Intelligent off-line programming control method and control system for stacker crane

Abstract

The invention relates to the technical field of stacker crane control, in particular to an intelligent offline programming control method and system for a stacker crane. A first multi-dimensional space coordinate parameter in the temporary stacking area and a second multi-dimensional space coordinate parameter of the designated stacking area are obtained, the first multi-dimensional space coordinate parameter and the second multi-dimensional space coordinate parameter are utilized to plan a space transportation path of the stacker crane, and a shortest obstacle avoidance transportation path is obtained; creating a plurality of reference stacking working conditions of the stacker crane according to an original factory specification of the stacker crane, and performing double quaternion interpolation on the shortest obstacle avoidance transportation path based on the plurality of reference stacking working conditions to obtain an offline control program; and performing tampered code query analysis and correction on the offline resource scheduling program code array written by the offline programmer based on the offline control program. According to the invention, the offline control program of the stacker crane can be accurately programmed, so that stacked products can reach an ideal stacking state, the offline stacking accuracy is improved, and intelligentization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of palletizer control, and in particular to an intelligent off-line programming control method and a control system for a palletizer. Background Art

[0002] Palletizer is an industrial automation equipment used to stack items on pallets in a specified order and level for easy transportation and storage. It is mainly used in packaging, logistics and warehousing at the end of the production line. It can greatly improve production efficiency, reduce the demand for manual labor, improve the neatness and stability of product stacking, and ensure the safety of product stacking. However, the offline programming control of existing palletizers is usually difficult to formulate reasonable palletizing rules and pattern logic for products based on product characteristics, palletizing scenarios and the stability of the stacking structure, which greatly reduces the stability and safety of product palletizing. In addition, a large amount of manual intervention is usually required in the writing of programs during the offline programming control process, which increases the output of labor costs and is time-consuming and labor-intensive. At the same time, the offline programming control technology of existing palletizers has a large error rate in the writing of the control program code of the palletizer, which causes code tampering that cannot be corrected, transportation collisions, and joint posture errors during transportation and palletizing of the palletizer, which reduces the offline control accuracy and intelligence of the palletizer and makes it difficult to meet the requirements of high-quality product palletizing in an offline state. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides an intelligent off-line programming control method and a control system for a palletizer.

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

[0005] The first aspect of the present invention provides an intelligent off-line programming control method for a palletizer, comprising the following steps:

[0006] S102: Obtain product categories and preset product characteristics of the palletizing product objects, calculate and determine the impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product categories and preset product characteristics, analyze and replace the error objects of the historical stacking pattern of the palletizer according to the impact degree, and generate the palletizing pattern replacement logic;

[0007] S104: obtaining space specification parameters of the stacked product objects and the designated stacking area, accumulating the transported stacked product objects through the space specification parameters and the designated stacking area to output a predetermined stacking topology boundary, adjusting the vector of the stacked product objects based on the structural stability index of each predetermined stacking topology boundary, and generating an optimal stacking space vector;

[0008] S106: Acquire the first multidimensional space coordinate parameters in the temporary stacking area, acquire the second multidimensional space coordinate parameters of the designated 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 multidimensional space coordinate parameters and the second multidimensional space coordinate parameters to obtain the shortest obstacle avoidance transportation path;

[0009] S108: creating several reference palletizing conditions of the palletizer according to the original factory manual of the palletizer, performing dual quaternion interpolation on the shortest obstacle avoidance transport path based on the several reference palletizing conditions, and obtaining an offline control program; querying and analyzing the tampered code of the offline resource scheduling program code array written by the offline programmer and correcting it based on the simulation of the offline control program.

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

[0011] Obtaining the product category of the palletized product object and the current palletizing scene, and determining the preset product characteristics of each palletized product object according to the product category;

[0012] Extract the historical intelligent stacking strategy of the palletizer in stacking preset product characteristics in the current palletizing scene through the work log of the palletizer, and create a historical stacking pattern model of each palletizing product object in the current palletizing scene according to the preset intelligent stacking strategy;

[0013] Using the historical stacking pattern model to obtain the regional stacking distribution pattern of each stacked product object, and dividing the historical stacking pattern model into a plurality of sub-pattern model areas based on the regional stacking distribution pattern;

[0014] Acquire multiple historical environmental parameter sets of the palletizer in the current palletizing scene, and acquire qualitative change data of each palletizing product object when it is in the multiple historical environmental parameter sets based on preset product characteristics;

[0015] Determine the covariates that cause the palletized product object to generate qualitative change data based on the multiple historical environmental parameter sets of the current palletizing scene, and perform linear regression calculation on the qualitative change data based on the covariates to eliminate the influence of the covariates and obtain the residual of the qualitative change data change;

[0016] Introducing a covariance analysis method to analyze and calculate the residual of the qualitative change data to obtain a net effect value, and determining the degree of influence of the current stacking scene environment change on the characteristics of each stacking product object according to the net effect value;

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

[0018] Extract the sub-pattern model area where the collapse probability is greater than the preset collapse probability, and calibrate the stacked product objects in the sub-pattern model area as incorrect stacking objects; at this time, perform pattern replacement on all incorrect stacking objects according to the degree of influence and formulate replacement logic until the collapse probability is output as the correct stacking object, and generate the stacking pattern replacement logic.

