Assembly line monitoring system based on full-automatic material supplementing unmanned logistics vehicle

Abstract

The invention belongs to the technical field of smart factories. The invention provides an assembly line monitoring system based on a full-automatic feeding unmanned logistics vehicle. The system comprises a distributed monitoring camera, a scheduling system and the full-automatic feeding unmanned logistics vehicle. Wherein the distributed monitoring camera shoots and obtains high-definition image data related to each material storage node of the assembly line; the scheduling system determines and obtains the material remaining amount of each material storage node based on the high-definition image data, determines and obtains a full-automatic material supplementing unmanned logistics vehicle for supplementing materials to the material storage nodes when the material remaining amount is lower than a preset value, and sends a material supplementing instruction to the corresponding full-automatic material supplementing unmanned logistics vehicle; and the full-automatic material supplementing unmanned logistics vehicle distributes the materials corresponding to the material distribution quantity to the material storage nodes corresponding to the identity information. According to the scheme, an existing distributed monitoring camera in an assembly line production workshop is fully utilized, and assembly line material distribution based on the full-automatic material supplementing unmanned vehicle can be achieved at low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factories, and in particular to an assembly line monitoring system based on a fully automatic material replenishment unmanned logistics vehicle. Background Art

[0002] In order to improve production efficiency, more and more factories have begun to use fully automatic refueling unmanned vehicles to provide automatic delivery of production materials to assembly lines.

[0003] However, the existing dispatching systems are mostly based on manual trigger signals to dispatch the corresponding fully automatic refueling unmanned vehicle for material delivery. When production workers find that the materials on the assembly line are insufficient, they press the dispatch button to send a material delivery request to the dispatching system, and the dispatching system dispatches the corresponding fully automatic refueling unmanned vehicle for material delivery. This method is overly dependent on manpower and it is difficult to further improve production efficiency. It is also difficult to implement in highly automated assembly lines with no or little human participation.

[0004] Some factories install material detection devices based on weighing sensors on the assembly line. When insufficient materials are detected, a material delivery request is sent to the dispatching system, and the dispatching system dispatches the corresponding fully automatic refueling unmanned vehicle to deliver the materials. This method requires the material storage mechanism of the assembly line to be modified, and the implementation cost is relatively high.

[0005] It can be seen that how to realize automatic analysis of the demand for assembly line materials and dispatch fully automatic refueling unmanned vehicles to refuel accordingly is a technical problem that needs to be solved at present. Summary of the invention

[0006] In this regard, the present invention provides a production line monitoring method, system, electronic equipment, computer storage medium and computer program product based on a fully automatic feeding unmanned logistics vehicle to solve the above-mentioned technical problems.

[0007] The present invention discloses a production line monitoring system based on a fully automatic material replenishment unmanned logistics vehicle, the system comprising a distributed monitoring camera, a dispatching system and a fully automatic material replenishment unmanned logistics vehicle; wherein,

[0008] The distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, and send the high-definition image data to the scheduling system;

[0009] The dispatching system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determine a fully automatic refueling unmanned logistics vehicle for refueling the material storage node, and send a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle; wherein the refueling instruction includes the identity information of the material storage node and the material delivery amount;

[0010] The fully automatic material replenishment unmanned logistics vehicle is used to respond to the material replenishment instruction and deliver the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

[0011] Preferably, the distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, including:

[0012] The distributed monitoring camera captures first high-definition image data of the assembly line, identifies the conveying equipment from the first high-definition image data, and extracts the movement direction of the product located on the conveying equipment, and reversely determines each material storage node according to the movement direction corresponding to each conveying equipment;

[0013] The distributed monitoring camera then captures the second high-definition image data of each of the material storage nodes and the third high-definition image data of other devices located downstream of the material storage nodes, and uses the second high-definition image data and the third high-definition image data as high-definition image data related to the material storage nodes.

[0014] In the embodiment of the present invention, the distributed monitoring camera of the present invention does not need to manually specify each material storage node of the assembly line, but can automatically determine which devices are material storage nodes based on image recognition.

