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

Abstract

The invention provides an intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection, and the method comprises the steps: firstly obtaining an electromagnetic detection data set composed of electromagnetic signal intensity and propagation delay information of different position nodes in an intelligent space, and then carrying out the sensing equipment state analysis of the electromagnetic detection data set; the method comprises the steps of obtaining a state feature set containing equipment connection stability and signal coverage range features, generating a dynamic topology optimization strategy containing an equipment connection relation adjustment rule and a signal coverage area distribution scheme according to the state feature set, and adjusting a sensor network topology structure according to the dynamic topology optimization strategy. And finally, performing optimization effect evaluation on the adjusted sensor network topological structure, and feeding back an evaluation result to trigger strategy updating, thereby dynamically optimizing the sensor network topological structure and improving the stability and reliability of the intelligent space sensor network.

Description

Technical Field

[0001] The present invention relates to the field of intelligent space technology, and in particular to an intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection. Background Art

[0002] In the smart space sector, such as smart homes, smart buildings, and the Industrial Internet of Things (IIoT), sensor networks are critical infrastructure for spatial environmental perception, data collection, and device control. Electromagnetic detection technology, a commonly used sensing method, acquires spatial information through electromagnetic signals from nodes at different locations. However, existing sensor networks based on electromagnetic detection suffer from numerous challenges.

[0003] On the one hand, the topology of traditional sensor networks is often static and fixed, unable to dynamically adjust to changes in the spatial environment and the actual status of sensor devices. In practical applications, the electromagnetic environment within smart spaces is affected by a variety of factors, such as device movement, the presence of obstacles, and electromagnetic interference. This can lead to decreased connection stability and changes in signal coverage for some sensor devices, thus affecting the accuracy and integrity of sensor data. On the other hand, existing sensor networks lack effective state analysis and optimization mechanisms, making it impossible to promptly detect abnormal sensor device states and implement targeted topological adjustments. For example, when a sensor device fails or its signal attenuates, it cannot automatically adjust the connection relationships and signal coverage areas of surrounding devices, 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 above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a method for dynamic topology optimization of intelligent spatial sensing based on electromagnetic detection, the method comprising: Acquire an electromagnetic detection data set within the smart space, wherein the electromagnetic detection data set includes electromagnetic signal strength information and signal propagation delay information of nodes at different locations; Performing sensor device state analysis on the electromagnetic detection data set to obtain a sensor device state feature set including a device connection stability feature and a signal coverage range feature; generating a dynamic topology optimization strategy based on the sensor device state feature set, wherein the dynamic topology optimization strategy includes a device connection relationship adjustment rule and a signal coverage area allocation plan; Adjusting the sensor network topology structure in the smart space according to the dynamic topology optimization strategy to obtain an adjusted sensor network; The adjusted sensor network is evaluated for optimization effects, and evaluation results are generated and fed back to the topology optimization strategy generation link to trigger a strategy update operation.

[0005] On the other hand, an embodiment of the present invention also provides an intelligent space sensing dynamic topology optimization system based on electromagnetic detection, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0006] Based on the above aspects, the embodiments of the present invention acquire a set of electromagnetic detection data within a smart space, including electromagnetic signal strength information and signal propagation delay information for nodes at different locations, and perform sensor device status analysis on the electromagnetic detection data set to obtain a sensor device status feature set including device connection stability characteristics and signal coverage range characteristics. This can accurately grasp the actual operating status of each sensor device, and generate a dynamic topology optimization strategy based on the sensor device status feature set, including device connection relationship adjustment rules and signal coverage area allocation schemes. This achieves dynamic adjustment of the sensor network topology structure, automatically optimizes the network structure based on the real-time status of the sensor devices and changes in the spatial environment, and improves 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 adjusted sensor network is evaluated for optimization effects, and the evaluation results are fed back to the topology optimization strategy generation process to trigger a strategy update operation. This can continuously improve the topology optimization strategy, ensuring that the sensor network always maintains the optimal operating state, thereby significantly improving the performance of the smart space sensor network and meeting the high-precision and high-reliability requirements of the smart space for environmental perception and device control. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the execution flow of the intelligent space sensing dynamic topology optimization method based on electromagnetic detection provided by an embodiment of the present invention.

[0008] Figure 2 Schematic diagram of exemplary hardware and software components of an intelligent spatial sensing dynamic topology optimization system based on electromagnetic detection provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a method for dynamic topology optimization of intelligent space sensing based on electromagnetic detection provided by an embodiment of the present invention. The method for dynamic topology optimization of intelligent space sensing based on electromagnetic detection is introduced in detail below.

[0010] Step S110: Acquire an electromagnetic detection data set in the smart space, where the electromagnetic detection data set includes electromagnetic signal strength information and signal propagation delay information of nodes at different locations.

[0011] In practical application scenarios, the scope and environment of smart spaces are complex and diverse, encompassing a large commercial building, an industrial plant, or a smart home. First, multiple electromagnetic detection nodes are deployed within the smart space according to a pre-defined node layout plan, each equipped with a directional antenna assembly. This pre-defined node layout plan is based on a thorough analysis of the smart space. For example, for a large commercial building, factors such as the building's floor structure, room layout, and personnel flow areas need to be considered. Electromagnetic detection nodes should be strategically placed in key locations on different floors, in corners of rooms, and in crowded areas to ensure coverage throughout the smart space. Directional antenna assemblies enable 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, directional antennas with higher gain and wider coverage can be used in open spaces, while more directive antennas with stronger directionality can be used in narrow corridors or rooms.

[0012] Step S111: Arrange multiple electromagnetic detection nodes in the smart space according to a preset node layout plan, and configure each electromagnetic detection node with a directional antenna component.

[0013] When deploying electromagnetic detection nodes, precise operation is required according to the pre-set node layout plan. First, a detailed mapping of the smart space is required to determine the specific location coordinates of each node. For complex smart spaces, specialized mapping equipment, such as laser rangefinders and 3D scanners, may be required to obtain accurate spatial information. Based on the mapping results, the electromagnetic detection nodes are then installed in the appropriate locations. During installation, the stability and robustness of the nodes must be ensured to prevent them from shifting due to external factors. Furthermore, the compatibility of the nodes with the 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 must be adjusted based on 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 to ensure effective signal exchange with other nodes.

[0014] Step S112: Control each electromagnetic detection node to transmit an electromagnetic signal of a set frequency to the surrounding space, and receive electromagnetic echo signals reflected or forwarded by other nodes.

[0015] In order for each electromagnetic detection node to transmit an electromagnetic signal of a set frequency into the surrounding space and receive electromagnetic echo signals reflected or forwarded by other nodes, a series of parameter settings and control operations are required. First, each electromagnetic detection node must be assigned a unique signal transmission frequency. This allocation of transmission frequencies is a critical step and requires comprehensive consideration based on factors such as the size of the smart space, the number of nodes, and signal interference. In a larger smart space with a large number of nodes, it may be necessary to use multiple frequency bands to allocate signal transmission frequencies to prevent signal interference. Furthermore, the electromagnetic signal transmission cycle parameter must be set to ensure that the interval between consecutive transmission cycles meets signal reception integrity requirements. The transmission cycle parameter setting should be adjusted based on factors such as signal propagation speed, the distance between nodes, and signal processing capabilities. For example, in a smart space with a relatively dispersed node distribution, where signal propagation distances are long, the transmission cycle may need to be appropriately extended to ensure that the node has sufficient time to receive electromagnetic echo signals reflected or forwarded by other nodes.

[0016] Step S1121: Allocate a unique signal transmission frequency to each electromagnetic detection node, and set the transmission cycle parameters of the electromagnetic signal so that the time interval between adjacent transmission cycles meets the signal reception integrity requirement.

