Cooperative scheduling method of industrial robot scheduling system

Abstract

The invention discloses a cooperative scheduling method of an industrial robot scheduling system, and relates to the technical field of cooperative scheduling, and the method comprises the steps: recording each robot participating in a scheduling task as a target robot, and obtaining a path transmission quality imbalance coefficient, a queuing coefficient and a transmission signal attenuation coefficient between the target robot and other target robots; a communication delay coefficient is obtained according to the path transmission quality imbalance coefficient, the queuing coefficient and the transmission signal attenuation coefficient, and communication delays between the target robot and other target robots are divided into different grades by combining a preset threshold value; and different optimization measures are adopted according to different communication delay levels to assist the cooperative scheduling of the industrial robot scheduling system, so that the communication delay between the industrial robots can be detected and corrected, timely task coordination and no data distortion are ensured, the influence on the execution efficiency of the overall assembly task is reduced, and the efficiency of the whole assembly task is improved. The production line stability and the product quality are ensured.

Description

Technical Field

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

[0002] The collaborative scheduling of the industrial robot scheduling system means that when multiple robots participate in a task together, through reasonable task allocation, path planning and time coordination, all robots can collaborate efficiently in space and time, avoid resource conflicts and delays, and ensure that the overall task is completed smoothly as planned. This scheduling is particularly suitable for industrial scenarios with high real-time and high-precision requirements, such as the synchronous operation of automobile assembly lines, where multiple robots need to collaborate to complete tasks such as welding, painting, and component installation, and each link is closely connected to achieve high-efficiency operation of the production line and consistency and accuracy of the product, ensuring that the production quality of automobile assembly parts is qualified and the production efficiency is high;

[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 automobile assembly lines, especially in the high-precision and high-beat production environment, it is crucial to ensure timely communication between industrial robots. However, the existing scheduling methods often assume that there is no communication delay between industrial robots and lack a mechanism to detect and correct communication delays. Communication delays may lead to untimely task coordination or data transmission errors, thereby 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 provides a collaborative scheduling method for an industrial robot scheduling system, the method comprising:

[0006] 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;

[0007] 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;

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

[0009] 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.

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

[0011] For each data transmission path, the signal strength within a preset time period is obtained and marked 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: 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, and commonly used mother wavelets include Haar wavelet and Morlet wavelet; 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;

[0013] 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;

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

[0015] Optionally, the nodes involved in the transmission between the target robot and other target robots are obtained, and the queuing coefficient is obtained by combining the key parameters of each node, including:

[0016] 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;

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

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

[0019] 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.

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

[0021] 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:

[0022]

[0023] In the formula, ev is the transmission signal attenuation coefficient, c is the speed of light, which is about 3×10 8 Meters / second.

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

[0025] 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.

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

[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, different optimization measures are taken according to different communication delay levels to assist the coordinated 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 is still carried out normally;

[0031] 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.

[0032] Beneficial effects of the present invention:

[0033] The present invention proposes a collaborative scheduling method for an industrial robot scheduling system, by recording each robot participating in the scheduling task as a target robot, obtaining the path transmission quality imbalance coefficient, queuing coefficient and transmission signal attenuation coefficient between the target robot and the remaining target robots; and obtaining the communication delay coefficient according to the path transmission quality imbalance coefficient, queuing coefficient and transmission signal attenuation coefficient, and dividing the communication delay between the target robot and the remaining target robots into different levels in combination with a preset threshold, and taking different optimization measures according to different communication delay levels to assist the collaborative scheduling of the industrial robot scheduling system, so that the communication delay between industrial robots can be detected and corrected, ensuring timely task coordination and undistorted data, reducing the impact on the overall assembly task execution efficiency, 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 in conjunction with the accompanying drawings.

[0035] Figure 1 The present invention is a flow chart of a collaborative scheduling method for an industrial robot scheduling system. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0038] The embodiment of the present invention provides a collaborative scheduling method for an industrial robot scheduling system. Figure 1 , Figure 1 A flowchart of a collaborative scheduling method for an industrial robot scheduling system provided by an embodiment of the present invention. The method comprises the following steps:

[0039] 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;

[0040] 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;

[0041] Obtaining the distance data between the target robot and other target robots, and obtaining the transmission signal attenuation coefficient according to the distance data;

[0042] 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.

