A TSN configuration method, system and terminal based on the robot operating environment

Abstract

The present application relates to a TSN configuration method, system and terminal based on a robot operating environment, and relates to the field of industrial Internet, including obtaining external environment parameters of the robot; analyzing the external environment parameters to determine the operation risk index of the robot; determining the switching operation mode of the robot according to the operation risk index and the preset index mode relationship; obtaining the actual operation mode and the actual TSN network architecture of the robot; judging whether the actual operation mode meets the requirements of the switching operation mode; if it meets, maintaining the actual TSN network architecture and continuing to obtain the external environment parameters of the robot for cyclic analysis and judgment; if it does not meet, reconfiguring the network architecture for the robot. The present application has the effect of improving the efficiency of network resource allocation.

Description

Technical Field

[0001] The present application relates to the technical field of industrial Internet, and in particular, to a TSN configuration method, system and terminal based on a robot operating environment. Background Art

[0002] Time Sensitive Network (TSN) is a set of communication standards aimed at achieving low-latency and deterministic high-bandwidth transmission in Ethernet, and these standards include time synchronization, traffic shaping, priority scheduling, reliable transmission and network management.

[0003] In related technologies, robots usually involve multiple types of data streams, and the requirements for time sensitivity and bandwidth of multiple types of data are usually different. For example, real-time control data requires low latency and high priority, visual sensor data requires large bandwidth, and status monitoring data is of lower priority. Therefore, when configuring a TSN network for a robot, time synchronization, bandwidth reservation and traffic scheduling, VLAN division and priority setting are performed in sequence, so as to ensure that all robots are synchronized within the same time domain and ensure that high-priority data is transmitted within a specified time window.

[0004] In view of the above related technologies, when configuring a TSN network for a robot, although the different requirements for priority and bandwidth of different types of data are considered, and priorities and specified transmission time windows are set for different types of data, the working environment of the robot is complex. Once situations such as collision, slipping or power failure occur, the priorities and bandwidths of the data required by the robot will change, resulting in the inability to reallocate network resources in time, and there is still room for improvement. Summary of the Invention

[0005] In order to improve the efficiency of network resource allocation, the present application provides a TSN configuration method, system and terminal based on a robot operating environment.

[0006] In a first aspect, the present application provides a TSN configuration method based on a robot operating environment, adopting the following technical solution:

[0007] A TSN configuration method based on a robot operating environment includes:

[0008] Obtain external environment parameters of the robot;

[0009] Analyze the external environment parameters to determine the operation risk index of the robot;

[0010] Determine the switching operation mode of the robot according to the operation risk index and the preset index mode relationship;

[0011] Obtain the actual operating mode of the robot and the actual TSN network architecture;

[0012] Determine whether the actual operating mode meets the requirements for switching the operating mode;

[0013] If it meets the requirements, maintain the actual TSN network architecture and continue to obtain the external environment parameters of the robot for cyclic analysis and judgment;

[0014] If it does not meet the requirements, reconfigure the network architecture for the robot.

[0015] By adopting the above technical solution, after analyzing the external environment parameters of the robot, the operation risk index of the robot is determined, and then the switching operation mode is determined according to the relationship between the operation risk index and the index mode. When it is determined that the switching operation mode is inconsistent with the actual operation mode, it indicates that the change in the external environment of the robot causes the change in the network resource configuration of the robot. Therefore, the network architecture of the robot is reconfigured, thereby improving the efficiency of network resource allocation.

[0016] Optionally, the steps for analyzing the external environment parameters to determine the operation risk index of the robot include:

[0017] Analyze the external environment parameters to determine the disaster parameter value, the obstacle dynamic parameter value, and the communication quality parameter value;

[0018] Analyze the disaster parameter value and the preset disaster parameter threshold to determine the disaster risk index;

[0019] Analyze the obstacle dynamic parameter value and the preset safety dynamic parameter value to determine the obstacle risk index;

[0020] Analyze the communication quality parameter value and the preset error code parameter threshold to determine the communication risk index;

[0021] Obtain the risk index weight coefficient;

[0022] Analyze the risk index weight coefficient, the disaster risk index, the obstacle risk index, and the communication risk index to determine the operation risk index.

[0023] By adopting the above technical solution, the disaster risk index, the obstacle risk index, and the communication risk index are respectively obtained by analyzing the disaster parameter value, the obstacle dynamic parameter value, and the communication quality parameter value. Then, the operation risk index is obtained by weighted fusion of the disaster risk index, the obstacle risk index, and the communication risk index according to the risk index weight coefficient. Thus, the risk index is calculated based on the factors affecting the operation of the robot in the external environment, and the accuracy of the operation risk index is improved.

[0024] Optionally, the steps for obtaining the risk index weight coefficient include:

[0025] Obtain historical environmental parameters and historical weight coefficients;

[0026] Control a preset environmental prediction model to make a prediction based on the historical environmental parameters to generate predicted environmental parameters;

[0027] Obtain an environmental transition probability matrix;

[0028] Analyze the environmental transition probability matrix and the predicted environmental parameters to determine the transition probability and the predicted transition environment;

[0029] Obtain the environmental weight coefficient of the predicted transition environment;

[0030] Analyze the environmental weight coefficient, the historical weight coefficient, and the transition probability to determine the risk index weight coefficient.