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

[0020] Acquire spatial specification parameters and palletizing transportation requirements of the palletizing product object, and acquire a designated palletizing area for the palletizing product object according to the palletizing transportation requirements;

[0021] Constructing a virtual stacking space domain of a designated stacking area, defining a stacking boundary of each stacking product object in the virtual stacking space domain based on the space specification parameters, and obtaining a plurality of stacking boundaries;

[0022] According to the stacking pattern replacement logic, each stacked product object is cumulatively transported to the virtual stacking space field. After the cumulative transportation is completed, the stacking boundary between each adjacent stacked product object forms a stacking topology boundary in the virtual stacking space field;

[0023] The stacking topological boundaries generated by each adjacent stacked product object in the virtual stacking space are stripped out and defined as the established stacking topological boundaries. The mechanical knowledge graph of the spatial object structure is obtained based on big data. The mechanical structure of each established stacking topological boundary is identified through the mechanical knowledge graph, and the structural stability index of each established stacking topological boundary is output.

[0024] If the structural stability index is lower than the preset structural stability index, the virtual stacking space domain is divided into a plurality of subspace domain layers, and the membership 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 domain layer corresponding to the membership degree greater than the preset membership degree is extracted, which is defined as the unstable subspace domain layer. The optimal vector limit of each unstable subspace domain layer is specified according to the optimal structural stability index, and the vector of the established stacking topology boundary in the unstable subspace domain layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated;

[0026] The historically established palletizing rules for different palletizing product objects when the palletizer is in an offline state are obtained, and the historically established palletizing rules are optimized based on the optimal stacking space vector to obtain new palletizing rules for the palletizing product objects.

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

[0028] Acquire a temporary stacking area for each current stacked product object, establish a regional coordinate space for the temporary stacking area, define it as a first regional coordinate space, acquire a multidimensional space coordinate parameter for each stacked product object through the first regional coordinate space, and calibrate it as the first multidimensional space coordinate parameter;

[0029] Constructing a regional coordinate space of a designated palletizing area, which is defined as a second regional coordinate space, obtaining multidimensional spatial coordinate parameters of each palletizing product object when it reaches an ideal palletizing structure and expected palletizing quality in the second regional coordinate space based on the palletizing pattern replacement logic and the new palletizing rule, and calibrating them as second multidimensional spatial coordinate parameters;

[0030] The spatial distance from the temporary stacking area to the designated stacking area of ​​each current stacked product object is calculated using the first multi-dimensional space coordinate parameter and the second multi-dimensional space coordinate parameter, and the special-shaped activity space area during the transportation process of the palletizer is planned according to the spatial distance;

[0031] Obtaining the obstacle point distribution information in the special-shaped activity space area, dividing the special-shaped activity space area into N sub-special-shaped areas, and judging whether two adjacent sub-special-shaped areas share a special-shaped edge in the special-shaped activity space area based on the obstacle point distribution information, and if so, obtaining the centroid points of the two sub-special-shaped areas at this time;

[0032] A dual graph framework is constructed with the palletizer as a transport fixed point, vertices of the dual graph framework are set based on the centroids of the two sub-heteromorphic regions, and vertices corresponding to the two sub-heteromorphic regions are connected in the dual graph framework;

[0033] Repeat the above steps of determining the sharing of special-shaped edges and connecting vertices until each special-shaped edge is traversed to generate a topological adjacency matrix of the dual graph. According to the topological adjacency matrix, the shortest obstacle-avoiding transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated stacking area is planned.

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

[0035] Obtaining the original factory manual of the palletizer, and extracting the mechanical joint structure of the palletizer, the degree of freedom of movement of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure through the original factory manual;

[0036] Simulating and creating several reference palletizing conditions of the palletizer based on the degrees of freedom of movement of each mechanical joint structure and the mechanical control principle; wherein the reference palletizing conditions include rotation, translation and lifting of each mechanical joint structure of the palletizer;

[0037] A dual quaternion algorithm is introduced to convert and calculate the several reference palletizing conditions, and a dual quaternion corresponding to each mechanical joint structure in the palletizer is obtained, and M segmentation points of the shortest obstacle avoidance transportation path are obtained. Based on each segmentation point, a weight of the reference palletizing condition corresponding to the associated mechanical joint structure is calculated in the dual quaternion algorithm to obtain a plurality of dual quaternion weights;

[0038] Taking the segmentation points as interpolation nodes, the dual quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the dual quaternion weight and rendered, so as to generate a joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport the palletized product object;

[0039] Constructing an offline simulation model of the palletizer, inputting the joint posture trajectory into the offline simulation model of the palletizer to write a program code for offline control, and finally generating an offline control program for the palletizer;

[0040] Based on the offline control program and the production order information of the palletized product object, the offline control simulates the resource scheduling tampering code, and 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, the offline control in step S108 based on the offline control program and the production order information of the palletized product object simulates the resource scheduling tampering code, and the offline resource scheduling program code array written by the offline programmer is queried, analyzed and corrected according to the resource scheduling tampering code, which specifically includes the following steps:

[0042] Acquire production order information of a palletizing product object, identify a resource scheduling batch of the palletizing product object through the production order information, run the offline control program in an offline simulation model of a palletizer operated manually online to simulate palletizing and debug the resource scheduling batch, and acquire an expected palletizing model of the palletizer under manual online operation;

