A digital twin-driven material distribution path optimization method for mixed-model assembly workshops

Abstract

The present invention discloses a material distribution path optimization method for a mixed-flow assembly workshop driven by a digital twin, including: 1. Using a digital twin system to monitor the material changes of assembly nodes and the changes of obstacles between nodes in the mixed-flow assembly workshop, and providing assembly node material demand information and obstacle status information in real time; 2. According to the requirements of different product assembly tasks, for assembly nodes that require materials in different time periods, using an improved artificial fish swarm algorithm to plan the optimal distribution sequence of the nodes requiring materials; 3. Statistically update the obstacle information between assembly nodes, and use an artificial potential field method to optimize the local paths between assembly nodes online. The present invention uses digital twin technology to monitor and obtain information from mixed-flow assembly workshops in real time, uses an improved artificial fish swarm algorithm to plan the global path of material distribution, and uses an artificial potential field method to optimize the local paths between assembly nodes online, saving storage space for assembly node materials, enhancing the material distribution capacity of the workshop, and improving the production efficiency of mixed-flow assembly workshops.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent manufacturing, and specifically relates to a material distribution path optimization method for a mixed-flow assembly workshop driven by a digital twin. Background Art

[0002] As the traditional single-variety, large-batch production mode is difficult to meet the increasingly personalized and diversified product needs, the multi-variety, small-batch production mode plays an increasingly important role in modern manufacturing. The mixed-flow assembly mode provides an effective and feasible new way for modern manufacturing to achieve multi-variety, small-batch production. In the production environment of a multi-variety, small-batch mixed-flow assembly workshop, there are many types of materials on the assembly nodes, the demand changes frequently, and the inventory capacity of the assembly nodes is limited. In order to achieve long-term operation of the mixed-flow assembly line and avoid excessive occupation of the limited inventory space on the assembly nodes, it is necessary to carry out multiple batches of material distribution activities on the assembly node inventory. There are assembly production equipment, semi-finished or finished product stacking, assembly tool storage, etc. in the assembly workshop, and the flow of operating and management personnel makes the assembly workshop environment complex and changeable, and the complexity of the material distribution path also increases accordingly. Traditional manual experience material distribution leads to unreasonable distribution path arrangement, which affects the efficiency of material distribution; the intelligent method currently used in the workshop material distribution path planning does not accurately respond to the dynamic changes in the workshop environment, and does not combine the real-time production status of the workshop with the material distribution demand, resulting in insufficient dynamic response capabilities of material distribution in the mixed-flow assembly workshop, waste of workshop space resources, and low workshop production efficiency. Summary of the invention

[0003] In response to the above-mentioned problems or shortcomings, the present invention provides a material distribution path optimization method for a mixed-flow assembly workshop driven by a digital twin, in order to optimize the material distribution demand supply and distribution planning path of the mixed-flow assembly workshop, accurately meet the material needs of the assembly nodes, and ensure timely supply of materials, thereby saving material storage space at the assembly nodes, enhancing the material resource distribution capabilities of the workshop, and improving the production efficiency of the mixed-flow assembly workshop.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0005] The material distribution path optimization method of a digital twin driven mixed-flow assembly workshop of the present invention comprises the following steps:

[0006] Step 1: Use the digital twin system to count the assembly nodes that need material replenishment during the production period in the mixed-flow assembly workshop online, so as to form a node material distribution sequence S = [s1 s2…s i …s n ], and get the i-th assembly node s i and the i+1th assembly node s i+1 Distance d i,i+1, where s1 represents the station number of the starting node of material distribution, s n Indicates the station number of the material delivery termination node;

[0007] Step 2: Use formula (1) to establish the optimization objective function f(S) of the node material distribution sequence S:

[0008]

[0009] Step 3: Initialize various parameters of the artificial fish swarm algorithm, including: population size k, field of view vis, maximum number of foraging attempts try_max, crowding degree δ, number of mutation optimization bits bit, current iteration number iter, maximum iteration number iter_max, and initialize iter=1;

[0010] Step 4: Initialize the m artificial fish of the artificial fish population as in, represents the station number of the assembly node at the i-th position in the m-th fish, and n represents the position number in the artificial fish;

[0011] Let the ath artificial fish in the iter generation population be Let the bth artificial fish in the iter generation population be in, It represents the workstation number of the ath artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration. It represents the workstation number of the bth artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration;

[0012] Define the optimization objective function f(S) as the fitness function of each fish;

