Intelligent sensor communication method

Abstract

The invention relates to the field of sensor communication, in particular to an intelligent sensor communication method. The method comprises the following steps: acquiring a physical quantity signal in an environment by using an intelligent sensor, generating a corresponding time domain signal, and converting the time domain signal into a frequency domain signal; based on the frequency domain signal, using a dynamic frequency hopping and power consumption perception optimization algorithm to select an optimal frequency; based on the optimal frequency, performing data block coding by using a nonlinear multi-level frequency coding algorithm; and evaluating the path state and the channel performance by using a path optimization selection algorithm, and calculating to obtain the optimal transmission path of each data block. The problems of frequency band overlapping and interference in sensor data acquisition are solved; the problems of fluctuation and inconsistency of sensor signals caused by changes of environmental conditions, reduction of signal reliability due to interference among different frequency channels, signal distortion due to frequency band interference among different data blocks, and fluctuation of channel quality and bandwidth of different paths along with time are solved.

Description

Technical Field

[0001] The present invention relates to the field of sensor communication, and in particular to an intelligent sensor communication method. Background Art

[0002] With the rapid development of emerging technologies, sensor technology has ushered in a major change. Traditional sensors are mainly used to monitor physical or chemical quantities in the environment, such as temperature, humidity, pressure, light intensity and gas concentration, and transmit the measured data to the controller or monitoring system in the form of analog signals. However, with the complexity and diversification of application scenarios, simple data measurement can no longer meet the needs of modern industry and consumer electronics. Therefore, smart sensors have gradually emerged and become a key component of future Internet of Things systems. Smart sensors can not only complete data collection and preliminary processing, but also realize remote data transmission and interconnection with other devices through communication interfaces.

[0003] In the future, with the further integration of communication technology, computing power and artificial intelligence, smart sensors will play an important role in more fields, provide strong technical support for the digital and intelligent transformation of human society, and promote the rapid evolution of industrial automation, smart life and smart medical care, so as to achieve more efficient resource allocation and more accurate services. The design of smart sensor communication methods needs to solve the problems of sensor equipment compatibility, real-time data transmission, stability and power consumption. At the same time, considering the complex and changeable communication network environment, such as electromagnetic interference in factories, attenuation of wireless signals, and transmission quality problems under severe weather conditions, smart sensor communication methods must also have certain robustness and anti-interference capabilities.

[0004] However, existing communication methods have at least the following technical problems: frequency band overlap and interference in sensor data acquisition; changes in environmental conditions lead to fluctuations and inconsistencies in sensor signals, which can cause signal distortion during transmission and reduce signal transmission quality; interference between different frequency channels can reduce signal reliability, while frequent frequency switching can increase sensor power consumption and affect device endurance; frequency band interference between different data blocks can cause signal distortion and affect communication quality; channel quality and bandwidth of different paths can fluctuate over time, and improper path selection can lead to transmission delays and data loss. Summary of the invention

[0005] The present invention provides an intelligent sensor communication method to solve the problems of frequency band overlap and interference in sensor data acquisition; and the fluctuation and inconsistency of sensor signals caused by changes in environmental conditions, which may cause signal distortion during transmission and reduce the transmission quality of the signal; interference between different frequency channels may reduce the reliability of the signal, and frequent frequency switching may increase the power consumption of the sensor and affect the endurance of the device; frequency band interference between different data blocks may cause signal distortion and affect the communication quality; the channel quality and bandwidth of different paths may fluctuate over time, and improper path selection may cause transmission delays and data loss.

[0006] A smart sensor communication method comprises the following steps:

[0007] S1: Use smart sensors to collect physical quantity signals in the environment and generate corresponding time domain signals; use adaptive time-frequency domain mapping algorithm to convert time domain signals into frequency domain signals;

[0008] S2: Based on the frequency domain signal, the optimal frequency is selected using dynamic frequency hopping and power-aware optimization algorithms;

[0009] S3: Based on the optimal frequency, a nonlinear multi-level frequency coding algorithm is used to encode the data block to obtain a coding value of the data block;

[0010] S4: Use the path optimization selection algorithm to evaluate the path status and channel performance, and calculate the optimal transmission path for each data block.

