CN115933684A - AGV-based intelligent dynamic dispatching logistics system for digital twin workshop - Google Patents
AGV-based intelligent dynamic dispatching logistics system for digital twin workshop Download PDFInfo
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Abstract
The invention discloses an AGV-based digital twin workshop intelligent dynamic dispatching logistics system, and particularly relates to the technical field of workshop intelligent dispatching, wherein the system comprises a data acquisition module, a digital twin platform building module, a task management module, an AGV dispatching module and an AGV handling robot, wherein the data acquisition module is used for acquiring workshop data and transmitting the acquired data to a virtual model building module and the AGV dispatching module; the AGV dispatching module analyzes the obtained task information, generates an optimal path and sends the optimal path to the AGV transfer robot for execution, monitors the execution condition of the AGV transfer robot and provides support for the AGV transfer robot; the AGV transfer robot is responsible for carrying out the task, uploads the video monitoring of task execution simultaneously to will meet the problem and transmit to the scheduling module through the communication unit, including health assessment unit, motion unit, information feedback unit.
Description
Technical Field
The invention relates to the technical field of in-vitro diagnosis, in particular to an AGV-based intelligent dynamic dispatching logistics system for a digital twin workshop.
Background
AGVs are acronyms of automated guided vehicles, that is, "automated guided vehicles" AGVs are vehicles equipped with electromagnetic or optical automatic guidance devices, which can travel along a predetermined guidance path, and which have safety protection and various transfer functions.
The workshop is a main place for production and manufacturing, comprises storage equipment and conveying tools, the workshop condition is mastered in real time, and a digital twin workshop is required to be built for prediction, wherein the digital twin workshop is a virtual model built by mastering workshop information.
The logistics system is an organic whole with specific functions, which is formed by materials to be conveyed and a plurality of dynamic elements which are mutually restricted and comprise related equipment, conveying tools, storage equipment, personnel, communication connection and the like in a certain time and space. The successful elements of a logistics system are to optimize and rationalize the logistics system as a whole and to comply with or improve the environment of a large social system.
When logistics is rapidly developed, more and more practitioners are engaged, but most of the personnel are engaged in simple and repeated physical labor, the intelligent AGV is combined with the logistics, the transportation and classification in the logistics system are completed, the intelligent logistics scheduling system has wide prospect and practical significance, and the existing intelligent logistics scheduling system has the problems of low efficiency, low automation degree.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides an AGV-based digital twin workshop intelligent dynamic dispatching logistics system, and solves the problems of low efficiency and low automation of the conventional dispatching logistics system in the background art by perfecting task management, efficiency dispatching and AGV transfer robot management.
In order to achieve the purpose, the invention provides the following technical scheme: the digital twin workshop intelligent dynamic dispatching logistics system based on the AGV comprises a data acquisition module, a digital twin platform building module, a task management module, an AGV dispatching module and an AGV transfer robot, wherein the data acquisition module is used for acquiring data of a workshop and transmitting the acquired data to a virtual model building module and the AGV dispatching module; the digital twin platform building module is used for building a digital twin workshop and comprises a three-dimensional model building unit, a digital twin building unit and a simulation prediction unit, wherein the simulation prediction unit obtains predicted task information according to real-time production data and sales data and transmits the predicted task information to the task management module; the task management module comprises a user unit, a task generation unit and a task grading unit, and is used for generating task information and transmitting the task information to the AGV scheduling module; the AGV dispatching module analyzes the obtained task information to carry out efficiency dispatching and generate an optimal path to be sent to the AGV carrying robot to be executed, monitors the execution condition of the AGV carrying robot and provides support for the AGV carrying robot, and the AGV dispatching module comprises an efficiency dispatching unit, a path planning unit, a monitoring display unit, a data interaction unit and a storage unit; the AGV transfer robot is responsible for carrying out the task, uploads the video monitoring of task execution simultaneously, transmits the performance to the scheduling module through the communication unit, including health assessment unit, motion unit, information feedback unit.
In a preferred embodiment, the simulation prediction unit is used for predicting a task to be processed, a virtual workshop is established in the digital twin building unit through a physical workshop, the virtual workshop is driven by real-time data, visual monitoring on equipment operation filling, product processing states, logistics states and sales data in a virtual space is achieved, a prediction task sample is obtained by collecting real-time data and historical rules of the workshop, the prediction task sample comprises n task subsets, simulation prediction is carried out by inputting the prediction task sample into the virtual workshop to achieve online prediction of the workshop, the prediction task sample is adjusted according to the simulation prediction condition until the obtained simulation prediction meets the requirement of a preset value, an ideal execution time of each task subset in the prediction task sample is obtained, and finally the adjusted prediction task sample and the ideal execution time are transmitted to the task generating unit through a communication mechanism.