[0013] Step 5: Use formula (2) to calculate the number of artificial fish in the iter generation population and the bth artificial fish Distance And compare Is it true? If true, it means artificial fish In artificial fish The visual range vis is used to determine whether other fish in the iter generation population are within the artificial fish Within the field of view vis, and count the artificial fish The number of all artificial fish k within the field of view vis iter ;

[0014]

[0015] In formula (2), sign(·) represents a binary function;

[0016] Step 6: Artificial fish a Perform foraging behavior within its visual range vis, and cyclically select other artificial fish within the visual range vis And compare Is it established? If established, then copy The first bit is the workstation number. The station numbers for the remaining positions will not be randomly generated; otherwise, Remain unchanged, if the condition is not met within the field of view vis The artificial fish Randomly generate a new workstation number sequence;

[0017] Step 7: Search and count artificial fish The visual range vis is the station number of each fish in the ith column of the school, and the one that appears the most and does not belong to The station number before the i-th column is assigned to the center of the fish school. At the i-th column position If the number of occurrences is the same, the first station number found is assigned to the center of the fish school. At the i-th column position Thus, artificial fish The center of the fish school within the field of view vis

[0018] Step 8: Artificial fish a Gathering behavior within its field of view vis, judging Is it established? If established, then Copy Center Location The first bit is the workstation number. The station numbers of the remaining positions are not randomly generated, otherwise, return to step 6;

[0019] Step 9: Artificial fish a The artificial fish performs tail-chasing behavior within its field of view vis. Search for the artificial fish with the highest fitness function value within the field of view vis if but Cloning artificial fish The first bit of the workstation number, The station numbers of the remaining positions are randomly generated without repeating, otherwise, go to step 6;

[0020] Step 10: each artificial fish in the iter generation population is processed according to the process of step 5 to step 9, the fitness function value of each artificial fish in the iter generation population is calculated, and the artificial fish with the best fitness function value is assigned to the bulletin board;

[0021] Step 11. After iter+1 is assigned to iter, if iter≤iter_max holds, repeat steps 5 to 10; otherwise, output the artificial fish with the best fitness function value in the iter_max generation population. And as the optimal distribution sequence of assembly nodes for material requirements, where Indicates the workstation number represented by the i-th node in the optimal material distribution sequence;

[0022] Step 12: Optimal distribution sequence S from assembly nodes of material requirements gbest Select two nodes in order and use the artificial force field method to optimize the local path between the two selected nodes:

[0023] Step 12.1: Obtain the optimal delivery sequence S of assembly nodes through the digital twin system gbest The i-th node Location coordinates in, Representation Node In the x-axis direction, Representation Node Coordinate in the y-axis direction, and initialize i=1;

[0024] Step 12.2: Optimal delivery sequence S gbest The i-th node Location is the current position G of the material transport vehicle, that is The i+1th node Location As the target position T of the material transport vehicle,

[0025] Step 12.3: Calculate the gravitational force F between the current position G and the target position T of the material transport vehicle using formula (3): att :

[0026] F att (G) = ηρ(G, T) (3)

[0027] In formula (3), η is a positive proportional coefficient, ρ(G, T) represents the geometric distance between the current position G of the material transport vehicle and the target position T, and the direction is from G to T;

[0028] Step 12.4: Use the digital twin system to count the number of obstacles m between the current position G and the target position T of the material transport vehicle online, and use formula (4) to calculate the repulsive force between the obstacle at the target position T and the current position G. The number of obstacles O for the tth obstacle is: t The repulsive force between the current position G is

[0029]

[0030] In formula (4), λ is the proportional coefficient, ρ(G,O t ) represents the distance between the material transport vehicle and the obstacle, and the direction is from O t Pointing to G; ρ0 represents the maximum distance at which obstacles affect the material transport vehicle; represents the gradient;

[0031] Step 12.5. Calculate the gravitational force F att (G) and repulsion The combined force And calculate the unit components of the resultant force F in the x and y directions Where |F| represents the modulus of the resultant force F, and F x represents the component of the resultant force F in the x-axis direction, F y represents the component of the resultant force F in the y-axis direction, z x represents the unit component of the resultant force F in the x-axis direction, z y represents the unit component of the resultant force F in the y-axis direction;

[0032] Step 12.6: According to the current position G of the material transport vehicle and the moving step length step, use formula (5) to calculate the next position G of the material transport vehicle: next :

[0033] G next =G+step*z (5)

[0034] Step 12.7: G next After assigning the value to G, return to step 12.3 and execute sequentially, so that the material conveyor vehicle can be completed at the starting node under the real-time monitoring of the changes of obstacles between the current position G and the target position T of the material conveyor vehicle by the digital twin system. To target node Path planning and material distribution between

[0035] Step 12.8, after assigning i+1 to i, return to step 12.2 and execute sequentially until i>n-1, thus completing all nodes Path planning and material delivery.