[0043] Preset a resource-balanced offline programming state, perform resource-balanced palletizing simulation for a preset time period on the palletizing product object in the offline simulation model based on the resource-balanced offline programming state, and output palletizing simulation point cloud data;

[0044] A resource-balanced palletizing model is constructed through the palletizing point cloud data, and a hash misalignment function between the resource-balanced palletizing model and the expected palletizing model is calculated. If the hash misalignment function is greater than a preset hash misalignment function, the program code in the resource-balanced palletizing 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] Preset the offline programming state of resource overload, perform secondary scheduling simulation of resource overload in a preset time period for the palletizing product object in the offline simulation model based on the offline programming state of resource overload, repeat the program code correction steps of the above-mentioned model hash misalignment and record the corrected scheduling tampering code data, marking it as the second type of resource scheduling tampering code;

[0046] Using a palletizer offline programmer to write an offline program code for the resource scheduling batch to obtain an offline resource scheduling program code array;

[0047] If any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource balanced scheduling; if any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource overload scheduling to eliminate the offline programming code tampering phenomenon.

[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 the palletizer, and when the intelligent offline programming control method program is executed by the processor, any one of the steps of the intelligent offline programming control method is implemented.

[0049] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0050] Obtain the product category and preset product characteristics of the palletizing product object, calculate and determine the 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 error objects of the historical stacking pattern of the replacement palletizer according to the impact degree, and generate the palletizing pattern replacement logic; obtain the spatial specification parameters and designated palletizing area of ​​the palletizing product object, accumulate and transport the palletizing product objects through the spatial specification parameters and designated palletizing area to output the established stacking topology boundary, adjust the vector of the palletizing product object based on the structural stability index of each established stacking topology boundary, and generate the optimal stacking space vector; obtain the first multidimensional space coordinate parameter in the temporary stacking area and the second multidimensional space coordinate parameter in the designated stacking area, use the first multidimensional space coordinate parameter and the second multidimensional space coordinate parameter to plan the spatial transportation path of the palletizer, and obtain the shortest obstacle avoidance transportation path; create several reference stacking conditions of the palletizer according to the original factory manual of the palletizer, perform double quaternion interpolation on the shortest obstacle avoidance transportation path based on several reference stacking conditions, and obtain an offline control program; query and analyze the tampered code of the offline resource scheduling program code array written by the offline programmer based on the offline control program and correct it. The present invention can accurately program the offline control program of the palletizer so that the stacked products can reach the ideal stacking state, improve the timeliness and accuracy of the offline stacking of the products, ensure the stability and safety index of the spatial stacking, and realize the intelligent offline stacking. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A first method flow chart of an intelligent off-line programming control method for a palletizer is shown;

[0053] Figure 2 A second method flow chart of an intelligent off-line programming control method for a palletizer is shown;

[0054] Figure 3 The system framework diagram of an intelligent off-line programming control system for a palletizer is shown. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0057] The first aspect of the present invention provides an intelligent off-line programming control method for a palletizer, such as Figure 1 As shown, the following steps are included:

[0058] S102: Obtain product categories and preset product characteristics of the palletizing product objects, calculate and determine the impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product categories and preset product characteristics, analyze and replace the error objects of the historical stacking pattern of the palletizer according to the impact degree, and generate the palletizing pattern replacement logic;

[0059] S104: obtaining space specification parameters of the stacked product objects and the designated stacking area, accumulating the transported stacked product objects through the space specification parameters and the designated stacking area to output a predetermined stacking topology boundary, adjusting the vector of the stacked product objects based on the structural stability index of each predetermined stacking topology boundary, and generating an optimal stacking space vector;

[0060] S106: Acquire the first multidimensional space coordinate parameters in the temporary stacking area, acquire the second multidimensional space coordinate parameters of the designated 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 multidimensional space coordinate parameters and the second multidimensional space coordinate parameters to obtain the shortest obstacle avoidance transportation path;

[0061] S108: creating several reference palletizing conditions of the palletizer according to the original factory manual of the palletizer, performing dual quaternion interpolation on the shortest obstacle avoidance transport path based on the several reference palletizing conditions, and obtaining an offline control program; querying and analyzing the tampered code of the offline resource scheduling program code array written by the offline programmer and correcting it based on the simulation of the offline control program.

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

[0063] Obtaining the product category of the palletized product object and the current palletizing scene, and determining the preset product characteristics of each palletized product object according to the product category;

[0064] Extract the historical intelligent stacking strategy of the palletizer in stacking preset product characteristics in the current palletizing scene through the work log of the palletizer, and create a historical stacking pattern model of each palletizing product object in the current palletizing scene according to the preset intelligent stacking strategy;

[0065] Using the historical stacking pattern model to obtain the regional stacking distribution pattern of each stacked product object, and dividing the historical stacking pattern model into a plurality of sub-pattern model areas based on the regional stacking distribution pattern;

[0066] Acquire multiple historical environmental parameter sets of the palletizer in the current palletizing scene, and acquire qualitative change data of each palletizing product object when it is in the multiple historical environmental parameter sets based on preset product characteristics;

[0067] Determine the covariates that cause the palletized product object to generate qualitative change data based on the multiple historical environmental parameter sets of the current palletizing scene, and perform linear regression calculation on the qualitative change data based on the covariates to eliminate the influence of the covariates and obtain the residual of the qualitative change data change;

[0068] Introducing a covariance analysis method to analyze and calculate the residual of the qualitative change data to obtain a net effect value, and determining the degree of influence of the current stacking scene environment change on the characteristics of each stacking product object according to the net effect value;

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

[0070] Extract the sub-pattern model area where the collapse probability is greater than the preset collapse probability, and calibrate the stacked product objects in the sub-pattern model area as incorrect stacking objects; at this time, perform pattern replacement on all incorrect stacking objects according to the degree of influence and formulate replacement logic until the collapse probability is output as the correct stacking object, and generate the stacking pattern replacement logic.