[0015] Preferably, the scheduling system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, including:

[0016] The scheduling system performs a first processing on the second high-definition image data to obtain vibration amplitude data of the material storage node, and predicts the remaining amount of the first material in the material storage node according to the vibration amplitude data;

[0017] The scheduling system performs a second processing on the third high-definition image data to obtain a conveying interval of products on a conveying device, and calculates a conveying interval change trend according to each of the conveying intervals;

[0018] If the transmission interval variation trend indicates that the transmission interval is gradually increasing, a first correction coefficient is set, otherwise a second correction coefficient is set; wherein the first correction coefficient is less than 1 and the second correction coefficient is equal to 1;

[0019] The first material remaining amount is corrected to a second material remaining amount using the first correction coefficient or the second correction coefficient.

[0020] Preferably, the determining of a fully automatic refueling unmanned logistics vehicle for refueling the material storage node and sending a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle includes:

[0021] Determine the material type of each material storage node by accessing the assembly line database, and determine an adapted fully automatic material replenishment unmanned logistics vehicle according to the material type;

[0022] The material replenishment instruction is generated according to the identity information of the material storage node and the material delivery quantity, and the material replenishment instruction is sent to the adapted fully automatic material replenishment unmanned logistics vehicle.

[0023] Preferably, the step of sending the refill instruction to the adapted fully automatic refill unmanned logistics vehicle comprises:

[0024] Determine whether the current time period belongs to the target time period, where the target time period is a time period obtained based on the historical operation stop data statistics of the assembly line;

[0025] If the current time period does not belong to the target time period, the refill instruction is sent to the adapted fully automatic refill unmanned logistics vehicle;

[0026] If the current time period belongs to the target time period, wait for a set time, control the distributed monitoring camera to capture the fourth high-definition image data of other equipment downstream of the material storage node, and track the movement status of the products on each conveying device according to the fourth high-definition image data; if there is no stationary trend in the movement status within the set time period, send the refill instruction to the adapted fully automatic refill unmanned logistics vehicle, otherwise cancel or suspend the refill instruction.

[0027] Preferably, the set duration is determined by:

[0028] Calculate the distance between the fully automatic refueling unmanned logistics vehicle and the corresponding material storage node, and predict the delivery time according to the distance and the historical running speed of the fully automatic refueling unmanned logistics vehicle;

[0029] A third correction coefficient is determined according to the number of the material storage nodes on the assembly line, and the set time is obtained by multiplying the third correction coefficient by the delivery time; wherein the third correction coefficient is greater than 1.

[0030] The present invention also discloses a production line monitoring method based on a fully automatic material replenishment unmanned logistics vehicle. The method is based on the system as described in any of the preceding items and includes the following method steps:

[0031] The distributed monitoring camera captures and obtains high-definition image data related to each material storage node of the assembly line, and sends the high-definition image data to the scheduling system;

[0032] The dispatching system determines the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determines a fully automatic unmanned logistics vehicle for replenishing the material storage node, and sends a replenishment instruction to the corresponding fully automatic unmanned logistics vehicle; wherein the replenishment instruction includes the identity information of the material storage node and the material delivery amount;

[0033] The fully automatic material replenishment unmanned logistics vehicle responds to the material replenishment instruction and delivers the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

[0034] The present invention also discloses an electronic device, characterized in that it is applied to the system as described in any of the preceding items, and comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0035] The present invention further discloses a computer storage medium, characterized in that: it is applied to the system as described in any of the preceding items, and the computer storage medium stores a computer program.

[0036] The present invention also discloses a computer program product, characterized in that: it is applied to the system as described in any of the preceding items, and includes a computer program stored on a non-transitory computer-readable medium.

[0037] The beneficial effects are:

[0038] On the one hand, the solution of the present invention does not require manual generation of delivery requests, nor does it require the installation of weighing sensors in material storage nodes. Instead, it makes full use of the existing distributed monitoring cameras in the assembly line production workshop, and can realize assembly line material distribution based on fully automatic material replenishment unmanned vehicles at low cost; on the other hand, the solution of the present invention also realizes automatic analysis of material distribution needs under the premise of low implementation cost, and combined with fully automatic material distribution, it can significantly improve the production efficiency of the assembly line. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a structural schematic diagram of a production line monitoring system based on a fully automatic material replenishment unmanned logistics vehicle disclosed in an embodiment of the present invention;

[0041] Figure 2 It is a flow chart of a method for monitoring an assembly line based on a fully automatic material replenishment unmanned logistics vehicle disclosed in an embodiment of the present invention;

[0042] Figure 3 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0044] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0045] Most existing dispatching systems dispatch the corresponding fully automatic refueling unmanned vehicle to deliver materials based on manual trigger signals. When production workers find that the materials on the assembly line are insufficient, they press the dispatch button to send a material delivery request to the dispatching system, and the dispatching system dispatches the corresponding fully automatic refueling unmanned vehicle to deliver the materials. This method relies too much on manpower and is difficult to further improve production efficiency. It is also difficult to implement in highly automated assembly lines with no or little human participation.

