Factory internal logistics system based on vehicle weighing

Abstract

The invention discloses a factory internal logistics system based on vehicle weighing, which effectively improves the efficiency of the logistics system, and comprises an intelligent weighing and identification module for generating an entrance trigger data packet containing a production order number; the edge scheduling server is in cluster communication connection with the intelligent weighing and identification module, the production execution system MES and the logistics execution unit through a high-speed communication network, and the edge scheduling server is configured to receive the entrance trigger data packet; based on the production order number, real-time production demand information of at least one corresponding target station is obtained from the MES, the structure is novel, the concept is ingenious, operation is easy and convenient, the function of judging the emergency degree of goods is effectively added, the emergency demand and traffic smoothness are balanced, delay is reduced, reliability is improved, and the production efficiency is improved. The fault-tolerant capability and robustness of the system are improved, and the survivability of the system in a more extreme fault scene is protected.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of factory logistics, and relates to a factory internal logistics system based on vehicle weighing. BACKGROUND

[0002] Factory internal logistics is one of the core links of modern manufacturing, and its efficiency directly affects the continuity of the production line, the rationality of resource allocation, and the final production cost. With the development of intelligent manufacturing, automated logistics systems such as automated guided vehicles (AGV) and autonomous mobile robots (AMR) have been widely used in material handling.

[0003] The existing factory internal logistics scheduling system usually relies on warehouse management systems (WMS) and warehouse control systems (WCS). The typical workflow is as follows: the system generates material distribution tasks according to the production plan and assigns them to specific AGV / AMR. After receiving the task, the vehicle usually drives to the target station according to the fixed path preset in the system or the path planned based on simple rules.

[0004] However, this traditional logistics scheduling method has obvious limitations:

[0005] Path rigidity and response lag: the path planned by the system when the task is issued is static and cannot respond to dynamic changes that occur during execution. When there is temporary congestion in the factory traffic, some path nodes are closed due to reasons, or there is an emergency order in the production site, the vehicle cannot adjust the route in real time, resulting in prolonged waiting time, reduced distribution efficiency, and even possible local deadlock.

[0006] Logistics and production are disconnected: traditional path planning mostly targets the efficiency within the logistics system and fails to deeply integrate with the real-time state of the production line. The logistics system often does not know which station is in an urgent "material waiting" state, resulting in high-priority materials not being quickly distributed, which may cause the production line to stop waiting and cause significant economic losses.

[0007] System flexibility is insufficient: the fixed path mode is difficult to adapt to the flexible production needs of modern manufacturing with multiple varieties and small batches. When the production process or layout needs to be adjusted, the vehicle driving path often needs to be re-planned and deployed, which is costly and time-consuming.

[0008] In addition, in the vehicle entry link, the existing weighing and information registration system is usually only used for data collection and record keeping, such as verifying the load and recording the entry time. The immediate decision-making value contained in this critical node is not fully utilized. After weighing, the vehicle still drives according to the original predetermined path, and the weighing link is separated from the path scheduling link.

[0009] Therefore, a factory internal logistics system based on vehicle weighing is needed to solve the above problems. SUMMARY

[0010] In order to solve the above problems, the application provides a factory internal logistics system based on vehicle weighing.

[0011] In order to achieve the above purpose, the technical scheme adopted by the application is as follows:

[0012] A factory internal logistics system based on vehicle weighing, comprising:

[0013] An intelligent weighing and identification module is arranged in a logistics entrance channel, and is used to acquire vehicle identity information, material information and real-time weight when a vehicle carrying materials passes, and generate an entry trigger data packet containing a production order number;

[0014] An edge scheduling server is in communication connection with the intelligent weighing and identification module, a manufacturing execution system (MES) and a logistics execution unit cluster through a high-speed communication network, and the edge scheduling server is configured to:

[0015] Receive the entry trigger data packet;

[0016] Based on the production order number, acquire real-time production demand information of at least one target station from the MES, wherein the real-time production demand information includes a demand urgency;

[0017] Acquire a global real-time traffic state of the logistics execution unit cluster;

[0018] Based on the real-time production demand information and the global real-time traffic state, calculate a dynamic optimization path for the vehicle with the current position of the vehicle as a starting point; and

[0019] Issue navigation instructions of the dynamic optimization path to the vehicle;

[0020] The logistics execution unit cluster comprises a plurality of self-moving vehicles, and is used to receive and execute the navigation instructions to deliver materials to target stations.

[0021] Preferably, the real-time production demand information includes a latest demand timestamp of the target station.