[0017] When allocating signal transmission frequencies to electromagnetic detection nodes, it's important to adhere to established frequency allocation rules. For example, a frequency planning algorithm can be used to divide the available frequency band into multiple sub-bands based on the actual conditions and requirements of the smart space. Each node is then assigned a unique sub-band as its signal transmission frequency. For example, a frequency allocation algorithm based on graph theory can be used, treating nodes as vertices and interference relationships between nodes as edges. Frequency allocation is achieved by solving a graph coloring problem. When setting the electromagnetic signal transmission cycle parameters, the signal propagation characteristics and the processing capabilities of the nodes must be considered. Experimentation and simulation can be used to determine the optimal transmission cycle parameters. For example, a simulation scenario similar to an actual smart space can be built in a laboratory environment. By varying the transmission cycle parameters, the quality and integrity of the signal received by the nodes can be observed to determine an appropriate transmission cycle range.

[0018] Step S1122: Control the electromagnetic detection node to start the receiving module after transmitting the signal, and continuously monitor the electromagnetic echo signals reflected or forwarded by other nodes.

[0019] After an electromagnetic detection node transmits a signal, the receiving module must promptly activate and continuously monitor electromagnetic echo signals reflected or forwarded by other nodes. The receiving module's startup timing must be precisely controlled to ensure that reception begins immediately after the signal is transmitted. Furthermore, a reasonable monitoring duration must be set to ensure that all possible echo signals are received. During the monitoring process, the received signal must be processed and analyzed in real time to determine its source and quality. Signal filtering and amplification techniques can be used to improve signal quality and remove noise and interference. For example, a bandpass filter can be used to filter out signals in unwanted frequency bands, and an amplifier can be used to boost signal strength.

[0020] Step S113: Record the intensity value of the electromagnetic echo signal received by each electromagnetic detection node as electromagnetic signal intensity information.

[0021] Recording the intensity of the electromagnetic echo signal received by each electromagnetic detection node is a key step in obtaining electromagnetic signal strength information. During the recording process, it is crucial to ensure measurement accuracy and consistency. First, the measuring equipment must be calibrated to ensure that its measurement accuracy meets the required accuracy. This can be done using a standard signal source, and the equipment's performance indicators should be regularly checked. When recording the electromagnetic echo signal intensity, appropriate measurement methods and units must be used. For example, a power meter can be used to measure signal power intensity in milliwatts (mW) or decibel milliwatts (dBm). Furthermore, information such as the measurement time and node location must be recorded to facilitate subsequent data analysis and processing.

[0022] Step S114: measuring a time interval parameter of the electromagnetic signal from the transmitting node to the receiving node as signal propagation delay information.

[0023] Measuring the time interval between an electromagnetic signal's transmission and reception is crucial for obtaining signal propagation delay information. To ensure measurement accuracy, high-precision clock synchronization 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. Clock synchronization can be achieved using technologies such as the Global Positioning System (GPS) and the Network Time Protocol (NTP). When measuring the time interval, timestamps can be used: recording timestamps at the instants of signal transmission and reception, then calculating the difference between the two timestamps. To improve measurement accuracy, multiple measurements can be averaged. For example, the same signal can be transmitted and received multiple times, with the time interval parameters recorded each time. The average of these parameters can then be calculated as the final signal propagation delay information.

[0024] Step S115: collecting electromagnetic signal strength information and signal propagation delay information of all electromagnetic detection nodes within a continuous time window, and integrating them to generate an electromagnetic detection data set containing a timestamp.

[0025] Collecting electromagnetic signal strength and signal propagation delay information from all electromagnetic detection nodes within a continuous time window and integrating it to generate a timestamped electromagnetic detection data set 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 personnel flow, the continuous time window can be set shorter to capture electromagnetic signal changes in a timely manner; whereas for a relatively stable industrial plant, the continuous time window can be set longer. When collecting data, it is important to ensure data integrity and accuracy. 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 each data point needs to be timestamped. The timestamp can be accurate to milliseconds or even microseconds to accurately record the data collection time. Finally, all data is integrated into a single dataset, forming an electromagnetic detection data set that contains electromagnetic signal strength and signal propagation delay information for nodes at different locations.

[0026] Step S120: performing sensor device status analysis on the electromagnetic detection data set to obtain a sensor device status feature set including a device connection stability feature and a signal coverage range feature.

[0027] After acquiring the electromagnetic detection data set, it is necessary to analyze and process the sensor device status to obtain a sensor device status feature set that includes device connection stability characteristics and signal coverage characteristics. This process requires in-depth mining and analysis of the electromagnetic detection data set to extract information related to the sensor device status.

[0028] Step S121: extracting electromagnetic signal strength information of the same device node in the electromagnetic detection data set at consecutive time stamps, and calculating a fluctuation amplitude parameter of the signal strength at adjacent time stamps.

[0029] Extracting electromagnetic signal strength information for the same device node at consecutive timestamps from an electromagnetic detection data set is the basis for calculating the signal strength fluctuation amplitude parameter for adjacent timestamps. First, the electromagnetic detection data set must be filtered and sorted, organizing the data by device node and timestamp order. A database management system can be used to store and query the data, allowing queries to be written to extract electromagnetic signal strength information for the same device node at consecutive timestamps. After extracting the data, the signal strength fluctuation amplitude parameter for adjacent timestamps needs to be calculated. This can be obtained by calculating the absolute value of the difference between the signal strengths of adjacent timestamps. For example, for device node A, the electromagnetic signal strength at times T1 and T2 is S1 and S2, respectively. The signal strength fluctuation amplitude parameter for adjacent timestamps can be expressed as |S2-S1|. To more comprehensively analyze signal strength fluctuations, the fluctuation amplitude parameter for multiple adjacent timestamps can be calculated and statistically analyzed.

[0030] Step S122: Evaluate the stability of the communication link between the device nodes according to the fluctuation amplitude parameter, and generate a device connection stability feature representing the reliability of the link.

[0031] Assessing the stability of the communication link between device nodes based on the fluctuation amplitude parameter is a key step in generating device connection stability characteristics. First, the stability threshold range for the fluctuation amplitude parameter must be set. The stability threshold range should be determined based on the actual application scenario and experience. For example, in an industrial control scenario with high communication stability requirements, the stability threshold range may be set relatively narrow, while in a relatively relaxed smart home scenario, the stability threshold range may be set relatively wide. When the fluctuation amplitude parameter is less than the lower stability threshold, the communication link is very stable, and a first-class stability identifier can be generated. When the fluctuation amplitude parameter is within the stability threshold range, the communication link is basically stable, and a second-class stability identifier can be generated. When the fluctuation amplitude parameter is greater than the upper stability threshold, the communication link is unstable, possibly due to signal interference or device failure, and a third-class stability identifier can be generated. These stability identifiers serve as the core description of the device connection stability characteristics for subsequent analysis and processing.

[0032] Step S1221: Set a stability threshold range for the fluctuation amplitude parameter, and generate a first type of stability identifier when the fluctuation amplitude parameter is less than the lower limit of the stability threshold.

[0033] Setting the stability threshold range for the fluctuation amplitude parameter requires comprehensive consideration of multiple factors. An appropriate stability threshold range can be determined through analysis and experimentation of extensive historical data. For example, in an actual smart space, electromagnetic signal strength data can be collected over a period of time, and the distribution of the fluctuation amplitude parameter can be statistically analyzed. Based on this distribution, the upper and lower limits of the stability threshold can be determined. When the fluctuation amplitude parameter is less than the lower limit of the stability threshold, the stability of the communication link is very high, and a first-class stability identifier can be generated. This first-class stability identifier can be represented by a specific code or symbol, such as the number "1."

