Image quality enhancement method and system based on MESH ad hoc network

Abstract

The application discloses a MESH self-organizing network-based image quality enhancement method and system, and relates to the technical field of self-organizing network image processing. The MESH self-organizing network-based image quality enhancement method comprises the following steps: node data acquisition delay error evaluation; signal collision delay error evaluation; and link breakage delay error evaluation. Whether to perform detection node optimization is determined according to the data acquisition delay error result, whether to perform route signal transmission power fraction optimization is determined according to signal collision data for signal delay error evaluation, and whether to perform link gain optimization is determined according to link breakage data for link delay error evaluation, so that the accuracy of real-time passenger flow image analysis at a smart city traffic intersection is improved, and the problem that the accuracy of real-time passenger flow image analysis at a smart city traffic intersection is not high due to the delay of MESH self-organizing network route switching caused by node image data in the multi-hop transmission process in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of image processing technology for self-organizing networks, and in particular to an image quality enhancement method and system based on MESH self-organizing networks. Background Technology

[0002] In complex, large-scale locations with high density of people and equipment, such as smart city intersections, transportation hubs, and industrial parks, applications like image surveillance and intelligent inspection play a crucial role in security and operational management. MESH (Mesh Network) refers to a wireless mesh network; in the communications field, it specifically refers to a decentralized, self-organizing network structure with multi-hop interconnection between nodes. Existing technologies use cross-layer collaborative mechanisms to perceive network status and dynamically adjust image encoding parameters (such as network layer congestion and transport layer throughput), progressively ensuring reliable network operation. By combining edge computing nodes and time synchronization characteristics, lightweight convolutional neural network models are deployed to enhance images in real time, ultimately achieving a balance between image quality and transmission efficiency while ensuring transmission reliability.

[0003] For example, the invention patent announcement CN110415184B discloses a multimodal image enhancement method based on orthogonal metaspace, which includes: extracting style and content codes of a high-aesthetic-quality image using an encoder-decoder and mutual information optimization strategy; mapping the style code of the reference image to a style metaspace spanned by a set of orthogonal bases; improving the decoupling of style and content codes of the reference image using an adaptive instance normalization module and a mutual information-optimized feature decoupling method; constructing a generative adversarial network based on encoder-decoder for model training; and in the testing phase, inputting any ordinary image into the trained model, extracting the content code by the content encoder, and randomly sampling multiple style codes in the style metaspace, fusing the content code and style code, and then feeding the result into the generator to obtain the multimodal enhanced image.

[0004] For example, the invention patent announcement CN114972107B discloses a low-light image enhancement method based on a multi-scale stacked attention network, which includes: preprocessing training image pairs of the original low-light image and the normal-light image to obtain training image pairs composed of the original low-light image and the normal-light image; designing a multi-scale adaptive fusion stacked attention network as a low-light image enhancement network, which includes a multi-scale adaptive feature fusion module, a stacked attention residual module, and a Fourier reconstruction module; designing a target loss function for the low-light image enhancement network, training the network until it converges to a threshold or the number of iterations reaches a threshold; inputting the image to be tested into the designed low-light image enhancement network, and using the trained network to predict and generate a normal-light image.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0006] Dynamic routing protocols need to perceive and update routing information in mobility scenarios in real time, i.e., the routing information of node links in a MESH ad hoc network. This means that in high mobility scenarios (such as smart city traffic intersections), the frequency of topology changes in node links in a MESH ad hoc network may be much higher than the routing convergence time (e.g., frequent link disconnections caused by vehicle movement). This results in the topology changing before the dynamic routing protocol has converged, making the topology change speed much faster than the routing convergence time. During data packet transmission, the transmission channel path formed by node links in the MESH ad hoc network may be broken or congested, and data packets can only accumulate in the node buffer. Secondly, the data packets accumulated in the buffer queue waiting for forwarding, resulting in a significant increase in end-to-end latency, and even the formation of network islands. Ultimately, this leads to a decrease in the adaptability of MESH ad hoc networks for image data transmission in dynamic environments, making it unable to meet real-time requirements. There is a problem that the corresponding MESH ad hoc network routing switching delay during multi-hop transmission of node image data leads to low accuracy in real-time passenger flow image analysis at smart city traffic intersections. Summary of the Invention

[0007] This application provides an image quality enhancement method and system based on MESH self-organizing networks, which solves the problem in the prior art that the accuracy of real-time passenger flow image analysis at smart city traffic intersections is low due to the delay in the corresponding MESH self-organizing network route switching during multi-hop transmission of node image data. It improves the accuracy of real-time passenger flow image analysis at smart city traffic intersections caused by the delay in the corresponding MESH self-organizing network route switching during multi-hop transmission of node image data.

[0008] This application provides an image quality enhancement method based on a MESH self-organizing network, comprising the following steps: Step 1, evaluating the data acquisition delay error of the passenger flow image data acquisition process based on the signal collision rate of the monitoring nodes, and determining whether to optimize the monitoring nodes; if so, evaluating the signal delay error, otherwise optimizing the monitoring nodes; Step 2, monitoring the node signal collision status of the monitoring nodes in a specified area of ​​the MESH network in real time during the signal delay error evaluation period, evaluating the signal delay error based on the node signal collision data, and determining whether to optimize the routing signal transmission power fraction; Step 3, monitoring the node link breakage status of the monitoring nodes in a specified area of ​​the MESH network in real time during the link delay error evaluation period, evaluating the link delay error based on the node link breakage data, and determining whether to optimize the link gain.

