A collaborative scheduling method for an industrial robot scheduling system

Abstract

The present invention discloses a collaborative scheduling method for an industrial robot scheduling system, which relates to the technical field of collaborative scheduling. Each robot participating in the scheduling task is denoted as a target robot, and the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient between the target robot and the other target robots are obtained. Then, the communication delay coefficient is obtained based on the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient, and the communication delay between the target robot and the other target robots is divided into different levels in combination with a preset threshold. Different optimization measures are taken according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system. In this way, the detection and correction of the communication delay between industrial robots can be carried out, ensuring timely task coordination and undistorted data, reducing the impact on the execution efficiency of the overall assembly task, and ensuring the stability of the production line and the product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative scheduling, and particularly to a collaborative scheduling method for an industrial robot scheduling system. Background Art

[0002] The collaborative scheduling of an industrial robot scheduling system refers to the situation where multiple robots participate in tasks together. Through reasonable task allocation, path planning, and time coordination, all robots can cooperate efficiently in space and time, avoid resource conflicts and delays, and ensure the smooth completion of the overall task as planned. This kind of scheduling is especially suitable for industrial scenarios with high real-time and high-precision requirements, such as the synchronous operation of an automotive assembly line, where multiple robots need to cooperate to complete tasks such as welding, painting, and component installation, and the various links are closely connected; in order to achieve the high-efficiency operation of the production line and the consistency and precision of products, ensure the qualified production quality and high production efficiency of automotive assemblies;

[0003] In the actual working process, industrial robots need to communicate frequently to share task progress and operation status; in the complex synchronous operation of an automotive assembly line, especially in a high-precision and high-tempo production environment, it is crucial to ensure timely communication between industrial robots; however, existing scheduling methods often default that there is no communication delay between industrial robots and lack a detection and correction mechanism for communication delay; communication delay may lead to untimely task coordination or data transmission errors, thus affecting the execution efficiency of the overall assembly task and reducing the stability of the production line and product quality. Summary of the Invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide a collaborative scheduling method for an industrial robot scheduling system.

[0005] The present invention proposes a collaborative scheduling method for an industrial robot scheduling system, and the method includes:

[0006] Denote each robot participating in the scheduling task as a target robot, obtain the number of data transmission paths between the target robot and the other target robots, and obtain the signal strength of each data transmission path to obtain the path transmission quality imbalance coefficient;

[0007] Obtain the nodes involved in the transmission between the target robot and the other target robots, and combine the key parameters of each node to obtain the queuing coefficient;

[0008] Obtain the distance data between the target robot and the other target robots, and obtain the transmission signal attenuation coefficient according to the distance data;

[0009] Obtain the communication delay coefficient based on the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient, divide the communication delay between the target robot and the remaining target robots into different levels in combination with a preset threshold, and take different optimization measures according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system.

[0010] Optionally, obtaining the signal strength of each data transmission path to obtain the path transmission quality imbalance coefficient includes:

[0011] For each data transmission path, obtain the signal strength within a preset time period and mark it as x(τ).

[0012] Perform wavelet transform on the delay data of each path to extract its frequency domain information. The wavelet transform formula is as follows: In the formula, W ψ (s,t) is the wavelet transform coefficient, representing the local characteristics of the signal at scale s and position t; x(τ) is the signal strength, ψ is the mother wavelet function, and common mother wavelets such as Haar wavelet, Morlet wavelet, etc.; s is the scale factor, controlling the time-frequency decomposition of the signal; t is the time translation parameter, representing the local position of the signal in time.

[0013] Calculate the quality difference between every two paths. The calculation expression is: In the formula, D i，j represents the quality difference between paths i and j, t represents the total number of time instants t, usually the length of the time series.

[0014] Calculate the path transmission quality imbalance coefficient. The calculation formula is: fg = In the formula, fg is the path transmission quality imbalance coefficient, and N represents the total number of data transmission paths.