[0022] The edge scheduling server is configured to calculate a demand urgency coefficient of the target station according to the latest demand timestamp and a current timestamp.

[0023] Preferably, the edge scheduling server is configured to:

[0024] Construct an evaluation function for each potential target station ,

[0025] wherein, is an identification of a target station, is an estimated passing time to the station is a congestion cost of a path is positively correlated with the demand urgency coefficient of the station, is a fixed weight coefficient; is selected as the final target station of this transportation, and the corresponding path

[0026] is selected as the final target station of this transportation, and the corresponding path is selected as the final target station of this transportation, and the corresponding path is selected as the final target station of this transportation, and the corresponding path is selected as the final target station of this transportation, and the corresponding path

[0027] Preferably, the high-speed communication network is a 5G private network or a time-sensitive network (TSN).

[0028] Preferably, the vehicle is an autonomous mobile robot (AMR) or an automatic guided vehicle (AGV).

[0029] Preferably, the autonomous mobile robot (AMR) adopts a SLAM navigation method.

[0030] Preferably, the edge scheduling server is further configured to:

[0031] When communication with the MES is interrupted, the vehicle is planned a path based on locally cached production plan information.

[0032] Preferably, the system further comprises a degraded operation mode:

[0033] When the edge scheduling server fails, the intelligent weighing and identification module guides the vehicle to a pre-set buffer staging area.

[0034] When the vehicle does not receive the navigation instruction, a pre-set fixed path is executed.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] The present application has an intelligent weighing and identification module and an edge scheduling server, which instantly acquires vehicle information at the starting point of the logistics chain, and fuses the most real-time production demand and traffic state, so that the system can calculate the current global optimal path for each vehicle entering the field within milliseconds, rather than making it follow a fixed route that may be outdated or congested, greatly reducing the empty running, waiting and congestion time of the AMR, and significantly shortening the overall cycle of materials from entering to going online. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 ​​A system structure diagram of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0039] The accompanying drawings are described below. Figure 1 The specific embodiments of the present application are further described in detail.

[0040] As shown in the accompanying drawings, in order to improve the efficiency of the logistics system, the present application comprises: Figure 1

[0041] An intelligent weighing and identification module is arranged in the logistics entrance channel, which is used to obtain vehicle identity information, material information and real-time weight when the vehicle carrying the material passes, and generate an entry trigger data packet containing a production order number;

[0042] An edge scheduling server is connected with the intelligent weighing and identification module, the production execution system MES and the logistics execution unit cluster through a high-speed communication network. The edge scheduling server is configured to:

[0043] receive the entry trigger data packet;

[0044] obtain real-time production demand information of at least one target station corresponding to the production order number from the MES, wherein the real-time production demand information includes demand urgency;

[0045] obtain the global real-time traffic state of the logistics execution unit cluster;

[0046] based on the real-time production demand information and the global real-time traffic state, calculate a dynamic optimization path for the vehicle with the current location of the vehicle as the starting point; and

[0047] issue navigation instructions of the dynamic optimization path to the vehicle;

[0048] The logistics execution unit cluster comprises a plurality of self-moving vehicles, which are used to receive and execute the navigation instructions, and transport the materials to the target station.

[0049] ​It should be noted that the above design discloses the core architecture of the system and the basic functions of each module. In one embodiment, the intelligent weighing and identification module can further integrate an RFID reader for reading an RFID tag installed on the AMR vehicle body to obtain the vehicle ID, and identifying a two-dimensional code on the material package through the visual recognition unit to obtain the material number and production order number. The high-speed communication network is preferably a 5G private network, which has low delay and high reliability to ensure that the entry trigger data packet, real-time production demand information, global real-time traffic state and dynamic path navigation instruction can be transmitted in real time and reliably. The vehicles in the logistics execution unit cluster can be selected as autonomous mobile robots (AMRs) equipped with laser SLAM navigation systems, which have the ability to move autonomously and dynamically avoid obstacles in complex environments, thereby efficiently executing the issued dynamic path.

[0050] Further, in order to judge the urgency of the goods, the real-time production demand information includes the latest demand timestamp of the target station;

[0051] The edge scheduling server is configured to calculate the demand urgency coefficient of the target station according to the latest demand timestamp and the current timestamp;

[0052] It should be noted that the above design discloses the real-time production demand information. In one embodiment, the MES system can periodically publish the demand event stream of all stations, and in this embodiment, each cycle is set to 1 second. The edge scheduling server obtains the "latest demand timestamp" of each station for a specific material by subscribing to the event stream. The calculation of the demand urgency coefficient is one of the key decision bases for dynamic path planning.