[0036] The present invention discloses an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the material distribution path optimization method, and the processor is configured to execute the program stored in the memory.

[0037] The present invention provides a computer-readable storage medium on which a computer program is stored. The computer-readable storage medium is characterized in that when the computer program is executed by a processor, the steps of the material distribution path optimization method are performed.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention uses digital twin technology to monitor the dynamic changes of materials at assembly nodes in a mixed-flow assembly workshop. During the material distribution planning process of the workshop, the number of assembly nodes that need to be replenished during the distribution period is counted, and an improved artificial fish school algorithm is used to plan the global distribution path sequence of assembly nodes that need to be replenished during the period, thereby improving the synergy between workshop material demand distribution and the dynamic production process.

[0040] 2. The present invention uses digital twin technology to monitor the changes of obstacles in the mixed-flow assembly workshop, obtains the status and position information of obstacles between assembly nodes in real time, uses the artificial potential field method to update the local paths between assembly nodes online, optimizes the driving paths of material delivery vehicles, and enhances the adaptability of workshop material distribution path planning to the dynamically changing workshop environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a schematic diagram of a mixed-flow assembly workshop scene of the present invention;

[0042] Figure 2 This is a schematic diagram of the foraging behavior of the artificial fish school of the present invention;

[0043] Figure 3 A schematic diagram of the gathering behavior of artificial fish schools of the present invention;

[0044] Figure 4 This is a schematic diagram of the artificial fish school chasing behavior of the present invention;

[0045] Figure 5 It is a schematic flow chart of the method for optimizing the material distribution path in a mixed-flow assembly workshop of the present invention. DETAILED DESCRIPTION

[0046] In this embodiment, a digital twin-driven material distribution path optimization method for a mixed-flow assembly workshop is firstly to obtain material demand information of assembly nodes in the mixed-flow assembly workshop through monitoring by a digital twin system, and to count changes in material demand of each assembly node in different production periods, and then to plan and update the material distribution sequence of the assembly nodes for the assembly nodes that need materials in the period using an improved artificial fish swarm algorithm; then to plan the local paths between the assembly nodes online using an artificial potential field method, and when the material transport vehicle moves between the assembly nodes, the digital twin system is used to count the obstacle change information between the assembly nodes in real time, and to autonomously update the local material distribution path between the assembly nodes. Specifically, the steps are as follows:

[0047] Step 1 Figure 1 The assembly nodes that need material replenishment in the mixed-flow assembly workshop shown in the figure are in dynamic change. The assembly nodes that need material replenishment in a certain production period in the mixed-flow assembly workshop are counted by the digital twin system S = [s1 s2…s i …s n ], where s1 represents the starting node of material distribution, s n Indicates the final node of material distribution, [s1 s2…s i …s n ] is a sequence of 1 2…i…n, which represents the order of nodes required for material replenishment in a distribution process, and they are not repeated.

[0048] Step 2: Obtain assembly node s through the digital twin system i and assembly nodes i+1 Distance d i,i+1 , for the assembly node sequence S that needs material replenishment during the distribution process, establish formula (1) as the optimization objective function of the node material distribution sequence:

[0049]

[0050] Step 3: Initialize various parameters of the artificial fish swarm algorithm, including: population size k, field of view vis, maximum number of foraging attempts try_max, crowding degree δ, number of mutation optimization bits bit, current iteration number iter, maximum iteration number iter_max, and initialize iter=1;

[0051] Step 4: Initialize the m artificial fish of the artificial fish population as in, represents the station number of the assembly node at the i-th position in the m-th fish, and n represents the n-th position number in the artificial fish;

[0052] Let the ath artificial fish in the iter generation population be Let the bth artificial fish in the iter generation population be It represents the workstation number of the ath artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration. represents the workstation number of the bth artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration; defines the optimization objective function f(S) as the fitness function of each fish;

[0053] Step 5: Define the distance between two artificial fish as the sum of the number of different station numbers at their corresponding positions, and determine whether other fish in population k are in the artificial fish. Within the field of view vis.

