A network state monitoring and optimization processing method, device and readable storage medium

Abstract

The application provides a network state monitoring and optimization processing method, device and readable storage medium, the method comprises the following steps: obtaining a network state parameter by monitoring; determining a data transmission parameter according to the network state parameter; determining a transmission priority of target data to be transmitted according to importance requirements and real-time requirements of the target data; and performing data transmission on the target data by using a target path based on the data transmission parameter and the transmission priority, wherein the target path is a preset main path or a backup path with the highest stability when the preset main path is unavailable. The method of the application can not only adjust parameters in real time according to network conditions, but also optimize data delivery processes according to different business requirements and has the ability to flexibly respond to unexpected situations, significantly improving data communication quality and user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, and particularly relates to a network state monitoring and optimization processing method and device and a readable storage medium. BACKGROUND

[0002] Under the condition of unstable network, the loss of data packets may cause the interruption of inspection tasks. This situation not only affects the inspection efficiency, but also may cause the omission of important information, thereby causing the device operation hidden danger unable to be discovered and processed in time.

[0003] In addition, the interruption of the inspection task may also cause a series of chain reactions, such as production scheduling confusion, safety hazard increase, maintenance cost increase and the like, which have adverse effects on the normal operation of the enterprise.

[0004] Therefore, in the environment of unstable network, how to take effective measures to reduce the data packet loss rate and guarantee the smooth progress of the inspection task has important significance for ensuring the production safety and improving the enterprise benefit. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a network state monitoring and optimization processing method and device and a readable storage medium to solve the problems in the prior art.

[0006] In a first aspect, the present application provides a network state monitoring and optimization processing method, which comprises the following steps:

[0007] S1, obtaining network state parameters by monitoring;

[0008] S2, determining data transmission parameters according to the network state parameters;

[0009] S3, determining the transmission priority of target data according to the importance requirement and real-time requirement of the target data to be transmitted;

[0010] S4, performing data transmission on the target data by using a target path based on the data transmission parameters and the transmission priority, wherein the target path is a preset main path or a backup path with the highest stability when the preset main path is unavailable.

[0011] In some embodiments, the network state parameters include network bandwidth and data packet transmission delay, and the data transmission parameters include transmission rate.

[0012] S2 comprises the following steps:

[0013] determining the transmission rate according to the network bandwidth and the data packet transmission delay.

[0014] In some embodiments, the transmission rate is determined according to network bandwidth and packet transmission delay by the following formula:

[0015] R = W / (1 + D)

[0016] wherein W represents network bandwidth, D represents packet transmission delay, and R represents transmission rate.

[0017] In some embodiments, the network status parameters include packet loss rate and network delay, and the data transmission parameters include retransmission interval time, retransmission times, and data packet size.

[0018] S2, comprising:

[0019] determining retransmission interval time, retransmission times of the data transmission protocol according to packet loss rate and network delay, and dynamically adjusting data packet size.

[0020] In some embodiments, determining retransmission interval time, retransmission times of the data transmission protocol according to packet loss rate and network delay comprises:

[0021] determining an adjustment factor based on packet loss rate and network delay;

[0022] when the adjustment factor is greater than a first preset threshold, increasing the retransmission interval time, and when the adjustment factor is less than or equal to the first preset threshold, keeping the retransmission interval time unchanged;

[0023] when the adjustment factor is greater than a second preset threshold, increasing the retransmission times, and when the adjustment factor is less than or equal to the second preset threshold, keeping the retransmission times unchanged;

[0024] wherein the second preset threshold is greater than the first preset threshold.

[0025] In some embodiments, the adjustment factor is determined according to packet loss rate and network delay by the following formula:

[0026] K = P x T / C

[0027] wherein P represents packet loss rate, T represents network delay, K represents adjustment factor, and C is a constant representing a reference value.

[0028] In some embodiments, the data packet size is dynamically adjusted by the following formula:

[0029] data_packet_size

[0030] = max (default_packet_size - delay_factor

[0031] x delay, min_packet_size)

[0032] wherein, data_packet_size represents a data packet size, default_packet_size represents a default packet size, delay represents a network delay, min_packet_size represents a minimum packet size, and delay_factor represents a delay adjustment factor.

[0033] In some embodiments, in S3, a transmission priority of target data is determined according to an importance requirement and a real-time requirement of the target data by the following formula:

[0034] P = (Q + V) / 2

[0035] wherein, P represents a transmission priority, Q represents an importance requirement of target data, and V represents a real-time requirement of target data.

[0036] In some embodiments, in S4, a standby path with the highest stability when the preset main path is unavailable is determined by the following formula:

[0037] S = min(Pi, Ci),

[0038] wherein, S represents a standby path with the highest stability when the preset main path is unavailable, Pi represents a priority of an i-th transmission path, and Ci represents a stability of the i-th transmission path.

[0039] In a second aspect, the present application provides a network state monitoring and optimization processing device, which comprises:

[0040] a parameter acquisition module configured to acquire network state parameters by monitoring;

[0041] a parameter determination module configured to determine data transmission parameters according to the network state parameters;

[0042] a priority determination module configured to determine a transmission priority of target data according to an importance requirement and a real-time requirement of the target data;

[0043] a data transmission module configured to perform data transmission on the target data by a target path based on the data transmission parameters and the transmission priority, wherein the target path is a preset main path or a standby path with the highest stability when the preset main path is unavailable.

[0044] In a third aspect, the present application provides a network state monitoring and optimization processing device, which comprises a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to realize the network state monitoring and optimization processing method of the first aspect.

[0045] In a fourth aspect, the present application provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program, when executed by a processor, implements the network state monitoring and optimization processing method of the first aspect.

[0046] The network state monitoring and optimization processing method, device and readable storage medium provided by the present application, the method comprising: obtaining network state parameters by monitoring; determining data transmission parameters according to the network state parameters; determining the transmission priority of target data to be transmitted according to the importance requirement and real-time requirement of the target data; and transmitting the target data by using a target path based on the data transmission parameters and the transmission priority, wherein the target path is a preset main path or a backup path with the highest stability when the preset main path is unavailable. The method of the present application can not only adjust parameters in real time according to network conditions, but also optimize data transmission processes according to different business requirements, and has the ability to flexibly respond to unexpected situations, significantly improving data communication quality and user experience. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0048] Figure 1 A flowchart of a network state monitoring and optimization processing method provided by an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a network state monitoring and optimization processing method provided by an embodiment of the present application;

[0050] Figure 3 A structural schematic diagram of a network state monitoring and optimization processing device provided by an embodiment of the present application;

[0051] Figure 4 A structural schematic diagram of another network state monitoring and optimization processing device provided by an embodiment of the present application.

