Intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection

Abstract

The application provides an intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection. First, electromagnetic detection data sets composed of electromagnetic signal strength and propagation delay information of different position nodes in the intelligent space are obtained. Then, the electromagnetic detection data sets are analyzed to obtain state feature sets containing device connection stability and signal coverage range characteristics. Dynamic topology optimization strategies containing device connection relationship adjustment rules and signal coverage area allocation schemes are generated according to the state feature sets. The sensing network topology is adjusted according to the dynamic topology optimization strategies. Finally, the optimized sensing network topology is evaluated, and the evaluation results are fed back to trigger strategy updating, so that the sensing network topology can be dynamically optimized, and the stability and reliability of the intelligent space sensing network can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent space technology, and more specifically, to an intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection. Background Technology

[0002] In the field of smart spaces, such as smart homes, smart buildings, and the Industrial Internet of Things (IIoT), sensor networks are a key infrastructure for realizing spatial environmental perception, data acquisition, and equipment control. Electromagnetic detection technology, as a commonly used sensing method, acquires spatial information through electromagnetic signals from nodes at different locations. However, existing electromagnetic detection-based sensor networks have many problems.

[0003] On the one hand, the topology of traditional sensor networks is often static and fixed, unable to dynamically adjust according to changes in the spatial environment and the actual state of the sensing devices. In practical applications, the electromagnetic environment within smart spaces is affected by various factors, such as device movement, the appearance of obstacles, and electromagnetic interference. This can lead to decreased connection stability of some sensing devices and changes in signal coverage, thus affecting the accuracy and integrity of sensing data. On the other hand, existing sensor networks lack effective state analysis and optimization mechanisms, making it impossible to promptly detect abnormal states of sensing devices and make targeted topology adjustments. For example, when a sensing device malfunctions or experiences signal attenuation, it cannot automatically adjust the connection relationships of surrounding devices and the signal coverage area, resulting in a decline in the performance of the entire sensor network and failing to meet the high precision and high reliability requirements of smart spaces for environmental perception and device control. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for intelligent space sensing dynamic topology optimization based on electromagnetic detection, the method comprising:

[0005] Acquire an electromagnetic detection data set within the intelligent space, the electromagnetic detection data set containing electromagnetic signal strength information and signal propagation delay information at different location nodes;

[0006] The electromagnetic detection data set is subjected to sensor device status analysis processing to obtain a sensor device status feature set that includes device connection stability characteristics and signal coverage range characteristics;

[0007] A dynamic topology optimization strategy is generated based on the set of state features of the sensing devices. The dynamic topology optimization strategy includes device connection relationship adjustment rules and signal coverage area allocation scheme.

[0008] The sensor network topology within the intelligent space is adjusted according to the dynamic topology optimization strategy to obtain the adjusted sensor network.

[0009] The adjusted sensor network is evaluated to assess its optimization effect, and the evaluation results are fed back to the topology optimization strategy generation stage to trigger the strategy update operation.

[0010] In another aspect, embodiments of the present invention also provide an intelligent space sensing dynamic topology optimization system based on electromagnetic detection, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0011] Based on the above, this embodiment of the invention acquires an electromagnetic detection data set within a smart space, including electromagnetic signal strength information and signal propagation delay information from nodes at different locations. It then performs sensor device status analysis on the electromagnetic detection data set to obtain a sensor device status feature set containing device connection stability characteristics and signal coverage range characteristics. This allows for accurate understanding of the actual operating status of each sensor device. A dynamic topology optimization strategy is generated based on the sensor device status feature set, including rules for adjusting device connection relationships and signal coverage area allocation schemes. This enables dynamic adjustment of the sensor network topology, automatically optimizing the network structure according to the real-time status of the sensor devices and changes in the spatial environment, thereby improving the stability and reliability of the sensor network. After adjusting the sensor network topology within the smart space according to the dynamic topology optimization strategy, the optimized sensor network is evaluated, and the evaluation results are fed back to the topology optimization strategy generation stage to trigger a strategy update operation. This allows for continuous improvement of the topology optimization strategy, ensuring the sensor network always maintains optimal operating conditions. This significantly improves the performance of the smart space sensor network, meeting the high-precision and high-reliability requirements of smart spaces for environmental perception and device control. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the execution flow of the intelligent space sensing dynamic topology optimization method based on electromagnetic detection provided in an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of exemplary hardware and software components of the intelligent space sensing dynamic topology optimization system based on electromagnetic detection provided in an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for dynamic topology optimization of intelligent space sensing based on electromagnetic detection, according to an embodiment of the present invention. The following is a detailed description of this method.

[0015] Step S110: Obtain an electromagnetic detection data set within the intelligent space, wherein the electromagnetic detection data set includes electromagnetic signal strength information and signal propagation delay information at different location nodes.

[0016] In practical applications, smart spaces are complex and diverse in scope and environment, ranging from large commercial buildings and industrial plants to smart home environments. First, multiple electromagnetic detection nodes are deployed within the smart space according to a pre-defined node layout scheme, and each node is equipped with a directional antenna assembly. The pre-defined node layout scheme is based on in-depth analysis of the smart space. For example, for a large commercial building, factors such as the building's floor structure, room distribution, and pedestrian traffic areas need to be considered. Electromagnetic detection nodes should be strategically placed at key locations on different floors, in room corners, and in densely populated areas to ensure coverage of the entire smart space. The directional antenna assembly enables the electromagnetic detection nodes to transmit and receive electromagnetic signals more accurately, improving signal quality and directionality. Different types of directional antennas are suitable for different scenarios. For example, in open spaces, a directional antenna with higher gain and wider coverage can be chosen, while in narrower passageways or rooms, a more directional antenna is preferable.

[0017] Step S111: Arrange multiple electromagnetic detection nodes in the intelligent space according to a preset node layout scheme, and configure a directional antenna component for each electromagnetic detection node.

[0018] When deploying electromagnetic detection nodes, precise operation is required based on a pre-defined node layout plan. First, a detailed survey of the intelligent space must be conducted to determine the specific coordinates of each node. For complex intelligent spaces, specialized surveying equipment, such as laser rangefinders and 3D scanners, may be necessary to obtain accurate spatial information. Then, based on the survey results, electromagnetic detection nodes are installed at the appropriate locations. During installation, the stability and robustness of the nodes must be ensured to prevent changes in node position due to external factors. Simultaneously, compatibility between the nodes and their surrounding environment must be considered to prevent interference from other devices or objects. When configuring directional antenna components, the angle and direction of the antennas need to be adjusted according to the node's location and orientation to maximize their effectiveness. For example, in a multi-story building, the angle of the directional antennas of nodes located on different floors may need to be adjusted based on their relative positions between floors to ensure effective signal interaction with other nodes.

[0019] Step S112: Control each electromagnetic detection node to emit electromagnetic signals of a set frequency into the surrounding space, and receive electromagnetic echo signals reflected or forwarded by other nodes.

[0020] To enable each electromagnetic detection node to transmit electromagnetic signals of a set frequency into the surrounding space and receive electromagnetic echo signals reflected or relayed by other nodes, a series of parameter settings and control operations are required. First, a unique signal transmission frequency must be assigned to each electromagnetic detection node. Frequency allocation is a crucial step, requiring comprehensive consideration of factors such as the size of the intelligent space, the number of nodes, and signal interference. In a large intelligent space with a large number of nodes, multiple different frequency bands may be needed to allocate signal transmission frequencies to avoid interference between signals. Simultaneously, the transmission period parameter of the electromagnetic signal needs to be set to ensure that the time interval between adjacent transmission periods meets the signal reception integrity requirements. The transmission period parameter setting must be adjusted based on factors such as signal propagation speed, distance between nodes, and signal processing capabilities. For example, in an intelligent space where nodes are widely distributed, the signal propagation distance is long, and the transmission period needs to be appropriately extended to ensure that nodes have sufficient time to receive electromagnetic echo signals reflected or relayed by other nodes.

[0021] Step S1121: Assign a unique signal transmission frequency to each electromagnetic detection node and set the transmission period parameter of the electromagnetic signal so that the time interval between adjacent transmission periods meets the signal reception integrity requirements.

[0022] When allocating signal transmission frequencies to electromagnetic detection nodes, it is necessary to follow established frequency allocation rules. For example, a frequency planning algorithm can be used to divide the available frequency bands into multiple sub-bands based on the actual conditions and needs of the smart space, and assign a unique sub-band as the signal transmission frequency to each node. Alternatively, a graph-based frequency allocation algorithm can be used, treating nodes as vertices in a graph and interference relationships between nodes as edges, achieving frequency allocation by solving a graph coloring problem. When setting the transmission period parameters for electromagnetic signals, the signal propagation characteristics and the node's processing capabilities need to be considered. The optimal transmission period parameters can be determined through experiments and simulations. For instance, a simulated scenario similar to the actual smart space can be built in a laboratory environment, and by changing the transmission period parameters, the quality and integrity of the signals received by the nodes can be observed to determine a suitable transmission period range.