[0011] Preferably, the S1 specifically includes:

[0012] The adaptive time-frequency domain mapping algorithm is a spectrum mapping method that combines adaptive adjustment with Fourier transform. It is used to convert time domain signals into frequency domain signals, and dynamically capture the changing trend of time domain signals, and use derivative calculation and adaptive coefficients to correct the fluctuation characteristics of time domain signals.

[0013] Preferably, the S1 specifically includes:

[0014] By performing derivative analysis on the changes in the time domain signal, the rate of change of the time domain signal is calculated; an adaptive correction mechanism is designed, and after the rate of change of the time domain signal is calculated, the time domain signal is corrected by applying an adaptive correction coefficient, and the response speed of the signal is dynamically adjusted according to environmental conditions; the time signal after adaptive correction is converted into a frequency domain signal.

[0015] Preferably, the S2 specifically includes:

[0016] In the process of implementing the dynamic frequency hopping and power consumption awareness optimization algorithm, the signal strength and interference conditions at different frequencies are detected in real time, the available frequencies are traversed one by one, and compared with the signals of adjacent sensors. The differences are evaluated to determine whether there is a conflict in the use of the current frequency. The interference level of each frequency is determined by taking the average of the statistical differences.

[0017] Preferably, the S2 specifically includes:

[0018] A time-related periodic function is introduced to simulate the periodic changes in power consumption of the sensor during operation; the fluctuation frequency coefficient is used to control the frequency of the periodic fluctuation; the fluctuation amplitude coefficient is used to control the amplitude of the periodic fluctuation; the interference and power consumption balance coefficient is introduced to control the trade-off between interference and power consumption in frequency selection.

[0019] Preferably, the S3 specifically includes:

[0020] In the nonlinear multi-level frequency coding algorithm, data blocks are encoded based on different combinations of frequency components, and the weight of each frequency component in the data block follows an exponential decay function; among all frequency components, the weight of the data block that is far away from the optimal frequency component is reduced.

[0021] Preferably, the S3 specifically includes:

[0022] During the encoding process, the frequency components are periodically enhanced and a sinusoidal modulation mechanism is used to adapt to the dynamic changes of the environment.

[0023] Preferably, the S4 specifically includes:

[0024] The path optimization selection algorithm integrates the transmission status of the path signal over time based on the state performance of the path signal in different transmission paths; it also evaluates the channel bandwidth of the path, combines the channel bandwidth of the path with the path adjustment coefficient to form a bandwidth adjustment model; and uses the time attenuation factor to capture the change trend of the transmission path performance over time.

[0025] Preferably, the S4 specifically includes:

[0026] The optimal transmission path selection formula for data blocks is:

[0027]

[0028] in, Indicates the optimal transmission path number of the nth data block; It means finding the transmission path that minimizes the loss function among all transmission paths p; represents the integral part of the loss function; θ p is the inclination angle of the transmission path p; sin(θp ·t) represents the sine value of the transmission inclination of the signal of the transmission path p at time t; c p represents the channel bandwidth of the transmission path p; δ represents the path adjustment coefficient; e -k·t represents the time attenuation factor; κ represents the time attenuation coefficient, which reflects the changing trend of the transmission path performance over time; represents the logarithmic part of the loss function; Q p Indicates the current channel quality of the transmission path p; Indicates the average channel quality of all transmission paths.

[0029] The beneficial effects of the technical solution of the present invention are:

[0030] 1. Based on the adaptive time-frequency domain mapping algorithm, accurate signal conversion and transmission are achieved. The signal change trend is dynamically captured and modified using derivatives. The frequency band conflict problem caused by the superposition of multiple physical quantity signals is solved, ensuring that the frequency characteristics of different signals do not overlap. By using adaptive coefficients to dynamically adjust the signal, the distortion problem caused by signal fluctuations of sensors in complex environments is avoided. The accurate separation and reliable transmission of multi-sensor data in different frequency bands are achieved, ensuring the stability of subsequent data analysis and communication. It is suitable for signal acquisition and transmission scenarios of remote monitoring and control systems.

