Disease prevention environmental sensor electric signal processing method

Abstract

The invention provides a disease prevention environment sensor electric signal processing method, which relates to the technical field of signal processing, and comprises the following specific steps: making a disease prevention environment electric signal data set, introducing a gain factor, and combining a graph gain and a spatial distance to carry out fusion processing on a disease prevention environment electric signal to obtain a disease prevention environment electric signal data set; the method comprises the following steps: extracting an abnormal signal mode by using multi-scale fractional differential and wavelet basis function decomposition, further reversely enhancing abnormal components through manifold geometric gradient and fractional differential, introducing a self-adaptive threshold to calculate abnormal confidence, fusing confidence of different sensors through Riemannian manifold distance, and dynamically adjusting parameters in combination with a feedback mechanism to obtain the abnormal signal mode. Optimizing disease prevention environment electric signal processing; meanwhile, a disease prevention environment electric signal processing model is provided, and disease prevention environment electric signals are optimized through the model; the model efficiently completes the detection and optimization of the electric signals of the disease prevention environment, and provides accurate data support for disease prevention.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to a disease prevention environmental sensor electrical signal processing method. Background Art

[0002] In the field of disease prevention, the disease prevention environmental sensor electrical signals collected by environmental sensors provide important data support for health monitoring and early diagnosis of diseases; disease prevention environmental sensor electrical signals can provide real-time health data to help timely detect disease risks; however, existing signal processing methods have some technical bottlenecks. When dealing with disease prevention environmental sensor electrical signals, traditional signal processing methods are difficult to accurately extract useful information and are easily affected by noise, interference and nonlinear changes in signals, resulting in unstable and inaccurate signal processing results; traditional anomaly detection methods usually rely on fixed thresholds and simple statistical models, perform poorly in dynamically changing environments, and cannot adapt to rapidly changing health conditions and environmental conditions, resulting in false alarms and missed alarms, affecting the accuracy of disease prevention.

[0003] In addition, the existing sensor data fusion methods fail to fully consider the temporal and spatial relationship between sensors. The accuracy of data fusion from different sensors is not high, and it is unable to effectively improve the monitoring capability of disease prevention. In disease prevention, the parameter values ​​of temperature and humidity, air quality, and gas concentration have a significant impact on human health, and the precise processing of electrical signals from disease prevention environmental sensors is directly related to the effect of disease prevention. Environmental changes have a significant impact on human health. Accurately processing disease prevention environmental electrical signals and timely discovering potential risks have become urgent issues to be addressed in the field of disease prevention. Summary of the invention

[0004] The present invention provides a disease prevention environment sensor electrical signal processing method, aiming to propose a disease prevention environment electrical signal processing model, wherein a gain factor and a graph gain method are introduced to optimize the fusion of disease prevention environment electrical signals, thereby improving signal quality and consistency; a multi-scale processing technology of fractional differential and wavelet basis function is adopted to accurately extract abnormal patterns of disease prevention environment electrical signals and calculate initial abnormal probability; a mixed Gaussian-Riemann manifold space and manifold geometric gradient are used to enhance abnormal components, thereby improving the recognition accuracy of abnormal patterns of disease prevention environment electrical signals; an adaptive threshold is generated by calculating abnormal confidence and combining spatial consistency, thereby optimizing the detection of abnormal disease prevention environment electrical signals; the confidences of different sensors are fused based on the Riemann manifold distance, and parameters are dynamically adjusted to achieve efficient response in complex environments; and a disease prevention environment electrical signal processing model is used to complete accurate processing and abnormality detection of disease prevention environment electrical signals.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a disease prevention environmental sensor electrical signal processing method, the specific steps are as follows: S1, collect electrical signals collected by environmental sensors and create a disease prevention environmental electrical signal dataset; S2. According to the spatiotemporal relationship of the disease prevention environment electrical signals, a gain factor is introduced, and the disease prevention environment electrical signals are fused by combining the graph gain method and the spatial distance; S3, decompose the signal by multi-scale fractional differential and wavelet basis function to generate multi-scale components, extract the abnormal signal pattern, and calculate the initial abnormal probability by combining the sensor spatial distance and electrical signal difference; S4, mapping the disease prevention environment electrical signal to the mixed Gaussian-Riemann manifold space, enhancing the disease prevention environment electrical signal, and inversely enhancing the abnormal component through the manifold geometric gradient and fractional differential; S5, generating an adaptive threshold based on the mean, standard deviation and spatial consistency measurement of the local disease prevention environment electrical signal, and generating an abnormality confidence by combining the enhanced disease prevention environment electrical signal with the initial abnormality probability; S6, fusing the confidences of different sensors through the Riemann manifold distance, verifying the abnormal mode, and feeding back the fusion results to the initial step S2, dynamically adjusting the fractional-order differential parameters, performing feedback optimization on the disease prevention environment electrical signal, iterating until convergence, and obtaining the disease prevention environment electrical signal after the final processing and the final fusion confidence; S7. Construct a disease prevention environment electrical signal processing model, input a disease prevention environment electrical signal data set, go through steps S2 to S6 in sequence, and perform iterative training until convergence to complete the optimization processing of the disease prevention environment electrical signal.