[0015] Optionally, obtain the nodes involved in the transmission between the target robot and the remaining target robots, and combine the key parameters of each node to obtain the queuing coefficient, including:

[0016] Obtain all the nodes involved in the transmission between the target robot and the remaining target robots, and obtain the arrival rate λ of each node; service rate μ; variance Var(S) of the service time, utilization rate ρ;

[0017] Calculate the queuing length Lq of each node. The calculation formula is:

[0018] According to the queuing length Lq of each node, calculate the queuing delay Wq of each node. The calculation formula is:

[0019] Calculate the queuing coefficient qs, and the calculation formula is: In the formula, r is the sequence number of the node, and k is the total number of nodes.

[0020] Optionally, obtain the distance data between the target robot and the other target robots, and obtain the transmission signal attenuation coefficient according to the distance data, including:

[0021] Obtain the shortest physical distance d between the target robot and the other target robots, the signal frequency f, and calculate the transmission signal attenuation coefficient. The calculation formula is:

[0022]

[0023] In the formula, ev is the transmission signal attenuation coefficient, c is the speed of light, approximately 3×10 8 m / s.

[0024] Optionally, obtain the communication delay coefficient according to the path transmission quality imbalance coefficient, the queuing coefficient, and the transmission signal attenuation coefficient, including:

[0025] Remove the units of the path transmission quality imbalance coefficient, the queuing coefficient, and the transmission signal attenuation coefficient, and obtain the communication delay coefficient through the path transmission quality imbalance coefficient, the queuing coefficient, and the transmission signal attenuation coefficient after unit removal.

[0026] Optionally, divide the communication delay between the target robot and the other target robots into different levels in combination with a preset threshold, including:

[0027] When the communication delay coefficient is less than the preset threshold, it means that the communication delay between the target robot and the other target robots is relatively slight, and the communication delay is recorded as a slight level;

[0028] When the communication delay coefficient is not less than the preset threshold, it means that the communication delay between the target robot and the other target robots is relatively serious, and the communication delay is recorded as a serious level.

[0029] Optionally, take different optimization measures according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system, including:

[0030] When the communication delay between the target robot and the other target robots is at a slight level, no measures need to be taken, and the collaborative scheduling between the industrial robot scheduling systems still proceeds normally;

[0031] When the communication delay between the target robot and the other target robots is at a serious level, reduce the data packet size and optimize the communication protocol to optimize the communication delay and ensure the normal collaborative scheduling between the industrial robot scheduling systems.

[0032] Advantages of the present invention:

[0033] The present invention provides a cooperative scheduling method for an industrial robot scheduling system. By designating each robot participating in the scheduling task as a target robot, the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient between the target robot and the other target robots are obtained. Based on the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient, a communication delay coefficient is obtained. Combining with a preset threshold, the communication delay between the target robot and the other target robots is divided into different levels, and different optimization measures are taken according to different communication delay levels to assist the cooperative scheduling of the industrial robot scheduling system. In this way, the detection and correction of communication delay between industrial robots can be achieved, ensuring timely task coordination and data integrity, reducing the impact on the execution efficiency of the overall assembly task, and ensuring the stability of the production line and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] Figure 1 It is a flowchart of a cooperative scheduling method for an industrial robot scheduling system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] The embodiments of the present invention provide a cooperative scheduling method for an industrial robot scheduling system. Refer to Figure 1 , Figure 1 It is a flowchart of a cooperative scheduling method for an industrial robot scheduling system provided by the embodiments of the present invention. The method includes the following steps:

[0039] Designate each robot participating in the scheduling task as a target robot, obtain the number of data transmission paths between the target robot and the other target robots, and obtain the signal strength of each data transmission path to obtain the path transmission quality imbalance coefficient;

[0040] Obtain the nodes involved in the transmission between the target robot and the other target robots, and combine the key parameters of each node to obtain the queuing coefficient;

[0041] Obtain the distance data between the target robot and the remaining target robots, and obtain the transmission signal attenuation coefficient according to the distance data;

[0042] Obtain the communication delay coefficient according to the path transmission quality imbalance coefficient, queuing coefficient and transmission signal attenuation coefficient, divide the communication delay between the target robot and the remaining target robots into different levels in combination with a preset threshold, and take different optimization measures according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system.