[0009] This application provides an image quality enhancement system based on a MESH self-organizing network, including: a data acquisition delay error assessment module, a signal delay error assessment module, and a link delay error assessment module. The data acquisition delay error assessment module assesses the data acquisition delay error of passenger flow image data based on the signal collision rate of monitoring nodes, and determines whether to optimize monitoring nodes. If so, it performs signal delay error assessment; otherwise, it performs monitoring node optimization. The signal delay error assessment module monitors the node signal collision status of monitoring nodes in a specified area of ​​the MESH network in real time during the signal delay error assessment period, assesses signal delay error based on node signal collision data, and determines whether to optimize routing signal transmission power fraction. The link delay error assessment module monitors the node link breakage status of monitoring nodes in a specified area of ​​the MESH network in real time during the link delay error assessment period, assesses link delay error based on node link breakage data, and determines whether to optimize link gain.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0011] 1. By assessing the signal collision rate of monitoring nodes, data acquisition delay error is evaluated to determine whether to optimize the detection nodes, reduce data errors, and ensure data acquisition quality. Then, signal delay error is assessed using signal collision data to determine whether to optimize the transmission power fraction of routing signals, which can reduce signal overlap and interference and lower signal delay. Finally, link delay error is assessed using link breakage data to determine whether to optimize link gain, thereby reducing link breakage caused by signal attenuation or external interference and improving link reliability. This improves the accuracy of real-time passenger flow image analysis at smart city traffic intersections.

[0012] 2. By comparing the obtained route reconstruction time, communication channel access time, and data packet retransmission time with the preset node signal collision data in the database at the end of the signal delay error assessment period, and processing them in conjunction with the node signal collision data compensation values, a signal delay error index is obtained. Compared with existing technologies that only focus on a single factor affecting signal delay, this method compares and processes these three key parameters, comprehensively covering the main links that generate delay during signal transmission, reflecting changes in network status in real time, and thus reducing the impact of transmission interruptions caused by monitoring node signal collisions on MESH self-organizing network route switching delay.

[0013] 3. By comparing the obtained link breakage frequency, link status update duration, and link repair triggering time with the preset node link breakage data in the database at the end of the link delay error assessment period, and processing them in conjunction with the node link breakage data compensation value, a link delay error index is obtained. Compared with the static environment compensation mechanism of the existing technology, this method describes the link delay error from different dimensions, captures link status changes in a timely manner through dynamic assessment, and reflects them in the link delay error index, thereby improving the reliability of the assessment and reducing the impact of transmission interruption caused by link breakage during network topology changes on the routing switching delay of MESH self-organizing network. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the image quality enhancement method based on MESH self-organizing networks provided in this application embodiment;

[0015] Figure 2 A logical framework diagram of the image quality enhancement method based on MESH self-organizing network provided in the embodiments of this application;

[0016] Figure 3 A flowchart for evaluating data acquisition delay error provided in this application embodiment;

[0017] Figure 4 This is a flowchart of the signal delay error evaluation provided in an embodiment of this application;

[0018] Figure 5 A flowchart for link delay error assessment provided in an embodiment of this application;

[0019] Figure 6 This is a schematic diagram of the structure of an image quality enhancement system based on a MESH self-organizing network provided in an embodiment of this application. Detailed Implementation

[0020] The embodiments of the present application provide an image quality enhancement method and system based on MESH ad hoc network, which solve the problem in the prior art that the real-time passenger flow image analysis accuracy at the smart city traffic intersection is not high due to the routing switching delay of the corresponding MESH ad hoc network during the multi-hop transmission of node image data. By monitoring the node signal collision rate, the data acquisition delay error of the passenger flow image data acquisition process is evaluated, and at the same time, it is judged whether to optimize the monitoring nodes. If it is determined that the data acquisition is qualified, the node signal collision state and the node link break state of the monitoring nodes in the specified area in the MESH network during the communication transmission stage are monitored in real time: based on the node signal collision data, the signal delay error of the node signal collision state is evaluated, and at the same time, it is judged whether to optimize the routing signal transmission power fraction; based on the node link break data, the link delay error of the node link break state is evaluated, and at the same time, it is judged whether to optimize the link gain, achieving the effect of improving the real-time passenger flow image analysis accuracy at the smart city traffic intersection, and solving the problem in the prior art that the real-time passenger flow image analysis accuracy at the smart city traffic intersection is not high due to the routing switching delay of the corresponding MESH ad hoc network during the multi-hop transmission of node image data.

[0021] The technical solution in the embodiments of the present application is to solve the problem that the real-time passenger flow image analysis accuracy at the smart city traffic intersection is not high due to the routing switching delay of the corresponding MESH ad hoc network during the multi-hop transmission of node image data. The general idea is as follows:

[0022] The data acquisition delay error is evaluated by monitoring the node signal collision rate to judge whether to optimize the detection nodes, then the signal delay error is evaluated by the signal collision data to judge whether to optimize the routing signal transmission power fraction, and finally the link delay error is evaluated by the link break data.

[0023] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0024] As Figure 1 shown, it is a flowchart of the image quality enhancement method based on MESH ad hoc network provided by the embodiments of the present application. The image quality enhancement method based on MESH ad hoc network provided by the embodiments of the present application includes the following steps:

[0025] Step 1: Based on the monitoring node signal collision rate, the passenger flow image data acquisition process is evaluated for data acquisition delay error. Simultaneously, it is determined whether monitoring node optimization is necessary. If so, signal delay error evaluation is performed; otherwise, monitoring node optimization is performed. Monitoring node optimization refers to reducing the delay caused by the monitoring node signal collision rate to real-time passenger flow image analysis by adjusting the monitoring node transmission power and the monitoring node movement distance fraction. The data acquisition delay error evaluation measures the impact of the monitoring node signal collision rate on the MESH ad hoc network routing switching delay during data acquisition. The monitoring node signal collision rate represents the probability of a collision when multiple monitoring nodes simultaneously transmit signals in a wireless communication network. Monitoring node optimization refers to reducing the delay caused by the monitoring node signal collision rate to real-time passenger flow image analysis by adjusting the monitoring node transmission power and the monitoring node movement distance fraction.