[0031] 2. Optimize frequency selection based on dynamic frequency hopping and power consumption perception algorithm to improve communication efficiency. By adjusting the balance coefficient between frequency hopping and interference and power consumption, reduce signal conflicts, optimize power consumption, and dynamically adapt to changes in the external environment. When signal interference increases, the frequency automatically jumps to a frequency band with lower interference to ensure the continuity and stability of communication. At the same time, considering the characteristics of sensor power consumption changing with frequency, efficient energy management is achieved, the battery life of sensor equipment is extended, and the reliability of the wireless communication network is enhanced, which meets the technical requirements of energy saving and anti-interference in sensor communication.

[0032] 3. Use a nonlinear multi-level frequency coding algorithm to independently encode different data blocks by combining exponential decay, periodic modulation and sinusoidal enhancement. By enhancing the signal strength of important frequency components and reducing the weight of data blocks composed of optimal frequency components, the stability of signal transmission in different communication environments is ensured. In high-interference environments, reliable data transmission is achieved by enhancing signal strength. In low-interference environments, power consumption is reduced through sinusoidal modulation, achieving a dynamic balance between power consumption and signal strength, and improving the anti-interference ability and stability of the overall communication.

[0033] 4. Based on the path optimization selection algorithm, the status and channel performance of different transmission paths are evaluated by calculating the loss function, and the optimal transmission path is selected in real time. The transmission inclination, time attenuation and channel bandwidth of the transmission path signal are considered to ensure the rationality of the transmission path selection. By smoothing the channel quality differences and the attenuation of path performance over time, the dynamic optimization selection of the channel is realized, avoiding the communication interruption problem caused by the sudden change of the transmission path quality, ensuring the stability of signal transmission in complex environments, and meeting the strict requirements of multi-path data transmission on communication quality and response time. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flow chart of a smart sensor communication method. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0037] A specific solution of an intelligent sensor communication method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0038] Refer to the attached Figure 1 , which shows a flow chart of a smart sensor communication method provided by an embodiment of the present invention, the method comprising the following steps:

[0039] S1. Use intelligent sensors to collect physical quantity signals in the environment and generate corresponding time domain signals; use adaptive time-frequency domain mapping algorithm to convert time domain signals into frequency domain signals;

[0040] Smart sensors are used to collect physical quantity signals in the environment, such as temperature, humidity and air pressure, and generate corresponding time domain signals S(t). However, since the sensors need to collect different physical quantity signals at the same time, directly transmitting time domain signals will cause frequency band overlap and interference. It is necessary to use an adaptive time-frequency domain mapping algorithm to convert time domain signals into frequency domain signals so that the frequency characteristics of each time domain signal can be identified and distinguished, so that physical quantity signals collected by different sensors can be transmitted on different frequency bands to avoid frequency band conflicts.

[0041] The adaptive time-frequency domain mapping algorithm is a spectrum mapping method that combines adaptive adjustment with Fourier transform, and aims to accurately convert the physical signal collected by the sensor from the time domain signal to the frequency domain signal. The core idea is to dynamically capture the changing trend of the time domain signal, use derivative calculation and adaptive coefficients to correct the fluctuation characteristics of the time domain signal, and finally convert it into a frequency form that is easy to analyze and process through frequency domain mapping, which solves the problem of time domain signal fluctuation and inconsistency encountered by the sensor when collecting physical quantity signals in a complex environment;

[0042] In order to capture the subtle changes of time domain signals, the calculation of the change rate of time domain signals is introduced, that is, the derivative analysis of the changes of time domain signals is performed. The change rate of time domain signals reflects the change speed and direction of time domain signals at different time points, thereby revealing the key details of the dynamic characteristics of time domain signals. By calculating the change rate of time domain signals, the changes of time domain signals can be accurately identified, avoiding the distortion of time domain signals caused by instantaneous fluctuations.

