Adaptive wireless connection optimization method for intelligent environment perception

Abstract

The invention discloses a self-adaptive wireless connection optimization method for intelligent environment perception, which relates to the technical field of wireless connection, and comprises the following steps: S1, constructing a perception data set GW by utilizing a sensor and scanning equipment in the step S1, so that wireless equipment can obtain structure semantic information of a propagation environment for the first time; therefore, an extended path loss model which is more accurate than a traditional model is constructed in S2. Compared with an existing fixed power configuration scheme, the mechanism has environment self-sensing and path self-correcting capabilities, and energy waste and non-uniform signal coverage caused by blind emission are avoided. And comparing a communication stability index gamma s calculated in real time with a target channel stability threshold gamma tar, reversely deducing the minimum current transmitting power PT meeting the communication quality in the current region, and obtaining the actual transmitting power PTadj in combination with a disturbance fine tuning mechanism. According to the mechanism, bidirectional coupling control between power and stability is achieved, redundant power is not configured for the whole situation any more, and the upper limit of power consumption can be effectively controlled.

Description

Technical Field

[0001] The present invention relates to the field of wireless connection technology, and in particular to an adaptive wireless connection optimization method for intelligent environment perception. Background Art

[0002] As the backbone of modern communication networks, wireless communication systems undertake the main function of data interaction between terminal devices, and have been widely used in scenarios such as Wi-Fi, Internet of Things, smart homes, and smart offices. As the layout of physical space becomes increasingly complex, the interaction between wireless communication and the building environment has gradually become a research hotspot. Among them, the dynamic behavior modeling of signal propagation in the structural environment and the power regulation and control of equipment are particularly critical. Therefore, how to perceive information based on spatial topology and realize the device's cognition and adaptive control of the current environment has become an important research direction for improving communication performance and energy efficiency.

[0003] Most existing wireless communication devices use a fixed transmission power mechanism, that is, the device sets a uniform signal transmission strength during initialization and maintains it unchanged during the entire operation process. However, there are a large number of channel disturbance factors in the actual environment, such as differences in wall thickness, frequent changes in furniture layout, and different reflectivity of ground materials. These factors will significantly affect the signal path attenuation and reflection angle distribution.

[0004] The limitation of the fixed transmission power strategy is that it cannot sense environmental changes and cannot make timely adjustments. When environmental disturbances such as furniture movement, personnel flow, and door and window opening and closing occur frequently, the original channel model is broken and the propagation path changes significantly at the physical level. However, the communication equipment still maintains the initial power parameters, resulting in a disconnect between the transmission power and the actual requirements. This disconnection will cause a series of abnormal consequences: in high-interference areas, the signal cannot reach the receiving end due to severe penetration attenuation, resulting in unstable connection, reduced rate, and increased delay; in low-impedance areas, excessive power is prone to co-channel interference, equipment overheating, increased energy consumption and other side effects. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an adaptive wireless connection optimization method based on intelligent environment perception, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an adaptive wireless connection optimization method based on intelligent environment perception, comprising the following steps: S1, using sensors and scanning equipment to collect information in the propagation environment, fitting it into the original data set SW, and preprocessing it to obtain the perception data set GW; S2. By using the sensing data set GW, an extended path loss model is constructed and the total signal attenuation Lt on different propagation paths is calculated; S3, combining the total signal attenuation Lt obtained with the target area receiving power Pr and the topology disturbance data CL received on site, evaluating the communication stability index Γs of each area in the space, and drawing a space energy efficiency diagram; S4. By combining the acquired communication stability index Γs with the preset target channel stability threshold Γtar, the current transmission power PT required in the current environment is reversed, and the actual transmission power PTadj is calculated; S5. Perform a comprehensive performance evaluation on the global region based on the actual transmission power PTadj and the communication stability index Γs, and obtain an evaluation index TΦ; S6. According to the obtained evaluation index TΦ, topology disturbance data CL and communication stability index Γs, after detecting the dynamic topological changes of furniture position changes and personnel movement, the disturbance degree of the signal environment is adjusted to obtain the power adjustment value ΔPT required for real-time compensation.

[0007] Preferably, S1 includes S11 and S12; S11, by using sensor equipment to scan and perceive the environment, collect information data in the propagation environment, including the wall dielectric constant change rate QD, the average reflection angle Er, the surface material reflection coefficient Pm, the furniture interference factor Hf, the topological disturbance data CL and the target area receiving power Pr, and construct the original data set SW; Among them, the wall dielectric constant change rate QD is obtained by millimeter wave radar collection; The average reflection angle Er is acquired through the ToF depth camera; The surface material reflectance Pm is acquired through a multi-spectral infrared scanner; The furniture interference factor Hf is obtained through high-frequency sonar collection; The topological disturbance data CL is acquired through the IMU dynamic attitude sensor; The target area receiving power Pr is acquired through the receiving signal monitor; S12, performing data cleaning and normalization processing on the acquired original data set SW to obtain the perception data set GW; Data cleaning includes using statistical outlier detection methods to remove outliers from the original data set SW, remove noise data above the mean, and obtain the data cleaning set CW; The normalization process includes normalizing the data cleaning set CW, uniformly mapping the data to the interval [0, 1], and obtaining the perception data set GW; The perception data set GW is obtained by the following formula: ; Wherein, GMb represents the b-th data in the perception data set GW, CWb represents the b-th data in the data cleaning set CW, minCWb represents the valley value of the b-th data in the data cleaning set CW, and maxCWb represents the peak value of the b-th data in the data cleaning set CW.