[0026] Step 2: Monitor the node signal collision status of monitoring nodes in a specified area of ​​the MESH network in real time during the signal delay error assessment period. Based on the node signal collision data, perform signal delay error assessment on the node signal collision status. At the same time, determine whether to perform routing signal transmission power score optimization. The signal delay error assessment is used to measure the impact of transmission interruption caused by monitoring node signal collision on the routing switching delay of the MESH ad hoc network. The routing signal transmission power score optimization means adjusting the routing signal transmission power to reduce the delay of real-time passenger flow image analysis caused by monitoring node signal collision.

[0027] Step 3: Monitor the link breakage status of monitoring nodes in a specified area of ​​the MESH network in real time during the link delay error assessment period. Based on the node link breakage data, perform link delay error assessment on the node link breakage status and determine whether to perform link gain optimization. The link delay error assessment is used to measure the impact of transmission interruption caused by link breakage during network topology changes on the routing switching delay of MESH ad hoc network. Link gain optimization means adjusting the antenna gain value and coding gain value to reduce the delay of real-time passenger flow image analysis caused by link breakage.

[0028] like Figure 2 The diagram shown is a logical framework diagram of the image quality enhancement method based on MESH self-organizing network provided in this application embodiment. The specific design logic is as follows: First, evaluate the data acquisition process to avoid blind optimization, save node optimization resources, enable system resources to be allocated reasonably, improve the overall resource utilization efficiency, speed up the data acquisition speed in the network, enable the data to reach the target node quickly, improve the stability and efficiency of data acquisition, ensure smooth and accurate data service, and meet the application scenario requirements of real-time requirements.

[0029] like Figure 3The diagram shown is a flowchart of the data acquisition delay error assessment provided in an embodiment of this application. The specific design logic is as follows: The process begins by evaluating the data acquisition process, obtaining the signal collision rate of the monitoring node, and evaluating the data acquisition delay error based on the obtained signal collision rate of the monitoring node. Based on the evaluation result of the data acquisition delay error, it is determined whether to optimize the monitoring node. If so, the transmission power and moving distance of the monitoring node are adjusted; otherwise, the data transmission process is evaluated.

[0030] Furthermore, the process for determining whether to optimize monitoring nodes based on the data acquisition delay error assessment results is as follows: Based on the obtained monitoring node signal collision rate and the preset monitoring node signal collision rate in the database, it is determined whether there is a need for monitoring node optimization. If there is a need for optimization, i.e., the obtained monitoring node signal collision rate is not less than the preset monitoring node signal collision rate, the data acquisition delay error assessment result is judged as data acquisition failure, and monitoring node optimization is performed. The monitoring node signal collision rate represents the probability that multiple monitoring nodes in a specified area of ​​the MESH network will simultaneously send signals, causing signal collisions. In wireless communication protocols (such as IEEE 802.11), nodes can identify signal collisions through physical layer collision detection mechanisms (such as CSMA / CA). If there is no need for optimization, i.e., the obtained monitoring node signal collision rate is less than the preset monitoring node signal collision rate, the data acquisition delay error assessment result is judged as data acquisition success, and a node status monitoring command for the communication transmission phase is sent. The node status monitoring command is used to monitor the node signal collision status and node link breakage status of monitoring nodes in the MESH network during the communication transmission phase in real time.

[0031] The specific steps for monitoring node optimization are as follows: The average of the obtained monitoring node signal collision rate deviation and monitoring node overlap deviation is used as the monitoring node transmission power adjustment value to dynamically adjust the monitoring node transmission power. The monitoring node signal collision rate deviation represents the difference between the acquired monitoring node signal collision rate at the end of the data acquisition period and the preset monitoring node signal collision rate. The monitoring node overlap deviation represents the difference between the acquired monitoring node overlap at the end of the data acquisition period and the preset monitoring node overlap. The monitoring node overlap represents the degree to which the coverage areas of multiple monitoring nodes in a specified area of ​​the MESH network overlap spatially, typically obtained by simulating node coverage using network simulation tools (such as NS-3, OMNeT++). If the re-acquired monitoring node signal collision rate after one monitoring node overlap optimization is less than the preset monitoring node signal collision rate, the monitoring node overlap optimization is completed and a node status monitoring command for the communication transmission phase is sent; otherwise, monitoring node movement distance score optimization is performed.

[0032] The optimization of the monitoring node movement distance score involves the following steps: The average of the collision rate deviation of the monitoring node signal re-acquired after one monitoring node overlap optimization and the deviation of the monitoring node movement distance score is used as the adjustment value for the monitoring node movement distance. This dynamically adjusts the movement distance of the monitoring nodes. The monitoring node movement distance score deviation represents the difference between the monitoring node movement distance score at the end of the data acquisition delay error assessment period and the preset monitoring node movement distance score. The node movement distance score represents the ratio of the node movement distance at the end of the data acquisition delay error assessment period to the preset node movement distance. The node movement distance is obtained through distance sensor monitoring. If the collision rate of the monitoring node signal re-acquired after one monitoring node movement distance score optimization is less than the preset monitoring node signal collision rate, the monitoring node optimization is completed and a node status monitoring command for the communication transmission phase is sent; otherwise, a monitoring node data acquisition collision alarm is triggered.