[0071] It should be noted that the preset product characteristics include fragile, heavy and easy to deform. The offline control of the palletizer is usually completely separated from human intervention. Therefore, compared with manual online control, it is difficult for the palletizer to realize intelligent decision-making of the stacking pattern according to its own product characteristics during the stacking process. For example, if the stacked products are fragile, if they are stacked under heavy objects at this time, it may cause the products to break and be damaged, resulting in a significant decline in product quality and reducing the control reliability of the palletizer. Therefore, this method calculates the residual of the qualitative change data change by combining the qualitative change data of the multiple historical environmental parameter sets when the palletizer is in the current palletizing scene and the qualitative change data of each palletizing product object in the multiple historical environmental parameter sets. The residual represents the change of the characteristics of the palletizing product after excluding the influence of the covariate of the current palletizing scene, which can further ensure the accuracy of the subsequent impact amplitude; then the residual is analyzed by variance to determine the net effect of the current palletizing scene on the characteristics of the palletizing product, that is, the degree of influence of the change of the palletizing scene environment on the characteristics of each palletizing product object. Based on the degree of influence, the palletizer can formulate the pattern stacking logic of different palletizing product characteristics according to the surrounding palletizing scene, so as to improve the rationality of product palletizing; wherein, if the collapse probability is greater than the preset collapse probability, it means that the local pattern of the palletizing product in the sub-pattern model area is easy to damage the palletizing product, so the palletizing product object in the sub-pattern model area is calibrated as an erroneous stacking object and replaced. This method can reasonably formulate the decision logic for the pattern replacement of palletized products during the offline control process of the palletizer according to the characteristics of the palletized products and the surrounding palletizing scenes, thereby ensuring that the palletizing quality of the palletized products is improved and the damage of the palletized products is reduced without manual intervention control, with high economic benefits.

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

[0073] Acquire spatial specification parameters and palletizing transportation requirements of the palletizing product object, and acquire a designated palletizing area for the palletizing product object according to the palletizing transportation requirements;

[0074] Constructing a virtual stacking space domain of a designated stacking area, defining a stacking boundary of each stacking product object in the virtual stacking space domain based on the space specification parameters, and obtaining a plurality of stacking boundaries;

[0075] According to the stacking pattern replacement logic, each stacked product object is cumulatively transported to the virtual stacking space field. After the cumulative transportation is completed, the stacking boundary between each adjacent stacked product object forms a stacking topology boundary in the virtual stacking space field;

[0076] The stacking topological boundaries generated by each adjacent stacked product object in the virtual stacking space are stripped out and defined as the established stacking topological boundaries. The mechanical knowledge graph of the spatial object structure is obtained based on big data. The mechanical structure of each established stacking topological boundary is identified through the mechanical knowledge graph, and the structural stability index of each established stacking topological boundary is output.

[0077] If the structural stability index is lower than the preset structural stability index, the virtual stacking space domain is divided into a plurality of subspace domain layers, and the membership 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 domain layer corresponding to the membership degree greater than the preset membership degree is extracted, which is defined as the unstable subspace domain layer. The optimal vector limit of each unstable subspace domain layer is specified according to the optimal structural stability index, and the vector of the established stacking topology boundary in the unstable subspace domain layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated;

[0079] The historically established palletizing rules for different palletizing product objects when the palletizer is in an offline state are obtained, and the historically established palletizing rules are optimized based on the optimal stacking space vector to obtain new palletizing rules for the palletizing product objects.

[0080] It should be noted that the vectors of the predetermined stacking topological boundary include contact area, direction and position. For stacked products, the stacking pattern in space is one aspect of the stacking standard to ensure product quality, but on the other hand, the structural stability of the stacked products after the stacking pattern is formed in space is also a crucial stacking standard. If the overall structure formed after the stacker stacks all the stacked products in the designated area is unreasonable or unstable, the stacking structure in the designated area will be shaken, tilted or even collapsed under certain external effects such as vibration and manpower, which may cause accidental injuries to surrounding personnel and damage to the stacked products themselves. Therefore, this method constructs a virtual stacking space domain to perform virtual accumulation and transportation of the stacked product objects to simulate the form of the stacked products in the designated area. Since the contact surface between the objects will present a clear intersection boundary when they are stacked in different stacking space vectors, that is, the stacking topological boundary, which can reflect the stability of the stacking structure between the stacked products. Therefore, by analyzing the stacking topological boundary between the stacked products in the virtual stacking space domain and using the recognition of the mechanical knowledge graph, it is possible to know whether the overall structure of the stacking is stable or not; if it is unstable, that is, the structural stability index is lower than the preset structural stability index, it means that the stacking vector between the stacked products at this location is unreasonable and should be adjusted. This method provides a reasonable adjustment basis for the space vectors of each stacked product in the unstable subspace domain layer by setting the limitation of the optimal structural stability index, replacing the traditional manual intervention adjustment steps and saving labor cost output; through this method, the offline programming control of the palletizer can be given a more intelligent stacking rule control, which improves the stability of the stacking structure of the palletizer for the stacked products, reduces the accidental injury and product damage caused by the unstable condition, and improves the neatness of the stacking of the stacked products.

