A method and control system for intelligent offline programming of a palletizer

Abstract

This invention relates to the field of palletizer control technology, specifically an intelligent offline programming control method and control system for palletizers. The method involves acquiring first multi-dimensional spatial coordinate parameters of a temporary stacking area and second multi-dimensional spatial coordinate parameters of a designated palletizing area. Using these parameters, the spatial transport path of the palletizer is planned to obtain the shortest obstacle-avoidance transport path. Several baseline palletizing conditions are created based on the original manufacturer's instructions. The shortest obstacle-avoidance transport path is then subjected to biquaternion interpolation based on these baseline conditions to obtain the offline control program. Based on the offline control program, the code array of the offline resource scheduling program written by the offline programmer is analyzed and corrected for any tampering. This invention enables accurate programming of the offline control program for the palletizer, achieving ideal stacking of products, improving offline palletizing accuracy, and realizing intelligent operation.

Description

Technical Field

[0001] This invention relates to the field of palletizer control technology, and in particular to an intelligent offline programming control method and control system for palletizers. Background Technology

[0002] A palletizer is an industrial automation device used to stack goods onto pallets in a specified order and hierarchy for transportation and storage. It is primarily used in packaging, logistics, and warehousing at the end of the production line, significantly improving production efficiency, reducing manual labor, enhancing the neatness and stability of product stacking, and ensuring product safety. However, existing offline programming control systems for palletizers often struggle to formulate reasonable palletizing rules and layout logic based on product characteristics, palletizing scenarios, and the stability of the stacking structure. This leads to a significant decrease in product palletizing stability and safety. Furthermore, offline programming control typically requires substantial manual intervention in program writing, increasing labor costs and being time-consuming and labor-intensive. Additionally, existing offline programming control technologies have a high error rate in writing the control program code, resulting in issues such as uncorrectable code tampering, transportation collisions, and incorrect joint postures during transportation and palletizing. This reduces the accuracy and intelligence of offline control, making it difficult to meet the high-quality product palletizing requirements in offline conditions. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides an intelligent offline programming control method and control system for palletizing machines.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of this invention provides an intelligent offline programming control method for a palletizer, comprising the following steps:

[0006] S102: Obtain the product category and preset product characteristics of the palletizing product object, calculate and determine the degree of impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product category and preset product characteristics, analyze the erroneous object of the historical stacking pattern of the palletizing machine according to the degree of impact, and generate the palletizing pattern replacement logic.

[0007] S104: Obtain the spatial specification parameters of the palletized product object and the specified palletizing area. Accumulate the transport and palletized product objects through the spatial specification parameters and the specified palletizing area to output the predetermined stacking topology boundary. Adjust the vector of the palletized product object based on the structural stability index of each predetermined stacking topology boundary to generate the optimal stacking space vector.

[0008] S106: Obtain the first multi-dimensional spatial coordinate parameters in the temporary stacking area, obtain the second multi-dimensional spatial coordinate parameters of the specified stacking area based on the stacking pattern replacement logic and the optimal stacking space vector, and plan the spatial transportation path of the palletizer using the first and second multi-dimensional spatial coordinate parameters to obtain the shortest obstacle avoidance transportation path.

[0009] S108: Based on the original manufacturer's instructions for the palletizer, create several benchmark palletizing conditions for the palletizer. Based on these benchmark palletizing conditions, perform double quaternion interpolation on the shortest obstacle avoidance transport path to obtain the offline control program. Based on the simulation of the offline control program, perform code query analysis and correction on the offline resource scheduling program code array written by the offline programmer.

[0010] More specifically, step S102 includes the following steps:

[0011] Obtain the product category and current palletizing scenario of the palletizing product object, and determine the preset product characteristics of each palletizing product object through the product category;

[0012] Extract the historical intelligent stacking strategies of the palletizer in the current palletizing scenario by the palletizer's work log, and create a historical stacking pattern model of each palletized product object in the current palletizing scenario based on the preset intelligent stacking strategies.

[0013] The historical stacking pattern model is used to obtain the regional stacking distribution pattern of each palletized product object, and the historical stacking pattern model is divided into several sub-pattern model regions based on the regional stacking distribution pattern.

[0014] The system acquires multiple historical environmental parameter sets for the palletizer in the current palletizing scenario, and simultaneously acquires qualitative change data for each palletized product object when it is in the multiple historical environmental parameter sets based on preset product characteristics.

[0015] Based on the multiple historical environmental parameter sets of the current palletizing scenario, the covariates that cause qualitative changes in the palletized product objects are determined. Then, linear regression calculation is performed on the qualitative changes based on the covariates to eliminate the influence of the covariates and obtain the residuals of the qualitative changes in the data.

[0016] Covariance analysis is introduced to analyze and calculate the residuals of the qualitative change data to obtain a net effect value. Based on the net effect value, the degree of influence of the current palletizing scenario environment change on the characteristics of each palletized product object is determined.

[0017] Obtain the real-time environmental parameters of the current palletizing scene in each sub-pattern model area, and calculate the probability of collapse of the characteristics of the palletized product object based on the degree of influence of the real-time environmental parameters of each sub-pattern model area;

[0018] Extract sub-pattern model regions where the failure probability is greater than the preset failure probability, and label the palletized product objects in the sub-pattern model regions as incorrect palletized objects; then, perform pattern replacement on all incorrect palletized objects according to the degree of influence and formulate replacement logic until the failure probability output is a correct palletized object, and generate palletized pattern replacement logic.