[0033] It should be understood that if the collision rate of the monitoring node signal reacquired after a monitoring node overlap optimization is not less than the preset monitoring node signal collision rate, it indicates that the monitoring node optimization includes a monitoring node overlap optimization and a monitoring node movement distance score optimization. If the collision rate of the monitoring node signal reacquired after a monitoring node overlap optimization is less than the preset monitoring node signal collision rate, it indicates that the monitoring node optimization refers to a monitoring node overlap optimization.

[0034] In this embodiment, the acquired monitoring node transmission power adjustment value and monitoring node movement distance adjustment value are used as inputs to the PID (Proportional-Integral-Derivative) control algorithm in the monitoring node controller. Through the synergistic effect of the proportional, integral, and derivative components, the transmission power adjustment result and movement distance adjustment result are output, thereby reducing the signal coverage area, reducing signal contention among monitoring nodes, and effectively improving the spatial layout balance of monitoring nodes. The preset monitoring node signal collision rate is represented by the sum of the average monitoring node signal collision rates corresponding to the end of the historical monitoring node data collection period in the database, and the preset node movement distance is represented by the sum of the average node movement distances corresponding to the end of the historical monitoring node data collection period in the database.

[0035] This example uses dynamic calculation of signal collision rate deviation to reflect the current network collision status in real time, thereby improving real-time performance, reducing overlap of node coverage areas, avoiding collisions caused by multiple nodes transmitting simultaneously, and improving transmission efficiency. This dynamic adjustment mechanism can quickly respond to changes in the current network status, thereby ensuring the quality and integrity of data acquisition. This "real-time monitoring-instant adjustment" mechanism avoids long-term transmission blockage caused by the accumulation of collisions, ensuring that data is transmitted in a short time, which meets the requirements of real-time systems for rapid response.

[0036] like Figure 4 The diagram shown is a flowchart of the signal delay error assessment provided in an embodiment of this application. The specific design logic is as follows: The process begins by evaluating the data transmission process, obtaining node signal collision data, and performing signal delay error assessment based on the obtained node signal collision data. Based on the signal delay error assessment result, it is determined whether to optimize the signal transmission power. If so, the routing signal transmission power is adjusted; otherwise, the time alignment is obtained and a judgment is made to ensure that the subsequent link delay error assessment is performed in a time-synchronized state.

[0037] Furthermore, based on node signal collision data, signal delay error is assessed for node signal collision status. The specific process is as follows: at the end of the signal delay error assessment period, the acquired node signal collision data is compared with the preset node signal collision data in the database. Simultaneously, compensation is applied to each comparison result using node signal collision data compensation values, and these results are coupled to obtain the signal delay error index. The signal delay error index represents the quantitative data on the delay of signal collision data in real-time passenger flow image analysis. Node signal collision data includes route reconstruction time, communication channel access time, and data packet retransmission time. The preset node signal collision data represents the preset route reconstruction time, communication channel access time, and data packet retransmission time. The route reconstruction time and communication channel access time are obtained by monitoring the built-in timer of the monitoring node. The data packet retransmission time is calculated by capturing the data packets sent and received by the node using a network packet capture tool deployed in the network. The signal delay error index represents the result of the coupled processing of the route reconstruction time index, communication channel access time index, and data packet retransmission time index. The node signal collision data compensation value includes the route reconstruction time compensation value, the communication channel access time compensation value, and the data packet retransmission time compensation value.

[0038] The specific constraint expression for the route reconstruction time index NCI1 is as follows: In the formula, NCI1 represents the route reconstruction time index corresponding to the end of the signal delay error assessment period of the monitoring node, ρ1 represents the route reconstruction time compensation value, X1 represents the route reconstruction time corresponding to the end of the signal delay error assessment period of the monitoring node, and X10 represents the preset route reconstruction time. The preset route reconstruction time is the result obtained by summing up the historical route reconstruction times corresponding to the end of the historical signal delay error assessment period and then calculating the average value of the sum.

[0039] The specific constraint expression for the communication channel access time index NCI2 is as follows: In the formula, NCI2 represents the communication channel access time index corresponding to the end of the signal delay error assessment period of the monitoring node, ρ2 represents the communication channel access time compensation value, X2 represents the communication channel access time corresponding to the end of the signal delay error assessment period of the monitoring node, and X20 represents the preset communication channel access time. The preset communication channel access time is the result obtained by summing up the historical communication channel access times corresponding to the end of the historical signal delay error assessment period and then calculating the average value of the sum.

[0040] The specific expression for the data packet retransmission time index NCI3 is as follows: In the formula, NCI3 represents the data packet retransmission time consumption index corresponding to the end of the signal delay error assessment period of the monitoring node, ρ3 represents the data packet retransmission time consumption compensation value, X3 represents the data packet retransmission time consumption corresponding to the end of the signal delay error assessment period of the monitoring node, and X30 represents the preset data packet retransmission time consumption. The preset data packet retransmission time consumption is the result obtained by summing up the retransmission times consumption of each historical data packet corresponding to the end of the historical signal delay error assessment period, and then calculating the average value of the summation.

[0041] The specific limiting expression for the signal delay error index NCI is: NCI = NCI1 + NCI2 + NCI3, where NCI represents the signal delay error index corresponding to the monitoring node at the end of the signal delay error assessment period.

[0042] In this example, the database pre-stores preset compensation values ​​that are highly correlated with the signal delay error index. These compensation values ​​are mapped to route reconstruction time, communication channel access time, and data packet retransmission time according to clear business requirements and system characteristics. This mapping can be a one-to-one correspondence between a single parameter and a compensation value, or a combination of multiple parameters corresponding to a single compensation value. For example, during signal delay error assessment, the system can collect the actual values ​​of route reconstruction time, communication channel access time, and data packet retransmission time in real time, and invoke the preset mapping rules to accurately match and extract the corresponding compensation values.