[0023] Step S1122: Control the electromagnetic detection node to start the receiving module after transmitting the signal, and continuously listen to the electromagnetic echo signals reflected or forwarded by other nodes.

[0024] After the electromagnetic detection node transmits a signal, the receiving module needs to be activated promptly to continuously monitor electromagnetic echo signals reflected or relayed by other nodes. The activation time of the receiving module must be precisely controlled to ensure immediate reception after signal transmission. Simultaneously, a reasonable monitoring duration needs to be set to ensure that all possible echo signals are received. During monitoring, the received signals need to be processed and analyzed in real time to determine their source and quality. Signal filtering and amplification techniques can be used to improve signal quality and remove noise and interference. For example, bandpass filters can be used to filter out unwanted frequency band signals, and amplifiers can be used to enhance signal strength.

[0025] Step S113: Record the intensity value of the electromagnetic echo signal received by each electromagnetic detection node as electromagnetic signal intensity information.

[0026] Recording the intensity of the electromagnetic echo signal received by each electromagnetic detection node is a crucial step in obtaining electromagnetic signal intensity information. During the recording process, it is essential to ensure the accuracy and consistency of the measurements. First, the measuring equipment must be calibrated to ensure its measurement accuracy meets requirements. A standard signal source can be used to calibrate the measuring equipment, and its performance indicators should be checked periodically. When recording the intensity of the electromagnetic echo signal, appropriate measurement methods and units must be used. For example, a power meter can be used to measure the signal power intensity, with units such as milliwatts (mW) or decibel-milliwatts (dBm). Simultaneously, information such as the measurement time and node location needs to be recorded for subsequent data analysis and processing.

[0027] Step S114: Measure the time interval parameter of the electromagnetic signal from the transmitting node to the receiving node as signal propagation delay information.

[0028] Measuring the time interval parameter of an electromagnetic signal from the transmitting node to the receiving node is a crucial step in obtaining signal propagation delay information. To ensure measurement accuracy, high-precision clock synchronization techniques and time measurement methods can be employed. First, the clocks of all electromagnetic detection nodes must be synchronized to ensure a consistent time base across all nodes. Technologies such as the Global Positioning System (GPS) and Network Time Protocol (NTP) can be used for clock synchronization. When measuring the time interval parameter, timestamp technology can be used, recording timestamps at the instants of signal transmission and reception, and then calculating the difference between the two timestamps. To improve measurement accuracy, multiple measurements can be taken and the average value calculated. For example, the same signal can be transmitted and received multiple times, the time interval parameter recorded each time, and the average value of these parameters can be used as the final signal propagation delay information.

[0029] Step S115: Collect electromagnetic signal strength information and signal propagation delay information of all electromagnetic detection nodes within a continuous time window, and integrate them to generate an electromagnetic detection data set containing timestamps.

[0030] Collecting electromagnetic signal strength and propagation delay information from all electromagnetic detection nodes within a continuous time window and integrating it to generate a timestamped electromagnetic detection dataset is a complex process. First, the length and start time of the continuous time window must be determined. The length of the continuous time window should be determined based on the dynamic changes in the smart space and the needs of data analysis. For example, for a commercial building with frequent pedestrian traffic, the continuous time window can be set shorter to promptly capture changes in electromagnetic signals; while for a relatively stable industrial plant, the continuous time window can be set longer. During data collection, it is necessary to ensure the integrity and accuracy of the data. A data acquisition system can be used to automatically collect data from each electromagnetic detection node and transmit the data to a data processing center. At the data processing center, the collected data needs to be integrated and processed, and a timestamp needs to be added to each data point. The timestamp can be accurate to milliseconds or even microseconds to accurately record the data acquisition time. Finally, all the data is integrated into a single dataset, forming an electromagnetic detection dataset containing electromagnetic signal strength and propagation delay information from nodes at different locations.

[0031] Step S120: Perform sensor device status analysis processing on the electromagnetic detection data set to obtain a sensor device status feature set that includes device connection stability features and signal coverage range features.

[0032] After acquiring the electromagnetic detection data set, it is necessary to perform sensor device status analysis and processing to obtain a set of sensor device status features, including device connection stability characteristics and signal coverage range characteristics. This process requires in-depth mining and analysis of the electromagnetic detection data set to extract information related to the sensor device status.

[0033] Step S121: Extract the electromagnetic signal strength information of the same device node in the electromagnetic detection data set at consecutive timestamps, and calculate the fluctuation amplitude parameter of the signal strength at adjacent timestamps.

[0034] Extracting electromagnetic signal strength information of the same device node at consecutive time stamps from an electromagnetic detection dataset is fundamental to calculating the fluctuation amplitude parameter of signal strength between adjacent time stamps. First, the electromagnetic detection dataset must be filtered and sorted, organized according to device node and time stamp order. A database management system can be used to store and retrieve the data, and a query statement can be written to extract the electromagnetic signal strength information of the same device node at consecutive time stamps. After data extraction, the fluctuation amplitude parameter of signal strength between adjacent time stamps needs to be calculated. This fluctuation amplitude parameter can be obtained by calculating the absolute value of the difference between the signal strengths of adjacent time stamps. For example, for device node A, the electromagnetic signal strengths at times T1 and T2 are S1 and S2 respectively, then the fluctuation amplitude parameter of signal strength between adjacent time stamps can be expressed as |S2-S1|. To more comprehensively analyze the signal strength fluctuation, the fluctuation amplitude parameter of multiple adjacent time stamps can be calculated and statistically analyzed.

[0035] Step S122: Evaluate the stability of the communication link between device nodes based on the fluctuation amplitude parameter, and generate device connection stability features representing the reliability of the link.

[0036] Assessing the stability of communication links between device nodes based on fluctuation amplitude parameters is a crucial step in generating device connection stability characteristics. First, a stability threshold range for the fluctuation amplitude parameter must be set. This threshold range should be determined based on the specific application scenario and experience. For example, in an industrial control scenario with high communication stability requirements, the stability threshold range might be set relatively narrow, while in a more relaxed smart home scenario, it might be set relatively wide. When the fluctuation amplitude parameter is below the lower limit of the stability threshold, the communication link is considered very stable, and a first-type stability identifier can be generated. When the fluctuation amplitude parameter is within the stability threshold range, the communication link is considered basically stable, and a second-type stability identifier can be generated. When the fluctuation amplitude parameter is above the upper limit of the stability threshold, the communication link is considered unstable, potentially due to signal interference, device malfunction, or other issues, and a third-type stability identifier can be generated. These stability identifiers serve as the core descriptive content of the device connection stability characteristics for subsequent analysis and processing.

[0037] Step S1221: Set the stability threshold range for the fluctuation amplitude parameter. When the fluctuation amplitude parameter is less than the lower limit of the stability threshold, generate a first-class stability identifier.

[0038] Setting a stable threshold range for fluctuation amplitude parameters requires comprehensive consideration of multiple factors. A suitable stable threshold range can be determined through analysis and experimentation with a large amount of historical data. For example, in a real-world smart space, electromagnetic signal strength data can be collected over a period of time, and the distribution of fluctuation amplitude parameters can be statistically analyzed. Based on this distribution, the upper and lower limits of the stable threshold can be determined. When the fluctuation amplitude parameter is less than the lower limit of the stable threshold, it indicates that the communication link is very stable, and a first-class stability identifier can be generated. The first-class stability identifier can be represented using specific codes or symbols, such as the number "1".

[0039] Step S1222: Generate a second type of stability identifier when the fluctuation amplitude parameter is within the stability threshold range.

[0040] When the fluctuation amplitude parameter is within a stable threshold range, it indicates that the communication link's stability is at a normal level. At this point, a second type of stability identifier can be generated. This second type of stability identifier can be represented using a different code or symbol than the first type, such as the number "2". When generating the second type of stability identifier, it is necessary to ensure the accuracy of the judgment regarding the fluctuation amplitude parameter and avoid misjudgments.

[0041] Step S1223: Generate a third type of stability identifier when the fluctuation amplitude parameter is greater than the upper limit of the stability threshold.

