Welding operation resource scheduling method, system and equipment and storage medium

Abstract

The invention provides a welding operation resource scheduling method, system and device and a storage medium, and belongs to the technical field of production scheduling. The method comprises the following steps: constructing a Petri net model based on given data of a welding operation task; based on the Petri net model, constructing an objective function of the minimum completion time of the welding operation task; the target function represents the minimum completion time required from a start library where the welding process starts to a termination library where the welding process ends in the Petri net model; solving the target function through an A star search algorithm to obtain a target reachable path of the Petri net model; and based on the target reachable path, configuring operation information of the welding operation resources so as to realize scheduling of the welding operation resources. According to the method, the Petri network model is combined with the A star search algorithm to perform resource scheduling of the welding operation, so that the efficiency and the effect of resource scheduling of the welding operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling, and in particular to a scheduling method for welding operation resources, a scheduling system for welding operation resources, an electronic device and a machine-readable storage medium. Background Art

[0002] In recent years, due to the rapid development of the construction machinery industry and the market demand for personalized customization, Robotic Cellular Manufacturing Systems (RCMSs) based on the lean production concept have been increasingly widely used in small and medium-sized batch production and large-scale personalized customization enterprises.

[0003] In practical applications, RCMSs have evolved from early single-robot units to multi-robot units. For example, in the current welding process using multi-robot units in welding workshops, the operation planning or resource scheduling of multi-robot units relies on previous common sense and experience, lacking theoretical guidance, resulting in low efficiency and poor effect in resource scheduling of welding workshops in current robot welding workshops. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system, device and storage medium for scheduling welding operation resources, so as to solve the problems of low efficiency and poor effect of welding operation resource scheduling in current robot welding operations.

[0005] In order to achieve the above object, an embodiment of the present invention provides a scheduling method for welding operation resources, the scheduling method comprising:

[0006] Construct a Petri net model based on the given data of the welding task;

[0007] An objective function of minimizing the completion time of the welding task is constructed based on the Petri net model; the objective function represents the minimum completion time required from the starting location at the beginning of the welding process to the ending location at the end of the welding process;

[0008] Solving the objective function by using an A-star search algorithm to obtain a target reachable path of the Petri net model;

[0009] Based on the target reachable path, the operation information of the welding operation resource is configured to achieve the scheduling of the welding operation resource.

[0010] Optionally, solving the objective function by using an A-star search algorithm to obtain a target reachable path of a Petri net model includes:

[0011] Initialize the algorithm parameters of the A-star search algorithm, wherein the algorithm parameters at least include an initial identifier, a target identifier, a to-be-explored list, and an explored list, wherein the to-be-explored list is used to store identifiers to be explored, and the explored list is used to store identifiers that have been explored, and the initial identifier and the target identifier represent a starting place and a target delay place in a Petri net model, respectively;

[0012] Repeat the following steps until the target identifier is found or the to-be-explored list is empty:

[0013] Finding the delay location in the Petri net model with the smallest total evaluation value from the to-be-explored list as the current node, removing the current node from the to-be-explored list, and adding it to the explored list;

[0014] Calculate the actual cost, heuristic cost and total evaluation of the adjacent nodes around the current node, and update the to-be-explored list and the explored list;

[0015] The target function is solved and the target reachable path of the Petri net model is obtained by tracing back from the target identifier according to the parent node pointer until the initial identifier is reached.

[0016] Optionally, before calculating the actual cost, heuristic cost and total evaluation of the target nodes around the current node and updating the to-be-explored list and the explored list, the method further includes:

[0017] A secondary search is performed on the nodes around the current node through a heuristic search algorithm to obtain target nodes around the current node.

[0018] Optionally, after configuring the operation information of the welding operation resource based on the target reachable path to implement the scheduling of the welding operation resource, the method further includes:

[0019] Receiving fault information fed back by the welding operation resource;

[0020] Determining a type of equipment failure based on an estimated repair time associated with the failure information;

[0021] Determining a rescheduling strategy based on the device failure type;

[0022] The operation information of the welding operation resource is configured based on the rescheduling strategy to achieve the rescheduling of the welding operation resource.

[0023] Optionally, the equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type;

[0024] Determining the rescheduling strategy based on the device failure type includes:

[0025] When the equipment failure type indicates that the current equipment is of the first failure type, adding idle equipment to the welding operation resources;

[0026] In the Petri net model, the current device is configured not to perform a welding process, and the idle device is configured to perform the welding process of the current device;

[0027] Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0028] Optionally, the equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type;

[0029] Determining the rescheduling strategy based on the device failure type includes:

[0030] When the device fault type indicates that the current device is of the second fault type, adjusting the delay value of the fault library mapped to the current device in the Petri net model;

[0031] Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0032] Optionally, the adjusting the delay value of the fault library mapped to the current device in the Petri net model includes:

[0033] The delay value of the fault pool in the Petri net model is adjusted to the sum of the original delay value of the fault pool before the fault and the estimated repair time.

[0034] Optionally, the Petri net model includes a start location representing the start of a welding process, a stop location representing the end of a welding process, and a delay location representing a delay value required to perform welding on a workpiece.

[0035] On the other hand, an embodiment of the present invention provides a scheduling system for welding operation resources, the scheduling system comprising:

[0036] A building module for building a Petri net model based on given data of welding work tasks;

[0037] An objective function construction module is used to construct an objective function of minimizing the completion time of the welding task based on the Petri net model; the objective function represents the minimum completion time required from the starting library at the beginning of the welding process to the ending library at the end of the welding process;

[0038] A path search module is used to solve the objective function by using an A-star search algorithm to obtain a target reachable path of a Petri net model;

[0039] The scheduling module is used to configure the operation information of the welding operation resources based on the target reachable path to realize the scheduling of the welding operation resources.

[0040] In another aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0041] at least one processor;

[0042] a memory connected to the at least one processor;

[0043] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the aforementioned method by executing the instructions stored in the memory.

[0044] On the other hand, an embodiment of the present invention provides a machine-readable storage medium storing machine instructions, which, when executed on a machine, enables the machine to execute the aforementioned method.

[0045] The present invention constructs a Petri net model based on given data of welding tasks, and establishes an objective function of minimizing completion time based on the Petri net model, thereby realizing the optimization problem of solving the dynamic resource scheduling of welding operations by using the Petri net model. Then, the A-star search algorithm is used to solve the optimization problem, and an optimal path from an initial identifier to an end identifier is searched in the form of a reachable graph in the Petri net model, thereby realizing the dynamic optimization of the resource scheduling of the welding operation. Compared with the existing welding operation resource scheduling, which relies on previous common sense and experience and lacks theoretical guidance, the present invention uses the Petri net model combined with the A-star search algorithm to perform resource scheduling of the welding operation, thereby improving the efficiency and effect of the resource scheduling of the welding operation.