[0034] Step S1222: Generate a second type of stability identifier when the fluctuation amplitude parameter is within the stability threshold range.

[0035] When the fluctuation amplitude parameter is within the stability threshold, it indicates that the stability of the communication link is normal. At this point, a second stability identifier can be generated. This second stability identifier can be represented by a different code or symbol than the first, such as the number "2." When generating this second stability identifier, it is important to ensure that the fluctuation amplitude parameter is accurately determined to avoid misjudgments.

[0036] Step S1223: Generate a third type of stability identifier when the fluctuation amplitude parameter is greater than the upper limit of the stability threshold.

[0037] When the fluctuation amplitude parameter exceeds the upper stability threshold, it indicates that the communication link is unstable and may have some problems. In this case, a third type of stability identifier can be generated. This third type of stability identifier can be represented by 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 should be promptly inspected and maintained to identify the cause of the instability, such as signal interference or equipment failure, and take appropriate measures to resolve it.

[0038] Step S123: Analyze the correspondence between the signal propagation delay information and the spatial position in the electromagnetic detection data set to determine the maximum spatial range boundary that can be covered by the electromagnetic signal of each device node.

[0039] Analyzing the relationship between signal propagation delay and spatial location in electromagnetic detection data sets is key to determining the maximum spatial range that each device node's electromagnetic signal can cover. First, a mathematical model must be established to correlate signal propagation delay with spatial location. Based on the principles of electromagnetic wave propagation and incorporating the geometric structure and electromagnetic characteristics of the smart space, a functional relationship between signal propagation delay and spatial location can be established. By fitting and analyzing the signal propagation delay and node location information in the electromagnetic detection data sets, the parameters of the mathematical model can be determined. Ray tracing or simulation methods can be used to determine the maximum spatial range that each device node's electromagnetic signal can cover. Ray tracing simulates the propagation path of electromagnetic waves in the smart space to calculate the maximum reach of the signal. Simulation methods use computer software to model and simulate the smart space, simulating the propagation of electromagnetic waves and thereby determining the signal's coverage range.

[0040] Step S124: Count the number of other device nodes contained within the boundary of the spatial range, and generate a signal coverage range feature representing the signal coverage capability.

[0041] Counting the number of other device nodes within the spatial boundary is a key step in generating signal coverage characteristics. First, the specific coordinates and shape of the spatial boundary must be determined. This can be determined by analyzing signal propagation delay information and electromagnetic signal strength, 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 must be counted. By querying the location information of device nodes, it can be determined whether a node is within the spatial boundary. The statistical results can be used as a signal coverage characteristic to represent signal coverage capability. To more comprehensively describe signal coverage capability, a comprehensive analysis can be conducted based on factors such as spatial scope size and signal strength.

[0042] Step S125: Correlate and integrate the device connection stability feature and the signal coverage range feature to generate a sensor device state feature set including a multi-dimensional state description.

[0043] The final step in generating the sensor device status feature set is to correlate and integrate the device connection stability feature and the signal coverage feature. Data fusion techniques can be used to correlate and integrate these two features. For example, a weighted average method can be used to fuse the device connection stability feature and the signal coverage feature to generate a comprehensive sensor device status feature. The weighting can be adjusted based on the actual application scenario and requirements. During the correlation and integration process, data consistency and accuracy must be ensured to avoid data conflicts or errors. The generated sensor device status feature set contains multi-dimensional status description information, such as device connection stability and signal coverage.

[0044] Step S130: generating a dynamic topology optimization strategy according to the sensor device state feature set, wherein the dynamic topology optimization strategy includes a device connection relationship adjustment rule and a signal coverage area allocation scheme.

[0045] The core of the approach is to generate a dynamic topology optimization strategy based on the sensor device state feature set. By analyzing and processing the sensor device state feature set, reasonable device connection relationship adjustment rules and signal coverage area allocation schemes are formulated to optimize the sensor network topology within the smart space.

[0046] Step S131: identifying device node pairs in the sensor device state feature set whose device connection stability feature is lower than a preset benchmark, and generating an adjustment instruction to disconnect the original communication link.

[0047] Identifying device node pairs whose device connection stability characteristics in the sensor device status feature set are lower than a preset benchmark is a prerequisite for generating adjustment instructions to disconnect the original communication link. First, a preset benchmark must be set, and the setting of the preset benchmark must be determined based on the actual application scenario and the requirements for communication link stability. A suitable preset benchmark can be determined through analysis and experimentation of historical data. When identifying device node pairs, the device connection stability characteristics in the sensor device status feature set can be queried to filter out device node pairs whose characteristics are lower than the preset benchmark. For the filtered device node pairs, an adjustment instruction to disconnect the original communication link is generated. The adjustment instruction can include identification information of the device node, disconnection time, and other content to accurately perform the disconnection operation.

[0048] Step S132: Screen device node pairs whose device connection stability characteristics are higher than a preset benchmark and whose signal coverage characteristics overlap, and generate adjustment instructions for establishing new communication links.

[0049] Screening for device node pairs whose connection stability characteristics exceed a preset benchmark and whose signal coverage characteristics overlap is key to generating adjustment instructions for establishing new communication links. First, the set of sensor device status characteristics must be screened and compared to identify pairs whose connection stability characteristics exceed a preset benchmark and whose signal coverage characteristics overlap. This screening and comparison process can be implemented by developing query statements and comparison algorithms. For these screened pairs, adjustment instructions for establishing new communication links are generated. These adjustment instructions can include device node identification information, communication protocol parameters, and establishment time, ensuring accurate execution of the establishment operation.

[0050] Step S133: Integrate the adjustment instruction for disconnecting the original communication link and the adjustment instruction for establishing a new communication link to form a device connection relationship adjustment rule.

[0051] Integrating the adjustment instructions for disconnecting existing communication links and establishing new ones is a crucial step in developing device connection relationship adjustment rules. These two adjustment instructions can be integrated using data integration and sorting methods. First, the adjustment instructions must be categorized and numbered, sorted by device node identification information and operation type. These integrated adjustment instructions are then stored in a rule database for subsequent query and execution. Device connection relationship adjustment rules define the method and sequence for adjusting communication links between device nodes, providing guidance for adjusting sensor network topology.

[0052] Step S134: perform spatial area division processing on the signal coverage range characteristics, extract the coordinates of the spatial area with overlapping coverage, determine the boundary range of the overlapping area, and obtain the device connection stability characteristics of all device nodes involved in the overlapping area based on the boundary range of the overlapping area.

[0053] Spatial region division of signal coverage characteristics is the foundation for generating a signal coverage area allocation plan. First, spatial visualization of signal coverage characteristics is required, with the signal coverage of each device node marked on a map. Geographic Information System (GIS) technology can be used to achieve spatial visualization. Based on this visualization, spatial region division is performed to extract the coordinates of spatial regions with overlapping coverage. Spatial analysis algorithms, such as buffer analysis and overlay analysis, can be used to determine the coordinates and boundaries of the overlapping regions. After determining the boundaries of the overlapping regions, it is necessary to obtain the device connection stability characteristics of all device nodes involved in the overlapping regions. By querying the sensor device status feature set, device nodes within the overlapping regions can be screened based on their location information, and their device connection stability characteristics can be obtained.

[0054] Step S135: Compare the device connection stability characteristics of each device node, and select the device node with the highest priority of the stability identifier as the main coverage node of the overlapping area.

[0055] Compare the device connection stability characteristics of each device node and select the device node with the highest priority stability identifier as the primary coverage node in the overlap area. This process is based on the stability identifiers in the previously determined device connection stability characteristics. Stability identifiers are divided into different categories, each representing a different level of device connection stability. When faced with multiple device nodes in the overlap area, the stability identifiers of these nodes need to be compared one by one.

