Intelligent robot path planning optimization method

Abstract

The invention discloses an intelligent robot path planning optimization method, which relates to the technical field of path planning, and comprises the following steps: analyzing a process flow of a processing plant, and determining a target flow; obtaining the volume of a target object and determining a volume grade; setting a training period and an acquisition time point, counting the number of target articles, and calculating a flow feature score and grade; obtaining the spatial position of the target process, drawing a planar graph, dividing working areas, and determining the operation level of each area; generating an equipment initialization path according to the job level; if the initialized path encounters an obstacle, measuring a gap, and judging whether the path needs to be adjusted; calculating a flow production score, and continuing if the flow production score reaches a threshold value; establishing a feature path, evaluating a production score, and setting a standard path; the environment complexity and the comprehensive score of the standard path are calculated according to the historical record, the path with the highest score is selected and transmitted to the equipment, the path planning is optimized, and the working efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of path planning, and in particular to a path planning optimization method for an intelligent robot. Background Art

[0002] In automated processing plants, with the advancement of automation and intelligence, intelligent robots are gradually introduced into various production links. However, the cleaning of production waste is still a challenge. These wastes not only affect the workshop environment, but also cause equipment wear and failure, and even threaten the safety of workers. Therefore, how to clean up these wastes efficiently has become an urgent problem to be solved;

[0003] At present, most processing plants have introduced intelligent robots for cleaning, so the cleaning path planning of intelligent robots has become particularly important. Traditional path planning methods are usually based on fixed routes or simple obstacle avoidance. However, in the actual production environment, equipment layout, waste distribution and other dynamic factors may change at any time, resulting in traditional path planning methods. It is difficult to achieve the ideal cleaning effect. Therefore, path planning is optimized to reduce repeated routes, shorten cleaning time, thereby saving energy and improving work efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing path planning of an intelligent robot to solve the problems raised in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent robot path planning optimization method, the method comprising:

[0006] Step S100: Obtain the process flow of the automated processing plant, analyze the process flow of producing the target work item, and determine the target process;

[0007] Step S200: obtaining the volume of the target operation object and determining the volume level of the target operation object;

[0008] Step S300: setting a training cycle, setting a number of collection time points, setting the time period corresponding to two adjacent collection time points as a training time period, collecting the number of target operation items generated by the target process in a certain training time period, calculating the feature score of the target process, and determining the feature level of the target process;

[0009] Step S400: obtaining the spatial position of each target process in the automated processing plant, drawing a plan of the automated processing plant, dividing the plan into a plurality of work areas, and determining the operation level of each work area;

[0010] Step S500: generating an initialization path for the operating equipment according to the operating level of the working area;

[0011] Step S600: when the operating equipment executes the initialization path, it is detected that there are obstacles on the initialization path, the gap distance between the obstacles is measured, and the widest size of the operating equipment is obtained. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed;

[0012] Step S700: Obtain the moving speed of the obstacle, calculate the production score of the target process to produce the target work item, and when the production score of the target process is greater than or equal to the production score threshold, execute step S800;

[0013] Step S800: obtaining the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establishing several characteristic paths, and calculating the production score of the target process in the characteristic path. If the production score of each target process in the characteristic path is less than the production score threshold, the characteristic path is set as the standard path;

[0014] Step S900: Obtain the historical operation records of the operating equipment executing a certain standard path, calculate the environmental complexity of the standard path, collect the time spent by the operating equipment to execute a certain standard path, calculate the comprehensive score of the standard path, select the standard path with the highest comprehensive score, transmit it to the operating equipment, and assist the operating equipment in operation.

[0015] Furthermore, step S100 includes:

[0016] Step S101: obtaining the processing equipment used by each process in the automated processing plant, determining the processing tasks performed by a process through the processing equipment used by the process, and collecting the production items generated by the process when performing the processing tasks;

[0017] Step S102: Detecting the production items generated by a certain process flow, and if a target operation item is detected, setting the process flow as a target process;

[0018] By obtaining the processing equipment and its corresponding processing tasks, the specific operations of each process can be effectively determined, thereby better managing the production process;

[0019] By detecting production items, the process flow for generating target work items can be quickly identified and located, and it can be set as the target process to improve process identification efficiency.