[0019] More specifically, step S104 includes the following steps:

[0020] Obtain the spatial specification parameters and palletizing and transportation requirements of the palletized product object, and obtain the designated palletizing area of ​​the palletized product object according to the palletizing and transportation requirements;

[0021] Construct a virtual stacking space domain for a specified palletizing area, and define the stacking boundary of each palletized product object in the virtual stacking space domain based on the space specification parameters to obtain multiple stacking boundaries;

[0022] According to the palletizing pattern replacement logic, each palletized product object is accumulated and transported to the virtual stacking space domain. After the accumulation and transportation are completed, the stacking boundary between each adjacent palletized product object forms a stacking topology boundary in the virtual stacking space domain.

[0023] The stacking topology boundary generated by each adjacent stacked product object in the virtual stacking space is extracted and defined as a given stacking topology boundary. Based on big data, a mechanical knowledge graph of the spatial object structure is obtained. The mechanical structure of each given stacking topology boundary is identified through the mechanical knowledge graph, and the structural stability index of each given stacking topology boundary is output.

[0024] If the structural stability index is lower than the preset structural stability index, the virtual stacking space is divided into several subspace domain layers, and the membership degree between each predetermined stacking topology boundary and each subspace domain layer is calculated based on the structural stability index.

[0025] The optimal structural stability index is preset, and only the subspace neighborhood layers with a membership degree greater than the preset membership degree are extracted and defined as unstable subspace neighborhood layers. The optimal vector limit of each unstable subspace neighborhood layer is specified according to the optimal structural stability index. The vector of the predetermined stacking topology boundary in the unstable subspace neighborhood layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated.

[0026] The historical established palletizing rules for different palletized product objects are obtained when the palletizer is offline. The historical established palletizing rules are optimized based on the optimal stacking space vector to obtain the new palletizing rules for the palletized product objects.

[0027] More specifically, step S106 includes the following steps:

[0028] Obtain the temporary stacking area of ​​each palletized product object, establish the regional coordinate space of the temporary stacking area, define it as the first regional coordinate space, obtain the multi-dimensional spatial coordinate parameters of each palletized product object through the first regional coordinate space, and calibrate them as the first multi-dimensional spatial coordinate parameters.

[0029] Construct a regional coordinate space for a specified palletizing area, which is defined as the second regional coordinate space. Based on the palletizing pattern replacement logic and the new palletizing rules, obtain the multi-dimensional spatial coordinate parameters of each palletized product object when it reaches the ideal palletizing structure and the expected palletizing quality in the second regional coordinate space, and calibrate them as the second multi-dimensional spatial coordinate parameters.

[0030] The spatial distance from the temporary stacking area to the designated stacking area for each palletized product object is calculated using the first multidimensional spatial coordinate parameters and the second multidimensional spatial coordinate parameters. Based on the spatial distance, the irregular activity space area during the palletizer transportation process is planned.

[0031] Obtain the obstacle point distribution information in the irregular activity space area, divide the irregular activity space area into N sub-irregular areas, and determine whether two adjacent sub-irregular areas share an irregular edge in the irregular activity space area based on the obstacle point distribution information. If they do share an edge, then obtain the centroid of the two sub-irregular areas.

[0032] A dual graph framework is constructed using the palletizer as the transport point. The vertices of the dual graph framework are set based on the centroids of the two sub-irregular regions. The corresponding vertices of the two sub-irregular regions are connected in the dual graph framework.

[0033] Repeat the above steps of determining the shared irregular edges and connecting vertices until every irregular edge is traversed, generating a topological adjacency matrix of the dual graph. Based on the topological adjacency matrix, plan the shortest obstacle-avoidance transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated palletizing area.

[0034] More specifically, step S108 includes the following steps:

[0035] Obtain the original manufacturer's instruction manual for the palletizer, and extract the mechanical joint structure, the degrees of freedom of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure from the original manufacturer's instruction manual.

[0036] Based on the degrees of freedom of movement and mechanical control principles of each mechanical joint structure, several benchmark palletizing conditions of the palletizer are simulated and created; wherein, the benchmark palletizing conditions include the rotation, translation and lifting of each mechanical joint structure of the palletizer;

[0037] A double quaternion algorithm is introduced to transform and calculate the several benchmark palletizing conditions to obtain the double quaternion corresponding to each mechanical joint structure in the palletizer. M segment points of the shortest obstacle avoidance transportation path are obtained. Based on the weight of the benchmark palletizing condition of the corresponding associated mechanical joint structure in the double quaternion algorithm, multiple double quaternion weights are obtained.

[0038] Using the segmentation point as the interpolation node, the double quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the double quaternion weight and rendered to generate the joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport and palletize product objects.

[0039] An offline simulation model of the palletizer is constructed. The joint posture trajectory is input into the offline simulation model of the palletizer to write the offline control program code, and finally the offline control program of the palletizer is generated.

[0040] Based on the offline control program and the production order information of the palletized product objects, the resource scheduling tampering code is simulated offline. The offline resource scheduling program code array written by the offline programmer is queried, analyzed and corrected according to the resource scheduling tampering code.

[0041] More specifically, in step S108, the offline control program, based on the production order information of the palletized product object, simulates resource scheduling tampering code offline. The offline resource scheduling program code array written by the offline programmer is then queried, analyzed, and corrected according to the resource scheduling tampering code. This specifically includes the following steps:

[0042] Obtain production order information of palletized product objects, identify resource scheduling batches of palletized product objects through the production order information, run the offline control program to simulate and debug the resource scheduling batches in the offline simulation model of the palletizer in a manual online state, and obtain the desired palletizing model of the palletizer under manual online operation.

[0043] A resource-balanced offline programming state is preset, and based on the resource-balanced offline programming state, the palletized product object is simulated for a preset time period in the offline simulation model, and the palletized simulation point cloud data is output.