[0031] By adopting the above technical solution, control the environmental prediction model to generate predicted environmental parameters based on the historical environmental parameters, and then identify the transition probability and the predicted transition environment in the environmental transition probability matrix according to the predicted environmental parameters and the historical environmental parameters, so as to calculate the risk index weight coefficient based on the environmental weight coefficient, the historical weight coefficient, and the transition probability of the predicted transition environment, thereby ensuring the efficiency of determining the risk index weight coefficient.

[0032] Optionally, the steps of analyzing the environmental weight coefficient, the historical weight coefficient, and the transition probability to determine the risk index weight coefficient include:

[0033] Determine a probability amplification coefficient according to the predicted transition environment and a preset environmental coefficient relationship;

[0034] Analyze the transition probability and the probability amplification coefficient to determine the amplified probability;

[0035] Analyze the environmental weight coefficient, the amplified probability, and the historical weight coefficient to determine the risk index weight coefficient.

[0036] By adopting the above technical solution, determine the probability amplification coefficient according to the predicted transition environment and the environmental coefficient relationship, then amplify the transition probability with the probability amplification coefficient to obtain the amplified probability, so as to analyze and calculate the environmental weight coefficient and the historical weight coefficient with the amplified probability to obtain the risk index weight coefficient, and calculate the risk index weight coefficient with the amplified transition probability, actively adapt to the prediction trend, and improve the efficiency of determining the weight coefficient.

[0037] Optionally, the steps of reconfiguring the network architecture for the robot include:

[0038] Determine a switching network architecture according to the switching operation mode and a preset mode architecture relationship; the switching network architecture includes a centralized architecture, a distributed edge architecture, and an ad hoc network architecture;

[0039] Reconfigure the network architecture for the robot according to the switched network architecture, and determine whether the switched network architecture is an ad-hoc network architecture;

[0040] If not, continue to obtain the external environment parameters of the robot for loop analysis and judgment;

[0041] If so, perform data protection on the robot according to the operation risk index.

[0042] By adopting the above technical solution, determine the switched network architecture according to the switched operation mode and the relationship between the mode architectures, and reconfigure the network architecture for the robot according to the switched network architecture, thereby improving the efficiency of configuring the network architecture.

[0043] Optionally, the steps of performing data protection on the robot according to the operation risk index include:

[0044] Judge whether the operation risk index meets the requirements of the preset protection index;

[0045] If not, continue to obtain the external environment parameters of the robot for loop analysis and judgment;

[0046] If it meets, obtain the compliance time of the operation risk index;

[0047] Judge whether the compliance time exceeds the preset protection time;

[0048] If it does not exceed, continue to obtain the compliance time of the operation risk index for loop judgment;

[0049] If it exceeds, activate the preset fuse mechanism to perform data protection on the robot.

[0050] By adopting the above technical solution, when it is determined that the operation risk index meets the requirements of the protection index and the compliance time exceeds the protection time, activate the fuse mechanism to perform data protection on the robot, and determine whether to perform data protection on the robot with double thresholds, thereby avoiding instantaneous misjudgment and causing data loss of the robot, and further improving the accuracy of data protection for the robot.

[0051] Optionally, the steps of activating the preset fuse mechanism to perform data protection on the robot include:

[0052] Obtain the real-time transmission data stream of the robot;

[0053] Identify the real-time transmission data stream to determine the non-critical data stream and the critical data stream;

[0054] Abort the non-critical data stream transmission and activate the preset encryption channel;

[0055] Transmit the critical data stream through the encryption channel.

[0056] By adopting the above technical solution, the transmission of non-critical data streams is aborted, thereby giving priority to ensuring the transmission of critical data streams, and activating the encrypted channel to transmit the critical data streams, thereby improving the efficiency and security of the transmission of critical data streams.

[0057] Optionally, the steps of activating the preset encrypted channel include:

[0058] Analyze the critical data stream to determine the data type and data transmission speed;

[0059] Determine the activation channel according to the data type and the preset data channel relationship;

[0060] Analyze the data transmission speed and the preset effective bandwidth to determine the reserved bandwidth redundancy;

[0061] Activate the encrypted channel according to the activation channel, and reserve the bandwidth according to the reserved bandwidth redundancy.

[0062] By adopting the above technical solution, the activation signal is determined according to the data type and the data channel relationship, so as to match a suitable channel for the critical data stream, which not only ensures the security of data transmission, but also does not occupy the remaining channels. The reserved bandwidth redundancy is calculated and determined according to the data transmission speed and the effective bandwidth, so that the encrypted channel reserves the bandwidth with the reserved bandwidth redundancy. In case of an abnormal situation, the channel still has enough bandwidth to ensure the quality of the transmitted data stream.

[0063] In a second aspect, the present application provides a TSN configuration system based on a robot operating environment, adopting the following technical solution:

[0064] A TSN configuration system based on a robot operating environment, comprising:

[0065] An acquisition module, configured to acquire external environment parameters, an actual operation mode, and an actual TSN network architecture;

[0066] A memory, configured to store a program of a TSN configuration method based on a robot operating environment as described in any one of the above;

[0067] A processor, the program in the memory can be loaded and executed by the processor and implement a TSN configuration method based on a robot operating environment as described in any one of the above.

[0068] By adopting the above technical solution, the processor loads and executes a program of a TSN configuration method based on the robot operating environment stored in the memory, controls the acquisition module to acquire a series of data related to the TSN configuration based on the robot operating environment, thereby analyzing the external environment parameters of the robot to determine the operation risk index of the robot, and then determining the switching operation mode according to the relationship between the operation risk index and the index mode. When it is determined that the switching operation mode is inconsistent with the actual operation mode, it indicates that the change in the external environment of the robot causes the change in the network resource configuration of the robot. Therefore, the network architecture is reconfigured for the robot, thereby improving the efficiency of network resource allocation.