[0046] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0048] Figure 1 A schematic diagram of the steps of the main method of an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a multi-robot welding unit under a simplified timed Petri net model of an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of an exemplary Petri net model mapped with dual robotic arms according to an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of an exemplary processing flow using an A*-BAS hybrid algorithm in a scheduling method according to an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of a processing flow of an exemplary scheduling method using an auxiliary decision system according to an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of a Petri net model before and after a first fault type according to an exemplary embodiment of the present invention;

[0054] Figure 7 Schematic diagram of an exemplary Petri net model before and after the second fault type in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0056] The welding resource scheduling in the embodiment of the present invention can be the welding resource scheduling implemented in the welding operation scene. The welding operation scene can realize the welding resource scheduling in the fields of machinery manufacturing, construction, automobile manufacturing, aerospace, electronic equipment, medical equipment and chemical equipment. Among them, the method of the embodiment of the present invention can be the welding resource scheduling of large ships, aircraft, bridges and steel structures in the field of machinery manufacturing; in the field of construction, it can be the welding resource scheduling of metal building components; in the field of automobile manufacturing, it can be the welding resource scheduling of automobile bodies; in the field of aerospace, it can be the welding resource scheduling of aircraft fuselages, wings, engines and other parts; in the field of electronic equipment, it can be the welding resource scheduling of tiny components, circuit boards or connectors; in the field of medical equipment, it can be the welding resource scheduling of surgical instruments, stents, etc.; in the field of chemical equipment, it can be the welding resource scheduling of manufacturing reactors, heat exchangers and other equipment.

[0057] Taking the welding operation scene as an example of a welding operation workshop, the welding operation workshop can be an automated workshop using welding robot units, and the welding operation workshop can be used to process workpieces, process materials, and manufacture products. The automated workshop has an automation system, which includes welding (operation) workshop resources and a scheduling control system. Welding operation workshop resources may include multiple welding robot units, each welding robot unit includes an automatic guided vehicle (or a transport vehicle or a transport robot) and an (automated) robotic arm, and welding operation workshop resources may also include workpieces to be processed. The scheduling control system configures the operation information to the welding robot unit according to the welding operation task, and the welding robot unit processes the workpiece to complete welding and processing processes. The welding operation workshop may be a flexible operation workshop. In the flexible operation workshop of the embodiment of the present invention, multiple welding robot units are respectively scheduled to complete multiple sections of welding processes of multiple or multiple types of workpieces, and all possible behaviors in the flexible operation workshop can be tracked by a reachable graph or tree or data of a Petri net (Petrie network) model, wherein the identifier in the reachable graph (or reachable identifier graph) can be mapped to the state of the welding operation workshop resources, and the identifiers are connected by transitions. In the embodiment of the present invention, identifiers and nodes are equivalent and interchangeable terms.

[0058] In the embodiment of the present invention, in any state of the welding operation resources, an optimal reachable path from the initial identifier to the end identifier can be searched in the form of a reachable graph. Therefore, the Petri net model can be used to solve a series of dynamic scheduling problems, thereby obtaining the optimal welding process sequence in the welding operation. However, even a very simple Petri net model requires a large amount of time to optimize the path of the reachable graph.

[0059] As a classic heuristic search algorithm, the A-star (A*) search algorithm has been widely used in fields such as unmanned driving and artificial intelligence, but the current A* search algorithm has not yet been applied to the technical field of production scheduling of welding operations. In view of this, the embodiment of the present invention uses a Petri net model combined with the A-star search algorithm to perform resource scheduling of welding operations, thereby improving the efficiency and effect of resource scheduling of welding operations.

[0060] Example 1

[0061] The embodiment of the present invention provides a method for scheduling welding operation resources, such as Figure 1 , the scheduling method may include:

[0062] S1) constructing a Petri net model based on given data of welding task;

[0063] S2) constructing an objective function of minimizing the completion time of the welding task based on the Petri net model; the objective function represents the minimum completion time required from the starting location at the beginning of the welding process to the ending location at the end of the welding process;

[0064] S3) solving the objective function by using an A-star search algorithm to obtain a target reachable path of a Petri net model;

[0065] S4) Based on the target reachable path, configuring the operation information of the welding operation resource to achieve the scheduling of the welding operation resource.

[0066] In the embodiment of the present invention, the step of S1) constructing a Petri net model based on given data of the welding task includes:

[0067] A1) Associate and map the Petri net model with welding operation resources.

[0068] In an exemplary Petri net model example disclosed in the present invention, according to the events that may occur in the welding process (welding process events), a multi-robot welding unit Petri net model PN (N, M0) and its association matrix A are established, and each element is defined and associated with the mapping of welding operation resources, which may include:

[0069] ①P={P1,P2,…,P m} is a finite set of places, representing the state of the robot or workpiece, that is, the mapping between places and states;

[0070] ②T={T1,T2,…,T n} is a set of transitions that must satisfy and Indicates the start or end of the robot's welding operation;

[0071] ③I:P×T→{0,1,2,…} is the input arc, O:T×P→{0,1,2,…} is the output arc, which represents the flow of resources when the robot arm performs the welding task;

[0072] ④Token is a dynamic object or token in the library. The number of tokens can represent the number of robotic arms or workpieces;

[0073] ⑤M:P→{0,1,2,…} is the identifier of the number of tokens in each place, M0 is the initial identifier, M f It is the target (or end) mark. If there is a token in the place, it means that the robot or workpiece is available. The mark exists as a vector, and the sum of the elements in each vector is equal to the total number of places in the Petri net model.

[0074] ⑥d:P→R+ ∪{0} is a delay function of the Petri net model. The delay function associates the time delay with the library, indicating the time of the robot movement, the loading and unloading and transportation time of the workpiece, R + is a positive real number.

[0075] Based on the above definition, a timed Petri net model of the welding system can be established. This Petri net model can be written as PN(N,M0)=(P,T;I,O,d,M0), N=(P,T;I,O,d). The association matrix A can be n rows and m columns, and the elements are determined by the judgment conditions and calculation relationship between P, T and the configuration. The association matrix A can be used to generate a reachability graph. Specifically: each element in the matrix A is an integer, A(p,t)=O(p,t)-I(p,t). When A(p,t) is 0, it means that the emission of transition t will not change the number of tokens in p; when A(p,t) is negative, it means that |A(p,t)| tokens will be removed from p when transition t is emitted; when A(p,t) is positive, it means that |A(p,t)| tokens will be moved into p when transition t is emitted. When the reachability graph changes from label M to M' through transition t, the number of tokens in each place can be calculated by the following formula:

[0076] M′(p)=M(p)+O(p,t)-I(p,t);

[0077] In the above Petri net model, a multi-robot welding unit can also be defined, and the multi-robot welding unit can belong to the welding operation resources, such as Figure 2 , the multi-robot welding cell can be expressed as follows:

[0078] a1.R={R1,R2,...,R r} is a resource collection containing r robotic arms;

[0079] a2.J={J1,J2,...,J q} is a set of q welding tasks;

[0080] a3. The i-th welding task J i The welding batch is the i-th welding batch I i , the i-th welding batch I i Includes i processing sequence;

[0081] a4. Based on the above, the jth processing sequence S ij There are t processing tasks in , and the t processing tasks have a predetermined task order;

[0082] a5. Based on the above, the kth processing task T ijkThere are L welding paths, and the lth path among the L welding paths corresponds to a basic welding operation O ijkl ;

[0083] a6. Based on the above, basic welding operations ijkl Requires ijkl Different robotic arm resources, time delay is p ijkl , i, j, k, and l are positive integers.