[0006] Preferably, in step S1, for the production of a disease prevention environmental electrical signal data set, environmental sensors suitable for disease prevention monitoring are selected. The environmental sensors include temperature and humidity sensors, air quality sensors, and gas concentration sensors, which are mainly used to monitor environmental parameters such as temperature and humidity, air quality, and gas concentration. Nursing homes are selected as collection sites for disease prevention environmental electrical signal data sets, and sensors are installed at different monitoring locations to ensure that various environmental conditions in the target area are covered. Environmental sensors are used to continuously collect real-time data and record electrical signals under different time and space conditions. All collected disease prevention environmental electrical signals will be stored together with the corresponding sensor sampling values ​​and spatial coordinate information, and data annotation will be performed. The disease prevention environmental electrical signals and annotation information will be organized into a unified format to complete the production of the disease prevention environmental electrical signal data set.

[0007] Preferably, in step S2, the specific steps of fusing the disease prevention environment electrical signal are: S21, input sensor original electrical signal and sensor space coordinates , represents the sampling value of the i-th sensor node at time t, is the total number of sensor nodes, is the three-dimensional spatial coordinate of sensor i. After the original electrical signal is processed by mean zeroing and variance normalization, the spatial and temporal relationship between the disease prevention environment electrical signals is calculated, and the signal association model is established to obtain the gain factor between the disease prevention environment electrical signals. The mathematical model of the gain factor is: ; In the formula, is the gain factor, which represents the signal gain of sensors i and j between time t, and are the signal values ​​of sensors i and j at time t, is an exponential function, and is the adjustment coefficient; S22. Use the graph gain method to perform signal fusion, construct the graph structure between the disease prevention environment electrical signals and optimize the signal fusion process. The mathematical model of the disease prevention environment electrical signal fusion is: ; In the formula, is the fusion signal matrix after graph gain optimization, is the eigenvector matrix. The first three principal components are extracted by principal component analysis of the disease prevention environmental electrical signal, and the energy of the principal components is retained. is the graph gain matrix, which is a diagonal matrix with diagonal elements being gain factors. is the relationship matrix of the disease prevention environmental electrical signal, and the relationship matrix is ​​obtained by calculating the covariance matrix of the disease prevention environmental electrical signal. for The transposed matrix of is element-wise multiplication; S23. By combining the gain factor, the graph gain and the spatial distance and processing the signal using an exponential function, the optimized disease prevention environmental electrical signal is obtained. The mathematical model is: ; In the formula, For optimized disease prevention environmental electrical signals, represents the position of sensor i, is the adjustment factor for controlling the spatial distance attenuation term, is the average value of the sensor positions.