[0044] A resource-balanced stacking model is constructed using the stacking point cloud data. The hash misalignment function between the resource-balanced stacking model and the desired stacking model is calculated. If the hash misalignment function is greater than the preset hash misalignment function, the program code in the resource-balanced stacking simulation process is corrected until it is less than the preset hash misalignment function. At this time, the corrected scheduling tampering code data is recorded and marked as a type of resource scheduling tampering code.

[0045] The offline programming state of preset resource overload is used. Based on the offline programming state of preset resource overload, the palletized product object is subjected to secondary scheduling simulation of resource overload for a preset time period in the offline simulation model. The above-mentioned program code correction steps for model hash misalignment are repeated and the corrected scheduling tampering code data is recorded and marked as the second type of resource scheduling tampering code.

[0046] Using the offline programmer of the palletizer, offline program code is written for the resource scheduling batch to obtain an array of offline resource scheduling program code;

[0047] If any array node in the offline resource scheduler code array can be found to have one type of resource scheduling tampering code, then the code tampering correction for resource balancing scheduling is performed on the array node; if any array node in the offline resource scheduler code array can be found to have two types of resource scheduling tampering code, then the code tampering correction for resource overload scheduling is performed on the array node to eliminate the phenomenon of offline programming code tampering.

[0048] A second aspect of the present invention provides an intelligent offline programming control system for a palletizer, the intelligent offline programming control system comprising a memory and a processor, the memory storing an intelligent offline programming control method program for a palletizer, the intelligent offline programming control method program being executed by the processor to implement any of the steps of the intelligent offline programming control method described in the present invention.

[0049] This invention addresses the technical deficiencies in the prior art, and its beneficial technical effects are as follows:

[0050] The process involves: acquiring the product category and preset product characteristics of palletized product objects; calculating the impact of changes in the current palletizing environment on the characteristics of each palletized product object based on the product category and preset product characteristics; analyzing erroneous objects in the historical stacking pattern of the palletizer based on the impact level; and generating palletizing pattern replacement logic. It also involves acquiring the spatial specification parameters and designated palletizing areas of the palletized product objects; accumulating transported palletized product objects using the spatial specification parameters and designated palletizing areas to output a predetermined stacking topology boundary; and adjusting the vector of the palletized product objects based on the structural stability index of each predetermined stacking topology boundary to generate the optimal stacking space. The invention employs a vector method to obtain the first multidimensional spatial coordinate parameters of the temporary stacking area and the second multidimensional spatial coordinate parameters of the specified stacking area. Using these parameters, it plans the spatial transport path of the palletizer to obtain the shortest obstacle-avoidance transport path. Based on the original manufacturer's manual, it creates several baseline stacking conditions for the palletizer. Using these baseline conditions, it performs double quaternion interpolation on the shortest obstacle-avoidance transport path to obtain the offline control program. Based on the offline control program, it analyzes and corrects any tampering code in the offline resource scheduling program code array written by the offline programmer. This invention can accurately program the offline control program of the palletizer to achieve an ideal stacking state for the products, improving the timeliness and accuracy of offline palletizing, ensuring the stability and safety of spatial stacking, and realizing intelligent offline palletizing. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0052] Figure 1 A flowchart of the first method of an intelligent offline programming control method for a palletizer is shown;

[0053] Figure 2 A flowchart of a second method for intelligent offline programming control of a palletizer is shown;

[0054] Figure 3 A system framework diagram of an intelligent offline programming control system for a palletizer is shown. Detailed Implementation

[0055] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0057] The first aspect of this invention provides an intelligent offline programming control method for a palletizer, such as... Figure 1 As shown, it includes the following steps:

[0058] S102: Obtain the product category and preset product characteristics of the palletizing product object, calculate and determine the degree of impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product category and preset product characteristics, analyze the erroneous object of the historical stacking pattern of the palletizing machine according to the degree of impact, and generate the palletizing pattern replacement logic.

[0059] S104: Obtain the spatial specification parameters of the palletized product object and the specified palletizing area. Accumulate the transport and palletized product objects through the spatial specification parameters and the specified palletizing area to output the predetermined stacking topology boundary. Adjust the vector of the palletized product object based on the structural stability index of each predetermined stacking topology boundary to generate the optimal stacking space vector.

[0060] S106: Obtain the first multi-dimensional spatial coordinate parameters in the temporary stacking area, obtain the second multi-dimensional spatial coordinate parameters of the specified stacking area based on the stacking pattern replacement logic and the optimal stacking space vector, and plan the spatial transportation path of the palletizer using the first and second multi-dimensional spatial coordinate parameters to obtain the shortest obstacle avoidance transportation path.

[0061] S108: Based on the original manufacturer's instructions for the palletizer, create several benchmark palletizing conditions for the palletizer. Based on these benchmark palletizing conditions, perform double quaternion interpolation on the shortest obstacle avoidance transport path to obtain the offline control program. Based on the simulation of the offline control program, perform code query analysis and correction on the offline resource scheduling program code array written by the offline programmer.

[0062] More specifically, step S102 includes the following steps:

[0063] Obtain the product category and current palletizing scenario of the palletizing product object, and determine the preset product characteristics of each palletizing product object through the product category;

[0064] Extract the historical intelligent stacking strategies of the palletizer in the current palletizing scenario by the palletizer's work log, and create a historical stacking pattern model of each palletized product object in the current palletizing scenario based on the preset intelligent stacking strategies.

[0065] The historical stacking pattern model is used to obtain the regional stacking distribution pattern of each palletized product object, and the historical stacking pattern model is divided into several sub-pattern model regions based on the regional stacking distribution pattern.