[0020] Furthermore, in step S200, the target operation items generated by a certain target process are collected and summarized, the volume of a certain target operation item in the target process is obtained, the volume is divided into several volume levels, the volume range of the target operation items corresponding to a certain volume level is obtained, and the volume level of each target operation item in a certain target process is obtained.

[0021] Furthermore, step S300 includes:

[0022] Step S301: Selecting a number of consecutive days as a training cycle, setting a number of acquisition time points within the training cycle, and setting the time period corresponding to two adjacent acquisition time points as the training time period;

[0023] Step S302: In a certain training time period, the number of a certain target operation item generated by a certain target process is collected, all training time periods are summarized, the frequency of generating a certain target operation item is calculated, and the characteristic score of the target process is calculated according to the following formula:

[0024]

[0025] Among them, A represents the characteristic score of the target process, B a is represented by the volume level of the ath target job item, α is represented by the weight of the volume level, C a It is represented as the frequency of the ath target job item, β is represented as the weight of the frequency, and b is represented as the number of target job items;

[0026] Step S303: Divide the feature scores into several feature levels, obtain the feature score range of a certain feature level, and obtain the feature level of a certain target process;

[0027] By setting the training cycle and collection time point, the performance of the target process in different time periods can be analyzed more accurately to ensure the representativeness and integrity of the data;

[0028] By calculating feature scores through formulas and classifying features, the production performance of each process can be quickly and automatically evaluated and compared, thus supporting lean management of the factory.

[0029] Furthermore, step S400 includes:

[0030] Step S401: obtaining the spatial position of each target process in the automated processing plant, drawing a plan view of the automated processing plant, and marking the characteristic level of each target process in the plan view;

[0031] Step S402: Divide the floor plan of the processing plant into several work areas, extract the target process existing in a certain work area, obtain the characteristic level of the target process, and calculate the work score of the work area according to the following formula:

[0032]

[0033] Among them, D represents the homework score of the work area, A d It is expressed as the characteristic level of the d-th target process, Ed It is represented as the weight of the dth target process, and e is represented as the number of target processes in the workspace;

[0034] Step S403: Divide the homework score into several homework levels, obtain the homework score range of a certain homework level, and obtain the homework level of a certain work area;

[0035] By drawing a factory floor plan and marking the location and characteristic level of each target process, you can fully understand the factory layout and help optimize process arrangements;

[0036] Calculating job scores for each work area allows for quick identification of efficient and inefficient areas.

[0037] Furthermore, in step S500, the work areas are sorted according to the work levels of the work areas, the paths are optimized by the Dijkstra algorithm, and the initialization path of the work equipment is generated.

[0038] Furthermore, step S600 includes:

[0039] Step S601: installing sensors in the operating equipment to monitor obstacles encountered by the operating equipment when performing the operating task;

[0040] Step S602: When the operating equipment is executing the initialization path, when the sensor detects that there is an obstacle on the initialization path, the gap distance between the obstacles is measured to obtain the widest size of the operating equipment. When the gap distance between the obstacles is greater than the widest size of the operating equipment, the obstacles are avoided and the initialization path is continued. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed;

[0041] By installing sensors to monitor obstacles in the equipment path in real time, it can effectively reduce pauses and waiting caused by obstacles, and ensure the continuity and smoothness of the operating equipment.

[0042] The equipment can automatically determine whether it can pass through the obstacle based on the size of the gap. It has the ability to avoid obstacles autonomously, which reduces the frequency of manual intervention and improves the degree of automation of operations.

[0043] Furthermore, step S700 includes:

[0044] Step S701: Obtain the moving speed of the obstacle, collect the distance from the operating equipment to the target process to be executed, and calculate the time required for the operating equipment to move according to the following formula:

[0045]

[0046] Where T represents the moving time, S represents the distance from the operating equipment to the target process being executed, and V represents the moving speed of the obstacle;

[0047] Step S702: Obtain the frequency of the target process producing a target work item, and calculate the production score of the target process producing the target work item according to the following formula:

[0048]

[0049] Where P represents the production score of the target process, C i It is expressed as the frequency of producing the i-th target operation item, H i It is represented as the weight of the i-th target operation item, and r is represented as the number of target operation items generated by the target process;

[0050] Step S703: Compare the target production score of the target process with the production score threshold. When the production score of the target process is less than the production score threshold, continue to execute the initialization path. When the production score of the target process is greater than or equal to the production score threshold, execute step S800.