[0084] A2) Construct a Petri net model based on the actual equipment and production methods of the welding operation scenario.

[0085] For example, a multi-robot welding unit may include a specified number of robotic arms and / or automatic guided vehicles, the specified number is less than a specified threshold, and the specified threshold may be selected based on the production scale, such as 5%, 10%, 20%, etc., of the number of robotic arms and / or automatic guided vehicles in the production line. It is understandable that such point value examples are not the only way to limit, and may also be less than 5%, less than 30%, less than 40%, etc., and may also be less than half of the total number of robotic arms and automatic guided vehicles in the production scale. For example, a welding workshop may have 10 robotic arms and 10 automatic guided vehicles, and each multi-robot welding unit may include 2 robotic arms R1, R2 and 2 automatic guided vehicles, and implement a mixed processing method for two types of workpieces. For the Petri net model established in the above example, the welding tasks of the two types of workpieces may have the following processes:

[0086] The process of the first type of workpiece is: first welded by robot R1, then welded by robot R2, which takes 3 units of time and 5 units of time respectively;

[0087] The process of the second type of workpiece is as follows: first, it is welded by robot arm R2 and then processed by robot arm R1, which takes 2 units of time and 4 units of time respectively.

[0088] The Petri net model includes a start location representing the start of a welding process, a stop location representing the end of a welding process, and a delay location representing the delay value required to perform welding on a workpiece. Figure 3 , Place P1, Place P2, Place P9, Place P 10 The black solid circles in the figure represent Tokens, and the number of black solid circles is the number of workpieces of each type that need to be processed. 10 Represents two automatic guided vehicles. Places P1 and P2 are the starting places of the two types of processes, and places P7 and P8 are the ending places of the two types of processes. The starting place and the ending place represent the start and end of workpiece welding respectively; places P3, P5, P4, and P6 are delay places, representing the time required to weld the workpiece (or delay value). Figure 3 In the configuration Petri net model, the places P1 to P8 represent the operation places. Places P1, P3, P5, and P7 are arranged in the order or flow of welding operations according to the workpiece process. Places P2, P4, P6, and P8 are also arranged in the order or flow of welding operations according to the workpiece process. In the configuration Petri net model, the places are connected by transitions T1 to T6. Figure 3 In the figure, it is represented by a black filled rectangle, which can be instantaneous transition or delayed transition. Instantaneous transition means that the transition operation can be completed instantly, and delayed transition means that the transition operation takes a certain amount of time.

[0089] Table 1 defines the mapping relationship between the library location and the welding operation meaning when holding a token

[0090]

[0091] In the embodiment of the present invention, the scheduling problem of welding operation resources can be set as follows:

[0092] b1. Each robot arm can only handle one welding task at the same time;

[0093] b2. Each welding operation processes only one workpiece at a time and outputs only one workpiece at a time;

[0094] b3. The cache between processing tasks is infinite;

[0095] b4. Ignore the robot startup time and loading time.

[0096] Based on the above content, according to the order of delivery date of the orders, a hierarchical modeling method can be used to establish a timed Petri net model of the multi-robot welding unit. The so-called hierarchical modeling means that different modeling methods are used for orders with different urgency, which can also speed up the algorithm to find the optimal solution.

[0097] In an embodiment of the present invention, S2) constructs an objective function of minimizing the completion time of the welding task based on the Petri net model, including: the A star search algorithm uses the minimized completion time of the multi-robot welding unit defined above as an evaluation function or an objective function or a cost function, and the cost function can be expressed as minf(M), f(M) = h(M) + g(M), wherein the value of the function g(M) represents the number of steps from the initial identifier M0 to the proposed identifier M cThe maximum completion time of a task, h(M) is the heuristic function. In the A-star algorithm, when for any identifier M, h(M) <= h*(M), the heuristic function is called a permissible heuristic function, and the algorithm can definitely find the optimal scheduling solution. Among them, h*(M) refers to the distance of the optimal path from the current node to the target node, that is, the minimum distance. Of course, in the embodiment of the present invention, the distance refers to the processing time. In the embodiment of the present invention, the value of the function h(M) represents the optimal path from the proposed identifier M. c To target mark M f The estimated value of the remaining cost (completion time) of the task. In the algorithm, h(M) guides the search direction of the algorithm by determining the sequence of transition triggers of a certain identifier.

[0098] The calculation method is:

[0099]

[0100] In the above formula, R represents the set of machine resources; R i represents the i-th machine; P k represents the kth processing step; RM i For machine R i The collection of places of machines that have not started processing; d(P k ) indicates P k The remaining time of the operation; w(P k ) indicates P k The number of operations remaining, ζ i It represents the estimated processing time of the i-th machine to complete all subsequent workpieces. ζ (M) = max Ri∈R ζi is the specific calculation method of the above heuristic function h(M) in the scenario of the embodiment of the present invention.

[0101] This estimation method does not take into account the idle time of the machine during the production process, so the estimated value is less than or equal to the actual processing time. It is an admissible heuristic function and is therefore selected as the heuristic function.

[0102] After the model building and configuration steps are executed, the aforementioned scheduling method can be started. Step S3) Solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model, including:

[0103] S310) Initializing algorithm parameters of the A-star search algorithm, the algorithm parameters at least including an initial identifier, a target identifier, a to-be-explored list, and an explored list, the to-be-explored list is used to store identifiers to be explored, the explored list is used to store identifiers that have been explored, the initial identifier and the target identifier represent a starting place and a target delay place in a Petri net model, respectively;

[0104] S320) Repeat the following steps until the target identifier is found or the to-be-explored list is empty:

[0105] Finding the delay library in the Petri net model with the smallest total evaluation value from the to-be-explored list as the current node, removing the current node from the to-be-explored list, and adding it to the explored list;

[0106] Calculate the actual cost, heuristic cost and total evaluation of the adjacent nodes around the current node, and update the to-be-explored list and the explored list;

[0107] S330) Backtracking from the target identifier according to the parent node pointer until backtracking to the initial identifier, thereby solving the target function and obtaining the target reachable path of the Petri net model.

[0108] In step S1), the given data of the welding task may include the depth constraint value dc, the initial mark M0 and the target mark M f The vector representation of Figure 4 Step S310) Initialize the algorithm parameters of the A star search algorithm including initializing the list M to be explored Open and the explored list M Close , initialize M Open 、M Close This can include:

[0109] S311) Define M Open 、M Close for a list;

[0110] S312) Settings is empty;

[0111] S313) The initial identifier M0 is placed in the OPEN table of the A star search algorithm, and the OPEN table after the initial identifier M0 is placed is recorded as the to-be-explored list M Open , the CLOSE table of the A star search algorithm is recorded as the explored list M Close .