[0043] Most importantly, to ensure that the evaluation process can proceed smoothly and that the evaluation results are comparable under different conditions, this example uniformly limits the range of values ​​for route reconstruction time compensation, communication channel access time compensation, and data packet retransmission time compensation, all of which are controlled within the range of 0 to 1, and the sum of them is always kept to be 1.

[0044] In this embodiment, the signal delay error index increases with the increase of route reconstruction time, communication channel access time, and data packet retransmission time. Specifically, an increase in route reconstruction time can lead to network topology instability, exacerbate monitoring node collisions and communication channel access delays. An increase in communication channel access time and retransmission time can lead to an increase in the transmission delay of routing protocol messages, thereby prolonging the route reconstruction time. An increase in route reconstruction time means that if an acknowledgment is not received within a specified time after the data packet is sent, the sender will trigger a retransmission. An increase in channel access time will directly increase the data packet transmission delay, leading to timeout retransmissions and an increase in data packet retransmission time.

[0045] This example accurately identifies the root cause of the problem by comprehensively considering the interrelationship between the signal delay error index and the time consumed by route reconstruction, communication channel access, and data packet retransmission. By optimizing the route reconstruction strategy, it improves network optimization efficiency, optimizes the communication channel access and data packet transmission process, reduces monitoring node collisions and communication delays, shortens route reconstruction time, and improves the success rate of data packet transmission.

[0046] Furthermore, based on the signal delay error assessment results, it is determined whether to perform routing signal transmit power score optimization. The specific process is as follows: Based on the obtained signal delay error index and the preset signal delay error index in the database, it is determined whether there is a need for routing signal transmit power score optimization. If there is a need for routing signal transmit power score optimization, that is, the obtained signal delay error index is greater than the preset signal delay error index in the database, then the signal delay error assessment result is determined to be unqualified for routing signal and routing signal transmit power score optimization is performed. If there is no need for routing signal transmit power score optimization, that is, the obtained signal delay error index is not greater than the preset signal delay error index in the database, then the signal delay error assessment result is determined to be qualified for routing signal and link delay error assessment is performed.

[0047] The optimization of routing signal transmission power score involves the following steps: The average of the obtained signal delay error index deviation and the routing signal transmission power score deviation is used as the adjustment amount for the routing signal transmission power score. This reduces signal collisions between monitoring nodes, thereby lowering the communication interruption rate. The signal delay error index deviation measures the difference between the obtained signal delay error index and the preset signal delay error index in the database. The routing signal transmission power score deviation represents the difference between the preset routing signal transmission power score and the actual routing signal transmission power score at the end of the signal delay error assessment period. The actual routing signal transmission power score represents the ratio of the corresponding routing transmission power at the end of the signal delay error assessment period to the preset routing transmission power. The routing transmission power is obtained through power sensor monitoring. If the re-obtained signal delay error index after one optimization is not greater than the preset signal delay error index, the routing signal transmission power score optimization is completed and link delay error assessment is performed; otherwise, a monitoring node signal transmission alarm is triggered.

[0048] In this example, the PID control algorithm in the routing controller takes the obtained routing signal transmission power fractional adjustment amount as input, and dynamically adjusts the routing signal transmission power through the coordinated action of the proportional, integral, and derivative components, thereby outputting the routing signal transmission power adjustment result, which reduces signal collisions between nodes and lowers the communication interruption rate. The preset signal delay error index is represented by the sum and average of the preprocessing control efficiency index corresponding to the end of the historical signal delay error assessment period in the database, and the preset routing signal transmission power is represented by the sum and average of the routing signal transmission power corresponding to the end of the historical signal delay error assessment period in the database.

[0049] This example compares the acquired signal delay error index with a preset value in the database to promptly determine whether the routing signal is qualified. If it is not qualified, an optimization process is initiated to prevent the signal delay problem from continuing to worsen. This ensures a rapid response in the event of a signal collision that causes a signal interruption, enabling the network to adapt to changes in the communication environment and load, reducing data retransmission and waiting time, thereby ensuring the continuity and stability of data transmission and improving the real-time performance of data transmission.

[0050] like Figure 5The diagram shown is a flowchart of the link delay error assessment provided in an embodiment of this application. The specific design logic is as follows: The process begins by evaluating the data transmission process and obtaining the time alignment. Based on the time alignment, it is determined whether to perform monitoring node movement speed score optimization. If so, the monitoring node movement speed is adjusted. Otherwise, node link breakage data is obtained. At the same time, the link delay error is assessed based on the obtained node link breakage data. Based on the link delay error assessment result, it is determined whether to perform link gain optimization. If so, the antenna gain value and coding gain value are adjusted. Otherwise, the process ends.

[0051] Furthermore, based on the node link breakage data, a link delay error assessment is performed on the node link breakage status. The specific process is as follows: Based on the time alignment of the acquired monitoring node and the preset time alignment in the database, it is determined whether there is a need for monitoring node movement speed score optimization. If there is a need for monitoring node movement speed score optimization, that is, the acquired time alignment is greater than the preset time alignment in the database, then it is determined that the time synchronization is unqualified and a link delay error assessment command is sent. If there is no need for monitoring node movement speed score optimization, that is, the acquired time alignment is not greater than the preset time alignment in the database, then it is determined that the time synchronization is qualified and node link breakage data is acquired.