[0052] Through the above-described drawings, the specific embodiments of the present application have been shown, and will be described in more detail hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] In order for those skilled in the art to better understand the technical solutions of the present application, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0054] It can be understood that the specific embodiments and drawings described herein are only used to explain the present application, but not limited to the present application.

[0055] It can be understood that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0056] It can be understood that, for the convenience of description, only parts related to the present application are shown in the drawings of the present application, and parts unrelated to the present application are not shown in the drawings.

[0057] It can be understood that each unit and module involved in the embodiments of the present application can only correspond to one entity structure, or can be composed of multiple entity structures, or multiple units and modules can be integrated into one entity structure.

[0058] It can be understood that the terms "first", "second" and the like in the embodiments of the present application are used to distinguish different objects, or to distinguish different treatments of the same object, rather than to describe a specific order of the object.

[0059] It can be understood that the functions and steps marked in the flowcharts and block diagrams of the present application can occur in an order different from that marked in the drawings without conflict.

[0060] It can be understood that in the flowcharts and block diagrams of the present application, the system, device, equipment, method according to the embodiments of the present application are shown. Each block in the flowchart or block diagram can represent a unit, module, program segment, code, which contains executable instructions for realizing the specified function. Moreover, each block or combination of blocks in the block diagram and flowchart can be realized by a hardware-based system for realizing the specified function, or by a combination of hardware and computer instructions.

[0061] It can be understood that the units and modules involved in the embodiments of the present application can be realized by software or hardware, for example, the units and modules can be located in a processor.

[0062] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0063] The present application provides a network state monitoring and optimization processing method, and the working process of the method can be realized by electronic equipment, such as computer, handheld intelligent terminal, etc. For the convenience of explanation, the method execution subject in the embodiments of the present application is described as a computer.

[0064] Figure 1 The schematic diagram of the network state monitoring and optimization processing method provided by the embodiments of the present application is shown in FIG. 1, and the present application provides a network state monitoring and optimization processing method, which in some embodiments includes S1-S4, and the details are as follows: Figure 1

[0065] S1, obtaining network state parameters through monitoring;

[0066] The network state parameters are a series of indicators for describing network performance and health status, which are used to represent the running situation of the network, diagnose problems, and optimize network performance.

[0067] S2, determining data transmission parameters according to the network state parameters;

[0068] The data transmission parameters refer to a series of performance indicators involved in data transmission in the network, which are used to measure the efficiency and quality of data movement on the network.

[0069] In some embodiments, the network state parameters include network bandwidth and data packet transmission delay, and the data transmission parameters include transmission rate.

[0070] S2 includes determining the transmission rate according to the network bandwidth and the data packet transmission delay.

[0071] Specifically, the network bandwidth (W) and the data packet transmission delay (D) on the network link can be obtained by monitoring the network bandwidth and the delay in real time.

[0072] The bandwidth represents the amount of data that can be transmitted per unit of time, usually measured in bps (bits per second), and usually varies between a few kbps and a few Gbps. The ideal state is the maximum physical transmission capacity of the network, and the optimal value is usually 70-80 Mbps.

[0073] The data packet transmission delay represents the time difference of data packets from the sending end to the receiving end, usually measured in milliseconds (ms), and the lower the better. The data packet transmission delay ranges from 0 to 1000 milliseconds, and the optimal value is usually 50-100 milliseconds.

[0074] These two indicators comprehensively reflect the actual transmission capacity and performance of the current network. For example, in an actual application of a smart inspection control automation method based on communication, when the smart inspection device is sending detection results to the central server, if the network bandwidth suddenly decreases or the delay increases, W will decrease accordingly and D will increase.

[0075] In some embodiments, the transmission rate is determined according to the network bandwidth and the data packet transmission delay by the following formula:​

[0076] R = W / (1 + D)

[0077] Wherein, W represents network bandwidth, D represents data packet transmission delay, and R represents transmission rate.

[0078] In this step, the above formula defines a relationship proportional to bandwidth and inversely proportional to delay to calculate the optimal transmission rate R. The higher the bandwidth, the more data can be processed per unit time, so the transmission rate should be increased accordingly. When the delay increases, it is more reasonable to appropriately reduce the transmission rate in order to avoid the efficiency loss caused by congestion and retransmission. The denominator plus 1 in the formula is mainly to avoid the division by zero error that may occur when the delay is close to 0, and also to slightly smooth the transmission rate change, avoiding large range changes in transmission rate caused by slight delay fluctuations. The optimal transmission rate should be within the bandwidth allowed range, and the maximum transmission efficiency should be achieved under the premise of ensuring timely data arrival.

[0079] The above setting can ensure that even in the case of large network bandwidth but high delay, the system can select a more stable lower transmission rate to avoid packet loss or retransmission problems caused by delay. For example, if the network bandwidth is stable at 100Mbps in a certain period of time, and the delay suddenly rises from 10ms to 50ms, the new transmission rate calculated according to the above formula will be reduced, thereby reducing the error rate during data transmission.

[0080] The present application can fully utilize bandwidth resources when network delay is low, and appropriately reduce transmission rate when network delay is high, thereby avoiding packet loss or retransmission caused by excessive use of bandwidth. This setting makes the transmission rate flexible to adapt to changes in network conditions and improves transmission efficiency.

[0081] In some embodiments, the network status parameters include packet loss rate and network delay, and the data transmission parameters include retransmission interval time, retransmission times, and data fragment size.

[0082] S2, comprising: determining the retransmission interval time, the retransmission times of the data transmission protocol according to the packet loss rate and the network delay, and dynamically adjusting the data fragment size.

[0083] This step specifically includes: monitoring the packet loss rate and network delay of the current network; adjusting the retransmission interval time and the retransmission times of the data transmission protocol according to the monitoring results; dynamically adjusting the data fragment size to reduce the packet loss phenomenon caused by large packet transmission; and gradually restoring the default data transmission parameter settings when the network state recovers to stable.

