CN116501002A - AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf - Google Patents
AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf Download PDFInfo
- Publication number
- CN116501002A CN116501002A CN202310760958.XA CN202310760958A CN116501002A CN 116501002 A CN116501002 A CN 116501002A CN 202310760958 A CN202310760958 A CN 202310760958A CN 116501002 A CN116501002 A CN 116501002A
- Authority
- CN
- China
- Prior art keywords
- agv
- carrying
- cargo container
- dangerous cargo
- dangerous
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000006698 induction Effects 0.000 title claims description 5
- 238000013439 planning Methods 0.000 claims abstract description 14
- 238000004088 simulation Methods 0.000 claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 7
- 238000004891 communication Methods 0.000 claims abstract description 6
- 231100001261 hazardous Toxicity 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 2
- 238000011269 treatment regimen Methods 0.000 claims description 2
- 230000001939 inductive effect Effects 0.000 claims 1
- 238000011282 treatment Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000013068 supply chain management Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4189—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system
- G05B19/41895—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/60—Electric or hybrid propulsion means for production processes
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a safety guidance configuration method for an AGV (automatic guided vehicle) for carrying dangerous cargo containers at an intelligent wharf, which relates to the technical field of container AGVs and comprises the following steps: installing sensors on AGVs, importing a determined communication protocol, monitoring the positions and surrounding environments of all AGVs in real time, and establishing a real-time position database; detecting the running state of the AGV, constructing a running data set, and calculating and analyzing to obtain a running coefficient Yx; acquiring a task list and the running states of all AGVs, distributing tasks to the AGVs, and obtaining the optimal route planning of each AGV by using an A-type algorithm; when an emergency occurs, an emergency scheme library is constructed, whether an emergency scheme is feasible or not is verified through simulation analysis, and if the emergency scheme is feasible, the optimal scheme is output; if not, an early warning is sent to the outside. The feasibility of the emergency scheme under different conditions can be rapidly evaluated, the success rate of the scheme is improved, the cost of system shutdown and maintenance is reduced, and finally the total cost is reduced.
Description
Technical Field
The invention relates to the technical field of container AGVs, in particular to a safety guidance configuration method for an AGV carrying dangerous cargo containers at an intelligent wharf.
Background
The handling operation of the conventional dangerous cargo container for the wharf has a plurality of problems, such as manual operation required by the conventional technology, easy omission and errors of operators, and influence on the wharf operation efficiency and safety. In recent years, along with the development of new technologies such as big data, the Internet of things and the like, the AGV has a wider application prospect in the logistics industry. AGV (Automated Guided Vehicle) automatic navigation trolley is a logistics automation solution realized based on an automation technology, can carry out automatic transportation and supply chain management in a warehouse, realizes centralized management of materials, reduces manual intervention, and greatly facilitates business processes. The AGV technology for carrying the dangerous cargo containers by the intelligent wharf can optimize the operation flow, and the problems of potential safety hazards, logistics delays and the like are reduced.
In the Chinese application of the invention with the application publication number of CN 115049324A, a wharf AGV dispatching method, a wharf AGV dispatching device, computer equipment and a storage medium are disclosed, wherein the method comprises the steps of acquiring historical operation data of historical AGVs of a container wharf of all operation routes; analyzing and training a model for the historical operation data; acquiring current operation data at a preset frequency; the current operation data is input into an AGV vehicle evaluation time model corresponding to each operation path, and the predicted remaining time proportion of each operation path is obtained; resetting the dispatching numerical values of all AGVs in the current operation data according to the AGV state information judging model and the operation path AGV dispatching model based on the remaining time proportion to obtain an AGV dispatching adjustment value; and adjusting and limiting the AGV dispatching adjustment value according to the dispatching limit rule to obtain the AGV dispatching update value of all the operation paths. Through the processing scheme of the disclosure, the wharf AGV is dynamically and reasonably distributed.
In the application of the invention, various algorithms are utilized to dynamically and reasonably allocate the wharf AGVs, but when an emergency occurs, part of AGVs possibly cannot work normally, so that the allocation sent by the algorithm cannot be realized, the intelligent monitoring and control of the AGVs are not enough only in a normal state, especially, the AGVs carrying dangerous cargo containers possibly cause unpredictable losses once avoiding or collision and the like, according to the cargo types and the number of the cargoes of the dangerous cargo containers, the personal risks, the fire and explosion radiuses, the maximum running speed of the AGVs carrying the dangerous cargo containers, the running path of the AGVs, other obstacles around the AGVs and the prediction of other AGVs are calculated in advance, and the running of the AGVs of the intelligent wharf is guaranteed to the greatest extent and how to regulate and process the AGVs under the emergency.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a safety guidance configuration method for an AGV (automatic guided vehicle) for carrying dangerous cargo containers at an intelligent wharf.