[0008] Preferably, S2 includes S21 and S22; S21. Extract path features from the acquired perception data set GW, including material structure correction factor QPD, angular multipath attenuation factor Qer, environmental interference factor HCf, and path length index correction factor DMp, and fit them into the path data set LF, perform model training, and construct a multi-factor extended path loss model; The material structure correction factor QPD is obtained by the following formula: ; The angular multipath attenuation factor Qer is obtained by the following formula: ; In the formula, cos represents the cosine function, and π represents pi; The environmental interference factor HCf is obtained by the following formula: ; The path length index correction factor DMp is obtained by the following formula: ; Where CD represents the path loss factor; S22, calculating the total signal attenuation Lt on different propagation paths by using the acquired path data set LF and the multi-factor extended path loss model; The total signal attenuation Lt is obtained by the following formula: ; Where Lo represents the free space reference path loss, log represents the logarithmic function, They respectively represent the preset weight values ​​of the material structure correction factor QPD, the angular multipath attenuation factor Qer and the environmental interference factor HCf, and d represents the physical length of the propagation path.

[0009] Preferably, S3 includes S31 and S32; S31, combining the acquired total signal attenuation Lt with the target area receiving power Pr and topology disturbance data CL received on site, and calculating the communication stability index Γs of the area; The communication stability index Γs is obtained by the following formula: ; Where X represents the disturbance sensitivity coefficient, the set value is 0.1~0.5, which represents the sensitivity control of the structural disturbance.

[0010] Preferably, S32, constructing a regional signal energy efficiency index CKs according to the acquired communication stability index Γs and in combination with an actual space topology coordinate system; The energy efficiency index CKs is obtained by the following formula: ; In the formula, ck represents a constant; Combine the spatial points (x, y, z) in the three-dimensional environment with the energy efficiency indicators CKs to form an energy efficiency mapping color gradient map in the three-dimensional space; The gradient map is obtained by matching in the following way: Red area: 0<energy efficiency index CKs<0.3, indicating unstable signal and low transmission efficiency; Yellow area: 0.3≤energy efficiency index CKs≤0.7, indicating signal coverage acceptance; Green area: 0.7 < energy efficiency index CKs, indicating stable communication and efficient power use.

[0011] Preferably, S4 includes S41 and S42; S41, first establish a theoretical receiving power-transmitting power relationship; Recover the target area received power Pr=PT-Lt from the channel loss model, substitute it into the formula for obtaining the communication stability index Γs, and obtain the theoretical stability expression: ; Where Γtheo represents the theoretical communication stability index; Reversely solve the target power formula: According to the communication stability index Γs, reverse the current transmission power PT required in the current environment; The current transmit power PT is obtained by the following formula: ; Where Γtar represents the preset target channel stability threshold.

[0012] Preferably, by introducing a disturbance response fine-tuning mechanism and constructing a disturbance response compensation model to adjust the current transmit power PT, the actual transmit power PTadj is obtained, and the interference caused by the dynamic disturbance characteristics of the environment, the movement of personnel and furniture is eliminated to ensure that the system still operates stably under slightly changing conditions; The disturbance change rate ΔCL is obtained through the disturbance response compensation model: The disturbance change rate ΔCL is obtained by the following formula: ; In the formula, ∂ represents the partial derivative; According to the obtained disturbance change rate ΔCL, the dynamic disturbance response function φ(CL) is defined to correct the transmission power: The dynamic disturbance response function φ(CL) is obtained by the following formula: ; Where, β represents the disturbance response coefficient; The actual transmit power PTadj is obtained by the following formula: .

[0013] Preferably, S5 includes S51 and S52; S51, constructing the regional energy efficiency index Qus through the actual transmission power PTadj and the communication stability index Γs; The energy efficiency index Qus is obtained by the following formula: ; Where Qus(i) represents the energy efficiency index of the ith region, Γs(i) represents the communication stability index of the ith region, PTadj(i) represents the actual transmission power of the ith region, and B represents a real number to prevent division by 0, which is usually set to 0.01.

[0014] Preferably, S52, integrating the energy efficiency index Qus of all regions, performing a comprehensive performance evaluation on the global region, obtaining the evaluation index TΦ, and judging the overall signal distribution state; The evaluation index TΦ is obtained by the following formula: ; Where N represents the number of evaluation areas, e represents a constant, k represents the distance attenuation coefficient, and D(i) represents the distance from the i-th area to the signal source; The overall signal distribution status is obtained by matching: When 0<evaluation index TΦ<0.5, it means there is a signal blind area and power waste; When 0.5≤evaluation index TΦ≤0.8, it means that the communication is generally acceptable, but there is power redundancy; When 0.8<evaluation index TΦ<1.0, it means that the overall configuration of the system is reasonable, the power usage is efficient, and the communication is stable.