[0056] The priority ranking of stability identifiers is based on the rules that have been determined when the device connection stability was previously evaluated. For example, the first type of stability identifier set previously represents the highest stability, the second type is second, and the third type is relatively the lowest. When comparing the device connection stability characteristics of each device node, the judgment is made according to this priority rule. Starting from the first device node in the overlapping area, check its stability identifier. If it is a first-type stability identifier, then it has a higher priority in terms of stability; then check the stability identifier of the second device node. If it is a second-type stability identifier, then the first device node has an advantage in stability; and so on, perform the above comparison on all device nodes in the overlapping area.

[0057] During the comparison process, it is necessary to ensure that the stability identifier of each device node is accurately determined. This requires strict quality control of the previously generated device connection stability characteristics to ensure that the stability identifier information is accurate and reliable. Data verification and audit mechanisms can be used to check and confirm the device connection stability characteristics multiple times to avoid incorrect stability identifier information.

[0058] After comparing the stability identifiers of all device nodes, the device node with the highest stability identifier priority is selected as the primary coverage node in the overlap area. The electromagnetic signal of this primary coverage node will play a leading role in the overlap area, providing stable and reliable communication support for other devices in the area.

[0059] Step S136: Adjust the boundary of the overlapping area to be within the signal coverage range boundary of the primary coverage node, and re-define non-overlapping remaining coverage areas for other involved device nodes so that the coverage areas of all device nodes have no overlap.

[0060] After determining the primary coverage nodes in the overlapped area, the boundaries of the overlapped area need to be adjusted. First, the signal coverage boundaries of the primary coverage nodes must be clearly defined. This can be determined, for example, by analyzing the correspondence between signal propagation delay information and spatial location in the electromagnetic detection data set. The signal coverage boundaries of the primary coverage nodes are a precise spatial range determined based on the propagation characteristics of electromagnetic signals and the actual environment of the smart space.

[0061] Adjusting the overlap area's boundaries to fit within the primary coverage node's signal coverage requires spatial geometry manipulation. Geographic Information System (GIS) technology or other spatial analysis tools can be used to visualize and analyze the overlap area boundaries and the primary coverage node's signal coverage boundaries. Adjust the overlap area's boundary coordinates to ensure they are completely within the primary coverage node's signal coverage boundaries. During this adjustment process, ensure that the adjusted overlap area boundaries are spatially reasonable, without any broken boundaries or unreasonable shapes.

[0062] For other device nodes involved in the overlapping area, in addition to the primary coverage nodes, it is necessary to re-define non-overlapping residual coverage areas for them. This requires a comprehensive consideration of the electromagnetic signal coverage of the entire smart space. First, analyze the original signal coverage of other device nodes and, combined with the adjusted overlapping area, 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 provide signal coverage within a reasonable range.

[0063] During the re-demarcation of 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 monitoring and adjusting the coverage area of ​​each device node in real time. Spatial conflict detection algorithms can be used to monitor the coverage areas of device nodes and, if overlap is detected, make timely adjustments. Adjustments can be made by further reducing the coverage area of ​​a device node or changing the shape of its coverage area until all device nodes have no overlap.

[0064] Step S137: retaining the coverage authority of the original device node for the independent coverage areas without overlap, and generating a signal coverage area allocation plan.

[0065] After adjusting the overlapping areas and delineating the remaining coverage areas, the original device node's coverage rights must be retained for any independent coverage areas that originally had no overlap. Independent coverage areas are those where, according to the previous analysis, their signal coverage does not overlap with the coverage of other device nodes. Signal coverage in these areas is relatively stable, and the original device node has established effective signal coverage within them.

[0066] Retaining the coverage rights of the original device nodes means that these device nodes can continue to transmit and propagate electromagnetic signals within their independent coverage areas in the same manner as before, providing communication support for other devices in the area. No additional adjustments to the signal transmission parameters or coverage range of these device nodes are required.

[0067] The coverage area information of all device nodes, including adjusted overlapping areas, redefined remaining coverage areas, and independent coverage areas that retain their original coverage permissions, is integrated to generate a signal coverage area allocation plan. This signal coverage area allocation plan details the specific coverage area of ​​each device node within the smart space and clarifies the signal coverage responsibilities of each device node. The signal coverage area allocation plan can be presented in text form or as a visual map, facilitating subsequent management and maintenance of the sensor network.

[0068] Step S138: combining the device connection relationship adjustment rule and the signal coverage area allocation plan to generate a dynamic topology optimization strategy including link adjustment logic and coverage allocation logic.

[0069] The key outcome of the entire topology optimization process is the generation of a dynamic topology optimization strategy, combining device connection relationship adjustment rules and signal coverage area allocation plans. The device connection relationship adjustment rules define how communication links between device nodes are adjusted, including specific instructions for disconnecting existing links and establishing new ones. The signal coverage area allocation plan, on the other hand, specifies the signal coverage range and responsibilities of each device node within the smart space.

[0070] To organically integrate these two components, the first step is to unify the formatting and data integration of the device connection relationship adjustment rules and the signal coverage area allocation plan. They can be stored in the same database or data structure to facilitate subsequent query and access. When generating dynamic topology optimization strategies, it is necessary to ensure the coordination between the link adjustment logic and the coverage allocation logic. For example, when establishing a new communication link, it is necessary to consider whether the signal coverage area of ​​the device nodes at both ends of the new link is reasonable and whether it will affect the coverage range of other device nodes. When adjusting the signal coverage area, it is necessary to consider whether it will affect the existing device connection relationship.

[0071] The link adjustment logic of the dynamic topology optimization strategy requires a detailed description of the specific conditions and steps for disconnecting existing communication links and establishing new ones. For disconnecting existing communication links, the specific circumstances under which the disconnection operation is performed must be clearly defined, along with the specific process for executing the disconnection operation, such as how the disconnection command is sent and when the disconnection is scheduled. For establishing new communication links, the rationale for selecting which device nodes to establish the new link, as well as the communication protocol parameters and steps required for configuring the new link, must be clearly stated.

[0072] 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 each device node's coverage area, as well as how to adjust and optimize the coverage area in different situations. For example, when the environment within the smart space changes, such as when a new obstacle appears or a device node malfunctions, how to adjust the coverage area of ​​the relevant device nodes according to the signal coverage area allocation plan.

[0073] By integrating the link adjustment logic and coverage allocation logic, a complete dynamic topology optimization strategy is formed. This dynamic topology optimization 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.

[0074] Step S140: adjusting the sensor network topology structure in the smart space according to the dynamic topology optimization strategy to obtain an adjusted sensor network.

[0075] Adjusting the sensor network topology within a smart space according to a 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.

[0076] Step S141: parsing the device connection relationship adjustment rules in the dynamic topology optimization strategy, locating the device node pairs that need to be disconnected and performing a link interruption operation.

[0077] Parsing the device connection relationship adjustment rules within the dynamic topology optimization strategy is the first step in link adjustment. These rules contain information about the device node pairs that need to be disconnected and newly connected. First, the rules must be parsed in detail to extract information such as the device node identifier and the operation type (disconnection or establishment).

[0078] During the parsing process, it's important to ensure accurate understanding of the rules. This can be accomplished by writing a specialized parser to automatically parse device connection adjustment rules. This parser must be able to identify the various information within the rules and convert it into executable instructions.

[0079] To locate the device node pair to be disconnected, the corresponding device node must be searched within the sensor network within the smart space based on the parsed device node identification information. Unique identifiers, such as device numbers and MAC addresses, can be used to query and locate the device node in the device management system. This ensures accurate identification of the device node pair to be disconnected.