In a preferred embodiment, the task management module includes subscriber unit, task generation unit and task classification unit, the subscriber unit is used for managing user information, and authorized user uploads the task, confirms the task priority, task management unit receives the task that subscriber unit uploaded and the information generation task that emulation prediction unit forecasted, task classification unit is used for confirming the task priority, transmits task and priority information to AGV scheduling module, task generation unit receives the task that emulation prediction unit transmitted and the task that the user directly assigned, task classification unit package user directly confirms task priority and automatic determination task priority two kinds of mode.
In a preferred embodiment, the automatic task priority mode determination uses a task priority ranking algorithm to determine task priority, and the factors most relevant to determining task priority are: the method comprises the steps that the value V of a task, the urgency T of the task, the value V of the task and the urgency T of the task have the characteristics of changing along with time, the task in execution has instant value and instant urgency, the influence factors of the urgency T of the task include the ending time of the task and the working efficiency E of the AGV carrying robot, and the task is divided into the task to be processed, the task in normal execution, the interrupted task and the completed task according to different states.
In a preferred embodiment, the task prioritization algorithm obtains the task priority order by:
step S01, calculating the value V of the current task: the value V of the task refers to the benefit brought by the task, the economic benefit which can be realized by the life cycle of the workshop production product is taken as the total value, and the value of the task which needs to be completed to realize the life cycle of the product is accumulated to obtain the total value of the life cycle of the workshop product;
step S02, calculating the urgency T of the current task: obtaining the ideal execution time t1 for completing the task, obtaining the residual time of the task according to the difference value between the current time t2 and the task deadline t3, if the residual time is not more than the time required by the task, the urgency value of the task is high, inputting the task into a digital virtual workshop, and obtaining the time required by completing the task through a simulation prediction unit;
step S03, calculating the instant value of the task: task IVi represents the immediate worth of the ith task in the task set, IV i ＝k×t P K represents the ratio of the expected value of the task to the theoretical execution time of the task, and p represents the speed change generated by the instant value of the taskWhen p =1 indicates that the instant value of the task is generated at a constant speed, when p =1 indicates that the instant value of the task is generated at an accelerated speed, and p < 11 indicates that the instant value of the task is generated at a decelerated speed;
step S04, calculating the instant urgency of the task: by the formula
Calculating the instant urgency of the task, wherein Ti represents the instant urgency of the ith task in the task set, and t represents the time when the task has been executed;
step S05, calculating task priority: by calculating IVi Q1 ×Ti Q2 The product of (2) obtains the value of task priority, the task with the largest value is the first priority task, wherein Q1 represents the weight coefficient of task value, and Q2 represents the weight coefficient of task urgency.
In a preferred embodiment, the efficiency scheduling unit determines whether the current AGV working efficiency can meet the first priority task requirement, if yes, directly executes the AGV, and if not, screens out interruptible tasks from normally executed tasks, so as to improve the current efficiency, where the interruptible tasks are tasks with a small calculated task priority value.
In a preferred embodiment, the path planning unit in the AGV scheduling module generates an optimal path corresponding to the task by using a path planning algorithm, and transmits the task and the optimal path to the AGV transfer robot, where the path planning algorithm is any one of an ant colony algorithm, a particle swarm algorithm, and a genetic algorithm.
In a preferred embodiment, the AGV transfer robot has a navigation positioning function, a motion function, a communication interface, and a power supply, and the AGV transfer robot completes the transfer function according to the instruction, transmits the article to the designated position, and feeds back the execution condition, and uses the non-inductive identification technology — the radio frequency identification technology to realize the positioning module of the AGV robot, and the communication function is realized by the Zigbee technology.