[0051] By calculating the moving time of the device and arranging the movement of the device according to the moving speed of the obstacle, time wasted due to waiting or detouring can be avoided, thus improving the efficiency of path planning.

[0052] By measuring the production efficiency of the target process through production scoring, it can be determined whether production needs to be optimized or adjusted to ensure that efficient processes are executed first;

[0053] Comparing the production scores against thresholds helps determine whether to continue executing the current path, reducing the selection of inefficient paths and improving the efficiency of production plan execution.

[0054] Furthermore, step S800 includes:

[0055] Step S801: obtain the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establish several characteristic paths, collect the target processes passed in a certain characteristic path, sort the target processes in the order of passing, and obtain the target process set of the Kth characteristic path as K={Q1, Q2, ..., Qu}, where K represents the target process set of the Kth characteristic path, and Q1, Q2, ..., Qu represent the 1st, 2nd, ..., uth target processes passed respectively;

[0056] Step S802: In a certain characteristic path, the time taken for the operating equipment to move from a certain target process to the next target process is obtained, and the production score of the target process is calculated according to the following formula:

[0057]

[0058] Where P represents the production score of the target process, C q It is expressed as the frequency of producing the qth target job item, Y q is the weight of the qth target operation item, y is the number of target operation items generated by the target process, T g It is represented as the time taken to move to the g-th target process, and w is the number of target processes;

[0059] Step S803: Summarize the production scores of the target processes in a certain characteristic path. If the production score of each target process in a certain characteristic path is less than the production score threshold, the characteristic path is set as a standard path and summarized.

[0060] By acquiring the location information of the operating equipment and the target process location in real time, establishing characteristic paths and sorting the target processes, the path optimization is made more accurate and the ineffective movement of equipment in the factory is reduced;

[0061] Calculate the production score of each target process of the operating equipment on the characteristic path to facilitate the evaluation of the production efficiency of different paths, ensure that the equipment gives priority to the more efficient path, and improve the overall production timeliness;

[0062] According to the production score of the target process in each characteristic path, the inefficient path is excluded or adjusted, and the appropriate standard path is dynamically selected to improve the scheduling flexibility of the operating equipment between different processes.

[0063] Furthermore, step S900 includes:

[0064] Step S901: In the standard path set, obtain the historical operation records of the operation equipment executing a certain standard path, collect the number of times a certain abnormal behavior occurs, and calculate the environmental complexity of the standard path according to the following formula:

[0065]

[0066] Among them, R represents the environmental complexity of the standard path, n x Expressed as the number of the xth abnormal behavior, m x It is represented as the weight of the x-th abnormal behavior, and N is represented as the number of abnormal behavior types;

[0067] Step S902: Collect the time taken by the operation equipment to execute a certain standard path, and calculate the comprehensive score of the standard path according to the following formula:

[0068] M=t f ×R f ×F;

[0069] Where M represents the comprehensive score of the standard path, t f It is expressed as the time taken by the fth standard path, R f It is represented as the environmental complexity of the fth standard path, and F is represented as the weight of the comprehensive score;

[0070] Step S903: Summarize the comprehensive scores of all standard paths from low to high, select the standard path with the highest comprehensive score, and transmit it to the operating equipment to assist the operating equipment in operation;

[0071] By calculating the environmental complexity of the standard path, the impact of various abnormal behaviors can be identified and quantified, so that the most suitable path can be selected more reliably, path risks can be reduced, and the safety of equipment operation can be improved;

[0072] Based on the comprehensive scores of standard paths, the equipment is ensured to prioritize the path with the highest score, thereby avoiding waste of resources and time and achieving more efficient production operations;

[0073] By summarizing the scores of all standard paths, the device can select the best path based on the real-time changing scores, realize flexible dynamic adjustment of the path, and adapt to changes in the production environment;

[0074] The comprehensive scores of all paths are ranked according to their merits, providing managers with visual data support, making the path decision process more scientific and facilitating efficient production management.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] Through detailed analysis of the work area and optimization of the path, this solution can effectively reduce the robot's movement time in the automated processing plant, thereby improving overall production efficiency;

