Truss robot scheduling system and control method based on data model drive

Abstract

The present invention discloses a truss robot scheduling system driven by a data model and a control method thereof. Through data modeling, different physical scenes are abstracted and mapped into a database relational table, and the differences in physical scenes are abstracted into differences in data models, separating the process logic from the physical scenes; at the same time, in response to changes in the process flow, the process logic is finely granularly stripped, and the chain-like process logic is then transformed into a mesh-like sub-process combination. The above technical solution enhances the modifiability of the system and the high scalability of the system to the process flow.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial robot control. More specifically, the present invention relates to a truss robot scheduling system driven by a data model. The present invention also relates to a control method of the scheduling system. Background Art

[0002] In recent years, with the continuous advancement of the wave of informatization and intelligence, the intelligent transformation of traditional manufacturing is in full swing. Among them, intelligent warehouse management is the top priority. As the end of intelligent transformation, it reflects the concentration and crystallization of the entire process.

[0003] Traditional truss robot scheduling systems are based on the target environment and continuously accumulate process logic modules. Ultimately, the scheduling algorithm cannot adapt to the needs of process flow adjustments.

[0004] Defects and problems of existing technologies:

[0005] Because of the different physical layouts of various warehouses and frequent changes in process techniques, the system will be frequently modified, resulting in the system being unable to achieve stable operation.

[0006] Traditional industries have large differences in warehouse layout and process flow. Such differences and changes exist not only in static scenarios, but more often they are dynamically adjusted according to market business, resulting in high risks of system changes and uncertainty, as well as losses in economic benefits. Summary of the invention

[0007] The invention provides a truss robot scheduling system based on data model driving, the purpose of which is to improve the modifiability of the system and the high expansibility of the process flow.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] The data model-driven truss robot scheduling system of the present invention includes a two-dimensional code visual recognition subsystem, a photoelectric sensor, and an industrial control computer (PLC); the scheduling system abstracts different physical scenes through data modeling and maps them to a database relational table, abstracts the differences in physical scenes into differences in data models, and separates the process logic from the physical scene; at the same time, in response to changes in the process flow, the process logic is finely granularly stripped, and the chain-type process logic is converted into a meshed sub-process combination.

[0010] In order to achieve the same invention purpose as the above technical solution, the present invention also provides the above-mentioned data model driven truss robot scheduling system control method, the process of the control method is as follows:

[0011] 1) The industrial computer (PLC) initiates a task request;

[0012] 2) The system receives the message information and analyzes the task type;

[0013] 3) Determine the task type and make a corresponding message correctness determination;

[0014] 4) Enter the corresponding functional submodule, read the database information, and perform logical comparison;

[0015] 5) If the judgment is correct, call the task generation function and pass in the function code;

[0016] 6) Generate subtask sequence and store it in data task data table;

[0017] 7) Reply to the corresponding message from PLC;

[0018] 8) PLC resumes executing subtask command message;

[0019] 9) Execute the task sequence in sequence and interact with PLC;

[0020] 10) After the subtask sequence is completed, reply to PLC;

[0021] 11) The process ends;

[0022] The present invention adopts the above technical solution, abstracts different physical scenarios through data modeling, and maps them into the database relational table, abstracts the differences in physical scenarios into differences in data models, separates the process logic from the physical scenarios, and enhances the modifiability of the system; in response to changes in the process flow, the process logic is fine-grainedly stripped, and then the chain-type process logic is transformed into a meshed combination of sub-processes, thereby enhancing the system's high scalability to the process flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of warehouse data modeling of the present invention;

[0024] Figure 2 It is a flow chart of the dispatching system control method of the present invention. DETAILED DESCRIPTION

[0025] The specific implementation modes of the present invention are further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention.

[0026] like Figure 1The warehouse data modeling schematic diagram of the present invention is a truss robot scheduling system driven by a data model, which is applied to a truss robot; the scheduling system includes a two-dimensional code visual recognition system, a photoelectric sensor, and an industrial control computer (PLC) to assist in task execution, raw material device identification, safety detection, and program deployment and operation.