[0046] Some factories install material detection devices based on weighing sensors on the assembly line. When insufficient materials are detected, a material delivery request is sent to the dispatching system, and the dispatching system dispatches the corresponding fully automatic refueling unmanned vehicle to deliver the materials. This method requires the material storage mechanism of the assembly line to be modified, and the implementation cost is relatively high.

[0047] It can be seen that both of the above-mentioned two existing methods have many defects, and it is difficult to realize assembly line material distribution based on fully automatic refueling unmanned vehicles at low cost.

[0048] In response to the above technical issues, such as Figure 1 As shown, an embodiment of the present invention discloses a production line monitoring system based on a fully automatic material replenishment unmanned logistics vehicle, the system comprising a distributed monitoring camera, a scheduling system and a fully automatic material replenishment unmanned logistics vehicle; wherein,

[0049] The distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, and send the high-definition image data to the scheduling system;

[0050] The dispatching system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determine a fully automatic refueling unmanned logistics vehicle for refueling the material storage node, and send a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle; wherein the refueling instruction includes the identity information of the material storage node and the material delivery amount;

[0051] The fully automatic material replenishment unmanned logistics vehicle is used to respond to the material replenishment instruction and deliver the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

[0052] In the above scheme of the present invention, the distributed monitoring cameras arranged in the assembly line production workshop are used to capture the relevant high-definition image data of each material storage node of the corresponding assembly line. The scheduling system analyzes and processes the relevant high-definition image data to obtain the remaining material quantity of each material storage node. When the remaining material quantity is lower than the preset value, it is determined that the material storage node needs to be replenished with materials. At this time, a fully automatic replenishment unmanned logistics vehicle is dispatched to carry a suitable amount of materials to the material storage node, thereby realizing fully automatic distribution of materials.

[0053] Therefore, on the one hand, the solution of the present invention does not need to manually generate delivery requests, nor does it need to set up weighing sensors in material storage nodes. Instead, it makes full use of the existing distributed monitoring cameras in the assembly line production workshop, and can realize assembly line material distribution based on fully automatic material replenishment unmanned vehicles at low cost; on the other hand, the solution of the present invention, under the premise of low implementation cost, also realizes the automatic analysis of material distribution needs, and then assisted by fully automatic distribution of materials, can significantly improve the production efficiency of the assembly line.

[0054] It should be noted that the assembly line production workshop in the present invention is, for example, a motorcycle assembly production line, and the corresponding materials include but are not limited to screws, nuts, powder coatings, lubricating powder, etc.

[0055] Preferably, the distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, including:

[0056] The distributed monitoring camera captures first high-definition image data of the assembly line, identifies the conveying equipment from the first high-definition image data, and extracts the movement direction of the product located on the conveying equipment, and reversely determines each material storage node according to the movement direction corresponding to each conveying equipment;

[0057] The distributed monitoring camera then captures the second high-definition image data of each of the material storage nodes and the third high-definition image data of other devices located downstream of the material storage nodes, and uses the second high-definition image data and the third high-definition image data as high-definition image data related to the material storage nodes.

[0058] In the embodiment of the present invention, the distributed monitoring camera of the present invention does not need to manually specify each material storage node on the assembly line, but can automatically determine which devices are material storage nodes based on image recognition.

[0059] Specifically, the distributed monitoring camera first captures the first high-definition image data of the production line, identifies the transmission equipment from the first high-definition image data, such as transmission rollers, conveyor belts, etc., and extracts the movement direction of the products on these transmission equipment. Based on the product movement direction corresponding to each transmission equipment on the production line, each material storage node can be inferred in the reverse direction, that is, the "starting point" of the overall movement direction is determined as the material storage node. Then, the distributed monitoring camera captures the second high-definition image data of each material storage node, as well as the third high-definition image data of other equipment located downstream of the material storage node. The second high-definition image data and the third high-definition image data are the high-definition image data related to the material storage node.