(II) technical scheme
In order to achieve the above object, the present invention is achieved by the following technical solutions:
the AGV safety induction configuration method for carrying the dangerous cargo container at the intelligent wharf comprises the following steps: installing necessary sensors and detection equipment on an AGV carrying dangerous cargo containers, importing a determined communication protocol, ensuring that the AGV carrying dangerous cargo containers can correctly upload data to a cloud, monitoring the positions and surrounding environment conditions of all AGVs carrying dangerous cargo containers in real time by using the sensors and cameras, uploading the data to the cloud, and establishing a real-time position database;
detecting the running state of the AGV carrying the dangerous cargo container through a plurality of sensors carried on the AGV carrying the dangerous cargo container, wherein the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh, constructing an running data set, and obtaining an running coefficient Yx after calculation and analysis;
acquiring an operation coefficient Yx, judging the relation between the operation coefficient Yx and a corresponding first preset threshold value and a corresponding second preset threshold value, acquiring the safety risk level of the AGV carrying the dangerous cargo container, and selecting a corresponding safety processing strategy according to different safety risk levels of the AGV carrying the dangerous cargo container;
acquiring a task list and the running states of all AGVs carrying dangerous cargo containers, distributing tasks to the AGVs carrying dangerous cargo containers, enabling each AGV carrying dangerous cargo container to complete all tasks within the capacity range of the AGVs, planning individual paths of the AGVs carrying dangerous cargo containers by using an A-type algorithm, searching conflicts of the AGVs planning schemes of the AGVs carrying dangerous cargo containers and avoiding the conflicts one by one, and obtaining the optimal route planning of the AGVs carrying dangerous cargo containers;
setting a control center, acquiring a real-time position database and an operation data set, monitoring the position and the operation state of an AGV carrying a dangerous cargo container in real time, constructing an emergency scheme library when an emergency occurs, verifying whether an emergency scheme is feasible through simulation analysis, and outputting an optimal scheme if the emergency scheme is feasible; if not, an early warning is sent to the outside.
Further, environmental information such as obstacles, personnel and cargoes around the AGV carrying the dangerous cargo container is obtained through a camera, a laser sensor and ultrasonic sensor detection equipment, and the position and the direction of the AGV carrying the dangerous cargo container in a working area are determined by using GPS and RFID label technology; the laser range sensor can detect obstacles around the AGV carrying the dangerous cargo container, the camera can be used for identifying landmarks and two-dimensional codes, and the ultrasonic sensor can detect the distance and the shape of an object;
further, the running state of the AGV carrying the dangerous cargo container is detected through a plurality of sensors carried on the AGV carrying the dangerous cargo container, the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh, an operation data set is constructed after the running data set is summarized, the vehicle speed Vs and the turning radius Od are periodically detected through a wheel type encoder, the electric quantity Dc is periodically detected through the electric quantity sensor, and the load Zh is periodically detected through the load sensor.
Further, an operation data set is obtained, and an operation coefficient Yx is obtained after dimensionless processing is carried out on the vehicle speed Vs, the turning radius Od, the electric quantity Dc and the load Zh; the obtaining mode of the operation coefficient Yx accords with the following formula:
wherein, the parameter meaning is: vehicle speed influencing factor，/>Turning influencing factor->，Electric quantity influencing factor->，/>Load influencing factor->h，/>C is a constant correction coefficient.
It should be noted that, a person skilled in the art collects multiple sets of sample data and sets a corresponding preset scaling factor for each set of sample data; preset to be setSubstituting the proportionality coefficient and the collected sample data into a formula, forming a quaternary once equation set by any four formulas, screening the calculated coefficient and taking an average value to obtain、/>Is a value of (2);
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
Further, the operation coefficient Yx is obtained and judged to be greater than a preset first threshold value and a preset second threshold value, and a corresponding safety risk level is determined for the AGV carrying the dangerous cargo container according to the relation with the corresponding preset threshold value, wherein the first safety risk level is greater than the second safety risk level, and when the operation coefficient Yx is greater than the preset first threshold value, the operation of the AGV carrying the dangerous cargo container is difficult.
Further, according to different AGV security risk levels for carrying dangerous cargo containers, a corresponding security treatment strategy is selected, specifically:
when the operation coefficient isWhen the first threshold value is set, the current running state of the AGV for carrying the dangerous cargo container is fed back to be good, and the AGV for carrying the dangerous cargo container does not send an early warning signal and does not need to be processed;
when the operation coefficient isWhen the operating state of the AGV for carrying the dangerous cargo container is not good enough, the AGV for carrying the dangerous cargo container is in a second safety risk level, and the corresponding AGV for carrying the dangerous cargo container sends a second to the control consoleThe control console can send a command of slowing down, stopping work or going to charge to the AGV carrying the dangerous cargo container, and the AGV carrying the dangerous cargo container can automatically solve the problem according to the command;
when the operation coefficient isWhen being greater than first threshold value, feedback is present carrying dangerous cargo container AGV unable normal operating, carries dangerous cargo container AGV and is in first safe risk level, and the corresponding dangerous cargo container AGV that carries sends first early warning signal to the control cabinet, and the control cabinet adopts to suspend the use of carrying dangerous cargo container AGV, uses safer equipment to replace dangerous equipment.