[0027] The present invention is based on the idea of ​​First Come First Served (FCFS) and combines data modeling technology to propose a truss robot scheduling and control technology for traditional manufacturing warehouse stacking. The easily changeable scene layout is data modeled to form a data model with appropriate redundancy. Therefore, it has strong scalability. The frequently modified stacking logic is vertically minimized and functionally cut, which has strong loose coupling and forms a robust combined algorithm module.

[0028] When faced with changes in warehouse layout and process flow, the present invention can achieve flexible combination of business logic algorithms according to business processes and digitization of physical scenes through data modeling of the location layout of stacked objects and fine-grained granularity of handling process tasks, thereby greatly ensuring the robustness and flexible adaptability of the system.

[0029] Specifically, in order to solve the problems existing in the prior art and overcome its defects, and to achieve the invention purpose of improving the modifiability of the system and the high scalability of the process flow, the technical solution adopted by the present invention is:

[0030] like Figure 1 , Figure 2 As shown, the truss robot scheduling system driven by a data model of the present invention abstracts different physical scenes through data modeling and maps them to the database relational table, abstracts the differences in physical scenes into differences in data models, and separates the process logic from the physical scene; at the same time, according to the changes in the process flow, the process logic is fine-grainedly stripped, and then the chain process logic is transformed into a network of sub-process combinations.

[0031] The above technical solution of the present invention solves the defects and problems of the traditional manufacturing warehouse truss scheduling system, namely: in the traditional manufacturing industry, due to the different physical layout of the warehouse and the frequent changes in the process technology, the system is frequently modified, which leads to the system always being unable to achieve the effect of stable operation, especially in the production of multiple varieties of industrial products, the problem is particularly prominent. However, the present invention solves this problem well.

[0032] The technical principle of the present invention is: through data modeling, different physical scenes are abstracted and mapped into the database relational table, the differences in physical scenes are abstracted into differences in data models, the process logic is separated from the physical scenes, and the modifiability of the system is enhanced. At the same time, in response to changes in the process flow, the process logic is finely granularly stripped, and then the chain process logic is transformed into a meshed sub-process combination, enhancing the system's high scalability to the process flow.

[0033] The key points of the present invention are: abstracting the physical scene and converting it into a corresponding data model, a warehouse data model building method, and a corresponding database mapping table design method.

[0034] The dispatching system and the truss robot PLC communicate using Modbus TCP messages.

[0035] Among them, the system modules mainly include raw material warehousing function module, raw material outbound function module, empty barrel outbound function module, warehouse transfer function module, and abnormal warning processing function module, each of which uses the calling function task generation and execution module.

[0036] like Figure 2 As shown, in order to achieve the same invention purpose as the above technical solution, the present invention also provides the above-mentioned data model driven truss robot scheduling system control method, the process of the control method is as follows:

[0037] 1) The industrial computer (PLC) initiates a task request;

[0038] 2) The system receives the message information and analyzes the task type;

[0039] 3) Determine the task type and make a corresponding message correctness determination;

[0040] 4) Enter the corresponding functional submodule, read the database information, and perform logical comparison;

[0041] 5) If the judgment is correct, call the task generation function and pass in the function code;

[0042] 6) Generate subtask sequence and store it in data task data table;

[0043] 7) Reply to the corresponding message from PLC;

[0044] 8) PLC resumes executing subtask command message;

[0045] 9) Execute the task sequence in sequence and interact with PLC;

[0046] 10) After the subtask sequence is completed, reply to PLC;

[0047] 11) The process ends;

[0048] In the above 1), the PLC sends a message request to the truss scheduling system program; the message is transmitted via the Modbus-TCP protocol;

[0049] The message includes request bits 1-49 and response bits 50-100;

[0050] The tasks include raw material warehousing tasks, raw material warehousing tasks, warehouse transfer tasks, empty barrel warehousing tasks, and barrel recycling tasks.