[0042] When the fluctuation amplitude parameter exceeds the upper limit of the stability threshold, it indicates poor stability of the communication link and potential problems. In this case, a third type of stability identifier can be generated. This third type of stability identifier can be represented using a different code or symbol than the first two types, such as the number "3". After generating the third type of stability identifier, the communication link needs to be checked and maintained promptly to identify the causes of instability, such as signal interference or equipment failure, and appropriate measures should be taken to resolve them.

[0043] Step S123: Analyze the correspondence between signal propagation delay information and spatial location in the electromagnetic detection data set, and determine the maximum spatial range boundary that the electromagnetic signal of each device node can cover.

[0044] Analyzing the correspondence between signal propagation delay information and spatial location in the electromagnetic detection dataset is crucial for determining the maximum spatial range boundary that the electromagnetic signal of each device node can cover. First, a mathematical model of the signal propagation delay information and spatial location must be established. This can be based on the propagation principle of electromagnetic waves, combined with the geometric structure and electromagnetic characteristics of the intelligent space, to establish a functional relationship between signal propagation delay and spatial location. The parameters of the mathematical model are then solved by fitting and analyzing the signal propagation delay information and node location information in the electromagnetic detection dataset. When determining the maximum spatial range boundary that the electromagnetic signal of each device node can cover, either ray tracing or simulation methods can be used. Ray tracing calculates the maximum range that the signal can reach by simulating the propagation path of electromagnetic waves in the intelligent space. Simulation methods use computer software to model and simulate the intelligent space, simulating the propagation process of electromagnetic waves to determine the signal coverage boundary.

[0045] Step S124: Count the number of other device nodes contained within the spatial range boundary and generate a signal coverage range feature representing the signal coverage capability.

[0046] Counting the number of other device nodes within the spatial boundary is a crucial step in generating signal coverage range characteristics. First, the specific coordinates and shape of the spatial boundary must be determined. This can be achieved by analyzing signal propagation delay and electromagnetic signal strength information, combined with the geometric structure of the smart space. Geographic Information System (GIS) technology can be used to visualize and manage the spatial boundary. After determining the spatial boundary, the number of other device nodes within it needs to be counted. This can be done by querying the location information of device nodes to determine if a node is within the spatial boundary. The statistical results can serve as signal coverage range characteristics representing signal coverage capability. To more comprehensively describe signal coverage capability, a combined analysis can be performed considering factors such as the size of the spatial range and signal strength.

[0047] Step S125: Associate and integrate the device connection stability feature and the signal coverage feature to generate a set of sensor device state features containing multi-dimensional state descriptions.

[0048] The final step in generating the sensor device state feature set is to correlate and integrate device connectivity stability features and signal coverage range features. Data fusion techniques can be used to correlate and integrate these two features. For example, a weighted average method can be used to fuse device connectivity stability features and signal coverage range features to obtain a comprehensive sensor device state feature set. The weights can be adjusted according to the actual application scenario and requirements. During the correlation and integration process, it is necessary to ensure data consistency and accuracy to avoid data conflicts or errors. The generated sensor device state feature set contains multi-dimensional state description information such as device connectivity stability and signal coverage range.

[0049] Step S130: Generate a dynamic topology optimization strategy based on the set of sensor device state features. The dynamic topology optimization strategy includes device connection relationship adjustment rules and signal coverage area allocation scheme.

[0050] The core of this method is generating a dynamic topology optimization strategy based on the set of sensor device state characteristics. By analyzing and processing the set of sensor device state characteristics, reasonable rules for adjusting device connection relationships and signal coverage area allocation schemes are formulated to optimize the sensor network topology within the intelligent space.

[0051] Step S131: Identify device node pairs in the sensor device status feature set whose device connection stability features are lower than a preset benchmark, and generate an adjustment command to disconnect the original communication link.

[0052] Identifying device node pairs whose connection stability characteristics in the sensor device status feature set are lower than a preset benchmark is a prerequisite for generating a command to disconnect the existing communication link. First, a preset benchmark must be set, determined based on the actual application scenario and the requirements for communication link stability. A suitable preset benchmark can be determined through analysis of historical data and experimentation. When identifying device node pairs, the connection stability characteristics in the sensor device status feature set can be queried to filter out pairs that are lower than the preset benchmark. For the selected device node pairs, a command to disconnect the existing communication link is generated. The command can include the device node's identification information, disconnection time, etc., to ensure accurate execution of the disconnection operation.

[0053] Step S132: Select device node pairs whose device connection stability characteristics are higher than the preset benchmark and whose signal coverage characteristics overlap, and generate adjustment instructions to establish a new communication link.

[0054] Selecting device node pairs whose connection stability characteristics exceed a preset benchmark and whose signal coverage characteristics overlap is crucial for generating adjustment instructions to establish a new communication link. First, the set of sensor device status characteristics must be filtered and compared to identify device node pairs whose connection stability characteristics exceed the preset benchmark and whose signal coverage characteristics overlap. This filtering and comparison process can be implemented by writing query statements and comparison algorithms. For the selected device node pairs, adjustment instructions to establish a new communication link are generated. These instructions can include device node identification information, communication protocol parameters, establishment time, etc., to ensure accurate execution of the establishment operation.

[0055] Step S133: Integrate the adjustment command to disconnect the original communication link and the adjustment command to establish a new communication link to form a device connection relationship adjustment rule.

[0056] Integrating the adjustment commands for disconnecting existing communication links and establishing new communication links is a crucial step in forming device connection adjustment rules. These two types of adjustment commands can be integrated using data integration and sorting methods. First, the adjustment commands must be categorized and numbered, sorted according to the device node's identification information and operation type. Then, the integrated adjustment commands are stored in a rule database for subsequent querying and execution. The device connection adjustment rules clarify the adjustment methods and order of communication links between device nodes, providing guidance for adjusting the sensor network topology.

[0057] Step S134: Perform spatial region division processing on the signal coverage range feature, extract the coordinates of the spatial regions with overlapping coverage, determine the boundary range of the overlapping region, and obtain the device connection stability features of all device nodes involved in the overlapping region based on the boundary range of the overlapping region.

[0058] Spatial region segmentation of signal coverage characteristics is fundamental to generating a signal coverage area allocation scheme. First, the signal coverage characteristics must be spatially visualized, marking the signal coverage area of ​​each device node on a map. Geographic Information System (GIS) technology can be used for spatial visualization. Based on visualization, spatial region segmentation is performed, extracting the coordinates of overlapping areas. Spatial analysis algorithms, such as buffer analysis and overlay analysis, can be used to determine the coordinates and boundary ranges of overlapping areas. After determining the boundary ranges of overlapping areas, it is necessary to obtain the device connection stability characteristics of all device nodes involved in the overlapping areas. This can be done by querying the sensor device status feature set, filtering the device nodes within the overlapping areas based on their location information, and obtaining their device connection stability characteristics.

[0059] Step S135: Compare the device connection stability characteristics of each device node, and select the device node with the highest priority of stability identifier as the main coverage node of the overlapping area.

[0060] The process involves comparing the device connection stability characteristics of each device node and selecting the device node with the highest priority stability identifier as the primary coverage node for the overlapping area. This process relies on the stability identifiers identified in the previously determined device connection stability characteristics. These stability identifiers are divided into different categories, each representing a different level of device connection stability. When dealing with multiple device nodes within an overlapping area, the stability identifiers of each node must be compared individually.

[0061] The priority ranking of stability identifiers was determined during the initial evaluation of device connection stability. For example, the first type of stability identifier represents the highest stability, the second type is next, and the third type is relatively the lowest. This priority rule is followed when comparing the device connection stability characteristics of each device node. Starting with the first device node in the overlapping area, its stability identifier is examined. If it is a first-type stability identifier, it has a higher priority in terms of stability. Next, the stability identifier of the second device node is examined; if it is a second-type stability identifier, the first device node has a greater advantage in stability. This process is repeated for all device nodes in the overlapping area.

[0062] During the comparison process, it is crucial to ensure the accuracy of the stability identifier for each device node. This necessitates rigorous quality control of the previously generated device connection stability characteristics to guarantee the accuracy and reliability of the stability identifier information. Data verification and auditing mechanisms can be used to perform multiple checks and confirmations on the device connection stability characteristics, preventing the generation of erroneous stability identifiers.

[0063] After comparing the stability identifiers of all device nodes, the device node with the highest priority stability identifier is selected as the primary coverage node for the overlapping area. The electromagnetic signal of this primary coverage node will play a dominant role in the overlapping area, providing stable and reliable communication support for other devices in the area.

[0064] Step S136: Adjust the boundary of the overlapping area to within the signal coverage range boundary of the main coverage node, and redefine the remaining non-overlapping coverage areas for other involved device nodes so that the coverage areas of all device nodes do not overlap.