[0043] In view of the possible fluctuations and noise interference of time domain signals, an adaptive correction mechanism is designed. After calculating the change rate of the time domain signal, the time domain signal is corrected by applying the adaptive correction coefficient according to the change trend. The correction process makes the dynamic characteristics of the time domain signal conform to the actual situation, and the response speed of the time domain signal can be dynamically adjusted according to different environmental conditions.

[0044] After the adaptive correction, the time domain signal will be further converted into a frequency domain signal. The frequency domain signal is realized by analyzing the performance of the time domain signal at different frequencies. The core is to map the time information in the time domain signal to the frequency space, thereby revealing the periodic laws that may exist in the time domain signal.

[0045] The conversion formula of frequency domain signal is:

[0046]

[0047] Among them, F(ω, t) represents the frequency domain signal converted from the time domain signal; S(t) represents the time domain signal, that is, the physical signal collected by the sensor at time t; It represents the rate of change of the time domain signal, that is, the rate of change of the time domain signal over time, reflecting the increase and decrease trend of the physical quantity signal at different time points, and is used to capture the dynamic characteristics of the time domain signal; β represents the adaptive correction coefficient, which is used to balance the weight coefficient of the time domain signal and the rate of change of the time domain signal, ensuring the accuracy of the spectrum mapped by the time domain signal; e -jωtrepresents the kernel function of Fourier transform, j is the imaginary unit, ω is the frequency component of the time domain signal in Fourier transform; T represents the integration interval of Fourier transform, that is, the time period covered when calculating the frequency domain signal, which is used to determine the time length of spectrum analysis; Represents the time integral in Fourier transform, which is used to convert a time domain signal into a frequency domain signal.

[0048] S2, based on the frequency domain signal, uses dynamic frequency hopping and power consumption awareness optimization algorithm to select the optimal frequency;

[0049] In order to solve the interference problem in different frequency communications and extend the battery life of the sensor, dynamic frequency hopping and power consumption awareness optimization algorithm are used to select the optimal frequency;

[0050] By detecting the signal strength and interference of sensors at different frequencies in real time, the available frequencies are traversed one by one, and compared with the signals of adjacent sensors, the differences are evaluated to determine whether there is a conflict in the use of the current frequency. By calculating the average value of the statistical differences, the interference level of each frequency can be determined. The frequency with the lower interference level will be given priority for communication to ensure signal quality.

[0051] At different frequencies, the power consumption level of sensor devices will be different. The exponential decrease method is used to describe the decrease in power consumption when the frequency is higher. When the sensor device selects a higher frequency channel for communication, the power consumption will decrease, but the degree of decrease will also be adjusted by the power consumption attenuation coefficient to ensure the rationality of the frequency selection. It is also necessary to consider the power consumption fluctuations caused by the external environment during the actual operation of the sensor. A time-related periodic function is introduced to simulate the periodic changes in power consumption of the sensor during operation, and the fluctuation frequency coefficient is used to control the frequency of the periodic fluctuation, and the fluctuation amplitude coefficient is used to control the amplitude of the periodic fluctuation.

[0052] During the frequency band selection process, the signal status of the current frequency is continuously detected. Once a conflict or increased signal interference is found, it will immediately jump to other available frequencies. The jumping process is automatic to ensure the continuity and stability of communication. In order to reduce the impact of frequent jumping on communication quality, the balance coefficient of interference and power consumption is introduced to control the trade-off between interference and power consumption in frequency selection.