[0060] Preferably, the scheduling system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, including:

[0061] The scheduling system performs a first processing on the second high-definition image data to obtain vibration amplitude data of the material storage node, and predicts the remaining amount of the first material in the material storage node according to the vibration amplitude data;

[0062] The scheduling system performs a second processing on the third high-definition image data to obtain a conveying interval of products on a conveying device, and calculates a conveying interval change trend according to each of the conveying intervals;

[0063] If the transmission interval variation trend indicates that the transmission interval is gradually increasing, a first correction coefficient is set, otherwise a second correction coefficient is set; wherein the first correction coefficient is less than 1 and the second correction coefficient is equal to 1;

[0064] The first material remaining amount is corrected to a second material remaining amount using the first correction coefficient or the second correction coefficient.

[0065] In the embodiment of the present invention, the material storage node is generally a barrel or box directly connected to the production equipment on the assembly line. The material can be a powder material (such as powder coating, lubricating powder), a granular material (such as screws, nuts), etc. The lower opening of the barrel or box is connected to the production equipment, and the material falls into the production equipment through the lower opening. The barrel or box can also be equipped with stirring, scraping and other equipment to facilitate the powder material and granular material therein to fall smoothly into the production equipment.

[0066] Therefore, when the stirring, scraping and other equipment are working, the barrel or box will naturally vibrate to a certain extent, and this vibration is related to the amount of residual material in the barrel or box. Generally speaking, when the amount of residual material in the barrel or box is more, the stirring, scraping and other equipment need to overcome a greater mass, and the vibration energy of the stirring, scraping and other equipment will be dispersed and absorbed by more materials, and the vibration amplitude will be reduced at this time; on the contrary, when the amount of residual material in the barrel or box is more, the vibration energy of the stirring, scraping and other equipment will be more transferred to the barrel or box, causing it to vibrate with a greater amplitude.

[0067] Based on the above actual situation, the present invention sets a scheduling system to extract the vibration amplitude data of the material storage node from the second high-definition image data of the material storage node, and based on the above principle, predicts the remaining amount of the first material in the material storage node according to the vibration amplitude data. Here, a reference table can be preset for barrels or boxes of specific sizes and weights. The reference table will contain multiple sets of correlations between vibration amplitudes and the remaining amount of the first material. By comparing the reference table, the remaining amount of the first material corresponding to the measured vibration amplitude data can be obtained. Of course, a prediction model can also be built based on CNN, Transformer, etc. The prediction model conducts a comprehensive analysis of the vibration amplitude data of the material storage node and the material storage node's own attribute data (size, weight, etc.), and predicts the remaining amount of the above-mentioned first material, which will not be described in detail.

[0068] At the same time, the vibration amplitude of the barrel or box is not only affected by the remaining amount of material inside, but also by the transmission stability of the stirring, scraping and other equipment. For example, when the stirring, scraping and other equipment are seriously aged, the vibration amplitude of the barrel or box will be higher, which will cause the vibration amplitude of the barrel or box to increase, which will cause the first remaining amount of material obtained above to be inaccurate. In response to this technical problem, the present invention sets a scheduling system to analyze the operating status of other equipment downstream of the material storage node to obtain the corresponding correction coefficient to assist in obtaining a more accurate remaining amount of material. Specifically, the scheduling system identifies the spacing between each product on the conveying equipment from the third high-definition image data, that is, the above-mentioned conveying interval, and the conveying interval change trend can be calculated based on these continuous conveying intervals. When there is sufficient material in the material storage node, the material storage node can continuously provide stable materials for the production equipment. At this time, the time taken by the production equipment to produce products will be very stable, and then the conveying interval on the conveying equipment will be very stable, and the conveying interval change trend will not indicate that the conveying interval is gradually increasing. At this time, the correction coefficient is set to 1, that is, the above-mentioned first remaining amount of material is not corrected. When the materials in the material storage nodes begin to be insufficient, the materials falling from the material storage nodes into the production equipment are no longer uniform, that is, the production equipment cannot continue to be provided with stable materials. At this time, the production equipment needs to wait for sufficient material supply before producing products. The time consumed in producing products will gradually increase, and then the transmission interval on the transmission equipment will become unstable. The transmission interval change trend will indicate that the transmission interval is gradually increasing. At this time, the correction coefficient is set to a value less than 1, that is, the correction coefficient is used to appropriately reduce the aforementioned first material remaining amount, so that the obtained second material remaining amount is more in line with the actual situation.