[0065] After identifying the main coverage nodes in the overlapping area, the boundaries of the overlapping area need to be adjusted. First, the signal coverage boundaries of the main coverage nodes must be clearly defined, for example, by using the results obtained from the previous analysis of the correspondence between signal propagation delay information and spatial location in the electromagnetic detection dataset. The signal coverage boundary of the main coverage nodes is a precise spatial range, determined based on the propagation characteristics of electromagnetic signals and the actual environment of the smart space.

[0066] Adjusting the boundary of the overlapping area to fall within the signal coverage range of the primary coverage node requires spatial geometric operations. Geographic Information System (GIS) technology or other spatial analysis tools can be used to visualize and analyze the boundary of the overlapping area and the signal coverage range of the primary coverage node. The boundary coordinates of the overlapping area are adjusted to ensure it is completely within the signal coverage range of the primary coverage node. During the adjustment process, it is crucial to ensure that the adjusted boundary of the overlapping area conforms to spatial geometric principles, avoiding boundary breaks or unreasonable shapes.

[0067] For other device nodes within the overlapping area besides the main coverage node, it is necessary to redefine their non-overlapping remaining coverage areas. This requires a comprehensive consideration of the electromagnetic signal coverage of the entire smart space. First, analyze the original signal coverage range of other device nodes, and combine this with the adjusted overlapping area to determine the remaining space they can cover. When delineating the remaining coverage area, it is necessary to fully consider the electromagnetic signal transmission capabilities of the device nodes and the actual environment of the smart space. For example, for some device nodes located in corners or obstructed by obstacles, the size and shape of their remaining coverage area need to be appropriately adjusted to ensure that they can play a signal coverage role within a reasonable range.

[0068] During the process of redefining the remaining coverage area, it is necessary to ensure that the coverage areas of all device nodes do not overlap. This can be achieved by real-time monitoring and adjustment of the coverage area of ​​each device node. Spatial conflict detection algorithms can be used to detect the coverage areas of device nodes, and adjustments can be made promptly once overlapping is detected. Adjustments can be made by further reducing the coverage area of ​​a particular device node or changing the shape of its coverage area until the coverage areas of all device nodes are free from overlap.

[0069] Step S137: Retain the coverage permissions of the original device nodes for independent coverage areas without overlap, and generate a signal coverage area allocation scheme.

[0070] After adjusting the overlapping areas and defining the remaining coverage areas, the coverage permissions of the original device nodes should be retained for independent coverage areas that did not originally overlap. Independent coverage areas refer to regions whose signal coverage does not intersect with the coverage areas of other device nodes in the previous analysis. The signal coverage in these areas is relatively stable, and the original device nodes have already established effective signal coverage within these areas.

[0071] Retaining the original coverage permissions of the device nodes means that these nodes can continue to transmit and propagate electromagnetic signals within their independent coverage areas in the same way, providing communication support for other devices in that area. No additional adjustments to the signal transmission parameters or coverage range of these device nodes are required.

[0072] The coverage area information of all device nodes, including adjusted overlapping areas, newly defined remaining coverage areas, and independent coverage areas retaining their original coverage permissions, is integrated to generate a signal coverage area allocation scheme. This scheme details the specific coverage area of ​​each device node within the smart space, clearly defining the signal coverage responsibilities of each node. The signal coverage area allocation scheme can be presented in text form or as a visual map, facilitating subsequent management and maintenance of the sensor network.

[0073] Step S138: Combine the device connection relationship adjustment rules and the signal coverage area allocation scheme to generate a dynamic topology optimization strategy that includes link adjustment logic and coverage allocation logic.

[0074] The key outcome of the entire topology optimization process is generating a dynamic topology optimization strategy by combining device connection adjustment rules and signal coverage area allocation schemes. Device connection adjustment rules clarify how communication links between device nodes are adjusted, including specific instructions for disconnecting existing communication links and establishing new ones; while the signal coverage area allocation scheme specifies the signal coverage range and responsibilities of each device node within the intelligent space.

[0075] To organically combine these two parts, the rules for adjusting device connections and the signal coverage area allocation scheme must first undergo unified format processing and data integration. They can be stored in the same database or data structure for easy subsequent querying and retrieval. When generating dynamic topology optimization strategies, it's crucial to ensure coordination between the link adjustment logic and the coverage allocation logic. For example, when establishing a new communication link, it's necessary to consider whether the signal coverage areas of the device nodes at both ends of the new link are reasonable and whether they will affect the coverage of other device nodes; when adjusting the signal coverage area, it's necessary to consider whether it will impact existing device connections.

[0076] The link adjustment logic portion of the dynamic topology optimization strategy needs a detailed description of the specific conditions and operational steps for disconnecting existing communication links and establishing new ones. For disconnecting existing links, it needs to clarify the circumstances under which a disconnection operation is required, as well as the specific procedure, such as the method of sending the disconnection command and the determination of the disconnection time. For establishing new communication links, it needs to explain the basis for selecting which device nodes to establish the new link, and the communication protocol parameters and operational steps required for establishing the new link.

[0077] The signal coverage area allocation logic requires a detailed description of the coverage area for each device node. This includes the specific coordinates, shape, and size of the coverage area for each device node, as well as how to adjust and optimize the coverage area under different circumstances. For example, when the environment within the smart space changes, such as the appearance of new obstacles or a device node malfunction, how should the coverage area of ​​the relevant device nodes be adjusted according to the signal coverage area allocation scheme?

[0078] By integrating the link adjustment logic and coverage allocation logic, a complete dynamic topology optimization strategy is formed. This strategy will serve as a guide for subsequent adjustments to the sensor network topology within the smart space, ensuring that the sensor network can maintain efficient and stable operation in a constantly changing environment.

[0079] Step S140: Adjust the topology of the sensor network in the smart space according to the dynamic topology optimization strategy to obtain the adjusted sensor network.

[0080] Adjusting the sensor network topology within the smart space according to the dynamic topology optimization strategy is a key step in achieving sensor network optimization. This process requires performing corresponding operations on each device node within the smart space based on the link adjustment logic and coverage allocation logic in the dynamic topology optimization strategy.

[0081] Step S141: Analyze the device connection relationship adjustment rules in the dynamic topology optimization strategy, locate the device node pairs that need to be disconnected, and perform link interruption operation.

[0082] Parsing the device connection adjustment rules in the dynamic topology optimization strategy is the first step in link adjustment. These rules contain information about device node pairs that need to be disconnected or have new connections established. The first step is to parse these rules in detail, extracting information such as device node identifiers and operation types (disconnect or establish).

[0083] During the parsing process, it is crucial to ensure an accurate understanding of the rules. This can be achieved by writing a dedicated parsing program to automate the parsing of device connection relationship adjustment rules. The parsing program must be able to identify various information within the rules and translate it into executable operational instructions.

[0084] Locating the pair of device nodes that need to be disconnected requires searching for the corresponding device nodes in the sensor network within the smart space based on the parsed device node identification information. This can be done by querying and locating the device nodes in the device management system using their unique identifiers, such as device number or MAC address. This ensures that the pair of device nodes that need to be disconnected can be accurately located.

[0085] When performing a link interruption operation, a pre-defined procedure must be followed. First, a disconnect command must be sent to the device nodes that need to be disconnected. This disconnect command can be sent via network communication, such as using wireless communication protocols or wired network protocols. When sending the disconnect command, it is necessary to ensure its accuracy and completeness to avoid command loss or errors. After receiving the disconnect command, the device nodes need to perform corresponding operations to sever the communication link between them. Link interruption can be achieved by closing the communication ports between the device nodes or stopping the relevant communication services.

[0086] Step S142: Locate the device node pair that needs to establish a new connection, configure the communication protocol parameters, and perform the link establishment operation.

[0087] After parsing the device connection relationship adjustment rules, it is necessary not only to locate the device node pairs that need to be disconnected, but also to locate the device node pairs that need to establish new connections. Similarly, based on the parsed device node identification information, the corresponding device nodes are searched in the sensor network within the smart space. This ensures that the device node pairs that need to establish new connections can be accurately located.

[0088] Configuring communication protocol parameters is a crucial step in establishing a new connection. Different communication protocols have different parameter requirements, necessitating the selection of the appropriate protocol based on the specific application scenario and device characteristics. For example, in wireless sensor networks, protocols such as ZigBee and Wi-Fi might be used. Each communication protocol requires configuring corresponding parameters, such as channel, frequency, and encryption key. When configuring communication protocol parameters, it is essential to ensure consistency and compatibility. Appropriate parameter values ​​can be determined by consulting the standard documentation of the communication protocol and the device's user manual. Furthermore, the configured parameters need to be tested and verified to ensure proper communication between device nodes.