[0015] Preferably, S6, performing feature extraction on the disturbance change rate ΔCL obtained according to the topological disturbance data CL to obtain a disturbance trend factor λp; The disturbance trend factor λp is obtained by the following formula: ; In the formula, tanh represents the hyperbolic tangent function, λa represents the change sensitivity adjustment coefficient; According to the obtained disturbance trend factor λp, combined with the evaluation index TΦ and the communication stability index Γs, the power adjustment value ΔPT to be added is calculated, and the state of the wireless power is judged; The power adjustment value ΔPT is obtained by the following formula: As shown in Table 1: ; In the formula, represents the power adjustment sensitivity factor; The wireless power status is obtained by matching: When 0<power adjustment value ΔPT<0.5, it means that the power is stable and no additional compensation is required; When 0.5≤power adjustment value ΔPT<1, it indicates power abnormality and power adjustment is required. The final power output value PTfin is obtained by combining the obtained power adjustment value ΔPT with the actual transmit power PTadj; The final power output value PTfin is obtained by adding the actual transmission power PTadj and the power adjustment value ΔPT.

[0016] The present invention provides an adaptive wireless connection optimization method based on intelligent environment perception, which has the following beneficial effects: (1) Using sensors and scanning devices to build a perception data set GW, wireless devices can obtain structural semantic information of the propagation environment for the first time, thereby building an extended path loss model in S2 that is more accurate than the traditional model. Compared with the existing fixed power configuration scheme, this mechanism has the ability of self-perception of the environment and self-correction of the path, avoiding energy waste and uneven signal coverage caused by blind transmission.

[0017] Based on the real-time calculated communication stability index Γs, compared with the target channel stability threshold Γtar, the current minimum transmission power PT that meets the communication quality in the current area is inferred, and the actual transmission power PTadj is obtained by combining the disturbance fine-tuning mechanism. This mechanism realizes the two-way coupling control between power and stability, that is, redundant power is no longer configured globally, but "quantified on demand" to effectively control the upper limit of power consumption.

[0018] (2) Introduce a variety of heterogeneous sensors to carry out targeted collection and classification of key physical variables in the wireless signal propagation environment, including millimeter wave radar to obtain the wall dielectric constant change rate QD, ToF depth camera to obtain the average reflection angle Er, infrared scanner to obtain the material reflection coefficient Pm, and model the channel environment from multiple dimensions such as material properties, reflection behavior, and structural disturbance. Compared with the traditional model with RSSI or distance as a single variable, this embodiment significantly enhances the system's spatial resolution and propagation semantic understanding capabilities of the real physical environment.

[0019] The original data set SW is cleaned and standardized, and data with different dimensions and distribution characteristics are uniformly mapped to the [0,1] interval. In particular, the statistical outlier removal algorithm is used to eliminate high noise points and environmental disturbance pseudo values ​​in the perception data, and finally a highly reliable perception data set GW is obtained. This not only improves the consistency of model input and data reliability, but also lays a high-stability and high-reliability basic data support for subsequent path modeling and power regulation.

[0020] (3) Using the acquired total signal attenuation Lt, the target area received power Pr, and the topological disturbance data CL, combined with the disturbance sensitivity coefficient X, a calculation mechanism for the communication stability index Γs is established. This approach enables the system to perform partitioned and dynamic quantitative perception of the communication quality in different areas. Compared with the traditional single judgment method that relies on RSSI, it is more robust and has more spatial accuracy, effectively solving the key problem of "signal blind areas cannot be judged and excessive signal waste is not detected".

[0021] In step S32, by coupling the communication stability index Γs with factors such as device power to construct the regional energy efficiency index CKs, the system has realized the dual-layer index linkage evaluation from communication stability to energy efficiency utilization for the first time. This mechanism not only measures the "strength" of the signal, but also measures the "effect produced by unit power", so that wireless connection no longer only focuses on whether the signal is "covered", but more on whether it is "reasonably covered and used efficiently", which improves the overall system perspective of energy management and service quality control.

[0022] (4) Through the combined relationship between actual transmission power and communication stability indicators, regional energy efficiency indicators are constructed to quantify the communication quality return brought by unit power in each spatial area. This mechanism realizes the evaluation method of the "output power cost performance" dimension, breaking through the evaluation limitations of traditional wireless systems that only rely on static indicators such as signal strength or reception success rate, providing a more scientific and comparable optimization basis for power regulation, and making energy efficiency control have a measurable and traceable basis.