[0080] When performing a link disconnection operation, a pre-defined process must be followed. First, a disconnection command must be sent to the device node pair to be disconnected. This disconnection command can be sent via network communication, such as a wireless or wired network protocol. When sending the disconnection command, the accuracy and completeness of the disconnection command must be ensured to avoid command loss or errors. Upon receiving the disconnection command, the device node must execute the appropriate action to sever the communication link between them. This can be achieved by closing the communication port between the device nodes or stopping the relevant communication services.

[0081] Step S142: Locate the device node pair that needs to establish a new connection, configure communication protocol parameters and perform link establishment operations.

[0082] After parsing the device connection relationship adjustment rules, in addition to locating the device node pairs that need to be disconnected, the system also needs to locate the device node pairs that need to be reconnected. 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 be reconnected are accurately located.

[0083] Configuring communication protocol parameters is a crucial step in establishing a new connection. Different communication protocols have different parameter requirements, and the appropriate protocol must be selected based on the specific application scenario and device characteristics. For example, in wireless sensor networks, protocols such as ZigBee and Wi-Fi may be used. For each communication protocol, corresponding parameters such as channel, frequency, and encryption key must be configured. When configuring communication protocol parameters, ensure consistency and compatibility. You can determine appropriate parameter values ​​by consulting the communication protocol's standard documentation and the device's manual. Furthermore, the configured parameters must be tested and verified to ensure proper communication between device nodes.

[0084] Link establishment must be performed in accordance with the communication protocol. First, the device nodes that wish to establish a new connection must discover and identify each other. This can be achieved by broadcasting signals or performing active scanning. After discovery, the nodes perform authentication and parameter negotiation. Authentication ensures connection security and prevents unauthorized access. Parameter negotiation is the process of reaching consensus on communication protocol parameters between the nodes. After authentication and parameter negotiation are complete, a stable communication link is established between the nodes. Link establishment is achieved by opening the communication ports between the nodes and activating the relevant communication services.

[0085] Step S143: parsing the signal coverage area allocation scheme in the dynamic topology optimization strategy, and adjusting the signal transmission power parameters of the device nodes to match the allocated coverage area boundaries.

[0086] Analyzing the signal coverage area allocation plan within the dynamic topology optimization strategy is a prerequisite for adjusting the signal transmission power parameters of device nodes. The signal coverage area allocation plan specifies the coverage area boundaries and related information for each device node. First, the signal coverage area allocation plan must be analyzed in detail to extract information such as the device node identifier, coverage area boundary coordinates, and coverage area size.

[0087] During the analysis process, it is necessary to ensure a correct understanding of the signal coverage area allocation plan. This can be automated by writing a specialized analysis program. The analysis program must be able to identify various information in the plan and convert it into executable instructions.

[0088] 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 speaking, larger coverage areas require higher signal transmission power, while smaller coverage areas can require lower signal transmission power. Through experiments and simulations, a relationship model between signal transmission power and coverage area size and shape can be established. Based on this relationship model and the analyzed coverage area boundary information, the required signal transmission power parameters for each device node can be calculated.

[0089] When adjusting signal transmission power parameters, ensure that the device node is functioning properly. Different device nodes have different transmission power limits, and adjustments must be made within these limits. You can send power adjustment commands to the device node through the device's configuration interface to automatically adjust the signal transmission power. During the adjustment process, monitor the device node's signal transmission power in real time to ensure that the adjusted power meets the requirements.

[0090] Step S144: Connectivity verification is performed on the adjusted device connection relationship and signal coverage area, and the verification content includes whether all device nodes can communicate with the central control node through at least one path.

[0091] Connectivity verification of the adjusted device connection relationship and signal coverage area is an important step to ensure the normal operation 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.

[0092] Starting from the central control node, a breadth-first search tree of device connectivity relationships is constructed. This breadth-first search tree is an algorithm for traversing a graph. Starting from the central control node, it expands layer by layer, traversing all device nodes connected to the central control node. When constructing the breadth-first search tree of device connectivity relationships, a connectivity graph between device nodes must be determined based on the adjusted device connectivity relationships. Communication link information between device nodes can be used to construct a graph structure, with device nodes as vertices and communication links as edges.

[0093] 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. During the traversal, use a breadth-first search algorithm to visit each device node in hierarchical order. For each device node visited, record the path length and path node sequence from the central control node to the device node. This can be achieved by maintaining a path record list during the search process.

[0094] Count the number of device nodes that cannot be reached through the search tree, and use this as the disconnected node parameter for connectivity verification. If a device node is found to be unreachable during the search tree traversal, it is marked as disconnected. Count all disconnected nodes to obtain the disconnected node parameter.

[0095] If the parameter for the number of disconnected nodes is zero, connectivity verification is considered successful. This means that all device nodes can communicate with the central control node via at least one path, and the sensor network's connectivity is good. If the parameter for the number of disconnected nodes is greater than zero, the disconnected nodes are located and link establishment is re-executed until all device nodes are connected. Disconnected nodes can be located in the sensor network based on their identification information. The cause of the disconnection is analyzed, which may be due to device failure, communication link interruption, or other factors. Appropriate measures are taken to address the cause. If the cause is a device failure, the device needs to be repaired or replaced; if the communication link is interrupted, the link establishment operation needs to be re-executed to ensure that the disconnected node can establish a communication connection with other nodes in the sensor network.

[0096] Step S145: After completing the connectivity verification, an adjusted sensor network including a new connection relationship list and a coverage area allocation table is generated.

[0097] 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 coverage area allocation table.

[0098] The new connection relationship list records the adjusted connection relationships between device nodes. This list includes information such as the device node's identification, connection status (connected or disconnected), and the identification of the connected peer device node. This information can be organized into a list by sorting and summarizing the adjusted device connection relationships. The new connection relationship list should clearly reflect the connection status between device nodes in the sensor network after the adjustment, facilitating subsequent network management and maintenance.

[0099] The coverage area allocation table records the signal coverage area information for each device node. This 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 organized into a table based on the previously adjusted and delineated signal coverage areas. The coverage area allocation table should accurately reflect the signal coverage range of each device node within the smart space.

[0100] The new connection relationship list and coverage area allocation table are combined to form an adjusted sensor network. This adjusted sensor network contains the connection relationships between device nodes and signal coverage area information, forming a complete description of the sensor network. This can be stored in a device management system or database for subsequent query, analysis, and use.

[0101] Step S150: performing optimization effect evaluation on the adjusted sensor network, generating an evaluation result and feeding it back to the topology optimization strategy generation link to trigger a strategy update operation.

[0102] Evaluating the optimization effectiveness of the adjusted sensor network is the final step in the dynamic topology optimization process and a key part of the feedback loop. This evaluation reveals whether the adjusted sensor network has achieved the desired results and whether further optimization is needed.

[0103] Step S151: reacquire the electromagnetic detection data set in the smart space, including the electromagnetic signal strength information and signal propagation delay information of the adjusted device nodes.

[0104] Re-acquiring electromagnetic detection data within the smart space is fundamental to evaluating optimization results. After adjusting the sensor network topology, the connectivity between device nodes and the signal coverage area change, requiring re-collection of electromagnetic detection data to understand the adjusted electromagnetic environment.

[0105] The process for reacquiring electromagnetic detection data sets is similar to the previous data acquisition process. Following the pre-set 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 forwarded by other nodes. The intensity of the electromagnetic echo signal received by each electromagnetic detection node is recorded as electromagnetic signal strength information, and the time interval between the electromagnetic signal transmitting node and the receiving node is measured as signal propagation delay information.

[0106] When reacquiring data, it is important to ensure data accuracy and consistency. Electromagnetic detection nodes can be recalibrated and debugged to ensure stable performance. Furthermore, data collection should be performed using the same time window and data acquisition method to facilitate comparison and analysis with previously acquired electromagnetic detection data sets.