[0069] It should be noted that the first correction coefficient less than 1 in the present invention may not be a fixed value, but is negatively correlated with the speed at which the transmission interval represented by the transmission interval change trend gradually increases. For example, each transmission interval is mapped to a coordinate system, and a transmission interval change curve is obtained by fitting. The slope of the latter segment (i.e., the latest segment) of the transmission interval change curve is used as the transmission interval change trend, and the first correction coefficient less than 1 is negatively correlated with the slope.

[0070] Preferably, the determining of a fully automatic refueling unmanned logistics vehicle for refueling the material storage node and sending a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle includes:

[0071] Determine the material type of each material storage node by accessing the assembly line database, and determine an adapted fully automatic material replenishment unmanned logistics vehicle according to the material type;

[0072] The material replenishment instruction is generated according to the identity information of the material storage node and the material delivery quantity, and the material replenishment instruction is sent to the adapted fully automatic material replenishment unmanned logistics vehicle.

[0073] In an embodiment of the present invention, there may be multiple material storage nodes on the assembly line, and different material storage nodes store different types of materials. Different types of materials require corresponding types of fully automatic material replenishment unmanned logistics vehicles for delivery. For example, powder materials require type A fully automatic material replenishment unmanned logistics vehicles for delivery, and granular materials require type B fully automatic material replenishment unmanned logistics vehicles for delivery. In this regard, the assembly line database is accessed to determine the material type of each material storage node of the current production task, and the adapted fully automatic material replenishment unmanned logistics vehicle that is currently idle is screened according to the determined material type. Then, the identity information and material delivery quantity of the material storage node that needs to be delivered are packaged as a replenishment instruction, and the replenishment instruction is sent to the adapted fully automatic material replenishment unmanned logistics vehicle. After receiving the replenishment instruction, the fully automatic material replenishment unmanned logistics vehicle goes to the warehouse to automatically obtain the corresponding type of material that meets the material delivery quantity, and delivers it to the corresponding material storage node, thereby completing the fully automatic delivery of the material.

[0074] The material distribution quantity may be a fixed value and may be preset according to the capacity of the material storage node and the material type, which will not be elaborated in the present invention.

[0075] Preferably, the method of sending the refill instruction to the adapted fully automatic refill unmanned logistics vehicle further comprises:

[0076] Determine whether the current time period belongs to the target time period, where the target time period is a time period obtained based on the historical operation stop data statistics of the assembly line;

[0077] If the current time period does not belong to the target time period, the refill instruction is sent to the adapted fully automatic refill unmanned logistics vehicle;

[0078] If the current time period belongs to the target time period, wait for a set time, control the distributed monitoring camera to capture the fourth high-definition image data of other equipment downstream of the material storage node, and track the movement status of the products on each conveying device according to the fourth high-definition image data; if there is no stationary trend in the movement status within the set time period, send the refill instruction to the adapted fully automatic refill unmanned logistics vehicle, otherwise cancel or suspend the refill instruction.

[0079] In an embodiment of the present invention, the factory assembly line is not in continuous operation, but has intermittent operation, which mainly depends on the number of production orders. In this regard, the present invention is based on the above-mentioned target period obtained by the statistics of the historical operation stop data of the assembly line, and the target period belongs to the period when the assembly line is likely to stop. If the current period belongs to the above-mentioned target period, it is not appropriate to directly send the refill instruction to the adapted fully automatic refill unmanned logistics vehicle, because if a shutdown occurs, the materials added to the material storage node will be damp and deteriorate due to long-term exposure. At this time, the distributed monitoring camera is controlled to shoot the fourth high-definition image data of other devices downstream of the material storage node, and the movement state of the products on each transmission device is tracked according to the fourth high-definition image data; if there is no static trend in the movement state within the set time, it indicates that the assembly line has no shutdown behavior at this time, and the refill instruction is sent to the adapted fully automatic refill unmanned logistics vehicle, that is, the material distribution is carried out normally; otherwise, it is determined that the assembly line has a shutdown behavior at this time, and the refill instruction is canceled or suspended, and the material distribution is temporarily not carried out. If the current time period does not belong to the above target time period, there is no need to wait, but the refilling instruction can be directly sent to the adapted fully automatic refilling unmanned logistics vehicle.