Further, starting and ending point positions, cargo information, task types and task priority information are input from a system or a user to determine the number of AGVs required for carrying dangerous cargo containers for a task, wherein the cargo information comprises names, numbers, weights and volumes of specified cargos; task types can be generally classified into transportation tasks, handling tasks, and picking tasks; priority of tasks if the urgency of different tasks is different, the priority may be set to allow the AGV carrying the hazardous cargo container to perform certain tasks preferentially.
Further, the particle swarm algorithm is adopted to distribute tasks to the AGVs carrying the dangerous cargo containers, so that each AGV carrying the dangerous cargo containers can complete all tasks within the capacity range, and in consideration of the problems that collision or congestion and the like can be encountered among the AGVs carrying the dangerous cargo containers, cooperation among the AGVs carrying the dangerous cargo containers needs to be achieved in the process, so that the tasks can be completed smoothly.
Further, when an emergency occurs, for example: and adding new tasks or canceling some tasks, solving problems of position deviation and running state of the AGV carrying the dangerous cargo container caused by environmental changes and other reasons in the running process of the AGV carrying the dangerous cargo container, searching corresponding emergency schemes according to the working scheme and the safety risk of the AGV carrying the dangerous cargo container, and constructing an emergency scheme library.
Further, according to different emergency events, selecting a corresponding emergency scheme from an emergency scheme library, performing simulation analysis on the emergency scheme, analyzing whether the running state of the AGV carrying the dangerous cargo container after emergency is expected, if the running state of the AGV carrying the dangerous cargo container is expected, adjusting the path of the AGV carrying the dangerous cargo container, reallocating tasks, outputting an optimal task allocation scheme and an optimal route plan, if the running state of the AGV carrying the dangerous cargo container is not expected, selecting the emergency scheme again in the emergency scheme library for further simulation until the running state of the AGV carrying the dangerous cargo container is expected, and if the running state of the AGV carrying the dangerous cargo container cannot be expected for 3 times continuously, sending early warning to the outside.
(III) beneficial effects
The invention provides an AGV safety induction configuration method for carrying dangerous cargo containers at an intelligent wharf, which has the following beneficial effects:
1. through some simple monitoring devices and sensors, the operation data of the AGV carrying the dangerous cargo container can be obtained, then the operation coefficient Yx is calculated and judged, a corresponding safety processing strategy is adopted, the operation complexity is low, the operation condition of the device can be accurately analyzed, and errors and miss evaluation risks caused by an intuitive and fuzzy safety evaluation mode are avoided.
2. Through obtaining task list and all the running state of carrying dangerous cargo container AGV, we can carry out reasonable task allocation to reach the purpose that the utilization ratio of each dangerous cargo container AGV that carries, can avoid necessary return journey as far as possible, thereby reduced the transportation time, improved production efficiency.
3. The emergency scheme library is constructed, emergency schemes suitable for various emergency situations can be provided for the AGV system for carrying the dangerous cargo container, and the optimal scheme is determined by means of simulation analysis, when the AGV system for carrying the dangerous cargo container breaks down or encounters an emergency situation, if the emergency scheme library and the simulation analysis support, the optimal emergency scheme can be selected to reduce the risk of accidents, avoid extra loss, improve the stability of the system, quickly evaluate the feasibility of the emergency scheme under different conditions, improve the success rate of the scheme, reduce the shutdown and maintenance cost of the system, and finally reduce the total cost.
Drawings
FIG. 1 is a flow chart of the intelligent dock carrying dangerous cargo container AGV safety guidance configuration method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a method for guiding and configuring the safety of an AGV carrying a dangerous cargo container in an intelligent dock, comprising the following steps:
firstly, installing necessary sensors and detection equipment on an AGV carrying dangerous cargo containers, importing a determined communication protocol, ensuring that the AGV carrying dangerous cargo containers can correctly upload data to a cloud, monitoring the positions and surrounding environment conditions of all AGVs carrying dangerous cargo containers in real time by using the sensors and cameras, uploading the data to the cloud, and establishing a real-time position database;
the first step comprises the following steps:
step 101, carrying various sensors on an AGV carrying a dangerous cargo container, for example, acquiring environmental information around the AGV carrying the dangerous cargo container through a camera, a laser sensor and an ultrasonic sensor detection device, such as: the method comprises the steps of determining the position and the direction of an AGV carrying a dangerous cargo container in a working area by using a GPS and RFID tag technology; the laser range sensor can detect obstacles around the AGV carrying the dangerous cargo container, the camera can be used for identifying landmarks and two-dimensional codes, and the ultrasonic sensor can detect the distance and the shape of an object;
step 102, selecting a communication protocol to ensure that the AGV carrying the dangerous cargo container can correctly upload data to the server, and optionally adopting HTTP, TCP/IP or MQTT protocol, uploading the data to the cloud, and establishing a real-time location database.