[0052] The optimization of the monitoring node movement speed score involves the following steps: mapping the time alignment deviation in the database to obtain an adjustment value for the monitoring node movement speed score to reduce the time synchronization error caused by the mismatch in monitoring node speeds, thereby improving the time synchronization degree of the monitoring nodes. The monitoring node movement speed score represents the ratio of the monitoring node movement speed at the end of the link delay error assessment period to the preset monitoring node movement speed. The preset time alignment is represented by the sum and average of the time alignment values ​​at the end of the historical link delay error assessment periods in the database. The preset monitoring node movement speed is represented by the sum and average of the monitoring node movement speed values ​​at the end of the historical link delay error assessment periods in the database. The time alignment deviation represents the difference between the time alignment obtained at the end of the link delay error assessment period and the preset time alignment. The monitoring node movement speed score deviation represents the absolute value of the difference between the monitoring node movement speed score obtained at the end of the link delay error assessment period and the preset monitoring node movement speed score.

[0053] At the end of the link delay error assessment period, the acquired node link breakage data is compared with the preset node link breakage data in the database. Simultaneously, the comparison results are compensated and coupled with the node link breakage data compensation value to obtain the link delay error index. The link delay error index represents the quantitative data on the delay of node link breakage data in real-time passenger flow image analysis. Node link breakage data includes link breakage frequency, link status update duration, and trigger link repair time. The preset node link breakage data includes preset link breakage frequency, link status update duration, and trigger link repair time. The link breakage frequency is monitored by a network analyzer, while the link status update duration and trigger link repair time are monitored by the timer built into the monitoring node. The link delay error index represents the coupling result of the link breakage frequency index, link status update duration index, and trigger link repair time index. The node link breakage data compensation value includes the link breakage frequency compensation value, the link status update duration compensation value, and the trigger link repair time compensation value.

[0054] Specifically, the specific constraint expression for the link breakage frequency index LDI1 is as follows: In the formula, LDI1 represents the link breakage frequency index corresponding to the end of the link delay error assessment period of the monitoring node, σ1 represents the link breakage frequency compensation value, Y1 represents the link breakage frequency corresponding to the end of the link delay error assessment period of the monitoring node, and Y10 represents the preset link breakage frequency. The preset link breakage frequency is the result obtained by summing up the historical link breakage frequencies corresponding to the end of the historical link delay error assessment period and then calculating the average value of the sum.

[0055] The specific constraint expression for the Link State Update Duration Index (LDI2) is as follows: In the formula, LDI2 represents the link status update duration index corresponding to the end of the link delay error assessment period of the monitoring node, σ2 represents the link status update duration compensation value, Y2 represents the link status update duration corresponding to the end of the link delay error assessment period of the monitoring node, and Y20 represents the preset link status update duration. The preset link status update duration is the result obtained by summing up the historical link status update durations corresponding to the end of the historical link delay error assessment period and then calculating the average value of the sum.

[0056] The specific constraint expression for the trigger link repair time index LDI3 is as follows: In the formula, LDI3 represents the trigger link repair time index corresponding to the end of the link delay error assessment period of the monitoring node, σ3 represents the trigger link repair time compensation value, Y3 represents the trigger link repair time corresponding to the end of the link delay error assessment period of the monitoring node, and Y30 represents the preset trigger link repair time. The preset trigger link repair time is the result obtained by summing up the historical trigger link repair times corresponding to the end of the historical link delay error assessment period and then calculating the average value of the sum.

[0057] The specific limiting expression for the Link Delay Error Index (LDI) is: LDI = LDI1 + LDI2 + LDI3, where LDI represents the link delay error index corresponding to the monitoring node at the end of the link delay error assessment period.

[0058] The database pre-stores preset compensation values ​​that are highly correlated with the link latency error index. These compensation values ​​are mapped to link breakage frequency, link status update duration, and link repair trigger time using clear rules based on business requirements and system characteristics. This mapping can be a one-to-one correspondence between a single parameter and a compensation value, or a combination of multiple parameters corresponding to a single compensation value. For example, during link latency error assessment, the system can collect real-time values ​​of link breakage frequency, link status update duration, and link repair trigger time, and then invoke the preset mapping rules to accurately match and extract the corresponding compensation values.

[0059] Most importantly, to ensure that the evaluation process can proceed smoothly and that the evaluation results under different conditions are comparable, this example uniformly limits the range of values ​​for link breakage frequency compensation, link status update duration compensation, and link repair time compensation, all of which are controlled within the range of 0 to 1, and the sum of them is always kept to be 1.

[0060] In this embodiment, the link delay error index increases with the increase of link breakage frequency, link status update duration, and link repair triggering time. Specifically, when the link status update period increases, the control message overhead increases to ensure timely synchronization of link status, which may exacerbate communication channel congestion and increase the link repair triggering time. When the update period decreases, the monitoring node can quickly detect link breakage, triggering repair earlier and reducing the overall repair time. When links break frequently, the link breakage frequency increases, and the monitoring node needs to obtain the latest topology information faster to avoid using broken paths. When the link status update period decreases, multiple link repair requests occur simultaneously, and the routing protocol is delayed due to resource contention, increasing the link repair triggering time.

[0061] This example clarifies the complex relationship between the link status update cycle and the link delay error index, communication channel congestion, and the time required to trigger link repair. Based on the actual network operation, it precisely adjusts the link status update cycle, speeds up the acquisition of the latest topology information by monitoring nodes, avoids the use of broken paths, reduces message overhead and link repair resource consumption, and improves the utilization rate of network resources.

[0062] Furthermore, based on the link delay error assessment results, it is determined whether link gain optimization should be performed. The specific process is as follows: Based on the obtained link delay error index and the preset link delay error index in the database, it is determined whether there is a need for link gain optimization: If there is a need for link gain optimization, that is, the obtained link delay error index is greater than the preset link delay error index, then the signal delay error assessment result is judged as link communication unqualified and link gain optimization is performed; if there is no need for link gain optimization, that is, the obtained link delay error index is not greater than the preset link delay error index, then the signal delay error assessment result is judged as link communication qualified and the node status monitoring command for the next communication transmission stage is sent.