[0084] First, monitor the packet loss rate and network delay of the current network. This step involves obtaining real-time packet loss rate and delay information during network communication through network monitoring tools or algorithms. This can be achieved by deploying sensors or monitoring points at key nodes in the data transmission path to capture and record the sending and receiving of data packets. This information is crucial for subsequent parameter adjustment, ensuring the rationality and effectiveness of the transmission strategy.

[0085] Adjust the retransmission interval time and the number of retransmissions of the data transmission protocol according to the monitoring results. Specifically, when a higher packet loss rate and network delay are monitored, the retransmission interval time needs to be reduced, and the number of retransmissions needs to be increased, so as to detect and recover lost data packets faster, and improve the reliability and efficiency of data transmission. For example, in one embodiment, if the packet loss rate reaches 10%, the system can adjust the retransmission interval time from 1 second to 0.5 seconds, and increase the number of retransmissions from 3 to 5.

[0086] In some embodiments, the retransmission interval time and the number of retransmissions of the data transmission protocol are determined according to the packet loss rate and the network delay, including:

[0087] Determine an adjustment factor based on the packet loss rate and the network delay;

[0088] When the adjustment factor is greater than a first preset threshold, increase the retransmission interval time, and when the adjustment factor is less than or equal to the first preset threshold, keep the retransmission interval time unchanged;

[0089] When the adjustment factor is greater than a second preset threshold, increase the number of retransmissions, and when the adjustment factor is less than or equal to the second preset threshold, keep the number of retransmissions unchanged;

[0090] Wherein, the second preset threshold is greater than the first preset threshold.

[0091] In some embodiments, the adjustment factor is determined based on the packet loss rate and the network delay by the following formula:

[0092] K = P x T / C

[0093] Where P represents the packet loss rate, T represents the network delay, K represents the adjustment factor, and C is a constant representing a reference value.

[0094] Specifically, the above process aims to dynamically adjust the parameters of the data transmission protocol through real-time monitoring of network conditions, ensuring the efficiency and reliability of data transmission. Specifically, an adjustment factor K is calculated based on the packet loss rate P and network delay T. The packet loss rate P represents the proportion of lost data packets during network transmission, usually between 0 and 1, and the network delay T represents the time required from sending to receiving, usually in milliseconds. C is a constant representing a baseline value for evaluating the overall stability of the network. The specific value of this constant depends on the network environment and application requirements, and is generally set in the range of 1 to 1000, with the optimal value determined according to actual conditions. The formula K = P x T / C aims to consider the impact of packet loss rate and network delay on network transmission, generating a quantitative index K for subsequent decision-making.

[0095] If the calculated K is greater than the first preset threshold Th1, the system will increase the retransmission interval time Tc1. This is because in the case of high packet loss rate and large network delay, frequent retransmission may increase network burden, so appropriately extending the retransmission interval can reduce network pressure and improve transmission efficiency. If K is less than or equal to Th1, it indicates that the network condition is acceptable, and there is no need to change the existing retransmission interval time to maintain the current performance level.

[0096] In addition, if K is greater than the second preset threshold Th2, it indicates that the network condition is poor, and the number of retransmissions Rc1 needs to be increased to ensure successful data transmission. Conversely, if K is less than or equal to Th2, the number of retransmissions remains unchanged to avoid unnecessary retransmission waste of resources. Generally, the value of Th2 is higher than Th1, reflecting a stricter standard suitable for more severe network environments.

[0097] Finally, after each adjustment, the changes in retransmission interval time and retransmission number are recorded in the system log for subsequent analysis and debugging. This helps to track historical data, optimize algorithm parameters, and improve the overall performance of the system.

[0098] For example, in a specific embodiment, a power company's inspection robot is receiving instructions for operation through a remote network. Suppose the monitoring data shows that the current network has a packet loss rate of 0.1 and a network delay of 100 milliseconds, and the reference value C is set to 500. The calculated adjustment factor K is (0.1 * 100) / 500 = 0.02. The system's preset threshold Th1 is 0.1 and Th2 is 0.5. Since K (0.02) is less than Th1, the system decides not to adjust the retransmission interval time, and K is much less than Th2, so it decides to keep the retransmission number unchanged. However, if the network condition worsens and the packet loss rate rises to 0.2 and the network delay increases to 200 milliseconds, the new K value is (0.2 * 200) / 500 = 0.08. At this time, K is greater than Th1 but less than Th2, so the system will increase the retransmission interval time but still keep the retransmission number unchanged until the network condition further deteriorates to K exceeding Th2, at which point the retransmission number will be increased. Through this dynamic adjustment mechanism, the system can maintain efficient inspection control in various network environments.

[0099] In some embodiments, the step of calculating the adjustment factor K based on the packet loss rate P and the network delay T further comprises the following steps:

[0100] initializing initial values P0 and T0 of the packet loss rate P and the network delay T;

[0101] real-time acquisition of the current packet loss rate P and the network delay T;

[0102] using the formula K = (P - P0) * (T - T0) / C for calculation, where C is a constant representing a reference value for measuring network stability;

[0103] when K is greater than a third preset threshold Th3, further increasing the retransmission interval time Tc2, and when K is less than or equal to the threshold Th3, restoring the original retransmission interval time.

[0104] initializing initial values P0 and T0 of the packet loss rate P and the network delay T. This step is to determine a baseline performance indicator before starting the system to run, so as to have a reference value in the subsequent running process. The initial values P0 and T0 are usually the packet loss rate and network delay of the system under the best network condition, or the average value of historical data. These initial values can ensure a reliable comparison standard in the dynamically changing network environment.

[0105] real-time acquisition of the current packet loss rate P and the network delay T. This step is to monitor the actual performance of the current network. The packet loss rate P reflects the proportion of data packet loss in network transmission, and the network delay T reflects the time taken for data to travel from the sender to the receiver. These real-time data are obtained through network monitoring tools or sensors to ensure that the system can quickly respond to changes in network status.

[0106] The formula K = (P - P0) x (T - T0) / C is used for calculation, where C is a constant representing a reference value for measuring network stability. P0 and T0 in the formula are the initial values in the previous step, and P and T are the current real-time data. The constant C is a predetermined reference value, usually ranging from 0.1 to 1, and the optimal value can be selected according to different network environments and requirements. The purpose of the formula is to comprehensively consider the influence of packet loss rate and network delay on network performance. If the value of K is large, it means that the current network environment has deteriorated significantly compared to the initial state; on the contrary, it means that the network performance is stable or slightly improved.