[0043] Based on a collaborative scheduling method of an industrial robot scheduling system provided by an embodiment of the present invention, the communication delay between industrial robots can be detected and corrected through the above method, ensuring timely task coordination and data distortion, reducing the impact on the overall assembly task execution efficiency, and ensuring the stability of the production line and product quality.

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

[0045] For each data transmission path, the signal strength within a preset time period is obtained and marked 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: 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, commonly used mother wavelets include Haar wavelet and Morlet wavelet, etc.; s is the scale factor, which controls 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 shift parameter, which represents the local position of the signal in time;

[0047] 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;

[0048] 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.

[0049] It should be noted that the preset time period is set by professionals based on actual circumstances and is not limited or elaborated on in detail.

[0050] It should be noted that the data transmission path of the industrial robot refers to the several communication lines through which the data transmission between the robots passes, and the path transmission quality imbalance coefficient is an indicator used to measure the difference in signal transmission quality between all data transmission paths. The quality difference between the paths is calculated by signal strength to evaluate the balance of path transmission performance; if the imbalance coefficient is high, it means that the quality difference between the paths is large, which may cause some paths to be overloaded or have low transmission efficiency, causing communication delays between robots, thereby affecting the information interaction and task coordination between robots; the signal strength of the data transmission path of the industrial robot can be obtained in real time through the signal receiving module of the communication device, for example, by recording the signal strength through a wireless communication module (such as Wi-Fi, Zigbee, LoRa, 5G, etc.), or in other ways, which are not specifically limited.

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

[0052] Optimize communication path selection: The path transmission quality imbalance coefficient can reveal the quality differences between different transmission paths. If the imbalance coefficient is high, it means 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 give priority to paths with better signal quality for communication, thereby reducing delays and improving communication efficiency.

[0053] Identify load imbalance: A higher imbalance coefficient indicates that some paths may be overloaded. This load imbalance may cause network congestion, thereby increasing communication delays. By evaluating the difference in path quality, overloaded paths can be identified and measures (such as redistributing data flows or adding relay nodes) can be taken to relieve pressure and avoid increased delays.

[0054] Early warning of potential communication problems: By calculating the quality differences and imbalance coefficients of the paths, the system can predict which paths may have 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 problems occur to ensure the real-time and reliability of communication during task execution.

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

[0056] Improve overall task coordination: The communication delay between robots directly affects the collaborative efficiency of the task. A higher imbalance coefficient often means a larger delay difference, which may lead to poor coordination between robots and even low synchronization in the synchronous operation of the automobile assembly line, greatly increasing the defective rate of assembled products; through the analysis of the imbalance coefficient, the system can promptly discover and correct these communication problems, improve the ability of robots to perform 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, the nodes involved in the transmission between the target robot and the other target robots are obtained, and the queuing coefficient is obtained by combining the key parameters of each node, including:

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

[0059] Calculate the queue length Lq of each node. The calculation formula is:

[0060] According to the queue length Lq of each node, the queue delay Wq of each node is calculated. The calculation formula is:

[0061] 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.

[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); service rate (the number of data packets processed by the server per unit time, which can be obtained through the server's processing capacity and performance monitoring); variance of service time (an indicator reflecting the fluctuation of service time, usually calculated by analyzing historical service data); utilization (the workload of the node, calculated by the ratio of arrival rate to service rate). These data can be obtained through network monitoring tools, node performance analysis, server logs, and router statistics.

[0063] It should be noted that the data transmission nodes of industrial robots refer to the various network exchange nodes or devices that data passes through during the data transmission between robots, including routers, switches, signal relay devices, and direct communication links between robots. These nodes are responsible for data reception, forwarding, routing, caching and other operations to ensure the flow and communication of data between different robots. The performance of data transmission nodes, such as network bandwidth, processing power and response time, directly affects the delay and efficiency of communication; the queuing coefficient is an indicator to measure the queuing delay of data packets in a network or communication system. In a complex transmission process, data packets may need to 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 node, thereby affecting the overall delay of communication. A high queuing coefficient usually means that data transmission is bottlenecked at certain nodes, which may lead to increased delays, thereby affecting task execution and robot collaboration efficiency.

