and data feedback for the operator, facilitating timely adjustment of the movement strategy to ensure that the lifting fixture and energy storage block can accurately reach the predetermined stacking area. The entire moving process is automated and intelligent, reducing manual intervention and improving operational efficiency. At the same time, through precise path planning and real-time monitoring, potential safety risks are effectively avoided and the safety of operators and equipment is guaranteed.

[0118] In another preferred embodiment of the present invention, the above step 151 uses The algorithm calculates the path from the current lifting fixture position to the predetermined palletizing area while avoiding obstacles, which may include:

[0119] Step 1511, defining a starting point and a target point, wherein the starting point represents the current position of the hoisting fixture, and the target point represents the center point of the predetermined stacking area;

[0120] Step 1512, add the starting point to the open list, and create a closed list, which is initially empty;

[0121] Step 1513, defining a g value and an h value for each node, wherein the g value represents the actual cost from the starting point to the current node, and the h value represents the heuristic estimated cost from the current node to the target point;

[0122] Step 1514, defining an f value, which is equal to the sum of the g value and the h value, for evaluating the priority of the node;

[0123] Step 1515: when the open list is not empty, repeat the following steps:

[0124] Step 1516, select the node corresponding to the f value from the open list as the current node;

[0125] Step 1517, move the current node from the open list to the closed list. If the current node is the target node, the path has been found;

[0126] Step 1518, check each reachable node around the current node, for each reachable node:

[0127] Step 1519: If the reachable node is not accessible, ignore it; if the reachable node is already in the closed list, ignore it;

[0128] Step 1520, calculate the g value of reaching the reachable node from the starting point through the current node; if the g value of reaching the reachable node from the starting point through the current node is not in the open list, add the g value of reaching the reachable node from the starting point through the current node to the open list, and set the current node as the parent node of the reachable node;

[0129] Step 1521, starting from the target node, trace back to the starting node along the parent node of each node to form a path, and the node sequence on the path is the point that the lifting fixture needs to move through;

[0130] Step 1522, convert the calculated path into a movement instruction sequence executed by the lifting device, and send a movement instruction to the lifting device to instruct it to move the lifting fixture and the energy storage block to the predetermined stacking area according to the path.

[0131] In an embodiment of the present invention, the current position of the lifting fixture is set as the center point of the predetermined palletizing area, and the starting point is added to the open list, which is initially empty. A g value and an h value are defined for each node, where the g value represents the actual moving cost from the starting point to the current node. The h value represents the cost from the current node to the target point estimated by the heuristic function, using the straight-line distance (Euclidean distance) between the two points as an estimate, where f = g + h, which is used to evaluate the priority of the node.

[0132] When the open list is not empty, perform the following steps:

[0133] Select the node with the smallest f value from the open list as the current node, move the current node from the open list to the closed list, if the current node is the target node, the path has been found, traverse all reachable (adjacent) nodes of the current node, if the reachable node is not accessible (such as an obstacle), ignore it. If the reachable node is already in the closed list, ignore it. If the g value of the node is undefined (that is, not in the open list), calculate the g value from the starting point through the current node to the reachable node, add it to the open list, and set the current node as the parent node of the reachable node. If the node is already in the open list, check whether a smaller g value can be obtained through the current node (that is, a better path is found); if so, update the g value and parent node of the node to the current node; if the open list is empty and the target node is not found, it means that there is no path to reach, and the algorithm ends; if the target node is found, start from the node, trace back to the starting node along the parent node of each node, and record all the nodes on the path.

[0134] The calculated path points are converted into a sequence of movement instructions that can be understood by the lifting equipment, which may involve converting the path points into specific joint rotation angles, speeds, and acceleration parameters; the movement instruction sequence is sent to the lifting equipment to instruct it to move the lifting fixture and energy storage block to the predetermined stacking area according to the path. After receiving the movement instruction, the lifting equipment begins to move step by step. During the movement, the sensors on the lifting equipment monitor the position, posture, and environmental information of the lifting fixture and energy storage block in real time to ensure the safety and accuracy of the movement process.