[0053] A specific calculation method is: urgency coefficient E = K / (latest demand timestamp-current timestamp), where K is an amplification coefficient. When the time difference is smaller, the urgency coefficient E is larger, indicating that the demand of the station is more urgent. This coefficient will directly affect the weight distribution in the subsequent path planning.

[0054] Further, in order to balance the urgent demand and traffic flow, the edge scheduling server is configured to:

[0055] construct an evaluation function for each potential target station ,

[0056] wherein, is the target station identifier, is the estimated travel time to the station , is the congestion cost of the path , the weight is positively related to the demand urgency coefficient of the station To fix the weight coefficient;

[0057] Select the target station with the minimum evaluation function value as the final target station of this transport, and the corresponding path as the dynamic optimization path;

[0058] It should be noted that the above design discloses the core path planning algorithm. In specific implementation, the real-time path planning engine in the edge scheduling server performs the following steps: first, the urgency coefficient of each potential target station is calculated according to claim 2 . Then, the evaluation function is constructed for each station .

[0059] Among them, the shortest time path from the depot to the station on the real-time traffic state map can be searched by using the A* algorithm to obtain. The total number of current AMRs on the path can be defined as the congestion degree, and the weight can be set to be proportional to , which can be set to in one embodiment, and is a system constant that can be adjusted. Finally, the station with the minimum is selected as the optimal target, and the corresponding path is the dynamic optimization path to be obtained. This algorithm ingeniously balances the delivery time and global traffic efficiency.

[0060] Further, in order to reduce delay and improve reliability, the high-speed communication network is a 5G private network or a time-sensitive network (TSN);

[0061] It should be noted that the 5G private network (Private 5G) is a specific implementation scheme in this embodiment, which can provide ultra-reliable low-latency communication (uRLLC) characteristics to ensure the instantaneous issuance of control instructions.

[0062] In another embodiment, the time-sensitive network (TSN) is also a feasible industrial-grade network scheme, which is especially suitable for wired network deployment scenarios, and can guarantee the deterministic delay of critical data streams.

[0063] Both of the above network technologies can provide the necessary real-time communication capability for the system of the present application.

[0064] Further, the vehicle is an autonomous mobile robot (AMR) or an automatic guided vehicle (AGV);

[0065] ​The autonomous mobile robot (AMR) adopts a SLAM navigation mode;

[0066] It should be noted that the autonomous mobile robot (AMR) is an ideal carrier for implementing the present application due to its fixed path-free and high flexibility. In the present embodiment, an AMR equipped with a laser radar and a simultaneous localization and mapping (SLAM) algorithm is specifically selected. The SLAM technology enables the AMR to achieve accurate positioning and navigation without relying on ground two-dimensional codes or magnetic strips, thereby being able to flexibly adapt to a new path of dynamic planning. In a specific scenario, such as a fixed path or an environment with low flexibility requirement, a traditional automatic guided vehicle (AGV) can also be used to implement the present application.

[0067] Further, in order to improve the fault tolerance and robustness of the system, the edge scheduling server is further configured to:

[0068] When the communication with the MES is interrupted, the production plan information based on the local cache is used to plan the path for the vehicle;

[0069] In the present embodiment, the edge scheduling server can pull the full-plant production plan from the MES every minute and cache it locally. When it is detected that the network connection with the MES is interrupted, the path planning engine will automatically switch to a degraded mode, determine the target station and plan the path based on the locally cached production plan information. This can ensure that the logistics system can still maintain basic operation when the core production data source is temporarily unavailable, greatly improving the robustness of the system.

[0070] Further, in order to protect the survival ability of the system in more extreme failure scenarios, the system further includes a degraded operation mode:

[0071] When the edge scheduling server fails, the intelligent weighing and identification module guides the vehicle to a preset buffer staging area;

[0072] When the vehicle does not receive navigation instructions, a preset fixed path is executed;

[0073] In the present embodiment, a centralized central buffer staging area is provided in the factory. When the intelligent weighing module detects that the edge scheduling server is unresponsive through a heartbeat detection mechanism, it will send a preset instruction to the AMR, guiding it to directly drive to the staging area for subsequent processing, and at the same time, an alarm will be sent to the maintenance personnel. In addition, at least one fixed path leading to a default unloading point is pre-stored in the on-board controller of each AMR. When the AMR does not receive a new navigation instruction after a preset time of more than 5 seconds after weighing, the fixed path will be automatically executed. This multi-level degradation strategy ensures that the system can still operate in an orderly manner in extreme failure situations, avoiding a complete breakdown.