[0089] When establishing a link, it must be done according to the communication protocol. First, the device nodes needing to establish a new connection must discover and identify each other. This can be done by sending broadcast signals or actively scanning to allow the device nodes to discover each other's existence. After discovery, the device nodes must perform authentication and parameter negotiation. Authentication ensures connection security and prevents unauthorized devices from accessing the connection. Parameter negotiation refers to the process by which the device nodes agree on the communication protocol parameters. After authentication and parameter negotiation are completed, a stable communication link is established between the device nodes. Link establishment can be achieved by opening communication ports between the device nodes and starting relevant communication services.

[0090] Step S143: Analyze the signal coverage area allocation scheme in the dynamic topology optimization strategy, and adjust the signal transmission power parameters of the device nodes to match the allocated coverage area boundaries.

[0091] Analyzing the signal coverage area allocation scheme in the dynamic topology optimization strategy is a prerequisite for adjusting the signal transmission power parameters of device nodes. The signal coverage area allocation scheme specifies in detail the coverage area boundaries and related information for each device node. First, this signal coverage area allocation scheme needs to be analyzed in detail to extract information such as device node identification, coverage area boundary coordinates, and coverage area size.

[0092] During the analysis process, it is crucial to ensure an accurate understanding of the signal coverage area allocation scheme. This can be achieved by writing a specialized analysis program to automate the analysis of the scheme. The program must be able to identify various information within the scheme and translate it into executable operational instructions.

[0093] Adjusting the signal transmission power parameters of device nodes to match the assigned coverage area boundaries requires determining the appropriate signal transmission power based on the size and shape of the coverage area. Generally, the larger the coverage area, the higher the required signal transmission power; conversely, the smaller the coverage area, the lower the required signal transmission power. A model relating signal transmission power to the size and shape of the coverage area can be established through experiments and simulations. Based on this model, and combined with the analytically obtained coverage area boundary information, the signal transmission power parameters that each device node needs to adjust can be calculated.

[0094] When adjusting signal transmission power parameters, it is essential to ensure that the device nodes are functioning correctly. Different device nodes have different limitations on signal transmission power, and adjustments must be made within these limits. Power adjustment commands can be sent to the device nodes through their configuration interfaces, allowing them to automatically adjust the signal transmission power. During the adjustment process, the signal transmission power of the device nodes needs to be monitored in real time to ensure that the adjusted power meets the requirements.

[0095] Step S144: Verify the connectivity of the adjusted device connection relationship and signal coverage area. The verification includes whether all device nodes can communicate with the central control node through at least one path.

[0096] Verifying the connectivity of the adjusted device connections and signal coverage areas is a crucial step in ensuring the proper functioning of the sensor network. The purpose of connectivity verification is to check whether all device nodes can communicate with the central control node through at least one path.

[0097] Starting from the central control node, a breadth-first search tree (BFS) is constructed to represent the device connections. BFS is an algorithm for traversing graphs, starting from the central control node and expanding layer by layer to traverse all device nodes connected to it. When constructing the BFS, the connection graph between device nodes needs to be determined based on the adjusted device connection relationships. A graph structure can be constructed using the communication link information between device nodes, where device nodes are the vertices and communication links are the edges.

[0098] Traverse all device nodes in the search tree, recording the path length and path node sequence from the central control node to each device node. During traversal, a breadth-first search algorithm is used, visiting each device node sequentially according to its level. For each visited device node, record its path length to the central control node and the sequence of path nodes traversed. This can be achieved by maintaining a list of path records during the search process.

[0099] The number of device nodes that cannot be reached through the search tree is counted and used as the parameter for the number of unconnected nodes in connectivity verification. If a device node is found to be inaccessible during the traversal of the search tree, it is marked as an unconnected node. The total number of unconnected nodes is counted to obtain the parameter for the number of unconnected nodes.

[0100] If the number of unconnected nodes is zero, the connectivity verification is considered successful. This means that all device nodes can communicate with the central control node through at least one path, and the sensor network has good connectivity. If the number of unconnected nodes is greater than zero, the unconnected nodes are located and the link establishment operation is re-executed until all device nodes are connected. These nodes can be located in the sensor network based on their identification information. The reasons for the disconnection can be analyzed, such as equipment failure or communication link interruption. Appropriate measures are taken to resolve different reasons. If it is equipment failure, the equipment needs to be repaired or replaced; if it is communication link interruption, the link establishment operation needs to be re-executed to ensure that the unconnected nodes can establish communication connections with other nodes in the sensor network.

[0101] Step S145: After completing the connectivity verification, generate an adjusted sensor network containing a list of new connectivity relationships and a coverage area allocation table.

[0102] After completing connectivity verification, the adjusted device connection relationships and signal coverage area information need to be organized and recorded to generate the adjusted sensor network. This includes generating a new connection relationship list and a coverage area allocation table.

[0103] The new connection list records the connection relationships between device nodes after the adjustment. The list includes the device node's identification information, connection status (connected or disconnected), and the identification of the connected peer device node. This information can be compiled into a list by sorting and summarizing the adjusted device connection relationships. The new connection list should clearly reflect the connection status between device nodes in the adjusted sensor network, facilitating subsequent network management and maintenance.

[0104] The coverage area allocation table records the signal coverage area information for each device node. The table includes the device node's identification information, the boundary coordinates of the coverage area, and the size and shape of the coverage area. This information can be compiled into a single table based on previously adjusted and defined signal coverage areas. The coverage area allocation table should accurately reflect the signal coverage range of each device node within the smart space.

[0105] The new connection list and coverage area allocation table are integrated to form the adjusted sensor network. This adjusted sensor network contains the connection relationships between device nodes and signal coverage area information, providing a complete sensor network description. It can be stored in a device management system or database for easy subsequent querying, analysis, and use.

[0106] Step S150: Evaluate the optimization effect of the adjusted sensor network, generate evaluation results, and feed them back to the topology optimization strategy generation stage to trigger the strategy update operation.

[0107] Evaluating the optimization effect of the adjusted sensor network is the final step in the entire dynamic topology optimization process and a crucial link in the feedback loop. By evaluating the optimization effect, we can understand whether the adjusted sensor network has achieved the expected results and whether further optimization is needed.

[0108] Step S151: Reacquire the electromagnetic detection data set in the intelligent space, including the electromagnetic signal strength information and signal propagation delay information of the adjusted device nodes.

[0109] Reacquiring a new set of electromagnetic detection data within the smart space is fundamental to evaluating the optimization effect. After the sensor network topology is adjusted, the connectivity of device nodes and the signal coverage area change, necessitating the collection of electromagnetic detection data again to understand the adjusted electromagnetic environment.

[0110] The process of reacquiring the electromagnetic detection data set is similar to the previous data acquisition process. Following the pre-defined node layout, electromagnetic detection nodes equipped with directional antenna components transmit electromagnetic signals of a set frequency into the surrounding space and receive electromagnetic echo signals reflected or relayed by other nodes. The intensity value of the electromagnetic echo signal received by each electromagnetic detection node is recorded as electromagnetic signal intensity information, and the time interval parameter from the transmitting node to the receiving node is measured as signal propagation delay information.

[0111] When reacquiring data, it is essential to ensure its accuracy and consistency. Electromagnetic detection nodes can be recalibrated and debugged to guarantee stable performance. Simultaneously, data should be collected using the same time windows and data acquisition methods to allow for comparison and analysis with previously acquired electromagnetic detection data sets.

[0112] Step S152: Perform sensor device status analysis processing on the newly acquired electromagnetic detection data set to obtain the adjusted sensor device status feature set.

[0113] The newly acquired electromagnetic detection data set undergoes sensor device status analysis and processing, following the same procedure as before. First, electromagnetic signal strength information for the same device node at consecutive timestamps is extracted from the electromagnetic detection data set, and the fluctuation amplitude parameter of signal strength between adjacent timestamps is calculated. Based on the fluctuation amplitude parameter, the stability of the communication link between device nodes is assessed, generating device connection stability characteristics representing the link reliability.

[0114] The correspondence between signal propagation delay information and spatial location in the reacquired electromagnetic detection data set is analyzed to determine the maximum spatial range boundary that the electromagnetic signal of each device node can cover. The number of other device nodes within the spatial range boundary is counted to generate a signal coverage range feature representing the signal coverage capability. The device connection stability feature and the signal coverage range feature are correlated and integrated to generate an adjusted sensor device state feature set containing multi-dimensional state descriptions.