[0054] Use formula (2) to calculate the number of artificial fish in the iter generation population: and the bth artificial fish Distance And compare Is it true? If true, it means artificial fish In artificial fish The visual range vis is used to determine whether other fish in the iter generation population are within the artificial fish Within the field of view vis, and count the artificial fish The number of all artificial fish k within the field of view vis iter ;

[0055]

[0056] In formula (2), sign(·) represents a binary function, that is, like , the return value is 1. If The return value is 0;

[0057] Step 6: Artificial fish a Foraging behavior is performed within its field of vision, such as Figure 2 As shown, loop to select other artificial fish within the field of view vis And compare Is it established? If established, then copy The first bit is the workstation number. The station numbers for the remaining positions will not be randomly generated; otherwise, Remain unchanged, if the condition is not met within the field of view vis The artificial fish Randomly generate a new workstation number sequence;

[0058] Step 7. Search for artificial fish Fish school within the field of view vis Central location Select the station number that appears most frequently in the 1st, 2nd, ..., i, ..., nth position of each fish as The center of the school of fish within the field of view vis The station number at the corresponding position 1 2 ... i ... n in the , thus obtaining the artificial fish The center of the fish school within the field of view vis

[0059] Step 7.1: Search and count artificial fish The workstation number of the first column of each fish in the school of fish within the field of view vis For different workstation numbers with the same statistical quantity, the first searched workstation number is selected first, and the workstation number num1 with the largest statistical quantity is set as iter Giving fish a center The first position of

[0060] Step 7.2: Search and count artificial fish The workstation number of the second column of each fish in the school of fish within the field of view vis Exclude fish center The existing station number in For different workstation numbers with the same statistical quantity, the first searched workstation number is selected first, and the workstation number num2 with the largest statistical quantity is selected as iter Giving fish a center The second position of

[0061] Step 7.3: Search and count artificial fish The workstation number of each fish in the ith column of the school of fish within the field of view vis Exclude fish center The station number obtained before the i-th column For different workstation numbers with the same number of statistics, the first workstation number found will be given priority, and the workstation number with the largest number of statistics will be given priority. Giving fish a center At the i-th position of Use the same method until you get the center of the school of fish. The station number at the nth position, that is,

[0062] Step 8: Artificial fish a Perform grouping behavior within its field of view vis, such as Figure 3 As shown, judge Is it established? If established, then Copy Center Location The first bit is the workstation number. The station numbers of the remaining positions are not randomly generated, otherwise, return to step 6;

[0063] Step 9: Artificial fish a Carry out rear-end collision within its field of view vis, such as Figure 4 As shown, artificial fish Search for the artificial fish with the highest fitness function value within the field of view vis if but Cloning artificial fish The first bit of the workstation number, The station numbers of the remaining positions are randomly generated without repeating, otherwise, go to step 6;

[0064] Step 10: each artificial fish in the iter generation population is processed according to the process of step 5 to step 9, the fitness function value of each artificial fish in the iter generation population is calculated, and the artificial fish with the best fitness function value is assigned to the bulletin board;

[0065] Step 11. After iter+1 is assigned to iter, if iter≤iter_max holds, repeat steps 5 to 10; otherwise, output the artificial fish with the best fitness function value in the iter_max generation population. And as the optimal distribution sequence of assembly nodes for material requirements, where Indicates the workstation number represented by the i-th node in the optimal material distribution sequence;

[0066] Step 12: Optimal distribution sequence S from assembly nodes of material requirements gbest Select two nodes in order and use the artificial force field method to optimize the local path between the two selected nodes:

[0067] Step 12.1: Obtain the optimal delivery sequence S of assembly nodes through the digital twin system gbest The position coordinates of the i-th node in Where i = 1, ..., n, Representation Node In the x-axis direction, Representation Node Coordinate in the y-axis direction, and initialize i=1;

[0068] Step 12.2: Optimal delivery sequence S gbest The i-th node Location is the current position G of the material transport vehicle, that is The i+1th node Location As the target position T of the material transport vehicle,

[0069] Step 12.3: Establish the gravitational field U between the current position G and the target position T of the material transport vehicle att (G), use formula (3) to calculate the gravitational field U att (G);

[0070]

[0071] In formula (3), η is the positive proportional coefficient, ρ(G, T) represents the geometric distance between the current position G of the material conveyor and the target position T, and the direction is from G to T.