[0135] The algorithm combines the advantages of the best first search and Dijkstra algorithm, guides the search direction through the heuristic function (h value), reduces unnecessary node expansion, and can quickly find the optimal path from the starting point to the target point. This improves the efficiency of the lifting equipment in moving the lifting fixture and energy storage block. During the implementation of the algorithm, by checking whether the reachable node is passable (i.e. whether it is an obstacle), the algorithm can automatically avoid obstacles to ensure that the lifting fixture and energy storage block will not collide with obstacles during movement, thus ensuring the safety of the operation. The algorithm can plan paths based on different maps and environmental information, so it has strong flexibility and adaptability. No matter how the position of the palletizing area changes or how the layout of obstacles in the environment is adjusted, the algorithm can effectively find a feasible path. Through automated path planning and instruction sending, the need for manual intervention is reduced, reducing the burden on operators. At the same time, it also reduces the possibility of human error and improves the accuracy and reliability of operations. In the process of the lifting equipment executing the moving instructions, the position, posture and environmental information of the lifting fixture and energy storage block are monitored in real time through sensors, which can promptly detect and handle any abnormal situation to ensure the safety and smooth progress of the moving process. Through precise path planning and efficient execution of moving instructions, the operation trajectory and time of the lifting equipment can be optimized, thereby reducing energy consumption and equipment wear, extending the service life of the equipment and reducing operating costs.

[0136] In a preferred embodiment of the present invention, the above step 15, after reaching the predetermined stacking area, fine-tuning the position and angle of the fixture again according to the boundary point of the predetermined stacking area; the above step 16, after completing the stacking operation, the hoisting equipment automatically returns to the initial state, and all the fixtures are reset to the original position to prepare for the next operation, may include:

[0137] In an embodiment of the present invention, when the lifting fixture and the energy storage block arrive at the predetermined stacking area, the precise boundary point information of the stacking area is obtained from the pre-set data. Based on the boundary point data and the current position and posture information of the lifting fixture, the position and angle fine-tuning amount required for the fixture is calculated, which may involve the translation of the fixture on the X, Y, and Z axes and the rotation around each axis. The calculated fine-tuning amount is converted into movement and rotation instructions that can be executed by the lifting equipment. A fine-tuning instruction is sent to the lifting equipment, instructing it to adjust the position and angle of the fixture according to the calculated fine-tuning amount to ensure that the fixture and the energy storage block can be accurately placed in the stacking area. After the fixture position and angle fine-tuning is completed, the stacking operation is performed to place the energy storage block in the predetermined position. The stacking process can be monitored by sensors to ensure that the energy storage block is stably and accurately placed in the target position. The initial state of the lifting equipment and fixture is clarified, including the angle of each joint, the position and posture of the fixture, etc. Use The algorithm or other path planning method calculates the path from the current position back to the initial state. Considering that there is no load of the energy storage block at this time, the path planning may be more efficient. Convert the calculated return path into a sequence of movement instructions that can be executed by the hoisting device. Send a return instruction to the hoisting device, instructing it to move step by step along the path to the initial state.

[0138] During the movement of the lifting equipment, ensure that the fixture gradually returns to its original position. The precise resetting of the fixture can be achieved by setting a specific resetting procedure or using sensor feedback. After the lifting equipment and fixture return to the initial state, a status check is performed to ensure that all systems are working properly and are ready for the next operation. The lifting equipment enters the standby state and waits to receive the instruction for the next operation.

[0139] An embodiment of the present invention further provides an energy storage block stacking positioning system, comprising:

[0140] The acquisition module is used to scan the flatbed cars of the train to identify the boundaries of each flatbed car; determine the boundary points of the predetermined stacking area according to the boundaries of each flatbed car; determine the adjustment range of the clamp position and angle according to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, including the coordinates of the clamp in three-dimensional space and the rotation angle of the clamp; initialize a population, in which each individual in the population represents a set of candidate solutions for the clamp position and angle; during initialization, a certain number of individuals are randomly generated within a determined parameter range to form an initial population; for each individual in the population, a corresponding random individual is selected and a mutant individual is generated; the mutant individual is cross-operated with the target individual to generate a test individual; the fitness of the test individual is evaluated until a preset number of iterations is reached to obtain a final solution, wherein the final solution represents the final position and angle of the lifting fixture;

[0141] The processing module is used to clamp the energy storage block according to the final position and angle of the lifting fixture. After successful clamping, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area; after reaching the predetermined stacking area, the position and angle of the fixture are fine-tuned again according to the boundary point of the predetermined stacking area; after completing the stacking operation, the lifting equipment automatically returns to the initial state, and all fixtures are reset to their original positions to prepare for the next operation.