[0069] In a third aspect, the present application provides an intelligent terminal, adopting the following technical solution:

[0070] An intelligent terminal includes a memory and a processor, and a computer program capable of being loaded and executed by the processor and being a TSN configuration method based on the robot operating environment as described in any one of the above is stored on the memory.

[0071] By adopting the above technical solution, by operating the intelligent terminal, the processor loads and executes a program of a TSN configuration method based on the robot operating environment stored in the memory, thereby analyzing the external environment parameters of the robot to determine the operation risk index of the robot, and then determining the switching operation mode according to the relationship between the operation risk index and the index mode. When it is determined that the switching operation mode is inconsistent with the actual operation mode, it indicates that the change in the external environment of the robot causes the change in the network resource configuration of the robot. Therefore, the network architecture is reconfigured for the robot, thereby improving the efficiency of network resource allocation.

[0072] In summary, the present application includes at least one of the following beneficial technical effects:

[0073] 1. By analyzing the external environment parameters of the robot to determine the operation risk index of the robot, and then determining the switching operation mode according to the relationship between the operation risk index and the index mode. When it is determined that the switching operation mode is inconsistent with the actual operation mode, it indicates that the change in the external environment of the robot causes the change in the network resource configuration of the robot. Therefore, the network architecture is reconfigured for the robot, thereby improving the efficiency of network resource allocation;

[0074] 2. By separately analyzing the disaster parameter value, the obstacle dynamic parameter value, and the communication quality parameter value to obtain the disaster risk index, the obstacle risk index, and the communication risk index, and then performing weighted fusion on the disaster risk index, the obstacle risk index, and the communication risk index according to the risk index weight coefficient to obtain the operation risk index, thereby calculating the risk index according to the factors affecting the operation of the robot in the external environment, and then improving the accuracy of the operation risk index;

[0075] 3. When it is determined that the running risk index meets the requirements of the protection index and the compliance time exceeds the protection time, activate the fuse mechanism to protect the data of the robot. Determine whether to protect the data of the robot with a double threshold, so as to avoid instantaneous misjudgment and cause the loss of robot data, thereby improving the accuracy of robot data protection. Description of the Drawings

[0076] Figure 1 It is a flowchart of a TSN configuration method based on the robot running environment in an embodiment of the present application.

[0077] Figure 2 It is a flowchart of the steps of analyzing external environmental parameters to determine the running risk index of the robot in an embodiment of the present application.

[0078] Figure 3 It is a flowchart of the steps of obtaining the risk index weight coefficient in an embodiment of the present application.

[0079] Figure 4 It is a flowchart of the steps of analyzing the environmental weight coefficient, historical weight coefficient and transition probability to determine the risk index weight coefficient in an embodiment of the present application.

[0080] Figure 5 It is a flowchart of the steps of reconfiguring the network architecture for the robot in an embodiment of the present application.

[0081] Figure 6 It is a flowchart of the steps of protecting the data of the robot according to the running risk index in an embodiment of the present application.

[0082] Figure 7 It is a flowchart of the steps of activating a preset fuse mechanism to protect the data of the robot in an embodiment of the present application.

[0083] Figure 8 It is a flowchart of the steps of activating a preset encryption channel in an embodiment of the present application. Detailed Embodiment

[0084] In order to make the purpose, technical solution and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying Figures 1 to 8 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0085] An embodiment of the present application discloses a TSN configuration method based on the operating environment of a robot. After the sensor detects the external environment parameters of the robot, the external environment parameters are sent to the processing terminal. The processing terminal analyzes the external environment parameters to determine the operation risk index, and looks up the switching operation mode in the mapping table corresponding to the index mode relationship according to the operation risk index. When the processing terminal determines that the actual operation mode of the robot is the same as the switching operation mode, the actual TSN network architecture is continued to be maintained. If they are inconsistent, the network architecture is reconfigured for the robot, so as to reallocate network resources for the robot in a timely manner according to the changes in the external environment, thereby improving the efficiency of network resource allocation.

[0086] Referring to Figure 1 , an embodiment of the present application discloses a TSN configuration method based on the operating environment of a robot, including the following steps:

[0087] Step S100: Obtain the external environment parameters of the robot.

[0088] Among them, the external environment parameters refer to the data in the operating environment of the robot, including disaster parameter values, obstacle dynamic parameter values, and communication quality parameter values. The disaster parameter values include numerical values such as smoke concentration, temperature, and radiation intensity, which are detected by relevant sensors, such as temperature sensors and smoke sensors, and then sent to the processing terminal; the obstacle dynamic parameter value refers to the approaching speed of the obstacle, which is measured in real time by a lidar and sent to the processing terminal; the communication quality parameter value refers to the bit error rate of the communication link, and the bit error rate is obtained by actually testing and comparing the communication link with test data.

[0089] Step S101: Analyze the external environment parameters to determine the operation risk index of the robot.

[0090] Among them, the operation risk index refers to the risk value caused by the external environment parameters to the operation of the robot, which is determined by the processing terminal analyzing the external environment parameters. The specific method refers to Figure 2 the steps of

[0091] Step S102: Determine the switching operation mode of the robot according to the operation risk index and the preset index mode relationship.