In use, the contents of steps 101 and 102 are combined:
install necessary sensor and check out test set on carrying dangerous cargo container AGV to leading-in definite communication protocol provides data basis for carrying dangerous cargo container AGV's intelligent control and real-time supervision, in discovery barrier and other risks, can in time take measures, prevent the occurence of failure, reinforcing carries dangerous cargo container AGV transportation security.
Detecting the running state of the AGV carrying the dangerous cargo container by a plurality of sensors carried on the AGV carrying the dangerous cargo container, wherein the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh, constructing an running data set, and calculating and analyzing to obtain an running coefficient Yx;
the second step comprises the following steps:
step 201, detecting the running state of an AGV carrying a dangerous cargo container through a plurality of sensors carried on the AGV carrying the dangerous cargo container, wherein the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh, and constructing an running data set after summarizing, wherein the vehicle speed Vs and the turning radius Od are periodically detected through wheel encoders, the electric quantity Dc is periodically detected through the electric quantity sensors, and the load Zh is periodically detected through the load sensors;
step 202, acquiring an operation data set, and acquiring an operation coefficient Yx after performing dimensionless processing on a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh; the obtaining mode of the operation coefficient Yx accords with the following formula:
wherein, the parameter meaning is: vehicle speed influencing factor，/>Turning influencing factor->，Electric quantity influencing factor->，/>Load influencing factor->h，/>C is a constant correction coefficient.
It should be noted that, a person skilled in the art collects multiple sets of sample data and sets a corresponding preset scaling factor for each set of sample data; substituting the preset proportionality coefficient and the collected sample data into a formula, forming a quaternary once equation set by any four formulas, screening the calculated coefficient and taking an average value to obtain、/>Is a value of (2);
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
In use, the contents of steps 201 and 202 are combined:
the operation coefficient Yx is obtained through analysis, so that the transport performance of the AGV carrying the dangerous cargo container can be evaluated and optimized, such as properly increasing the transport speed, reducing the turning radius, optimizing the electric quantity and the load, and the like, thereby improving the logistics transport efficiency, shortening the transport time, reducing the logistics transport cost and increasing the profit capability of enterprises.
Step three, acquiring an operation coefficient Yx, judging the relation between the operation coefficient Yx and a corresponding first preset threshold value and a corresponding second preset threshold value, acquiring the safety risk level of the AGV carrying the dangerous cargo container, and selecting a corresponding safety processing strategy according to different safety risk levels of the AGV carrying the dangerous cargo container;
the third step comprises the following steps:
step 301, acquiring an operation coefficient Yx and judging whether the operation coefficient Yx exceeds a preset first threshold value and a second threshold value, and determining a corresponding security risk level for the AGV carrying the dangerous cargo container according to the relation with the corresponding preset threshold value, wherein the first security risk level is larger than the second security risk level, and when the operation coefficient Yx is larger than the preset first threshold value, indicating that the AGV carrying the dangerous cargo container is difficult to operate;
step 302, selecting a corresponding safety processing strategy according to different safety risk levels of AGVs for carrying dangerous cargo containers, wherein the safety processing strategy specifically comprises the following steps:
when the operation coefficient isWhen the value is smaller than the second threshold value, the operation state of the AGV carrying the dangerous cargo container is fed back to be good, the AGV carrying the dangerous cargo container does not send out an early warning signal, and the AGV does not need to be processed;
when the operation coefficient isWhen the first threshold value is set between the second threshold value and the first threshold value, the current operation state of the AGV carrying the dangerous cargo container is fed back to be poor, the AGV carrying the dangerous cargo container is in a second safety risk level, the corresponding AGV carrying the dangerous cargo container sends a second early warning signal to the control console, and the control console can send a warning signal to the control consoleThe AGV for carrying the dangerous cargo container sends a command of slowing down, stopping working or going to charge, and the AGV for carrying the dangerous cargo container can automatically solve the problem according to the command;
when the operation coefficient isWhen being greater than first threshold value, feedback is present carrying dangerous cargo container AGV unable normal operating, carries dangerous cargo container AGV and is in first safe risk level, and the corresponding dangerous cargo container AGV that carries sends first early warning signal to the control cabinet, and the control cabinet adopts to suspend the use of carrying dangerous cargo container AGV, uses safer equipment to replace dangerous equipment.
In use, the contents of steps 301 and 302 are combined:
through some simple monitoring devices and sensors, the operation data of the AGV carrying the dangerous cargo container can be obtained, then the operation coefficient Yx is calculated and judged, a corresponding safety processing strategy is adopted, the operation complexity is low, the operation condition of the device can be accurately analyzed, and errors and miss evaluation risks caused by an intuitive and fuzzy safety evaluation mode are avoided.