[0080] Preferably, the set duration is determined by:

[0081] Calculate the distance between the fully automatic refueling unmanned logistics vehicle and the corresponding material storage node, and predict the delivery time according to the distance and the historical running speed of the fully automatic refueling unmanned logistics vehicle;

[0082] A third correction coefficient is determined according to the number of the material storage nodes on the assembly line, and the set time is obtained by multiplying the third correction coefficient by the delivery time; wherein the third correction coefficient is greater than 1.

[0083] In an embodiment of the present invention, the set duration can be based on the delivery duration of the fully automatic replenishment unmanned logistics vehicle to the material storage node to be delivered associated with it, and the delivery duration is obtained by first calculating the distance between the fully automatic replenishment unmanned logistics vehicle and the material storage node, and then dividing the distance by the historical running speed of the fully automatic replenishment unmanned logistics vehicle. At the same time, the delivery duration of the fully automatic replenishment unmanned logistics vehicle will also be determined by the number of other fully automatic replenishment unmanned logistics vehicles on the delivery route, and this number is obviously positively correlated with the number of material storage nodes on the assembly line, that is, the more the number of material storage nodes on the assembly line, the greater the probability that more other logistics vehicles will appear on the delivery route of the fully automatic replenishment unmanned logistics vehicle to hinder the operation of the logistics vehicle. In this regard, the present invention is set to determine the third correction coefficient according to the number of material storage nodes on the assembly line, and then use the third correction coefficient to multiply the aforementioned delivery duration to obtain the final set duration. Among them, the third correction coefficient is positively correlated with the number of material storage nodes on the assembly line, but the present invention does not limit the specific positive correlation expression formula.

[0084] like Figure 2 As shown, an embodiment of the present invention further discloses a production line monitoring method based on a fully automatic material replenishment unmanned logistics vehicle, the method is based on the system as described in any of the preceding items, and includes the following method steps:

[0085] The distributed monitoring camera captures and obtains high-definition image data related to each material storage node of the assembly line, and sends the high-definition image data to the scheduling system;

[0086] The dispatching system determines the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determines a fully automatic unmanned logistics vehicle for replenishing the material storage node, and sends a replenishment instruction to the corresponding fully automatic unmanned logistics vehicle; wherein the replenishment instruction includes the identity information of the material storage node and the material delivery amount;

[0087] The fully automatic material replenishment unmanned logistics vehicle responds to the material replenishment instruction and delivers the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

[0088] like Figure 3 As shown, an embodiment of the present invention also discloses an electronic device, applied to the system as described in any of the preceding items, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0089] An embodiment of the present invention further discloses a computer storage medium, which is applied to the system as described in any of the preceding items, and the computer storage medium stores a computer program.

[0090] An embodiment of the present invention further discloses a computer program product, which is applied to the system as described in any of the preceding items, and includes a computer program stored on a non-transitory computer-readable medium.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A production line monitoring system based on a fully automatic refueling unmanned logistics vehicle, characterized by: The system includes distributed monitoring cameras, a dispatching system and a fully automatic material replenishment unmanned logistics vehicle; wherein, The distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, and send the high-definition image data to the scheduling system; The dispatching system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determine a fully automatic refueling unmanned logistics vehicle for refueling the material storage node, and send a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle; wherein the refueling instruction includes the identity information of the material storage node and the material delivery amount; The fully automatic material replenishment unmanned logistics vehicle is used to respond to the material replenishment instruction and deliver the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

2. According to claim 1, a production line monitoring system based on a fully automatic material replenishment unmanned logistics vehicle is characterized by: The distributed monitoring camera is used to capture high-definition image data related to each material storage node of the assembly line, including: The distributed monitoring camera captures first high-definition image data of the assembly line, identifies the conveying equipment from the first high-definition image data, and extracts the movement direction of the product located on the conveying equipment, and reversely determines each material storage node according to the movement direction corresponding to each conveying equipment; The distributed monitoring camera then captures the second high-definition image data of each of the material storage nodes and the third high-definition image data of other devices located downstream of the material storage nodes, and uses the second high-definition image data and the third high-definition image data as high-definition image data related to the material storage nodes.