[0043] Based on the collaborative scheduling method of an industrial robot scheduling system provided by an embodiment of the present invention, in the above manner, it is possible to detect and correct the communication delay between industrial robots, ensure timely task coordination and data integrity, reduce the impact on the execution efficiency of the overall assembly task, and ensure the stability of the production line and product quality.

[0044] In one embodiment, obtaining the path transmission quality imbalance coefficient by obtaining the signal strength of each data transmission path includes:

[0045] For each data transmission path, obtain the signal strength within a preset time period and mark it as x(τ).

[0046] Perform wavelet transform on the delay data of each path to extract its frequency domain information. The wavelet transform formula is as follows: In the formula, W ψ (s,t) is the wavelet transform coefficient, representing the local characteristics of the signal at scale s and position t; x(τ) is the signal strength, ψ is the mother wavelet function, and common mother wavelets such as Haar wavelet, Morlet wavelet, etc.; s is the scale factor, controlling the time-frequency decomposition of the signal (large s corresponds to low-frequency components, small s corresponds to high-frequency components); t is the time translation parameter, representing the local position of the signal in time.

[0047] Calculate the quality difference between every two paths, and the calculation expression is: In the formula, D i，j represents the quality difference between paths i and j, T represents the total number of time instants t, usually the length of the time series;

[0048] Calculate the path transmission quality imbalance coefficient, and the calculation formula is: In the formula, fg is the path transmission quality imbalance coefficient, and N represents the total number of data transmission paths.

[0049] It should be noted that the preset time period is set by professionals according to the actual situation, and specific limitations and details are not elaborated.

[0050] It should be noted that the data transmission path of industrial robots refers to several communication lines through which data is transmitted between robots, and the path transmission quality imbalance coefficient is an index used to measure the signal transmission quality differences between all data transmission paths. The quality differences between paths are calculated through signal strength to evaluate the balance of path transmission performance. If the imbalance coefficient is relatively high, it indicates that the quality differences between paths are relatively large, which may lead to overloading of some paths or low transmission efficiency, resulting in communication delays between robots, thereby affecting the information interaction and task collaboration effect between robots. The signal strength of the data transmission path of industrial robots can be obtained in real time through the signal receiving module of the communication device. For example, the signal strength can be recorded through a wireless communication module (such as Wi-Fi, Zigbee, LoRa, 5G, etc.), or it can be other methods, which are not specifically limited.

[0051] The benefits of analyzing the path transmission quality imbalance coefficient for judging the degree of communication delay between industrial robots are as follows:

[0052] Optimizing communication path selection: The path transmission quality imbalance coefficient can reveal the quality differences between different transmission paths. If the imbalance coefficient is relatively high, it indicates that the signal quality of some paths is poor, which may lead to slow transmission speed and large delay. By calculating and analyzing the imbalance coefficient, the system can preferentially select paths with better signal quality for communication, thereby reducing delay and improving communication efficiency.

[0053] Identifying load imbalance: A relatively high imbalance coefficient indicates that some paths may bear excessive load, and this load imbalance may lead to network congestion, thereby increasing communication delay. By evaluating the path quality differences, paths with excessive load can be identified, and measures (such as reallocating data streams or adding relay nodes) can be taken to relieve the pressure and avoid an increase in delay.

[0054] Early warning of potential communication problems: By calculating the path quality differences and imbalance coefficient, the system can predict which paths may have relatively large delay problems at a specific moment. This provides a forward-looking optimization strategy for the industrial robot scheduling system, which can be adjusted before the problem occurs to ensure the real-time and reliable communication during task execution.

[0055] Dynamically adjusting communication strategies: When the system detects an imbalance in path transmission quality, it can dynamically adjust communication strategies, such as enhancing the signal strength of weak signal paths, adding communication relay devices, or adjusting task scheduling strategies, to ensure that the information transmission between robots is not affected by excessive delay, thereby improving the collaborative working efficiency of the entire system.