[0092] Among them, the index mode relationship refers to the corresponding relationship between the risk index and the operation mode. There are three operation modes in the embodiment of the present application, including the safety mode, the warning mode, and the emergency mode. Among them, when the risk index is lower than 1, it corresponds to the safety mode. When the risk index is between 1 and 2, it corresponds to the warning mode. When the risk index is greater than 2, it corresponds to the emergency mode. The operator forms a mapping table by corresponding the risk index and the operation mode one by one.

[0093] The switched operating mode refers to the operating mode suitable for the current external environmental parameters, which is obtained by the processing terminal looking up in the mapping table corresponding to the exponential mode relationship according to the operating risk index.

[0094] Step S103: Obtain the actual operating mode of the robot and the actual TSN network architecture.

[0095] Among them, the actual operating mode refers to the mode in which the robot is currently operating, which is obtained by the processing terminal identifying and calling the operating mode of the robot. The actual TSN network architecture refers to the current TSN architecture of the robot, which is obtained by the processing terminal identifying and calling the network architecture of the robot.

[0096] Step S104: Determine whether the actual operating mode meets the requirements of the switched operating mode.

[0097] Among them, the requirements for the switched operating mode refer to being consistent with the switched operating mode, which are stored in the processing terminal by the operator.

[0098] The processing terminal determines whether the actual operating mode is consistent with the switched operating mode, so as to determine whether the robot needs to reconfigure the network architecture.

[0099] Step S1041: If it meets the requirements, maintain the actual TSN network architecture, and continue to obtain the external environmental parameters of the robot for cyclic analysis and judgment.

[0100] Among them, if the processing terminal determines that the actual operating mode is consistent with the switched operating mode, it indicates that the operating mode of the robot has not changed. Therefore, there is no need to reconfigure the network architecture, so the actual TSN network architecture is continued to be maintained, and the external environmental parameters of the robot are continuously detected to continuously monitor the changes in the external environment of the robot.

[0101] Step S1042: If it does not meet the requirements, reconfigure the network architecture for the robot.

[0102] Among them, if the processing terminal determines that the actual operating mode is inconsistent with the switched operating mode, it indicates that the operating mode of the robot needs to change. At this time, reconfigure the network architecture for the robot. The specific method refers to Figure 5 the steps, so as to trigger the corresponding network scheduling strategy according to the changes in the environment, and then improve the efficiency of network resource allocation.

[0103] Refer to Figure 2 , the steps of analyzing the external environmental parameters to determine the operating risk index of the robot include:

[0104] Step S200: Analyze the external environmental parameters to determine the disaster parameter value, obstacle dynamic parameter value, and communication quality parameter value.

[0105] Among them, the disaster parameter value refers to the physical factors in the external environment that affect the operation of the robot, including values such as smoke concentration, temperature, and radiation intensity, which are detected by relevant sensors, such as temperature sensors, smoke sensors, etc., and then sent to the processing terminal; the dynamic parameter value of the obstacle refers to the approaching speed of the obstacle, which is measured in real time by the lidar and sent to the processing terminal; the communication quality parameter value refers to the bit error rate of the communication link, and the bit error rate is obtained by actually testing and comparing the communication link with test data. The three are identified and called by the processing terminal in the data packet corresponding to the external environment parameters.

[0106] Step S201: Analyze the disaster parameter value and the preset disaster parameter threshold to determine the disaster risk index.

[0107] Among them, the disaster parameter threshold refers to the safety threshold when a disaster occurs. For example, the smoke concentration threshold is set to 500 ppm, which is determined by the operator according to the actual situation.

[0108] The disaster risk index refers to the risk index caused by the disaster parameter to the robot, which is obtained by the processing terminal calculating the quotient between the disaster parameter value and the disaster parameter threshold.

[0109] Step S202: Analyze the dynamic parameter value of the obstacle and the preset safety dynamic parameter value to determine the obstacle risk index.

[0110] Among them, the safety dynamic parameter value refers to the maximum safe approaching speed allowed by the robot. In this embodiment of the application, 2 m / s is taken as an example.

[0111] The obstacle risk index refers to the risk index caused by the dynamic obstacle to the robot, which is obtained by the processing terminal calculating the quotient between the dynamic parameter value of the obstacle and the safety dynamic parameter value.

[0112] Step S203: Analyze the communication quality parameter value and the preset bit error parameter threshold to determine the communication risk index.

[0113] Among them, the bit error parameter threshold refers to the maximum bit error rate threshold tolerated by the communication quality. In this embodiment of the application, 0.0005 is taken as an example.

[0114] The communication risk index refers to the risk index caused by the communication quality to the operation of the robot, which is obtained by the processing terminal calculating the quotient between the communication quality parameter value and the bit error parameter threshold.

[0115] Step S204: Obtain the risk index weight coefficient.

[0116] Among them, the risk index weight coefficient refers to the weight of different risk indices in the total risk, including the disaster weight coefficient, the obstacle weight coefficient, and the communication weight coefficient. The specific acquisition method refers to Figure 3 the steps.

[0117] Step S205: Analyze the risk index weight coefficient, disaster hazard index, obstacle hazard index, and communication hazard index to determine the operation risk index.

[0118] Among them, the operation risk index in this step is consistent with the operation risk index in step S101. The processing terminal calls the disaster weight coefficient, obstacle weight coefficient, and communication weight coefficient in the risk index weight coefficient, so as to perform weighted summation of the disaster weight coefficient, obstacle weight coefficient, and communication weight coefficient with the disaster hazard index, obstacle hazard index, and communication hazard index respectively to obtain the operation risk index.