[0053] The formula for calculating the optimal frequency is:

[0054]

[0055] Among them, ω o represents the optimal frequency; Represents the "minimum value" operator, which is used to find the frequency ω that makes the expression inside the formula reach the minimum value; represents the average value of the difference in neighboring sensor signals, which is used to measure the overall interference level at frequency ω; M represents the number of neighboring sensors, reflecting how many neighboring sensors participate in the communication in the current communication environment; F m (ω, t) represents the frequency domain signal of the adjacent m-th sensor at frequency ω, which is used to measure the frequency conflict or signal interference between adjacent sensors; |F(ω, t)-F m (ω, t)| 2 represents the square of the difference between the current sensor signal and the mth adjacent sensor signal, and measures the conflict intensity between sensors at the current frequency ω; α represents the balance coefficient between interference and power consumption, and is used to control the trade-off between interference and power consumption in frequency selection; P(ω, t) represents the power consumption at frequency ω and time t, and is used to measure the power consumption level of the current frequency. The specific calculation formula is:

[0056] P(ω, t)=P0·e -γ·ω +η·sin(λt)

[0057] Among them, P0 represents the initial power consumption, which is the basic power consumption of the sensor when no frequency jump is performed, and reflects the power consumption level of the sensor in static operation; -γ·ω represents the frequency-related exponential decay term, γ represents the power consumption decay coefficient, which is used to control the decay speed of power consumption with increasing frequency, reflecting the power consumption optimization capability of the sensor for high-frequency operation; η·sin(λt) represents the periodic power consumption fluctuation term, which is used to simulate the periodic power consumption changes of the sensor during operation; η represents the fluctuation amplitude coefficient, which is used to control the amplitude of periodic fluctuations, reflecting the magnitude of the sensor power consumption fluctuation; sin(λt) represents the time-related periodic function, which is used to simulate the periodic changes of power consumption. The length of the period is determined by λ, and λ represents the fluctuation frequency coefficient, which is used to control the frequency of periodic fluctuations, that is, the speed of power consumption fluctuations.

[0058] S3, based on the optimal frequency, using a nonlinear multi-level frequency coding algorithm to encode the data block to obtain a coding value of the data block;

[0059] Based on the optimal frequency, a nonlinear multi-level frequency coding algorithm is used to calculate the coding value of the data block. The nonlinear multi-level frequency coding algorithm combines exponential decay, periodic modulation and sinusoidal enhancement method to improve the anti-interference ability and communication stability, and can flexibly cope with different communication environments;

[0060] Each data block needs to be independently encoded to ensure that different data blocks are transmitted using different frequency components to reduce frequency band interference; the encoding of each data block is based on a combination of several different frequency components. In order to achieve anti-interference, the weight of each frequency component in the data block follows an exponential decay function. Among all frequency components, the weight of the data block that is far away from the optimal frequency component is reduced to reduce mutual interference between different data blocks;

[0061] At the same time, during the encoding process, the frequency components are periodically enhanced so that some data blocks have enhanced signal strength at specific times. The enhancement effect is controlled by a cosine function to ensure that the periodic changes of the signal can penetrate the frequency barrier and ensure reliable transmission of the signal.

[0062] A sinusoidal modulation mechanism is also used to adapt to dynamic changes in the environment. During the transmission of each data block, the signal strength will fluctuate over time. Through sinusoidal modulation, power consumption can be reduced in low-interference environments and signal strength can be enhanced in high-interference environments.

[0063] The calculation formula of the encoding value of the data block is:

[0064]

[0065] Wherein, E(n) represents the encoding value of the nth data block; represents the cumulative summation symbol, which accumulates the code values ​​of all frequency components, and the cumulative range is from the k=1th frequency component to the k=Kth frequency component; F(ω o，k ) represents the signal strength of the kth frequency component at the optimal frequency; n represents the number of the current data block; k represents the number of the frequency component; K represents the total number of frequency components; represents an exponential decay function, which reduces the weight of the data block composed of the frequency components far away from the optimal frequency through exponential decay, thereby reducing the mutual interference between different data blocks; a represents the coefficient of controlling the exponential decay, which is dynamically adjusted according to the interference intensity and channel conditions; represents the periodic enhancement term, which is implemented by the cosine function to enhance the data block at the optimal frequency to cope with the periodic interference in the environment; b represents the periodic enhancement coefficient; Represents a nonlinear sinusoidal modulation term. The nonlinear change is introduced by sinusoidal modulation, which makes the spectrum distribution of the data block complex and effectively improves the anti-interference ability.