[0107] Step S152: performing sensor device state analysis processing on the reacquired electromagnetic detection data set to obtain an adjusted sensor device state feature set.

[0108] The sensor device status analysis and processing for the newly acquired electromagnetic detection data set follows the same process as before. First, the electromagnetic signal strength information for the same device node at consecutive timestamps within the electromagnetic detection data set is extracted, and the fluctuation amplitude parameter of the signal strength between adjacent timestamps is calculated. This fluctuation amplitude parameter is used to assess the stability of the communication link between the device nodes and generate a device connection stability feature that represents the link reliability.

[0109] Analyze the correspondence between signal propagation delay information and spatial position in the recovered electromagnetic detection data set to determine the maximum spatial range boundary of the electromagnetic signal of each device node. Count the number of other device nodes within the spatial range boundary to generate a signal coverage feature that represents the signal coverage capability. Correlate and integrate the device connection stability feature with the signal coverage feature to generate an adjusted sensor device state feature set containing a multi-dimensional state description.

[0110] To extract electromagnetic signal strength information for the same device node at consecutive timestamps, the newly acquired electromagnetic detection data set must be carefully screened and sorted again. Based on the unique identifier of the device node, the electromagnetic signal strength information is sorted in chronological order by timestamp. For this screened and sorted information, the absolute difference between the electromagnetic signal strengths at adjacent timestamps is calculated to obtain the fluctuation amplitude parameter. This process requires ensuring the accuracy of the difference calculation, which can be achieved through multiple verifications of the data and calculation results.

[0111] 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 clear boundaries. When the fluctuation amplitude parameter is less than the lower limit of the stability threshold, a first-class stability identifier is generated; when it is within the stability threshold range, a second-class stability identifier is generated; and when it is greater than the upper limit of the stability threshold, a third-class stability identifier is generated. These stability identifiers serve as the core content of the adjusted device connection stability characteristics. It is necessary to ensure that the generation of the identifiers strictly follows the established rules to avoid identification errors.

[0112] Analyzing the relationship between signal propagation delay and spatial location still relies on the previously established mathematical model. This model is based on the principles of electromagnetic wave propagation and the realities of smart spaces. By fitting and analyzing the signal propagation delay and node location information from the recovered 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. Ray tracing or simulation methods can be used to determine this boundary. Ray tracing simulates the propagation path of electromagnetic waves in the smart space and calculates the maximum reach of the signal. Simulation methods use computer software to model and simulate the smart space, simulating the electromagnetic wave propagation process to determine the coverage boundary. In the implementation of both methods, the accuracy of the model parameters and the authenticity of the simulation process must be ensured to obtain reliable spatial range boundary results.

[0113] When counting the number of other device nodes within a spatial boundary, the precise coordinates and shape of the boundary must 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 smart 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 signal coverage characteristics, representing signal coverage capability. To comprehensively describe signal coverage capability, a comprehensive analysis can also be conducted incorporating factors such as spatial range size and signal strength.

[0114] Finally, the device connection stability and signal coverage features are correlated and integrated. Data fusion techniques, such as weighted averaging, are used to combine these two features. The weights should be adjusted based on the actual application scenario and requirements to ensure that the fusion results accurately reflect the status of the sensor device. During the correlation and integration process, data consistency and accuracy must be strictly guaranteed to avoid data conflicts or errors. Ultimately, an adjusted set of sensor device status features containing a multi-dimensional description of the status is generated.

[0115] Step S1521: extracting electromagnetic signal strength information of the same device node at consecutive time stamps in the re-acquired electromagnetic detection data set, and calculating a fluctuation amplitude parameter of the signal strength at adjacent time stamps.

[0116] To extract electromagnetic signal strength information for the same device node at consecutive timestamps from the newly acquired electromagnetic detection data set, the data set must first be preprocessed. This preprocessing involves data cleaning to remove any noise or errors. This can be done by setting a reasonable range for the data to filter out eligible data. For example, if the electromagnetic signal strength value exceeds the normal operating range of the device, it is considered erroneous and discarded.

[0117] Data is grouped according to the unique identifier of the device node, and each group is sorted by timestamp. After sorting, the absolute difference in electromagnetic signal strength between consecutive timestamps is calculated, starting from the first timestamp in each group, to obtain the fluctuation amplitude parameter. During the calculation process, the continuity and order of timestamps must be ensured to avoid jumps or reversals. A timestamp checking mechanism can be implemented to perform real-time checks on timestamps during the calculation process. If anomalies are detected, the data order can be adjusted or supplemented promptly.

[0118] Step S1522: Evaluate the stability of the communication link between the device nodes according to the fluctuation amplitude parameter, and generate a device connection stability feature representing the reliability of the link.

[0119] When evaluating the stability of communication links between device nodes based on fluctuation amplitude parameters, it is necessary to strictly adhere to the previously established stability threshold range. This stability threshold range is based on extensive experimental data and practical application experience, ensuring high reliability. During the evaluation process, the calculated fluctuation amplitude parameter is compared with the stability threshold range.

[0120] If the fluctuation amplitude parameter is less than the lower stability threshold, the communication link between the device nodes is very stable, and a first-class stability identifier is generated. When generating identifiers, it is necessary to ensure that the format and content of the identifiers comply with unified regulations. This can be achieved by writing a dedicated identifier generation program. This program accepts the fluctuation amplitude parameter and the stability threshold range as input and generates a corresponding stability identifier based on the comparison results.

[0121] When the fluctuation amplitude parameter is within the stability threshold, it indicates that the communication link is basically stable, and a second type of stability identifier is generated. Similarly, it is necessary to ensure that the identifier is generated accurately. The generated identifier can be verified multiple times and checked for correctness by comparing it with a preset identifier template.

[0122] If the fluctuation amplitude parameter exceeds the upper stability threshold, the communication link is unstable, and a third-level stability identifier is generated. After this identifier is generated, further inspection and analysis of the communication link is necessary to identify the cause of the instability. Possible causes include signal interference and equipment failure, and appropriate remedial measures should be implemented accordingly. For example, if signal interference is the cause of the instability, the device node's communication frequency can be adjusted or a signal shielding device can be added. If the device is faulty, the device can be repaired or replaced.

[0123] Step S1523: Analyze the correspondence between the signal propagation delay information and the spatial position in the re-acquired electromagnetic detection data set to determine the maximum spatial range boundary that can be covered by the electromagnetic signal of each device node.

[0124] When analyzing the correspondence between signal propagation delay information and spatial position in a newly acquired electromagnetic detection data set, the signal propagation delay information and spatial position information must be synchronized. First, the signal propagation delay information must be calibrated to ensure its accuracy. The timestamps in the signal propagation delay information can be corrected by comparing it with a standard time source.

[0125] When establishing a relationship between signal propagation delay and spatial location, it's necessary to consider the complex environmental factors of smart spaces, such as the presence of obstacles and changes in electromagnetic properties. This can be achieved by partitioning the smart space and establishing different relationship models for different areas. For example, areas with numerous metal obstacles significantly affect electromagnetic wave propagation, necessitating a specialized model to describe the relationship between signal propagation delay and spatial location.

[0126] To determine the maximum spatial range that each device node's electromagnetic signal can cover, ray tracing requires precise simulation of the electromagnetic wave's propagation path. Considering that electromagnetic waves may experience reflection, refraction, and scattering during propagation, the simulation incorporates appropriate physical models 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 encountering obstacles.

[0127] In simulation, the accuracy of the computer software modeling of the smart space must be ensured. The model must include detailed information such as the smart space's geometry and material properties to accurately reflect the electromagnetic wave propagation environment. During the simulation process, appropriate simulation parameters, such as the electromagnetic wave transmission power and frequency, must be set to ensure the reliability of the simulation results.