[0119] Refer to Figure 3 , the steps for obtaining the risk index weight coefficient include:

[0120] Step S300: Obtain the historical environmental parameters and historical weight coefficients.

[0121] Among them, the historical environmental parameters refer to the external environmental parameters of the robot before detecting the current external environmental parameters, which are detected by relevant sensors and sent to the processing terminal for backup for the processing terminal to call. The historical weight coefficient refers to the weight coefficient when the robot determined the risk index last time, which is backed up by the processing terminal for future use.

[0122] Step S301: Control the preset environmental prediction model to make a prediction based on the historical environmental parameters to generate predicted environmental parameters.

[0123] Among them, the environmental prediction model is a model for predicting the environmental parameters at the next moment. In the embodiment of the present application, the prediction is based on a long short-term memory network (LSTM), including an input layer that abstracts the historical environmental parameters into a feature vector with expandable dimensions, where each component represents an environmental parameter; a hidden layer that uses a bidirectional LSTM layer (64 units) and an Attention mechanism; and an output layer that uses a fully connected layer to predict the environmental parameters at the next moment.

[0124] The predicted environmental parameters refer to the environmental parameters at the predicted next moment, which are obtained by the processing terminal inputting the historical environmental parameters into the environmental prediction model for prediction.

[0125] Step S302: Obtain the environmental transition probability matrix.

[0126] Among them, the environmental transition probability matrix refers to the transition probability matrix in the Markov decision process, which represents the probability of transitioning from one scenario to another scenario. It can be obtained by maximum likelihood estimation statistics. First, record a series of scenario sequences that change over time, then calculate the number of times of transitioning from the first scenario to the second scenario, divide the number of times by the total number of times to obtain the probability of a single scenario transition, and finally organize the probabilities of all scenario transitions to form the environmental transition probability matrix.

[0127] Step S303: Analyze the environmental transition probability matrix and the predicted environmental parameters to determine the transition probability and the predicted transition environment.

[0128] The predicted transition environment refers to the predicted transition scenario, which is obtained by the processing terminal looking up in the scenario mapping table corresponding to the environmental parameters according to the predicted environmental parameters, including scenarios such as fire spread, communication interruption, and group obstacle avoidance.

[0129] The transition probability refers to the probability of scenario transition. First, the processing terminal calls the corresponding probability in the environmental transition probability matrix according to the historical scenario and the transition scenario, then uses the Softmax function to convert the predicted environmental parameters output by the environmental prediction model into a probability distribution, and finally calls the empirical smoothing coefficients corresponding to the probability matrix and the probability distribution, which are 0.3 and 0.7 respectively, and uses the empirical smoothing coefficients, the probability matrix, and the probability distribution to perform weighted summation to obtain the transition probability.

[0130] Step S304: Obtain the environmental weight coefficient of the predicted transition environment.

[0131] The environmental weight coefficient refers to the disaster weight coefficient, obstacle weight coefficient, and communication weight coefficient in the predicted environment, which are obtained by the processing terminal looking up in the mapping table of the environment and the weight coefficient according to the predicted transition environment. For example, in the fire spread scenario, the disaster weight coefficient is 0.7, the obstacle weight coefficient is 0.2, and the communication weight coefficient is 0.1; in the communication interruption scenario, the disaster weight coefficient is 0.3, the obstacle weight coefficient is 0.2, and the communication weight coefficient is 0.5, etc.

[0132] Step S305: Analyze the environmental weight coefficient, historical weight coefficient, and transition probability to determine the risk index weight coefficient.

[0133] The risk index weight coefficient in this step is the same as the risk index weight coefficient in Step S204, which is calculated by the processing terminal according to the environmental weight coefficient, historical weight coefficient, and transition probability. The specific method refers to Figure 4 the steps, so as to avoid system oscillation caused by sudden weight changes through probability-weighted linear interpolation.

[0134] Refer to Figure 4 and the steps of analyzing the environmental weight coefficient, historical weight coefficient, and transition probability to determine the risk index weight coefficient include:

[0135] Step S400: Determine the probability amplification coefficient according to the predicted transition environment and the preset environmental coefficient relationship.

[0136] Among them, the environmental coefficient relationship refers to the corresponding relationship between different environments and the probability amplification coefficient. For example, the amplification coefficient in a low-risk scenario is small, while that in an emergency scenario is large. After the operator corresponds the scenarios with the probability amplification coefficients one by one, a mapping table is formed.

[0137] The probability amplification coefficient is a coefficient used to amplify the transfer probability, which is obtained by the processing terminal looking up in the mapping table corresponding to the environmental coefficient relationship according to the predicted transfer environment.

[0138] Step S401: Analyze the transfer probability and the probability amplification coefficient to determine the amplified probability.

[0139] Among them, the amplified probability refers to the amplified transfer probability, the purpose of which is to enhance the influence of the prediction signal and solve the control lag problem caused by prediction uncertainty and execution delay in the dynamic environment of the system. It is obtained by the processing terminal calculating the product between the transfer probability and the probability amplification coefficient. When amplifying the transfer probability, if it exceeds 1, it is truncated to 1.

[0140] Step S402: Analyze the environmental weight coefficient, the amplified probability, and the historical weight coefficient to determine the risk index weight coefficient.