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

[0082] S202: Acquire a temporary stacking area for each current stacked product object, establish a regional coordinate space for the temporary stacking area, define it as a first regional coordinate space, acquire a multidimensional space coordinate parameter for each stacked product object through the first regional coordinate space, and calibrate it as the first multidimensional space coordinate parameter;

[0083] S204: constructing a regional coordinate space of a designated palletizing area, defining it as a second regional coordinate space, obtaining multidimensional spatial coordinate parameters of each palletizing product object when it reaches an ideal palletizing structure and expected palletizing quality in the second regional coordinate space based on the palletizing pattern replacement logic and the new palletizing rule, and calibrating them as second multidimensional spatial coordinate parameters;

[0084] S206: Calculate the spatial distance of each current palletized product object from the temporary stacking area to the designated palletizing area using the first multi-dimensional space coordinate parameter and the second multi-dimensional space coordinate parameter, and plan the special-shaped activity space area during the transportation process of the palletizer according to the spatial distance;

[0085] S208: Obtaining the obstacle point distribution information in the special-shaped activity space area, dividing the special-shaped activity space area into N sub-special-shaped areas, and judging whether two adjacent sub-special-shaped areas share a special-shaped edge in the special-shaped activity space area based on the obstacle point distribution information. If so, obtaining the centroid points of the two sub-special-shaped areas at this time;

[0086] S210: constructing a dual graph framework with the palletizer as the transport fixed point, setting vertices of the dual graph framework based on the centroids of the two sub-heteromorphic regions, and connecting the vertices corresponding to the two sub-heteromorphic regions in the dual graph framework;

[0087] S212: Repeat the above steps of determining the sharing of special-shaped edges and connecting vertices until each special-shaped edge is traversed to generate a topological adjacency matrix of the dual graph, and plan the shortest obstacle-avoiding transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated stacking area based on the topological adjacency matrix.

[0088] It should be noted that since the working environment of the palletizer is not restricted, there are no high requirements for the setting points of the palletizer. This also means that there may be obstacles of different types and specifications around the palletizer. These obstacles cause friction, collision or collision during the operation of the palletizer, which is not conducive to the smooth transportation and palletizing of the goods by the palletizer. In view of this, the method establishes a temporary stacking area for the palletizing product object and a regional coordinate space corresponding to the designated palletizing area. The coordinate information of the distance between the two regional coordinate spaces can be used to plan an activity space area where the palletizer may contact obstacles during the transportation from the temporary stacking area to the designated palletizing area. On the one hand, due to the differences in the types and specifications of the possible obstacles around, the boundary of the activity space area is irregular. On the other hand, the transportation activity of the palletizer is multi-dimensional, so the activity space area is a multi-dimensional space, that is, an irregular activity space area. Then, according to the position of the obstacle in the irregular activity space area, it is judged whether two adjacent sub-irregular areas share a special-shaped edge in the irregular activity space area. If there is a sharing, it means that the palletizer may contact the obstacle point when running in the sub-irregular area. Therefore, a dual graph can be constructed and the vertices corresponding to the two sub-irregular areas on the special-shaped edge can be connected in the dual graph to eliminate the collision between the palletizer and the obstacle when running in the sub-irregular area. Finally, the topological adjacency matrix output after traversing all the special-shaped edges represents the running trajectory to avoid collision with obstacles, so as to plan a shortest transportation path for the palletizer to transport from the temporary stacking area to the designated stacking area without colliding with obstacles based on the stacking pattern replacement logic and the new stacking rules in an offline state. This method can intelligently plan the shortest, accurate obstacle avoidance and efficient transportation path for the palletizer in the offline control state, thereby avoiding the collision 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, the step S108 includes the following steps:

[0090] Obtaining the original factory manual of the palletizer, and extracting the mechanical joint structure of the palletizer, the degree of freedom of movement of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure through the original factory manual;

[0091] Simulating and creating several reference palletizing conditions of the palletizer based on the degrees of freedom of movement of each mechanical joint structure and the mechanical control principle; wherein the reference palletizing conditions include rotation, translation and lifting of each mechanical joint structure of the palletizer;

[0092] A dual quaternion algorithm is introduced to convert and calculate the several reference palletizing conditions, and a dual quaternion corresponding to each mechanical joint structure in the palletizer is obtained, and M segmentation points of the shortest obstacle avoidance transportation path are obtained. Based on each segmentation point, a weight of the reference palletizing condition corresponding to the associated mechanical joint structure is calculated in the dual quaternion algorithm to obtain a plurality of dual quaternion weights;

[0093] Taking the segmentation points as interpolation nodes, the dual quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the dual quaternion weight and rendered, so as to generate a joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport the palletized product object;