[0128] Step S1524: Count the number of other device nodes contained within the boundary of the spatial range, and generate a signal coverage range feature representing the signal coverage capability.

[0129] When counting the number of other device nodes within the spatial boundary, the spatial boundary must first be accurately digitized. The coordinate information of the spatial boundary is converted into a computer-processable format, which can be stored and managed using the data format of the Geographic Information System (GIS) software.

[0130] When counting device nodes, determine whether they are within the spatial range boundaries by querying their location information in the device management system. A specialized query program can be developed to compare the device node's location coordinates with the coordinates of the spatial range boundaries. To improve statistical accuracy, multiple verifications of the device node's location information can be performed to avoid statistical errors caused by location information errors.

[0131] When generating signal coverage features that represent signal coverage capability, in addition to counting the number of device nodes, factors such as spatial range and signal strength can also be comprehensively considered. These factors can be quantified and weighted to integrate them into the signal coverage feature. The weighting should be adjusted based on the actual application scenario and the importance of each factor to ensure that the signal coverage feature comprehensively and accurately reflects the signal coverage capability of the device nodes.

[0132] Step S1525: Correlate and integrate the device connection stability feature and the signal coverage range feature to generate an adjusted sensor device state feature set including a multi-dimensional state description.

[0133] When correlating and integrating the device connection stability and signal coverage features, these two features must first be normalized. Because the device connection stability and signal coverage features may have different dimensions and value ranges, they must be normalized to ensure the rationality of the fusion results. Normalization can be used to map the value of each feature to a uniform interval, such as [0, 1].

[0134] When using a weighted average approach for fusion, determining weights is crucial. Weights can be determined through expert evaluation, experimental analysis, and other methods. For example, in scenarios where device connection stability is critical, the weight of the device connection stability feature can be appropriately increased; in scenarios where signal coverage is more important, the weight of the signal coverage feature can be increased.

[0135] During the fusion process, data consistency and accuracy must be ensured. The fusion results are repeatedly checked and verified, and their reliability is ensured through comparative analysis with the original data. The resulting adjusted sensor device state feature set contains a multi-dimensional state description that more comprehensively reflects the actual state of the sensor device.

[0136] Step S153: Compare the sensor device state feature sets before and after the adjustment, and calculate the difference parameter of the device connection stability feature and the overlap area change parameter of the signal coverage range feature.

[0137] When comparing the sensor device status feature sets before and after adjustment, for the device connection stability feature, first categorize the stability identifiers before and after the adjustment. Assign the first category of stability identifiers to the same category, and similarly for the second and third categories. Then, by counting the changes in the number of stability identifiers in different categories, calculate the difference parameter of the device connection stability feature. For example, count the number of device nodes with the first category of stability identifiers before and after the adjustment and calculate the difference between the two.

[0138] When calculating the difference parameter, it is necessary to consider that changes in the stability identifier may affect the overall device connection stability assessment. If the number of device nodes with the first type of stability identifier increases after the adjustment, it means that the device connection stability has improved; otherwise, it means that the stability has decreased.

[0139] To characterize signal coverage, it's necessary to analyze changes in spatial boundaries before and after adjustments. First, perform a spatial overlay analysis of the boundaries before and after adjustments. Using Geographic Information System (GIS) technology, display and analyze the boundaries on the same map. Determine the extent and area of ​​the overlapping region.

[0140] When calculating the overlap change parameter for the signal coverage feature, you can calculate the difference between the overlap area before and after adjustment. If the overlap area decreases after adjustment, it indicates that the signal coverage area is more rationally divided and overlap is reduced. If the area increases, further analysis is required, as it may be due to improper signal coverage area adjustment.

[0141] During the comparison and calculation process, data accuracy and consistency must be ensured. Multiple checks should be conducted on the sensor device status feature sets before and after adjustments to ensure data integrity and accuracy. Calculation results should also be verified, either through different calculation methods or by comparing them with other relevant data.

[0142] Step S154: Counting the average communication delay parameters and energy consumption parameters of the device nodes in the sensor network after the adjustment.

[0143] To calculate the average communication delay parameters of device nodes in an adjusted sensor network, it is necessary to record the communication delay between each device node. Communication delay refers to the time it takes for a signal to be sent from one device node to be received by another. This can be achieved by sending a test signal between the device nodes, recording the signal's transmission and reception times, and calculating the difference between the two to obtain the communication delay.

[0144] To obtain an accurate average communication delay parameter, it is necessary to statistically analyze the communication delay times between multiple pairs of device nodes. Select representative pairs of device nodes for testing to ensure that the test results reflect the communication delay characteristics of the entire sensor network. Taking an average of multiple tests can reduce test errors. For example, perform multiple communication delay tests on each device node pair, record the results of each test, and then calculate the average of these results.

[0145] When counting 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 node to collect real-time energy consumption data of the device node, such as changes in battery power and power consumption.

[0146] Collected energy consumption data is aggregated and analyzed. The average energy consumption of each device node over a period of time is calculated, and then the average energy consumption of all device nodes is aggregated to obtain the average energy consumption parameter for the entire sensor network. The statistical process needs to consider the impact of different device node operating modes and task loads on energy consumption. For example, device nodes in a high-frequency communication state will have relatively high energy consumption, and the energy consumption data of these nodes needs to be analyzed and processed separately.

[0147] Step S155: Integrate the difference parameter, the overlapping area change parameter, the average communication delay parameter and the energy consumption parameter to generate an evaluation result including quantitative indicators.

[0148] When integrating the difference parameter, overlap area variation parameter, average communication delay parameter, and energy consumption parameter, it is necessary to determine the weights of these parameters based on their importance and actual application requirements. For example, in scenarios where device connection stability is a high priority, the difference parameter weight of the device connection stability feature can be set higher; in scenarios where the rationality of the signal coverage area is important, the overlap area variation parameter weight of the signal coverage range feature can be appropriately increased.

[0149] 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 indicator. During the integration process, the dimensional consistency of the parameters must be ensured. If the parameters have different dimensions, they can be normalized to map them to the same dimensional range.

[0150] When generating evaluation results that include quantitative metrics, they need to be visualized. These results can be presented in graphical form, such as bar charts or line graphs. Visualizing these results provides a more intuitive understanding of the optimization effects of the adjusted sensor network. Detailed explanations and descriptions of the evaluation results are also necessary to facilitate subsequent analysis and decision-making.

[0151] Step S156: input the evaluation result into the topology optimization strategy generation link as a basis for strategy update to trigger the next round of dynamic topology optimization operation.

[0152] When inputting evaluation results into the topology optimization strategy generation phase, it is important to ensure accurate transmission of these results. This can be accomplished through network communication. During this transmission process, a reliable communication protocol must be used to ensure data integrity and accuracy.

[0153] After receiving the evaluation results, the topology optimization strategy generation phase requires in-depth analysis. Based on the quantitative indicators in the evaluation results, it is determined whether the current topology optimization strategy has achieved the desired 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 decrease in overlap area, the average communication delay parameter is reduced, and the energy consumption parameter is reasonable, the current topology optimization strategy has achieved good results, but fine-tuning is still possible based on the specific situation.

[0154] If the evaluation results indicate that certain aspects are not meeting expectations, such as decreased device connection stability or significant overlap in signal coverage areas, the topology optimization strategy generation phase will need to adjust the strategy based on these issues. This adjustment may involve re-defining the device connection relationship adjustment rules and the signal coverage area allocation plan. For example, if device connection stability decreases, the connection relationships between device nodes can be re-evaluated to add more stable connections. If there is significant overlap in signal coverage areas, the signal coverage area division can be further optimized.