[0107] When K is greater than the threshold Th3, the retransmission interval time Tc2 is further increased, and when K is less than or equal to the threshold Th3, the original retransmission interval time is restored. The threshold Th3 is a limit for judging whether the current network state needs to be adjusted, and is usually set to a reasonable positive value. If K is greater than Th3, it means that the network condition is not good, and the retransmission interval time Tc2 needs to be increased to reduce unnecessary network congestion. On the contrary, if K is less than or equal to Th3, it means that the network condition is good, and there is no need to adjust the retransmission interval time, so as to restore the original transmission efficiency. This mechanism can ensure that the system automatically adapts when the network condition deteriorates, and restores normal operation when the condition improves, improving the reliability and performance of the overall system.

[0108] For example, in one embodiment, a certain intelligent patrol control system measures the packet loss rate P0 to be 0.1% and the network delay T0 to be 20 milliseconds in the initial state. Through real-time monitoring, the system finds that the current packet loss rate P rises to 0.3% and the network delay T increases to 50 milliseconds. Using the above formula, the value of K is calculated as (0.3% - 0.1%) x (50ms - 20ms) / 0.5 = 2. Assuming that the threshold Th3 is set to 1, since the value of K is greater than 1, the system will increase the retransmission interval time Tc2 to avoid network congestion. In another scenario, if the packet loss rate drops to 0.1% and the network delay remains at 20 milliseconds, the value of K is calculated to be 0, which is less than the threshold Th3, and the system will restore the original retransmission interval time to ensure efficient communication efficiency.

[0109] In some embodiments, the initial values P0 and T0 of the packet loss rate P and the network delay T further include the following steps:

[0110] When the system starts, record the first monitored packet loss rate and network delay as P0 and T0;

[0111] Periodically re-evaluate and update the initial values P0 and T0, with an evaluation period of 10 minutes;

[0112] When the changes of P0 and T0 exceed a certain threshold D, re-initialize P0 and T0;

[0113] The updated P0 and T0 will be used for subsequent adjustment factor K calculation.

[0114] At system startup, the first monitored packet loss rate and network delay are recorded as P0 and T0. This is to establish a baseline value for subsequent comparison and adjustment. For example, during the startup process of the network monitoring system, the first recorded packet loss rate is 0.05%, and the network delay is 20 milliseconds, which are set as P0 and T0.

[0115] The initial values P0 and T0 are periodically re-evaluated and updated, with an evaluation period of 10 minutes. This step is to ensure that the initial values can reflect the latest state of the network, preventing the baseline value from being invalid due to changes in network environment. In an embodiment, the system will monitor the network packet loss rate and network delay again every 10 minutes. If the new packet loss rate is 0.06% and the network delay is 22 milliseconds, then update P0 and T0 to these new values.

[0116] When the changes of P0 and T0 exceed a certain threshold D, P0 and T0 are reinitialized. The purpose of this step is to timely reset the baseline value when the network conditions change significantly, avoiding the error baseline leading to inaccurate subsequent calculation results. The threshold D can be set according to specific application scenarios, and the common range is that the packet loss rate changes more than 0.1% and the network delay changes more than 5 milliseconds. Specifically, if after multiple 10-minute evaluations, the packet loss rate rises from 0.05% to 0.16%, exceeding the threshold of 0.1%, then reinitialization will be triggered, and P0 and T0 will be reset to the currently monitored packet loss rate and network delay.

[0117] The updated P0 and T0 will be used for subsequent adjustment factor K calculation. Adjustment factor K is a weight value calculated based on P0 and T0, used to adjust the optimization strategy of network performance. Specifically, assuming the calculation formula of K is:

[0118] K = alpha × times P + beta × times T

[0119] Where alpha and beta are weight coefficients of packet loss rate and network delay, respectively, with a value range of 0 to 1, and alpha + beta = 1. The optimal value is usually adjusted according to the actual application effect, for example, if more attention is paid to packet loss rate, alpha can be set to 0.7 and beta to 0.3.

[0120] The meaning of this formula is that when calculating the adjustment factor K, both the impact of packet loss rate on network performance and the impact of network delay should be considered, and the weight coefficients reflect the importance of the two. The formula set in this way can make the system flexibly adjust the network optimization strategy in different situations and improve the overall performance.

[0121] Optionally, when the changes in P0 and T0 exceed a certain threshold D, reinitializing P0 and T0 further comprises:

[0122] Monitoring the changes in P0 and T0;

[0123] Using the formulae |(P0_new - P0_old) / P0_old| > D and |(T0_new - T0_old) / T0_old| > D to judge the change amplitude, where P0_new, T0_new are the new measurement values, P0_old, T0_old are the old measurement values, and D is the threshold value, to determine whether the change amplitude is too large;

[0124] When any of the conditions are met, re-record the values of P0 and T0; reset the system-related parameters to adapt to the new network state.

[0125] First, monitor the changes in P0 and T0, which involves periodically collecting the current pressure (P0) and temperature (T0) values of the system, and comparing these new measurement values with the previously stored old measurement values. Through continuous monitoring, the trend of changes in these two key parameters can be obtained in real time, and any abnormal fluctuations can be detected in a timely manner.

[0126] Second, use the formulae |(P0_new - P0_old) / P0_old| > D and |(T0_new - T0_old) / T0_old| > D to judge the change amplitude. Here, P0_new and T0_new are the latest measurement values, P0_old and T0_old are the last recorded measurement values, and D is the pre-set threshold value. The threshold value D is usually set between 0.1 and 0.3, and the specific value is optimized according to the requirements of the system and the stability requirements. The formula indicates the change rate of the current measurement value relative to the old measurement value, and when the change rate exceeds the threshold value D, it indicates that the system state has changed significantly, and appropriate measures need to be taken.

[0127] When any of the above conditions are met, i.e. when the change rate of P0 or T0 exceeds the threshold value D, the system will automatically re-record the new values of P0 and T0. This step ensures that the measurement data of the system is always up-to-date, reflecting the current system state.

[0128] Finally, reset the system-related parameters to adapt to the new network state. After the system re-records the values of P0 and T0, it will adjust the relevant control parameters and algorithms according to these new measurement results, so that the system can better cope with the new working environment. For example, in one embodiment, if the pressure P0 is detected to suddenly increase by 10%, which exceeds the pre-set threshold value D (e.g. 0.2), the system will re-record the new pressure value and adjust the working frequency of the pump and the valve opening to maintain the stable operation of the pipe network.