[0056] Improve overall task coordination: The communication delay between robots directly affects the collaborative efficiency of tasks. A higher imbalance coefficient often means a larger delay difference, which may lead to poor coordination between robots and even a lower synchronization in the synchronous operation of the automotive assembly line, resulting in a significant increase in the defective rate of assembled products. By analyzing the imbalance coefficient, the system can promptly detect and correct these communication problems, enhance the ability of robots to execute tasks collaboratively, and thus improve the overall rescue effect and safety. In summary, the path transmission quality imbalance coefficient not only helps to judge and reduce the communication delay between industrial robots but also provides data support for optimizing scheduling, enhancing system robustness, and improving rescue efficiency.

[0057] In one embodiment, obtain the nodes involved in the transmission between the target robot and the other target robots, and combine the key parameters of each node to obtain the queuing coefficient, including:

[0058] Obtain all the nodes involved in the transmission between the target robot and the other target robots, and obtain the arrival rate λ (the number of data packets arriving per unit time, such as the number of requests arriving per second) of each node; the service rate μ (the number of customers or data packets that the server can process per unit time, such as the number of requests processed per second); the variance Var(S) of the service time (the variance of the service time, reflecting the degree of fluctuation of the service time); the utilization rate ρ (the workload of the system, defined as the ratio of the arrival rate to the service rate);

[0059] Calculate the queuing length Lq of each node, and the calculation formula is:

[0060] According to the queuing length Lq of each node, calculate the queuing delay Wq of each node, and the calculation formula is:

[0061] Calculate the queuing coefficient qs, and the calculation formula is: In the formula, r is the sequence number of the node, and k is the total number of nodes.

[0062] It should be noted that the key parameters of the nodes involved in the transmission process between the target robot and the other target robots include: the arrival rate of each node (the number of data packets arriving per unit time, usually obtained through network traffic monitoring or router data packet statistics); the service rate (the number of data packets processed by the server per unit time, which can be obtained through the processing capacity and performance monitoring of the server); the variance of the service time (an index reflecting the fluctuation of the service time, usually calculated by analyzing historical service data); the utilization rate (the workload of the node, calculated by the ratio of the arrival rate to the service rate). These data can be obtained through network monitoring tools, node performance analysis, server logs, and router statistical data, etc.

[0063] It should be noted that the data transmission nodes of industrial robots refer to the various network switching nodes or devices through which data passes during the process of data transmission between robots, including routers, switches, signal relay devices, and direct communication links between robots. These nodes are responsible for operations such as data reception, forwarding, routing, and caching, ensuring the flow and communication of data between different robots. The performance of data transmission nodes, such as network bandwidth, processing capacity, and response time, directly affects the latency and efficiency of communication; the queuing coefficient is an indicator that measures the queuing delay of data packets in a network or communication system. In a complex transmission process, data packets may pass through multiple nodes, and each node may introduce a certain queuing delay. The queuing coefficient reflects the queuing situation in the system by analyzing parameters such as the arrival rate, service rate, and variance of service time of the nodes, and thus affects the overall latency of communication. A high queuing coefficient usually means that data transmission is bottlenecked at some nodes, which may lead to an increase in latency, thereby affecting task execution and the collaborative efficiency of robots.

[0064] In one implementation method, the benefits of analyzing the queuing coefficient for judging the communication latency degree between industrial robots are as follows: First, the queuing coefficient can help identify potential bottlenecks or latency points in the data transmission process. By analyzing the queuing situation of each data transmission node, it is possible to accurately find which nodes have a high queuing delay, thus affecting the overall communication efficiency. When industrial robots are performing assembly tasks, the timely transmission of data is crucial. The analysis of queuing delay can provide a basis for optimizing the network layout and adjusting the data transmission strategy to ensure the maximization of the collaborative efficiency between robots. Second, the queuing coefficient can provide data support for system load balancing and resource scheduling. The communication between industrial robots often needs to pass through multiple relay nodes. If the service capacity of some nodes is low, it may lead to serious queuing delays. By calculating and monitoring the queuing coefficient, the workload of the nodes can be dynamically adjusted, the allocation of network resources can be optimized, and the pressure on high-load nodes can be reduced, thereby improving the overall response speed of the network. This has a direct impact on ensuring the rapid response ability and coordination of industrial robots in a complex environment, and helps to improve the success rate and efficiency of emergency rescue tasks.