In a preferred embodiment, the AGV transfer robot is configured to execute instructions and feed back execution information, and includes the following steps:
step S11, executing tasks: the AGV carrying robot moves according to the tasks and paths sent by the AGV dispatching module and carries the article A to the position B;
step S12, data feedback: an AGV transfer robot is adopted to recognize obstacles in a path by adopting machine vision, and collected pictures are transmitted to a monitoring display unit in an AGV dispatching module;
step S13, obstacle evaluation: the method comprises the steps that an AGV dispatching module calls a historical database in a storage unit to evaluate a barrier processing mode, the evaluation mode is to compare the historical database, whether the barrier processing mode can be carried out or not is judged according to the fact that whether the historical database has the same problem and the same processing mode or not, and if the barrier processing mode can be carried out, a solution is transmitted to an AGV carrying robot, and the problem is solved;
and S14, if the solution can not be processed, uploading the solution to a user unit in the task management module, and providing the solution by a user.
In a preferred embodiment, the health evaluation unit is configured to evaluate the health status of the AGV transfer robot, where the evaluation content includes a battery health degree, a positioning accuracy, and an average moving speed value, and obtains a health score of the AGV transfer robot through weighting calculation, and when the score is lower than a preset value, an alarm is sent to the AGV scheduling module to remind maintenance, the working efficiency E of the AGV transfer robot is affected by the health score, and the working efficiency E of the AGV transfer robot satisfies the following formula E = E × f, where E represents the initial working efficiency of the AGV transfer robot, and f represents the health score of the AGV transfer robot, and the higher the health score is, the higher the working efficiency is.
Advantageous effects
The method specifically comprises the steps that data are collected through a data collection module, a physical workshop is mapped in a digital twin platform to obtain a virtual workshop, tasks to be executed in the workshop are predicted through a prediction unit, the prediction unit can achieve automatic operation of the workshop, management of the tasks is achieved through a task management module, task grading units arrange the tasks according to the urgency and the value of the tasks in sequence, production efficiency of the workshop is improved beneficially, execution of the first priority tasks is guaranteed through an efficiency scheduling unit in an AGV scheduling module, monitoring of the AGV carrying robot is achieved through a monitoring display unit and a data interaction unit, management of the carrying robot is achieved through evaluation of the health state of the carrying robot in the AGV carrying robot module, and accurate working efficiency of the carrying robot can be obtained.
Drawings
FIG. 1 is a block diagram of the system architecture of the present invention.
FIG. 2 is a flow chart of a task prioritization algorithm of the present invention.
FIG. 3 is a flowchart illustrating the AGV handling robot performing tasks according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As used in this application, the terms "module," "system," and the like are intended to include a computer-related entity, such as but not limited to hardware, firmware, a combination of hardware and software, or software in execution. For example, a module may be, but is not limited to: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. For example, an application running on a computing device and the computing device may both be a module. One or more modules may reside within a process and/or thread of execution and a module may be localized on one computer and/or distributed between two or more computers.
The workshop of the invention is used for producing by utilizing resources to obtain finished products, and comprises a plurality of AGV transfer robots, wherein after receiving tasks, the AGV transfer robots transport production data to a production vehicle or transport finished products to a finished product storage workshop, and after receiving sales orders, the AGV transfer robots transport the products to a to-be-loaded area.
Example 1
The embodiment provides an AGV-based intelligent dynamic dispatching logistics system for a digital twin workshop, which comprises a data acquisition module, a digital twin platform building module, a task management module, an AGV dispatching module and an AGV transfer robot, wherein the data acquisition module is used for acquiring spatial information data, real-time production data and real-time sales data of the workshop and transmitting the acquired data to a virtual model building module and the AGV dispatching module, and the data acquisition module comprises a spatial information acquisition unit, a production data acquisition unit and a sales data acquisition unit; the digital twin platform building module is used for building a digital twin workshop and comprises a three-dimensional model building unit, a digital twin building unit and a simulation prediction unit, wherein the simulation prediction unit obtains predicted task information according to real-time production data and sales data and transmits the predicted task information to the task management module; the task management module comprises a user unit, a task generation unit and a task grading unit, and is used for generating task information and transmitting the task information to the AGV scheduling module; the system comprises an AGV dispatching module, a task management unit, a communication mechanism and a communication module, wherein the AGV dispatching module analyzes obtained task information to carry out efficiency dispatching and generate an optimal path, and sends the optimal path to the AGV carrying robot to be executed, monitors the execution condition of the AGV carrying robot and provides support for the AGV carrying robot; the AGV transfer robot is responsible for carrying out the task, uploads the video monitoring that the task was carried out simultaneously, transmits the execution situation to the scheduling module through the communication unit, including health assessment unit, motion unit, information feedback unit.