3. The assembly line monitoring system based on the fully automatic material replenishment unmanned logistics vehicle according to claim 2 is characterized in that: The scheduling system is used to determine the remaining amount of materials at each material storage node based on the high-definition image data, including: The scheduling system performs a first processing on the second high-definition image data to obtain vibration amplitude data of the material storage node, and predicts the remaining amount of the first material in the material storage node according to the vibration amplitude data; The scheduling system performs a second processing on the third high-definition image data to obtain a conveying interval of products on a conveying device, and calculates a conveying interval change trend according to each of the conveying intervals; If the transmission interval variation trend indicates that the transmission interval is gradually increasing, a first correction coefficient is set, otherwise a second correction coefficient is set; wherein the first correction coefficient is less than 1 and the second correction coefficient is equal to 1; The first material remaining amount is corrected to a second material remaining amount using the first correction coefficient or the second correction coefficient.

4. The assembly line monitoring system based on the fully automatic material replenishment unmanned logistics vehicle according to claim 3 is characterized by: Determining a fully automatic refueling unmanned logistics vehicle for refueling the material storage node, and sending a refueling instruction to the corresponding fully automatic refueling unmanned logistics vehicle, including: Determine the material type of each material storage node by accessing the assembly line database, and determine an adapted fully automatic material replenishment unmanned logistics vehicle according to the material type; The material replenishment instruction is generated according to the identity information of the material storage node and the material delivery quantity, and the material replenishment instruction is sent to the adapted fully automatic material replenishment unmanned logistics vehicle.

5. The assembly line monitoring system based on the fully automatic material replenishment unmanned logistics vehicle according to claim 4 is characterized in that: Sending the refill instruction to the adapted fully automatic refill unmanned logistics vehicle includes: Determine whether the current time period belongs to the target time period, where the target time period is a time period obtained based on the historical operation stop data statistics of the assembly line; If the current time period does not belong to the target time period, the refill instruction is sent to the adapted fully automatic refill unmanned logistics vehicle; If the current time period belongs to the target time period, wait for a set time, control the distributed monitoring camera to capture the fourth high-definition image data of other equipment downstream of the material storage node, and track the movement status of the products on each conveying device according to the fourth high-definition image data; if there is no stationary trend in the movement status within the set time period, send the refill instruction to the adapted fully automatic refill unmanned logistics vehicle, otherwise cancel or suspend the refill instruction.

6. The assembly line monitoring system based on the fully automatic material replenishment unmanned logistics vehicle according to claim 5 is characterized in that: The set duration is determined by: Calculate the distance between the fully automatic refueling unmanned logistics vehicle and the corresponding material storage node, and predict the delivery time according to the distance and the historical running speed of the fully automatic refueling unmanned logistics vehicle; A third correction coefficient is determined according to the number of the material storage nodes on the assembly line, and the set time is obtained by multiplying the third correction coefficient by the delivery time; wherein the third correction coefficient is greater than 1.

7. A method for monitoring an assembly line based on a fully automatic refueling unmanned logistics vehicle, the method being based on the system according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: Distributed monitoring cameras capture high-definition image data related to each material storage node of the assembly line, and send the high-definition image data to the scheduling system; The dispatching system determines the remaining amount of materials at each material storage node based on the high-definition image data, and when the remaining amount of materials is lower than a preset value, determines a fully automatic unmanned logistics vehicle for replenishing the material storage node, and sends a replenishment instruction to the corresponding fully automatic unmanned logistics vehicle; wherein the replenishment instruction includes the identity information of the material storage node and the material delivery amount; The fully automatic material replenishment unmanned logistics vehicle responds to the material replenishment instruction and delivers the material corresponding to the material delivery quantity to the material storage node corresponding to the identity information.

8. An electronic device, characterized in that: The system applied to any one of claims 1 to 6 comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

9. A computer storage medium, characterized in that: Applied to the system according to any one of claims 1 to 6, the computer storage medium stores a computer program.

10. A computer program product, characterized in that: A system as claimed in any one of claims 1 to 6, comprising a computer program stored on a non-transitory computer readable medium.

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