[0063] The link gain optimization process is as follows: Based on the acquired link breakage delay error index deviation, an antenna gain value is mapped from the database to reduce the probability of link breakage due to signal attenuation (i.e., antenna gain optimization), thereby shortening the repair time after a breakage. The link breakage delay error index deviation represents the difference between the acquired link breakage delay error index and the preset link breakage delay error index. Simultaneously, based on the acquired antenna gain deviation, a coding gain value is mapped from the database to improve the signal's error tolerance (i.e., coding gain value optimization), thereby reducing the probability of link breakage due to error accumulation. If the re-acquired link delay error index after one link gain optimization is not greater than the preset link delay error index, the link gain optimization is completed and a node status monitoring command for the next communication transmission stage is sent; otherwise, a transmission link breakage alarm is triggered. One link gain optimization includes one antenna gain optimization and one coding gain value optimization.

[0064] In this embodiment, the antenna gain adjustment and coding gain adjustment are taken as inputs by the least squares method in the linear regression algorithm. The antenna gain and coding gain are dynamically adjusted by the linear relationship equation set in the linear regression algorithm, and the antenna gain adjustment result and coding gain adjustment result are output. This accelerates the network node's perception of link status changes and rerouting, and improves the network recovery stability. The preset link delay error index is represented by the sum and average of the link delay error indices corresponding to the end of the historical link delay error assessment period in the database.

[0065] This example optimizes the link in a targeted manner, enabling signals to reach the receiving end quickly, monitoring the link status in real time, adjusting link parameters in a timely manner, ensuring stable operation of the link under data transmission conditions, enhancing signal anti-interference capability, accelerating the efficiency of network nodes in sensing changes in link status and rerouting after link gain is improved, improving network recovery stability, reducing link breaks and delays, and thus improving the accuracy of data acquisition in real-time passenger flow image analysis.

[0066] like Figure 6 The diagram shows the structure of an image quality enhancement system based on a MESH self-organizing network provided in this application embodiment. The system includes: a data acquisition delay error assessment module, a signal delay error assessment module, and a link delay error assessment module. The data acquisition delay error assessment module evaluates the data acquisition delay error of passenger flow image data based on the signal collision rate of monitoring nodes, and determines whether to optimize monitoring nodes. If so, it performs signal delay error assessment; otherwise, it performs monitoring node optimization. The signal delay error assessment module monitors the node signal collision status of monitoring nodes in a specified area of ​​the MESH network in real time during the signal delay error assessment period, evaluates the signal delay error based on the node signal collision data, and determines whether to optimize the routing signal transmission power fraction. The link delay error assessment module monitors the node link breakage status of monitoring nodes in a specified area of ​​the MESH network in real time during the link delay error assessment period, evaluates the link delay error based on the node link breakage data, and determines whether to optimize the link gain.

[0067] In this embodiment, the three modules are interconnected, monitoring and optimizing the entire process of multi-hop transmission of node image data, forming a complete feedback optimization closed loop. This effectively reduces the latency impact caused by MESH self-organizing network routing switching, improves the transmission efficiency and quality of real-time passenger flow image data at smart city traffic intersections, and thus ensures the accuracy of passenger flow image analysis.

[0068] In summary, this application embodiment assesses data acquisition delay error by monitoring node signal collision rate to determine whether to optimize detection nodes, thereby reducing data errors and ensuring data acquisition quality. Then, it assesses signal delay error by monitoring signal collision data to determine whether to optimize routing signal transmission power fraction, which can reduce signal overlap and interference and lower signal delay. Finally, it assesses link delay error by monitoring link breakage data to determine whether to optimize link gain, thereby reducing link breakage caused by signal attenuation or external interference and improving link reliability. This improves the accuracy of real-time passenger flow image analysis at smart city traffic intersections.