[0112] In the A-star search algorithm of the embodiment of the present invention, the comparison table of parameters and definitions is shown in Table 2 below.

[0113] Table 2 Comparison table of parameters and definitions

[0114]

[0115] Step S320) repeats the following steps until the target identifier is found or the to-be-explored list is empty, specifically including:

[0116] S321) If it is judged Then the result of planning failure is output and the plan ends, or the result of ending is output;

[0117] S322) If it is judged Then we get M Open The first node M Open The depth value d of [0] f , if d f ≤d c , then execute step S323), where M Open [0] refers to the first node in the list, with a depth of d f Refers to the number of the deepest node in the reachable identification graph of the Petri net model;

[0118] S323) Traverse M Open If the node in has a depth greater than or equal to the depth constraint value d c Node a is expanded, and f(first)≤f(a), then delete all nodes greater than M Open [0] The depth value is marked, and M Open Sort in ascending order according to the cost function value f, that is, M Open The cost function value of [0] is the smallest;

[0119] S324) the M Open [0] Insert M Close , then delete M Open [0], jump to step S321).

[0120] Step S330) of tracing back from the target identifier according to the parent node pointer until tracing back to the initial identifier to obtain the target reachable path of the Petri net model specifically includes:

[0121] S331) If M Open [0] = M f , then backtrack the pointer and get f The algorithm terminates and returns the transition sequence σ to M0, as well as the maximum completion time C. max , where the identification record in the sequence σ is the reachable path;

[0122] S332) If M Open [0]≠M f, the enabling transition of the current identifier M can be executed, that is, M[t>M′. Specifically, a new successor identifier M′ can be generated by the formula M′(P)=M(P)+O(P,T)-I(P,T), and a pointer from the current identifier M to the identifier M′ is set. The cost functions g(M′) and h(M′) of the identifier M′ are calculated. The formula M(P)≥I(P,T) is used to determine whether the reachability condition is met. If the reachability condition is met, step S333) is executed. If the reachability condition is not met, it is determined that the node in the path will fall into a deadlock state and should be abandoned, and go to step S321);

[0123] S333) For the generated M′, the following operations can be performed:

[0124] c1. Determine whether M′ already exists in M Open If it exists, point the pointer to M Open the identity of the smallest g(M′) in , and

[0125] c2. Determine whether M′ already exists in M Close If it exists, point the pointer to M Close the identity of the smallest g(M′) in , and

[0126] c3. If the identity of M′ is not equal to M Open and M Close The identifier of any node of M is calculated and M′ is placed in M Open In the table;

[0127] S334) for M Open Sort in ascending order by f value and go to step S321).

[0128] After the above processing steps, a reachable path can be obtained, and the operation information of the associated equipment can be configured according to the reachable path. The associated equipment can be the equipment in the aforementioned multi-robot welding unit. In some production scenarios, the production line may include a control system, and the control system may include a server for scheduling control or a control device with instruction processing and computing capabilities. The production line may also include welding operation resources (there may be multiple multi-robot welding units) and equipment, sites, etc. for equipment detection, power support and transportation. The control system may send the operation information to the aforementioned multi-robot welding unit, and the control system may schedule each device in the multi-robot welding unit to perform the welding operation task. The aforementioned scheduling method may be performed entirely by the control system, or steps S1) to S3) may also be performed by a scheduling system dedicated to calculating the scheduling scheme, and step S4) may be performed by the control system, and the scheduling system may also include a server or a control device with instruction processing and computing capabilities. It can be understood that the above disclosed steps can adjust the processing logic, configuration iteration and nesting and other program settings according to the computer program adapted to the needs.

[0129] The present invention constructs a Petri net model based on given data of welding tasks, and establishes an objective function of minimizing completion time based on the Petri net model, thereby realizing the optimization problem of solving the dynamic resource scheduling of welding operations by using the Petri net model. Then, the A-star search algorithm is used to solve the optimization problem, and an optimal path from an initial identifier to an end identifier is searched in the form of a reachable graph in the Petri net model, thereby realizing the dynamic optimization of the resource scheduling of the welding operation. Compared with the existing welding operation resource scheduling, which relies on previous common sense and experience and lacks theoretical guidance, the present invention uses the Petri net model combined with the A-star search algorithm to perform resource scheduling of the welding operation, thereby improving the efficiency and effect of the resource scheduling of the welding operation.

[0130] On the other hand, the current A-star search algorithm needs to spend a large amount of memory and time to identify nodes with similar costs on the search path. The Beetle Antennae Search (BAS) algorithm is an efficient intelligent optimization algorithm with excellent global search capabilities and is suitable for large-scale environments. In view of this, the embodiment of the present invention will use two heuristic search algorithms and apply them to the later node search of the Petri net model, which can greatly reduce the amount of calculation and realize the planning and scheduling solution in the welding operation scenario. Specifically, the A-star search algorithm can be executed in the aforementioned step S2), and the Beetle Antennae Search Algorithm is used to process the specified list in the A-star search algorithm. Before calculating the actual cost, heuristic cost and total valuation of the target nodes around the current node, and updating the steps of the list to be explored and the explored list, it also includes: performing a secondary search on the nodes around the current node by a heuristic search algorithm to obtain the target nodes around the current node.

[0131] Specifically, the embodiment of the present invention can perform a first search through the depth value of the node and the cost function value of the node, and perform a second search through the beetle whisker search algorithm on the list obtained after the first search. f >d c , then execute the following steps S325 and S326:

[0132] S325) traverse M Open Select all nodes whose value is less than or equal to f(first) and put them into the temporary node list Temlist;

[0133] S326) Use a heuristic search algorithm to perform a secondary search on the surrounding nodes of the current expanded node in the temporary node list Temlist, randomly delete nodes with similar costs (function values), reduce redundant key nodes in the A-star search algorithm, update the expanded nodes in the temporary node list Temlist, and return to execute step S324). Among them, the heuristic search algorithm can be selected as the longhorn beard search algorithm. In other embodiments, the heuristic search algorithm can also be other algorithms, such as genetic algorithms, particle swarm optimization algorithms, etc. The temporary node list Temlist can be the aforementioned specified list, but it is not the only implementation method of the embodiment of the present invention. The specific form of the specified list of the embodiment of the present invention can include expressions such as lists, arrays and / or vectors in computer languages.

[0134] It is worth noting that the embodiment of the present invention adopts the beetle whisker search algorithm to filter out nodes with similar cost function values ​​by random deletion during the iteration process of the A-star search algorithm, thereby limiting the expandable search direction of the A-star search algorithm, thereby achieving balanced use of equipment between various processes and operations in the welding process. At the same time, it avoids the optimization of the availability distribution of various workpieces in the same welding process to be restricted to some stages, which are some stages in which the workpieces are welded by the robot arm, and avoids the imbalance of workpiece production and robot arm work caused by the optimization restriction, and avoids the failure of equipment that is easily affected by uneven use and the resulting production line blockage when random disturbance events occur, thereby improving the quality and efficiency stability of the produced products.