[0129] wherein the step of using the formulae |(P0_new - P0_old) / P0_old| > D and |(T0_new - T0_old) / T0_old| > D to determine the change amplitude further comprises the following steps: obtaining the latest packet loss rate P0_new and network delay T0_new in real time; comparing with P0_old and T0_old in the historical record; triggering the re-initialization of P0 when |(P0_new - P0_old) / P0_old| > D is true; triggering the re-initialization of T0 when |(T0_new - T0_old) / T0_old| > D is true.

[0130] Obtaining the latest packet loss rate P0_new and network delay T0_new in real time refers to collecting the current packet loss rate P0_new and network delay T0_new in real time by monitoring the network status during the system running process. These two parameters reflect the quality of the current network environment, which is crucial for timely discovering and handling network problems.

[0131] Comparing with P0_old and T0_old in the historical record refers to comparing the packet loss rate P0_new and network delay T0_new collected in real time with the historical data P0_old and T0_old recorded previously. This process is usually completed automatically by the background system and is used to detect the changes in network performance.

[0132] Triggering the re-initialization of P0 when |(P0_new - P0_old) / P0_old| > D is true refers to re-initializing the packet loss rate parameter when the change amplitude of the current packet loss rate P0_new and the historical packet loss rate P0_old exceeds the preset threshold D. The value range of parameter D is generally 0 to 1, and the optimal value depends on the specific application scenario. In general, D can be set to 0.1 or 0.2, indicating a change amplitude of 10% or 20%. The purpose of this formula is to adjust the relevant parameters in time when the packet loss rate changes significantly, ensuring that the system can quickly adapt to the new network status.

[0133] Triggering the re-initialization of T0 when |(T0_new - T0_old) / T0_old| > D is true also refers to re-initializing the network delay parameter when the change amplitude of the current network delay T0_new and the historical network delay T0_old exceeds the preset threshold D. The value and optimization principle of D are the same as the previous formula. The purpose of setting the formula is to ensure that the system can quickly respond when the network delay changes significantly, adjust the corresponding control strategy, and avoid the impact of network problems on the overall service quality.

[0134] For example, in one embodiment, assume that the monitoring system of a certain intelligent system updates packet loss rate and network delay data every minute. At a certain moment, the current packet loss rate is 2%, and the historical packet loss rate is 1%, i.e., P0_new = 0.02, P0_old = 0.01. Calculate |(P0_new - P0_old) / P0_old| = |(0.02 - 0.01) / 0.01| = 1, which exceeds the preset threshold D = 0.1. At this time, the system will trigger the re-initialization of P0, and re-collect and adjust the packet loss rate parameter. This helps the system respond to network changes in a timely manner and ensure service quality. Similarly, if the current network delay is 100 milliseconds and the historical network delay is 80 milliseconds, i.e., T0_new = 100, T0_old = 80, calculate |(T0_new - T0_old) / T0_old| = |(100 - 80) / 80| = 0.25, which also exceeds the threshold D = 0.2, and the system will re-initialize T0 to ensure that the network delay parameter conforms to the actual network condition. Through such a mechanism, the system can quickly respond when the network condition changes, ensuring the automation and efficiency of the patrol control.

[0135] In this application, the size of data fragments is dynamically adjusted to reduce the packet loss phenomenon caused by large packet transmission. When the network quality is poor, large packets are more likely to be lost, so it is necessary to split large data packets into smaller data packets for transmission, thereby reducing the transmission failure probability of a single data packet. For example, if the monitored network delay exceeds 50 milliseconds, the size of the data packet can be adjusted from 1024 bytes to 512 bytes.

[0136] In some embodiments, the size of the data fragment is dynamically adjusted by the following formula:

[0137] data_packet_size

[0138] = max (default_packet_size - delay_factor

[0139] × delay, min_packet_size)

[0140] where default_packet_size is the default data packet size (e.g., 1024 bytes), delay is the network delay (unit: milliseconds), min_packet_size is the minimum data packet size (e.g., 512 bytes), and delay_factor is the delay adjustment coefficient (e.g., 1 byte / millisecond). This formula ensures that the data packet size can be effectively reduced when the network delay is high, but is still not less than the minimum value.

[0141] When the network state recovers to be stable, the default data transmission parameter settings are gradually restored. This step ensures that after the network conditions improve, the system can gradually fall back to the initial optimization parameters, avoiding resource waste due to excessive conservatism. For example, specifically, when the packet loss rate drops to 2% and the delay is less than 30 milliseconds, the system can gradually restore the data packet size to 1024 bytes per minute and gradually increase the retransmission interval time from 0.5 seconds to 1 second, and finally restore the default settings, ensuring optimal transmission effect after the network recovers to be stable.

[0142] S3, according to the importance requirement and real-time requirement of the target data to be transmitted, determining the transmission priority of the target data;

[0143] In some embodiments, in S3, according to the importance requirement and real-time requirement of the target data to be transmitted, the transmission priority of the target data is determined by the following formula:

[0144] P = (Q + V) / 2

[0145] Where P represents the transmission priority, Q represents the importance requirement of the target data, and V represents the real-time requirement of the target data.

[0146] Specifically, in a multi-task environment, different data have different requirements for timeliness and importance. This step ensures that critical data is transmitted first by dynamically allocating transmission priority P.

[0147] Where the importance requirement Q reflects the relative value or urgency of the information, and the larger the value represents the more important; the real-time requirement V indicates the urgency of the information that needs to be quickly conveyed, and the larger the value represents the higher the requirement.

[0148] The purpose of the above transmission priority calculation formula is to combine the two attributes to evaluate the priority of data transmission, so that those important and urgently needed data are given priority. The calculation of priority P value uses the two-factor average method to ensure that both the importance of the content and the actual time constraint are not ignored.

[0149] In some examples, Q generally ranges from 1 to 5, and the higher the value represents the more important the data; V is also between 1 and 5, reflecting the strictness of the arrival time of the batch of data. After averaging, a comprehensive index P between 1 and 5 is obtained, indicating the position level of the current task in all task queues to be executed. For example, assuming that a patrol robot captures an image of a significant pipeline leak, this information belongs to high-priority content (assuming Q = 4), and in order to timely warn potential risk incidents, it needs to be transmitted immediately (V = 5), then P is (4 + 5) / 2 = 4.5, indicating that this information should be uploaded and processed as soon as possible.