Step four, acquiring a task list and the running states of all AGVs carrying dangerous cargo containers, distributing tasks to the AGVs carrying dangerous cargo containers, enabling each AGV carrying dangerous cargo container to complete all tasks within the capacity range of the AGVs, planning individual paths of the AGVs carrying dangerous cargo containers by using an A-type algorithm, searching conflicts of the AGVs planning schemes of the AGVs carrying dangerous cargo containers and avoiding the conflicts one by one, and obtaining the optimal route planning of the AGVs carrying dangerous cargo containers;
the fourth step comprises the following steps:
step 401, inputting starting point and end point positions, cargo information, task types and task priority information from a system or a user to determine the number of AGVs for carrying dangerous cargo containers required by a task, wherein the cargo information comprises names, numbers, weights and volumes of specified cargos; task types can be generally classified into transportation tasks, handling tasks, and picking tasks; the priority of the tasks, if the emergency degree of different tasks is different, the AGV carrying the dangerous cargo container can preferentially execute certain tasks by setting the priority;
through reasonably distributing tasks and finding out an optimal route plan, each AGV carrying the dangerous cargo container can complete more tasks within the capacity range, so that the utilization rate of the AGV carrying the dangerous cargo container is improved;
step 402, a particle swarm algorithm is adopted to distribute tasks to the AGVs carrying the dangerous cargo containers, so that each AGV carrying the dangerous cargo containers can complete all tasks within the capacity range, and in consideration of the problems that collision or congestion and the like can be encountered among the AGVs carrying the dangerous cargo containers, cooperation among the AGVs carrying the dangerous cargo containers needs to be realized in a program so as to ensure that the tasks can be successfully completed;
and step 403, planning independent paths of the AGVs carrying the dangerous cargo containers by using an A-algorithm, searching for conflicts of the AGVs carrying the dangerous cargo containers, avoiding the conflicts one by one, obtaining the optimal path planning of the AGVs carrying the dangerous cargo containers, and outputting and executing the optimal path planning.
In use, the contents of steps 401 to 403 are combined:
through obtaining task list and all the running state of carrying dangerous cargo container AGV, we can carry out reasonable task allocation to reach the purpose that the utilization ratio of each dangerous cargo container AGV that carries, can avoid necessary return journey as far as possible, thereby reduced the transportation time, improved production efficiency.
Step five, a control center is arranged, a real-time position database and an operation data set are obtained, the position and the operation state of an AGV carrying a dangerous cargo container are monitored in real time, when an emergency occurs, an emergency scheme library is constructed, whether an emergency scheme is feasible or not is verified through simulation analysis, and if the emergency scheme is feasible, the optimal scheme is output; if not, sending out early warning to the outside;
the fifth step comprises the following steps:
step 501, a control center is set, a real-time position database and an operation data set are obtained, and the position and the operation state of an AGV carrying a dangerous cargo container are monitored in real time;
step 502, when an emergency occurs, for example: the method comprises the steps that new tasks are added or some tasks are cancelled, position deviation and running state of the AGV carrying the dangerous cargo container are caused to be problematic due to environmental changes and other reasons in the running process of the AGV carrying the dangerous cargo container, corresponding emergency schemes are searched according to working schemes and safety risks of the AGV carrying the dangerous cargo container, and an emergency scheme library is constructed;
various accidents are unavoidable during the actual operation of an AGV carrying a hazardous cargo container. If a perfect emergency scheme library exists, the emergency can be responded quickly, the dead time and the shutdown rate caused by operation faults or other problems are reduced, and the reliability and the production efficiency of the system are improved;
step 503, selecting a corresponding emergency scheme from an emergency scheme library according to different emergency events, performing simulation analysis on the emergency scheme, analyzing whether the running state of the AGV carrying the dangerous cargo container after emergency is expected, if the running state of the AGV carrying the dangerous cargo container is expected, adjusting the path of the AGV carrying the dangerous cargo container, reassigning tasks, outputting an optimal task assignment scheme and an optimal route plan, if the running state of the AGV carrying the dangerous cargo container is not expected, selecting the emergency scheme again in the emergency scheme library for re-simulation until the running state of the AGV carrying the dangerous cargo container is expected, and if the running state of the AGV carrying the dangerous cargo container cannot be expected for 3 times continuously, sending early warning to the outside.