Furthermore, the simulation prediction unit is used for predicting the tasks to be processed, a virtual workshop is established in the digital twin building unit through a physical workshop, the virtual workshop is driven by real-time data, visual monitoring of equipment operation filling, product processing states, logistics states and sales data in a virtual space is achieved, a prediction task sample is obtained by collecting real-time data and historical rules of the workshop, the prediction task sample comprises n task subsets, simulation prediction is carried out by inputting the prediction task sample into the virtual workshop, online prediction of the workshop is achieved, the prediction task sample is adjusted according to simulation prediction conditions until the obtained simulation prediction preset value meets requirements, meanwhile, the ideal execution time of each task subset in the prediction task sample is obtained, and finally the adjusted prediction task sample and the ideal execution time are transmitted to the task generation unit through a communication mechanism.
Further, the task management module comprises a user unit, a task generation unit and a task classification unit, wherein the user unit is used for managing user information, authorized users upload tasks and determine task priorities, the task management unit receives the tasks uploaded by the user unit and information predicted by the simulation prediction unit and generates the tasks, the task classification unit is used for determining the task priorities and transmitting the tasks and priority information to the AGV scheduling module, the task generation unit receives the tasks transmitted by the simulation prediction unit and the tasks directly issued by the users, and the task classification unit comprises two modes of directly determining the task priorities and automatically determining the task priorities.
Further, the automatic task priority mode determination is performed by using a task priority sorting algorithm to determine task priorities, and the factors most relevant to determining task priorities include: the method comprises the steps that the value V of a task, the urgency T of the task, the value V of the task and the urgency T of the task have the characteristics of changing along with time, the task in execution has instant value and instant urgency, the influence factors of the urgency T of the task include the ending time of the task and the working efficiency E of the AGV carrying robot, and the task is divided into the task to be processed, the task in normal execution, the interrupted task and the completed task according to different states.
Further, as shown in fig. 2, the task prioritization algorithm obtains the task priority order through the following steps:
step S01, calculating the value V of the current task: the value V of the task refers to the benefit brought by the task, the economic benefit which can be realized by the life cycle of the workshop production product is taken as the total value, and the value of the task which needs to be completed to realize the life cycle of the product is accumulated to obtain the total value of the life cycle of the workshop product;
step S02, calculating the urgency T of the current task: obtaining the ideal execution time t1 for completing the task, obtaining the residual time of the task according to the difference value between the current time t2 and the task deadline t3, if the residual time is not more than the time required by the task, the urgency value of the task is high, inputting the task into a digital virtual workshop, and obtaining the time required by completing the task through a simulation prediction unit;
step S03, calculating the instant value of the task: task IVi represents the immediate worth of the ith task in the task set, IV i ＝k×t P K represents the ratio of the expected value of the task to the theoretical execution time of the task, p represents the speed change generated by the instant value of the task, when p =1 represents that the instant value of the task is generated at a constant speed, when p =1 represents that the instant value of the task is generated in an acceleration mode, and p < 11 represents that the instant value of the task is generated in a deceleration mode;
step S04, calculating the instant urgency of the task: by the formula
Calculating the instant urgency of the task, wherein Ti represents the instant urgency of the ith task in the task set, and t represents the time when the task has been executed;
step S05, calculating task priority: by calculating IVi Q1 ×Ti Q2 The product of (2) obtains the value of task priority, the task with the largest value is the first priority task, wherein Q1 represents the weight coefficient of task value, and Q2 represents the weight coefficient of task urgency.
Further, the efficiency scheduling unit judges whether the current AGV working efficiency can meet the requirement of the first priority task, if so, the AGV working efficiency is directly executed, if not, the interruptible task is screened from the normally executed tasks, and the current efficiency is improved, wherein the interruptible task refers to a task with a small calculated task priority value.
Further, a path planning unit in the AGV scheduling module generates an optimal path corresponding to the task by using a path planning algorithm, and transmits the task and the optimal path to the AGV transfer robot, where the path planning algorithm is any one of an ant colony algorithm, a particle swarm algorithm, and a genetic algorithm.
Furthermore, the AGV transfer robot has a navigation positioning function, a movement function, a communication interface and a power supply, the AGV transfer robot completes the transfer function according to instructions, transmits articles to the designated position and feeds back the execution condition, the positioning module of the AGV robot is realized by adopting an inductionless identification technology-a radio frequency identification technology, and the communication function is realized by a Zigbee technology.