[0023] In S52, by integrating the energy efficiency data of all regions, a system-level comprehensive performance index is formed to judge the rationality of the overall signal distribution and the effectiveness of the power configuration. Compared with the traditional local optimization method, this embodiment realizes the transformation from "single-point optimization" to "global optimization", enabling the system to fully understand whether there are blind spots, signal waste or power overload in the current configuration, and has the system-level performance intelligent judgment capability, which improves the balance and stability of the overall network deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the process flow of the adaptive wireless connection optimization method for intelligent environment perception of the present invention; Figure 2 It is a block diagram flow chart of the power adjustment of the present invention; Figure 3 A line graph of power adjustment values ​​of the present invention; Figure 4 It is an area diagram of the power adjustment value of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Example 1 The present invention provides an adaptive wireless connection optimization method based on intelligent environment perception. Figures 1 to 4 , including the following steps: S1, using sensors and scanning equipment to collect information in the propagation environment, fitting it into the original data set SW, and preprocessing it to obtain the perception data set GW; S2. By using the sensing data set GW, an extended path loss model is constructed and the total signal attenuation Lt on different propagation paths is calculated; S3, combining the total signal attenuation Lt obtained with the target area receiving power Pr and the topology disturbance data CL received on site, evaluating the communication stability index Γs of each area in the space, and drawing a space energy efficiency diagram; S4. By combining the acquired communication stability index Γs with the preset target channel stability threshold Γtar, the current transmission power PT required in the current environment is reversed, and the actual transmission power PTadj is calculated; S5. Perform a comprehensive performance evaluation on the global region based on the actual transmission power PTadj and the communication stability index Γs, and obtain an evaluation index TΦ; S6. According to the obtained evaluation index TΦ, topology disturbance data CL and communication stability index Γs, after detecting the dynamic topological changes of furniture position changes and personnel movement, the disturbance degree of the signal environment is adjusted to obtain the power adjustment value ΔPT required for real-time compensation.

[0027] In this embodiment, by using sensors and scanning devices to build a perception data set GW in step S1, the wireless device can obtain the structural semantic information of the propagation environment for the first time, thereby building an extended path loss model that is more accurate than the traditional model in S2. Compared with the existing fixed power configuration scheme, this mechanism has the ability of self-perception of the environment and self-correction of the path, avoiding energy waste and uneven signal coverage caused by blind transmission.

[0028] Through steps S3 to S4, the system compares the communication stability index Γs calculated in real time with the target channel stability threshold Γtar, and infers the current minimum transmission power PT that meets the communication quality in the current area, and combines the disturbance fine-tuning mechanism to obtain the actual transmission power PTadj. This mechanism realizes the two-way coupling control between power and stability, that is, it no longer configures redundant power globally, but "quantifies on demand" to effectively control the upper limit of power consumption.

[0029] In S5, the present invention introduces for the first time a global comprehensive evaluation index TΦ based on unit stability and unit power, and forms a comprehensive perception of signal efficiency distribution through exponential decay combined with distance factors. This mechanism enables the system to not only see the local effect, but also judge whether the power configuration is reasonable and whether the signal distribution is balanced at the system level, providing data support for subsequent adjustments and expansions.

[0030] S6 innovatively introduces the time derivative of topological disturbance, which can quickly identify disturbance trends for sudden spatial topological changes such as furniture movement and personnel movement, and automatically adjust the power adjustment value ΔPT to achieve a true closed-loop capability of "environmental changes are regulation". This adaptive mechanism significantly enhances the system's adaptability and stability to complex scenarios.

[0031] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11 and S12; S11, by using sensor equipment to scan and perceive the environment, collect information data in the propagation environment, including the wall dielectric constant change rate QD, the average reflection angle Er, the surface material reflection coefficient Pm, the furniture interference factor Hf, the topological disturbance data CL and the target area receiving power Pr, and construct the original data set SW; Among them, the wall dielectric constant change rate QD is obtained by millimeter wave radar collection; The average reflection angle Er is acquired through the ToF depth camera; The surface material reflectance Pm is acquired through a multi-spectral infrared scanner; The furniture interference factor Hf is obtained through high-frequency sonar collection; The topological disturbance data CL is acquired through the IMU dynamic attitude sensor; The target area receiving power Pr is acquired through the receiving signal monitor; S12, performing data cleaning and normalization processing on the acquired original data set SW to obtain the perception data set GW; Data cleaning includes using statistical outlier detection methods to remove outliers from the original data set SW, remove noise data above the mean, and obtain the data cleaning set CW; The normalization process includes normalizing the data cleaning set CW, uniformly mapping the data to the interval [0, 1], and obtaining the perception data set GW; The perception data set GW is obtained by the following formula: ; Wherein, GMb represents the b-th data in the perception data set GW, CWb represents the b-th data in the data cleaning set CW, minCWb represents the valley value of the b-th data in the data cleaning set CW, and maxCWb represents the peak value of the b-th data in the data cleaning set CW.

[0032] S2 includes S21 and S22; S21. Extract path features from the acquired perception data set GW, including material structure correction factor QPD, angular multipath attenuation factor Qer, environmental interference factor HCf, and path length index correction factor DMp, and fit them into the path data set LF, perform model training, and construct a multi-factor extended path loss model; The material structure correction factor QPD is obtained by the following formula: ; The angular multipath attenuation factor Qer is obtained by the following formula: ; In the formula, cos represents the cosine function, and π represents pi; The environmental interference factor HCf is obtained by the following formula: ; The path length index correction factor DMp is obtained by the following formula: ; Where CD represents the path loss factor; S22, calculating the total signal attenuation Lt on different propagation paths by using the acquired path data set LF and the multi-factor extended path loss model; The total signal attenuation Lt is obtained by the following formula: ; Where Lo represents the free space reference path loss, log represents the logarithmic function, They respectively represent the preset weight values ​​of the material structure correction factor QPD, the angular multipath attenuation factor Qer and the environmental interference factor HCf, and d represents the physical length of the propagation path.