[0065] In one embodiment, obtaining the distance data between a target robot and other target robots and obtaining the transmission signal attenuation coefficient based on the distance data includes:

[0066] Obtain the shortest physical distance d between the target robot and other target robots, the signal frequency f, in hertz (Hz), calculate the transmission signal attenuation coefficient, and the calculation formula is:

[0067]

[0068] where ev is the attenuation coefficient of the transmission signal, c is the speed of light, approximately 3×10 8 m / s.

[0069] It should be noted that the shortest physical distance between target robots is obtained through the positioning system or sensors between robots, and is usually accurately calculated through GPS or indoor positioning systems (such as ultra-wideband UWB). Secondly, the signal frequency is usually determined by communication protocols (such as Wi-Fi, Bluetooth, Zigbee, etc.), and the frequency parameters can be obtained according to the wireless communication technology used by the robots.

[0070] It should be noted that in the industrial robot scheduling system, the attenuation coefficient of the transmission signal is used to evaluate the communication quality between robots. In the synchronous operation of complex automotive assembly lines, especially in high-precision and high-tempo production environments, the attenuation coefficient of the signal can reflect the degree of communication delay in the communication path. A larger attenuation coefficient of the transmission signal usually means that the signal encounters more obstacles or is at a greater distance during transmission, resulting in higher latency and poorer communication quality. In fire or emergency rescue missions, the communication quality directly affects the data transmission efficiency and collaborative operation between robots. The increase in signal attenuation may lead to untimely information exchange and inaccurate task coordination between robots, thus reducing the overall rescue efficiency.

[0071] In one implementation, the attenuation coefficient of the transmission signal plays an important role in judging the degree of communication delay between industrial robots because it can reflect the degree of attenuation of the signal due to distance, obstacles, or environmental factors during transmission. A higher attenuation coefficient means that the signal takes longer to reach the target robot, resulting in an increase in communication delay. By monitoring and analyzing the attenuation coefficient of the transmission signal, the scheduling system can timely identify potential communication bottlenecks, predict and optimize the delay, so as to ensure real-time data transmission and coordination between robots in the synchronous operation of complex automotive assembly lines, especially in high-precision and high-tempo production environments, and improve the efficiency and safety of the overall assembly task execution.

[0072] In one embodiment, obtaining the communication delay coefficient based on the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient includes:

[0073] Removing the units of the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient, and obtaining the communication delay coefficient through the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient after unit removal.

[0074] The calculation formula is:

[0075] Qsg = f1×Kg + f2×Xg + f3×Hg

[0076] Wherein, Qsg is the communication delay coefficient, and Kg, Wg, and Hg are respectively the path transmission quality imbalance coefficient, queuing coefficient, and transmission signal attenuation coefficient after unit removal. f1, f2, and f3 are respectively the proportionality coefficients of Kg, Wg, and Hg, and f1, f2, and f3 are all greater than 0;

[0077] In one embodiment, classifying the communication delay between the target robot and the remaining target robots into different levels in combination with a preset threshold includes:

[0078] When the communication delay coefficient is less than the preset threshold, it indicates that the communication delay between the target robot and the remaining target robots is relatively slight, and the communication delay is recorded as a slight level;

[0079] When the communication delay coefficient is not less than the preset threshold, it indicates that the communication delay between the target robot and the remaining target robots is relatively serious, and the communication delay is recorded as a serious level.