[0066] Each data block is encoded with different frequency components, which can effectively avoid frequency band conflicts, make the transmission of data blocks stable in different environments, and achieve flexible frequency use and reliable signal transmission.

[0067] S4, using the path optimization selection algorithm to evaluate the path status and channel performance, and calculate the optimal transmission path for each data block;

[0068] Use the path optimization selection algorithm to evaluate the path status and channel performance through the loss function, and calculate the optimal transmission path for each data block;

[0069] The path optimization selection algorithm takes into account the state performance of the path signal in different transmission paths. The path signal is the transmission signal on the path. In order to capture the changes of the path signal at different times, the transmission status of the path signal is time-integrated. The inclination information of the transmission path is an important parameter, which describes the impact of the incident or transmission angle of the path signal on the transmission quality. The inclination changes with time, and the sine function of the inclination is used to evaluate the performance dynamics of the path; the channel bandwidth of the path is further evaluated. The channel bandwidth characterizes the transmission capacity of the path. The higher the bandwidth of the path, the faster the transmission speed. The bandwidth of the path and the path adjustment coefficient are combined to form a bandwidth adjustment model to flexibly respond to the transmission status of different paths; in order to capture the change trend of the path performance over time, the time decay factor is used, that is, the path performance will decay over time;

[0070] By integrating the above path status over the entire time period, the comprehensive performance of the transmission path over a period of time is calculated. The integral part is used to evaluate the transmission quality of each transmission path to help identify and select the path with the best signal transmission performance;

[0071] To further ensure the accuracy of transmission path selection, the difference between the channel quality of each transmission path and the average channel quality of all transmission paths will be compared. The better the channel quality, the higher the stability of signal transmission. In order to avoid excessive deviation values ​​leading to excessively extreme transmission path selection, the deviation values ​​are logarithmically transformed, which can effectively avoid serious impact on overall transmission caused by sudden changes in the quality of individual transmission paths.

[0072] The optimal transmission path selection formula for data blocks is:

[0073]

[0074] in, Indicates the optimal transmission path number of the nth data block; It means finding the transmission path that minimizes the loss function among all possible transmission paths p; represents the integral part of the loss function, which is integrated from time t 0 to T to evaluate the performance of the transmission path in the entire time window; θ p is the inclination angle of the transmission path p, which is used to describe the physical characteristics and transmission state of the transmission path; sin(θ p·t) represents the sine value of the transmission inclination angle of the signal on the transmission path p at time t, which is used to measure the impact of the transmission angle of the signal on the performance in the transmission path; c p represents the channel bandwidth of the transmission path p, which is used to evaluate the transmission capacity of the transmission path; δ represents the path adjustment coefficient, which is used to dynamically adjust the priority of the transmission path; e -κ·t represents the time decay factor, which is used to describe the exponential decay of the transmission path performance over time, simulate the situation where the transmission path quality decreases over time, and ensure that stable paths are selected first; κ represents the time decay coefficient, which reflects the changing trend of the transmission path performance over time; It represents the logarithmic part of the loss function, which is used to ensure that the impact of the difference in transmission path channel quality is smoothed in the loss function to avoid excessively extreme transmission path selection due to excessive deviation value; Q p represents the current channel quality of the transmission path p, and is used to evaluate the channel status of the current transmission path in real time. The higher the channel quality, the better the transmission performance of the transmission path. It is calculated based on the quality and bit error rate of the real-time communication signal. The calculation method is a technical means well known to those skilled in the art and will not be described in detail here. Indicates the average channel quality of all transmission paths, which serves as a reference for the current transmission path p and helps determine whether the transmission path is better or worse than the overall average level; It represents the absolute value of the difference between the channel quality of the transmission path p and the average channel quality, and is used to measure the degree to which the performance of the current transmission path p deviates from the average level.

[0075] By dynamically selecting the optimal transmission path, the signal transmission quality and stability can be guaranteed.

[0076] In summary, a smart sensor communication method is completed.