[0135] Before jumping from step S326) to step S324), the nodes selected (not deleted) using the beetle search algorithm can be placed from the specified list into a new list, and the new list can include the selected nodes and the nodes that are not placed in the temporary node list Temlist in step S325). The new list can be an updated, current M Open and / or M Close After processing the nodes using the beetle whisker search algorithm, continue to use the A-star search algorithm, combined with the new list, to determine whether the target node is touched and to search for a reachable path.

[0136] In order to solve the problem that the A-star search algorithm is slow and consumes a large amount of memory to identify nodes with similar costs on the search path, an embodiment of the present invention combines the A-star search algorithm with the longicorn beard search algorithm to propose an A*-BAS algorithm, which effectively reduces the amount of computation of the A-star search algorithm, improves the operating efficiency of the A-star search algorithm, and further improves the efficiency and effect of welding operation resource scheduling.

[0137] The welding operation scenario of the embodiment of the present invention can be a flexible welding workshop. During the flexible welding process, the workpieces being processed will share a multi-robot welding unit. When the welding process disturbance or random interference event causes equipment failure, it is difficult for resources to flow normally, and actual deadlock is likely to occur. In the embodiment of the present invention, the strategy layer with the corresponding functions of the aforementioned scheduling method can be used as the first strategy layer, and the second strategy layer can be implemented on the basis of the first strategy layer. The second strategy layer can provide auxiliary strategies for handling equipment failures. Both the first strategy layer and the second strategy layer can be applied to the control system in the production line to avoid deadlock problems during the welding process.

[0138] In the embodiment of the present invention, Figure 5, the second strategy layer can be used to perform rescheduling according to the (estimated) fault duration (repair time) of the equipment, and the rescheduling may include generating a rescheduling plan and executing the rescheduling plan. The aforementioned production line may include a detection system, and the detection system can determine whether a sudden random event occurs in the welding operation resources (taking the robotic arm as an example), for example, based on the numerical value (characteristic change / mutation) and signal (characteristic change / mutation) of events such as sensor events and / or bus events. If the detection system determines that a sudden random event has occurred, the detection system sends a fault message to the aforementioned control system. If the detection system determines that no sudden random event has occurred, the robotic arm continues to perform the currently scheduled task. After the detection system determines that a sudden random event has occurred, the aforementioned scheduling method may also include:

[0139] S5) receiving fault information fed back by the welding operation resource;

[0140] S6) Determine the type of equipment failure based on the estimated repair time associated with the failure information.

[0141] In some examples disclosed in the present invention, the fault information may be the detection information of the sensor and / or controller of the robot arm or the transport trolley or the production line, and the detection information may include workpiece transportation fault information, communication fault information, power fault information, mechanical fault information such as the motor / hydraulic of the equipment, etc. There may be different fault durations between the fault information, and the fault duration may be mainly determined by the length of the repair time (the repair time will be directly used in the subsequent description). For example, the repair time of the workpiece transportation fault, the equipment network interruption, the equipment power interruption, etc. is shorter, and the repair time of the motor or mechanical fault of the equipment is longer. The fault information can be divided into types according to the specific content of the fault information, and the divided types are associated with the estimated repair time. The estimated repair time can be a statistical value or a specified value of the (actual) repair time associated with the fault information. At the same time, each type of fault information can be corresponded to the equipment fault type. In some favorable scenarios, the fault information can be respectively corresponded to two types of equipment fault types, and the two types of equipment fault types include a first fault type and a second fault type, wherein the severity of the first fault type is greater than that of the second fault type, that is, the first fault type and the second fault type are relative equipment faults with different (estimated) repair times. That is, the estimated repair time corresponding to the second fault type is shorter than the estimated repair time corresponding to the first fault type, and a threshold time can be specified. The estimated repair time greater than the threshold time is the estimated repair time under the first fault type classification, and the estimated repair time less than or equal to the threshold time is the estimated repair time under the second fault type classification. In this way, in production line applications, the equipment fault type can be determined based on the estimated repair time associated with the fault information, such as by querying a configuration table or a database. It should be noted that the two types of equipment fault types are not the only limited implementation methods, and can be adjusted according to the characteristics of the production line and the requirements for detection effects, as shown in the following exemplary Table 3.

[0142] Table 3 Relationship between equipment failure type, estimated repair time and failure information

[0143] Fault Information Estimated repair time Equipment failure type <![CDATA[D1,D2,D3,……,D x ]]> <![CDATA[τ x >t]]> 10 <![CDATA[d1,d2,d3,……,d y ]]> <![CDATA[0<τ x ≤τ]]> 01 E / 11 G 0 00

[0144] In Table 3, the control system can receive fault information D1, D2, D3, ..., D x and d1,d2,d3,...,d y , x, y are positive integers, fault information D x or y The information in can be associated with the identification information of the current workpiece and the occupied robot arm (which can be used to determine the object mapped in the Petri net model), and then query the received fault information D x or y , determine the estimated repair time τ associated with the fault information x If the estimated repair time τ xIf the estimated repair time is greater than the threshold time τ, the equipment fault type is determined to be the first fault type 10; if the estimated repair time τ x If the time is less than or equal to the threshold time τ, it is determined that the device fault type is the second fault type 01. In some exemplary application scenarios, other types of fault information may also be configured, for example, active maintenance fault information E or no fault information G (normal operation); the estimated repair time τ associated with the active maintenance fault information E x It can be specified as empty or -1, etc., which can determine that the equipment failure type at this time is active maintenance type 11; the estimated repair time τ associated with the no-fault information G x It can be specified as 0, and it can be determined that the equipment fault type at this time is a non-fault type or a normal working type 00. In this way, the embodiment of the present invention can adopt different scheduling methods according to the fault type to deal with the disturbance factors in the welding process. The scheduling method can also include:

[0145] S7) determining a rescheduling strategy based on the device failure type;

[0146] S8) configuring the operation information of the welding operation resources based on the rescheduling strategy to achieve rescheduling of the welding operation resources.

[0147] Among them, step S7) and step S8) can be implemented with step S5) and step S6) respectively, or can be implemented with step S5) and step S6) after being combined.

[0148] In some application scenarios, step S7) may include:

[0149] Step S711) when the equipment failure type indicates that the current equipment is of the first failure type, adding idle equipment to the welding operation resources;

[0150] Step S712) configuring the current device in the Petri net model not to perform the welding process, and configuring the idle device to perform the welding process of the current device;

[0151] Step S713) jumps to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0152] In some other application scenarios, step S7) may also include:

[0153] Step S721) When the device fault type indicates that the current device is of the second fault type, adjust the delay value of the fault library mapped to the current device in the Petri net model.