[0033] In this embodiment, in S11, multiple heterogeneous sensors are introduced for the first time to carry out directional collection and classification of key physical variables in the wireless signal propagation environment, including millimeter wave radar to obtain the wall dielectric constant change rate QD, ToF depth camera to obtain the average reflection angle Er, infrared scanner to obtain the material reflection coefficient Pm, and model the channel environment from multiple dimensions such as material properties, reflection behavior, and structural disturbance. Compared with the traditional model with RSSI or distance as a single variable, this embodiment significantly enhances the system's spatial resolution and propagation semantic understanding capabilities of the real physical environment.

[0034] By cleaning and standardizing the original data set SW in step S12, data with different dimensions and distribution characteristics are uniformly mapped to the [0,1] interval. In particular, the statistical outlier removal algorithm is used to eliminate high noise points and environmental disturbance pseudo values ​​in the perception data, and finally a highly reliable perception data set GW is obtained. This not only improves the consistency of model input and data reliability, but also lays a high-stability and high-reliability basic data support for subsequent path modeling and power regulation.

[0035] In S21 and S22, this embodiment no longer relies on the traditional path loss model, but introduces a four-dimensional loss factor structure based on the perception data set GW: material structure correction factor QPD, angle multipath attenuation factor Qer, environmental interference factor HCf, path length index correction factor DMp, by fitting into the path data set LF, combined with the actual path distance ddd, and finally constructing a multi-factor extended path loss model to calculate the total signal attenuation Lt. This allows the system to more accurately predict the signal strength attenuation trend in non-ideal spatial structures, and effectively solves the problem of large estimation deviation and rough path evaluation of traditional models in complex building environments.

[0036] This embodiment introduces multiple mathematical formulas to clearly quantify the contribution relationship of each factor, and at the same time sets the weight parameters in the loss model, retains the model's adjustability and learning ability, adapts to the propagation mechanism in different spatial, structural, and density scenarios, and enhances the system's cross-scenario generalization ability and deployment flexibility.

[0037] By completing the integrated data-to-model construction process from S1 to S2, a complete process from environmental perception → data conversion → path mapping → attenuation prediction was established, providing highly reliable and highly expressive model input support for subsequent steps, and solving the problems of large power control fluctuations and strong prediction deviations caused by inaccurate model basis in traditional methods.

[0038] Example 3 This embodiment is explained in Example 2. Please refer to Figure 1 and Figure 2 , specifically: S3 includes S31 and S32; S31, combining the acquired total signal attenuation Lt with the target area receiving power Pr and topology disturbance data CL received on site, and calculating the communication stability index Γs of the area; The communication stability index Γs is obtained by the following formula: ; Where X represents the disturbance sensitivity coefficient.

[0039] S32, constructing a regional signal energy efficiency index CKs based on the acquired communication stability index Γs and the actual space topology coordinate system; The energy efficiency index CKs is obtained by the following formula: ; In the formula, ck represents a constant; Combine the spatial points (x, y, z) in the three-dimensional environment with the energy efficiency indicators CKs to form an energy efficiency mapping color gradient map in the three-dimensional space; The gradient map is obtained by matching in the following way: Red area: 0<energy efficiency index CKs<0.3, indicating unstable signal and low transmission efficiency; Yellow area: 0.3≤energy efficiency index CKs≤0.7, indicating signal coverage acceptance; Green area: 0.7 < energy efficiency index CKs, indicating stable communication and efficient power use.

[0040] In this embodiment, the calculation mechanism of the communication stability index Γs is established by using the acquired total signal attenuation Lt, the target area received power Pr and the topological disturbance data CL, and combining the disturbance sensitivity coefficient X. This approach enables the system to perform partitioned and dynamic quantitative perception of the communication quality in different areas. Compared with the traditional single judgment method relying on RSSI, it is more robust and spatially accurate, and effectively solves the key problem of "signal blind areas cannot be judged and excessive signal waste is not detected".

[0041] In step S32, by coupling the communication stability index Γs with factors such as device power to construct the regional energy efficiency index CKs, the system has realized the dual-layer index linkage evaluation from communication stability to energy efficiency utilization for the first time. This mechanism not only measures the "strength" of the signal, but also measures the "effect produced by unit power", so that wireless connection no longer only focuses on whether the signal is "covered", but more on whether it is "reasonably covered and used efficiently", which improves the overall system perspective of energy management and service quality control.

[0042] By mapping the energy efficiency index CKs to the spatial coordinate system (x, y, z) and visually displaying it in the form of a color gradient map (red-yellow-green partition), the system achieves an intuitive presentation of the energy efficiency distribution of space communication. Compared with traditional parameters that are limited to background analysis, this method can: Let the system automatically identify weak signal areas (red areas) and locate blind spots; Accurately mark the energy efficiency balance area (yellow area) for maintenance; Emphasize the power excess area (green zone) for subsequent regulation and energy saving; This not only improves the interactivity and intuitiveness of spatial decision-making, but also significantly enhances the system's engineering visualization and real-time configuration assistance capabilities. It is particularly suitable for large-scale multi-region deployment scenarios such as smart buildings, industrial workshops, and smart warehouses.

[0043] By incorporating topological disturbance data CL into the stability and energy efficiency calculation path, the system can identify dynamic changes in the propagation environment caused by furniture movement, personnel activities, equipment migration, etc., and capture the fluctuations in communication stability caused by them in space. This capability constitutes a "perception-determination-mapping" spatial dynamic recognition mechanism. Compared with traditional static models, it can track changes in connection quality in real time and effectively prevent problems such as sudden disconnection, power redundancy lag, and increased channel interference.