[0077] The system automatically detects obstacles and adjusts the path, reducing the need for human intervention and allowing the robot to operate more independently;

[0078] Through real-time monitoring and path adjustment, the robot can adapt to changes in the factory environment, such as the appearance of obstacles, to ensure the continuity and stability of the production process;

[0079] By analyzing the production scores and environmental complexity of each target process, robot resources can be allocated more effectively, resource waste can be avoided, and work efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The present invention is a flowchart of an intelligent robot path planning optimization method. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] See also Figure 1 The present invention provides a technical solution: an intelligent robot path planning optimization method, the method comprising:

[0083] Step S100: Obtain the process flow of the automated processing plant, analyze the process flow of producing the target work item, and determine the target process;

[0084] Wherein, step S100 includes:

[0085] Step S101: obtaining the processing equipment used by each process in the automated processing plant, determining the processing tasks performed by a process through the processing equipment used by the process, and collecting the production items generated by the process when performing the processing tasks;

[0086] Step S102: Detect the production items generated by a certain process flow. If the target operation items are detected, the process flow is set as the target process.

[0087] Step S200: obtaining the volume of the target operation object and determining the volume level of the target operation object;

[0088] Among them, in step S200, the target operation items generated by a certain target process are collected and summarized, the volume of a certain target operation item in the target process is obtained, the volume is divided into several volume levels, the volume range of the target operation items corresponding to a certain volume level is obtained, and the volume level of each target operation item in a certain target process is obtained.

[0089] Step S300: setting a training cycle, setting a number of collection time points, setting the time period corresponding to two adjacent collection time points as a training time period, collecting the number of target operation items generated by the target process in a certain training time period, calculating the feature score of the target process, and determining the feature level of the target process;

[0090] Wherein, step S300 includes:

[0091] Step S301: Selecting a number of consecutive days as a training cycle, setting a number of acquisition time points within the training cycle, and setting the time period corresponding to two adjacent acquisition time points as the training time period;

[0092] Step S302: In a certain training time period, the number of a certain target operation item generated by a certain target process is collected, all training time periods are summarized, the frequency of generating a certain target operation item is calculated, and the characteristic score of the target process is calculated according to the following formula:

[0093]

[0094] Among them, A represents the characteristic score of the target process, B a is represented by the volume level of the ath target job item, α is represented by the weight of the volume level, C a It is represented as the frequency of the ath target job item, β is represented as the weight of the frequency, and b is represented as the number of target job items;

[0095] Step S303: Divide the feature scores into several feature levels, obtain the feature score range of a certain feature level, and obtain the feature level of a certain target process;

[0096] For example, in the first target process, three target operation items A, B and C are generated. The volume level of target operation item A is level 2 and the frequency is 50; the volume level of target operation item B is level 3 and the frequency is 30; the volume level of target operation item C is level 1 and the frequency is 20.

[0097] The calculated feature score of target work item A is 21.2, the feature score of target work item B is 16.5, and the feature score of target work item C is 12.4;

[0098] Then the characteristic level of the target operation item A is 3; the characteristic level of the target operation item B is 2; and the characteristic level of the target operation item C is 2.

[0099] Step S400: obtaining the spatial position of each target process in the automated processing plant, drawing a plan of the automated processing plant, dividing the plan into a plurality of work areas, and determining the operation level of each work area;

[0100] Wherein, step S400 includes:

[0101] Step S401: obtaining the spatial position of each target process in the automated processing plant, drawing a plan view of the automated processing plant, and marking the characteristic level of each target process in the plan view;

[0102] Step S402: Divide the floor plan of the processing plant into several work areas, extract the target process existing in a certain work area, obtain the characteristic level of the target process, and calculate the work score of the work area according to the following formula:

[0103]

[0104] Among them, D represents the homework score of the work area, A d It is expressed as the characteristic level of the d-th target process, E d It is represented as the weight of the dth target process, and e is represented as the number of target processes in the workspace;

[0105] Step S403: Divide the homework score into several homework levels, obtain the homework score range of a certain homework level, and obtain the homework level of a certain work area.