[0094] Constructing an offline simulation model of the palletizer, inputting the joint posture trajectory into the offline simulation model of the palletizer to write a program code for offline control, and finally generating an offline control program for the palletizer;

[0095] Based on the offline control program and the production order information of the palletized product object, the offline control simulates the resource scheduling tampering code, and 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 an 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 transportation path. Once the posture is determined, accurate program code can be written for it. However, the accuracy of the joint posture analysis of the existing offline controlled palletizer is low, which results in many errors in the final programmed program code, requiring subsequent manual intervention and correction, which is time-consuming and labor-intensive, and reduces the offline control accuracy of the palletizer. This method constructs the corresponding maximum operating benchmark palletizing conditions for the palletizer through the relevant mechanical contents recorded in the original factory manual, so as to reasonably define and adjust the posture analysis of the palletizer, and then calculates a dual quaternion weight for these benchmark palletizing conditions, which is used to concretely express the necessary degree of each working condition in the control of the shortest obstacle avoidance transportation path, calculates the dual quaternion of each mechanical joint structure, and interpolates the dual quaternion corresponding to each mechanical joint structure in the palletizer on the shortest obstacle avoidance transportation path based on the dual quaternion weight, so as to describe the joint posture required by each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path, so as to determine and write accurate program codes by executing these joint postures with the help of the offline simulation model of the palletizer. Through this method, the operating postures of all joints of the palletizer can be defined according to the corresponding analysis of the planned shortest obstacle avoidance transportation path, so as to make the posture control of the palletizer during transportation more stable and accurate, and make the program control of offline programming more reliable.

[0097] More specifically, the step S108 offline simulates the resource scheduling tampering code based on the offline control program and the production order information of the palletized product object, and queries, analyzes and corrects the offline resource scheduling program code array written by the offline programmer according to the resource scheduling tampering code, which specifically includes the following steps:

[0098] Acquire production order information of a palletizing product object, identify a resource scheduling batch of the palletizing product object through the production order information, run the offline control program in an offline simulation model of a palletizer operated manually online to simulate palletizing and debug the resource scheduling batch, and acquire an expected palletizing model of the palletizer under manual online operation;

[0099] Preset a resource-balanced offline programming state, perform resource-balanced palletizing simulation for a preset time period on the palletizing product object in the offline simulation model based on the resource-balanced offline programming state, and output palletizing simulation point cloud data;

[0100] A resource-balanced palletizing model is constructed through the palletizing point cloud data, and a hash misalignment function between the resource-balanced palletizing model and the expected palletizing model is calculated. If the hash misalignment function is greater than a preset hash misalignment function, the program code in the resource-balanced palletizing 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] Preset the offline programming state of resource overload, perform secondary scheduling simulation of resource overload in a preset time period for the palletizing product object in the offline simulation model based on the offline programming state of resource overload, repeat the program code correction steps of the above-mentioned model hash misalignment and record the corrected scheduling tampering code data, marking it as the second type of resource scheduling tampering code;

[0102] Using a palletizer offline programmer to write an offline program code for the resource scheduling batch to obtain an offline resource scheduling program code array;

[0103] If any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource balanced scheduling; if any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource overload scheduling to eliminate the offline programming code tampering phenomenon.

[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 amounts of resources, which may result in garbled or tampered program codes. This is one of the control factors that has a more serious impact on the transportation and palletizing of the palletizer. The existing offline control technology makes it difficult to achieve timely and efficient code tampering and correction for the program code output by the offline simulation programming of the palletizer, making it impossible for the offline controlled transportation and palletizing of the palletizer to meet the expected palletizing requirements. In view of this, the method simulates the palletizing and debugging of the resource scheduling batches of the palletizing product objects by manually operating the palletizer in the online state to execute the offline control program, so that the palletizer can output an expected palletizing model under human intervention, thereby ensuring that the subsequent code tampering correction is based on the expected palletizing requirements of human intervention; then simulate the resource scheduling of the palletizer for the palletizing product objects under resource balance and resource overload conditions respectively, and then compare the differences between the resource balance palletizing model and the resource overload palletizing model and the expected palletizing model. If the hash misalignment function is greater than the preset hash misalignment function, it means that the result of the resource scheduling palletizing simulation deviates from the expected palletizing requirements to a large extent, which may be caused by a program code error. Therefore, it is necessary to correct the program code in the palletizing simulation process, and then obtain the corrected scheduling tampering code data. Through these corrected scheduling tampering code data, query and analyze the offline resource scheduling program code array written by the subsequent offline programmer. If any array node in the offline resource scheduling program code array can query the first type of resource scheduling tampering code, it means that the written code has been tampered with by the code of resource balanced scheduling, so the array node needs to be corrected for the code tampering of resource balanced scheduling. If any array node can query the second type of resource scheduling tampering code, it means that the code written by the offline programmer has been tampered with by the code of resource overload scheduling, and the array node needs to be corrected for the code tampering of resource overload scheduling.

[0105] It should be noted that this method can correct the program code tampering of resource scheduling that occurs when the offline programmer writes the palletizer control program, thereby eliminating the offline programming code tampering phenomenon, improving the stability and reliability of the palletizer offline programming, and improving the accuracy of the palletizer offline control.