[0008] Preferably, in step S2, a gain factor is introduced according to the spatiotemporal relationship of the disease prevention environment electrical signal, and the fused disease prevention environment electrical signal is optimized by combining the graph gain method and the spatial distance; first, the design of the gain factor adjusts the relative gain between the disease prevention environment electrical signals by considering the spatiotemporal correlation between sensors, thereby improving the complementarity and effectiveness between the disease prevention environment electrical signals; the mathematical model of the gain factor uses an exponential function to dynamically adjust the gain of the signal according to the change of the disease prevention environment electrical signal, thereby enhancing the sensitivity and adaptability of the signal; secondly, a graph gain method is used to construct a relationship graph between the disease prevention environment electrical signals, and the signal fusion is achieved by optimizing the graph gain matrix, thereby further improving the accuracy and robustness of the disease prevention environment electrical signal processing; finally, combined with the spatial distance attenuation term, the weighted fusion of the disease prevention environment electrical signal is optimized by adjusting the influence of the spatial distance on the signal, so that the fused disease prevention environment electrical signal is more accurate, effectively reducing the interference of the spatial position on the signal, and improving the monitoring capability of the disease prevention system; the overall design significantly improves the flexibility and accuracy of the disease prevention environment electrical signal processing through the combination of gain factor, graph gain and spatial distance control, and provides a more efficient monitoring means for disease prevention.

[0009] Preferably, in step S3, the specific steps of calculating the initial abnormality probability are: S31. Decompose the disease prevention environmental electrical signal into multi-scale tensors and directly extract potential abnormal patterns. The mathematical model is: ; In the formula, is the disease prevention environmental electrical signal component after multi-scale decomposition, is a fractional differential operator, , controls the decomposition of disease prevention environmental electrical signals at different scales, s is the scale parameter, Corresponding to high frequency, medium frequency and low frequency components respectively, It is an adaptive wavelet basis function, and the frequency band component is controlled by the scale parameter s; The mathematical model of the fractional-order differential operator is: ; In the formula, is the gamma function, which normalizes the result of fractional differentiation. To obtain The integer part of , a is the historical time, and t is the current time point; The mathematical model of adaptive wavelet basis function is: ; S32. Combine multi-scale components with spatial correlation to generate initial anomaly probability. The mathematical model is: ; In the formula, is the initial abnormal probability, is the Euclidean space distance between sensor nodes i and j, is the smoothing constant, Variance of environmental electrical signals for disease prevention under scale parameter s.

[0010] Preferably, in step S3, the disease prevention environment electrical signal is decomposed by multi-scale fractional differentials and wavelet basis functions, the abnormal pattern of the disease prevention environment electrical signal is extracted, and the initial abnormal probability is calculated by combining the spatial distance of the sensor and the difference between the disease prevention environment electrical signal; first, the disease prevention environment electrical signal is multi-scale processed by fractional differentials to capture the multi-level features of the signal at different scales, and has better sensitivity to subtle changes in the disease prevention environment electrical signal, thereby improving the accuracy of anomaly detection; the fractional differential operator flexibly controls the response of the disease prevention environment electrical signal in different frequency bands by adjusting the order, and optimizes the signal. The time domain and frequency domain characteristics of the disease prevention environmental electrical signal are analyzed; the wavelet basis function is used to further decompose the disease prevention environmental electrical signal, and the potential abnormal patterns in the signal are extracted. Through the adjustment of the adaptive wavelet basis function, the accurate processing of signals in different frequency bands is achieved, and the key features in the time-varying signal are captured; finally, the initial abnormal probability is calculated based on the spatial distance between sensors and the difference in disease prevention environmental electrical signals, and the anomalies of disease prevention environmental electrical signals are identified at different spatial positions, providing a reliable basis for anomaly detection and signal enhancement; the overall design improves the accuracy of disease prevention environmental electrical signal analysis and the sensitivity of anomaly detection through the combination of multi-scale decomposition and spatial correlation.

[0011] Preferably, in step S4, the specific steps of enhancing the disease prevention environment electrical signal are: S41. Mapping the disease prevention environmental electrical signal to a mixed Gaussian-Riemann manifold to enhance the abnormal components. The mathematical model is: ; In the formula, For enhanced disease prevention environmental electrical signals, is the number of Gaussian components, is the uniform mixing coefficient, is the center of the manifold, is the covariance, is the adaptive fractional order parameter, , is the information entropy, is a stabilizing constant to prevent the denominator from being zero. ; S42, the abnormal signal is enhanced by reverse enhancement of fractional gradient, and the mathematical model is: ; In the formula, is the geometric gradient of the manifold computed via the Riemann logarithmic map, , is a logarithmic mapping on the Riemann manifold, mapping the disease prevention environmental electrical signal from the manifold space to the tangent space, To increase strength, , for of A value equal to 0.5, is the symbolic function, Is an assignment symbol, indicating that the signal is updated through gradient enhancement.