[0150] In some examples, the value range of Q can also be set to 0 to 10, and the Q value of important data is usually 8 or above; the value range of V can also be set to 0 to 10, and the V value of data with high real-time requirement is 8 or above. A new transmission priority P is obtained by calculation (P = (Q + V) / 2). The purpose of this formula is to balance the importance and real-time requirement of data, and to ensure that data with high priority can be transmitted in time. For example, if the data of a certain monitoring camera is not only important but also needs to be transmitted in real time, Q and V are both set to 9, so that P is calculated to be 9, indicating that the data of the camera has a very high transmission priority.

[0151] In some examples, the value range of Q and V can also be set to 0 to 1, and 1 represents the highest importance or the most urgent. The calculation formula of priority P is P = (Q + V) / 2, that is, the average value of data Q and V is taken as the priority. This ensures that tasks with high importance and urgency can obtain higher priority and be processed more quickly.

[0152] In this application, determining the transmission priority of the target data can include the following steps:

[0153] S31. Collecting importance and real-time information of data according to a preset time interval;

[0154] Specifically, collecting importance and real-time information of data according to a preset time interval is to ensure that the system can dynamically adjust the transmission priority according to the latest situation. The preset time interval can be flexibly set according to the requirements of the application scene, for example, data importance and real-time information can be collected once when the inspection robot detects an anomaly, or once every minute.

[0155] S32. Calculating importance requirement Q and real-time requirement V of each data;

[0156] Specifically, calculating importance requirement Q and real-time requirement V of each data is to analyze each data in detail to determine its importance and timeliness in the transmission process. Importance requirement Q reflects the importance of data in the overall inspection task, which can usually be represented by a score of 0 to 10, and 10 represents the most important; real-time requirement V reflects how quickly the data needs to be transmitted and processed, which can also be represented by a score of 0 to 10, and 10 represents the highest real-time requirement.

[0157] S33. Calculating transmission priority P using the formula P = (Q + V) / 2, where P is the transmission priority, Q is the importance requirement of data, and V is the real-time requirement of data;

[0158] Specifically, P is calculated by taking the average of Q and V, so as to balance the importance and real-time performance of the data, and avoid the influence of extreme cases on the whole transmission priority. This setting can ensure that the importance of the data and the real-time performance are considered in the transmission process.

[0159] In this application, the dynamic allocation of transmission priority P can better adapt to the changing network environment through dynamic adjustment of the transmission priority, ensuring the efficiency and stability of data transmission.

[0160] S4, based on the data transmission parameters and the transmission priority, using a target path to transmit the target data, wherein the target path is a preset main path or a backup path with the highest stability when the preset main path is unavailable.

[0161] In some embodiments, in S4, the backup path with the highest stability when the preset main path is unavailable is determined by the following formula:

[0162] S = min(Pi, Ci),

[0163] Where S represents the backup path with the highest stability when the preset main path is unavailable, Pi represents the priority of the i-th transmission path, and Ci represents the stability of the i-th transmission path.

[0164] Specifically, in the case of network instability, the backup transmission path S is automatically enabled, S = min(Pi, Ci), where Pi is the priority of the i-th transmission path, and Ci is the stability of the i-th transmission path. This strategy is a set of alternative solutions when the main transmission route is unavailable or inefficient, ensuring that the alternative line is quickly enabled when the main communication channel fails. The priority Pi of the path is comprehensively evaluated according to the aforementioned method, and the stability Ci evaluates the ability of the specific line to resist sudden failure and the ability to continuously and stably work, which is usually between 0 and 1. The closer the value is to 1, the more robust and reliable it is. The formula selects the lower of the two path scores as the final decision standard, aiming to ensure that even if faced with multiple candidate routes, the system can still select the best replacement option that is suitable and stable.

[0165] For example, when detecting that the original transmission channel is severely jittering, the algorithm quickly calculates the relevant parameters of all existing alternative paths, and selects the most suitable backup path according to the minimum principle to continue transmitting the important data collected by the robot, ensuring the smooth progress of the inspection work.

[0166] In one specific embodiment, if the backbone line is interrupted due to weather factors, the control system will make a judgment among the remaining two available paths: one has slightly lower priority (P2=3) but has been proven to be extremely stable through multiple tests (C2=0.9); the other has higher priority (P3=4) but frequently has problems (C3=0.6), so the system will ultimately choose the first line as the emergency channel to continue data transmission.

[0167] For example, during an intelligent inspection process, if it is found that the current transmission path has a decreased priority due to temporary network fluctuations, and the priority of the backup path S is higher than that of the current path, and the stability of the backup path is good, the system will automatically switch to the backup path to ensure the quality and reliability of data transmission. This automatic switching mechanism helps to improve the robustness and user experience of the overall system.

[0168] For example, in an application scenario of a smart inspection control automation method, assume that there is an inspection robot conducting equipment inspection, which collects the importance and real-time information of the detected data every minute. At a certain moment, the robot detects that a temperature sensor data is abnormal (importance requirement Q=9), which needs to be immediately transmitted to the central control center for further analysis (real-time requirement V=8). According to the formula P=(Q+V) / 2, the transmission priority of the data is calculated as P=(9+8) / 2=8.5. Then, the robot has three available transmission paths: path 1 has a priority Pi1=9 and a stability Ci1=7; path 2 has a priority Pi2=8 and a stability Ci2=8; path 3 has a priority Pi3=7 and a stability Ci3=9. According to the formula S=min(Pi,Ci), the S values of the paths are calculated as S1=min(9,7)=7, S2=min(8,8)=8, and S3=min(7,9)=7. Finally, the system will select either path 1 or path 3, because the S values of these two paths are both 7, meeting the stability requirement of the transmission path. This selection ensures that important data can be quickly transmitted and the reliability of the data is guaranteed during transmission.

[0169] For example, assume that there are three transmission paths in the inspection control automation system, path A has a priority of 0.7 and a stability of 0.6; path B has a priority of 0.8 and a stability of 0.8; path C has a priority of 0.6 and a stability of 0.9, then the score of path A is 0.6, the score of path B is 0.8, and the score of path C is 0.6. Therefore, path B is selected as the best transmission path.