[0115] When extracting electromagnetic signal strength information for the same device node at consecutive timestamps, the newly acquired electromagnetic detection data set needs to be carefully filtered and sorted again. Based on the unique identifier of the device node, the electromagnetic signal strength information is arranged in chronological order of timestamps. For the filtered and sorted information, the fluctuation amplitude parameter is obtained by calculating the absolute value of the difference between electromagnetic signal strength at adjacent timestamps. In this process, the accuracy of the difference calculation must be ensured, which can be achieved by repeatedly verifying the data and calculation results.

[0116] After calculating the fluctuation amplitude parameter, the stability of the communication link between device nodes is evaluated based on the previously set stability threshold range. The stability threshold range is determined during the initial assessment of device connection stability and has clearly defined boundaries. When the fluctuation amplitude parameter is less than the lower limit of the stability threshold, a first-type stability identifier is generated; when it is within the stability threshold range, a second-type stability identifier is generated; and when it is greater than the upper limit of the stability threshold, a third-type stability identifier is generated. These stability identifiers are used as the core content of the adjusted device connection stability characteristics, and the generation of these identifiers must strictly follow the established rules to avoid identification errors.

[0117] When analyzing the correspondence between signal propagation delay information and spatial location, the previously established mathematical model is still required. This model is constructed based on the principles of electromagnetic wave propagation and the actual conditions of the smart space. By fitting and analyzing the signal propagation delay information and node location information in the newly acquired electromagnetic detection data set, the model parameters are solved to determine the maximum spatial range boundary that the electromagnetic signal of each device node can cover. The boundary can be determined using either ray tracing or simulation. Ray tracing simulates the propagation path of electromagnetic waves in the smart space and calculates the maximum range the signal can reach; simulation uses computer software to model and simulate the smart space, simulating the electromagnetic wave propagation process to determine the coverage boundary. During the implementation of both methods, it is necessary to ensure the accuracy of the model parameters and the realism of the simulation process to obtain reliable spatial range boundary results.

[0118] When counting the number of other device nodes within a spatial boundary, the precise coordinates and shape of the boundary must first be accurately determined. This can be achieved through in-depth analysis of signal propagation delay and electromagnetic signal strength information, combined with the geometric structure of the intelligent space. Geographic Information System (GIS) technology is used to visualize and manage the spatial boundary, facilitating subsequent statistical operations. During the statistical process, the location information of device nodes is queried to determine whether they are within the spatial boundary; the statistical results serve as a signal coverage range characteristic representing signal coverage capability. To comprehensively describe signal coverage capability, a combined analysis can be conducted, incorporating factors such as the size of the spatial range and signal strength.

[0119] Finally, the device connection stability characteristics and signal coverage characteristics are correlated and integrated. Data fusion techniques, such as weighted averaging, are used to fuse these two features. The weights need to be adjusted according to the actual application scenario and requirements to ensure that the fusion result accurately reflects the state of the sensing device. During the correlation and integration process, strict consistency and accuracy of the data must be ensured to avoid data conflicts or errors, ultimately generating an adjusted set of sensing device state characteristics containing multi-dimensional state descriptions.

[0120] Step S1521: Extract the electromagnetic signal strength information of the same device node at consecutive timestamps from the reacquired electromagnetic detection data set, and calculate the fluctuation amplitude parameter of the signal strength at adjacent timestamps.

[0121] To extract electromagnetic signal strength information of the same device node at consecutive timestamps from a newly acquired electromagnetic detection data set, preprocessing of the data set is necessary. Preprocessing includes data cleaning to remove potentially noisy or erroneous data. Data that meets certain criteria can be filtered out by setting a reasonable range. For example, for electromagnetic signal strength information, if the value exceeds the range that the device can generate under normal operating conditions, it is considered erroneous data and discarded.

[0122] Data is grouped according to the unique identifier of each device node, and then each group is sorted based on its timestamp. After sorting, starting from the first timestamp of each group, the absolute value of the difference in electromagnetic signal intensity between adjacent timestamps is calculated to obtain the fluctuation amplitude parameter. During the calculation process, the continuity and order of timestamps must be ensured to avoid jumps or reversals. This can be achieved by setting up a timestamp checking mechanism to check the timestamps in real time during the calculation process; if anomalies are detected, the data order should be adjusted or data supplemented promptly.

[0123] Step S1522: Evaluate the stability of the communication link between device nodes based on the fluctuation amplitude parameter, and generate device connection stability features representing the reliability of the link.

[0124] When evaluating the stability of communication links between device nodes based on fluctuation amplitude parameters, it is necessary to strictly adhere to the previously set stability threshold range. The stability threshold range is determined based on extensive experimental data and practical application experience, and therefore possesses high reliability. During the evaluation process, the calculated fluctuation amplitude parameters are compared with the stability threshold range.

[0125] If the fluctuation amplitude parameter is less than the lower limit of the stability threshold, it indicates that the communication link between device nodes is very stable, and a first-type stability identifier is generated. When generating the identifier, it is necessary to ensure that the format and content of the identifier conform to uniform regulations, which can be achieved by writing a dedicated identifier generation program. This program receives the fluctuation amplitude parameter and the stability threshold range as input and generates the corresponding stability identifier based on the comparison result.

[0126] When the fluctuation amplitude parameter is within the stable threshold range, it indicates that the communication link is basically stable, and a second type of stability identifier is generated. Similarly, it is necessary to ensure the accuracy of the identifier generation. The generated identifier can be verified multiple times by comparing it with a preset identifier template to check its correctness.

[0127] If the fluctuation amplitude parameter exceeds the upper limit of the stability threshold, it indicates that the communication link is unstable, generating a third-type stability identifier. After generating this identifier, the communication link needs to be further inspected and analyzed promptly to find the cause of the instability. Possible causes include signal interference, equipment failure, etc., and corresponding solutions should be taken for different causes. For example, if the instability is determined to be caused by signal interference, the communication frequency of the equipment node can be adjusted or a signal shielding device can be added; if it is an equipment failure, the equipment should be repaired or replaced.

[0128] Step S1523: Analyze the correspondence between signal propagation delay information and spatial location in the reacquired electromagnetic detection data set, and determine the maximum spatial range boundary that the electromagnetic signal of each device node can cover.

[0129] When analyzing the correspondence between signal propagation delay information and spatial location in the reacquired electromagnetic detection data set, it is necessary to process the signal propagation delay information and spatial location information synchronously. First, the signal propagation delay information needs to be calibrated to ensure its accuracy. This can be done by comparing it with a standard time source to correct the timestamps in the signal propagation delay information.

[0130] When establishing the correspondence between signal propagation delay information and spatial location, it is necessary to consider the complex environmental factors of the intelligent space, such as the presence of obstacles and changes in electromagnetic properties. This can be achieved by dividing the intelligent space into zones and establishing different correspondence models for different areas. For example, in areas with numerous metal obstacles, the propagation of electromagnetic waves will be significantly affected, requiring a specialized model to describe the relationship between signal propagation delay and spatial location.

[0131] When determining the maximum spatial boundary that the electromagnetic signals of each device node can cover, ray tracing requires accurately simulating the propagation path of electromagnetic waves. Considering that electromagnetic waves may undergo reflection, refraction, and scattering during propagation, corresponding physical models must be incorporated into the simulation to describe these phenomena. For example, the laws of reflection and refraction are used to calculate the reflection and refraction paths of electromagnetic waves when they encounter obstacles.

[0132] In simulation methods, it is crucial to ensure the accuracy of the computer software's modeling of the intelligent space. The model must include detailed information such as the intelligent space's geometry and material properties to realistically reflect the electromagnetic wave propagation environment. During the simulation process, reasonable simulation parameters, such as the electromagnetic wave's transmission power and frequency, need to be set to ensure the reliability of the simulation results.

[0133] Step S1524: Count the number of other device nodes contained within the spatial range boundary and generate a signal coverage range feature representing the signal coverage capability.

[0134] When counting the number of other device nodes contained within a spatial boundary, the spatial boundary must first be accurately represented digitally. The coordinate information of the spatial boundary is then converted into a computer-processable format, which can be stored and managed using data formats found in Geographic Information System (GIS) software.

[0135] When counting device nodes, the location information of each node in the device management system is queried to determine if it is within the spatial boundary. A dedicated query program can be written to compare the device node's location coordinates with the spatial boundary coordinates. To improve the accuracy of the statistics, the device node's location information can be verified multiple times to avoid statistical errors caused by location information inaccuracies.

[0136] When generating signal coverage range features representing signal coverage capability, in addition to counting the number of device nodes, factors such as spatial range size and signal strength can be comprehensively considered. These factors can be quantified and incorporated into the signal coverage range features through weighted calculations. The weights should be adjusted based on the actual application scenario and the importance of each factor to ensure that the signal coverage range features comprehensively and accurately reflect the signal coverage capability of the device nodes.