[0077] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A smart sensor communication method, characterized in that: The following steps are involved: S1: Use smart sensors to collect physical quantity signals in the environment and generate corresponding time domain signals; use adaptive time-frequency domain mapping algorithm to convert time domain signals into frequency domain signals; S2: Based on the frequency domain signal, the optimal frequency is selected using dynamic frequency hopping and power-aware optimization algorithms; S3: Based on the optimal frequency, a nonlinear multi-level frequency coding algorithm is used to encode the data block to obtain a coding value of the data block; S4: Use the path optimization selection algorithm to evaluate the path status and channel performance, and calculate the optimal transmission path for each data block.

2. The intelligent sensor communication method according to claim 1, characterized in that: The S1 specifically includes: The adaptive time-frequency domain mapping algorithm is a spectrum mapping method that combines adaptive adjustment with Fourier transform. It is used to convert time domain signals into frequency domain signals, and dynamically capture the changing trend of time domain signals, and use derivative calculation and adaptive coefficients to correct the fluctuation characteristics of time domain signals.

3. The intelligent sensor communication method according to claim 2, characterized in that: The S1 specifically includes: By performing derivative analysis on the changes in the time domain signal, the rate of change of the time domain signal is calculated; an adaptive correction mechanism is designed, and after the rate of change of the time domain signal is calculated, the time domain signal is corrected by applying an adaptive correction coefficient, and the response speed of the signal is dynamically adjusted according to environmental conditions; the time signal after adaptive correction is converted into a frequency domain signal.

4. The intelligent sensor communication method according to claim 1, characterized in that: The S2 specifically includes: In the process of implementing the dynamic frequency hopping and power consumption awareness optimization algorithm, the signal strength and interference conditions at different frequencies are detected in real time, the available frequencies are traversed one by one, and compared with the signals of adjacent sensors. The differences are evaluated to determine whether there is a conflict in the use of the current frequency. The interference level of each frequency is determined by taking the average of the statistical differences.

5. The intelligent sensor communication method according to claim 4, characterized in that: The S2 specifically includes: A time-related periodic function is introduced to simulate the periodic changes in power consumption of the sensor during operation; the fluctuation frequency coefficient is used to control the frequency of the periodic fluctuation; the fluctuation amplitude coefficient is used to control the amplitude of the periodic fluctuation; the interference and power consumption balance coefficient is introduced to control the trade-off between interference and power consumption in frequency selection.

6. The intelligent sensor communication method according to claim 1, characterized in that: The S3 specifically includes: In the nonlinear multi-level frequency coding algorithm, data blocks are encoded based on different combinations of frequency components, and the weight of each frequency component in the data block follows an exponential decay function; among all frequency components, the weight of the data block that is far away from the optimal frequency component is reduced.

7. The intelligent sensor communication method according to claim 6, characterized in that: The S3 specifically includes: During the encoding process, the frequency components are periodically enhanced and a sinusoidal modulation mechanism is used to adapt to the dynamic changes of the environment.

8. The intelligent sensor communication method according to claim 1, characterized in that: The S4 specifically includes: The path optimization selection algorithm integrates the transmission status of the path signal over time based on the state performance of the path signal in different transmission paths; it also evaluates the channel bandwidth of the path, combines the channel bandwidth of the path with the path adjustment coefficient to form a bandwidth adjustment model; and uses the time attenuation factor to capture the change trend of the transmission path performance over time.

9. The intelligent sensor communication method according to claim 8, characterized in that: The S4 specifically includes: The optimal transmission path selection formula for data blocks is: in, Indicates the optimal transmission path number of the nth data block; It means finding the transmission path that minimizes the loss function among all transmission paths p; represents the integral part of the loss function; θ p is the inclination angle of the transmission path p; sin(θ p ·t) represents the sine value of the transmission inclination of the signal of the transmission path p at time t; c p represents the channel bandwidth of the transmission path p; δ represents the path adjustment coefficient; e -κ·t represents the time attenuation factor; κ represents the time attenuation coefficient, which reflects the changing trend of the transmission path performance over time; represents the logarithmic part of the loss function; Q p Indicates the current channel quality of the transmission path p; Indicates the average channel quality of all transmission paths.

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