[0170] In one embodiment, assume that a power company's inspection robot needs to transmit detected power grid anomaly information back to the command center in real time. The system discovers that the current network bandwidth is 10 Mbps and the delay is 50 ms through real-time monitoring, and then calculates the current transmission rate as about 6.7 Mbps according to R = W / (1+D). Assume that there are two tasks at this time: task 1 is used to transmit important but not urgent environmental monitoring data, and the data Q and V are both 0.5, so the priority P = (0.5+0.5) / 2 = 0.5; task 2 is used to transmit urgent and important abnormal alarm data, and the data Q and V are both 0.9, so the priority P = (0.9+0.9) / 2 = 0.9. When selecting a transmission path, considering the actual performance of paths A, B, and C and the evaluation method mentioned above, path B is finally selected as the optimal path to transmit the abnormal alarm data of task 2, ensuring that the alarm data can be accurately transmitted at the fastest speed.

[0171] In one embodiment, assume that an automated inspection system needs to send instructions from a central server to multiple remote devices. The system regularly collects Q and V data, and assume that at a certain time Q = 0.8 and V = 0.9, and then calculate P = (0.8+0.9) / 2 = 0.85. At the same time, during transmission, the system detects that the current path has a network quality W = 0.9 and a network delay D = 0.1, and then calculates R = 0.9- / (1+0.1) = 0.818. The system continues to detect path stability, and assume that path 1 has Pi = 0.85 and Ci = 0.8, and path 2 has Pi = 0.9 and Ci = 0.7, and finally select path S1 = min(0.85,0.8) = 0.8, because path 1 has higher stability and is more suitable as a transmission path.

[0172] This step selects the minimum value of priority and stability as the final routing basis when switching paths, ensuring that not only priority but also path stability is considered when selecting a path, thereby improving the overall reliability of transmission.

[0173] In summary, the method of the present application not only can adjust parameters in real time according to network conditions, but also can optimize data delivery processes according to different business needs, and has the ability to flexibly respond to unexpected situations, significantly improving data communication quality and user experience.

[0174] In addition, the existing inspection technology has other problems, specifically: under unstable network conditions, data packet loss can cause the interruption of inspection tasks; secondly, in the case of multiple devices online at the same time, resource preemption can cause delay in inspection scheduling; thirdly, in a frequently changing environment, the accuracy of sensors may decrease, leading to errors in inspection data; in addition, in the event of a sudden abnormal event, due to the imperfect processing mechanism, the system response speed may lag; finally, when a large amount of data needs to be processed, the high complexity of the algorithm can lead to excessive occupation of computing resources.

[0175] Based on the above problems, the present application further provides an intelligent communication-based inspection control automation method. This method aims to solve the inspection problems under unstable network, multiple device resource competition, frequent environmental changes, sudden abnormal events and large data processing through a series of intelligent technology and algorithm optimization.

[0176] Figure 2 The schematic diagram of the network state monitoring and optimization processing method provided by the embodiments of the present application is shown in Figure 2 As shown, the present application provides an intelligent communication-based inspection control automation method, which includes:

[0177] (1) Based on the real-time network state monitoring mechanism, dynamically adjust the data transmission parameters to reduce data packet loss and inspection task interruption.

[0178] The key to this step is to monitor the network status in real time, including but not limited to signal strength, bandwidth utilization and data transmission success rate. When the network status is detected to be poor, the system will automatically adjust the data packet size, retransmission times and other transmission parameters to ensure reliable transmission of data. For example, in one embodiment, the inspection robot suddenly encounters a situation of wireless signal attenuation while performing a task. At this time, the real-time network state monitoring system detects that the signal strength is lower than the threshold value, and immediately starts the adaptive transmission mechanism, reduces the data packet size from the original 1024 bytes to 256 bytes, and increases the data retransmission times to 5 times, thereby effectively avoiding data packet loss and ensuring the continuity of the inspection task.

[0179] (2) According to the multi-device resource demand prediction model, optimize the inspection task scheduling strategy to avoid inspection scheduling delay caused by resource preemption.

[0180] This step involves establishing a multi-device resource demand prediction model by analyzing historical task data and resource consumption patterns of individual inspection devices. Based on this model, the system can predict resource demand peaks in advance and dynamically adjust the execution order and priority of inspection tasks. Specifically, suppose in a smart chemical plant, multiple inspection robots need to perform inspections in different areas simultaneously. Through the multi-device resource demand prediction model, the system discovers that certain specific areas have concentrated access demand during a certain period of time, and immediately reallocates tasks to let some robots complete inspections in low-resource competition areas first, thereby effectively alleviating the problem of inspection delays caused by resource preemption.

[0181] (3) Real-time correction of sensor data through adaptive calibration algorithm to improve inspection data accuracy in frequently changing environments.

[0182] The core of this step is to detect and correct the accuracy deviation of sensors caused by changes in environmental factors such as temperature and humidity in real time during the inspection process. The adaptive calibration algorithm ensures the accuracy and reliability of the collected data through continuous comparison and error correction of sensor data. For example, in one embodiment, an inspection robot equipped with temperature and humidity sensors experiences a shift in readings when entering a high-temperature and high-humidity area. At this time, the adaptive calibration algorithm is immediately activated, comparing the current readings with historical normal data and adjusting the calibration parameters of the sensors in real time to restore the temperature and humidity readings to the standard range, thereby improving the accuracy of the inspection data.

[0183] (4) Build an abnormal event rapid response system to improve response speed to sudden situations during inspection.

[0184] The purpose of this step is to establish an intelligent monitoring and alarm system to quickly trigger pre-set emergency measures upon detection of abnormal events. The system will automatically classify responses based on the severity of the abnormal event and call the corresponding processing module. In a specific embodiment, a certain inspection robot discovers a leakage fault in a production device during the inspection process. The abnormal event rapid response system immediately detects this situation, triggers an alarm and generates an emergency handling work order, notifying maintenance personnel to arrive at the scene for processing in the first time, greatly shortening the accident handling time and reducing potential risks.

[0185] (5) Use lightweight machine learning models for data processing to reduce algorithm complexity and computational resource occupation.

[0186] This step realizes rapid processing and analysis of data by selecting or designing an efficient and lightweight machine learning model suitable for inspection data processing. The lightweight model not only maintains high accuracy, but also significantly reduces the consumption of computing resources. For example, in a large-scale inspection project, the system needs to process a large amount of data reported by thousands of inspection devices every day. By introducing a lightweight machine learning model, the computing efficiency of the system is greatly improved, and the processing time is shortened from several hours to tens of minutes, and the computing resource occupancy rate is significantly reduced, ensuring the continuous and stable operation of the system.