[0142] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0143] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0144] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0145] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for stacking and positioning energy storage blocks, characterized in that: The method comprises: Scanning the train flatbed cars to identify the boundaries of each flatbed car; Determine the boundary points of the predetermined stacking area based on the boundaries of each flatbed carriage; According to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, determine the adjustment range of the fixture position and angle, including the coordinates of the fixture in three-dimensional space and the rotation angle of the fixture; initialize a population, in this scenario, each individual in the population represents a set of candidate solutions for the fixture position and angle; during initialization, randomly generate a certain number of individuals within a determined parameter range to form an initial population; for each individual in the population, select the corresponding random individual and generate a mutant individual; cross-operate the mutant individual with the target individual to generate a test individual; evaluate the fitness of the test individual until the preset number of iterations is reached to obtain the final solution, where the final solution represents the final position and angle of the lifting fixture; According to the final position and angle of the lifting fixture, the energy storage block is clamped. After successful clamping, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area. After reaching the predetermined stacking area, the position and angle of the fixture are fine-tuned again according to the boundary points of the predetermined stacking area; After the palletizing operation is completed, the lifting equipment automatically returns to its initial state, and all fixtures are reset to their original positions, ready for the next operation; individual adaptability The calculation formula is: ； in, Indicates the straight-line distance between the fixture position and the target position; Indicates the difference between the fixture rotation angle and the target angle; Indicates the actual minimum distance between the fixture and surrounding obstacles; Indicates the set safety distance threshold; Indicates the time required for fixture adjustment; Indicates the energy consumption required for fixture adjustment; , , , , and represents weight; Represents a positive number.

2. The energy storage block stacking and positioning method according to claim 1, characterized in that: Scan the train flatbed cars to identify the boundaries of each flatbed car, including: Using a camera to shoot the train flatbed carriage to obtain train flatbed carriage image data; Transmit the train flatbed carriage image data to the image recognition system in real time; In the image recognition system, the received original image is preprocessed to obtain a preprocessed image; Scanning the pre-processed image to identify potential boundaries of the flatbed compartment; The potential boundary of the flatbed car is modified to obtain the final boundary of the flatbed car.

3. The energy storage block stacking and positioning method according to claim 2, characterized in that: The pre-processed image is scanned to identify potential boundaries of the flatbed compartment, including: Use the Sobel operator to calculate the horizontal and vertical gradients of the image, and calculate the gradient magnitude and direction based on the horizontal and vertical gradients; traverse each pixel of the image and compare its gradient magnitude with the adjacent pixels along the gradient direction. If the gradient magnitude of the current pixel is not the local maximum, set its value to 0; Setting a first threshold and a second threshold, marking pixels with a gradient magnitude higher than the first threshold as determined edges, and marking pixels with a gradient magnitude lower than the second threshold as non-edges; The pixels marked as determined edges are output in the form of a binary image, where the edge pixel value is 1 and the non-edge pixel value is 0, so as to obtain the potential boundary of the flatbed carriage.

4. The energy storage block stacking and positioning method according to claim 3 is characterized in that: According to the final position and angle of the lifting fixture, the energy storage block is clamped, including: Compare the final position and angle of the lifting fixture with the current position and posture of the energy storage block to obtain a comparison result; According to the comparison results, the final position and angle data of the lifting fixture are converted into specific control instructions, including the extension length, rotation angle and translation coordinates of the fixture; Send the converted control instructions to the lifting fixture to instruct it to make automatic adjustments; After receiving the control command, the lifting fixture automatically performs telescopic, rotating and translational actions according to the control command to achieve the clamping position of the energy storage block; The camera is used to capture the relative position and posture of the lifting fixture and the energy storage block in real time, and the accuracy of clamping is monitored based on the real-time image. If it is confirmed that the lifting fixture and the energy storage block are aligned and the posture matches, a clamping instruction is sent to the lifting fixture, and the lifting fixture executes the clamping action to grasp the energy storage block.