In use, the contents of steps 501 to 503 are combined:
the emergency scheme library is constructed, emergency schemes suitable for various emergency situations can be provided for the AGV system for carrying the dangerous cargo container, and the optimal scheme is determined by means of simulation analysis, when the AGV system for carrying the dangerous cargo container breaks down or encounters an emergency situation, if the emergency scheme library and the simulation analysis support, the optimal emergency scheme can be selected to reduce the risk of accidents, avoid extra loss, improve the stability of the system, quickly evaluate the feasibility of the emergency scheme under different conditions, improve the success rate of the scheme, reduce the shutdown and maintenance cost of the system, and finally reduce the total cost.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (10)
1. The intelligent wharf AGV safety induction configuration method for carrying dangerous cargo containers is characterized by comprising the following steps of: the method comprises the following steps:
installing sensors and detection equipment on an AGV carrying dangerous cargo containers, importing a determined communication protocol, ensuring that the AGV carrying dangerous cargo containers uploads data to a cloud end, monitoring the positions and surrounding environment conditions of all AGVs carrying dangerous cargo containers in real time by using the sensors and cameras, uploading the data to the cloud end, and establishing a real-time position database;
detecting the running state of the AGV carrying the dangerous cargo container by a sensor carried on the AGV carrying the dangerous cargo container, wherein the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh, constructing an running data set, and calculating and analyzing to obtain an running coefficient Yx;
acquiring an operation coefficient Yx, judging the relation between the operation coefficient Yx and a corresponding first preset threshold value and a corresponding second preset threshold value, acquiring the safety risk level of the AGV carrying the dangerous cargo container, and selecting a corresponding safety processing strategy according to different safety risk levels of the AGV carrying the dangerous cargo container;
acquiring a task list and the running states of all AGVs carrying dangerous cargo containers, distributing tasks to the AGVs carrying dangerous cargo containers, enabling each AGV carrying dangerous cargo container to complete all tasks within the capacity range of the AGVs, planning individual paths of the AGVs carrying dangerous cargo containers by using an A-type algorithm, searching conflicts of the AGVs planning schemes of the AGVs carrying dangerous cargo containers and avoiding the conflicts one by one, and obtaining the optimal route planning of the AGVs carrying dangerous cargo containers;
setting a control center, acquiring a real-time position database and an operation data set, monitoring the position and the operation state of an AGV carrying a dangerous cargo container in real time, constructing an emergency scheme library when an emergency occurs, verifying whether an emergency scheme is feasible through simulation analysis, and outputting an optimal scheme if the emergency scheme is feasible; if not, an early warning is sent to the outside.
2. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 1, wherein: through camera, laser sensor and ultrasonic sensor check out test set, acquire the environmental information around carrying dangerous cargo container AGV, include: obstacles, personnel and cargo, and determine the location and orientation of an AGV carrying a hazardous cargo container in the work area using GPS and RFID tags.
3. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 2, wherein: the running state of the AGV carrying the dangerous cargo container is detected through a sensor carried on the AGV carrying the dangerous cargo container, the detection indexes at least comprise a vehicle speed Vs, a turning radius Od, electric quantity Dc and a load Zh, an operation data set is constructed after the running data set is summarized, the vehicle speed Vs and the turning radius Od are periodically detected through a wheel type encoder, the electric quantity Dc is periodically detected through an electric quantity sensor, and the load Zh is periodically detected through a load sensor.
4. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 3, wherein: acquiring an operation data set, and acquiring an operation coefficient Yx after performing dimensionless treatment on a vehicle speed Vs, a turning radius Od, an electric quantity Dc and a load Zh; the obtaining mode of the operation coefficient Yx accords with the following formula:
wherein, the parameter meaning is: vehicle speed influencing factor，/>Turning influencing factor->，Electric quantity influencing factor->，/>Load influencing factor->h，/>C is a constant correction coefficient.
5. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 4, wherein: and acquiring an operation coefficient Yx and judging whether the operation coefficient Yx exceeds a preset first threshold value and a preset second threshold value, and determining a corresponding safety risk level for the AGV carrying the dangerous cargo container according to the relation with the corresponding preset threshold value, wherein the first safety risk level is larger than the second safety risk level, and when the operation coefficient Yx is larger than the preset first threshold value, indicating that the AGV carrying the dangerous cargo container is difficult to operate.
6. The intelligent terminal carrying hazardous cargo container AGV security-inducing configuration method of claim 5, wherein: according to different AGV security risk levels for carrying dangerous cargo containers, a corresponding security treatment strategy is selected, specifically:
when the operation coefficient isWhen the value is smaller than the second threshold value, the operation state of the AGV carrying the dangerous cargo container is fed back to be good, the AGV carrying the dangerous cargo container does not send out an early warning signal, and the AGV does not need to be processed;
when the operation coefficient isWhen the current running state of the AGV carrying the dangerous cargo container is poor and is in a second safety risk level, the corresponding AGV carrying the dangerous cargo container sends a second early warning signal to the control console, the control console can send a speed reduction command to the AGV carrying the dangerous cargo container, stop working or go to a charging command, and the AGV carrying the dangerous cargo container can solve the problem according to the command;
when the operation coefficient isWhen being greater than first threshold value, feedback is present carrying dangerous cargo container AGV unable normal operating, carries dangerous cargo container AGV and is in first safe risk level, and the corresponding dangerous cargo container AGV that carries sends first early warning signal to the control cabinet, and the control cabinet adopts to suspend the use of carrying dangerous cargo container AGV, uses safer equipment to replace dangerous equipment.
7. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 6, wherein: inputting start and end positions, cargo information, task type, and task priority information from a system or user to determine the number of AGVs required to carry dangerous cargo containers for a task, wherein the cargo information includes the name, number, weight, and volume of a specified cargo; task types can be generally classified into transportation tasks, handling tasks, and picking tasks; the priority of the tasks, if the emergency degree of different tasks is different, the priority is set to enable the AGV carrying the dangerous cargo container to execute the tasks preferentially.
8. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 7, wherein: the particle swarm algorithm is used to assign tasks to the AGVs carrying the dangerous cargo containers such that each AGV carrying the dangerous cargo container is able to complete all tasks within its capacity.
9. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 8, wherein: in the event of an emergency, comprising: when a new task is added or part of tasks are cancelled, and the position deviation occurs in the traveling process of the AGV carrying the dangerous cargo container, the corresponding emergency scheme is searched according to the working scheme and the safety risk of the AGV carrying the dangerous cargo container, and an emergency scheme library is constructed.
10. The intelligent terminal carrying hazardous cargo container AGV safety guidance configuration method of claim 9, wherein: according to different emergency events, selecting a corresponding emergency scheme from an emergency scheme library, carrying out simulation analysis on the emergency scheme, analyzing whether the running state of the AGV carrying the dangerous cargo container after emergency is expected, if the running state of the AGV carrying the dangerous cargo container is expected, adjusting the path of the AGV carrying the dangerous cargo container, reassigning tasks, outputting an optimal task assignment scheme and an optimal route planning, if the running state of the AGV carrying the dangerous cargo container is not expected, selecting the emergency scheme again from the emergency scheme library for re-simulation until the running state of the AGV carrying the dangerous cargo container is expected, and if the running state of the AGV carrying the dangerous cargo container cannot be expected for 3 times continuously, sending early warning to the outside.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310760958.XA CN116501002B (en) | 2023-06-27 | 2023-06-27 | AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310760958.XA CN116501002B (en) | 2023-06-27 | 2023-06-27 | AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116501002A true CN116501002A (en) | 2023-07-28 |
| CN116501002B CN116501002B (en) | 2023-09-01 |
Family
ID=87316922
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310760958.XA Active CN116501002B (en) | 2023-06-27 | 2023-06-27 | AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116501002B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894247A (en) * | 2023-09-07 | 2023-10-17 | 贵州正易科技有限公司 | Method and system for protecting computer system security |
| CN117566657A (en) * | 2023-11-27 | 2024-02-20 | 深圳市卓越信息技术有限公司 | Fork truck intelligent monitoring and early warning system and method based on artificial intelligence and Internet of things |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10143566A (en) * | 1996-11-11 | 1998-05-29 | Nippon Steel Corp | Scheduling device |
| CN104317855A (en) * | 2014-10-15 | 2015-01-28 | 江苏瑞中数据股份有限公司 | Metering producing real-time monitoring method based on real-time database |
| CN106991633A (en) * | 2017-03-31 | 2017-07-28 | 江苏远望神州软件有限公司 | The computer simulation system and emulation mode of a kind of public safety emergency management |
| CN107392435A (en) * | 2017-06-28 | 2017-11-24 | 北京交通大学 | A kind of track traffic station emergency evacuation capability evaluation system |
| KR20180028575A (en) * | 2016-09-08 | 2018-03-19 | (주)엠텍 | Operation Safety Motion Base with Sensor at the AGV and Control Method Thereof |
| CN208061027U (en) * | 2018-05-07 | 2018-11-06 | 温州大学瓯江学院 | A kind of unmanned storage intelligent comprehensive monitoring and dispatching system |
| CN110414895A (en) * | 2019-07-26 | 2019-11-05 | 珠海格力电器股份有限公司 | Vehicle dispatch system and method |
| CN110728612A (en) * | 2019-10-10 | 2020-01-24 | 北京全路通信信号研究设计院集团有限公司 | Rail transit emergency simulation evaluation method and system |
| DE102018125880A1 (en) * | 2018-10-18 | 2020-04-23 | Industrieanlagen-Betriebsgesellschaft Mbh | System, method and computer-readable memory for (online) monitoring the operation of at least one vehicle |
| CN111738663A (en) * | 2020-06-30 | 2020-10-02 | 昆明东电科技有限公司 | Intelligent warehouse management system based on AVG technology |
| CN111815158A (en) * | 2020-07-07 | 2020-10-23 | 中船重工信息科技有限公司 | Horizontal transportation scheduling system for container terminal |
| CN113534766A (en) * | 2020-04-15 | 2021-10-22 | 北京旷视机器人技术有限公司 | Exception handling method and device, electronic equipment and readable storage medium |
| CN115285618A (en) * | 2022-08-02 | 2022-11-04 | 成都振鑫丰机电设备有限公司 | AGV intelligent robot operation state monitoring analysis control system |
| CN115630849A (en) * | 2022-12-22 | 2023-01-20 | 交通运输部水运科学研究所 | Cargo container yard health risk assessment method based on complex system theory |
| CN115994689A (en) * | 2023-03-22 | 2023-04-21 | 交通运输部水运科学研究所 | Management method and device for safe transportation of dangerous goods in intelligent dock and harbor district |
-
2023
- 2023-06-27 CN CN202310760958.XA patent/CN116501002B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10143566A (en) * | 1996-11-11 | 1998-05-29 | Nippon Steel Corp | Scheduling device |
| CN104317855A (en) * | 2014-10-15 | 2015-01-28 | 江苏瑞中数据股份有限公司 | Metering producing real-time monitoring method based on real-time database |