[0044] Example 4 This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S4 includes S41 and S42; S41, first establish a theoretical receiving power-transmitting power relationship; Recover the target area received power Pr=PT-Lt from the channel loss model, substitute it into the formula for obtaining the communication stability index Γs, and obtain the theoretical stability expression: ; Where Γtheo represents the theoretical communication stability index; Reversely solve the target power formula: According to the communication stability index Γs, reverse the current transmission power PT required in the current environment; The current transmit power PT is obtained by the following formula: ; Where Γtar represents the preset target channel stability threshold.

[0045] By introducing a disturbance response fine-tuning mechanism and building a disturbance response compensation model, the current transmit power PT is adjusted to obtain the actual transmit power PTadj; The disturbance change rate ΔCL is obtained through the disturbance response compensation model: The disturbance change rate ΔCL is obtained by the following formula: ; In the formula, ∂ represents the partial derivative; According to the obtained disturbance change rate ΔCL, the dynamic disturbance response function φ(CL) is defined to correct the transmission power: The dynamic disturbance response function φ(CL) is obtained by the following formula: ; Where, β represents the disturbance response coefficient; The actual transmit power PTadj is obtained by the following formula: .

[0046] In this embodiment, the transmit power control strategy no longer relies on empirical settings or fixed values, but is based on the communication stability target, by establishing a mathematical relationship between the received power and the channel attenuation, and further combining the communication stability index for theoretical modeling and target matching reverse deduction. This mechanism realizes the reverse deduction of the minimum transmit power required under the current environmental conditions from the "communication quality target", so that the device transmit power can be dynamically adjusted strictly around the actual needs of the system, significantly improving the scientificity, accuracy and energy efficiency of the transmit power configuration.

[0047] In traditional wireless systems, when the environment structure changes rapidly due to factors such as furniture movement and personnel flow, the device transmission power is often difficult to respond in time, resulting in short-term connection interruption, sudden drop in channel stability and other problems. This embodiment introduces a disturbance response compensation model to obtain the rate of change of topological disturbances in real time and convert it into a power adjustment factor, thereby achieving instant perception of sudden scene disturbances and dynamic fine-tuning of transmission power. This mechanism effectively alleviates the problem of the communication system being "sluggish" to dynamic environments and improves the system's steady-state operation capability and anti-interference performance.

[0048] This embodiment adopts a two-level control path design; on the one hand, the initial power configuration is inferred based on the theoretical model to ensure that the communication stability meets the expectations; on the other hand, the power is fine-tuned through the compensation mechanism in combination with the change trend of the actual environmental disturbance, and the energy output is further finely controlled. While ensuring the connection quality, this strategy effectively avoids the energy waste and co-frequency interference caused by excessive transmission. It is an integrated power regulation architecture of "demand-driven + perception response".

[0049] In traditional methods, transmit power control often lacks basis and is not traceable, making it difficult for system operators to determine whether the power configuration is reasonable. In this embodiment, by explicitly modeling the theoretical power calculation logic and the disturbance response relationship, the power adjustment path has clear and explainable logical support, which can be transparently tracked and adjusted over time, environment, communication quality and other factors. This not only helps to realize intelligent control of the system, but also facilitates visual management and policy updates after deployment.

[0050] Example 5 This embodiment is explained in Example 4. Please refer to Figure 3 and Figure 4 , specifically: S5 includes S51 and S52; S51, constructing the regional energy efficiency index Qus through the actual transmission power PTadj and the communication stability index Γs; The energy efficiency index Qus is obtained by the following formula: ; Where Qus(i) represents the energy efficiency index of the i-th region, Γs(i) represents the communication stability index of the i-th region, PTadj(i) represents the actual transmission power of the i-th region, and B represents a real number.

[0051] S52, integrating the energy efficiency index Qus of all regions, performing a comprehensive performance evaluation on the global region, obtaining the evaluation index TΦ, and judging the overall signal distribution state; The evaluation index TΦ is obtained by the following formula: ; Where N represents the number of evaluation areas, e represents a constant, k represents the distance attenuation coefficient, and D(i) represents the distance from the i-th area to the signal source; The overall signal distribution status is obtained by matching: When 0<evaluation index TΦ<0.5, it means there is a signal blind area and power waste; When 0.5≤evaluation index TΦ≤0.8, it means that the communication is generally acceptable, but there is power redundancy; When 0.8<evaluation index TΦ<1.0, it means that the overall configuration of the system is reasonable, the power usage is efficient, and the communication is stable.