5. The energy storage block stacking and positioning method according to claim 4, characterized in that: After successful clamping, the hoisting equipment moves the clamp and energy storage block to the predetermined stacking area, including: Use the positioning sensor on the lifting equipment to obtain the current three-dimensional coordinates and posture information of the lifting fixture; Extracting the coordinates of the center point of the predetermined stacking area from the determined boundary point data of the stacking area in the flatbed carriage of the train; Calculate the path from the current lifting fixture position to the predetermined palletizing area while avoiding obstacles; Convert the path data from the current hoisting fixture position to the predetermined stacking area into a movement instruction sequence executed by the hoisting device, and send the movement instruction to the hoisting device, including the rotation angle, speed and acceleration parameters of each joint; After receiving the moving instruction, the lifting equipment starts to move step by step according to the instruction sequence. During the movement, the sensors on the lifting equipment monitor the position, posture and environmental information of the lifting fixture and energy storage block in real time, so that the lifting fixture and energy storage block can reach the determined predetermined stacking area.

6. The method for stacking and positioning energy storage blocks according to claim 5, characterized in that: Using the A* algorithm, the path from the current lifting fixture position to the predetermined palletizing area is calculated while avoiding obstacles, including: Define the starting point and target point, where the starting point represents the current position of the lifting fixture and the target point represents the center point of the predetermined stacking area; Add the starting point to the open list and create a closed list, which is initially empty; Define a g value and an h value for each node, where the g value represents the actual cost from the starting point to the current node, and the h value represents the heuristic estimated cost from the current node to the target point; Define an f value, which is equal to the sum of the g value and the h value, to evaluate the priority of the node; When the open list is not empty, repeat the following steps: Select the node corresponding to the f value from the open list as the current node; Move the current node from the open list to the closed list. If the current node is the target node, the path has been found. Check every reachable node around the current node. For each reachable node: If the reachable node is not accessible, it is ignored; if the reachable node is already in the closed list, it is ignored; Calculate the g value from the starting point through the current node to the reachable node; if the g value from the starting point through the current node to the reachable node is not in the open list, add the g value from the starting point through the current node to the reachable node to the open list, and set the current node as the parent node of the reachable node; Starting from the target node, trace back to the starting node along the parent node of each node to form a path. The node sequence on the path is the point that the lifting fixture needs to move through; The calculated path is converted into a sequence of movement instructions executed by the lifting equipment, and the movement instructions are sent to the lifting equipment to instruct it to move the lifting fixture and energy storage block to the predetermined stacking area according to the path.

7. An energy storage block stacking positioning system, characterized in that: Applied to the method according to any one of claims 1 to 6, comprising: The acquisition module is used to scan the flatbed cars of the train to identify the boundaries of each flatbed car; determine the boundary points of the predetermined stacking area according to the boundaries of each flatbed car; determine the adjustment range of the clamp position and angle according to the physical limitations of the lifting fixture and the characteristics of the predetermined stacking area, including the coordinates of the clamp in three-dimensional space and the rotation angle of the clamp; initialize a population, in which each individual in the population represents a set of candidate solutions for the clamp position and angle; during initialization, a certain number of individuals are randomly generated within a determined parameter range to form an initial population; for each individual in the population, a corresponding random individual is selected and a mutant individual is generated; the mutant individual is cross-operated with the target individual to generate a test individual; the fitness of the test individual is evaluated until a preset number of iterations is reached to obtain a final solution, wherein the final solution represents the final position and angle of the lifting fixture; The processing module is used to clamp the energy storage block according to the final position and angle of the lifting fixture. After successful clamping, the lifting equipment moves the fixture and the energy storage block to the predetermined stacking area; after reaching the predetermined stacking area, the position and angle of the fixture are fine-tuned again according to the boundary point of the predetermined stacking area; after completing the stacking operation, the lifting equipment automatically returns to the initial state, and all fixtures are reset to their original positions to prepare for the next operation.

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