[0106] Step S500: generating an initialization path for the operating equipment according to the operating level of the working area;

[0107] Wherein, in step S500, according to the operation level of the work area, the work area is sorted according to the operation level, the path is optimized by the Dijkstra algorithm, and the initialization path of the operation equipment is generated.

[0108] Step S600: when the operating equipment executes the initialization path, it is detected that there are obstacles on the initialization path, the gap distance between the obstacles is measured, and the widest size of the operating equipment is obtained. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed;

[0109] Wherein, step S600 includes:

[0110] Step S601: installing sensors in the operating equipment to monitor obstacles encountered by the operating equipment when performing the operating task;

[0111] Step S602: When the operating equipment is executing the initialization path, when the sensor detects that there are obstacles on the initialization path, the gap distance between the obstacles is measured to obtain the widest size of the operating equipment. When the gap distance between the obstacles is greater than the widest size of the operating equipment, the obstacles are avoided and the initialization path continues to be executed. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed.

[0112] Step S700: Obtain the moving speed of the obstacle, calculate the production score of the target process to produce the target work item, and when the production score of the target process is greater than or equal to the production score threshold, execute step S800;

[0113] Wherein, step S700 includes:

[0114] Step S701: Obtain the moving speed of the obstacle, collect the distance from the operating equipment to the target process to be executed, and calculate the time required for the operating equipment to move according to the following formula:

[0115]

[0116] Where T represents the moving time, S represents the distance from the operating equipment to the target process being executed, and V represents the moving speed of the obstacle;

[0117] Step S702: Obtain the frequency of the target process producing a target work item, and calculate the production score of the target process producing the target work item according to the following formula:

[0118]

[0119] Where P represents the production score of the target process, C i It is expressed as the frequency of producing the i-th target operation item, H i It is represented as the weight of the i-th target operation item, and r is represented as the number of target operation items generated by the target process;

[0120] Step S703: Compare the target production score of the target process with the production score threshold. When the production score of the target process is less than the production score threshold, continue to execute the initialization path. When the production score of the target process is greater than or equal to the production score threshold, execute step S800.

[0121] Step S800: obtaining the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establishing several characteristic paths, and calculating the production score of the target process in the characteristic path. If the production score of each target process in the characteristic path is less than the production score threshold, the characteristic path is set as the standard path;

[0122] Wherein, step S800 includes:

[0123] Step S801: obtain the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establish several characteristic paths, collect the target processes passed in a certain characteristic path, sort the target processes in the order of passing, and obtain the target process set of the Kth characteristic path as K={Q1, Q2, ..., Qu}, where K represents the target process set of the Kth characteristic path, and Q1, Q2, ..., Qu represent the 1st, 2nd, ..., uth target processes passed respectively;

[0124] Step S802: In a certain characteristic path, the time taken for the operating equipment to move from a certain target process to the next target process is obtained, and the production score of the target process is calculated according to the following formula:

[0125]

[0126] Where P represents the production score of the target process, C qIt is expressed as the frequency of producing the qth target job item, Y q is the weight of the qth target operation item, y is the number of target operation items generated by the target process, T g It is represented as the time taken to move to the g-th target process, and w is the number of target processes;

[0127] Step S803: Summarize the production scores of the target processes in a certain characteristic path. If the production score of each target process in a certain characteristic path is less than the production score threshold, the characteristic path is set as a standard path and summarized.

[0128] Step S900: Obtain the historical operation records of the operation equipment executing a certain standard path, calculate the environmental complexity of the standard path, collect the time spent by the operation equipment executing a certain standard path, calculate the comprehensive score of the standard path, select the standard path with the highest comprehensive score, transmit it to the operation equipment, and assist the operation equipment in operation;

[0129] Wherein, step S900 includes:

[0130] Step S901: In the standard path set, obtain the historical operation records of the operation equipment executing a certain standard path, collect the number of times a certain abnormal behavior occurs, and calculate the environmental complexity of the standard path according to the following formula:

[0131]

[0132] Among them, R represents the environmental complexity of the standard path, n x Expressed as the number of the xth abnormal behavior, m x It is represented as the weight of the x-th abnormal behavior, and N is represented as the number of abnormal behavior types;

[0133] Step S902: Collect the time taken by the operation equipment to execute a certain standard path, and calculate the comprehensive score of the standard path according to the following formula:

[0134] M=t f ×R f ×F;

[0135] Where M represents the comprehensive score of the standard path, t f It is expressed as the time taken by the fth standard path, R f It is represented as the environmental complexity of the fth standard path, and F is represented as the weight of the comprehensive score;

[0136] Step S903: The comprehensive scores of all standard paths are summarized from low to high, and the standard path with the highest comprehensive score is selected and transmitted to the operating equipment to assist the operating equipment in operation.