[0137] Step S1525: Associate and integrate the device connection stability feature and the signal coverage feature to generate an adjusted set of sensor device state features containing multi-dimensional state descriptions.

[0138] When integrating device connectivity stability features and signal coverage features, data standardization is required for both features. Since device connectivity stability features and signal coverage features may have different dimensions and value ranges, standardization is necessary to ensure the reasonableness of the fusion result. A normalization method can be used to map the value of each feature to a unified interval, such as the [0, 1] interval.

[0139] When using a weighted average method for fusion, determining the weights is crucial. Weights can be determined through expert evaluation, experimental analysis, or other methods. For example, in scenarios with high requirements for device connection stability, the weight of device connection stability characteristics can be appropriately increased; while in scenarios where signal coverage is more important, the weight of signal coverage characteristics should be increased.

[0140] During the data fusion process, it is crucial to ensure data consistency and accuracy. The fusion results should be checked and verified multiple times, and their reliability can be ensured through comparative analysis with the original data. The final adjusted set of sensor device state features contains multi-dimensional state descriptions, providing a more comprehensive reflection of the actual state of the sensor devices.

[0141] Step S153: Compare the sensor device state feature sets before and after adjustment, and calculate the difference parameter of device connection stability feature and the overlap area change parameter of signal coverage feature.

[0142] When comparing the sensor state feature sets before and after adjustment, for device connection stability features, the stability identifiers before and after adjustment are first classified and mapped. The first type of stability identifiers are assigned to the same category, and the second and third types are also assigned the same category. Then, by statistically analyzing the changes in the number of stability identifiers in different categories, the difference parameter of the device connection stability features is calculated. For example, the number of device nodes with the first type of stability identifiers before and after adjustment is counted, and the difference between the two is calculated.

[0143] When calculating the difference parameter, it is necessary to consider that changes in the stability identifier may affect the overall assessment of device connection stability. If the number of device nodes with the first type of stability identifier increases after adjustment, it indicates that the device connection stability has improved; conversely, it indicates that the stability has decreased.

[0144] To determine the characteristics of signal coverage, it is necessary to analyze the changes in spatial boundaries before and after the adjustment. First, a spatial overlay analysis is performed on the spatial boundaries before and after the adjustment. Using Geographic Information System (GIS) technology, the spatial boundaries before and after the adjustment are displayed and analyzed on the same map. The extent and area of ​​the overlapping region are then determined.

[0145] When calculating the overlapping area variation parameters of signal coverage characteristics, this can be achieved by calculating the difference in overlapping area before and after adjustment. If the overlapping area decreases after adjustment, it indicates that the division of the signal coverage area is more reasonable, reducing overlap; if the area increases, further analysis is needed, as it may be due to improper adjustment of the signal coverage area.

[0146] During the comparison and calculation process, it is essential to ensure the accuracy and consistency of the data. The set of sensor state characteristics before and after adjustment should be checked multiple times to ensure data integrity and accuracy. Simultaneously, the calculation results need to be verified, which can be done through different calculation methods or by comparing with other relevant data to validate the reliability of the results.

[0147] Step S154: Statistically analyze the average communication delay parameters and energy consumption parameters of the device nodes in the adjusted sensor network.

[0148] When calculating the average communication delay parameter of device nodes in a sensor network after adjustment, it is necessary to record the communication delay time between each device node. Communication delay time refers to the time elapsed from when one device node sends a signal to when another device node receives it. This can be obtained by sending test signals between device nodes, recording the transmission and reception times, and calculating the difference between the two.

[0149] To obtain accurate average communication delay parameters, it is necessary to statistically analyze the communication delay times between multiple device node pairs. Representative device node pairs should be selected for testing to ensure that the test results reflect the communication delay situation of the entire sensor network. A method of averaging multiple tests can be used to reduce testing errors. For example, multiple communication delay tests can be performed on each device node pair, the results of each test can be recorded, and then the average of these results can be calculated.

[0150] When calculating the energy consumption parameters of device nodes, it is necessary to monitor the energy consumption of each device node in real time. An energy monitoring module can be installed on the device nodes, which can collect energy consumption data in real time, such as changes in battery level and power consumption.

[0151] The collected energy consumption data is then aggregated and analyzed. The average energy consumption of each device node over a period of time is calculated, and the average energy consumption of all device nodes is statistically analyzed to obtain the average energy consumption parameter of the entire sensor network. During the statistical process, the impact of different device node operating modes and task loads on energy consumption needs to be considered. For example, device nodes in frequent communication states will have relatively higher energy consumption, requiring separate analysis and processing of their energy consumption data.

[0152] Step S155: Integrate the difference parameter, overlapping area change parameter, average communication delay parameter, and energy consumption parameter to generate an evaluation result containing quantitative indicators.

[0153] When integrating difference parameters, overlap area variation parameters, average communication delay parameters, and energy consumption parameters, the weights of these parameters need to be determined based on their importance and actual application requirements. For example, in scenarios where device connection stability is highly critical, the weight of the difference parameter for device connection stability characteristics can be set higher; in scenarios where the rationality of signal coverage area is emphasized, the weight of the overlap area variation parameter for signal coverage characteristics can be appropriately increased.

[0154] These parameters are integrated using a weighted average method. Each parameter is multiplied by its corresponding weight, and the results are summed to obtain a comprehensive quantitative index. During the integration process, it is necessary to ensure the consistency of the parameters' dimensions. If the parameters have different dimensions, they can be standardized first to map them to the same dimensional range.

[0155] When generating evaluation results that include quantitative indicators, the results need to be visualized. These results can be displayed in chart form, such as bar charts or line graphs. Visualized evaluation results can more intuitively reflect the optimization effect of the adjusted sensor network. At the same time, detailed explanations and interpretations of the evaluation results are necessary to facilitate subsequent analysis and decision-making.

[0156] Step S156: Input the evaluation results into the topology optimization strategy generation stage as the basis for strategy update to trigger the next round of dynamic topology optimization operation.

[0157] When inputting the evaluation results into the topology optimization strategy generation stage, it is necessary to ensure the accurate transmission of the results. The evaluation results can be sent to the topology optimization strategy generation module via network communication. During transmission, a reliable communication protocol must be used to guarantee the integrity and accuracy of the data.

[0158] After receiving the evaluation results, the topology optimization strategy generation stage needs to conduct in-depth analysis. Based on the quantitative indicators in the evaluation results, it is determined whether the current topology optimization strategy has achieved the expected results. If the difference parameter of the device connection stability characteristic shows improved stability, the overlap area change parameter of the signal coverage characteristic shows a reduced overlap area, the average communication delay parameter decreases, and the energy consumption parameter is reasonable, it indicates that the current topology optimization strategy has achieved good results, but it can still be fine-tuned according to specific circumstances.

[0159] If the evaluation results show that certain aspects have not met expectations, such as decreased device connection stability or severe overlap of signal coverage areas, the topology optimization strategy generation stage needs to readjust the strategy based on these issues. Adjustments may include redefining the rules for adjusting device connection relationships and the signal coverage area allocation scheme. For example, if device connection stability decreases, the connection relationships between device nodes can be re-evaluated, and some connections with higher stability can be added; if signal coverage areas overlap severely, the division of signal coverage areas can be further optimized.

[0160] When triggering the next round of dynamic topology optimization, the sensor network topology within the smart space needs to be readjusted according to the new topology optimization strategy. Starting with acquiring the electromagnetic detection data set, the process sequentially includes sensor device status analysis and processing, generating dynamic topology optimization strategies, adjusting the sensor network topology, and evaluating the optimization effect, forming a closed-loop dynamic optimization process that continuously improves the performance and stability of the sensor network.

[0161] Figure 2 The illustration shows exemplary hardware and software components of an electromagnetic detection-based intelligent space sensing dynamic topology optimization system 100, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the electromagnetic detection-based intelligent space sensing dynamic topology optimization system 100 and to perform the functions in this application.

[0162] The intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection can be a general-purpose server or a special-purpose server; both can be used to implement the intelligent space sensing dynamic topology optimization method based on electromagnetic detection of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0163] For example, the intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection also includes an I / O interface 150 between the computer and other input / output devices.

[0164] For ease of explanation, only one processor is described in the electromagnetic detection-based intelligent space sensing dynamic topology optimization system 100. However, it should be noted that the electromagnetic detection-based intelligent space sensing dynamic topology optimization system 100 of this application may also include multiple processors. Therefore, the steps executed by one processor as described in this application may also be executed jointly by multiple processors or individually. For example, if the processor of the electromagnetic detection-based intelligent space sensing dynamic topology optimization system 100 executes steps A and B, it should be understood that steps A and B may also be executed jointly by two different processors or individually by one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor jointly execute steps A and B.