[0106] The second aspect of the present invention provides an intelligent off-line 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, any one of the steps of the intelligent offline programming control method is implemented.

[0107] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent off-line programming control method for a palletizer, characterized in that: The following steps are involved: S102: Obtain product categories and preset product characteristics of the palletizing product objects, calculate and determine the impact of the current palletizing scene environment change on the characteristics of each palletizing product object based on the product categories and preset product characteristics, analyze and replace the error objects of the historical stacking pattern of the palletizer according to the impact degree, and generate the palletizing pattern replacement logic; S104: obtaining space specification parameters of the stacked product objects and the designated stacking area, accumulating the transported stacked product objects through the space specification parameters and the designated stacking area to output a predetermined stacking topology boundary, adjusting the vector of the stacked product objects based on the structural stability index of each predetermined stacking topology boundary, and generating an optimal stacking space vector; S106: Acquire the first multidimensional space coordinate parameters in the temporary stacking area, acquire the second multidimensional space coordinate parameters of the designated 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 multidimensional space coordinate parameters and the second multidimensional space coordinate parameters to obtain the shortest obstacle avoidance transportation path; S108: creating several reference palletizing conditions of the palletizer according to the original factory manual of the palletizer, performing dual quaternion interpolation on the shortest obstacle avoidance transport path based on the several reference palletizing conditions, and obtaining an offline control program; querying and analyzing the tampered code of the offline resource scheduling program code array written by the offline programmer and correcting it based on the simulation of the offline control program.

2. The intelligent off-line programming control method for a palletizer according to claim 1 is characterized in that: The step S102 specifically includes the following steps: Obtaining the product category of the palletized product object and the current palletizing scene, and determining the preset product characteristics of each palletized product object according to the product category; Extract the historical intelligent stacking strategy of the palletizer in stacking preset product characteristics in the current palletizing scene through the work log of the palletizer, and create a historical stacking pattern model of each palletizing product object in the current palletizing scene according to the preset intelligent stacking strategy; Using the historical stacking pattern model to obtain the regional stacking distribution pattern of each stacked product object, and dividing the historical stacking pattern model into a plurality of sub-pattern model areas based on the regional stacking distribution pattern; Acquire multiple historical environmental parameter sets of the palletizer in the current palletizing scene, and acquire qualitative change data of each palletizing product object when it is in the multiple historical environmental parameter sets based on preset product characteristics; Determine the covariates that cause the palletized product object to generate qualitative change data based on the multiple historical environmental parameter sets of the current palletizing scene, and perform linear regression calculation on the qualitative change data based on the covariates to eliminate the influence of the covariates and obtain the residual of the qualitative change data change; Introducing a covariance analysis method to analyze and calculate the residual of the qualitative change data to obtain a net effect value, and determining the degree of influence of the current stacking scene environment change on the characteristics of each stacking product object according to the net effect value; Acquire the real-time environmental parameters of the current palletizing scene in each sub-pattern model area, and calculate the probability of collapse of the real-time environmental parameters of each sub-pattern model area on the characteristics of the palletized product object based on the degree of influence; Extract the sub-pattern model area where the collapse probability is greater than the preset collapse probability, and calibrate the stacked product objects in the sub-pattern model area as incorrect stacking objects; at this time, perform pattern replacement on all incorrect stacking objects according to the degree of influence and formulate replacement logic until the collapse probability is output as the correct stacking object, and generate the stacking pattern replacement logic.

3. The intelligent off-line programming control method for a palletizer according to claim 1 is characterized in that: The step S104 specifically includes the following steps: Acquire spatial specification parameters and palletizing transportation requirements of the palletizing product object, and acquire a designated palletizing area for the palletizing product object according to the palletizing transportation requirements; Constructing a virtual stacking space domain of a designated stacking area, defining a stacking boundary of each stacking product object in the virtual stacking space domain based on the space specification parameters, and obtaining a plurality of stacking boundaries; According to the stacking pattern replacement logic, each stacked product object is cumulatively transported to the virtual stacking space field. After the cumulative transportation is completed, the stacking boundary between each adjacent stacked product object forms a stacking topology boundary in the virtual stacking space field; The stacking topological boundaries generated by each adjacent stacked product object in the virtual stacking space are stripped out and defined as the established stacking topological boundaries. The mechanical knowledge graph of the spatial object structure is obtained based on big data. The mechanical structure of each established stacking topological boundary is identified through the mechanical knowledge graph, and the structural stability index of each established stacking topological boundary is output. If the structural stability index is lower than the preset structural stability index, the virtual stacking space domain is divided into a plurality of subspace domain layers, and the membership 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 domain layer corresponding to the membership degree greater than the preset membership degree is extracted, which is defined as the unstable subspace domain layer. The optimal vector limit of each unstable subspace domain layer is specified according to the optimal structural stability index, and the vector of the established stacking topology boundary in the unstable subspace domain layer is adjusted until it infinitely approaches the optimal vector limit, and finally the optimal stacking space vector is generated; The historically established palletizing rules for different palletizing product objects when the palletizer is in an offline state are obtained, and the historically established palletizing rules are optimized based on the optimal stacking space vector to obtain new palletizing rules for the palletizing product objects.