Further, as shown in fig. 3, the AGV transfer robot is configured to execute instructions and feed back execution information, and includes the following steps:
step S11, executing tasks: the AGV carrying robot moves according to the tasks and paths sent by the AGV dispatching module and carries the article A to the position B;
step S12, data feedback: an AGV transfer robot is adopted to recognize obstacles in a path by adopting machine vision, and collected pictures are transmitted to a monitoring display unit in an AGV dispatching module;
step S13, obstacle evaluation: the method comprises the steps that an AGV dispatching module calls a historical database in a storage unit to evaluate a barrier processing mode, the evaluation mode is to compare the historical database, whether the barrier processing mode can be carried out or not is judged according to the fact that whether the historical database has the same problem and the same processing mode or not, and if the barrier processing mode can be carried out, a solution is transmitted to an AGV carrying robot, and the problem is solved;
and S14, if the solution can not be processed, uploading the solution to a user unit in the task management module, and providing the solution by a user.
Further, the health evaluation unit is used for evaluating the health state of the AGV transfer robot, evaluation contents comprise battery health degree, positioning accuracy and average movement speed numerical values, health scores of the AGV transfer robot are obtained through weighting calculation, when the scores are lower than a preset value, an alarm is sent to the AGV scheduling module to remind maintenance, the working efficiency E of the AGV transfer robot is influenced by the health scores, the working efficiency E of the AGV transfer robot meets the following formula E = E × f, wherein E represents the initial working efficiency of the AGV transfer robot, f represents the health scores of the AGV transfer robot, the health scores are higher, and the working efficiency is higher.
In summary, the following steps: the invention adopts a data acquisition module to acquire data to map a physical workshop into a digital twin platform to obtain a virtual workshop, a prediction unit is used for predicting tasks to be executed in the workshop, the prediction unit can realize automatic operation of the workshop, and a task management module is used for managing the tasks, wherein a task grading unit arranges the tasks according to the urgency and the value of the tasks in sequence, thereby being beneficial to improving the production efficiency of the workshop.
In addition, the embodiment of the present invention merely provides an implementation manner, and does not specifically limit the protection scope of the present invention.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. AGV-based intelligent dynamic dispatching logistics system for digital twin workshop, which is characterized in that: the system comprises a data acquisition module, a digital twin platform building module, a task management module, an AGV dispatching module and an AGV transfer robot, wherein the data acquisition module is used for acquiring data of a workshop and transmitting the acquired data to the virtual model building module and the AGV dispatching module; the digital twin platform building module is used for building a digital twin workshop and comprises a three-dimensional model building unit, a digital twin building unit and a simulation prediction unit, wherein the simulation prediction unit obtains predicted task information according to real-time production data and sales data and transmits the predicted task information to the task management module; the task management module comprises a user unit, a task generation unit and a task grading unit, and is used for generating task information and transmitting the task information to the AGV scheduling module; the AGV dispatching module analyzes the obtained task information to carry out efficiency dispatching and generate an optimal path to be sent to the AGV carrying robot to be executed, monitors the execution condition of the AGV carrying robot and provides support for the AGV carrying robot, and the AGV dispatching module comprises an efficiency dispatching unit, a path planning unit, a monitoring display unit, a data interaction unit and a storage unit; the AGV transfer robot is responsible for carrying out the task, uploads the video monitoring that the task was carried out simultaneously, transmits the execution situation to the scheduling module through the communication unit, including health assessment unit, motion unit, information feedback unit.
2. The AGV-based digital twin plant intelligent dynamic dispatch logistics system of claim 1 wherein: the simulation prediction unit is used for predicting tasks to be processed, a virtual workshop is built in the digital twin building unit through a physical workshop, the virtual workshop is driven by real-time data, visual monitoring of equipment operation filling, product processing states, logistics states and sales data in a virtual space is achieved, a prediction task sample is obtained by collecting real-time data and historical rules of the workshop, the prediction task sample comprises n task subsets, simulation prediction is conducted by inputting the prediction task sample into the virtual workshop, online prediction of the workshop is achieved, the prediction task sample is adjusted according to simulation prediction conditions until the obtained simulation prediction meets requirements, meanwhile, the ideal execution time of each task subset in the prediction task sample is obtained, and finally the adjusted prediction task sample and the ideal execution time are transmitted to the task generation unit through a communication mechanism.