[0165] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned intelligent space sensing dynamic topology optimization method based on electromagnetic detection is implemented.

[0166] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A dynamic topology optimization method for intelligent space sensing based on electromagnetic detection, characterized in that, The method includes: Acquire an electromagnetic detection data set within the intelligent space, the electromagnetic detection data set containing electromagnetic signal strength information and signal propagation delay information at different location nodes; The electromagnetic detection data set is subjected to sensor device status analysis processing to obtain a sensor device status feature set that includes device connection stability characteristics and signal coverage range characteristics; A dynamic topology optimization strategy is generated based on the set of state features of the sensing devices. The dynamic topology optimization strategy includes device connection relationship adjustment rules and signal coverage area allocation scheme. The sensor network topology within the intelligent space is adjusted according to the dynamic topology optimization strategy to obtain the adjusted sensor network. The adjusted sensor network is evaluated to assess its optimization effect, and the evaluation results are fed back to the topology optimization strategy generation stage to trigger the strategy update operation. The dynamic topology optimization strategy generated based on the set of sensor device state features includes device connection relationship adjustment rules and signal coverage area allocation schemes, including: Identify device node pairs in the sensor device status feature set whose device connection stability features are lower than a preset benchmark, and generate an adjustment command to disconnect the original communication link; Select device node pairs whose connection stability characteristics are higher than the preset benchmark and whose signal coverage characteristics overlap, and generate adjustment instructions to establish a new communication link; The adjustment instructions for disconnecting the original communication link and the adjustment instructions for establishing a new communication link are integrated to form a device connection relationship adjustment rule; The signal coverage range features are subjected to spatial region division processing to extract the coordinates of spatial regions with overlapping coverage, determine the boundary range of the overlapping regions, and obtain the device connection stability features of all device nodes involved in the overlapping regions based on the boundary range of the overlapping regions. Compare the device connection stability characteristics of each device node, and select the device node with the highest priority of stability identifier as the main coverage node of the overlapping area; The boundaries of the overlapping areas are adjusted to be within the signal coverage range boundaries of the main coverage nodes, and the remaining non-overlapping coverage areas are redefined for other involved device nodes so that the coverage areas of all device nodes do not overlap. For independent coverage areas without overlap, retain the coverage permissions of the original device nodes and generate a signal coverage area allocation scheme; By combining the device connection relationship adjustment rules and the signal coverage area allocation scheme, a dynamic topology optimization strategy is generated that includes link adjustment logic and coverage allocation logic.

2. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1, characterized in that, The acquired electromagnetic detection data set within the intelligent space includes electromagnetic signal strength information and signal propagation delay information at different location nodes, including: Multiple electromagnetic detection nodes are arranged in the intelligent space according to a preset node layout scheme, and each electromagnetic detection node is equipped with a directional antenna component. Each electromagnetic detection node is controlled to emit electromagnetic signals of a set frequency into the surrounding space and to receive electromagnetic echo signals reflected or forwarded by other nodes. Record the intensity value of the electromagnetic echo signal received by each electromagnetic detection node as electromagnetic signal intensity information; The time interval parameter between the transmitting node and the receiving node of the electromagnetic signal is measured as signal propagation delay information; Collect electromagnetic signal strength and propagation delay information from all electromagnetic detection nodes within a continuous time window, and integrate them to generate an electromagnetic detection data set containing timestamps.

3. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 2, characterized in that, The control of each electromagnetic detection node to emit electromagnetic signals of a set frequency into the surrounding space and to receive electromagnetic echo signals reflected or forwarded by other nodes includes: Each electromagnetic detection node is assigned a unique signal transmission frequency, and the transmission period parameter of the electromagnetic signal is set so that the time interval between adjacent transmission periods meets the signal reception integrity requirements. The electromagnetic detection node is controlled to start the receiving module after transmitting a signal, and continuously listen to the electromagnetic echo signals reflected or forwarded by other nodes; Record the arrival timestamp and signal strength value of the electromagnetic echo signal to form multi-dimensional signal recording data containing transmission frequency, timestamp and signal strength; The multidimensional signal recording data is used as the original data source for electromagnetic signal strength information and signal propagation delay information.

4. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1, characterized in that, The electromagnetic detection data set is subjected to sensor device status analysis processing to obtain a sensor device status feature set containing device connection stability characteristics and signal coverage range characteristics, including: Extract the electromagnetic signal strength information of the same device node at consecutive timestamps from the electromagnetic detection data set, and calculate the fluctuation amplitude parameter of the signal strength at adjacent timestamps; The stability of the communication link between device nodes is evaluated based on the fluctuation amplitude parameter, and device connection stability features representing the link reliability are generated. Analyze the correspondence between signal propagation delay information and spatial location in the electromagnetic detection data set to determine the maximum spatial range boundary that the electromagnetic signal of each device node can cover; The number of other device nodes contained within the spatial range boundary is counted to generate a signal coverage range feature representing the signal coverage capability; The device connection stability feature and the signal coverage feature are correlated and integrated to generate a set of sensor device state features containing multi-dimensional state descriptions.

5. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 4, characterized in that, The step of evaluating the stability of the communication link between device nodes based on the fluctuation amplitude parameter and generating device connection stability features representing the link reliability includes: Set a stable threshold range for the fluctuation amplitude parameter. When the fluctuation amplitude parameter is less than the lower limit of the stable threshold, generate a first-class stability identifier. A second type of stability identifier is generated when the fluctuation amplitude parameter is within the stability threshold range; A third type of stability identifier is generated when the fluctuation amplitude parameter is greater than the upper limit of the stability threshold. The first type of stability identifier, the second type of stability identifier, and the third type of stability identifier are used as the core description content of the device connection stability characteristics.

6. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1, characterized in that, The step of adjusting the sensor network topology within the smart space according to the dynamic topology optimization strategy to obtain the adjusted sensor network includes: Analyze the device connection relationship adjustment rules in the dynamic topology optimization strategy, locate the device node pairs that need to be disconnected, and perform link interruption operations; Locate the device node pair that needs to establish a new connection, configure the communication protocol parameters, and perform the link establishment operation; The signal coverage area allocation scheme in the dynamic topology optimization strategy is analyzed, and the signal transmission power parameters of the device nodes are adjusted to match the allocated coverage area boundaries. The connectivity of the adjusted device connection relationship and signal coverage area is verified. The verification includes whether all device nodes can communicate with the central control node through at least one path. After completing connectivity verification, an adjusted sensor network is generated, containing a list of new connectivity relationships and a coverage area allocation table.

7. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 6, characterized in that, The connectivity verification of the adjusted device connection relationships and signal coverage areas includes verifying whether all device nodes can communicate with the central control node through at least one path, including: Starting from the central control node, construct a breadth-first search tree for the device connection relationships; Traverse all device nodes in the search tree and record the path length and path node sequence from the central control node to each device node; The number of device nodes that cannot be reached through the search tree is counted and used as the parameter for the number of unconnected nodes in connectivity verification. If the number of unconnected nodes is zero, the connectivity verification is considered successful. If the number of unconnected nodes is greater than zero, locate the unconnected nodes and re-execute the link establishment operation until all device nodes are connected.

8. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1, characterized in that, The process of evaluating the optimization effect of the adjusted sensor network, generating evaluation results, and feeding them back to the topology optimization strategy generation stage to trigger a strategy update operation includes: Reacquire the electromagnetic detection data set within the intelligent space, including the electromagnetic signal strength information and signal propagation delay information of the adjusted device nodes; The newly acquired electromagnetic detection data set is processed by sensor device status analysis to obtain an adjusted set of sensor device status characteristics. Compare the set of sensor device state characteristics before and after adjustment, and calculate the difference parameter of device connection stability characteristics and the overlap area change parameter of signal coverage characteristics; The average communication delay and energy consumption parameters of device nodes in the sensor network were statistically adjusted. By integrating the difference parameter, overlapping area change parameter, average communication delay parameter, and energy consumption parameter, an evaluation result containing quantitative indicators is generated. The evaluation results are input into the topology optimization strategy generation stage as the basis for strategy updates to trigger the next round of dynamic topology optimization operations.

9. A smart space sensing dynamic topology optimization system based on electromagnetic detection, characterized in that, The system includes a processor and a memory, the memory being connected to the processor. The memory is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the memory to implement the intelligent space sensing dynamic topology optimization method based on electromagnetic detection as described in any one of claims 1-8.

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