[0137] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for optimizing intelligent robot path planning, characterized in that: Step S100: Obtain the process flow of the automated processing plant, analyze the process flow of producing the target work item, and determine the target process; Step S200: obtaining the volume of the target operation object and determining the volume level of the target operation object; Step S300: setting a training cycle, setting a number of collection time points, setting the time period corresponding to two adjacent collection time points as a training time period, collecting the number of target operation items generated by the target process in a certain training time period, calculating the feature score of the target process, and determining the feature level of the target process; Step S400: obtaining the spatial position of each target process in the automated processing plant, drawing a plan of the automated processing plant, dividing the plan into a plurality of work areas, and determining the operation level of each work area; Step S500: generating an initialization path for the operating equipment according to the operating level of the working area; Step S600: when the operating equipment executes the initialization path, it is detected that there are obstacles on the initialization path, the gap distance between the obstacles is measured, and the widest size of the operating equipment is obtained. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed; Step S700: Obtain the moving speed of the obstacle, calculate the production score of the target process to produce the target work item, and when the production score of the target process is greater than or equal to the production score threshold, execute step S800; Step S800: obtaining the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establishing several characteristic paths, and calculating the production score of the target process in the characteristic path. If the production score of each target process in the characteristic path is less than the production score threshold, the characteristic path is set as the standard path; Step S900: Obtain the historical operation records of the operating equipment executing a certain standard path, calculate the environmental complexity of the standard path, collect the time spent by the operating equipment to execute a certain standard path, calculate the comprehensive score of the standard path, select the standard path with the highest comprehensive score, transmit it to the operating equipment, and assist the operating equipment in operation.

2. The intelligent robot path planning optimization method according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: obtaining the processing equipment used by each process in the automated processing plant, determining the processing tasks performed by a process through the processing equipment used by the process, and collecting the production items generated by the process when performing the processing tasks; Step S102: Detect the production items generated by a certain process flow. If the target operation items are detected, the process flow is set as the target process.

3. The intelligent robot path planning optimization method according to claim 2 is characterized in that: The step S200: The target operation items generated by a certain target process are collected and summarized, the volume of a certain target operation item in the target process is obtained, the volume is divided into several volume levels, the volume range of the target operation items corresponding to a certain volume level is obtained, and the volume level of each target operation item in a certain target process is obtained.

4. The intelligent robot path planning optimization method according to claim 3 is characterized in that: The step S300 includes the following steps: Step S301: Selecting a number of consecutive days as a training cycle, setting a number of acquisition time points within the training cycle, and setting the time period corresponding to two adjacent acquisition time points as the training time period; Step S302: In a certain training time period, the number of a certain target operation item generated by a certain target process is collected, all training time periods are summarized, the frequency of generating a certain target operation item is calculated, and the characteristic score of the target process is calculated according to the following formula: Among them, A represents the characteristic score of the target process, B a is represented by the volume level of the ath target job item, α is represented by the weight of the volume level, C a It is represented as the frequency of the ath target job item, β is represented as the weight of the frequency, and b is represented as the number of target job items; Step S303: Divide the feature scores into several feature levels, obtain the feature score range of a certain feature level, and obtain the feature level of a certain target process.

5. The intelligent robot path planning optimization method according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: obtaining the spatial position of each target process in the automated processing plant, drawing a plan view of the automated processing plant, and marking the characteristic level of each target process in the plan view; Step S402: Divide the floor plan of the processing plant into several work areas, extract the target process existing in a certain work area, obtain the characteristic level of the target process, and calculate the work score of the work area according to the following formula: Among them, D represents the homework score of the work area, A d It is expressed as the characteristic level of the d-th target process, E d It is represented as the weight of the dth target process, and e is represented as the number of target processes in the workspace; Step S403: Divide the homework score into several homework levels, obtain the homework score range of a certain homework level, and obtain the homework level of a certain work area.