[0141] Among them, the risk index weight coefficient in this step is the same as the risk index weight coefficient in step S305. The processing terminal calculates the non-transfer probability according to the amplified probability and the total probability 1, then calculates the product of the amplified probability and the environmental weight coefficient, and then calculates the product of the non-transfer probability and the historical weight coefficient. The sum of the two products is the risk index weight coefficient.

[0142] Refer to Figure 5 , the steps for reconfiguring the network architecture for the robot include:

[0143] Step S500: Determine the switched network architecture according to the switched operation mode and the preset mode-architecture relationship; the switched network architecture includes a centralized architecture, a distributed edge architecture, and an ad hoc network architecture.

[0144] Among them, the mode-architecture relationship refers to the corresponding relationship between the operation mode and the network architecture. For example, the security mode corresponds to the centralized architecture, the warning mode corresponds to the distributed edge architecture, and the emergency mode corresponds to the ad hoc network architecture. After the operator corresponds the operation mode with the network architecture one by one, a mapping table is formed.

[0145] The switched network architecture refers to the network architecture to be switched to, which is obtained by the processing terminal looking up in the mapping table corresponding to the mode architecture relationship according to the switching operation mode, including a centralized architecture, a distributed edge architecture, and an ad hoc network architecture. All three architectures can be used in combination with TSN technology. Among them, in the centralized architecture, all computing, storage, and decision-making functions are concentrated in a single or a small number of central nodes (such as data centers, servers), and edge devices are only responsible for lightweight tasks (such as uploading sensor data), so as to achieve centralized resource scheduling, security policies, and data storage, and avoid synchronization conflicts. In the security mode, unified policies, global monitoring, and quick response are required, so the centralized architecture is selected; in the distributed edge architecture, computing, policy, and decision-making capabilities are sunk to edge nodes close to the data source (such as local servers, intelligent network management, edge devices), and the central node is only responsible for global coordination or complex tasks, reducing cloud dependence and data transmission time, and saving network resources. In the early warning mode, low-latency response, local decision-making, and high reliability are required, such as natural disaster early warning, equipment failure prediction, etc., so the distributed edge architecture is selected; the ad hoc network architecture is a decentralized dynamic network architecture, where nodes autonomously form a network and dynamically adjust the topology without relying on fixed infrastructure. In the emergency mode, such as disaster relief, the traditional network may have collapsed, so the ad hoc network architecture is selected.

[0146] Step S501: Reconfigure the network architecture for the robot according to the switched network architecture, and determine whether the switched network architecture is an ad hoc network architecture.

[0147] Among them, after determining the switched network architecture, the network architecture of the robot is automatically reconfigured according to the switched network architecture. For example, when switching from a centralized architecture to a distributed edge architecture, the switch is triggered by the decision engine, the computing tasks are distributed to the edge nodes, Kubernetes schedules the service instances to the edge, and the SDN controller updates the traffic routing. When switching to an ad hoc network architecture, the nodes automatically form a Mesh network, use the AODV protocol to maintain the routing, the edge nodes take over the local decision-making, the data is temporarily stored and synchronized after recovery. After switching the network architecture, it is determined whether the switched network architecture is an ad hoc network architecture, providing data support for subsequent determination of whether data protection for the robot is required.

[0148] Step S5011: If not, continue to obtain the external environment parameters of the robot for cyclic analysis and judgment.

[0149] Among them, if the processing terminal determines that the switched network architecture is not an ad hoc network architecture, it indicates that there is no emergency mode, and at this time, data protection for the robot is not required. Therefore, the external environment parameters of the robot are continuously detected to continuously monitor the changes in the robot's operating environment.

[0150] Step S5012: If so, perform data protection on the robot according to the operation risk index.

[0151] Among them, if the processing terminal determines that the switched network architecture is an ad-hoc network architecture, it indicates that the robot is in an extreme environment, and it is very likely to damage the data of the robot. Therefore, data protection is performed on the robot according to the operation risk index. For the specific method, refer to Figure 6 the steps of

[0152] Refer to Figure 6 , and the steps of performing data protection on the robot according to the operation risk index include:

[0153] Step S600: Determine whether the operation risk index meets the requirements of the preset protection index.

[0154] Among them, the protection index refers to the lowest risk index that requires data protection. The specific value is determined by the operator according to the actual situation. The requirement of the protection index means not less than the protection index.

[0155] The processing terminal determines whether the operation risk index is not less than the protection index, so as to determine whether the environment where the robot is located is extreme, and provide data support for determining whether it is necessary to protect the data of the robot subsequently.

[0156] Step S601: If not, continue to obtain the external environment parameters of the robot for loop analysis and judgment.

[0157] Among them, if the processing terminal determines that the operation risk index is lower than the protection index, it indicates that even if the robot is in the emergency mode, the risk has not reached the level that requires data protection. Therefore, continue to detect the external environment parameters of the robot, so as to continuously monitor the changes in the external environment of the robot.

[0158] Step S602: If it meets, obtain the compliance time of the operation risk index.

[0159] Among them, if the processing terminal determines that the operation risk index is not less than the protection index, it indicates that the robot is in an extreme environment and data protection is required. Therefore, detect the compliance time of the operation risk index to provide data support for determining whether there is an instantaneous misjudgment subsequently.

[0160] The compliance time refers to the time when the operation risk index exceeds the protection index, which is obtained by timing with a timer.

[0161] Step S603: Determine whether the compliance time exceeds the preset protection time.