3. The AGV-based digital twin plant intelligent dynamic dispatch logistics system of claim 1 wherein: the task management module comprises a user unit, a task generation unit and a task classification unit, wherein the user unit is used for managing user information, authorized users upload tasks and determine task priorities, the task management unit receives the tasks uploaded by the user unit and information predicted by the simulation prediction unit and generates the tasks, the task classification unit is used for determining the task priorities and transmitting the tasks and priority information to an AGV (automatic guided vehicle) scheduling module, the task generation unit receives the tasks transmitted by the simulation prediction unit and the tasks directly issued by the users, and the task classification unit comprises two modes of directly determining the task priorities and automatically determining the task priorities.
4. The AGV based digital twin plant intelligent dynamic dispatch logistics system of claim 3 wherein: the task priority is determined by automatically determining the task priority by adopting a task priority ordering algorithm, and the factors most relevant to determining the task priority are as follows: the method comprises the steps that the value V of a task, the urgency T of the task, the value V of the task and the urgency T of the task have the characteristic of changing along with time, the task in execution has instant value and instant urgency, the influence factors of the urgency T of the task include the ending time of the task and the working efficiency E of the AGV transfer robot, and the task is divided into the task to be processed, the task in normal execution, the interrupted task and the completed task according to different states.
5. The AGV based digital twin plant intelligent dynamic dispatch logistics system of claim 4 wherein: the task priority ordering algorithm obtains the task priority order through the following steps:
step S01, calculating the value V of the current task: the value V of the task refers to the benefit brought by the task, the economic benefit which can be realized by the life cycle of the workshop production product is taken as the total value, and the value of the task which needs to be completed to realize the life cycle of the product is accumulated to obtain the total value of the life cycle of the workshop product;
step S02, calculating the urgency T of the current task: obtaining the ideal execution time t1 for completing the task, obtaining the residual time of the task according to the difference value between the current time t2 and the task deadline t3, if the residual time is not more than the time required by the task, the urgency value of the task is high, inputting the task into a digital virtual workshop, and obtaining the time required by completing the task through a simulation prediction unit;
step S03, calculating the instant value of the task: the task IVi represents the instant value of the ith task in the task set, and the calculation formula is IV i ＝k×t P K represents the ratio of the expected value of the task to the theoretical execution time of the task, p represents the speed change generated by the instant value of the task, when p =1 represents that the instant value of the task is generated at a constant speed, when p =1 represents that the instant value of the task is generated in an acceleration mode, and p < 11 represents that the instant value of the task is generated in a deceleration mode;
step S04, calculating the instant urgency of the task: by the formula
Calculating the instant urgency of the task, wherein Ti represents the instant urgency of the ith task in the task set, and t represents the time when the task has been executed;
step S05, calculating task priority: by calculating IVi Q1 ×Ti Q2 The product of (2) obtains the value of task priority, the task with the largest value is the first priority task, wherein Q1 represents the weight coefficient of task value, and Q2 represents the weight coefficient of task urgency.
6. The AGV based digital twin plant intelligent dynamic dispatch logistics system of claim 5 wherein: the efficiency scheduling unit judges whether the current AGV working efficiency can meet the requirement of the first priority task, if so, the current AGV working efficiency is directly executed, if not, the interruptible task is screened out from the tasks in normal execution, and the current efficiency is improved, wherein the interruptible task refers to the task with a small calculation task priority value.
7. The AGV-based digital twin plant intelligent dynamic dispatch logistics system of claim 1 wherein: and a path planning unit in the AGV dispatching module generates an optimal path corresponding to the task by using a path planning algorithm, and transmits the task and the optimal path to the AGV transfer robot, wherein the path planning algorithm is any one of an ant colony algorithm, a particle swarm algorithm and a genetic algorithm.
8. The AGV-based digital twin plant intelligent dynamic dispatch logistics system of claim 1 wherein: the AGV transfer robot has a navigation positioning function, a movement function, a communication interface and a power supply, the AGV transfer robot completes the transfer function according to an instruction, articles are transmitted to an appointed position, the execution condition is fed back, a positioning module of the AGV robot is realized by adopting an noninductive identification technology-a radio frequency identification technology, and the communication function is realized by a Zigbee technology.