6. The intelligent robot path planning optimization method according to claim 5, characterized in that: The step S500; According to the operation level of the work area, the work area is sorted according to the operation level, the path is optimized by the Dijkstra algorithm, and the initialization path of the operation equipment is generated.

7. The intelligent robot path planning optimization method according to claim 6 is characterized in that: The step S600 includes the following steps: Step S601: installing sensors in the operating equipment to monitor obstacles encountered by the operating equipment when performing the operating task; Step S602: When the operating equipment is executing the initialization path, when the sensor detects that there are obstacles on the initialization path, the gap distance between the obstacles is measured to obtain the widest size of the operating equipment. When the gap distance between the obstacles is greater than the widest size of the operating equipment, the obstacles are avoided and the initialization path continues to be executed. When the gap distance between the obstacles is less than or equal to the widest size of the operating equipment, step S700 is executed.

8. The intelligent robot path planning optimization method according to claim 7, characterized in that: The step S700 includes the following steps: Step S701: Obtain the moving speed of the obstacle, collect the distance from the operating equipment to the target process to be executed, and calculate the time required for the operating equipment to move according to the following formula: Where T represents the moving time, S represents the distance from the operating equipment to the target process being executed, and V represents the moving speed of the obstacle; Step S702: Obtain the frequency of the target process producing a target work item, and calculate the production score of the target process producing the target work item according to the following formula: Where P represents the production score of the target process, C i It is expressed as the frequency of producing the i-th target operation item, H i It is represented as the weight of the i-th target operation item, and r is represented as the number of target operation items generated by the target process; Step S703: Compare the target production score of the target process with the production score threshold. When the production score of the target process is less than the production score threshold, continue to execute the initialization path. When the production score of the target process is greater than or equal to the production score threshold, execute step S800.

9. The intelligent robot path planning optimization method according to claim 8, characterized in that: The step S800 includes the following steps: Step S801: obtain the real-time position of the operating equipment and the position of the target process to be executed by the operating equipment, establish several characteristic paths, collect the target processes passed in a certain characteristic path, sort the target processes in the order of passing, and obtain the target process set of the Kth characteristic path as K={Q1, Q2, ..., Qu}, where K represents the target process set of the Kth characteristic path, and Q1, Q2, ..., Qu represent the 1st, 2nd, ..., uth target processes passed respectively; Step S802: In a certain characteristic path, the time taken for the operating equipment to move from a certain target process to the next target process is obtained, and the production score of the target process is calculated according to the following formula: Where P represents the production score of the target process, C q It is expressed as the frequency of producing the qth target job item, Y q is the weight of the qth target operation item, y is the number of target operation items generated by the target process, T g It is represented as the time taken to move to the g-th target process, and w is the number of target processes; Step S803: Summarize the production scores of the target processes in a certain characteristic path. If the production score of each target process in a certain characteristic path is less than the production score threshold, the characteristic path is set as a standard path and summarized.

10. The intelligent robot path planning optimization method according to claim 9, characterized in that: The step S900 includes the following steps: Step S901: In the standard path set, obtain the historical operation records of the operation equipment executing a certain standard path, collect the number of times a certain abnormal behavior occurs, and calculate the environmental complexity of the standard path according to the following formula: Among them, R represents the environmental complexity of the standard path, n x Expressed as the number of the xth abnormal behavior, m x It is represented as the weight of the x-th abnormal behavior, and N is represented as the number of abnormal behavior types; Step S902: Collect the time taken by the operation equipment to execute a certain standard path, and calculate the comprehensive score of the standard path according to the following formula: M=t f ×R f ×F; Where M represents the comprehensive score of the standard path, t f It is expressed as the time taken by the fth standard path, R f It is represented as the environmental complexity of the fth standard path, and F is represented as the weight of the comprehensive score; Step S903: The comprehensive scores of all standard paths are summarized from low to high, and the standard path with the highest comprehensive score is selected and transmitted to the operating equipment to assist the operating equipment in operation.

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