[0162] Among them, the protection time refers to the lowest time when the operation risk index exceeds the protection time. In the embodiment of the present application, 2 seconds is taken as an example.

[0163] The processing terminal determines whether the compliance time exceeds the protection time, so as to determine whether an instantaneous misjudgment occurs in the data protection of the robot.

[0164] Step S6031: If it does not exceed, continue to obtain the compliance time of the operation risk index for loop judgment.

[0165] Among them, if the processing terminal determines that the compliance time does not exceed the protection time, it indicates that the data protection of the robot is an instantaneous misjudgment. Therefore, continue to detect the compliance time of the operation risk index to continuously monitor the operation risk status of the robot.

[0166] Step S6032: If it exceeds, activate the preset fusing mechanism to protect the data of the robot.

[0167] Among them, if the processing terminal determines that the compliance time exceeds the protection time, it indicates that the data protection of the robot is not an instantaneous misjudgment. Therefore, activate the fusing mechanism to protect the data of the robot. For the specific method, refer to Figure 7 the steps.

[0168] The fusing mechanism refers to a method of aborting the non-critical data stream transmission of the robot and retaining the critical data stream transmission, which is stored in the processing terminal by the operator.

[0169] Refer to Figure 7 , the steps of activating the preset fusing mechanism to protect the data of the robot include:

[0170] Step S700: Obtain the real-time transmission data stream of the robot.

[0171] Among them, the real-time transmission data stream refers to the data stream being transmitted by the robot, which is captured and displayed by the network monitoring tool for all traffic of the network adapter.

[0172] Step S701: Identify the real-time transmission data stream to determine the non-critical data stream and the critical data stream.

[0173] Among them, the non-critical data stream refers to the data with little impact in the current external environment, and the critical data stream refers to the data that directly affects the operation and safety of the robot. For example, in the early warning or emergency mode, log files, performance statistics information, non-urgent software update requests, etc. are non-critical data streams, and control instructions (such as movement commands, stop instructions), real-time sensor data, and status feedback information, etc. are critical data, which are identified and classified by the processing terminal according to the external environment for the real-time transmission data stream.

[0174] Step S702: Abort the non-critical data stream transmission and activate the preset encryption channel.

[0175] Among them, after determining the non-critical data stream and the critical data stream, the transmission of the non-critical data stream is aborted, thereby reducing the amount of external data transmission, reducing the risk of external attacks, and activating the encryption channel. For the specific method, refer to Figure 8 for basic support for the subsequent transmission of critical data. The encryption channel refers to the transmission channel that protects communication data by using encryption technology.

[0176] Step S703: Transmit the critical data stream through the encryption channel.

[0177] Among them, after the encryption channel is activated, the critical data stream is transmitted through the encryption channel, thereby ensuring the normal transmission and data security of the robot's critical data.

[0178] Refer to Figure 8 , and the steps to activate the preset encryption channel include:

[0179] Step S800: Analyze the critical data stream to determine the data type and data transmission speed.

[0180] Among them, the data type refers to the type of the critical data stream, including types such as control instructions, sensor data, and status information. The data transmission speed refers to the amount of data transmitted per unit time, which is determined by the processing terminal after analyzing the critical data stream.

[0181] Step S801: Determine the activation channel according to the data type and the preset data channel relationship.

[0182] Among them, the data channel relationship refers to the corresponding relationship between different types of data and the encryption channel. Different types of data have different level divisions. The higher the level of the data, the higher the encryption level channel it corresponds to. The operator forms a mapping table by corresponding the data type with the encryption channel one by one.

[0183] The activation channel refers to the encryption channel that needs to be activated, which is obtained by the processing terminal looking up in the mapping table corresponding to the data channel relationship according to the data type.

[0184] Step S802: Analyze the data transmission speed and the preset effective bandwidth to determine the reserved bandwidth redundancy.

[0185] Among them, the effective bandwidth refers to the total available bandwidth of the channel, which is determined by the operator according to the actual situation of the encryption channel. The reserved bandwidth redundancy refers to the additional bandwidth ratio reserved to ensure the transmission quality. The processing terminal calculates the quotient of the data transmission speed and the effective bandwidth, and then subtracts the quotient from the total ratio to obtain the reserved bandwidth redundancy.

[0186] Step S803: Activate the encryption channel according to the activation channel and reserve the bandwidth according to the reserved bandwidth redundancy.

[0187] Among them, after determining the active channels and reserved bandwidth redundancy, the corresponding encryption channels are activated according to the active channels, and bandwidth is reserved for the encryption channels according to the reserved bandwidth redundancy, so that there is still enough bandwidth to ensure the transmission quality of critical data when the network conditions are poor or there is an unexpected increase in traffic.

[0188] Based on the same inventive concept, an embodiment of the present application provides a TSN configuration system based on a robot operating environment, including:

[0189] An acquisition module, configured to acquire external environment parameters, an actual operation mode, an actual TSN network architecture, a risk index weight coefficient, historical environment parameters, historical weight coefficients, an environment transition probability matrix, an environment weight coefficient, a compliance time, and a real-time transmission data stream;

[0190] A memory, configured to store a program of a TSN configuration method based on a robot operating environment;

[0191] A processor, and the program in the memory can be loaded and executed by the processor to implement a TSN configuration method based on a robot operating environment.

[0192] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described in detail here.