[0051] In the above 2), the message is divided into two parts: a request bit and a response bit;

[0052] The request bits include: function code, area code, XYZ coordinate information, custom A-axis coordinate information, barcode information, weight information, position number, sub-process number, working mode, working status, gripper status code, product information, granularity, 1-52 sensor status information, empty barrel outbound quantity, initial position information, and target position information;

[0053] The response bits include: a ready code, a confirmation code, a barrel code sub-flow program number, and an error code;

[0054] The tasks include raw material warehousing tasks, raw material warehousing tasks, warehouse transfer tasks, empty barrel warehousing tasks, and barrel recycling tasks.

[0055] In the above 3), the task type is determined according to the function code and the area code; the verification method for executing different tasks; taking the raw material warehousing task as an example: check whether the robot is in normal working state -> check whether the barcode information is empty -> check whether there is duplicate data -> check product matching information -> check whether the raw material information is entered -> check whether the target position generated by the system is correct -> execute the task; each step of the judgment result is based on data correctness and operation safety considerations; if the judgment is correct, continue to execute; if the judgment is wrong, an error code will be generated and uploaded to the central control system, and the task will be suspended.

[0056] In the above 4), the logic comparison includes: comparing whether there is duplicate data according to the barcode information; comparing whether the barrel information is valid; and comparing whether the product code of the target barrel is consistent.

[0057] In the above 5), the control system calculates the target bucket position according to the system algorithm, calls the task generation function, and passes the function code and the target bucket position information into the function, and generates the corresponding subtask sequence according to different function codes;

[0058] Task generation includes the following situations:

[0059] If there is no obstruction bucket, directly generate the fetch and place subtasks;

[0060] If there is an obstructed barrel, it is necessary to first generate the fetching and placing subtasks of the temporary storage area transfer, put the obstructed barrel in the temporary storage area, then generate the fetching and placing subtasks to fetch and place the barrel in the specified barrel position, and finally generate the fetching and placing subtasks to move the barrel in the temporary storage area to the original position.

[0061] In the above 6), the subtask is the smallest execution unit, including a picking up subtask and a releasing subtask.

[0062] In the above 7), the main function of the PLC corresponding message is that the dispatching system executes the corresponding algorithm logic according to the message content sent by the truss robot, and sorts out the corresponding message according to the algorithm output content, and sends it to the truss robot PLC in the communication mode of Modbus-TCP. See the table below:

[0063] 40050 40051 40052 40053 40054 Get Ready Confirmation Code Bucket position code Subflow Program Error Code 01 01 25 04 99 01 01 04 02 99 01 01 26 04 99

[0064] In the above 8), the truss robot scheduling program sends a subtask command message;

[0065] In the above 9), the truss robot PLC receives the subtask message and drives the execution unit to execute the task.

[0066] Through the above-mentioned technical scheme of the present invention, traditional warehouses can use truss robots more quickly and adapt to business changes when facing uncertain factors such as process changes and location changes. At the same time, it can also make the system more modifiable, highly scalable, robust and flexible.

[0067] For example, the position of a barrel changes; general scheduling system software must be modified from the underlying algorithm logic to adapt to the impact of the change factors on the system. Now most small and medium-sized enterprises are order-driven process production. As the market changes, process production including material storage must also change accordingly. This trend is becoming more and more obvious.

[0068] However, frequent system modifications and changes will reduce or even destroy the stability and availability of the system.

[0069] The technical solution of the present invention performs data modeling on the warehouse scene, abstracts the physical scene into a model structure, and then maps it into a two-dimensional table in the database, and finally converts the scene change into a modification of the table structure, thereby reducing the modification of the system code. At the same time, the system execution task is minimized, and the pick-and-place subtasks are combined together in the form of assembly, so as to meet the expansion and change of the system tasks in different combinations.

[0070] Ultimately, the physical scene is converted into a two-dimensional database table, and complex tasks are broken down into the smallest execution units. When the process or environment changes, the system can meet the requirements at the lowest cost.