[0012] Preferably, in step S4, the disease prevention environment electrical signal is mapped to a mixed Gaussian-Riemann manifold space, and the abnormal components are reversely enhanced using the manifold geometric gradient and fractional differential methods, thereby achieving enhanced processing of the disease prevention environment electrical signal; first, the disease prevention environment electrical signal is mapped to a mixed Gaussian-Riemann manifold space, so that the disease prevention environment electrical signal can be more effectively processed in a space with a geometric structure, which helps to retain the intrinsic characteristics and spatial structure of the disease prevention environment electrical signal; the abnormal components of the disease prevention environment electrical signal are enhanced by manifold geometric gradient calculation and combined with the geometric properties of the manifold, thereby improving the recognition accuracy of abnormal patterns; the abnormal components in the disease prevention environment electrical signal are optimized using the reverse enhancement method of fractional differentials to enhance recognizability; the design of fractional differentials enables fine adjustment of the disease prevention environment electrical signal at different scales, providing greater flexibility for detailed processing of abnormal patterns of the disease prevention environment electrical signal; the enhancement strategy of step S4 effectively improves the quality of the disease prevention environment electrical signal, and provides a clearer disease prevention environment electrical signal foundation for abnormal detection and pattern recognition.

[0013] Preferably, in step S5, the specific steps of calculating the anomaly confidence are: S51, generating an adaptive threshold based on signal statistical characteristics and spatial consistency, the mathematical model is: ; In the formula, is the adaptive threshold, is the local signal mean, , is the local standard deviation, , is half the width of the time window, is the total length of the time window, the calculation window is 5 seconds, is the spatial consistency measure, is the adaptive threshold amplitude adjustment coefficient, is the spatial consistency adjustment coefficient; The mathematical model of spatial consistency measurement is: ; In the formula, For the mutual correlation coefficient, the correlation of the disease prevention environmental electrical signals was calculated; The mathematical model of the mutual correlation coefficient is: ; In the formula, for The average value of for The average value of is the sum of all moments in time t; S52. The enhanced disease prevention environmental electrical signal is combined with the adaptive threshold to generate confidence. The mathematical model is: ; In the formula, is the abnormal confidence level, is the normalization constant, , max is the maximum value.

[0014] Preferably, in step S5, by calculating the abnormal confidence of the disease prevention environment electrical signal, combining the statistical characteristics and spatial consistency of the local disease prevention environment electrical signal, an adaptive threshold is generated and anomaly detection is further optimized; first, the adaptive threshold is generated by measuring the mean, standard deviation and spatial consistency of the disease prevention environment electrical signal. The adaptive threshold can be adaptively adjusted according to the local fluctuation and spatial distribution of the disease prevention environment electrical signal, and can flexibly respond to changes in the disease prevention environment electrical signal. The design of the adaptive threshold takes into account the time-varying characteristics of the disease prevention environment electrical signal, and can accurately distinguish normal signals from abnormal signals in different time periods and spatial positions; combining the enhanced disease prevention environment electrical signal with the initial abnormal probability, generating the abnormal confidence through weighted fusion, can effectively improve the determination accuracy of the abnormal mode, and by introducing the spatial consistency measurement, further improves the correlation processing between the disease prevention environment electrical signals, and can correctly identify abnormal situations in complex environments; the overall design improves the sensitivity and robustness of the disease prevention environment electrical signal processing by combining the statistical characteristics and spatial consistency of the local disease prevention environment electrical signal, providing a reliable foundation for accurate disease prevention.

[0015] Preferably, in step S6, the specific steps of fusion and feedback optimization of electrical signals of different disease prevention environments are: S61. Based on the information geometry distance, different disease prevention environmental electrical signals are fused to verify the abnormal pattern. The mathematical model is: ; In the formula, is the fusion confidence after fusing the confidences of different sensors. is the Riemann manifold distance, , is the fusion attenuation coefficient, , Var is the variance; S62, Fusion Confidence Feedback to S2 to adjust the fractional order differential parameters , the mathematical model is: ; The iteration termination condition is The variance is less than the preset threshold.

