Multi-data acquisition and image transmission method and system based on wireless network

Abstract

The invention discloses a multi-data acquisition and image transmission method and system based on a wireless network, and relates to the technical field of image data acquisition and transmission, and the method comprises the steps: constructing a wireless network architecture, determining a center node, an acquisition node and an edge node, enabling the acquisition node to obtain real-time acquired image data, and enabling the edge node to obtain the image data based on application scene information; the method comprises the following steps: determining position information of acquisition nodes, determining edge dynamic points based on a three-dimensional space coordinate system, determining coordinates of edge nodes through the edge dynamic points and coordinate points of an acquisition device, and determining coordinates of a center node in the three-dimensional space coordinate system through edge node basic coordinates and transmission quality indexes of each edge node. Appropriate acquisition nodes, edge nodes and center nodes are determined in an area with various network devices and complex terrains, and the edge nodes are prevented from being affected by factors such as the environment after receiving image data of the acquisition nodes and transmitting the image data to the center nodes.

Description

Technical Field

[0001] The present invention relates to the technical field of image data acquisition and transmission, and specifically to a multi-data acquisition and image transmission method and system based on a wireless network. Background Art

[0002] The multi-data acquisition and image transmission technology based on a wireless network uses wireless communication to achieve real-time acquisition, processing, and remote transmission of multiple data sources, breaking the limitations of traditional wired networks and enhancing the flexibility and adaptability of the system. This technology has extensive applications in fields such as remote monitoring, intelligent transportation, and environmental monitoring. Through efficient data and image transmission, it can improve the response speed and data update frequency, reduce the need for manual intervention, and promote the automation and intelligent development of the system. Its application not only promotes the popularization of Internet of Things technology but also drives innovation and technological progress in various industries, with important social and economic value.

[0003] In the existing multi-data acquisition and image transmission methods and systems based on a wireless network, in areas with a large number of network devices and complex terrain, due to the susceptibility of acquisition devices and edge devices to environmental impacts, it is difficult for the wireless network architecture to determine appropriate edge nodes and central nodes. At the same time, due to different acquisition devices set at different acquisition nodes, it is difficult to ensure time synchronization of the image data transmitted to the edge nodes, affecting the efficiency of subsequent image processing. Therefore, a multi-data acquisition and image transmission method and system based on a wireless network are needed to solve the above-mentioned problems. Summary of the Invention

[0004] To solve the above technical problems, a multi-data acquisition and image transmission method and system based on a wireless network are provided. The present technical solution solves the problems in the above background art that in areas with a large number of network devices and complex terrain, due to the susceptibility of acquisition devices and edge devices to environmental impacts, it is difficult for the wireless network architecture to determine appropriate edge nodes and central nodes. At the same time, due to different acquisition devices set at different acquisition nodes, it is difficult to ensure time synchronization of the image data transmitted to the edge nodes, affecting the efficiency of subsequent image processing.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-data acquisition and image transmission method based on a wireless network, including: Construct a wireless network architecture and determine the central node, acquisition nodes, and edge nodes; Acquire real-time acquisition image data with the acquisition nodes and obtain real-time time information with the edge nodes; Based on the acquisition nodes, edge nodes, and real-time time information, perform clock synchronization on the real-time acquired image data to obtain synchronized image data; Transmit the synchronized image data to the central node through the edge node; Perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; Obtain the data attribute matrix of the classified image data, and construct a data attribute pie chart with the data attribute matrix; Based on the data attribute pie chart, determine the image transmission parameters, and transmit the classified image data to the cloud platform for storage.

[0006] In an alternative embodiment, the construction of the wireless network architecture and the determination of the central node, acquisition nodes, and edge nodes specifically include: Obtain the application scenario information, and based on the application scenario information, determine the location information of the acquisition nodes; According to the location information of the acquisition nodes, obtain the environmental information of the acquisition nodes, and at the same time set the corresponding acquisition devices and obtain the type information of the acquisition devices; Based on the type information of the acquisition devices, classify the acquisition devices, and respectively obtain the location information of the same type of acquisition devices; Based on the location information of the same type of acquisition devices, determine the maximum distance information of the acquisition devices; Determine the maximum distance line segment based on the maximum distance information of the acquisition devices, and construct a three-dimensional space coordinate system with the center point of the maximum distance line segment as the origin; Based on the three-dimensional space coordinate system, determine the edge dynamic points; According to the location information of the same type of acquisition devices, determine the coordinate points of the same type of acquisition devices in the three-dimensional space coordinate system; Connect the edge dynamic points with the coordinate points of the same type of acquisition devices in the three-dimensional space coordinate system respectively to obtain edge dynamic line segments; Sum the length values of the edge dynamic line segments corresponding to the same type of acquisition devices to obtain the edge node dynamic decision value; Use the coordinates of the edge dynamic point when the edge node dynamic decision value is the smallest as the coordinates of the edge node; Based on the edge nodes corresponding to each type of acquisition device and the environmental information of the acquisition nodes, determine the central node.

[0007] In an alternative embodiment, the determination of the central node based on the edge nodes corresponding to each type of acquisition device and the environmental information of the acquisition nodes specifically includes: Based on the three-dimensional space coordinate system, determine the coordinates of the edge nodes corresponding to each type of acquisition device as the edge node basic coordinates; Collect the environmental information of the acquisition nodes to determine the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the acquisition devices of the same type; Obtain the acquisition quality influence coefficient corresponding to the acquisition nodes according to the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the acquisition devices of the same type; Determine the specification information of each type of acquisition device based on the type information of the acquisition device; Obtain the acquisition quality index of the acquisition nodes based on the type information, specification information, and acquisition quality influence coefficient of the acquisition devices; Classify the acquisition nodes through the acquisition quality index; Perform a correlation analysis on the acquisition nodes after classification with the edge nodes, and set multi-level edge devices at the edge nodes; Obtain the operating status information and multi-level environmental information of the multi-level edge devices set at each edge node, and obtain the operating status quality index corresponding to the multi-level edge devices set at each edge node; Obtain the transmission quality index of each edge node according to the operating status quality index and multi-level environmental information corresponding to the multi-level edge devices set at each edge node; Determine the coordinates of the central node in the three-dimensional space coordinate system through the basic coordinates of the edge node and the transmission quality index of each edge node.

[0008] In an alternative embodiment, the clock synchronization of the real-time acquisition image data based on the acquisition nodes, edge nodes, and real-time time information to obtain synchronized image data specifically includes: Determine the level information of the acquisition nodes, and extract the timestamp information in the real-time acquisition image data transmitted by the acquisition nodes through the edge nodes; Judge whether the timestamp information is empty through the edge nodes, determine the non-empty rate of timestamp transmission of the acquisition nodes at the same level, and set a non-empty rate threshold for timestamp transmission based on the edge nodes; If the non-empty rate of timestamp transmission of the acquisition nodes at the same level is greater than or equal to the non-empty rate threshold for timestamp transmission, determine the standard time information of the real-time acquisition image data based on the timestamp information and real-time time information of the acquisition nodes at this level; If the non-empty rate of timestamp transmission of the acquisition nodes at the same level is less than the non-empty rate threshold for timestamp transmission, determine the standard time information of the real-time acquisition image data based on the real-time time information.

[0009] In an alternative embodiment, the transmission of the synchronized image data to the central node through the edge nodes specifically includes: Determine the central node for edge node interaction, and generate a pre-shared key by combining the edge node and the central node; Based on the pre-shared key, determine whether the central node allows the edge node to perform image transmission. If not, the acquisition node continues to perform real-time image acquisition. If so, set a transmission quality threshold based on the transmission quality index of the edge node; Set the data processing information of the edge node for the synchronous image data according to the transmission quality index and the transmission quality threshold; Based on the data processing information and the pre-shared key, transmit the synchronous image data of the edge node to the central node.

[0010] In an alternative embodiment, the roulette classification and data processing of the synchronous image data received by the central node to obtain classified image data specifically includes: Determine the standard time information of the synchronous image data according to the standard time information of the real-time acquired image data; construct a time roulette with the standard time information, and classify the synchronous image data according to time periods; Extract the feature information of the synchronous image data, and at the same time construct a feature roulette based on the feature information of the synchronous image data, and classify the synchronous image data classified by the time roulette according to image features; Determine the task priority index according to the edge node corresponding to the synchronous image data, and construct a task priority roulette, and classify the synchronous image data classified by the feature roulette according to the task priority; Perform data processing on the synchronous image data that has completed the classification by the time roulette, the feature roulette, and the task priority roulette to obtain classified image data.

[0011] In an alternative embodiment, the determining the image transmission parameters based on the data attribute pie chart and transmitting the classified image data to the cloud platform for storage specifically includes: Determine the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission according to the data attribute pie chart; Determine the image transmission parameters with the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission; Transmit the classified image data to the cloud platform for storage in sequence through the image transmission parameters.

[0012] Furthermore, a multi-data acquisition and image transmission system based on a wireless network is proposed for implementing the transmission method as described in any one of the above, including: Edge processing module, which is used to obtain real-time acquisition image data by an acquisition node, obtain real-time time information by an edge node, and based on the acquisition node, the edge node and the real-time time information, perform clock synchronization on the real-time acquisition image data to obtain synchronized image data, and transmit the synchronized image data to a central node through the edge node; Central management module, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node and the real-time time information to obtain synchronized image data; Cloud platform module, which is used to monitor and manage the cloud platform, and is also used to build a wireless network architecture, determine the central node, the acquisition node and the edge node, determine the central node with which the edge node interacts, and generate a pre-shared key in combination with the edge node and the central node.

[0013] In an alternative embodiment, the central management module includes: Central receiving unit, which is used to receive data and information transmitted from the edge processing module; Classification unit, which is used to perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; Data sending unit, which is used to obtain the data attribute matrix of the classified image data, construct a data attribute pie chart based on the data attribute matrix, determine the image transmission parameters based on the data attribute pie chart, and transmit the classified image data to the cloud platform for storage.

[0014] In an alternative embodiment, the edge processing module includes: Acquisition unit, which is used to obtain real-time acquisition image data by an acquisition node and obtain real-time time information by an edge node; Data processing unit, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node and the real-time time information to obtain synchronized image data; Transmission unit, which is used to transmit the synchronized image data to the central node through the edge node.

[0015] Compared with the prior art, the beneficial effects of the present invention are: In the method and system for multi-data acquisition and image transmission based on a wireless network proposed in this solution, based on the application scenario information, the location information of the acquisition nodes is determined. Then, based on the three-dimensional space coordinate system, the edge dynamic points are determined, and the coordinates of the edge nodes are determined through the edge dynamic points and the coordinate points of the acquisition devices. Then, through the basic coordinates of the edge nodes and the transmission quality index of each edge node, the coordinates of the central node in the three-dimensional space coordinate system are determined, realizing the determination of appropriate acquisition nodes, edge nodes, and central nodes in areas with a large number of network devices and complex terrain, and avoiding the influence of environmental and other factors on the transmission of image data from the edge nodes to the central node after receiving the image data from the acquisition nodes; In the method and system for multi-data acquisition and image transmission based on a wireless network proposed in this solution, the level information of the acquisition nodes is determined, and the edge nodes extract the timestamp information in the real-time acquisition image data transmitted by the acquisition nodes. Then, the edge nodes determine whether the timestamp information is empty, and at the same time determine the non-empty rate of timestamp transmission of the acquisition nodes of the same level. Based on the edge nodes, a threshold for the non-empty rate of timestamp transmission is set. Through the threshold for the non-empty rate of timestamp transmission and the non-empty rate of timestamp transmission, it is ensured that the image data transmitted from different acquisition nodes to the edge nodes is time-synchronized, improving the efficiency of subsequent image processing. Description of the Drawings

[0016] Figure 1 It is a flowchart of the method for multi-data acquisition and image transmission based on a wireless network proposed by the present invention; Figure 2 It is a flowchart for constructing the wireless network architecture in the present invention; Figure 3 It is a flowchart for determining the central node in the present invention; Figure 4 It is a system framework diagram of the system for multi-data acquisition and image transmission based on a wireless network proposed by the present invention. Detailed Embodiment

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0018] Refer to Figure 1 - Figure 4 As shown, the method for multi-data acquisition and image transmission based on a wireless network includes: Construct a wireless network architecture and determine the central node, acquisition nodes, and edge nodes; The acquisition nodes obtain real-time acquisition image data, and the edge nodes obtain real-time time information; Based on the acquisition nodes, edge nodes, and real-time time information, clock synchronization is performed on the real-time acquired image data to obtain synchronized image data; The synchronized image data is transmitted to the central node through the edge node; Perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; Obtain the data attribute matrix of the classified image data, and construct a data attribute pie chart with the data attribute matrix; Based on the data attribute pie chart, determine the image transmission parameters, and transmit the classified image data to the cloud platform for storage.

[0019] Specifically, the wireless network architecture is the overall structure and design module diagram of the management multi-data acquisition and image transmission system constructed by the cloud platform module through various data and information, including the location information of the central node, acquisition node, and edge node determined later. The acquisition node is responsible for collecting image data and sending the image data to the edge node. The edge node processes the image data and performs preprocessing, and passes important information to the central node, or makes local decisions. The central node performs in-depth analysis and storage on all data, makes global decisions, and issues instructions to downstream nodes for in-depth analysis and storage, and makes global decisions.

[0020] Furthermore, obtain the data attribute matrix of the classified image data, and construct a data attribute pie chart with the data attribute matrix. Load the classified image data through common libraries in Python, such as PIL (Pillow) and torchvision.

[0021] Furthermore, construct a wireless network architecture and determine the central node, acquisition node, and edge node. Specifically include: Obtain the application scenario information, and based on the application scenario information, determine the location information of the acquisition node; According to the location information of the acquisition node, obtain the environmental information of the acquisition node, set the corresponding acquisition device at the same time, and obtain the type information of the acquisition device; Based on the type information of the acquisition device, classify the acquisition devices, and respectively obtain the location information of the same type of acquisition devices; Determine the maximum distance information of the acquisition device with the location information of the same type of acquisition device; Determine the maximum distance line segment with the maximum distance information of the acquisition device, and construct a three-dimensional space coordinate system with the center point of the maximum distance line segment as the origin; Based on the three-dimensional space coordinate system, determine the edge dynamic points; According to the location information of the same type of acquisition devices, determine the coordinate points of the same type of acquisition devices in the three-dimensional space coordinate system; Connect the edge dynamic points to the coordinate points of the same type of acquisition devices in the three-dimensional space coordinate system to obtain edge dynamic line segments; Sum up the length values of the edge dynamic line segments corresponding to the same type of acquisition devices to obtain the edge node dynamic decision value; Use the coordinates of the edge dynamic points when the edge node dynamic decision value is the smallest as the coordinates of the edge nodes; Determine the central node based on the environmental information of the edge nodes and acquisition nodes corresponding to each type of acquisition device.

[0022] Specifically, based on the application scenario information, to determine the location information of the acquisition nodes, first, it is necessary to understand the environmental characteristics in the scenario, such as terrain, buildings, obstacles, communication conditions, etc. Then, clarify the type of the application scenario, such as urban environment, indoor environment, agricultural monitoring, industrial environment, etc. Different types of scenarios have different impacts on the requirements and locations of the acquisition nodes. Then, determine the types of data to be collected, such as temperature and humidity, gas concentration, light intensity, motion monitoring, etc. Different types of data collection requirements may vary in the arrangement of sensors. At the same time, determine the area and scope that each acquisition node needs to cover. Based on the three-dimensional space coordinate system, determine the edge dynamic points. The edge dynamic points are a dynamic coordinate set in the three-dimensional space coordinate system, used to determine the coordinates of the edge nodes.

[0023] Furthermore, to determine the central node based on the environmental information of the edge nodes and acquisition nodes corresponding to each type of acquisition device, specifically including: Based on the three-dimensional space coordinate system, determine the coordinates of the edge nodes corresponding to each type of acquisition device as the edge node basic coordinates; Based on the environmental information of the acquisition nodes, determine the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the same type of acquisition devices; According to the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the same type of acquisition devices, obtain the acquisition quality impact coefficient corresponding to the acquisition nodes; Determine the specification information of each type of acquisition device based on the type information of the acquisition device; Based on the type information, specification information, and acquisition quality impact coefficient of the acquisition device, obtain the acquisition quality index of the acquisition nodes; Classify the acquisition nodes through the acquisition quality index; Conduct a correlation analysis on the acquisition nodes after classification and the edge nodes, and set multi-level edge devices at the edge nodes; Obtain the operating status information and multi-level environment information of the multi-level edge devices set by each edge node, and obtain the operating status quality index corresponding to the multi-level edge devices set by each edge node; According to the operating status quality index and multi-level environment information corresponding to the multi-level edge devices set by each edge node, obtain the transmission quality index of each edge node; Determine the coordinates of the central node in the three-dimensional space coordinate system through the edge node basic coordinates and transmission quality index of each edge node.

[0024] Specifically, the acquisition quality index is an indicator that comprehensively measures the performance of acquisition nodes in multiple dimensions. Through the acquisition quality index, the acquisition nodes are classified. According to factors such as the performance, data quality, and stability of the nodes, different acquisition nodes are divided into different levels to optimize network resource management and improve the efficiency of data acquisition. The acquisition nodes with an acquisition quality index close to 1 are classified as high-quality nodes, indicating that the node performs very well in all aspects, such as accurate data acquisition and stable communication, and is suitable for tasks with extremely high requirements for data quality. The acquisition nodes with an acquisition quality index between 0.5 and 0.8 are classified as medium-quality nodes, indicating that the performance of the node is at a medium level and may have deficiencies in some aspects (such as slightly poor communication quality or low energy efficiency), and are suitable for tasks with general requirements for data quality. The acquisition nodes with an acquisition quality index below 0.5 are classified as low-quality nodes, indicating that the node has problems in multiple aspects, such as inaccurate data acquisition and poor communication quality, and are not suitable for high-precision or high-requirement tasks and may need to be replaced or optimized.

[0025] Among them, the calculation formula for the acquisition quality influence coefficient is: ; In the formula, is the acquisition quality influence coefficient, is the influence function of the th factor, is the actual value of the th factor, is the weight of the th factor, represents the correction function related to the th environmental factor, is the intensity coefficient; The calculation formula for the acquisition quality index is: ; In the formula, is the acquisition quality index, is the acquisition quality influence coefficient, is the The type coefficient of the acquisition device at a moment is the specification coefficient of the acquisition device at the th moment, the device impact coefficient of the acquisition device at the th moment, and the configuration impact coefficient of the acquisition device at the th moment is the correction factor, and is the impact coefficient weight at the

[0026] th moment. Further, obtain the operating status information and multi-level environment information of the multi-level edge devices set for each edge node, and obtain the operating status quality index corresponding to the multi-level edge devices set for each edge node. The multi-level edge devices are multiple edge devices set for each edge node and are used for multi-batch processing of the image data transmitted by the acquisition nodes. The calculation steps for the operating status quality index corresponding to the multi-level edge devices set for each edge node are as follows: S1. Define various parameters required for calculating the operating status quality index, including edge device operating status parameters, multi-level device interaction impact coefficients, multi-level environment impact coefficients, multi-level edge device configuration quality coefficients, and multi-level device health degrees; S2. Input the above parameters into in sequence to calculate and obtain the operating status quality index corresponding to the multi-level edge devices set for each edge node. Among them, represents the edge device operating status parameter of device m on edge node n (such as the load, response time, processing capacity, stability, etc. of the device). These status parameters can be quantified based on the real-time monitoring data of the device (such as CPU usage rate, memory occupancy rate, response delay, etc.). represents device 's multi-level device interaction impact coefficient on device on the same edge node n (reflecting the impact of interaction, resource sharing, or conflict between devices on quality). These coefficients can be determined through historical data, dependencies between devices, resource allocation, etc. is the multi-level environment impact coefficient where edge node n is located (including factors such as temperature, humidity, air quality, network bandwidth, etc.). Environmental factors will affect the working stability and reliability of the device, represents the multi-level edge device configuration quality coefficient of edge node n (such as the robustness of the node hardware, configuration optimization degree, etc.), and the multi-level device health degree of device m on edge node n (such as device failure rate, service life, etc.).

[0027] Further, based on the acquisition nodes, edge nodes, and real-time time information, clock synchronization is performed on the real-time acquired image data to obtain synchronized image data, which specifically includes: Determine the level information of the acquisition nodes, and extract the timestamp information in the real-time acquired image data transmitted by the acquisition nodes through the edge nodes; Through the edge nodes, determine whether the timestamp information is empty, and at the same time determine the non-empty rate of timestamp transmission of acquisition nodes at the same level, and set a threshold for the non-empty rate of timestamp transmission based on the edge nodes; If the non-empty rate of timestamp transmission of acquisition nodes at the same level is greater than or equal to the threshold for the non-empty rate of timestamp transmission, then determine the standard time information of the real-time acquired image data based on the timestamp information and real-time time information of the acquisition nodes at this level; If the non-empty rate of timestamp transmission of acquisition nodes at the same level is less than the threshold for the non-empty rate of timestamp transmission, then determine the standard time information of the real-time acquired image data based on the real-time time information.

[0028] Further, the synchronized image data is transmitted to the central node through the edge nodes, which specifically includes: Determine the central node with which the edge node interacts, and generate a pre-shared key in combination with the edge node and the central node; Based on the pre-shared key, determine whether the central node allows the edge node to perform image transmission. If not, the acquisition node continues to perform real-time image acquisition. If so, set a transmission quality threshold based on the transmission quality index of the edge node; According to the transmission quality index and the transmission quality threshold, set the data processing information of the edge node for the synchronized image data; Based on the data processing information and the pre-shared key, transmit the synchronized image data of the edge node to the central node.

[0029] Specifically, according to the transmission quality index and the transmission quality threshold, set the data processing information of the edge node for the synchronized image data. When the transmission quality index is higher than or equal to the transmission quality threshold, it means that the transmission quality of the edge node is high, and lossless data transmission is used to ensure the image quality. When the transmission quality index is lower than the transmission quality threshold, it means that the transmission quality of the edge node is relatively low, and lossy compression is performed on the image data during transmission (such as using JPEG compression or video coding compression) to reduce the data volume and improve the transmission speed. Based on the data processing information and the pre-shared key, transmit the synchronized image data of the edge node to the central node. When the edge node uses the data processing information to perform data transmission, a key is set in the synchronized image data through the pre-shared key to encrypt the data transmission and ensure the security of the data transmission.

[0030] Further, perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data, which specifically includes: Determine the standard time information for synchronizing image data based on the standard time information of real-time acquired image data; construct a time roulette with the standard time information, and classify the synchronized image data according to time periods; Extract the feature information of the synchronized image data, and at the same time construct a feature roulette based on the feature information of the synchronized image data, and classify the synchronized image data after time roulette classification according to image features; Determine the task priority index according to the edge node corresponding to the synchronized image data, and construct a task priority roulette, and classify the synchronized image data after feature roulette classification according to task priority; Perform data processing on the synchronized image data that has completed classification by the time roulette, feature roulette, and task priority roulette to obtain classified image data. Specifically, according to the standard time information, construct a time roulette, divide the image data according to time periods, and each time period is a "sector" on the roulette, corresponding to the image data acquired within a time period. In one "sector" of the time roulette, based on the extracted image feature information, construct a feature roulette, which classifies the images according to the extracted features. Feature classification can be based on image similarity or through unsupervised learning algorithms (such as clustering). The image feature information includes color features and content features, etc. First, based on the color features, divide the synchronized image data with the same color features into the same "sector" of the feature roulette, and then based on the content features, divide the synchronized image data with the same content into the same "sector" of the feature roulette. Based on the processing capacity and task priority of each edge node (through information such as the load of the edge node, network bandwidth, and task type), assign a priority index to each synchronized image data, and then map the task priority index to a task priority roulette, and each sector represents a priority interval.

[0031] Further, based on the data attribute pie chart, determine the image transmission parameters, and transmit the classified image data to the cloud platform for storage, specifically including:

[0032] Based on the data attribute pie chart, determine the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission; Determine the image transmission parameters with the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission; Through the image transmission parameters, transmit the classified image data to the cloud platform for storage in sequence. Further, a multi-data acquisition and image transmission system based on a wireless network is proposed to implement the transmission method as described in any one of the above, including:

[0033] ​ An edge processing module, which is used to obtain real-time acquisition image data by an acquisition node, and obtain real-time time information by an edge node. Based on the acquisition node, the edge node, and the real-time time information, it is used to perform clock synchronization on the real-time acquisition image data to obtain synchronized image data, and transmit the synchronized image data to a central node through the edge node; A central management module, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node, and the real-time time information to obtain synchronized image data; A cloud platform module, which is used to monitor and manage the cloud platform, and is also used to construct a wireless network architecture, determine the central node, the acquisition node, and the edge node, determine the central node with which the edge node interacts, and generate a pre-shared key by combining the edge node and the central node.

[0034] Further, the central management module includes: A central receiving unit, which is used to receive data and information transmitted from the edge processing module; A classification unit, which is used to perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; A data sending unit, which is used to obtain the data attribute matrix of the classified image data, construct a data attribute pie chart based on the data attribute matrix, determine the image transmission parameters based on the data attribute pie chart, and transmit the classified image data to the cloud platform for storage.

[0035] Further, the edge processing module includes: An acquisition unit, which is used to obtain real-time acquisition image data by an acquisition node, and obtain real-time time information by an edge node; A data processing unit, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node, and the real-time time information to obtain synchronized image data; A transmission unit, which is used to transmit the synchronized image data to the central node through the edge node.

[0036] The advantages of the present invention are as follows: According to the type information, specification information and acquisition quality influence coefficient of the acquisition device, the acquisition quality index of the acquisition node is obtained. Then, through the acquisition quality index, the acquisition nodes are classified by level. Next, the correlation analysis is carried out between the acquisition nodes classified by level and the edge nodes, and multi-level edge devices are set at the edge nodes. The operating state information and multi-level environment information of the multi-level edge devices set at each edge node are obtained, and the operating state quality index corresponding to the multi-level edge devices set at each edge node is obtained. According to the operating state quality index and multi-level environment information corresponding to the multi-level edge devices set at each edge node, the transmission quality index of each edge node is obtained. By gradually analyzing each link, such as acquisition quality, node state, environmental impact, etc., the overall optimization from device performance to transmission quality is ensured, the risk of one-sided dependence on a certain factor is avoided, the environmental factors and device states are monitored in real time, so that the system can self-adjust to adapt to different operating conditions, the flexibility and reliability of the system are improved. By quantifying indicators such as node quality and transmission performance, the system can intelligently perform resource scheduling and load balancing, optimize device configuration, improve data transmission efficiency and systematicness. Through multi-dimensional evaluation methods, it is ensured that even if some nodes fail, the stable operation of the overall system can be guaranteed. At the same time, the model has good scalability and can adapt to changes in different scales and requirements; Based on the application scenario information, the location information of the acquisition node is determined. Then, based on the three-dimensional space coordinate system, the edge dynamic point is determined, and the coordinates of the edge node are determined through the edge dynamic point and the coordinate points of the acquisition device. Then, based on the basic coordinates of the edge node and the transmission quality index of each edge node, the coordinates of the central node in the three-dimensional space coordinate system are determined, realizing the determination of appropriate acquisition nodes, edge nodes and central nodes in areas with a large number of network devices and complex terrain, and avoiding the influence of environmental and other factors on the transmission of image data from the edge node to the central node after receiving the image data of the acquisition node; By determining the level information of the acquisition node, and extracting the timestamp information in the real-time acquisition image data transmitted by the acquisition node through the edge node. Then, the edge node judges whether the timestamp information is empty, and determines the non-empty rate of timestamp transmission of the acquisition nodes of the same level. Based on the edge node, a threshold for the non-empty rate of timestamp transmission is set. Through the threshold for the non-empty rate of timestamp transmission and the non-empty rate of timestamp transmission, it is ensured that the image data transmitted from different acquisition nodes to the edge node is time-synchronized, improving the efficiency of subsequent image processing.