[0154] Specifically, the present invention can adjust the delay value of the fault repository in the Petri net model to the sum of the original delay value of the fault repository before the fault and the estimated repair time.

[0155] Step S722) jumps to the step of solving the objective function by the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0156] Therefore, the embodiment of the present invention divides the fault type of the current device into a first fault type and a second fault type according to different estimated repair times, and executes different rescheduling strategies according to different fault types.

[0157] It should be pointed out that in the embodiment of the present invention, after determining the fault type between the faulty equipment, the reachable identification diagram of the Petri net model is adjusted and the reachable path is searched again after the fault, so as to realize the automatic handling of the fault problem. The beetle beard search algorithm in the embodiment of the present invention plays a role in supporting multiple and large-scale rescheduling after the equipment fails in the multi-robot welding unit. Even if a fault occurs, the welding process efficiency can be quickly restored, and after multiple rescheduling, a relatively stable balance of availability distribution can be achieved. In the production environment, it is difficult for the planning and scheduling scheme to perfectly adapt to all equipment or multi-robot welding units. While avoiding the equipment that actually has the second fault type to stop production and avoiding the equipment that actually has the first fault type to block the production line, the embodiment of the present invention can further optimize the planning and scheduling scheme. After multiple rescheduling, the scheduling coordination between the multi-robot welding unit and the control system is closer, which is more conducive to preventing the adverse effects of emergencies on the equipment. For equipment that has the second fault type but has not lost the ability to complete the work, it is not necessary to withdraw the equipment from the production line, which reduces the production cost; for equipment that has the first fault type, it is replaced with an idle equipment without manual participation. All associated workpieces will be processed, and the multi-robot welding unit can prepare for rescheduling by following the control system's changes to the identification of the reachable identification diagram in the Petri net model. The production loss caused by the failure will be reduced to a very low level, and the welding production efficiency can be quickly restored after rescheduling.

[0158] Based on the above-mentioned first fault type and second fault type fault type and equipment control, the present invention discloses an example of a scheduling method using an auxiliary decision-making system (or a second strategy layer), which can intervene in the second strategy layer after the control system obtains the fault information to deal with event interference and realize dynamic scheduling. The second strategy layer can be implemented by a program in the control system (the first strategy layer can also be implemented by a program), and the second strategy layer can also be implemented by a control system different from the first strategy layer, and the control system can be called an auxiliary decision-making system. The program can be configured with jumps, calculation logic, etc., and can be executed by the control system to implement the aforementioned steps S5) to S8), and the second strategy layer can be configured to:

[0159] k1. Receive the fault information sent by the detection system and obtain the current identification M of the welding operation scene cur , determine the estimated repair time according to the fault information. If the estimated repair time is greater than the threshold time, it belongs to the first fault type, and proceed to step k2. If the estimated repair time is less than or equal to the threshold time, it belongs to the second fault type, and proceed to step k5;

[0160] k2. When the equipment fault type is the first fault type, the Petri net model and the fault library P b The associated delay value d 故障设备 Set to a fault delay value, which can be a delay value that deviates from a normal (empirical or average) delay value by a specified multiple. For example, a normal delay value can be between 1 and 111, and a fault delay value can be 888, 999, 9999, etc., so that the faulty machine exits this scheduling and enters a maintenance state. The faulty machine can be a faulty robotic arm, and the robotic arm can be the current device associated with the fault information.

[0161] k3. The robot arm in step k2 releases the current workpiece being processed, adds the idle equipment to the welding operation resources in use, and replaces the workpiece originally in the fault library with the workpiece in use. b The processing steps are allocated to the idle equipment for processing, and the idle equipment is mapped to the Petri net model. At the same time, the reachable identification diagram of the Petri net model is adjusted. cur Change the current logo to M 故障 (Realize the update of availability status and mapping of idle devices to the Petri net model), and the idle devices complete the subsequent processing operations of the current tool;

[0162] k4 jumps to step S3 in the aforementioned scheduling method) to reschedule, determine a new reachable path, generate a rescheduling plan, and then jumps to step k6;

[0163] k5. Do not reallocate the robot arm or other equipment for the faulty workpiece and obtain the library location P before the fault occurs.b The original delay value d b , and obtain the estimated repair time d of the faulty device r , using d b ′=d r +d b Update the library P after the failure occurs b The faulty workpiece is processed again after the delay time, and the faulty tool, i.e., the current workpiece associated with the fault information, jumps to step S3) in the aforementioned scheduling method for rescheduling, determines a new target reachable path, and generates a rescheduling plan;

[0164] k6. Change the operation information of the equipment associated with the emergency or fault information and execute a new scheduling plan.

[0165] In some exemplary application scenarios of the dynamically adjusted Petri net model of the embodiments of the present invention, steps k2 to k4 may be used to process the first fault type, such as Figure 6 Before the equipment fails, Figure 6 As shown in (a), the mapping of welding operation resources in the Petri net model is library places p1~p4. For the convenience of explanation, the completion time of the transition delay value between the library places is recorded as time t1~t4. If at time t1, the library place p2 resource (current equipment, that is, the faulty equipment) fails, and the estimated repair time is greater than the threshold time (the threshold time is such as 50, 100, 200 unit time, etc.), it can be the first fault type at this time, and the identification M at the time t1 is obtained. cur , which can be [1 1 0 0]. Control the idle equipment to process the current workpiece and map the idle equipment to the Petri net model. The Petri net model can be adjusted to a model with 5 places. After a fault occurs, Figure 6 (b) Change the current time mark to mark M 故障 , which can be expressed as [1 0 0 0 1], where place p5 represents an idle device, at time t s Indicates the fault start time, time t e Indicates the moment when the fault ends. Place p5 can be used to replace the current device to execute the welding process originally executed by the current workpiece. Specifically, the delay value of place p5 is given according to the actual remaining processing time of the current workpiece. For example, if the current device associated with place p2 still needs 1 unit time to process the current workpiece, then the current device associated with place p2 is controlled to release the workpiece. At the same time, the delay value of the place is configured to 999 unit time, and the idle device associated with place p5 replaces the current device associated with place p2. The delay value of place p5 is configured to 1 unit time, and the idle device completes the remaining welding process. Steps k5 to k6 can be used to handle the second fault type, such as Figure 7 Before the equipment fails, Figure 7 As shown in (a), after the machine fails, Figure 7 As shown in (b), if at time t2, the resource in place p2 fails, it is estimated that 2 unit times are required to repair the equipment, that is, the estimated repair time is 2 unit times, which is less than the threshold time. At this time, it can be the second fault type. Therefore, based on the original delay value of place p2, two unit times can be added, and the current equipment associated with place p2 can be used to restart the processing of the workpiece, and then rescheduling is performed.