[0071] The present invention is described above by way of example in conjunction with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A control method for a truss robot scheduling system driven by a data model, wherein the scheduling system includes a two-dimensional code visual recognition subsystem, a photoelectric sensor, and an industrial control computer (PLC); It is characterized in that The process of the control method is as follows: 1) The industrial computer (PLC) initiates a task request; 2) The system receives the message information and analyzes the task type; 3) Determine the task type and make a corresponding message correctness determination; 4) Enter the corresponding functional submodule, read the database information, and perform logical comparison; 5) If the judgment is correct, call the task generation function and pass in the function code; 6) Generate subtask sequence and store it in data task data table; 7) Reply to the corresponding message from PLC; 8) PLC resumes executing subtask command message; 9) Execute the task sequence in sequence and interact with PLC; 10) After the subtask sequence is completed, reply to PLC; 11) The process ends; In the above 5), the control system calculates the target bucket position according to the system algorithm, calls the task generation function, and passes the function code and the target bucket position information into the function, and generates the corresponding subtask sequence according to different function codes; Task generation includes the following situations: If there is no obstruction bucket, directly generate the fetch and place subtasks; If there is an obstructed barrel, you need to generate the fetch and place subtasks of the temporary storage area transfer first, put the obstructed barrel in the temporary storage area, then generate the fetch and place subtasks to fetch and place the barrels in the specified barrel position, and finally generate the fetch and place subtasks to move the barrels in the temporary storage area to their original positions; In the above 6), the subtask is the smallest execution unit, including a picking up subtask and a releasing subtask.

2. The control method of the truss robot scheduling system based on data model drive according to claim 1 is characterized in that: In the above 1), the PLC sends a message request to the truss scheduling system program; the message is transmitted via the Modbus-TCP protocol; The message includes request bits 1-49 and response bits 50-100; The tasks include raw material warehousing tasks, raw material warehousing tasks, warehouse transfer tasks, empty barrel warehousing tasks, and barrel recycling tasks.

3. The control method of the truss robot scheduling system based on data model drive according to claim 1 is characterized in that: In the above 2), the message is divided into two parts: a request bit and a response bit; The request bits include: function code, area code, coordinate information, custom A-axis coordinate information, barcode information, weight information, position number, sub-process number, working mode, working status, gripper status code, product information, particle size, sensor status information, empty barrel outbound quantity, initial position information, and target position information; The response bits include: a ready code, a confirmation code, a barrel code sub-flow program number, and an error code; The tasks include raw material warehousing tasks, raw material warehousing tasks, warehouse transfer tasks, empty barrel warehousing tasks, and barrel recycling tasks.

4. The control method of the truss robot scheduling system based on data model drive according to claim 1 is characterized in that: In the above 3), the task type is determined according to the function code and the area code; the verification method for executing different tasks is different; the result of each step is determined based on data correctness and operation safety; if the judgment is correct, the execution continues; if the judgment is wrong, an error code will be generated and uploaded to the central control system, and the task will be suspended.

5. The control method of the truss robot scheduling system based on data model drive according to claim 1, characterized in that: In the above 4), the logic comparison includes: comparing whether there is duplicate data according to the barcode information; comparing whether the barrel information is valid; and comparing whether the product code of the target barrel is consistent.

6. The control method of the truss robot scheduling system based on data model drive according to claim 1 is characterized in that: In the above 7), the main function of the PLC corresponding message is that the scheduling system executes the corresponding algorithm logic according to the message content sent by the truss robot, and sorts out the corresponding message according to the algorithm output content, and sends it to the truss robot PLC in the Modbus-TCP communication mode.

7. The control method of the truss robot scheduling system based on data model drive according to claim 1 is characterized in that: In the above 8), the truss robot scheduling program sends a subtask command message; in the above 9), the truss robot PLC receives the subtask message and drives the execution unit to execute the task.

8. The data model driven truss robot scheduling system according to claim 1, characterized in that: The scheduling system abstracts different physical scenarios through data modeling and maps them to the database relational table, abstracting the differences in physical scenarios into differences in data models and separating the process logic from the physical scenarios. At the same time, the process logic is finely granularly stripped in response to changes in the process flow, thereby converting the chain-like process logic into a network of sub-process combinations.

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