[0016] Preferably, combined with the confidence of the disease prevention environment electrical signal, the disease prevention environment electrical signals of different sensors are fused through the Riemann manifold distance, so as to optimize the verification and adjustment of abnormal patterns; first, the disease prevention environment electrical signals collected by sensors at different spatial positions may have different degrees of errors and interference. The confidence of each sensor is fused through the Riemann manifold distance, and the data from different sensors can be weighted and merged according to the spatial relationship. The fusion method based on geometric distance can improve the recognition accuracy of abnormal patterns in multi-source signals, and can effectively improve the recognition of environmental changes in the environmental monitoring scenario of the disease prevention system. Sensitivity; the fused confidence results are fed back to the S2 step, and the fractional-order differential parameters are adjusted dynamically. During the real-time monitoring process, the processing parameters can be adjusted to adapt to the current environmental state according to the detected abnormal disease prevention environmental electrical signals, thereby improving the adaptability and accuracy under different situations; the iterative optimization process gradually improves the accuracy of abnormal signal detection, ensuring that the disease prevention system can operate stably under complex and changeable environmental conditions; the overall design improves the fusion accuracy and robustness of disease prevention environmental electrical signals by combining Riemann manifold distance and dynamic parameter adjustment, providing strong technical support for disease prevention and environmental monitoring.

[0017] Compared with the prior art, the present invention has the following technical effects: the technical solution provided by the present invention proposes a disease prevention environment electrical signal processing model, and combines the disease prevention environment electrical signal fusion and anomaly detection technology to significantly improve the overall monitoring capability of disease prevention, wherein the gain factor and graph gain method are introduced to optimize the fusion process of the disease prevention environment electrical signal, enhance the effective information in the signal, and improve the accuracy and reliability of anomaly detection; the multi-scale processing technology of fractional differentials and wavelet basis functions is used to accurately extract the abnormal pattern of the disease prevention environment electrical signal and calculate the initial abnormal probability, thereby improving the anomaly detection capability of the disease prevention environment electrical signal and timely discovering potential health risks; the mixed Gaussian-Riemann manifold space and manifold geometry ladder are used to detect the abnormality of the disease prevention environment electrical signal. The abnormal components are enhanced to effectively improve the recognition accuracy of abnormal patterns of disease prevention environmental electrical signals. By calculating the abnormal confidence and combining the spatial consistency to generate an adaptive threshold, high-precision anomaly detection is ensured. The confidence of different sensors is fused based on the Riemann manifold distance to optimize abnormal disease prevention environmental electrical signals. The disease prevention environmental electrical signal processing model is used to solve the shortcomings of traditional disease prevention methods in disease prevention environmental electrical signal processing and anomaly detection, significantly improve the accuracy of monitoring results, ensure that disease prevention can be carried out in a timely and effective manner, provide more accurate data support for disease prevention, reduce the risk of disease occurrence and spread, and make the identification and response of health risks more efficient, thereby improving the overall health management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the disease prevention environment electrical signal processing method provided by the present invention.

[0019] Figure 2 It is a structural diagram of the fused disease prevention environment electrical signal provided by the present invention.

[0020] Figure 3 It is a structural diagram of calculating the initial abnormality probability provided by the present invention.

[0021] Figure 4 It is a structural diagram of the disease prevention enhanced environmental electrical signal provided by the present invention.

[0022] Figure 5 It is a structural diagram of calculating anomaly confidence provided by the present invention.

[0023] Figure 6 It is a structural diagram of the electrical signal fusion and feedback optimization of different disease prevention environments provided by the present invention.

[0024] Figure 7 It is a comparison diagram of the disease prevention environment electric signal after enhancing abnormal components after being processed by the disease prevention environment electric signal processing model provided by the present invention and the original disease prevention environment electric signal. DETAILED DESCRIPTION

[0025] The present invention aims to propose a disease prevention environment sensor electrical signal processing method and a disease prevention environment electrical signal processing model, wherein a gain factor and a graph gain method are introduced to optimize the fusion of disease prevention environment electrical signals and improve signal quality and consistency; a multi-scale processing technology of fractional differentials and wavelet basis functions is used to accurately extract abnormal patterns of disease prevention environment electrical signals and calculate initial abnormal probability; a mixed Gaussian-Riemann manifold space and manifold geometric gradient are used to enhance abnormal components, thereby improving the recognition accuracy of abnormal patterns of disease prevention environment electrical signals, and an adaptive threshold is generated by calculating abnormal confidence and combining spatial consistency to optimize the detection of abnormal disease prevention environment electrical signals; the confidence of different sensors is fused based on the Riemann manifold distance, and parameters are dynamically adjusted to achieve efficient response in complex environments; and a disease prevention environment electrical signal processing model is used to complete accurate processing and abnormality detection of disease prevention environment electrical signals.