[0166] In summary, the present invention establishes an objective function with the goal of minimizing the completion time of the welding task through the Petri net model, and solves the objective function using the hybrid algorithm of the A-star search algorithm and the beetle whisker search algorithm (A*-BAS hybrid algorithm), thereby realizing the dynamic optimization of the flexible operation equipment. At the same time, the form of job rescheduling is analyzed, and the type of equipment failure is determined according to the estimated repair time associated with the fault information in the welding operation scenario. Different rescheduling strategies are adopted for different equipment failure types to further improve the resource scheduling efficiency of the automated flexible welding operation scenario.

[0167] Example 2

[0168] The embodiment of the present invention and embodiment 1 belong to the same inventive concept. The embodiment of the present invention provides an application method of the scheduling method of the beetle whisker search algorithm in embodiment 1. The application method may include:

[0169] B1) Initialize parameter x 0 ,d 0 ,δ 0 , set the number of iterations T max , where x 0 Indicates the identifier in the Temlist list obtained in step S205) of Example 1, and x 0 Set as the initial solution, d 0 represents the initial search range, δ 0 is the initial step size, set its value to 1, and then according to δ t =αδ t-1 Make changes, where α is a preset coefficient.

[0170] B2) During the iterative solution process, a search direction b is determined completely randomly in the search for feasible transitions of the current representation:

[0171]

[0172] In the formula, rand(·) represents a random function and k represents the dimension of the position. In addition, the search behavior on the left and right sides is also required to simulate the activity of the antennae of the longhorn beetle:

[0173] x r =xt +d t b

[0174] x l =x t -d t b

[0175] In the formula, x r Indicates the location of the search area on the right, x l Indicates the position of the search area on the left. The search parameter d is the sensing length of the antenna, which represents the exploration ability. Initially, this value should be large enough to cover a reasonable search area to ensure that the local optimal point is jumped out, and then gradually decays with the passage of time. t represents the number of iterations.

[0176] B3) In order to express the detection behavior, the following iterative model is further generated to associate odor detection by considering the search behavior:

[0177] x t =x t-1 +δ t bsign(f(x r )-f(x l ))

[0178] In the formula, δ is the search step size, which is used to adjust the convergence speed. It is a decreasing function of the number of iterations t, not an increasing function or a constant. δ should be initialized to the same value as the search, that is, equal to 1. sign(·) represents the sign function, that is, if the input is greater than 0, it returns 1, if it is equal to 0, it returns 0, and if it is less than 0, it returns -1. f(·) is the fitness function, and the maximum value of the fitness function is the value of the odor source position, that is, the fitness value of the target point to be found.

[0179] For the search parameter, antenna length d, use d t =βd t-1 +γ for update, where β and γ are preset coefficients.

[0180] B4) Reach T max After iterations, when the fitness function value f best When x no longer changes, best That is, the logo M best , return the mark M best Together with all the identifiers on the M0 path, an identifier list is formed. The number of identifiers in the identifier list is significantly reduced than the number of identifiers in the Temlist list. The identifiers in the identifier list can be added to the new list in Example 1.

[0181] The embodiment of the present invention aims to solve the problem that the A-star search algorithm has a slow search speed and occupies a large amount of memory and computing resources (high overhead) to identify nodes with similar costs on the search path, which makes it very difficult to deploy on embedded devices in the production line of welding operations. The embodiment of the present invention combines the A-star search algorithm with the longicorn beard search algorithm, effectively reducing the amount of algorithm calculations and improving the algorithm operation efficiency. It can be deployed in the control system of the production line, the control system's equipment such as controllers and industrial computers and other embedded hardware, and supports the scenario of multiple rescheduling for large and small equipment failures in Example 1, improving the scheduling efficiency of the multi-robot welding unit in Example 1 that automatically responds to random sudden interference events.

[0182] Example 3

[0183] The embodiment of the present invention and embodiments 1 and 2 all belong to the same inventive concept. The embodiment of the present invention provides a scheduling system for welding operation resources, which may include:

[0184] A building module for building a Petri net model based on given data of welding work tasks;

[0185] An objective function construction module is used to construct an objective function of minimizing the completion time of the welding task based on the Petri net model; the objective function represents the minimum completion time required from the starting library at the beginning of the welding process to the ending library at the end of the welding process;

[0186] A path search module is used to solve the objective function by using an A-star search algorithm to obtain a target reachable path of a Petri net model;

[0187] The scheduling module is used to configure the operation information of the welding operation resources based on the target reachable path to realize the scheduling of the welding operation resources.

[0188] Specifically, the objective function is solved by the A-star search algorithm to obtain the target reachable path of the Petri net model, including:

[0189] Initialize the algorithm parameters of the A-star search algorithm, wherein the algorithm parameters at least include an initial identifier, a target identifier, a to-be-explored list, and an explored list, wherein the to-be-explored list is used to store identifiers to be explored, and the explored list is used to store identifiers that have been explored, and the initial identifier and the target identifier represent a starting place and a target delay place in a Petri net model, respectively;

[0190] Repeat the following steps until the target identifier is found or the to-be-explored list is empty:

[0191] Finding the delay location in the Petri net model with the smallest total evaluation value from the to-be-explored list as the current node, removing the current node from the to-be-explored list, and adding it to the explored list;

[0192] Calculate the actual cost, heuristic cost and total evaluation of the adjacent nodes around the current node, and update the to-be-explored list and the explored list;

[0193] The target function is solved and the target reachable path of the Petri net model is obtained by tracing back from the target identifier according to the parent node pointer until the initial identifier is reached.

[0194] Specifically, before calculating the actual cost, heuristic cost and total evaluation of the target nodes around the current node and updating the to-be-explored list and the explored list, the method further includes:

[0195] A secondary search is performed on the nodes around the current node through a heuristic search algorithm to obtain target nodes around the current node.

[0196] Specifically, the scheduling system also includes:

[0197] A receiving module, used for receiving fault information fed back by the welding operation resource;

[0198] A fault type determination module, configured to determine a device fault type based on an estimated repair time associated with the fault information;

[0199] A rescheduling strategy determination module, used to determine a rescheduling strategy based on the device failure type;

[0200] A rescheduling module, used to configure the operation information of the welding operation resources based on the rescheduling strategy to achieve the rescheduling of the welding operation resources

[0201] Specifically, the equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type;

[0202] Determining the rescheduling strategy based on the device failure type includes:

[0203] When the equipment failure type indicates that the current equipment is of the first failure type, adding idle equipment to the welding operation resources;

[0204] In the Petri net model, the current device is configured not to perform a welding process, and the idle device is configured to perform the welding process of the current device;

[0205] Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0206] Specifically, the equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type;

[0207] Determining the rescheduling strategy based on the device failure type includes:

[0208] When the device fault type indicates that the current device is of the second fault type, adjusting the delay value of the fault library mapped to the current device in the Petri net model;

[0209] Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

[0210] Specifically, the adjusting the delay value of the fault library mapped to the current device in the Petri net model includes:

[0211] The delay value of the fault pool in the Petri net model is adjusted to the sum of the original delay value of the fault pool before the fault and the estimated repair time.