[0074] In a specific embodiment, an engine with an order number of PO-001 is required at station A of the general assembly line in an automobile assembly plant.

[0075] Trigger: An AMR carrying an engine enters the intelligent weighing station at the logistics entrance. The system automatically identifies its vehicle ID as "AMR-007", the material as "engine", and the associated order number as "PO-001", and records the weight.

[0076] Decision: After receiving the trigger data, the edge scheduling server immediately queries the MES and learns that order PO-001 corresponds to station A (the latest demand time is urgent, with an emergency coefficient very high) and station B (the demand is relatively relaxed). At the same time, the server obtains the full site map and finds that the path to station A is slightly congested.

[0077] Planning: The path planning engine calculates the evaluation function values F(A) and F(B) for the paths to stations A and B. Although the path to station A takes slightly longer, but due to its extremely high emergency coefficient the weight is very large, and ultimately F(A) < F(B). Therefore, the engine decisively chooses station A as the target and plans the best path that takes into account the congestion situation.

[0078] Execution: The path instructions are instantly transmitted to AMR-007 via the 5G network. AMR-007, based on SLAM navigation, immediately drives along the dynamic path and ultimately delivers the engine to station A on time, avoiding line stoppage.

[0079] Fault tolerance: Assuming that the network with the MES is temporarily interrupted during this delivery process, the system will automatically switch to a degraded mode based on cached data and still be able to correctly deliver the material to station A.

[0080] The present application has novel structure, ingenious concept, simple and convenient operation, effectively improves the efficiency of the logistics system through the design, increases the function of judging the emergency degree of goods, balances the urgent demand and traffic flow, reduces the delay, improves the reliability, improves the fault tolerance and robustness of the system, protects the survival ability of the system in more extreme fault scenarios.

[0081] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacements for part of the technical features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A factory intra-logistics system based on vehicle weighing, characterized in that, include: The intelligent weighing and identification module is installed at the logistics entrance channel. It is used to obtain vehicle identity information, material information and real-time weight when a vehicle carrying materials passes by, and generate an entry trigger data packet containing the production order number. The edge scheduling server communicates with the intelligent weighing and identification module, the production execution system (MES), and the logistics execution unit cluster via a high-speed communication network. The edge scheduling server is configured as follows: Receive the entry trigger data packet; Based on the production order number, real-time production demand information for at least one target workstation corresponding to the MES is obtained, and the real-time production demand information includes the urgency of the demand. Obtain the global real-time traffic status of the logistics execution unit cluster; Starting from the current location of the vehicle, a dynamically optimized path is calculated for the vehicle based on the real-time production demand information and the global real-time traffic status. as well as The navigation instructions for the dynamically optimized route are sent to the vehicle; The logistics execution unit cluster includes multiple autonomously moving vehicles used to receive and execute the navigation instructions to transport materials to the target workstation.

2. The factory intralogistics system based on vehicle weighing according to claim 1, characterized in that: The real-time production demand information includes the latest demand timestamp for the target workstation. The edge scheduling server is configured to calculate the urgency coefficient of the target workstation based on the latest demand timestamp and the current timestamp.

3. The factory intralogistics system based on vehicle weighing according to claim 2, characterized in that: The edge scheduling server is configured as follows: Construct an evaluation function for each potential target workstation , in, Identify the target workstation. To get to the workstation The estimated travel time, For path Congestion costs, weight With workstation The aforementioned demand urgency coefficient is positively correlated. Fixed weighting coefficients; Choose to make the evaluation function Minimum target workstation As the final target workstation for this transportation, the corresponding path will be... This serves as the dynamically optimized path.

4. The factory intralogistics system based on vehicle weighing according to claim 1, characterized in that: The high-speed communication network is a 5G private network or a Time Sensitive Network (TSN).

5. A factory intralogistics system based on vehicle weighing according to claim 1, characterized in that: The vehicle is either an autonomous mobile robot (AMR) or an automated guided vehicle (AGV).

6. A factory intralogistics system based on vehicle weighing according to claim 5, characterized in that: The autonomous mobile robot (AMR) uses SLAM navigation.

7. A factory intralogistics system based on vehicle weighing according to claim 1, characterized in that: The edge scheduling server is also configured to: When communication with the MES is interrupted, a route is planned for the vehicle based on the locally cached production plan information.

8. A factory intralogistics system based on vehicle weighing according to claim 1, characterized in that: The system also includes a degraded operation mode: When the edge scheduling server fails, the intelligent weighing and identification module guides the vehicle to a preset buffer storage area. When the vehicle does not receive the navigation command, it executes a preset fixed route.

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