[0080] In one embodiment, taking different optimization measures according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system includes:

[0081] When the communication delay between the target robot and the remaining target robots is at a slight level, no measures need to be taken, and the collaborative scheduling between the industrial robot scheduling systems still proceeds normally;

[0082] When the communication delay between the target robot and the remaining target robots is at a serious level, reduce the data packet size and optimize the communication protocol to optimize the communication delay, ensuring the normal collaborative scheduling between the industrial robot scheduling systems.

[0083] It should be noted that when the communication delay between the target robot and the remaining target robots is at a serious level, in order to optimize the communication delay, measures can be taken from two aspects: reducing the data packet size and optimizing the communication protocol. First, reducing the data packet size reduces the transmission load by compressing the data or simplifying the data content, which can effectively reduce the bandwidth and transmission time required during the transmission process, and reduce the attenuation and delay generated by the signal during the transmission process. Second, optimizing the communication protocol ensures that data can be transmitted more efficiently within a limited time by adjusting the processing logic of each layer in the protocol stack, such as adopting a more efficient coding method, reducing the acknowledgment delay, and optimizing the transmission rate, etc., reducing network congestion and delay.

[0084] The reason for taking these measures is that the increase in communication latency will lead to a lag in data transmission, thus affecting the task coordination and information sharing among robots. In emergency rescue scenarios, especially when industrial robots are performing tasks, the transmission of real-time data is crucial. Severe communication latency may result in inaccurate or delayed-updated information, which in turn affects the effectiveness of task scheduling and collaborative execution. By reducing the packet size and optimizing the protocol, the data transmission efficiency can be effectively improved, ensuring that robots can receive necessary instructions and environmental data in a timely manner during task execution, thus avoiding decision-making errors and task delays caused by excessive latency.

[0085] Optimizing communication latency to ensure the normal collaborative scheduling between industrial robot scheduling systems can bring significant benefits. First of all, reducing latency can ensure the real-time nature of data, enabling robots to respond to instructions and feedback more quickly. Especially in high-precision and high-tempo production environments, this is crucial for improving the response speed and processing ability. Secondly, the optimized communication protocol can not only reduce latency but also improve the stability and fault tolerance of the system, avoiding communication interruptions caused by excessive network load. In addition, ensuring that the communication latency is within a controllable range can also improve the efficiency of the entire group of robots in collaborative operations, avoiding coordination failures caused by poor communication, thereby improving the execution quality and safety of the overall assembly task.

[0086] It should be noted that the measures to reduce the packet size are as follows:

[0087] Optimize the data format: Compress the transmitted data or adopt a more efficient encoding format. For example, for status update data, avoid transmitting redundant information, reduce useless data fields, and transmit the most concise messages.

[0088] Segmented transmission: For large data sets, they can be divided into multiple small packets for separate transmission to avoid packet loss or retransmission caused by overly large packets, which in turn increases latency.

[0089] Example: Remove unnecessary additional information, such as the robot's battery status, temperature, or non-critical sensor data, and only retain the task status necessary for real-time operations. For example:

[0090] Original data:

[0091] {"robot_id":1,"position":{"x":10.123,"y":20.456,"z":30.789},"status":"operating","battery":85,"temperature":45}

[0092] Optimized data:

[0093] {"id":1,"pos":"10.1,20.4,30.7","st":"operating"}

[0094] This can reduce the size of the data packet while ensuring the integrity of the necessary information.

[0095] By transmitting only critical information directly related to the task, transmission delays can be effectively reduced.

[0096] The measures to optimize the communication protocol are: Choose the appropriate transport layer protocol: For example, choose UDP instead of TCP, because the UDP protocol does not guarantee the reliable transmission of data packets compared to TCP, but it can reduce the delay caused by the retransmission mechanism. In some applications with high real-time requirements, appropriately sacrificing reliability can be exchanged for lower delay.

[0097] Use custom compression algorithms: Designing lightweight custom compression algorithms for commonly used data (such as robot status information, etc.) can significantly reduce the amount of data and latency during transmission.