[0212] Specifically, the Petri net model includes a start location representing the start of a welding process, a stop location representing the end of a welding process, and a delay location representing a delay value required for performing welding on a workpiece.

[0213] Example 4

[0214] The embodiment of the present invention and embodiments 1 to 3 all belong to the same inventive concept. The embodiment of the present invention provides a production line, which may have welding operation resources or multiple multi-robot welding units and is used for workpiece production. The production line includes the detection system and control system in embodiment 1. The control system may include electronic equipment for executing the scheduling method of all or part of the steps in embodiment 1. The control system may use a multi-robot welding unit in embodiment 1 as the basic scheduling unit to control each multi-robot welding unit in the production line to perform welding operation tasks, thereby realizing a large-scale welding process.

[0215] An embodiment of the present invention further provides an electronic device, which may include:

[0216] at least one processor;

[0217] a memory connected to the at least one processor;

[0218] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the aforementioned method by executing the instructions stored in the memory.

[0219] The embodiment of the present invention further provides a machine-readable storage medium storing machine instructions, which, when executed on a machine, enables the machine to execute the aforementioned method.

[0220] In view of the emergencies that may occur during the multi-robot welding process, the present invention proposes a dynamic scheduling mode for multi-robot welding units, establishes an optimized Petri net model with the goal of minimizing the maximum processing time of the equipment, and solves the path optimization problem using the A*-BAS hybrid algorithm formed by Example 1 and Example 2, thereby achieving dynamic optimization of the fault size of flexible operation equipment and analyzing the form of job rescheduling, providing a solution for the efficient operation of automated flexible operations.

[0221] It should be noted that the A-star search algorithm in the above embodiment of the present invention should be understood as a path search algorithm that uses the initial identifier and the target identifier as initial parameters, searches for adjacent identifiers in a list within a specified distance range, and forms a path. The A-star search algorithm in the above embodiment of the present invention should include the classic A* search algorithm (proposed by Nils Nilsson, Peter E. Hart, etc.) and its improved search algorithms, such as the D-star search algorithm (Dynamic A Star) and the iterative deepening A-star search algorithm (Iterative Deepening A*).

[0222] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical scheme of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

[0223] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0224] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium can be non-transient, and the storage medium can include: U disk, hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), flash memory (Flash memory), disk or optical disk and other media that can store program codes.

[0225] In addition, various implementation modes of the embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed by the embodiments of the present invention.

Claims

1. A method for scheduling welding operation resources, characterized in that: The scheduling method includes: Construct a Petri net model based on the given data of the welding task; Based on the Petri net model, an objective function for minimizing the completion time of the welding task is constructed; the objective function represents the minimum completion time required from the starting library at the beginning of the welding process to the ending library at the end of the welding process in the Petri net model; Solving the objective function by using an A-star search algorithm to obtain a target reachable path of the Petri net model; Based on the target reachable path, the operation information of the welding operation resource is configured to achieve the scheduling of the welding operation resource.

2. The method for scheduling welding operation resources according to claim 1, characterized in that: The objective function is solved by the A-star search algorithm to obtain a target reachable path of the Petri net model, including: Initialize the algorithm parameters of the A-star search algorithm, wherein the algorithm parameters at least include an initial identifier, a target identifier, a to-be-explored list, and an explored list, wherein the to-be-explored list is used to store identifiers to be explored, and the explored list is used to store identifiers that have been explored, and the initial identifier and the target identifier represent a starting place and a target delay place in a Petri net model, respectively; Repeat the following steps until the target identifier is found or the to-be-explored list is empty: Finding the delay location in the Petri net model with the smallest total evaluation value from the to-be-explored list as the current node, removing the current node from the to-be-explored list, and adding it to the explored list; Calculate the actual cost, heuristic cost and total evaluation of the adjacent nodes around the current node, and update the to-be-explored list and the explored list; The target function is solved and the target reachable path of the Petri net model is obtained by tracing back from the target identifier according to the parent node pointer until the initial identifier is reached.

3. The method for scheduling welding operation resources according to claim 2, characterized in that: Before calculating the actual cost, heuristic cost and total evaluation of the target nodes around the current node and updating the to-be-explored list and the explored list, the method further includes: A secondary search is performed on the nodes around the current node through a heuristic search algorithm to obtain target nodes around the current node.

4. The method for scheduling welding operation resources according to claim 1, characterized in that: After configuring the operation information of the welding operation resource based on the target reachable path to realize the scheduling of the welding operation resource, the method further includes: Receiving fault information fed back by the welding operation resource; Determining a type of equipment failure based on an estimated repair time associated with the failure information; Determining a rescheduling strategy based on the device failure type; The operation information of the welding operation resource is configured based on the rescheduling strategy to achieve the rescheduling of the welding operation resource.

5. The method for scheduling welding operation resources according to claim 4, characterized in that: The equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type; Determining the rescheduling strategy based on the device failure type includes: When the equipment failure type indicates that the current equipment is of the first failure type, adding idle equipment to the welding operation resources; In the Petri net model, the current device is configured not to perform a welding process, and the idle device is configured to perform the welding process of the current device; Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

6. The method for scheduling welding operation resources according to claim 4, characterized in that: The equipment fault type includes a first fault type and a second fault type, and the severity of the first fault type is greater than that of the second fault type; Determining the rescheduling strategy based on the device failure type includes: When the device fault type indicates that the current device is of the second fault type, adjusting the delay value of the fault library mapped to the current device in the Petri net model; Jump to the step of solving the objective function by using the A-star search algorithm to obtain the target reachable path of the Petri net model.

7. The method for scheduling welding operation resources according to claim 6, characterized in that: The step of adjusting the delay value of the fault library mapped to the current device in the Petri net model includes: The delay value of the fault pool in the Petri net model is adjusted to the sum of the original delay value of the fault pool before the fault and the estimated repair time.

8. The method for scheduling welding operation resources according to any one of claims 1 to 7, characterized in that: The Petri net model includes a start location representing the start of a welding process, a stop location representing the end of a welding process, and a delay location representing a delay value required for welding a workpiece.

9. A scheduling system for welding operation resources, characterized in that: The dispatch system includes: A building module for building a Petri net model based on given data of welding work tasks; An objective function construction module is used to construct an objective function of minimizing the completion time of the welding task based on the Petri net model; the objective function represents the minimum completion time required from the starting library at the beginning of the welding process to the ending library at the end of the welding process in the Petri net model; A path search module is used to solve the objective function by using an A-star search algorithm to obtain a target reachable path of a Petri net model; The scheduling module is used to configure the operation information of the welding operation resources based on the target reachable path to realize the scheduling of the welding operation resources.

10. An electronic device, characterized in that: The electronic device includes: at least one processor; a memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the method of any one of claims 1 to 8 by executing the instructions stored in the memory.

11. A machine-readable storage medium storing machine instructions, which, when the machine instructions are executed on a machine, enable the machine to execute the method according to any one of claims 1 to 8.

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