[0187] In summary, the intelligent-based inspection control automation method effectively solves the inspection problems in the case of unstable network, multi-device resource competition, frequent environmental changes, sudden abnormal events, and large data processing, and provides a strong guarantee for efficient, stable, and accurate intelligent inspection.

[0188] It should be understood that although each step in the flowchart in the above embodiment is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other orders. Moreover, at least part of the steps in the figure can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0189] Figure 3 The schematic diagram of the network state monitoring and optimization processing device provided by the embodiment of the present application is shown in FIG. 1, and the present application provides a network state monitoring and optimization processing device, which comprises: Figure 3

[0190] The parameter acquisition module 11 is configured to acquire network state parameters by monitoring;

[0191] The parameter determination module 12 is configured to determine data transmission parameters according to the network state parameters;

[0192] The priority determination module 13 is configured to determine the transmission priority of target data according to the importance requirement and real-time requirement of the target data;

[0193] The data transmission module 14 is configured to perform data transmission on the target data by using a target path based on the data transmission parameters and the transmission priority, wherein the target path is a preset main path or a backup path with the highest stability when the preset main path is unavailable. ​

[0194] With reference to the limitation of the network state monitoring and optimization processing device, the limitation of the network state monitoring and optimization processing method in the above-mentioned embodiments of the present application can be referred to, and the present embodiment will not be described here again.

[0195] Figure 4 Another schematic diagram of the network state monitoring and optimization processing device provided by the embodiments of the present application is shown in FIG. 2. In some embodiments, the present application provides a network state monitoring and optimization processing device, which comprises a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the network state monitoring and optimization processing method in the above-mentioned embodiments of the present application. Figure 4

[0196] The memory is connected with the processor, and the memory can adopt a flash memory or a read-only memory or other memories, and the processor can adopt a central processing unit or a single-chip microcomputer.

[0197] In some embodiments, the present application provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the network state monitoring and optimization processing method in the above-mentioned embodiments of the present application is realized.

[0198] The computer readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, computer program modules or other data. The computer readable storage medium includes but is not limited to RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), digital versatile disc (DVD) or other optical disc storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer.

[0199] It can be understood that the above embodiments are only exemplary embodiments adopted for illustrating the principles of the present application, and the present application is not limited thereto. Various modifications and improvements can be made by those skilled in the art without departing from the spirit and essence of the present application, and these modifications and improvements are also regarded as the protection scope of the present application.​

Claims

1. A network state monitoring and optimization processing method, characterized by, The method comprises: S1, obtaining network state parameters by monitoring; S2, determining data transmission parameters according to the network state parameters; S3, determining the transmission priority of target data to be transmitted according to the importance requirement and real-time requirement of the target data; S4, based on the data transmission parameters and the transmission priority, using a target path to perform data transmission on the target data, wherein the target path is a preset main path or a backup path when the preset main path is unavailable; Wherein, the backup path when the preset main path is unavailable is determined by the following formula: S = min (Pi, Ci), Wherein, S represents the backup path when the preset main path is unavailable, Pi represents the priority of the i-th transmission path, and Ci represents the stability of the i-th transmission path.

2. The network state monitoring and optimization processing method of claim 1, wherein, The network state parameters include network bandwidth and data packet transmission delay, and the data transmission parameters include transmission rate; S2, comprising: Determine the transmission rate according to the network bandwidth and the data packet transmission delay.

3. The network state monitoring and optimization processing method of claim 1, wherein, The network state parameters include packet loss rate and network delay, and the data transmission parameters include retransmission interval time, retransmission times and data packet size; S2, comprising: According to the packet loss rate and the network delay, determine the retransmission interval time, the retransmission times of the data transmission protocol, and dynamically adjust the data packet size.

4. The network state monitoring and optimization processing method of claim 3, wherein, According to the packet loss rate and the network delay, determining the retransmission interval time, the retransmission times of the data transmission protocol, comprises: Determine the adjustment factor based on the packet loss rate and the network delay; When the adjustment factor is greater than a first preset threshold, increase the retransmission interval time, and when the adjustment factor is less than or equal to the first preset threshold, keep the retransmission interval time unchanged; When the adjustment factor is greater than a second preset threshold, increase the retransmission times, and when the adjustment factor is less than or equal to the second preset threshold, keep the retransmission times unchanged; Wherein, the second preset threshold is greater than the first preset threshold.

5. The network state monitoring and optimization processing method of claim 4, wherein, Based on the packet loss rate and the network delay, the adjustment factor is determined by the following formula: K = P × T / C Wherein, P represents the packet loss rate, T represents the network delay, K represents the adjustment factor, and C is a constant representing a reference value.

6. The network state monitoring and optimization processing method of claim 3, wherein, Dynamically adjust the data packet size by the following formula: data_packet_size = max (default_packet_size-delay_factor × delay, min_pecket_size) Wherein, data_packet_size represents the data packet size, default_packet_size represents the default data packet size, delay represents the network delay, min_packet_size represents the minimum data packet size, and delay_factor represents the delay adjustment coefficient.

7. The network state monitoring and optimization processing method according to any one of claims 1-6, wherein, In S3, according to the importance requirement and real-time requirement of the target data to be transmitted, the transmission priority of the target data is determined by the following formula: P = (Q + V) / 2 Wherein, P represents the transmission priority, Q represents the importance requirement of the target data, and V represents the real-time requirement of the target data.

8. A network state monitoring and optimization processing apparatus, characterized by comprising: The device comprises: a parameter obtaining module configured to obtain network state parameters by monitoring; a parameter determining module configured to determine data transmission parameters according to the network state parameters; a priority determining module configured to determine a transmission priority of target data to be transmitted according to importance and real-time requirements of the target data; a data transmission module configured to perform data transmission on the target data by using a target path based on the data transmission parameters and the transmission priority, wherein the target path is a preset main path or a backup path when the preset main path is unavailable; wherein the backup path when the preset main path is unavailable is determined by the following formula: S = min(Pi, Ci), wherein S represents the backup path when the preset main path is unavailable, Pi represents a priority of an i-th transmission path, and Ci represents a stability of the i-th transmission path.

9. A network state monitoring and optimization processing apparatus, characterized by comprising: a memory and a processor, the memory storing a computer program, and the processor being configured to run the computer program to implement the network state monitoring and optimization processing method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the network state monitoring and optimization processing method according to any one of claims 1-7.

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