[0052] S6, performing feature extraction on the disturbance change rate ΔCL obtained according to the topological disturbance data CL to obtain a disturbance trend factor λp; The disturbance trend factor λp is obtained by the following formula: ; In the formula, tanh represents the hyperbolic tangent function, λa represents the change sensitivity adjustment coefficient; According to the obtained disturbance trend factor λp, combined with the evaluation index TΦ and the communication stability index Γs, the power adjustment value ΔPT to be added is calculated, and the state of the wireless power is judged; The power adjustment value ΔPT is obtained by the following formula: ; In the formula, represents the power adjustment sensitivity factor; =0.7; Specific example: Table 1: Dynamic power adjustment table; The wireless power status is obtained by matching: When 0<power adjustment value ΔPT<0.5, it means that the power is stable and no additional compensation is required; When 0.5≤power adjustment value ΔPT<1, it indicates power abnormality and power adjustment is required. The final power output value PTfin is obtained by combining the obtained power adjustment value ΔPT with the actual transmit power PTadj; The final power output value PTfin is obtained by adding the actual transmission power PTadj and the power adjustment value ΔPT.

[0053] In this embodiment, the regional energy efficiency index is constructed through the combined relationship between the actual transmission power and the communication stability index, and the communication quality return brought by the unit power in each spatial area is quantified. This mechanism realizes the evaluation method of the "output power cost performance" dimension, breaking through the evaluation limitations of traditional wireless systems that only rely on static indicators such as signal strength or reception success rate, providing a more scientific and comparable optimization basis for power regulation, and making energy efficiency control have a measurable and traceable basis.

[0054] In S52, by integrating the energy efficiency data of all regions, a system-level comprehensive performance index is formed to judge the rationality of the overall signal distribution and the effectiveness of the power configuration. Compared with the traditional local optimization method, this embodiment realizes the transformation from "single-point optimization" to "global optimization", enabling the system to fully understand whether there are blind spots, signal waste or power overload in the current configuration, and has the system-level performance intelligent judgment capability, which improves the balance and stability of the overall network deployment.

[0055] Through the multi-factor coupling analysis of evaluation indicators, communication stability indicators and disturbance trend factors, this embodiment designs a dynamic calculation and response mechanism for power adjustment values, which can determine whether compensation adjustment is needed according to the current system status, and combine the adjustment value with the current transmit power to calculate the final output power. This closed-loop mechanism ensures that the system has the ability to adjust on demand, respond dynamically, and make rapid corrections, truly realizing the qualitative change from traditional static power strategies to intelligent, self-adjusting, and self-sensing directions.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive wireless connection optimization method based on intelligent environment perception, characterized in that: The following steps are involved: S1, using sensors and scanning equipment to collect information in the propagation environment, fitting it into the original data set SW, and preprocessing it to obtain the perception data set GW; S2. By using the sensing data set GW, an extended path loss model is constructed and the total signal attenuation Lt on different propagation paths is calculated; S3, combining the total signal attenuation Lt obtained with the target area receiving power Pr and the topology disturbance data CL received on site, evaluating the communication stability index Γs of each area in the space, and drawing a space energy efficiency diagram; S4. By combining the acquired communication stability index Γs with the preset target channel stability threshold Γtar, the current transmission power PT required in the current environment is reversed, and the actual transmission power PTadj is calculated; S5. Perform a comprehensive performance evaluation on the global region based on the actual transmission power PTadj and the communication stability index Γs, and obtain an evaluation index TΦ; S6. According to the obtained evaluation index TΦ, topology disturbance data CL and communication stability index Γs, after detecting the dynamic topological changes of furniture position changes and personnel movement, the disturbance degree of the signal environment is adjusted to obtain the power adjustment value ΔPT required for real-time compensation.

2. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 1, characterized in that: S1 includes S11 and S12; S11, by using sensor equipment to scan and perceive the environment, collect information data in the propagation environment, including the wall dielectric constant change rate QD, the average reflection angle Er, the surface material reflection coefficient Pm, the furniture interference factor Hf, the topological disturbance data CL and the target area receiving power Pr, and construct the original data set SW; Among them, the wall dielectric constant change rate QD is obtained by millimeter wave radar collection; The average reflection angle Er is acquired through the ToF depth camera; The surface material reflectance coefficient Pm is acquired through a multi-spectral infrared scanner; The furniture interference factor Hf is obtained through high-frequency sonar collection; The topological disturbance data CL is acquired through the IMU dynamic attitude sensor; The target area receiving power Pr is acquired through the receiving signal monitor; S12, performing data cleaning and normalization processing on the acquired original data set SW to obtain the perception data set GW; Data cleaning includes using statistical outlier detection methods to remove outliers from the original data set SW, remove noise data above the mean, and obtain the data cleaning set CW; The normalization process includes normalizing the data cleaning set CW, uniformly mapping the data to the interval [0, 1], and obtaining the perception data set GW; The perception data set GW is obtained by the following formula: ； Wherein, GMb represents the b-th data in the perception data set GW, CWb represents the b-th data in the data cleaning set CW, minCWb represents the valley value of the b-th data in the data cleaning set CW, and maxCWb represents the peak value of the b-th data in the data cleaning set CW.

3. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 2, characterized in that: S2 includes S21 and S22; S21. Extract path features from the acquired perception data set GW, including material structure correction factor QPD, angular multipath attenuation factor Qer, environmental interference factor HCf, and path length index correction factor DMp, and fit them into the path data set LF, perform model training, and construct a multi-factor extended path loss model; The material structure correction factor QPD is obtained by the following formula: ； The angular multipath attenuation factor Qer is obtained by the following formula: ； In the formula, cos represents the cosine function, and π represents pi; The environmental interference factor HCf is obtained by the following formula: ； The path length index correction factor DMp is obtained by the following formula: ； Where CD represents the path loss factor; S22, calculating the total signal attenuation Lt on different propagation paths by using the acquired path data set LF and the multi-factor extended path loss model; The total signal attenuation Lt is obtained by the following formula: ； Where Lo represents the free space reference path loss, log represents the logarithmic function, They respectively represent the preset weight values ​​of the material structure correction factor QPD, the angular multipath attenuation factor Qer and the environmental interference factor HCf, and d represents the physical length of the propagation path.

4. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 2, characterized in that: S3 includes S31 and S32; S31, combining the acquired total signal attenuation Lt with the target area receiving power Pr and topology disturbance data CL received on site, and calculating the communication stability index Γs of the area; The communication stability index Γs is obtained by the following formula: ； Where X represents the disturbance sensitivity coefficient.

5. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 4, characterized in that: S32, constructing a regional signal energy efficiency index CKs based on the acquired communication stability index Γs and the actual space topology coordinate system; The energy efficiency index CKs is obtained by the following formula: ； In the formula, ck represents a constant; Combine the spatial points (x, y, z) in the three-dimensional environment with the energy efficiency index CKs to form an energy efficiency mapping color gradient map in the three-dimensional space; The gradient map is obtained by matching in the following way: Red area: 0<energy efficiency index CKs<0.3, indicating unstable signal and low transmission efficiency; Yellow area: 0.3≤energy efficiency index CKs≤0.7, indicating signal coverage acceptance; Green area: 0.7 < energy efficiency index CKs, indicating stable communication and efficient power use.

6. The method for intelligent environment-aware adaptive wireless connection optimization according to claim 5, characterized in that: S4 includes S41 and S42; S41, first establish a theoretical receiving power-transmitting power relationship; Recover the target area received power Pr=PT-Lt from the channel loss model, substitute it into the formula for obtaining the communication stability index Γs, and obtain the theoretical stability expression: ； Where Γtheo represents the theoretical communication stability index; Reversely solve the target power formula: According to the communication stability index Γs, reverse the current transmission power PT required in the current environment; The current transmit power PT is obtained by the following formula: ； Where Γtar represents the preset target channel stability threshold.

7. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 6, characterized in that: By introducing a disturbance response fine-tuning mechanism and building a disturbance response compensation model, the current transmit power PT is adjusted to obtain the actual transmit power PTadj; The disturbance change rate ΔCL is obtained through the disturbance response compensation model: The disturbance change rate ΔCL is obtained by the following formula: ； In the formula, ∂ represents the partial derivative; According to the obtained disturbance change rate ΔCL, the dynamic disturbance response function φ(CL) is defined to correct the transmission power: The dynamic disturbance response function φ(CL) is obtained by the following formula: ； Where, β represents the disturbance response coefficient; The actual transmit power PTadj is obtained by the following formula: 。 8. The method for intelligent environment-aware adaptive wireless connection optimization according to claim 7, characterized in that: S5 includes S51 and S52; S51, constructing the regional energy efficiency index Qus through the actual transmission power PTadj and the communication stability index Γs; The energy efficiency index Qus is obtained by the following formula: ； Where Qus(i) represents the energy efficiency index of the i-th region, Γs(i) represents the communication stability index of the i-th region, PTadj(i) represents the actual transmission power of the i-th region, and B represents a real number.

9. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 8, characterized in that: S52, integrating the energy efficiency index Qus of all regions, performing a comprehensive performance evaluation on the global region, obtaining the evaluation index TΦ, and judging the overall signal distribution state; The evaluation index TΦ is obtained by the following formula: ； Where N represents the number of evaluation areas, e represents a constant, k represents the distance attenuation coefficient, and D(i) represents the distance from the i-th area to the signal source; The overall signal distribution status is obtained by matching: When 0<evaluation index TΦ<0.5, it means there is a signal blind area and power waste; When 0.5≤evaluation index TΦ≤0.8, it means that the communication is generally acceptable, but there is power redundancy; When 0.8<evaluation index TΦ<1.0, it means that the overall configuration of the system is reasonable, the power usage is efficient, and the communication is stable.

10. The method for optimizing the intelligent environment-aware adaptive wireless connection according to claim 9, characterized in that: S6, performing feature extraction on the disturbance change rate ΔCL obtained according to the topological disturbance data CL to obtain a disturbance trend factor λp; The disturbance trend factor λp is obtained by the following formula: ； In the formula, tanh represents the hyperbolic tangent function, λa represents the change sensitivity adjustment coefficient; According to the obtained disturbance trend factor λp, combined with the evaluation index TΦ and the communication stability index Γs, the power adjustment value ΔPT to be added is calculated, and the state of the wireless power is judged; The power adjustment value ΔPT is obtained by the following formula: ； In the formula, represents the power adjustment sensitivity factor; The wireless power status is obtained by matching: When 0<power adjustment value ΔPT<0.5, it means that the power is stable and no additional compensation is required; When 0.5≤power adjustment value ΔPT<1, it indicates power abnormality and power adjustment is required. The final power output value PTfin is obtained by combining the obtained power adjustment value ΔPT with the actual transmit power PTadj; The final power output value PTfin is obtained by adding the actual transmission power PTadj and the power adjustment value ΔPT.

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