[0193] An embodiment of the present application provides a computer-readable storage medium, storing a computer program that can be loaded and executed by a processor to implement a TSN configuration method based on a robot operating environment.

[0194] Computer storage media include, for example: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0195] Based on the same inventive concept, an embodiment of the present application provides an intelligent terminal, including a memory and a processor, and a computer program that can be loaded and executed by the processor to implement a TSN configuration method based on a robot operating environment is stored on the memory.

[0196] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. For the specific working processes of the system, device, and unit described above, reference can be made to the corresponding processes in the foregoing method embodiments, and details are not described herein again.

[0197] The above are all preferred embodiments of the present application. The protection scope of the present application is not limited thereby. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A TSN configuration method based on the operating environment of a robot, characterized in that, Including: Obtain the external environment parameters of the robot; Analyze the external environment parameters to determine the operation risk index of the robot; Determine the switched operation mode of the robot according to the operation risk index and the preset index-mode relationship; Obtain the actual operation mode and the actual TSN network architecture of the robot; Judge whether the actual operation mode meets the requirements of the switched operation mode; If it meets the requirements, maintain the actual TSN network architecture and continue to obtain the external environment parameters of the robot for cyclic analysis and judgment; If it does not meet the requirements, reconfigure the network architecture for the robot; The steps for reconfiguring the network architecture for the robot include: Determine the switched network architecture according to the switched operation mode and the preset mode-architecture relationship; the switched network architecture includes a centralized architecture, a distributed edge architecture, and an ad hoc network architecture; Reconfigure the network architecture for the robot according to the switched network architecture and judge whether the switched network architecture is an ad hoc network architecture; If not, continue to obtain the external environment parameters of the robot for cyclic analysis and judgment; If so, perform data protection on the robot according to the operation risk index; The steps for performing data protection on the robot according to the operation risk index include: Judge whether the operation risk index meets the requirements of the preset protection index; If it does not meet the requirements, continue to obtain the external environment parameters of the robot for cyclic analysis and judgment; If it meets the requirements, obtain the compliance time of the operation risk index; Judge whether the compliance time exceeds the preset protection time; If it does not exceed, continue to obtain the compliance time of the operation risk index for cyclic judgment; If it exceeds, activate the preset fusing mechanism to perform data protection on the robot.

2. The TSN configuration method based on the robot operating environment according to claim 1, wherein, The steps for analyzing the external environment parameters to determine the operation risk index of the robot include: Analyze the external environment parameters to determine the disaster parameter value, the obstacle dynamic parameter value, and the communication quality parameter value; Analyze the disaster parameter value and the preset disaster parameter threshold to determine the disaster risk index; Analyze the obstacle dynamic parameter value and the preset safety dynamic parameter value to determine the obstacle risk index; Analyze the communication quality parameter value and the preset error code parameter threshold to determine the communication risk index; Obtain the risk index weight coefficient; Analyze the risk index weight coefficient, the disaster risk index, the obstacle risk index, and the communication risk index to determine the operation risk index.

3. A TSN configuration method based on a robot operating environment according to claim 2, wherein The steps for obtaining the risk index weight coefficient include: Obtain the historical environment parameters and the historical weight coefficient; Control the preset environment prediction model to make a prediction according to the historical environment parameters to generate predicted environment parameters; Obtain the environment transition probability matrix; Analyze the environment transition probability matrix and the predicted environment parameters to determine the transition probability and the predicted transition environment; Obtain the environment weight coefficient of the predicted transition environment; Analyze the environment weight coefficient, the historical weight coefficient, and the transition probability to determine the risk index weight coefficient.

4. A TSN configuration method based on a robot operating environment according to claim 3, characterized in that The steps for analyzing the environment weight coefficient, the historical weight coefficient, and the transition probability to determine the risk index weight coefficient include: Determine the probability amplification coefficient according to the predicted transition environment and the preset environment coefficient relationship; Analyze the transition probability and the probability amplification coefficient to determine the amplified probability; Analyze the environmental weight coefficient, amplification probability, and historical weight coefficient to determine the risk index weight coefficient.

5. A TSN configuration method based on a robot operating environment according to claim 1, characterized in that The steps of activating the preset fusing mechanism for data protection of the robot include: Obtain the real-time transmission data stream of the robot; Identify the real-time transmission data stream to determine the non-critical data stream and the critical data stream; Abort the non-critical data stream transmission and activate the preset encryption channel; Transmit the critical data stream through the encryption channel.

6. The TSN configuration method based on a robot operating environment according to claim 5, wherein The steps of activating the preset encryption channel include: Analyze the critical data stream to determine the data type and data transmission speed; Determine the activation channel according to the data type and the preset data channel relationship; Analyze the data transmission speed and the preset effective bandwidth to determine the reserved bandwidth redundancy; Activate the encryption channel according to the activation channel and reserve the bandwidth according to the reserved bandwidth redundancy.

7. A TSN configuration system based on a robot operating environment, characterized in that, Include: An acquisition module for acquiring external environmental parameters, actual operating mode, and actual TSN network architecture; A memory for storing a program of a TSN configuration method based on the robot operating environment according to any one of claims 1 to 6; A processor, the program in the memory can be loaded and executed by the processor and implement a TSN configuration method based on the robot operating environment according to any one of claims 1 to 6.

8. An intelligent terminal, characterized in that, Include a memory and a processor, and a computer program capable of being loaded and executed by the processor and implementing a TSN configuration method based on the robot operating environment according to any one of claims 1 to 6 is stored on the memory.

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