[0072] Step 12.4: Use equation (4) to calculate the gravitational force F of the target position T on the material transport vehicle: att (G), F att (G) is the gravitational field U att (G) negative gradient;

[0073] F att (G) = ηρ(G, T) (4)

[0074] Step 12.5: Use the digital twin system to count the number of obstacles O between the current position G of the material conveyor and the target position T online, and establish a repulsive field U between the current position G of the material conveyor and the obstacles. rep (O) and repulsive force F rep (O), F rep (O) is U rep The negative gradient of (O). Establish the tth obstacle O t The repulsive field between the current position G Use formula (5) to calculate

[0075]

[0076] In formula (5), λ is the proportional coefficient, ρ(G,O t ) indicates the distance between the current position G of the material transport vehicle and the obstacle O t The geometric distance between them, from O t Points to G. ρ0 is a constant, representing the obstacle O t The maximum distance at which the material transport vehicle will be affected.

[0077] Step 12.6: Calculate obstacle O using formula (6) t Repulsion to material handling vehicles Repulsive field The negative gradient of

[0078]

[0079] Step 12.7. Calculate the gravitational force F att (G) and repulsion The combined force And calculate the unit components of the resultant force F in the x and y directions Where |F| represents the modulus of the resultant force F, and F x represents the component of the resultant force F in the x-axis direction, F y represents the component of the resultant force F in the y-axis direction, z x represents the unit component of the resultant force F in the x-axis direction, z y represents the unit component of the resultant force F in the y-axis direction;

[0080] Step 12.8: According to the current position G of the material transport vehicle and the moving step length step, use formula (7) to calculate the next position G of the material transport vehicle: next :

[0081] G next =G+step*z (7)

[0082] Step 12.9: G next After assigning the value to G, return to step 12.3 and execute sequentially, so that the material conveyor vehicle can be completed at the starting node under the real-time monitoring of the changes of obstacles between the current position G and the target position T of the material conveyor vehicle by the digital twin system. To target node Path planning and material distribution.

[0083] Step 12.10: After assigning i+1 to i, return to step 12.2 and execute sequentially until i>n-1, thus completing the optimal delivery sequence S for all assembly nodes. gbest Path planning and material delivery, such as Figure 5 shown.

[0084] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned material distribution path optimization method, and the processor is configured to execute the program stored in the memory.

[0085] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned material distribution path optimization method are executed.

[0086] In summary, the present invention uses a digital twin system to provide real-time and accurate production status information and environmental change information in a mixed-flow assembly workshop. According to the dynamic material demand of the assembly nodes and the dynamic changes in the assembly workshop environment, an improved artificial fish swarm algorithm is used to plan the global path of material distribution of the assembly nodes. The artificial potential field method is used to update and optimize the local paths between assembly nodes online. The synergy between workshop material distribution and the dynamic production process, and the adaptability to the dynamically changing workshop environment are considered to optimize the distribution demand supply and distribution planning path of workshop materials, accurately meet the material demand of assembly nodes, ensure timely supply of materials, and improve workshop production efficiency.