4. The intelligent off-line programming control method for a palletizer according to claim 1, characterized in that: The step S106 specifically includes the following steps: Acquire a temporary stacking area for each current stacked product object, establish a regional coordinate space for the temporary stacking area, define it as a first regional coordinate space, acquire a multidimensional space coordinate parameter for each stacked product object through the first regional coordinate space, and calibrate it as the first multidimensional space coordinate parameter; Constructing a regional coordinate space of a designated palletizing area, which is defined as a second regional coordinate space, obtaining multidimensional spatial coordinate parameters of each palletizing product object when it reaches an ideal palletizing structure and expected palletizing quality in the second regional coordinate space based on the palletizing pattern replacement logic and the new palletizing rule, and calibrating them as second multidimensional spatial coordinate parameters; The spatial distance from the temporary stacking area to the designated stacking area of ​​each current stacked product object is calculated using the first multi-dimensional space coordinate parameter and the second multi-dimensional space coordinate parameter, and the special-shaped activity space area during the transportation process of the palletizer is planned according to the spatial distance; Obtaining the obstacle point distribution information in the special-shaped activity space area, dividing the special-shaped activity space area into N sub-special-shaped areas, and judging whether two adjacent sub-special-shaped areas share a special-shaped edge in the special-shaped activity space area based on the obstacle point distribution information, and if so, obtaining the centroid points of the two sub-special-shaped areas at this time; A dual graph framework is constructed with the palletizer as a transport fixed point, vertices of the dual graph framework are set based on the centroids of the two sub-heteromorphic regions, and vertices corresponding to the two sub-heteromorphic regions are connected in the dual graph framework; Repeat the above steps of determining the sharing of special-shaped edges and connecting vertices until each special-shaped edge is traversed to generate a topological adjacency matrix of the dual graph. According to the topological adjacency matrix, the shortest obstacle-avoiding transportation path for the palletizer to transport each palletized product object from the temporary stacking area to the designated stacking area is planned.

5. The intelligent off-line programming control method for a palletizer according to claim 1, characterized in that: The step S108 specifically includes the following steps: Obtaining the original factory manual of the palletizer, and extracting the mechanical joint structure of the palletizer, the degree of freedom of movement of each mechanical joint structure, and the mechanical control principle of each mechanical joint structure through the original factory manual; Simulating and creating several reference palletizing conditions of the palletizer based on the degrees of freedom of movement of each mechanical joint structure and the mechanical control principle; wherein the reference palletizing conditions include rotation, translation and lifting of each mechanical joint structure of the palletizer; A dual quaternion algorithm is introduced to convert and calculate the several reference palletizing conditions, and a dual quaternion corresponding to each mechanical joint structure in the palletizer is obtained, and M segmentation points of the shortest obstacle avoidance transportation path are obtained. Based on each segmentation point, a weight of the reference palletizing condition corresponding to the associated mechanical joint structure is calculated in the dual quaternion algorithm to obtain a plurality of dual quaternion weights; Taking the segmentation points as interpolation nodes, the dual quaternion corresponding to each mechanical joint structure in the palletizer is interpolated on each interpolation node according to the dual quaternion weight and rendered, so as to generate a joint posture trajectory of each mechanical joint structure when the palletizer executes the shortest obstacle avoidance transportation path to transport the palletized product object; Constructing an offline simulation model of the palletizer, inputting the joint posture trajectory into the offline simulation model of the palletizer to write a program code for offline control, and finally generating an offline control program for the palletizer; Based on the offline control program and the production order information of the palletized product object, the offline control simulates the resource scheduling tampering code, and 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 off-line programming control method for a palletizer according to claim 5, characterized in that: The offline control in step S108 simulates the resource scheduling tampering code based on the offline control program and the production order information of the palletized product object, and queries, analyzes and corrects the offline resource scheduling program code array written by the offline programmer according to the resource scheduling tampering code, which specifically includes the following steps: Acquire production order information of a palletizing product object, identify a resource scheduling batch of the palletizing product object through the production order information, run the offline control program in an offline simulation model of a palletizer operated manually online to simulate palletizing and debug the resource scheduling batch, and acquire an expected palletizing model of the palletizer under manual online operation; Preset a resource-balanced offline programming state, perform resource-balanced palletizing simulation for a preset time period on the palletizing product object in the offline simulation model based on the resource-balanced offline programming state, and output palletizing simulation point cloud data; A resource-balanced palletizing model is constructed through the palletizing point cloud data, and a hash misalignment function between the resource-balanced palletizing model and the expected palletizing model is calculated. If the hash misalignment function is greater than a preset hash misalignment function, the program code in the resource-balanced palletizing 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; Preset the offline programming state of resource overload, perform secondary scheduling simulation of resource overload in a preset time period for the palletizing product object in the offline simulation model based on the offline programming state of resource overload, repeat the program code correction steps of the above-mentioned model hash misalignment and record the corrected scheduling tampering code data, marking it as the second type of resource scheduling tampering code; Using a palletizer offline programmer to write an offline program code for the resource scheduling batch to obtain an offline resource scheduling program code array; If any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource balanced scheduling; if any array node in the offline resource scheduling program code array can query a type of resource scheduling tampering code, the array node is executed for code tampering correction of resource overload scheduling to eliminate the offline programming code tampering phenomenon.

7. An intelligent off-line 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, the intelligent offline programming control method steps as described in any one of claims 1 to 6 are implemented.

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