[0066] The system acquires multiple historical environmental parameter sets for the palletizer in the current palletizing scenario, and simultaneously acquires qualitative change data for each palletized product object when it is in the multiple historical environmental parameter sets based on preset product characteristics.

[0067] Based on the multiple historical environmental parameter sets of the current palletizing scenario, the covariates that cause qualitative changes in the palletized product objects are determined. Then, linear regression calculation is performed on the qualitative changes based on the covariates to eliminate the influence of the covariates and obtain the residuals of the qualitative changes in the data.

[0068] Covariance analysis is introduced to analyze and calculate the residuals of the qualitative change data to obtain a net effect value. Based on the net effect value, the degree of influence of the current palletizing scenario environment change on the characteristics of each palletized product object is determined.

[0069] Obtain the real-time environmental parameters of the current palletizing scene in each sub-pattern model area, and calculate the probability of collapse of the characteristics of the palletized product object based on the degree of influence of the real-time environmental parameters of each sub-pattern model area;

[0070] Extract sub-pattern model regions where the failure probability is greater than the preset failure probability, and label the palletized product objects in the sub-pattern model regions as incorrect palletized objects; then, perform pattern replacement on all incorrect palletized objects according to the degree of influence and formulate replacement logic until the failure probability output is a correct palletized object, and generate palletized pattern replacement logic.

[0071] It should be noted that the preset product characteristics include fragile, heavy, and easily deformable. Offline control of palletizers is typically completely independent of human intervention. Therefore, compared to online manual control, offline control of palletizers makes it difficult to make intelligent decisions regarding the stacking pattern based on the product's characteristics during the palletizing process. For example, if the palletized product is fragile, stacking it under heavy objects may cause it to break and be damaged, resulting in a significant decrease in product quality and reducing the control reliability of the palletizer. Therefore, this method calculates the residual of qualitative change data by combining multiple historical environmental parameter sets of the palletizer in the current palletizing scenario and qualitative change data of each palletized product object in multiple historical environmental parameter sets. This residual represents the change in palletized product characteristics after excluding the influence of covariates in the current palletizing scenario, which can further ensure the accuracy of the subsequent impact magnitude. Then, the residual is analyzed by variance to determine the net effect of the current palletizing scenario on the characteristics of the palletized product, that is, the degree of influence of the change in the palletizing scenario environment on the characteristics of each palletized product object. Based on this degree of influence, the palletizer can formulate the pattern stacking logic of different palletized product characteristics according to the surrounding palletizing scenario, thereby improving the rationality of product stacking. Among them, if the failure probability is greater than the preset failure probability, it means that the local pattern of the palletized product in the sub-pattern model area is prone to damage to its palletized product. Therefore, the palletized product object in the sub-pattern model area is marked as an incorrect stacking object and replaced. This method enables the rational formulation of layout replacement decision logic for palletized products during offline control of the palletizer based on the characteristics of the palletized products and the surrounding palletizing environment. This ensures improved palletizing quality and reduced product damage without manual intervention, resulting in high economic benefits.

[0072] More specifically, step S104 includes the following steps:

[0073] Obtain the spatial specification parameters and palletizing and transportation requirements of the palletized product object, and obtain the designated palletizing area of ​​the palletized product object according to the palletizing and transportation requirements;

[0074] Construct a virtual stacking space domain for a specified palletizing area, and define the stacking boundary of each palletized product object in the virtual stacking space domain based on the space specification parameters to obtain multiple stacking boundaries;

[0075] According to the palletizing pattern replacement logic, each palletized product object is accumulated and transported to the virtual stacking space domain. After the accumulation and transportation are completed, the stacking boundary between each adjacent palletized product object forms a stacking topology boundary in the virtual stacking space domain.

[0076] The stacking topology boundary generated by each adjacent stacked product object in the virtual stacking space is extracted and defined as a given stacking topology boundary. Based on big data, a mechanical knowledge graph of the spatial object structure is obtained. The mechanical structure of each given stacking topology boundary is identified through the mechanical knowledge graph, and the structural stability index of each given stacking topology boundary is output.

[0077] If the structural stability index is lower than the preset structural stability index, the virtual stacking space is divided into several subspace domain layers, and the membership degree between each predetermined stacking topology boundary and each subspace domain layer is calculated based on the structural stability index.

[0078] The optimal structural stability index is preset, and only the subspace neighborhood layers with a membership degree greater than the preset membership degree are extracted and defined as unstable subspace neighborhood layers. The optimal vector limit of each unstable subspace neighborhood layer is specified according to the optimal structural stability index. The vector of the predetermined stacking topology boundary in the unstable subspace neighborhood layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated.

[0079] The historical established palletizing rules for different palletized product objects are obtained when the palletizer is offline. The historical established palletizing rules are optimized based on the optimal stacking space vector to obtain the new palletizing rules for the palletized product objects.