[0064] In one implementation, the benefits of analyzing the queuing coefficient for determining the degree of communication delay between industrial robots are as follows: First, the queuing coefficient can help identify possible bottlenecks or delay points in the data transmission process. By analyzing the queuing conditions of each data transmission node, it is possible to accurately find which nodes have high queuing delays, thereby affecting the overall communication efficiency. When industrial robots perform assembly tasks, timely data transmission is crucial. The analysis of queuing delays can provide a basis for optimizing network layout and adjusting data transmission strategies to ensure that the collaborative efficiency between robots is maximized. Secondly, the queuing coefficient can provide data support for the system's load balancing and resource scheduling. Communication between industrial robots often needs to pass through multiple relay nodes. If the service capacity of some nodes is low, it may cause serious queuing delays. By calculating and monitoring the queuing coefficient, the workload of the node 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 response speed of the overall network. This has a direct impact on ensuring the rapid response capability and coordination of industrial robots in complex environments, and helps to improve the success rate and efficiency of emergency rescue missions.

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

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

[0067]

[0068] In the formula, ev is the transmission signal attenuation coefficient, c is the speed of light, which is about 3×10 8 Meters / second.

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

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

[0071] In one implementation, the transmission signal attenuation coefficient plays an important role in determining the degree of communication delay between industrial robots, because it can reflect the degree to which the signal is attenuated during transmission due to distance, obstacles or environmental factors. A higher attenuation coefficient means that the signal takes longer to transmit to the target robot, resulting in increased communication delay. By monitoring and analyzing the transmission signal attenuation coefficient, the scheduling system can promptly identify potential communication bottlenecks, predict and optimize delays, thereby ensuring real-time data transmission and coordination between robots in complex automobile assembly line synchronization operations, especially in high-precision, high-beat production environments, and improving the efficiency and safety of overall assembly task execution.

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

[0073] 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.

[0074] The calculation formula is:

[0075] Qsg=f1×Kg+f2×Xg+f3×Hg

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

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

[0078] 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;

[0079] 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.

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

[0081] 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;

[0082] 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.

[0083] It should be noted that when the communication delay between the target robot and the other target robots is at a serious level, in order to optimize the communication delay, measures can be taken by reducing the size of the data packet and optimizing the communication protocol. First, reducing the size of the data packet is to reduce 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 of the signal during the transmission process. Secondly, optimizing the communication protocol is to ensure that data can be transmitted more efficiently within a limited time and reduce network congestion and delay by adjusting the processing logic of each layer in the protocol stack, such as using more efficient encoding methods, reducing confirmation delays, and optimizing transmission rates.

[0084] The reason for taking these measures is that the increase in communication delay will lead to a lag in data transmission, which will affect task coordination and information sharing between robots. In emergency rescue scenarios, especially when industrial robots are performing tasks, the transmission of real-time data is crucial. Severe communication delays may lead to inaccurate or delayed information updates, which in turn affects the effectiveness of task scheduling and collaborative execution. By reducing the size of data packets and optimizing protocols, the data transmission efficiency can be effectively improved, ensuring that the robot can receive the necessary instructions and environmental data in time during task execution, thereby avoiding decision-making errors and task delays caused by long delays.

[0085] Optimizing communication delays to ensure the normal coordinated scheduling between industrial robot scheduling systems can bring significant benefits. First, reducing delays can ensure the real-time nature of data, allowing robots to respond to instructions and feedback more quickly, especially in high-precision, high-beat production environments, which is crucial for improving response speed and processing capabilities. Secondly, optimized communication protocols can not only reduce delays, but also improve the stability and fault tolerance of the system, and avoid communication interruptions caused by excessive network load. In addition, ensuring that communication delays are within a controllable range can also improve the efficiency of the entire robot group's collaborative operations, avoid coordination failures caused by poor communication, and thus improve the execution quality and safety of the overall assembly task.

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

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

[0088] Segmented transmission: For larger data sets, they can be divided into multiple small packets for transmission separately to avoid packet loss or retransmission due to large data packets, thereby increasing latency.

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

[0090] Raw 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, and commonly used mother wavelets include Haar wavelet and Morlet wavelet; 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 each two paths, the calculation expression is: D i，j = 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: In the formula, ev is the transmission signal attenuation coefficient, c is the speed of light, which is about 3×10 8 Meters / second.

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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