| KR20180028575A (en) * | 2016-09-08 | 2018-03-19 | (주)엠텍 | Operation Safety Motion Base with Sensor at the AGV and Control Method Thereof |
| CN106991633A (en) * | 2017-03-31 | 2017-07-28 | 江苏远望神州软件有限公司 | The computer simulation system and emulation mode of a kind of public safety emergency management |
| CN107392435A (en) * | 2017-06-28 | 2017-11-24 | 北京交通大学 | A kind of track traffic station emergency evacuation capability evaluation system |
| CN208061027U (en) * | 2018-05-07 | 2018-11-06 | 温州大学瓯江学院 | A kind of unmanned storage intelligent comprehensive monitoring and dispatching system |
| DE102018125880A1 (en) * | 2018-10-18 | 2020-04-23 | Industrieanlagen-Betriebsgesellschaft Mbh | System, method and computer-readable memory for (online) monitoring the operation of at least one vehicle |
| CN110414895A (en) * | 2019-07-26 | 2019-11-05 | 珠海格力电器股份有限公司 | Vehicle dispatch system and method |
| CN110728612A (en) * | 2019-10-10 | 2020-01-24 | 北京全路通信信号研究设计院集团有限公司 | Rail transit emergency simulation evaluation method and system |
| CN113534766A (en) * | 2020-04-15 | 2021-10-22 | 北京旷视机器人技术有限公司 | Exception handling method and device, electronic equipment and readable storage medium |
| CN111738663A (en) * | 2020-06-30 | 2020-10-02 | 昆明东电科技有限公司 | Intelligent warehouse management system based on AVG technology |
| CN111815158A (en) * | 2020-07-07 | 2020-10-23 | 中船重工信息科技有限公司 | Horizontal transportation scheduling system for container terminal |
| CN115285618A (en) * | 2022-08-02 | 2022-11-04 | 成都振鑫丰机电设备有限公司 | AGV intelligent robot operation state monitoring analysis control system |
| CN115630849A (en) * | 2022-12-22 | 2023-01-20 | 交通运输部水运科学研究所 | Cargo container yard health risk assessment method based on complex system theory |
| CN115994689A (en) * | 2023-03-22 | 2023-04-21 | 交通运输部水运科学研究所 | Management method and device for safe transportation of dangerous goods in intelligent dock and harbor district |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894247A (en) * | 2023-09-07 | 2023-10-17 | 贵州正易科技有限公司 | Method and system for protecting computer system security |
| CN116894247B (en) * | 2023-09-07 | 2023-11-14 | 贵州正易科技有限公司 | Method and system for protecting computer system security |
| CN117566657A (en) * | 2023-11-27 | 2024-02-20 | 深圳市卓越信息技术有限公司 | Fork truck intelligent monitoring and early warning system and method based on artificial intelligence and Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116501002B (en) | 2023-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116501002B (en) | AGV safety induction configuration method for carrying dangerous cargo container at intelligent wharf | |
| Halawa et al. | Introduction of a real time location system to enhance the warehouse safety and operational efficiency | |
| US20200231386A1 (en) | Systems and methods for automated guided vehicle control | |
| US9650233B2 (en) | Method for operating an industrial vehicle to manage energy costs | |
| US11187547B2 (en) | Tire conditioning optimization for a collection of mining vehicles | |
| CN111738663A (en) | Intelligent warehouse management system based on AVG technology | |
| JP7165902B2 (en) | Comprehensive container terminal system and operation method using artificial intelligence | |
| US11959777B2 (en) | Methods and Internet of Things (IoT) systems for safety management of smart gas underground gas pipeline networks | |
| US20240070578A1 (en) | Engineering facility scheduling method and system, and engineering facility | |
| KR20150112049A (en) | Emergency Rescue System For Electric Vehicle Using Mobile | |
| CN111882474A (en) | FDS function design method for cluster scheduling of automatic driving vehicles | |
| CN116569118A (en) | Information management method and device | |
| CN109607013A (en) | A kind of method and device for handling to containment and transport of objects | |
| KR20230035987A (en) | Programmable logic controller operation system and method for each movement position of logistics robot | |
| CN110780651B (en) | AGV dispatching system and method | |
| CN112162557A (en) | Remote control system and method for automated guided vehicle | |
| CN115237131A (en) | Inspection control method and device of inspection robot and storage medium | |
| CN113419536B (en) | AGV unmanned automatic driving control method and system | |
| CN117350827B (en) | Dynamic demand matching-based mobile forklift lease management method and system | |
| Yan | Enhancing the performance of automated guided vehicles through reliability, operation and maintenance assessment | |
| CN209525679U (en) | A kind of logistics cloud monitoring alarm | |
| Chung | Introduction of a Real Time Location System to Enhance the Warehouse Safety and Operational Efficiency Farouq Halawa", Husam Daoud", lngyu Lee", Yinglei Li “, Sang Won Yoon", and | |
| Aksen | Using internet of things to improve manufacturing process | |
| CN115840429A (en) | Control method and device for production equipment in intelligent factory and intelligent factory | |
| CN117970891A (en) | Vehicle-mounted control system based on laser navigation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |