Low-energy-consumption oriented heterogeneous data interaction optimization processing method

Abstract

The invention discloses a low-energy-consumption oriented heterogeneous data interaction optimization processing method, which relates to the technical field of wireless communication, and comprises the following steps of: clustering a wireless sensor network through a low-energy-consumption clustering protocol to obtain a plurality of cluster heads with position identifiers; related cluster heads are screened out based on the adjacent areas of the target event, and an adjacent cluster head set is formed. And obtaining the heterogeneous data of the first layer from the adjacent cluster head set according to a layered sampling mechanism. And storing the data to a target cluster head, and selecting a relay cluster head through a multi-hop interaction strategy to obtain heterogeneous data of a second layer. And after the two layers of heterogeneous data are fused, the first relay cluster head transmits the fused data to an interactive processing base station. The technical problem of high energy consumption caused by too long data transmission distance and too high node energy consumption in the existing wireless sensor network is solved, and the technical effect of reducing the communication distance and energy consumption between the nodes is achieved by introducing a low-energy-consumption clustering protocol and a multi-hop interaction strategy to optimize a data transmission path.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a low-energy consumption-oriented heterogeneous data interaction optimization processing method. Background Art

[0002] Wireless sensor networks (WSNs) are increasingly being used in areas such as environmental monitoring, smart cities, and the Internet of Things. However, in practical applications, these networks face many challenges. First, sensor nodes are usually battery-powered, and wireless communications usually involve long-distance data transmission, which significantly increases the energy consumption of the nodes. As the network scale expands and the transmission distance increases, long-distance communication between nodes will lead to excessive energy consumption, which in turn affects the sustainable operation of the network. Second, due to the lack of an effective resource optimization mechanism, the nodes in the network may be in a high-load state for a long time, resulting in uneven energy consumption, which in turn exacerbates the overall energy consumption problem of the network. In addition, existing communication protocols often do not fully optimize node energy consumption. Especially in large-scale deployments, the communication distance and data transmission path are not reasonably planned, resulting in high energy consumption and low efficiency. Therefore, existing technologies cannot effectively solve the problem of high energy consumption caused by long-distance data transmission and excessive node energy consumption.

[0003] In the current related technologies, there are technical problems in wireless sensor networks that result in high energy consumption due to long data transmission distances and high node energy consumption. Summary of the invention

[0004] The present application solves the technical problem of high energy consumption in existing wireless sensor networks due to long data transmission distances and high node energy consumption by providing a low-energy consumption-oriented heterogeneous data interaction optimization processing method.

[0005] The present application provides a low-energy consumption oriented heterogeneous data interaction optimization processing method, comprising: A low energy consumption clustering protocol is introduced to cluster the wireless sensor network to obtain a network node clustering result, wherein the network node clustering result includes a plurality of cluster heads with location identifiers; a target adjacent area of ​​a target event is obtained, and the plurality of cluster heads with location identifiers are screened based on the target adjacent area to obtain an adjacent cluster head set; a stratified sampling mechanism is read, and first heterogeneous data of a first adjacent layer in the adjacent cluster head set is obtained according to the stratified sampling mechanism, wherein the stratified sampling mechanism refers to a mechanism for stratifying the adjacent cluster head set based on a base station-center connection line, and the base station-center connection line refers to a connection line between a base station position of an interactive processing base station and a center position of the target adjacent area; the first heterogeneous data is stored in a first target cluster head of the first adjacent layer, and a first relay cluster head of the first target cluster head is obtained in combination with a predetermined multi-hop interactive strategy, wherein the first relay cluster head corresponds to second heterogeneous data; the first heterogeneous data is fused with the second heterogeneous data to obtain first interactive data, and the first relay cluster head transmits the first interactive data to an interactive processing base station of the wireless sensor network.

[0006] The present application proposes a low-energy consumption-oriented heterogeneous data interaction optimization processing method. First, the wireless sensor network is clustered through a low-energy consumption clustering protocol to obtain multiple cluster heads with location identifiers. The relevant cluster heads are screened out based on the adjacent area of ​​the target event to form an adjacent cluster head set. According to the layered sampling mechanism, the heterogeneous data of the first layer is obtained from the adjacent cluster head set. The data is stored in the target cluster head, and the relay cluster head is selected through the multi-hop interaction strategy to obtain the heterogeneous data of the second layer. After the two layers of heterogeneous data are fused, the first relay cluster head transmits the fused data to the interactive processing base station, thereby achieving the technical effect of optimizing the data transmission path by introducing the low-energy consumption clustering protocol and the multi-hop interaction strategy, and reducing the communication distance and energy consumption between nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0008] Figure 1 A schematic diagram of a process flow of a low-energy consumption oriented heterogeneous data interaction optimization processing method provided in an embodiment of the present application; Figure 2 A flowchart of a low-energy consumption-oriented heterogeneous data interaction optimization processing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0010] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0011] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0012] The present application embodiment provides a low-energy consumption oriented heterogeneous data interaction optimization processing method, such as Figure 1 As shown, the method includes: Step S100, introduce a low-energy clustering protocol to cluster the wireless sensor network, and obtain a network node clustering result, wherein the network node clustering result includes multiple cluster heads with location identifiers. Specifically, in a wireless sensor network (WSN), the goal of introducing a low-energy clustering protocol is to reduce the long-distance communication between nodes by effectively dividing the network, thereby reducing the overall energy consumption. First, each node in the network is clustered according to its energy, location, signal strength and other factors, and finally forms multiple cluster heads. The cluster head node is responsible for aggregating sensor data within the cluster and communicating with other cluster heads or base stations. For example, in environmental monitoring, multiple temperature and humidity sensors may be distributed in different locations. The temperature and humidity data collected by each sensor node will be collected and processed by the cluster head node of the cluster, and then forwarded to other cluster heads or base stations, instead of each sensor reporting data directly to the base station, thereby effectively reducing communication energy consumption. Each cluster head has a location identifier, which is helpful for subsequent data routing and management of the network, especially in a dynamically changing network environment, such as in disaster monitoring, the location identifier helps to quickly determine the most suitable cluster head node for data transmission. The selection of cluster heads is usually determined by the energy sufficiency and location. Nodes that are close to the event site and have sufficient energy are selected as cluster heads to ensure the accuracy and reliability of data collection. For example, in forest fire monitoring, the nodes that are closest to the fire area and have sufficient battery energy will be selected as cluster heads, which can ensure that fire information is quickly collected and transmitted to the base station. At the same time, the selection of cluster heads is not static. As time goes by, the energy consumption of some cluster heads may accelerate. The network protocol can reselect cluster heads based on real-time energy levels to avoid overloading some cluster heads and ensure the continuous and stable operation of the network. In the case of excessive load, the protocol will dynamically adjust the cluster head selection and distribute the data transmission tasks to other nodes to avoid some cluster heads from running out of energy quickly. Dynamic adjustment ensures the long-term and effective operation of the network, especially in large-scale deployment application scenarios, such as environmental sensor networks in smart cities. The above mechanism can effectively extend the service life of the network and reduce maintenance costs.

[0013] Step S200, obtaining the target adjacent area of ​​the target event, and filtering the multiple cluster heads with location identifiers based on the target adjacent area to obtain an adjacent cluster head set. Specifically, in a wireless sensor network, it is first necessary to obtain the target adjacent area of ​​the target event, which is an area related to a specific event, usually determined by sensor data or external information (such as GPS positioning, geographic information system, etc.). For example, if the target event is an abnormal temperature change in a certain area, then the target adjacent area may be the temperature monitoring range of the area. Then, based on the definition of the target adjacent area, multiple cluster heads in the network will be filtered according to their geographical location and relative distance from the target area, and the cluster heads closest to the target event will be filtered out to form an "adjacent cluster head set". The cluster head will undertake subsequent data collection and transmission tasks to ensure that the network can focus on the location of the target event and avoid unnecessary node participation. For example, in meteorological monitoring, if the cluster head is located in the area where heavy rain occurs, it will be selected first because it can quickly collect and transmit data related to heavy rain. When selecting cluster heads, not only the location but also factors such as the energy consumption and load capacity of the cluster heads may be considered to ensure that the selected cluster heads can perform tasks efficiently. Through screening, the system will only activate cluster heads related to the target event, reducing the collection and transmission of irrelevant data, thereby saving network resources and reducing energy consumption. Ultimately, the screening process ensures the efficiency and accuracy of the wireless sensor network when processing target events. For example, if the target event is a forest fire, the system will select the cluster heads closest to the fire point for data collection and transmit fire-related data through these cluster heads, rather than involving all nodes in the entire network.

[0014] Step S300, read the stratified sampling mechanism, and obtain the first heterogeneous data of the first adjacent layer in the adjacent cluster head set according to the stratified sampling mechanism, wherein the stratified sampling mechanism refers to a mechanism for stratifying the adjacent cluster head set based on the base station-center connection line, and the base station-center connection line refers to the connection line between the base station position of the interactive processing base station and the center position of the target adjacent area. Specifically, firstly, by introducing the stratified sampling mechanism, it is ensured that data collection and transmission in the wireless sensor network can be carried out efficiently. Specifically, after determining the target adjacent area of ​​the target event, the system divides the cluster heads in the network into levels according to the scope and importance of the event, wherein the first adjacent layer contains the cluster heads closest to the target event. For example, when monitoring air pollution events, the cluster heads of the first adjacent layer may be sensor nodes located in the area around the pollution source, and the air quality data collected by the sensor nodes will reflect the direct impact of the event. The cluster head of the first adjacent layer is responsible for collecting heterogeneous data from multiple sensors in the cluster (such as temperature, humidity, and gas concentration sensors). These data usually come from different types of sensors with different formats and measurement units. For example, the temperature sensor provides temperature data, while the gas concentration sensor provides pollutant concentration data. In the above process, the cluster head will perform preliminary processing and storage on the collected data, such as data compression or denoising, to ensure that the data can be transmitted efficiently and reduce redundancy. In this way, the data can maintain efficiency and accuracy during transmission. For example, in pollution monitoring, the cluster head node will summarize all sensor data in the area and organize it into a simplified data packet, ready to be transmitted to the next level of cluster head or base station. Through the hierarchical sampling mechanism, the cluster head at each level in the network is responsible for different levels of data collection tasks according to its geographical distance from the target event and the relevance of the event, ensuring that the network can accurately respond to the target event, while reducing the collection and transmission of irrelevant data, thereby effectively saving network energy and bandwidth resources.

[0015] In one possible implementation, Figure 2As shown, the stratified sampling mechanism is read, and the first heterogeneous data of the first adjacent layer in the adjacent cluster head set is obtained according to the stratified sampling mechanism, wherein the stratified sampling mechanism refers to a mechanism for stratifying the adjacent cluster head set based on the base station-center connection line, and the base station-center connection line refers to the connection line between the base station position of the interactive processing base station and the center position of the target adjacent area. Step S300 further includes step S310, obtaining the target center of the target adjacent area, wherein the target center corresponds to the center position. Specifically, it is first necessary to obtain the target center of the target adjacent area, which represents the geometric center of the area where the target event is located. For example, in a meteorological monitoring network, the target adjacent area may be a temperature anomaly area of ​​a specific city, and the target center is the geometric center point of the area, which may correspond to the center position of the city. The target center is obtained by calculating the average value of the coordinates of the sensor nodes or by weighting according to the data distribution to ensure that the position can most accurately reflect the core area of ​​the event. On this basis, the next step is to obtain the connection line between the base station position and the target center, which will serve as a reference for subsequent data transmission path optimization. Assuming that the base station is located on the outskirts of a city and the target center is in the urban area, this connection line can help the system determine the distance from the base station to the core area of ​​the event, providing a basis for the selection of the data transmission path. Then, combined with the predetermined interval segment in the stratified sampling mechanism, the connection line between the base station and the target center is divided into several levels. The cluster heads in the network can be divided into multiple levels according to the distance from the target center. The cluster heads at each level are responsible for collecting data related to their corresponding areas. For example, the first layer may contain the cluster heads closest to the target center, which are responsible for monitoring the real-time dynamics of the event, while the farthest adjacent layer is responsible for collecting data in the peripheral area, and the data will be gradually transmitted to the base station. Through the stratified strategy, the system can more accurately control data collection and transmission, ensuring that the task load of each cluster head is reasonably distributed. Finally, the system selects the adjacent layer farthest from the base station as the first adjacent layer, and the cluster head is responsible for collecting more extensive data, ensuring that there is no blind spot in the monitoring of the entire area and optimizing the use of network resources. Through the above steps, the system can accurately locate the event center, reasonably divide the data collection area, and efficiently organize the data transmission path.

[0016] Step S320, obtain the base station position of the interactive processing base station and the base station-center connection line of the center position. Specifically, it is necessary to first obtain the position of the interactive processing base station and the position of the target center. The determination of these two positions is crucial for subsequent path optimization and data collection. The base station is usually located at the periphery or fixed position of the network, which may be a remote communication tower or data center in the city, responsible for receiving and processing data from each cluster head. For example, the base station is located on the east side of the city, and the center of the target event (such as air pollution) may be located in the central area of ​​the city. By clarifying these two positions, the system can calculate the straight line between the base station and the target center, and the connection line represents the spatial relationship between the base station and the core area of ​​the target event. When calculating, the longitude and latitude coordinates of the base station and the target center are usually used, and the distance between the two is obtained by simple geometric calculations (such as Euclidean distance). Through the base station-center connection line, the system can provide data support for subsequent multi-hop transmission path selection and cluster head screening. For example, the base station-center connection line can help the system select the shortest path to transmit data, avoid energy waste caused by long-distance transmission, and ensure that the efficiency of data transmission from the cluster head node close to the target center to the base station is maximized. If the target event is temperature anomaly, the calculation of the target center may be determined by the geographical distribution of temperature sensors, and the base station-center connection line can further guide the selection of the most appropriate cluster head for real-time data collection, avoiding the waste of peripheral irrelevant areas.

[0017] Step S330, the base station-center connection line is segmented in combination with the predetermined interval segment in the stratified sampling mechanism, and the stratification result of the adjacent cluster head set is obtained. Specifically, first, the connection line between the base station and the target center is segmented according to the predetermined interval segment of the stratified sampling mechanism. The segmentation process is to divide the distance between the base station and the target center into several sections by the preset interval segment. For example, assuming that the straight-line distance between the base station and the target center is 10 kilometers, and the predetermined interval segment is set to 1 kilometer, then the connection line will be segmented into 10 sections. Each section represents a level, and the cluster head of the level is responsible for collecting data in the section. Segmentation helps to reasonably distribute data collection tasks according to the distance, avoid excessive concentration on certain cluster heads, and ensure that the workload of each cluster head is moderate. Next, according to the segmentation result, the system obtains the stratification result of the adjacent cluster head set. Each cluster head is responsible for collecting data in the corresponding area according to the level it is in. For example, the first-layer cluster head is responsible for collecting data in the area close to the base station, which may be air quality monitoring data, while the third-layer cluster head is responsible for data in the farther area, such as ambient temperature or humidity data. Through stratification, the system can ensure the comprehensiveness and efficiency of data collection, while avoiding redundant and overlapping data collection, thereby saving network resources and energy. Finally, based on the stratification results, the data collection path is also optimized, ensuring that the data of each layer of cluster heads can be efficiently transmitted to the next layer and gradually converged to the base station for final processing. The stratified sampling mechanism significantly improves the accuracy of data collection and transmission, while reducing energy waste, allowing wireless sensor networks to respond to target events more efficiently.

[0018] Step S340, the adjacent layer farthest from the base station in the stratification result is used as the first adjacent layer. Specifically, firstly, by dividing the connecting line between the base station and the target center, the system assigns different data collection tasks to each cluster head, and each segment represents a layer. Next, the system selects the adjacent layer farthest from the base station as the first adjacent layer. The first adjacent layer usually covers the peripheral area of ​​the target event and is responsible for collecting data in the farthest range. For example, in a forest fire monitoring system, the first adjacent layer may include sensors located outside the fire, and the sensors are responsible for collecting data from areas far away from the fire source, such as temperature changes and smoke concentrations. By dividing the cluster heads into the farthest adjacent layer, the system ensures that the coverage of data collection is not limited to the area around the base station, but extends to the impact area of ​​the entire event, avoiding deviations or omissions in data collection. As data collection proceeds, the cluster heads of the first adjacent layer will collect and process the data, and then gradually transmit them to the central node through multi-hop transmission. At the same time, the burden on the cluster heads will not be too heavy, because each cluster head only needs to pay attention to the data of the area farthest from it, and does not need to participate in the tasks around the base station. Through the hierarchical strategy, the system can reasonably allocate resources, reduce the transmission of redundant data, and ensure that the data collection task of each cluster head is both accurate and efficient. In addition, during the data transmission process of the first adjacent layer, due to the distance from the base station, the system will specially design the transmission path to reduce the energy loss caused by long-distance data transmission and optimize the energy efficiency and performance of the overall network.

[0019] Step S350, according to the sampling plan in the stratified sampling mechanism, the first sampling cluster head and the second sampling cluster head of the first adjacent layer are obtained. Specifically, the implementation of the stratified sampling mechanism depends on the sampling plan to effectively select the sampling cluster head of the first adjacent layer. First, the cluster heads of the first adjacent layer are divided according to the distance from the core area of ​​the target event, and the cluster heads are usually responsible for collecting peripheral data of the target event. Through the sampling plan, the system selects the first sampling cluster head, which is responsible for collecting regional data close to the base station. For example, in air pollution monitoring, the first sampling cluster head may be located near the pollution source and is responsible for real-time monitoring of the pollutant concentration and gas composition in the area. Next, the system further selects the second sampling cluster head according to the sampling plan, which will be responsible for collecting regional data outside the impact range of the target event. Regional data is crucial for a comprehensive understanding of the expansion of the event. For example, in forest fire monitoring, the second sampling cluster head is located outside the fire and collects smoke concentration and temperature data in the area. The data collection task allocation of the first and second sampling cluster heads ensures comprehensive coverage from the event center to the peripheral area, while avoiding duplication and redundancy of data collection. Each sampling cluster head is not only responsible for collecting data in the area, but also needs to pre-process the data, such as denoising and compression, to improve data transmission efficiency. After data collection, the sampling cluster head transmits the data to a higher-level cluster head or base station through multi-hop transmission, ensuring that the data can be accurately and timely transmitted to the network center for further analysis and processing, ultimately providing efficient data support for decision-making.

[0020] Step S360, aggregate the first sensing information of the first sampling cluster head and the second sensing information of the second sampling cluster head to obtain the first heterogeneous data. Specifically, the sensing information from the first sampling cluster head and the second sampling cluster head needs to be aggregated first. The first sampling cluster head may be responsible for collecting environmental data of the central area of ​​the target event, such as temperature, humidity, gas concentration, etc., while the second sampling cluster head may be responsible for collecting data of the peripheral area at a longer distance, such as wind speed, smoke concentration, etc. in the area. Although the data comes from different cluster heads and may contain different types of sensor data, they need to be integrated through data fusion to form the first heterogeneous data. For example, the temperature data collected by the first sampling cluster head may be 10°C, while the smoke concentration data collected by the second sampling cluster head may be 50ppm. The system will integrate the heterogeneous data into a unified data set through weighted averaging, data splicing or other mathematical methods for subsequent analysis and transmission. During the aggregation process, the system also needs to pre-process the data to remove outliers and noise to ensure that the final first heterogeneous data is more accurate and reliable. For example, if a temperature sensor fails and the data is too high, the system will identify and exclude these abnormal data to avoid affecting the final analysis results. The aggregated first heterogeneous data not only provides multi-dimensional information about the target event, but also reflects the changing trends of the event in different regions, such as changes in pollutant concentrations, temperature fluctuations, etc. Ultimately, the data will be transmitted to the processing center of the network to provide decision makers with comprehensive data support and help formulate response measures. For example, in forest fire monitoring, by integrating temperature, humidity and smoke concentration data, the path and speed of fire spread can be accurately predicted.

[0021] Step S400, storing the first heterogeneous data in the first target cluster head of the first adjacent layer, and obtaining the first relay cluster head of the first target cluster head in combination with the predetermined multi-hop interaction strategy, wherein the first relay cluster head corresponds to the second heterogeneous data. Specifically, firstly, the first heterogeneous data is stored in the first target cluster head of the first adjacent layer, and the above process ensures that the data from different regions and different types of sensors can be reliably stored and prepared for subsequent processing. For example, the first target cluster head stores the temperature, humidity and pollutant concentration data from the central area of ​​the target event, which represent the core impact area of ​​the event. Then, the system selects the first relay cluster head according to the predetermined multi-hop interaction strategy, and its task is to transmit the data stored in the first target cluster head to a more distant area or base station. The first relay cluster head may be located in an area far away from the base station, such as the edge area of ​​the event impact range, to ensure that the data can be gradually transmitted through multiple cluster heads, avoiding the energy consumption caused by direct long-distance transmission. For example, in forest fire monitoring, the first relay cluster head may be located outside the fire, responsible for transmitting the collected data of the fire extension area to the base station. Then, the first relay cluster head will fuse the second heterogeneous data (such as temperature changes and smoke concentration data in the fire spreading area) with the first heterogeneous data to ensure that the data is more comprehensive and accurate. These data are transmitted through multiple jumps and gradually transmitted to the base station or other processing nodes for analysis, and ultimately provide support for decision-making. For example, the fused data can help predict the spread path, speed, and possible control measures of the fire. The entire process not only improves the efficiency of data transmission through reasonable layering and multi-hop strategies, but also ensures the optimal use of network resources.

[0022] In a possible implementation, the first heterogeneous data is stored in the first target cluster head of the first adjacent layer, and the first relay cluster head of the first target cluster head is obtained in combination with a predetermined multi-hop interaction strategy, wherein the first relay cluster head corresponds to the second heterogeneous data, and step S400 further includes step S410, wherein the first target cluster head refers to the adjacent cluster head closest to the base station in the first adjacent layer. Specifically, the first target cluster head refers to the adjacent cluster head closest to the base station in the first adjacent layer. The adjacent cluster heads are located at a position close to the base station, so they play a vital role in the data collection and transmission process. For example, in an environmental monitoring system, assuming that the target event is pollution diffusion, the first target cluster head usually selects a sensor located near the pollution source, and the first target cluster head is responsible for collecting real-time data in the area, such as air quality, temperature and humidity, and quickly feedbacks the degree and change trend of pollution. Because the first target cluster head is close to the base station, they can transmit data to the base station with a lower delay to ensure the timeliness of the data. The first target cluster head not only undertakes the data collection task, but also is responsible for transmitting the data to the next layer of cluster heads or directly to the base station through multi-hop transmission. For example, in forest fire monitoring, the first target cluster head may be located outside the fire, timely collecting temperature data of the fire spread and transmitting it to cluster heads farther away, which eventually reach the base station for processing and analysis through multiple transmission paths. Since the first target cluster head is closer to the base station, it can also undertake the preliminary filtering and processing of the data, such as denoising and compression, to ensure the efficiency and accuracy of the transmission. In addition, the selection of the first target cluster head not only optimizes the data transmission path, but also improves the stability of the wireless sensor network and reduces the energy loss caused by long-distance transmission. Through the above design, the system can ensure that the data in the target area can be quickly and accurately transmitted to the base station, supporting timely decision-making response, especially in the monitoring and early warning process of emergencies.

[0023] In a possible implementation, the first heterogeneous data is stored in the first target cluster head of the first adjacent layer, and the first relay cluster head of the first target cluster head is obtained in combination with a predetermined multi-hop interaction strategy, wherein the first relay cluster head corresponds to the second heterogeneous data, and step S400 further includes step S420, extracting the second adjacent layer in the stratification result. Specifically, the second adjacent layer, that is, the cluster head of the area far from the base station, is first extracted, and the cluster head is responsible for collecting data in the peripheral area of ​​the target event. For example, in environmental pollution monitoring, the cluster head of the second adjacent layer may be located at the far end of the pollution source, and collects data such as air quality and pollutant concentration in the area. By extracting the second adjacent layer, the system ensures that the data collection task can cover a wider area, thereby comprehensively monitoring the impact range of the target event, especially the extended part of the periphery of the event. The main task of the cluster head is to collect data from the periphery of the event and pass it to the upper layer cluster head or base station for processing. For example, in a large-scale forest fire monitoring, the cluster head of the second adjacent layer may be located at the periphery of the fire spread, collecting temperature and smoke concentration data in the fire impact area, and the data is crucial for judging the spread trend of the fire. In addition, the cluster head of the second adjacent layer is also responsible for the data forwarding function, and transmits the collected data to the cluster heads or base stations of other layers through multi-hop transmission. This not only avoids the blind spots of data transmission, but also ensures that the data from the area far away from the base station to the target area can smoothly reach the central node for analysis. In this process, the selection of the cluster head of the second adjacent layer and the design of the data transmission path optimize the load balancing of the network, reduce the overload of a certain cluster head, and improve the stability and efficiency of the entire network. This layered sampling strategy ensures complete monitoring from the core of the event to the periphery, further improving the accuracy and response speed of target event monitoring.

[0024] Step S430, randomly extract any cluster head in the second adjacent layer, and obtain any position of the arbitrary cluster head. Specifically, first randomly select a cluster head from the second adjacent layer to perform data collection tasks, and the location of the cluster head is usually far away from the base station and the target event center. For example, in an environmental pollution monitoring system, the cluster head of the second adjacent layer may be located at the far end of the pollution source, responsible for collecting environmental data such as air quality, temperature or humidity in the area. By randomly selecting cluster heads, the system avoids some cluster heads from carrying too many data transmission tasks for a long time, thereby ensuring the load balancing of the network and avoiding a certain cluster head from becoming a bottleneck. After selecting the cluster head, the system will obtain the geographical location of the cluster head and its coordinates in the network, which is crucial for the subsequent transmission path optimization. For example, if the cluster head is located in an area far away from the base station, the system will calculate the shortest data transmission path based on this location, and may select multiple relay cluster heads for multi-hop transmission, thereby avoiding energy waste and delay caused by long-distance direct transmission. After obtaining the location, the system will also comprehensively analyze the distance between the cluster head and the base station and other cluster heads to evaluate whether it can be used as an effective relay node for data transmission. Through the above methods, the system can achieve flexible load distribution, optimize the data collection and transmission process, ensure that the data in each area can be uploaded to the central node efficiently and timely, and provide support for subsequent data processing and decision-making.

[0025] Step S440, evaluate and analyze the arbitrary position, the first cluster head position of the first target cluster head and the base station position according to the multi-hop decision evaluation function in the predetermined multi-hop interaction strategy to obtain the first decision fitness. Specifically, the system first evaluates the transmission path between the arbitrary cluster head position, the first cluster head position of the first target cluster head and the base station position according to the multi-hop decision evaluation function in the multi-hop interaction strategy. The purpose of this evaluation is to calculate the first decision fitness of the transmission path. The smaller the fitness value, the shorter the data transmission distance, the lower the energy consumption, and the higher the transmission efficiency. Specifically, assuming that the first target cluster head is located at the center of the target event, and the arbitrary cluster head is located in the peripheral area, the system will first calculate the distance between the cluster head and the first target cluster head, and then combine it with the distance of the base station to obtain the total transmission distance through the distance formula. For example, if the distance between the first target cluster head and the base station is short, and the arbitrary cluster head is located at a farther position, the system will evaluate the distance from the first target cluster head to the cluster head and then to the base station. At this time, the larger the fitness value, the higher the energy consumption will be. The system will select other cluster heads or adjust the path to ensure the minimum energy consumption. In this process, the dimension reduction strategy is also used. The data of multiple sensors will be integrated to reduce redundant information, thereby further reducing the energy consumption during data transmission. For example, the temperature and humidity data collected by multiple sensors can be merged by averaging or weighting to reduce the amount of data transmitted, thereby reducing the energy required during the transmission process. Finally, by calculating the fitness, if the fitness value of a cluster head is lower than the predetermined threshold, the system will select it as the first relay cluster head and gradually pass the data to the next layer of cluster heads or base stations through a multi-hop strategy. This method ensures that the path selection during data transmission is both efficient and energy-saving, avoids unnecessary long-distance transmission, and optimizes the energy use of wireless sensor networks, improving the stability of the system and the timeliness of data transmission.

[0026] Step S450, if the first decision fitness reaches a predetermined fitness threshold, the arbitrary cluster head is recorded as the first relay cluster head. Specifically, when the first decision fitness is calculated and compared with the preset fitness threshold, the system will decide whether to use a cluster head as the first relay cluster head. The fitness value is calculated based on a multi-hop interaction strategy, and the goal is to evaluate the effectiveness of the data transmission path. The smaller the fitness value, the shorter the path and the lower the energy consumption. For example, assuming that the first target cluster head is located at the center of the target event, and a cluster head of a second adjacent layer is located at the periphery of the event, the system will calculate the total transmission distance from the first target cluster head to the cluster head, and then to the base station. If the fitness value of the cluster head is less than the preset threshold, it means that it has a shorter transmission path and lower energy consumption, which meets the requirements of the relay cluster head. At this time, the system will select the cluster head as the first relay cluster head, and forward the data from the first target cluster head to the next layer of cluster heads or base stations through multi-hop transmission. In this way, the selection of relay cluster heads not only optimizes the data transmission path and reduces the energy consumption of long-distance transmission, but also ensures that data can be transmitted to the target node efficiently and timely through flexible multi-hop transmission strategies. In addition, during the data transmission process, the system will also combine dimensionality reduction technology to remove redundant information through data fusion, further reducing energy consumption and ensuring the efficient operation of the entire network. For example, in environmental pollution monitoring, the first relay cluster head may collect and forward data from different sensors, such as temperature, humidity, and pollutant concentration, to ensure the system's real-time response and decision support to target events. In this way, the system can achieve energy optimization, network load balancing, and ensure the timeliness and accuracy of data transmission.

[0027] In a possible implementation, the arbitrary position, the first cluster head position of the first target cluster head, and the base station position are evaluated and analyzed according to the multi-hop decision evaluation function in the predetermined multi-hop interaction strategy to obtain a first decision fitness, and step S440 further includes step S441, and the expression of the multi-hop decision evaluation function is as follows: ;in, It means that any position The fitness of the corresponding arbitrary cluster head as the first relay cluster head, refers to any position The first cluster head position The distance between refers to any position With the base station location The distance between and are weight coefficients, and Specifically, the core of the multi-hop decision evaluation function is to select the optimal cluster head path by calculating the first decision fitness. The fitness function is expressed as ,in, represents the fitness value, From any location (such as the cluster head in the second adjacent layer) to the first cluster head position The distance From any cluster head location To base station location The distance and are weight coefficients, and Assume that in environmental monitoring, the cluster heads of the second adjacency layer are located at the periphery of the pollution source, while the base station is located at the monitoring center. The goal is to transmit data from the peripheral area to the base station for processing. If If the value is larger, the system will pay more attention to optimizing the transmission efficiency between the cluster head and the first target cluster head and reducing the distance between the collected data points; If the distance between the cluster head and the base station is large, the system will pay more attention to the transmission distance from the cluster head to the base station, reducing the energy consumption caused by long-distance data transmission. By calculating the fitness value of each cluster head, the system can select the cluster head with a lower fitness value as the relay cluster head to ensure that the data can be transmitted through the shortest path. For example, if a cluster head is closer to the first cluster head and the transmission distance to the base station is moderate, its fitness value will be lower, so it will be selected as the relay cluster head to forward the data from the first cluster head to the base station. This not only optimizes the data transmission path, reduces transmission delay and energy consumption, but also ensures that the system always selects the most effective path to transmit data through the setting of the fitness threshold, thereby improving the transmission efficiency and energy efficiency of the entire network.

[0028] In a possible implementation, the arbitrary position, the first cluster head position of the first target cluster head and the base station position are evaluated and analyzed according to the multi-hop decision evaluation function in the predetermined multi-hop interaction strategy to obtain a first decision fitness, and step S440 further includes step S442, obtaining the bit spacing between the first cluster head position and the arbitrary position. Specifically, the system first needs to obtain the bit spacing between the first cluster head position and the arbitrary position (i.e., the arbitrary cluster head position). The bit spacing refers to the physical distance between the two cluster heads, which plays a key role in the subsequent data transmission and clock synchronization process. For example, assuming that the first cluster head is located in the central area of ​​the target event, and the arbitrary cluster head is located in the edge area of ​​the event, the system obtains the precise coordinates (such as longitude and latitude) of the two cluster heads through GPS positioning or other positioning technologies. Then, the Euclidean distance formula (calculated ) to calculate the bit spacing between them. If the bit spacing between the first cluster head and any cluster head is large, it means that the data transmission may experience a long delay. In this way, the system can evaluate the communication delay between these cluster heads, which helps optimize the subsequent data transmission path. For example, assuming that the temperature data collected by the first cluster head needs to be further transmitted to the base station through any cluster head, if the bit spacing is large, the system will consider multi-hop transmission to reduce energy consumption. Based on the calculation of the bit spacing, the system can choose a shorter path or a suitable relay cluster head to transmit data to avoid energy waste of direct transmission. In addition, the bit spacing also provides the necessary basis for clock synchronization. The system can adjust the clocks of each cluster head according to the delay difference between nodes to ensure that the data is transmitted in the correct order and avoid data conflicts or losses. In this way, the system can accurately control the data transmission in the network and improve the overall energy efficiency and transmission stability.

[0029] Step S443, clock backoff is performed on the bit spacing to obtain a backoff result. Specifically, the system first calculates the possible transmission delay according to the bit spacing (i.e., the physical distance between the first cluster head and any cluster head), and then performs clock backoff based on this information. For example, if the bit spacing between the first cluster head and any cluster head is large, the system will estimate a large delay difference. In order to ensure that the data can be transmitted in the correct order, the system will adjust the sending timing of each cluster head according to the bit spacing. If the bit spacing is large, the system will delay the sending time of any cluster head through clock backoff to avoid conflicts with the transmission timing of other cluster heads. The calculation result of the backoff delay forms a backoff result, which defines a specific delay value (such as a few milliseconds) and determines the time that each cluster head needs to be delayed. This adjustment ensures that the clocks of all cluster heads can work synchronously during multi-hop data transmission. For example, in a pollutant monitoring system, it is assumed that the first cluster head collects air quality data in the city center, and any cluster head far away from the center is located in the edge area, and the data needs to be transmitted to the base station. In this case, through the calculation of the bit spacing and the clock backoff mechanism, the system can ensure that the pollutant concentration data from the first cluster head and any cluster head are transmitted in the correct timing, avoiding data loss or conflict. Ultimately, the backoff result not only makes the data transmission timing correct, but also helps the system avoid errors caused by clock asynchrony in multi-hop transmission, ensuring efficient data synchronization and accuracy.

[0030] Step S444, synchronize the first heterogeneous data and the second heterogeneous data according to the backoff result. Specifically, first, the system will adjust the data transmission timing of different cluster heads based on the backoff result to ensure that the data transmission between the nodes is carried out in the correct time sequence, thereby avoiding conflicts caused by clock asynchronization. For example, assuming that the timestamp of the temperature data collected by the first cluster head is t1, and the timestamp of the humidity data collected by the second cluster head is t2, due to the long distance, there is a slight difference in their collection time. Through the backoff mechanism, the system will calculate the delay time according to the bit spacing, and postpone the data transmission of the second cluster head according to this delay, so that the humidity data and the temperature data can be transmitted in the same time frame. Then, the system will adjust the timing of the data according to the backoff result so that the timestamps of the two are aligned to avoid inconsistency. After synchronization, the first heterogeneous data (temperature) and the second heterogeneous data (humidity) are merged into a unified time series. In the process of data fusion, they may be integrated into a comprehensive environmental data set, such as temperature and humidity joint monitoring data, by using weighted average, interpolation method, etc. This process ensures the consistency and accuracy of the data. In the environmental monitoring system, the unified data can be directly input into the model for trend prediction and real-time analysis, providing more accurate decision support. In addition, the system also ensures that the transmission of each node does not conflict by synchronizing the data, avoiding data loss or duplication due to time asynchrony, thereby improving the timeliness and reliability of the data. In this way, clock backoff and synchronization processing significantly optimize the energy efficiency and stability of the data transmission process, and improve the performance and response speed of the overall system.

[0031] In a possible implementation, the first heterogeneous data and the second heterogeneous data are synchronously processed according to the backoff result, and step S444 further includes step S4441, obtaining the first interactive time sequence of the first interactive data. Specifically, the system first extracts the timestamp of each data point from the first interactive data and records the collection time of each data. For example, in an environmental monitoring system, the first cluster head may collect temperature data, and the second cluster head may collect humidity data. The system will generate a corresponding timestamp for each data point, such as the timestamp of the temperature data is "2023-10-10_14:30:00", and the timestamp of the humidity data is "2023-10-10_14:30:05". These timestamps can accurately mark the collection time of each data and ensure that the data is processed in time sequence. Next, the system will sort the data according to the timestamp to ensure that the data is transmitted and processed in the correct order. If there is a time difference between the data collected by the first cluster head and the second cluster head, the system will adjust according to the timestamp so that all data can be synchronized in a unified time frame. For example, if the temperature data and humidity data are collected a few seconds apart, the system will adjust the two sets of data according to the time series to ensure that they are used in the same time period, ready for subsequent analysis and fusion. Finally, after sorting and synchronization, the system can store the data of the first interactive time series in the database for subsequent trend analysis, data fusion or real-time prediction. In environmental monitoring, time series data provides the system with the necessary time frame to ensure that the system can respond in real time and predict future trends based on historical data. Through this process, the system ensures the timeliness and consistency of the data, avoids data confusion caused by clock asynchrony, and thus improves the overall data processing efficiency and network performance.

[0032] Step S4442, trend prediction analysis is performed on the first interaction time series to obtain first predicted interaction data. Specifically, the system first infers the future data change trend through trend prediction analysis based on the historical data extracted from the first interaction time series. For example, suppose the first cluster head records the temperature data of the past week and finds that the data shows a gradually rising trend. Based on the data trend, the system applies the ARIMA model for analysis and finds that the temperature may continue to rise in the next few days. Assume that the prediction result is that the temperature in the next three days is 19°C, 20°C and 21°C, respectively, and the prediction error range is ±0.5°C. The predicted value is the first predicted interaction data. The predicted data represents the future temperature change trend and can provide a basis for the system to respond in advance. After generating the predicted data, the system transmits the data to the interaction processing base station so that the base station can make resource scheduling or start the early warning system in advance according to the predicted data. For example, if the system predicts that the temperature will rise, the base station may deploy equipment with large energy consumption in advance, or start the cooling system to ensure that the system can operate efficiently. In addition, the system can also adjust the acquisition frequency of the sensor based on the predicted data to avoid wasting energy due to excessive data collection, thereby optimizing the operation efficiency of the system. Through the above process, the system can not only accurately predict data change trends, but also provide forward-looking support for subsequent decision-making, thereby improving the stability and efficiency of the overall system.

[0033] Step S4443, transmitting the first predicted interaction data to the interaction processing base station. Specifically, after the trend prediction analysis, the first predicted interaction data will first be formatted and prepared for transmission. For example, suppose the system predicts that the temperature will reach 19°C, 20°C, and 21°C in the next three days, and each data point is accompanied by a timestamp, such as "2023-10-10_14:00:00". During the formatting process, the system may package these data in a compressed format to reduce the bandwidth and time required for transmission. If the amount of data is large, the system may use a compression algorithm (such as gzip) to reduce the burden of data transmission, and package the temperature and related timestamp data into a standard format for easy reception and processing by the base station. Then, these predicted data will be transmitted to the interaction processing base station through wireless communication protocols such as 5G, Wi-Fi, or LoRa. During the transmission process, the system will use error detection mechanisms, such as CRC (cyclic redundancy check), to ensure the integrity of the data. If data is lost or damaged during transmission, the system triggers the automatic retransmission request (ARQ) mechanism to ensure that the lost data packet is resent to avoid data errors affecting subsequent processing. For example, in a temperature and humidity monitoring system, if a data packet is lost, the system automatically re-requests the transmission of the data packet until the complete data reaches the base station. Finally, when the data is successfully transmitted to the base station, the base station analyzes and stores it based on the received forecast data and adjusts the strategy based on real-time data. Assuming that the temperature forecast data indicates that the temperature will rise in the next few days, the base station will start the air conditioning system in advance to adjust the temperature, or adjust the energy use strategy based on the forecast to reduce the load caused by high temperature. The above process not only ensures the efficient and accurate transmission of data, but also optimizes the system's resource allocation and responsiveness by foreseeing future trends.

[0034] Step S500, the first heterogeneous data and the second heterogeneous data are merged to obtain the first interactive data, and the first relay cluster head transmits the first interactive data to the interactive processing base station of the wireless sensor network. Specifically, the system first obtains the first heterogeneous data and the second heterogeneous data from different cluster heads and merges them into a unified data set. For example, the temperature data collected by the first cluster head is 25°C, and the humidity data collected by the second cluster head is 70%. The system combines these two sets of data to form a data pair of "temperature: 25°C, humidity: 70%". Then, the system ensures that the timestamps of the two sets of data are consistent through time synchronization so that they are processed in the same time frame. Then, the fused first interactive data will be transmitted by the first relay cluster head. As a relay node for data transmission, the first relay cluster head will send the fused data to the interactive processing base station, which may be transmitted in a single hop or multi-hop manner. Assume that in an intelligent agricultural system, the temperature and humidity data are collected by different cluster heads respectively. After the first cluster head merges the temperature and humidity data, the first relay cluster head transmits them to the base station, and finally reaches the base station after two relays. After receiving the data, the base station may make adjustments based on the changing trends of temperature and humidity, such as starting the automatic temperature control system or adjusting the operating status of the water pump to cope with possible future environmental changes. This process not only ensures the accuracy and integrity of the data, but also optimizes the data transmission path and improves the response speed and efficiency of the system.

[0035] The embodiment of the present application uses a low-energy clustering protocol to cluster the wireless sensor network to obtain multiple cluster heads with location identifiers. Relevant cluster heads are screened out based on the adjacent area of ​​the target event to form an adjacent cluster head set. According to the layered sampling mechanism, the heterogeneous data of the first layer is obtained from the adjacent cluster head set. The data is stored in the target cluster head, and the relay cluster head is selected through the multi-hop interaction strategy to obtain the heterogeneous data of the second layer. After the two layers of heterogeneous data are fused, the first relay cluster head transmits the fused data to the interactive processing base station, thereby achieving the technical effect of optimizing the data transmission path by introducing the low-energy clustering protocol and the multi-hop interaction strategy, and reducing the communication distance and energy consumption between nodes.

[0036] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A low-energy consumption oriented heterogeneous data interaction optimization processing method, characterized in that: include: A low-energy clustering protocol is introduced to cluster the wireless sensor network to obtain a network node clustering result, wherein the network node clustering result includes a plurality of cluster heads with location identifiers; Acquire a target adjacent area of ​​a target event, and screen the plurality of cluster heads with location identifiers based on the target adjacent area to obtain an adjacent cluster head set; Reading a stratified sampling mechanism, and acquiring first heterogeneous data of a first adjacent layer in the adjacent cluster head set according to the stratified sampling mechanism, wherein the stratified sampling mechanism refers to a mechanism for stratifying the adjacent cluster head set based on a base station-center connection line, and the base station-center connection line refers to a connection line between a base station position of an interactive processing base station and a center position of the target adjacent area; The first heterogeneous data is stored in a first target cluster head of the first adjacent layer, and a first relay cluster head of the first target cluster head is acquired in combination with a predetermined multi-hop interaction strategy, wherein the first relay cluster head corresponds to the second heterogeneous data; The first heterogeneous data and the second heterogeneous data are integrated to obtain first interactive data, and the first relay cluster head transmits the first interactive data to an interactive processing base station of the wireless sensor network.

2. According to the low-energy consumption oriented heterogeneous data interaction optimization processing method of claim 1, it is characterized in that: Reading a stratified sampling mechanism, and acquiring first heterogeneous data of a first adjacent layer in the adjacent cluster head set according to the stratified sampling mechanism, including: Obtaining a target center of the target adjacent area, wherein the target center corresponds to a center position; Acquire a base station position of the interactive processing base station and a base station-center connection line of the center position; Segmenting the base station-center connection line in combination with the predetermined interval segments in the stratified sampling mechanism, and obtaining a stratified result of the adjacent cluster head set; The adjacent layer farthest from the base station in the layering result is used as the first adjacent layer.

3. According to the low-energy consumption oriented heterogeneous data interaction optimization processing method of claim 2, it is characterized in that: Reading a stratified sampling mechanism, and acquiring first heterogeneous data of a first adjacent layer in the adjacent cluster head set according to the stratified sampling mechanism, including: Acquire a first sampling cluster head and a second sampling cluster head of the first adjacent layer according to a sampling plan in the stratified sampling mechanism; The first perception information of the first sampling cluster head and the second perception information of the second sampling cluster head are aggregated to obtain the first heterogeneous data.

4. According to the low-energy consumption oriented heterogeneous data interaction optimization processing method of claim 2, it is characterized in that: The first target cluster head refers to an adjacent cluster head in the first adjacent layer that is closest to the base station.

5. According to the low-energy consumption oriented heterogeneous data interaction optimization processing method of claim 2, it is characterized in that: Storing the first heterogeneous data in a first target cluster head of the first adjacent layer, and acquiring a first relay cluster head of the first target cluster head in combination with a predetermined multi-hop interaction strategy, including: Extracting the second adjacent layer in the stratification result; Randomly extract any cluster head in the second adjacent layer, and obtain any position of the arbitrary cluster head; According to the multi-hop decision evaluation function in the predetermined multi-hop interaction strategy, the arbitrary position, the first cluster head position of the first target cluster head and the base station position are evaluated and analyzed to obtain a first decision fitness; If the first decision fitness reaches a predetermined fitness threshold, the arbitrary cluster head is recorded as the first relay cluster head.

6. A low-energy consumption oriented heterogeneous data interaction optimization processing method according to claim 5, characterized in that: The expression of the multi-hop decision evaluation function is as follows: ; in, It means that any position The fitness of the corresponding arbitrary cluster head as the first relay cluster head, refers to any position The first cluster head position The distance between refers to any position With the base station location The distance between and are weight coefficients, and .

7. The low-energy consumption oriented heterogeneous data interaction optimization processing method according to claim 5 is characterized in that: Before fusing the first heterogeneous data with the second heterogeneous data to obtain first interactive data, the method further includes: Obtaining a bit distance between the first cluster head position and the arbitrary position; Performing clock backoff on the bit spacing to obtain a backoff result; The first heterogeneous data and the second heterogeneous data are synchronously processed according to the backoff result.

8. A low-energy consumption oriented heterogeneous data interaction optimization processing method according to claim 7, characterized in that: After synchronizing the first heterogeneous data with the second heterogeneous data according to the backoff result, the method further includes: Acquire a first interaction time sequence of the first interaction data; Performing trend prediction analysis on the first interaction time series to obtain first predicted interaction data; The first predicted interaction data is transmitted to the interaction processing base station.

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