[0155] When the next round of dynamic topology optimization is triggered, the sensor network topology within the smart space needs to be readjusted according to the new topology optimization strategy. Starting with acquiring electromagnetic detection data, the process continues with sensor device status analysis, dynamic topology optimization strategy generation, sensor network topology adjustment, and optimization results evaluation. This forms a closed-loop dynamic optimization process, continuously improving the performance and stability of the sensor network.

[0156] Figure 2 A schematic diagram illustrating exemplary hardware and software components of an electromagnetic detection-based intelligent spatial sensing dynamic topology optimization system 100 that can implement the concepts of the present application is provided in some embodiments of the present application. For example, a processor 120 can be used in the electromagnetic detection-based intelligent spatial sensing dynamic topology optimization system 100 to perform the functions of the present application.

[0157] The electromagnetic detection-based intelligent spatial sensing dynamic topology optimization system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the electromagnetic detection-based intelligent spatial sensing dynamic topology optimization method 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 processing load.

[0158] 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 storage media 140 in different forms, 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 method of the present 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 and output devices.

[0159] For ease of explanation, only one processor is described in the intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection. However, it should be noted that the intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection in this application may also include multiple processors, so the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the intelligent space sensing dynamic topology optimization system 100 based on electromagnetic detection executes step A and step B, it should be understood that step A and step B may also be executed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0160] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned intelligent space sensing dynamic topology optimization method based on electromagnetic detection is implemented.

[0161] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A dynamic topology optimization method for intelligent space sensing based on electromagnetic detection, characterized in that: The method comprises: Acquire an electromagnetic detection data set within the smart space, wherein the electromagnetic detection data set includes electromagnetic signal strength information and signal propagation delay information of nodes at different locations; Performing sensor device state analysis on the electromagnetic detection data set to obtain a sensor device state feature set including a device connection stability feature and a signal coverage range feature; generating a dynamic topology optimization strategy based on the sensor device state feature set, wherein the dynamic topology optimization strategy includes a device connection relationship adjustment rule and a signal coverage area allocation plan; Adjusting the sensor network topology structure in the smart space according to the dynamic topology optimization strategy to obtain an adjusted sensor network; The adjusted sensor network is evaluated for optimization effects, and evaluation results are generated and fed back to the topology optimization strategy generation link to trigger a strategy update operation.

2. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1 is characterized in that: The acquisition of electromagnetic detection data sets within the smart space, including electromagnetic signal strength information and signal propagation delay information of nodes at different locations, includes: Arrange multiple electromagnetic detection nodes in the smart space according to a preset node layout plan, and each electromagnetic detection node is equipped with a directional antenna assembly; Control each electromagnetic detection node to transmit electromagnetic signals of a set frequency into the surrounding space and 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; Measuring the time interval parameter of the electromagnetic signal from the transmitting node to the receiving node as signal propagation delay information; The electromagnetic signal strength information and signal propagation delay information of all electromagnetic detection nodes in a continuous time window are collected and integrated to generate an electromagnetic detection data set containing a timestamp.

3. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 2 is characterized in that: The controlling of each electromagnetic detection node to transmit an electromagnetic signal of a set frequency to the surrounding space and to receive electromagnetic echo signals reflected or forwarded by other nodes includes: Assign a unique signal transmission frequency to each electromagnetic detection node and set the transmission cycle parameters of the electromagnetic signal so that the time interval between adjacent transmission cycles meets the signal reception integrity requirements; Control the electromagnetic detection node to start the receiving module after transmitting the signal, and continuously monitor the electromagnetic echo signals reflected or forwarded by other nodes; Recording the arrival timestamp and signal strength value of the electromagnetic echo signal to form multi-dimensional signal recording data including the emission frequency, timestamp and signal strength; The multi-dimensional signal recording data is used as the original data source of 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 is characterized in that: The performing of sensor device state analysis processing on the electromagnetic detection data set to obtain a sensor device state feature set including a device connection stability feature and a signal coverage range feature includes: Extracting electromagnetic signal strength information of the same device node in the electromagnetic detection data set at consecutive timestamps, and calculating a fluctuation amplitude parameter of the signal strength at adjacent timestamps; Evaluate the stability of the communication link between the device nodes based on the fluctuation amplitude parameter, and generate a device connection stability feature indicating the reliability of the link; Analyzing the correspondence between signal propagation delay information and spatial position in the electromagnetic detection data set to determine the maximum spatial range boundary that can be covered by the electromagnetic signal of each device node; Counting the number of other device nodes contained within the boundary of the spatial range to generate a signal coverage range feature representing signal coverage capability; The device connection stability feature and the signal coverage feature are associated and integrated to generate a sensor device state feature set containing a multi-dimensional state description.

5. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 4 is characterized in that: The step of evaluating the stability of the communication link between the device nodes according to the fluctuation amplitude parameter and generating a device connection stability feature indicating the reliability of the link includes: Setting a stability threshold range for the fluctuation amplitude parameter, and generating a first-class stability identifier when the fluctuation amplitude parameter is less than the lower limit of the stability threshold; The second type of stability identifier is generated when the volatility parameter is within the stability threshold range; The 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 is characterized in that: The generating of a dynamic topology optimization strategy based on the sensor device state feature set, including device connection relationship adjustment rules and signal coverage area allocation scheme, includes: Identifying device node pairs whose device connection stability characteristics are lower than a preset benchmark in the sensor device state feature set, and generating an adjustment instruction to disconnect the original communication link; Screening device node pairs whose device connection stability characteristics are higher than a preset benchmark and whose signal coverage characteristics overlap, and generating adjustment instructions for establishing new communication links; Integrating the adjustment instruction for disconnecting the original communication link and the adjustment instruction for establishing a new communication link to form a device connection relationship adjustment rule; Performing spatial region division processing on the signal coverage range characteristics, extracting the coordinates of the spatial region where overlapping coverage exists, determining the boundary range of the overlapping region, and obtaining the device connection stability characteristics of all device nodes involved in the overlapping region based on the boundary range of the overlapping region; Compare the device connection stability characteristics of each device node and select the device node with the highest priority of the stability identifier as the main coverage node of the overlapping area; Adjust the boundaries of the overlapping area to within the signal coverage range of the primary coverage node, and re-define the non-overlapping remaining coverage areas for other involved device nodes so that the coverage areas of all device nodes do not overlap; For independent coverage areas without overlap, the coverage authority of the original device node is retained to generate a signal coverage area allocation plan; In combination with the device connection relationship adjustment rule and the signal coverage area allocation scheme, a dynamic topology optimization strategy including link adjustment logic and coverage allocation logic is generated.

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

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

9. The intelligent space sensing dynamic topology optimization method based on electromagnetic detection according to claim 1 is characterized in that: The optimization effect evaluation process is performed on the adjusted sensor network, and the evaluation result is generated and fed back to the topology optimization strategy generation link to trigger the strategy update operation, including: Re-acquire the electromagnetic detection data set in the smart space, including the electromagnetic signal strength information and signal propagation delay information of the adjusted device nodes; Performing sensor device state analysis and processing on the reacquired electromagnetic detection data set to obtain an adjusted sensor device state feature set; Compare the sensor device state feature sets before and after the adjustment, and calculate the difference parameter of the device connection stability feature and the overlap area change parameter of the signal coverage feature; The average communication delay parameters and energy consumption parameters of device nodes in the statistically adjusted sensor network; Integrating the difference parameter, the overlap area change parameter, the average communication delay parameter, and the energy consumption parameter to generate an evaluation result including quantitative indicators; The evaluation results are input into the topology optimization strategy generation link as the basis for strategy update to trigger the next round of dynamic topology optimization operation.

10. An intelligent space sensing dynamic topology optimization system based on electromagnetic detection, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes 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 to 9.

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