9. The AGV based digital twin plant intelligent dynamic scheduling logistics system of claim 1 wherein: the AGV transfer robot is used for executing instructions and feeding back execution information, and comprises the following steps:
step S11, executing tasks: the AGV carrying robot moves according to the tasks and paths sent by the AGV dispatching module and carries the article A to the position B;
step S12, data feedback: an AGV transfer robot is adopted to recognize obstacles in a path by adopting machine vision, and collected pictures are transmitted to a monitoring display unit in an AGV dispatching module;
step S13, obstacle evaluation: the AGV dispatching module calls a historical database in the storage unit to evaluate a barrier processing mode, wherein the evaluation mode is to compare the historical database, judge whether the processing can be carried out according to the fact that whether the experience database has the same problem and processing mode, and if the processing can be carried out, transmit the solution to the AGV carrying robot, so that the problem is solved;
and S14, if the solution can not be processed, uploading the solution to a user unit in the task management module, and providing the solution by a user.
10. The AGV-based digital twin plant intelligent dynamic dispatch logistics system of claim 1 wherein: the health evaluation unit is used for evaluating the health state of the AGV transfer robot, evaluation contents comprise battery health degree, positioning accuracy and average movement speed numerical values, health scores of the AGV transfer robot are obtained through weighted calculation, when the scores are lower than preset values, an alarm is sent to the AGV dispatching module to remind maintenance, the working efficiency E of the AGV transfer robot is influenced by the health scores, the working efficiency E of the AGV transfer robot meets the following formula E = E x f, wherein E represents the initial working efficiency of the AGV transfer robot, f represents the health scores of the AGV transfer robot, the health scores are higher, and the working efficiency is higher.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116339389A (en) * | 2023-05-30 | 2023-06-27 | 北自所(北京)科技发展股份有限公司 | Digital twinning-based rotary wing unmanned aerial vehicle inventory making method, device and storage medium |
| CN116661466A (en) * | 2023-07-28 | 2023-08-29 | 深圳市磐锋精密技术有限公司 | Operation control management system for AGV equipment |
| CN117350609A (en) * | 2023-09-21 | 2024-01-05 | 广东省有色工业建筑质量检测站有限公司 | Construction method of intelligent transport control system of detection laboratory based on AGV |
| CN117494953A (en) * | 2023-12-29 | 2024-02-02 | 深圳市控汇智能股份有限公司 | New energy visual inspection robot management system and method |
| CN117933515A (en) * | 2024-01-25 | 2024-04-26 | 上海智远慧智能技术股份有限公司 | Transfer robot cluster scheduling method and device based on task merging |
| CN118071227A (en) * | 2024-02-20 | 2024-05-24 | 珠海市格努信息技术有限公司 | Heterogeneous scheduling system based on digital twinning |
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2022
- 2022-12-27 CN CN202211684433.4A patent/CN115933684A/en active Pending
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116339389A (en) * | 2023-05-30 | 2023-06-27 | 北自所(北京)科技发展股份有限公司 | Digital twinning-based rotary wing unmanned aerial vehicle inventory making method, device and storage medium |
| CN116339389B (en) * | 2023-05-30 | 2023-07-28 | 北自所(北京)科技发展股份有限公司 | Digital twinning-based rotary wing unmanned aerial vehicle inventory making method, device and storage medium |
| CN116661466A (en) * | 2023-07-28 | 2023-08-29 | 深圳市磐锋精密技术有限公司 | Operation control management system for AGV equipment |
| CN116661466B (en) * | 2023-07-28 | 2023-09-26 | 深圳市磐锋精密技术有限公司 | Operation control management system for AGV equipment |
| CN117350609A (en) * | 2023-09-21 | 2024-01-05 | 广东省有色工业建筑质量检测站有限公司 | Construction method of intelligent transport control system of detection laboratory based on AGV |
| CN117494953A (en) * | 2023-12-29 | 2024-02-02 | 深圳市控汇智能股份有限公司 | New energy visual inspection robot management system and method |
| CN117494953B (en) * | 2023-12-29 | 2024-04-05 | 深圳市控汇智能股份有限公司 | New energy visual inspection robot management system and method |
| CN117933515A (en) * | 2024-01-25 | 2024-04-26 | 上海智远慧智能技术股份有限公司 | Transfer robot cluster scheduling method and device based on task merging |
| CN118071227A (en) * | 2024-02-20 | 2024-05-24 | 珠海市格努信息技术有限公司 | Heterogeneous scheduling system based on digital twinning |
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