Claims

1. A material distribution path optimization method for a mixed-flow assembly workshop driven by a digital twin, the characteristics of which include Follow these steps: Step 1: Use the digital twin system to count the assembly nodes that need material replenishment during the production period in the mixed-flow assembly workshop online, so as to form a node material distribution sequence S = [s1 s2…s i …s n ], and get the i-th assembly node s i and the i+1th assembly node s i+1 Distance d i,i+1 , where s1 represents the station number of the starting node of material distribution, s n Indicates the station number of the material delivery termination node; Step 2: Use formula (1) to establish the optimization objective function f(S) of the node material distribution sequence S: Step 3: Initialize various parameters of the artificial fish swarm algorithm, including: population size k, field of view vis, maximum number of foraging attempts try_max, crowding degree δ, number of mutation optimization bits bit, current iteration number iter, maximum iteration number iter_max, and initialize iter=1; Step 4: Initialize the m artificial fish of the artificial fish population as in, represents the station number of the assembly node at the i-th position in the m-th fish, and n represents the position number in the artificial fish; Let the ath artificial fish in the iter generation population be Let the bth artificial fish in the iter generation population be in, It represents the workstation number of the ath artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration. It represents the workstation number of the bth artificial fish at the i-th node in the material distribution node sequence at the iter-th iteration; Define the optimization objective function f(S) as the fitness function of each fish; Step 5: Use formula (2) to calculate the number of artificial fish in the iter generation population and the bth artificial fish Distance And compare Is it true? If true, it means artificial fish In artificial fish The visual range vis is used to determine whether other fish in the iter generation population are within the artificial fish Within the field of view vis, and count the artificial fish The number of all artificial fish k within the field of view vis iter ; In formula (2), sign(·) represents a binary function; Step 6: Artificial fish a Perform foraging behavior within its visual range vis, and cyclically select other artificial fish within the visual range vis And compare Is it established? If established, then copy The first bit is the workstation number. The station numbers for the remaining positions will not be randomly generated; otherwise, Remain unchanged, if the condition is not met within the field of view vis The artificial fish Randomly generate a new workstation number sequence; Step 7: Search and count artificial fish The visual range vis is the station number of each fish in the ith column of the school, and the one that appears the most and does not belong to The station number before the i-th column is assigned to the center of the fish school. At the i-th column position If the number of occurrences is the same, the first station number found is assigned to the center of the fish school. At the i-th column position Thus, artificial fish The center of the fish school within the field of view vis Step 8: Artificial fish a Gathering behavior within its field of view vis, judging Is it established? If established, then Copy Center Location The first bit is the workstation number. The station numbers of the remaining positions are not randomly generated, otherwise, return to step 6; Step 9: Artificial fish a The artificial fish performs tail-chasing behavior within its field of view vis. Search for the artificial fish with the highest fitness function value within the field of view vis if but Cloning artificial fish The first bit of the workstation number, The station numbers of the remaining positions are randomly generated without repeating, otherwise, go to step 6; Step 10: each artificial fish in the iter generation population is processed according to the process of step 5 to step 9, the fitness function value of each artificial fish in the iter generation population is calculated, and the artificial fish with the best fitness function value is assigned to the bulletin board; Step 11. After iter+1 is assigned to iter, if iter≤iter_max holds, repeat steps 5 to 10; otherwise, output the artificial fish with the best fitness function value in the iter_max generation population. And as the optimal distribution sequence of assembly nodes for material requirements, where Indicates the workstation number represented by the i-th node in the optimal material distribution sequence; Step 12: Optimal distribution sequence S from assembly nodes of material requirements gbest Select two nodes in order and use the artificial force field method to optimize the local path between the two selected nodes: Step 12.1: Obtain the optimal delivery sequence S of assembly nodes through the digital twin system gbest The i-th node Location coordinates in, Representation Node In the x-axis direction, Representation Node Coordinate in the y-axis direction, and initialize i=1; Step 12.2: Optimal delivery sequence S gbest The i-th node Location is the current position G of the material transport vehicle, Right now The i+1th node Location As the target position T of the material transport vehicle, Step 12.3: Calculate the gravitational force F between the current position G and the target position T of the material transport vehicle using formula (3): att : F att (G)=ηρ(G,T) (3) In formula (3), η is a positive proportional coefficient, ρ(G, T) represents the geometric distance between the current position G of the material transport vehicle and the target position T, and the direction is from G to T; Step 12.4: Use the digital twin system to count the number of obstacles m between the current position G and the target position T of the material transport vehicle online, and use formula (4) to calculate the repulsive force between the obstacle at the target position T and the current position G. The number of obstacles O for the tth obstacle is: t The repulsive force between the current position G is In formula (4), λ is the proportional coefficient, ρ(G,O t ) represents the distance between the material transport vehicle and the obstacle, and the direction is from O t Pointing to G; ρ0 represents the maximum distance at which obstacles affect the material transport vehicle; represents the gradient; Step 12.

5. Calculate the gravitational force F att (G) and repulsion The combined force And calculate the unit components of the resultant force F in the x and y directions Where |F| represents the modulus of the resultant force F, and F x represents the component of the resultant force F in the x-axis direction, F y represents the component of the resultant force F in the y-axis direction, z x represents the unit component of the resultant force F in the x-axis direction, z y represents the unit component of the resultant force F in the y-axis direction; Step 12.6: According to the current position G of the material transport vehicle and the moving step length step, use formula (5) to calculate the next position G of the material transport vehicle: next : G next =G+step*z (5) Step 12.7, G next After assigning the value to G, return to step 12.3 and execute sequentially, so that the material conveyor vehicle can be completed at the starting node under the real-time monitoring of the changes of obstacles between the current position G and the target position T of the material conveyor vehicle by the digital twin system. To target node Path planning and material distribution between Step 12.8, after assigning i+1 to i, return to step 12.2 and execute sequentially until i>n-1, thus completing all nodes Path planning and material delivery.

2. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the material distribution path optimization method according to claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the material distribution path optimization method according to claim 1 are executed.

Images