[0069] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An image quality enhancement method based on MESH self-organizing networks, characterized in that, Includes the following steps: Step 1: Based on the signal collision rate of the monitoring nodes, the data acquisition delay error of the passenger flow image data acquisition process is evaluated. At the same time, it is determined whether to optimize the monitoring nodes. If so, the signal delay error is evaluated; otherwise, the monitoring nodes are optimized. The monitoring node optimization means reducing the delay of the monitoring node signal collision rate on the real-time passenger flow image analysis by adjusting the transmission power and the moving distance fraction of the monitoring nodes. The specific process for determining whether to optimize the monitoring node is as follows: Based on the obtained collision rate of the monitoring node signal and the preset collision rate of the monitoring node signal in the database, it is determined whether there is a need for monitoring node optimization. If so, the data acquisition delay error assessment result is judged as unqualified data acquisition and the monitoring node is optimized. Otherwise, the data acquisition delay error assessment result is judged as qualified data acquisition and the node status monitoring command in the communication transmission stage is sent. The optimization of the monitoring nodes involves the following steps: The summation and average of the obtained signal collision rate deviation and overlap deviation of the monitoring nodes are used as the monitoring node transmission power adjustment value to dynamically adjust the monitoring node transmission power. If the collision rate of the monitoring node signal reacquired after one monitoring node overlap optimization is less than the preset monitoring node signal collision rate, then the monitoring node overlap optimization is completed and the node status monitoring command for the communication transmission stage is sent; otherwise, the monitoring node movement distance score optimization is performed. Step 2: Monitor the node signal collision status of the monitoring nodes in the specified area of ​​the MESH network in real time during the signal delay error assessment period, evaluate the signal delay error based on the node signal collision data, and determine whether to optimize the routing signal transmission power fraction. The specific process for evaluating signal delay error based on node signal collision data is as follows: At the end of the signal delay error assessment period, the acquired node signal collision data is compared with the preset node signal collision data in the database. At the same time, the comparison results are compensated by combining the node signal collision data compensation value and coupled to obtain the signal delay error index. The node signal collision data includes route reconstruction time, communication channel access time, and data packet retransmission time. The signal delay error index represents the quantitative data of the delay of the signal collision data to the real-time passenger flow image analysis. The specific process for determining whether to perform routing signal transmit power fraction optimization is as follows: Based on the obtained signal delay error index and the preset signal delay error index in the database, determine whether there is a need for optimization of the route signal transmit power fraction: If there is a need to optimize the transmission power score of the routing signal, the signal delay error assessment result will be judged as the routing signal being unqualified and the transmission power score of the routing signal will be optimized. If there is no need to optimize the transmission power fraction of the routing signal, the signal delay error assessment result will be judged as qualified for routing signal and a link delay error assessment will be performed. Step 3: Monitor the link breakage status of the monitoring nodes in the specified area of ​​the MESH network in real time during the link delay error assessment period, assess the link delay error based on the node link breakage data, and determine whether to perform link gain optimization. The specific process for evaluating link delay error based on node link breakage data is as follows: Based on the time alignment of the acquired monitoring nodes and the preset time alignment in the database, it is determined whether there is a need to optimize the movement speed score of the monitoring nodes: If there is a need to optimize the moving speed score of the monitoring node, it will be determined that the time synchronization is unqualified and a link delay error assessment command will be sent. If there is no need to optimize the node movement speed score, the time synchronization is deemed qualified and node link breakage data is obtained. The optimization of the monitoring node's moving speed score involves the following steps: The time alignment deviation and the monitoring node movement speed fraction deviation are mapped in the database to obtain the monitoring node movement speed adjustment value to reduce the time synchronization error caused by the mismatch of monitoring node speeds. At the end of the link delay error assessment period, the obtained node link breakage data is compared with the preset node link breakage data in the database. At the same time, the node link breakage data compensation value is combined to compensate for each difference comparison result and perform coupling processing to obtain the link delay error index. The link delay error index represents the quantitative data of the delay of node link breakage data to real-time passenger flow image analysis. The specific process for determining whether to perform link gain optimization is as follows: Based on the obtained link delay error index and the preset link delay error index in the database, determine whether there is a need for link gain optimization: If there is a need for link gain optimization, the signal delay error assessment result will be judged as unqualified link communication and link gain optimization will be performed. If there is no need for link gain optimization, the signal delay error assessment result will be judged as the link communication is qualified, and the node status monitoring command for the next communication transmission stage will be sent.

2. The image quality enhancement method based on MESH self-organizing network as described in claim 1, characterized in that, The optimization of the monitoring node movement distance score involves the following steps: The summation and average of the collision rate deviation of the monitoring node signal and the fractional deviation of the monitoring node movement distance after the first optimization of the monitoring node overlap is used as the adjustment value of the monitoring node movement distance to improve the spatial layout balance of the monitoring nodes. If the collision rate of the monitoring node signal after the optimization of the moving distance score of the monitoring node is less than the preset collision rate of the monitoring node signal, the optimization of the monitoring node is completed and the node status monitoring command of the communication transmission stage is sent; otherwise, a collision alarm for monitoring node data acquisition is triggered.

3. The image quality enhancement method based on MESH self-organizing network as described in claim 1, characterized in that, The optimization of the transmission power fraction of the routing signal is specifically carried out through the following steps: The summation and average of the obtained signal delay error exponential deviation and the routing signal transmission power fraction deviation is used as the routing signal transmission power fraction adjustment amount to reduce signal collisions between monitoring nodes. If the signal delay error index reacquired after the optimization of the routing signal transmission power score is not greater than the preset signal delay error index, then the optimization of the routing signal transmission power score is completed and the link delay error is evaluated; otherwise, an alarm is triggered for the signal transmission of the monitoring node.

4. The image quality enhancement method based on MESH self-organizing network as described in claim 1, characterized in that, The link gain optimization process is as follows: The antenna gain value is obtained by mapping the obtained link breakage delay error exponential deviation into the database to reduce the probability of link breakage caused by signal attenuation. At the same time, the obtained antenna gain deviation is mapped into the database to obtain the coding gain value in order to improve the signal's error resistance; If the link delay error index reacquired after a link gain optimization is not greater than the preset link delay error index, then the link gain optimization is completed and the node status monitoring command for the next communication transmission stage is sent; otherwise, a transmission link breakage alarm is issued. The link gain optimization includes an antenna gain optimization and a coding gain value optimization.

5. A system applying the image quality enhancement method based on MESH self-organizing network as described in any one of claims 1-4, characterized in that, include: Data acquisition delay error assessment module, signal delay error assessment module, and link delay error assessment module; The data acquisition delay error assessment module is used to assess the data acquisition delay error of the passenger flow image data acquisition process based on the signal collision rate of the monitoring node, and at the same time determine whether to optimize the monitoring node. If so, the signal delay error assessment is performed; otherwise, the monitoring node optimization is performed. The signal delay error assessment module is used to monitor the node signal collision status of the monitoring nodes in a specified area of ​​the MESH network in real time during the signal delay error assessment period, and to assess the signal delay error based on the node signal collision data, while determining whether to perform routing signal transmission power fraction optimization. The link delay error assessment module is used to monitor the link breakage status of monitoring nodes in a specified area of ​​the MESH network in real time during the link delay error assessment period. Based on the node link breakage data, the module assesses the link delay error of the node link breakage status and determines whether to perform link gain optimization.

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