[0098] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A collaborative scheduling method for an industrial robot scheduling system, characterized in that: The following steps are involved: Each robot participating in the scheduling task is recorded as a target robot, the number of data transmission paths between the target robot and the remaining target robots is obtained, and the signal strength of each data transmission path is obtained to obtain the path transmission quality imbalance coefficient; Obtain the nodes involved in the transmission between the target robot and other target robots, and obtain the queuing coefficient by combining the key parameters of each node; Obtaining the distance data between the target robot and other target robots, and obtaining the transmission signal attenuation coefficient according to the distance data; The communication delay coefficient is obtained according to the path transmission quality imbalance coefficient, queuing coefficient and transmission signal attenuation coefficient, and the communication delay between the target robot and the other target robots is divided into different levels according to the preset threshold. Different optimization measures are taken according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system.

2. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: Obtaining the signal strength of each data transmission path to obtain the path transmission quality imbalance coefficient includes: For each data transmission path, the signal strength within a preset time period is obtained and marked as x(τ). Perform wavelet transform on the delay data of each path to extract its frequency domain information; the wavelet transform formula is as follows: Where W ψ (s, t) is the wavelet transform coefficient, which represents the local characteristics of the signal at scale s and position t; x(τ) is the signal strength, ψ is the mother wavelet function; s is the scale factor, which controls the time-frequency decomposition of the signal; t is the time shift parameter, which represents the local position of the signal in time; Calculate the quality difference between every two paths, and the calculation expression is: Where D i，j represents the quality difference between paths i and j, T represents the total number of time t, usually the length of the time series; Calculate the path transmission quality imbalance coefficient. The calculation formula is: Where fg is the path transmission quality imbalance coefficient, and N represents the total number of data transmission paths.

3. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: Obtain the nodes involved in the transmission between the target robot and other target robots, and combine the key parameters of each node to obtain the queuing coefficient including: Obtain all nodes involved in the transmission between the target robot and other target robots, and obtain the arrival rate λ of each node; service rate μ; variance of service time Var(S), utilization rate ρ; Calculate the queue length Lq of each node. The calculation formula is: According to the queue length Lq of each node, the queue delay Wq of each node is calculated. The calculation formula is: Calculate the queuing coefficient qs, the calculation formula is: In the formula, r is the sequence number of the node, and k is the total number of nodes.

4. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: Obtaining the distance data between the target robot and the other target robots, and obtaining the transmission signal attenuation coefficient according to the distance data includes: Obtain the shortest physical distance d and signal frequency f between the target robot and the other target robots, and calculate the transmission signal attenuation coefficient. The calculation formula is: Where ev is the transmission signal attenuation coefficient and c is the speed of light.

5. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: The communication delay coefficients obtained based on the path transmission quality imbalance coefficient, queuing coefficient and transmission signal attenuation coefficient include: The communication delay coefficient is obtained by removing the unit of the path transmission quality imbalance coefficient, the queuing coefficient and the transmission signal attenuation coefficient.

6. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: The communication delay between the target robot and other target robots is divided into different levels in combination with the preset threshold, including: When the communication delay coefficient is less than the preset threshold, it means that the communication delay between the target robot and the other target robots is relatively slight, and the communication delay is recorded as a slight level; When the communication delay coefficient is not less than the preset threshold, it means that the communication delay between the target robot and the other target robots is relatively serious, and the communication delay is recorded as a serious level.

7. The collaborative scheduling method of an industrial robot scheduling system according to claim 1, characterized in that: Different optimization measures are taken according to different communication delay levels to assist the coordinated scheduling of industrial robot scheduling systems, including: When the communication delay between the target robot and the other target robots is at a slight level, no measures need to be taken, and the collaborative scheduling between the industrial robot scheduling systems is still carried out normally; When the communication delay between the target robot and the remaining target robots is at a serious level, the data packet size is reduced, and the communication protocol is optimized to optimize the communication delay to ensure normal collaborative scheduling between the industrial robot scheduling systems.

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