[0080] It should be noted that the vector of the predetermined stacking topology boundary includes the contact area, direction, and position. For palletized products, the stacking pattern in space is one aspect of ensuring product quality. However, the structural stability of the palletized products after forming the stacking pattern in space is also a crucial palletizing standard. If the overall structure formed after the palletizer stacks all the palletized products in the designated area is unreasonable or unstable, it may cause the stacking structure in the designated area to shake, tilt, or even collapse under certain external forces such as vibration or human intervention, which may easily injure surrounding workers and damage the palletized products themselves. Therefore, this method constructs a virtual stacking space domain to perform virtual accumulation and transportation of palletized product objects, simulating the form of palletized products after stacking in a designated area. Since the contact surfaces between objects when stacked and in contact with different stacking space vectors will present a clear intersection boundary, i.e., the stacking topological boundary, this boundary can reflect the stacking structural stability between palletized products. Therefore, by analyzing the stacking topological boundary between palletized products in the virtual stacking space domain and using the identification of mechanical knowledge graphs, it is possible to know whether the overall stacking structure is stable. If it is unstable, i.e., the structural stability index is lower than the preset structural stability index, it means that the stacking vector between palletized products at that location is unreasonable and should be adjusted. This method provides a reasonable adjustment basis for the spatial vector of each palletized product in the unstable subspace domain layer by setting the limit of the optimal structural stability index, replacing the traditional manual intervention adjustment steps and saving labor costs. This method can give the offline programming control of the palletizer more intelligent palletizing rule control, improve the stability of the palletizer's stacking structure for palletized products, reduce accidental damage and product damage caused by unstable conditions, and improve the neatness of the palletized products.

[0081] More specifically, step S106, as follows: Figure 2 As shown, the specific steps include:

[0082] S202: Obtain the temporary stacking area of ​​each palletized product object, establish the regional coordinate space of the temporary stacking area, define it as the first regional coordinate space, obtain the multi-dimensional spatial coordinate parameters of each palletized product object through the first regional coordinate space, and calibrate them as the first multi-dimensional spatial coordinate parameters.

[0083] S204: Construct the regional coordinate space of the specified palletizing area, which is defined as the second regional coordinate space. Based on the palletizing pattern replacement logic and the new palletizing rule, obtain the multi-dimensional spatial coordinate parameters of each palletized product object when it reaches the ideal palletizing structure and the expected palletizing quality in the second regional coordinate space, and calibrate them as the second multi-dimensional spatial coordinate parameters.

[0084] S206: Calculate the spatial distance from the temporary stacking area to the designated stacking area for each palletized product object using the first multi-dimensional spatial coordinate parameters and the second multi-dimensional spatial coordinate parameters, and plan the irregular activity space area during the palletizer transportation process based on the spatial distance.

[0085] S208: Obtain the obstacle point distribution information in the irregular activity space area, divide the irregular activity space area into N sub-irregular areas, and determine whether two adjacent sub-irregular areas share an irregular edge in the irregular activity space area based on the obstacle point distribution information. If they do share an edge, then obtain the centroid of the two sub-irregular areas at this time.

[0086] S210: Construct a dual graph framework with the palletizer as the transport point, set the vertices of the dual graph framework based on the centroids of the two sub-irregular regions, and connect the corresponding vertices of the two sub-irregular regions in the dual graph framework.

[0087] S212: Repeat the above steps of irregular edge sharing determination and vertex connection until each irregular edge is traversed, generate the topological adjacency matrix of the dual graph, and plan the shortest obstacle avoidance transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated palletizing area according to the topological adjacency matrix.

[0088] It should be noted that since the working environment of palletizers is unrestricted, there are no high requirements for their placement. This also means that there may be obstacles of different types and sizes around the palletizer. These obstacles can cause friction, collisions, or head-on collisions during operation, which is detrimental to the smooth transport and palletizing of goods. To address this, this method establishes a temporary stacking area for the palletized product and a coordinate space corresponding to a designated palletizing area. Using the coordinate information of the distance between the two coordinate spaces, an activity space area that the palletizer may come into contact with obstacles during the transport from the temporary stacking area to the designated palletizing area can be planned. On the one hand, due to the differences in the types and sizes of the surrounding obstacles, the boundary of this activity space area is irregular. On the other hand, the transport activity of the palletizer is multi-dimensional; therefore, this activity space area is a multi-dimensional space, i.e., an irregularly shaped activity space area. Next, based on the location of obstacles in the irregular activity space area, it is determined whether two adjacent sub-irregular areas share an irregular edge within the irregular activity space area. If a shared edge exists, it indicates that the palletizer may encounter the obstacle point while operating in the sub-irregular area. Therefore, a dual graph can be constructed, and the corresponding vertices of the two sub-irregular areas on the irregular edge can be connected in the dual graph to eliminate collisions between the palletizer and obstacles while operating in the sub-irregular area. Finally, the topological adjacency matrix output after traversing all irregular edges represents the trajectory that avoids collisions with obstacles. Thus, in offline mode, a shortest transportation path is planned for the palletizer to transport products from the temporary stacking area to the designated palletizing area without collisions with obstacles, based on the palletizing pattern replacement logic and new palletizing rules. This method can intelligently plan the shortest, most accurate, obstacle-avoiding, and efficient transportation path for the palletizer in offline control mode, thereby avoiding collisions between the palletizer and obstacles during the transportation of palletized products and improving the safety factor and control stability of palletizing transportation.

[0089] More specifically, step S108 includes the following steps:

[0090] Obtain the original manufacturer's instruction manual for the palletizer, and extract the mechanical joint structure, the degrees of freedom of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure from the original manufacturer's instruction manual.

[0091] Based on the degrees of freedom of movement and mechanical control principles of each mechanical joint structure, several benchmark palletizing conditions of the palletizer are simulated and created; wherein, the benchmark palletizing conditions include the rotation, translation and lifting of each mechanical joint structure of the palletizer;

[0092] A double quaternion algorithm is introduced to transform and calculate the several benchmark palletizing conditions to obtain the double quaternion corresponding to each mechanical joint structure in the palletizer. M segment points of the shortest obstacle avoidance transportation path are obtained. Based on the weight of the benchmark palletizing condition of the corresponding associated mechanical joint structure in the double quaternion algorithm, multiple double quaternion weights are obtained.

[0093] Using the segmentation point as the interpolation node, the double quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the double quaternion weight and rendered to generate the joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport and palletize product objects.