[0037] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for multi-data acquisition and image transmission based on a wireless network, characterized in that, Including: Construct a wireless network architecture and determine the central node, acquisition nodes, and edge nodes; Use the acquisition nodes to obtain real-time acquired image data and use the edge nodes to obtain real-time time information; Based on the acquisition nodes, edge nodes, and real-time time information, perform clock synchronization on the real-time acquired image data to obtain synchronized image data; Transmit the synchronized image data to the central node through the edge nodes; Perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; Obtain the data attribute matrix of the classified image data and construct a data attribute pie chart with the data attribute matrix; Based on the data attribute pie chart, determine the image transmission parameters and transmit the classified image data to the cloud platform for storage.

2. The multi-data acquisition and image transmission method based on a wireless network according to claim 1, characterized in that The construction of the wireless network architecture and the determination of the central node, acquisition nodes, and edge nodes specifically include: Obtain application scenario information and, based on the application scenario information, determine the location information of the acquisition nodes; According to the location information of the acquisition nodes, obtain the environmental information of the acquisition nodes, set the corresponding acquisition devices at the same time, and obtain the type information of the acquisition devices; Based on the type information of the acquisition devices, classify the acquisition devices and respectively obtain the location information of the acquisition devices of the same type; Use the location information of the acquisition devices of the same type to determine the maximum distance information of the acquisition devices; Use the maximum distance information of the acquisition devices to determine the maximum distance line segment and construct a three-dimensional space coordinate system with the center point of the maximum distance line segment as the origin; Based on the three-dimensional space coordinate system, determine the edge dynamic points; According to the location information of the acquisition devices of the same type, determine the coordinate points of the acquisition devices of the same type in the three-dimensional space coordinate system; Connect the edge dynamic points with the coordinate points of the acquisition devices of the same type in the three-dimensional space coordinate system respectively to obtain edge dynamic line segments; Sum the length values of the edge dynamic line segments corresponding to the acquisition devices of the same type to obtain the edge node dynamic decision value; Use the coordinates of the edge dynamic points when the edge node dynamic decision value is the smallest as the coordinates of the edge nodes; Use the edge nodes corresponding to each type of acquisition device and the environmental information of the acquisition nodes to determine the central node.

3. The method for multi-data acquisition and image transmission based on a wireless network according to claim 2, characterized in that, The determination of the central node using the edge nodes corresponding to each type of acquisition device and the environmental information of the acquisition nodes specifically includes: Based on the three-dimensional space coordinate system, determine the coordinates of the edge nodes corresponding to each type of acquisition device as the edge node basic coordinates; Use the environmental information of the acquisition nodes to determine the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the acquisition devices of the same type; According to the temperature information, humidity information, electromagnetic interference information, air flow information, vibration information, and noise information of the acquisition nodes corresponding to the acquisition devices of the same type, obtain the acquisition quality influence coefficient corresponding to the acquisition nodes; Based on the type information of the acquisition devices, determine the specification information of each type of acquisition device; Based on the type information, specification information, and acquisition quality influence coefficient of the acquisition devices, obtain the acquisition quality index of the acquisition nodes; Classify the acquisition nodes according to the acquisition quality index. Perform a correlation analysis on the collected nodes after level division and the edge nodes, and set multi-level edge devices at the edge nodes; Obtain the operating status information and multi-level environment information of the multi-level edge devices set at each edge node, and obtain the operating status quality index corresponding to the multi-level edge devices set at each edge node; Obtain the transmission quality index of each edge node according to the operating status quality index and multi-level environment information corresponding to the multi-level edge devices set at each edge node; Determine the coordinates of the central node in the three-dimensional space coordinate system through the basic coordinates of the edge node and the transmission quality index of each edge node.

4. The method for multi-data acquisition and image transmission based on a wireless network according to claim 3, wherein, The clock synchronization of the real-time collected image data based on the collected nodes, edge nodes and real-time time information to obtain synchronized image data specifically includes: Determine the level information of the collected nodes, and extract the timestamp information in the real-time collected image data transmitted by the collected nodes through the edge nodes; Judge whether the timestamp information is empty through the edge nodes, and at the same time determine the non-empty rate of timestamp transmission of the collected nodes of the same level, and set the non-empty rate threshold of timestamp transmission based on the edge nodes; If the non-empty rate of timestamp transmission of the collected nodes of the same level is greater than or equal to the non-empty rate threshold of timestamp transmission, determine the standard time information of the real-time collected image data based on the timestamp information and real-time time information of the collected nodes of this level; If the non-empty rate of timestamp transmission of the collected nodes of the same level is less than the non-empty rate threshold of timestamp transmission, determine the standard time information of the real-time collected image data based on the real-time time information.

5. The multi-data acquisition and image transmission method based on a wireless network according to claim 4, characterized in that The transmission of the synchronized image data to the central node through the edge nodes specifically includes: Determine the central node interacted by the edge node, and generate a pre-shared key by combining the edge node and the central node; Based on the pre-shared key, judge whether the central node allows the edge node to perform image transmission. If not, the collected node continues to perform real-time image acquisition. If so, set the transmission quality threshold based on the transmission quality index of the edge node; Set the data processing information of the edge node for the synchronized image data according to the transmission quality index and the transmission quality threshold; Transmit the synchronized image data of the edge node to the central node based on the data processing information and the pre-shared key.

6. The multi-data acquisition and image transmission method based on a wireless network according to claim 5, characterized in that, The roulette classification and data processing of the synchronized image data received by the central node to obtain classified image data specifically includes: Determine the standard time information of the synchronized image data according to the standard time information of the real-time collected image data; construct a time roulette with the standard time information, and classify the synchronized image data according to time periods; Extract the feature information of the synchronized image data, and at the same time construct a feature roulette based on the feature information of the synchronized image data, and classify the synchronized image data after time roulette classification according to image features; Determine the task priority index according to the edge node corresponding to the synchronized image data, and construct a task priority roulette, and classify the synchronized image data after feature roulette classification according to the task priority; Perform data processing on the synchronized image data that has completed time roulette, feature roulette and task priority roulette classification to obtain classified image data.

7. The method for multi-data acquisition and image transmission based on a wireless network according to claim 6, wherein Based on the data attribute pie chart, determine the image transmission parameters, and transmit the classified image data to the cloud platform for storage, specifically including: According to the data attribute pie chart, determine the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission; Determine the image transmission parameters based on the bandwidth requirement information, data compression information, transmission protocol information, transmission frequency information, and transmission encryption information for image transmission; Transmit the classified image data to the cloud platform for storage in sequence through the image transmission parameters.

8. A multi-data acquisition and image transmission system based on a wireless network, for implementing the transmission method according to any one of claims 1-7, characterized in that Including: An edge processing module, which is used to obtain real-time acquisition image data by the acquisition node, and use the edge node to obtain real-time time information. Based on the acquisition node, the edge node, and the real-time time information, it is used to perform clock synchronization on the real-time acquisition image data to obtain synchronized image data, and transmit the synchronized image data to the central node through the edge node; A central management module, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node, and the real-time time information to obtain synchronized image data; A cloud platform module, which is used to monitor and manage the cloud platform, and is also used to build a wireless network architecture, determine the central node, the acquisition node, and the edge node, determine the central node for edge node interaction, and generate a pre-shared key by combining the edge node and the central node.

9. The multi-data acquisition and image transmission system based on a wireless network according to claim 8, wherein The central management module includes: A central receiving unit, which is used to receive the data and information transmitted from the edge processing module; A classification unit, which is used to perform roulette classification and data processing on the synchronized image data received by the central node to obtain classified image data; A data sending unit, which is used to obtain the data attribute matrix of the classified image data, construct a data attribute pie chart based on the data attribute matrix, determine the image transmission parameters based on the data attribute pie chart, and transmit the classified image data to the cloud platform for storage.

10. The multi-data acquisition and image transmission system based on a wireless network according to claim 8, characterized in that The edge processing module includes: An acquisition unit, which is used to obtain real-time acquisition image data by the acquisition node and use the edge node to obtain real-time time information; A data processing unit, which is used to perform clock synchronization on the real-time acquisition image data based on the acquisition node, the edge node, and the real-time time information to obtain synchronized image data; A transmission unit, which is used to transmit the synchronized image data to the central node through the edge node.

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