[0094] An offline simulation model of the palletizer is constructed. The joint posture trajectory is input into the offline simulation model of the palletizer to write the offline control program code, and finally the offline control program of the palletizer is generated.

[0095] Based on the offline control program and the production order information of the palletized product objects, the resource scheduling tampering code is simulated offline. The offline resource scheduling program code array written by the offline programmer is queried, analyzed and corrected according to the resource scheduling tampering code.

[0096] It should be noted that after planning the offline obstacle avoidance control path for the palletizer, it is necessary to determine the joint posture of the palletizer when executing the shortest obstacle avoidance transport path. Once the posture is determined, accurate program code can be written. However, the joint posture analysis accuracy of existing offline control palletizers is low, resulting in many errors in the final programmed program code. This requires subsequent manual intervention for correction, which is time-consuming and labor-intensive, and reduces the offline control accuracy of the palletizer. This method constructs baseline palletizing conditions for the palletizer based on the mechanical details described in the original manufacturer's manual, facilitating the reasonable definition and adjustment of the palletizer's attitude analysis. Then, a double quaternion weight is calculated for these baseline palletizing conditions to concretely represent the necessity of each condition in the shortest obstacle avoidance transport path control. The double quaternion for each mechanical joint is calculated, and based on the double quaternion weights, the double quaternions corresponding to each mechanical joint are interpolated onto the shortest obstacle avoidance transport path. This describes the joint attitude required for each mechanical joint when the palletizer executes the shortest obstacle avoidance transport path. By using an offline simulation model of the palletizer to execute these joint attitudes, accurate program code can be determined and written. This method can define the operating attitude of all joints of the palletizer according to the analysis of the planned shortest obstacle avoidance transport path, resulting in smoother and more precise attitude control during transport, and more reliable offline program control.

[0097] More specifically, in step S108, resource scheduling tampering code is simulated offline based on the offline control program and the production order information of the palletized product object. The offline resource scheduling program code array written by the offline programmer is then queried, analyzed, and corrected according to the resource scheduling tampering code. This specifically includes the following steps:

[0098] Obtain production order information of palletized product objects, identify resource scheduling batches of palletized product objects through the production order information, run the offline control program to simulate and debug the resource scheduling batches in the offline simulation model of the palletizer in a manual online state, and obtain the desired palletizing model of the palletizer under manual online operation.

[0099] A resource-balanced offline programming state is preset, and based on the resource-balanced offline programming state, the palletized product object is simulated for a preset time period in the offline simulation model, and the palletized simulation point cloud data is output.

[0100] A resource-balanced stacking model is constructed using the stacking point cloud data. The hash misalignment function between the resource-balanced stacking model and the desired stacking model is calculated. If the hash misalignment function is greater than the preset hash misalignment function, the program code in the resource-balanced stacking simulation process is corrected until it is less than the preset hash misalignment function. At this time, the corrected scheduling tampering code data is recorded and marked as a type of resource scheduling tampering code.

[0101] The offline programming state of preset resource overload is used. Based on the offline programming state of preset resource overload, the palletized product object is subjected to secondary scheduling simulation of resource overload for a preset time period in the offline simulation model. The above-mentioned program code correction steps for model hash misalignment are repeated and the corrected scheduling tampering code data is recorded and marked as the second type of resource scheduling tampering code.

[0102] Using the offline programmer of the palletizer, offline program code is written for the resource scheduling batch to obtain an array of offline resource scheduling program code;

[0103] If any array node in the offline resource scheduler code array can be found to have one type of resource scheduling tampering code, then the code tampering correction for resource balancing scheduling is performed on the array node; if any array node in the offline resource scheduler code array can be found to have two types of resource scheduling tampering code, then the code tampering correction for resource overload scheduling is performed on the array node to eliminate the phenomenon of offline programming code tampering.

[0104] It should be noted that when the offline programmer has a low level of intelligence, it is difficult to identify and judge the scheduling and transportation of different resource quantities. This may lead to problems such as garbled characters or tampering in the program code, which is one of the more serious control factors affecting the transportation and palletizing of the palletizer. Existing offline control technology cannot achieve timely and efficient code tampering correction for the program code output by the offline simulation programming of the palletizer, so the transportation and palletizing controlled by the offline palletizer cannot meet the expected palletizing requirements. To address this, this method involves manually operating the palletizer online to execute an offline control program to simulate and debug resource scheduling batches of palletized product objects. This allows the palletizer to output a desired palletizing model under manual intervention, ensuring that subsequent code tampering corrections are based on the desired palletizing requirements under manual intervention. Next, the method simulates resource scheduling of palletized product objects under both resource-balanced and resource-overloaded conditions. The differences between the resource-balanced and resource-overloaded palletizing models and the desired palletizing model are then analyzed. If the hash misalignment function is greater than the preset hash misalignment function, it indicates that the simulated resource scheduling palletizing results deviate significantly from the desired palletizing requirements. This may be due to a program code error, thus requiring correction of the program code during the palletizing simulation. At this point, the corrected scheduling tampering code data is obtained. By analyzing the modified scheduling code data, the offline resource scheduler code array subsequently written by the offline programmer is queried. If any array node in the offline resource scheduler code array shows one type of resource scheduling modification code, it indicates that the written code has been modified for resource balancing scheduling, and therefore the array node needs to be corrected for resource balancing scheduling. If any array node shows two types of resource scheduling modification code, it indicates that the code written by the offline programmer has been modified for resource overload scheduling, and the array node needs to be corrected for resource overload scheduling.

[0105] It should be noted that this method can correct the program code tampering that occurs when writing palletizer control programs using offline programmers, thereby eliminating the phenomenon of offline programming code tampering, improving the stability and reliability of offline programming of palletizers, and improving the accuracy of offline control of palletizers.

[0106] A second aspect of the present invention provides an intelligent offline programming control system for a palletizer, such as... Figure 3 As shown, the intelligent offline programming control system includes a memory 31 and a processor 32. The memory 31 stores an intelligent offline programming control method program for a palletizer. When the intelligent offline programming control method program is executed by the processor 32, it implements any of the steps of the intelligent offline programming control method described above.

[0107] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent offline programming control method for a palletizer, characterized in that, Includes the following steps: S102: Obtain the product category and preset product characteristics of the palletizing product object, calculate and determine the degree of impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product category and preset product characteristics, analyze the erroneous object of the historical stacking pattern of the palletizing machine according to the degree of impact, and generate the palletizing pattern replacement logic. S104: Obtain the spatial specification parameters of the palletized product object and the specified palletizing area. Accumulate the transport and palletized product objects through the spatial specification parameters and the specified palletizing area to output the predetermined stacking topology boundary. Adjust the vector of the palletized product object based on the structural stability index of each predetermined stacking topology boundary to generate the optimal stacking space vector. S106: Obtain the first multi-dimensional spatial coordinate parameters in the temporary stacking area, obtain the second multi-dimensional spatial coordinate parameters of the specified stacking area based on the stacking pattern replacement logic and the optimal stacking space vector, and plan the spatial transportation path of the palletizer using the first and second multi-dimensional spatial coordinate parameters to obtain the shortest obstacle avoidance transportation path. S108: Based on the original manufacturer's instructions for the palletizer, create several benchmark palletizing conditions for the palletizer. Based on these benchmark palletizing conditions, perform double quaternion interpolation on the shortest obstacle avoidance transport path to obtain the offline control program. Based on the simulation of the offline control program, perform code query analysis and correction on the offline resource scheduling program code array written by the offline programmer.

2. The intelligent offline programming control method for a palletizer according to claim 1, characterized in that, Step S102 specifically includes the following steps: Obtain the product category and current palletizing scenario of the palletizing product object, and determine the preset product characteristics of each palletizing product object through the product category; Extract the historical intelligent stacking strategies of the palletizer in the current palletizing scenario by the palletizer's work log, and create a historical stacking pattern model of each palletized product object in the current palletizing scenario based on the preset intelligent stacking strategies. The historical stacking pattern model is used to obtain the regional stacking distribution pattern of each palletized product object, and the historical stacking pattern model is divided into several sub-pattern model regions based on the regional stacking distribution pattern. The system acquires multiple historical environmental parameter sets for the palletizer in the current palletizing scenario, and simultaneously acquires qualitative change data for each palletized product object when it is in the multiple historical environmental parameter sets based on preset product characteristics. Based on the multiple historical environmental parameter sets of the current palletizing scenario, the covariates that cause qualitative changes in the palletized product objects are determined. Then, linear regression calculation is performed on the qualitative changes based on the covariates to eliminate the influence of the covariates and obtain the residuals of the qualitative changes in the data. Covariance analysis is introduced to analyze and calculate the residuals of the qualitative change data to obtain a net effect value. Based on the net effect value, the degree of influence of the current palletizing scenario environment change on the characteristics of each palletized product object is determined. Obtain the real-time environmental parameters of the current palletizing scene in each sub-pattern model area, and calculate the probability of collapse of the characteristics of the palletized product object based on the degree of influence of the real-time environmental parameters of each sub-pattern model area; Extract sub-pattern model regions where the failure probability is greater than the preset failure probability, and label the palletized product objects in the sub-pattern model regions as incorrect palletized objects; then, perform pattern replacement on all incorrect palletized objects according to the degree of influence and formulate replacement logic until the failure probability output is a correct palletized object, and generate palletized pattern replacement logic.

3. The intelligent offline programming control method for a palletizer according to claim 1, characterized in that, Step S104 specifically includes the following steps: Obtain the spatial specification parameters and palletizing and transportation requirements of the palletized product object, and obtain the designated palletizing area of ​​the palletized product object according to the palletizing and transportation requirements; Construct a virtual stacking space domain for a specified palletizing area, and define the stacking boundary of each palletized product object in the virtual stacking space domain based on the space specification parameters to obtain multiple stacking boundaries; According to the palletizing pattern replacement logic, each palletized product object is accumulated and transported to the virtual stacking space domain. After the accumulation and transportation are completed, the stacking boundary between each adjacent palletized product object forms a stacking topology boundary in the virtual stacking space domain. The stacking topology boundary generated by each adjacent stacked product object in the virtual stacking space is extracted and defined as a given stacking topology boundary. Based on big data, a mechanical knowledge graph of the spatial object structure is obtained. The mechanical structure of each given stacking topology boundary is identified through the mechanical knowledge graph, and the structural stability index of each given stacking topology boundary is output. If the structural stability index is lower than the preset structural stability index, the virtual stacking space is divided into several subspace domain layers, and the membership degree between each predetermined stacking topology boundary and each subspace domain layer is calculated based on the structural stability index. The optimal structural stability index is preset, and only the subspace neighborhood layers with a membership degree greater than the preset membership degree are extracted and defined as unstable subspace neighborhood layers. The optimal vector limit of each unstable subspace neighborhood layer is specified according to the optimal structural stability index. The vector of the predetermined stacking topology boundary in the unstable subspace neighborhood layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated. The historical established palletizing rules for different palletized product objects are obtained when the palletizer is offline. The historical established palletizing rules are optimized based on the optimal stacking space vector to obtain the new palletizing rules for the palletized product objects.

4. The intelligent offline programming control method for a palletizer according to claim 1, characterized in that, Step S106 specifically includes the following steps: Obtain the temporary stacking area of ​​each palletized product object, establish the regional coordinate space of the temporary stacking area, define it as the first regional coordinate space, obtain the multi-dimensional spatial coordinate parameters of each palletized product object through the first regional coordinate space, and calibrate them as the first multi-dimensional spatial coordinate parameters. Construct a regional coordinate space for a specified palletizing area, which is defined as the second regional coordinate space. Based on the palletizing pattern replacement logic and the new palletizing rules, obtain the multi-dimensional spatial coordinate parameters of each palletized product object when it reaches the ideal palletizing structure and the expected palletizing quality in the second regional coordinate space, and calibrate them as the second multi-dimensional spatial coordinate parameters. The spatial distance from the temporary stacking area to the designated stacking area for each palletized product object is calculated using the first multidimensional spatial coordinate parameters and the second multidimensional spatial coordinate parameters. Based on the spatial distance, the irregular activity space area during the palletizer transportation process is planned. Obtain the obstacle point distribution information in the irregular activity space area, divide the irregular activity space area into N sub-irregular areas, and determine whether two adjacent sub-irregular areas share an irregular edge in the irregular activity space area based on the obstacle point distribution information. If they do share an edge, then obtain the centroid of the two sub-irregular areas. A dual graph framework is constructed using the palletizer as the transport point. The vertices of the dual graph framework are set based on the centroids of the two sub-irregular regions. The corresponding vertices of the two sub-irregular regions are connected in the dual graph framework. Repeat the above steps of determining the shared irregular edges and connecting vertices until every irregular edge is traversed, generating a topological adjacency matrix of the dual graph. Based on the topological adjacency matrix, plan the shortest obstacle-avoidance transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated palletizing area.

5. The intelligent offline programming control method for a palletizer according to claim 1, characterized in that, Step S108 specifically includes the following steps: Obtain the original manufacturer's instruction manual for the palletizer, and extract the mechanical joint structure, the degrees of freedom of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure from the original manufacturer's instruction manual. Based on the degrees of freedom of movement and mechanical control principles of each mechanical joint structure, several benchmark palletizing conditions of the palletizer are simulated and created; wherein, the benchmark palletizing conditions include the rotation, translation and lifting of each mechanical joint structure of the palletizer; A double quaternion algorithm is introduced to transform and calculate the several benchmark palletizing conditions to obtain the double quaternion corresponding to each mechanical joint structure in the palletizer. M segment points of the shortest obstacle avoidance transportation path are obtained. Based on the weight of the benchmark palletizing condition of the corresponding associated mechanical joint structure in the double quaternion algorithm, multiple double quaternion weights are obtained. Using the segmentation point as the interpolation node, the double quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the double quaternion weight and rendered to generate the joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport and palletize product objects. An offline simulation model of the palletizer is constructed. The joint posture trajectory is input into the offline simulation model of the palletizer to write the offline control program code, and finally the offline control program of the palletizer is generated. Based on the offline control program and the production order information of the palletized product objects, the resource scheduling tampering code is simulated offline. The offline resource scheduling program code array written by the offline programmer is queried, analyzed and corrected according to the resource scheduling tampering code.

6. The intelligent offline programming control method for a palletizer according to claim 5, characterized in that, Step S108 involves simulating resource scheduling tampering code offline based on the offline control program and the production order information of the palletized product objects. The offline resource scheduling program code array written by the offline programmer is then queried, analyzed, and corrected according to the resource scheduling tampering code. Specifically, this includes the following steps: Obtain production order information of palletized product objects, identify resource scheduling batches of palletized product objects through the production order information, run the offline control program to simulate and debug the resource scheduling batches in the offline simulation model of the palletizer in a manual online state, and obtain the desired palletizing model of the palletizer under manual online operation. A resource-balanced offline programming state is preset, and based on the resource-balanced offline programming state, the palletized product object is simulated for a preset time period in the offline simulation model, and the palletized simulation point cloud data is output. A resource-balanced stacking model is constructed using the stacking point cloud data. The hash misalignment function between the resource-balanced stacking model and the desired stacking model is calculated. If the hash misalignment function is greater than the preset hash misalignment function, the program code in the resource-balanced stacking simulation process is corrected until it is less than the preset hash misalignment function. At this time, the corrected scheduling tampering code data is recorded and marked as a type of resource scheduling tampering code. The offline programming state of preset resource overload is used. Based on the offline programming state of preset resource overload, the palletized product object is subjected to secondary scheduling simulation of resource overload for a preset time period in the offline simulation model. The above-mentioned program code correction steps for model hash misalignment are repeated and the corrected scheduling tampering code data is recorded and marked as the second type of resource scheduling tampering code. Using the offline programmer of the palletizer, offline program code is written for the resource scheduling batch to obtain an array of offline resource scheduling program code; If any array node in the offline resource scheduler code array can be found to have one type of resource scheduling tampering code, then the code tampering correction for resource balancing scheduling is performed on the array node; if any array node in the offline resource scheduler code array can be found to have two types of resource scheduling tampering code, then the code tampering correction for resource overload scheduling is performed on the array node to eliminate the phenomenon of offline programming code tampering.

7. An intelligent offline programming control system for a palletizer, characterized in that, The intelligent offline programming control system includes a memory and a processor. The memory stores an intelligent offline programming control method program for a palletizer. When the intelligent offline programming control method program is executed by the processor, it implements the steps of the intelligent offline programming control method as described in any one of claims 1-6.

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