[0026] See also Figure 1 As shown, a disease prevention environmental sensor electrical signal processing method in an embodiment of the present application, the specific steps are as follows.

[0027] S1. Collect the electrical signals collected by environmental sensors and create a disease prevention environmental electrical signal dataset.

[0028] Furthermore, in step S1, for the production of the disease prevention environmental electrical signal dataset, environmental sensors suitable for disease prevention monitoring are selected. The environmental sensors include temperature and humidity sensors, air quality sensors, and gas concentration sensors, which are mainly used to monitor environmental parameters such as temperature and humidity, air quality, and gas concentration. Nursing homes are selected as the collection sites for the disease prevention environmental electrical signal datasets, and sensors are installed at different monitoring locations to ensure that various environmental conditions in the target area are covered. Environmental sensors are used to continuously collect real-time data and record electrical signals under different time and space conditions. All collected disease prevention environmental electrical signals will be stored together with the corresponding sensor sampling values ​​and spatial coordinate information, and data annotation will be performed. The disease prevention environmental electrical signals and the annotation information will be organized into a unified format to complete the production of the disease prevention environmental electrical signal dataset.

[0029] S2. According to the spatiotemporal relationship of the disease prevention environmental electrical signals, a gain factor is introduced, and the disease prevention environmental electrical signals are fused and processed by combining the graph gain method and the spatial distance.

[0030] Further, in step S2, for optimizing the fusion environment disease prevention environment electrical signal, its structure is as follows Figure 2 As shown, the specific steps for fusing and processing the disease prevention environment electrical signals are as follows.

[0031] S21. Input original disease prevention environment electrical signal and sensor space coordinates , represents the sampling value of the i-th sensor node at time t, is the total number of sensor nodes, N is set to 50, 50 sensors continuously collect environmental data at different locations, the data collected by each sensor is a 1-hour time series signal, the sampling interval is 10 seconds, and the total number of sampling points of disease prevention environmental electrical signals is 18,000. is the three-dimensional spatial coordinate of sensor i. After the original disease prevention environment electrical signal is processed by mean zeroing and variance normalization, the spatial and temporal relationship between the disease prevention environment electrical signals is calculated, and the signal association model is established to obtain the gain factor between the disease prevention environment electrical signals. The mathematical model of the gain factor is: ; In the formula, is the gain factor, which represents the signal gain of sensors i and j between time t, and are the signal values ​​of sensors i and j at time t, is an exponential function, and is the adjustment coefficient. In this embodiment, Set to 0.5 to control the decay rate of the signal difference. The value is 0.2, which controls the decay rate of spatial distance.

[0032] S22. Use the graph gain method to perform signal fusion, construct the graph structure between the disease prevention environment electrical signals and optimize the signal fusion process. The mathematical model of the disease prevention environment electrical signal fusion is: ; In the formula, is the fusion signal matrix after graph gain optimization, is the eigenvector matrix. The first three principal components are extracted by principal component analysis of the disease prevention environmental electrical signal, and the energy of the principal components is retained. is the graph gain matrix, which is a diagonal matrix with diagonal elements being gain factors. is the relationship matrix of the disease prevention environmental electrical signal, and the relationship matrix is ​​obtained by calculating the covariance matrix of the disease prevention environmental electrical signal. for The transposed matrix of is element-wise multiplication.

[0033] S23. By combining the gain factor, the image gain and the spatial distance and processing the signal using an exponential function, the optimized sensor signal is obtained. The mathematical model is: ; In the formula, For optimized disease prevention environmental electrical signals, represents the position of sensor i, To control the adjustment factor of the spatial distance attenuation term, in this embodiment, Set it to 0.1 to avoid overfitting. is the average value of the sensor positions.

[0034] S3. Decompose the signal by multi-scale fractional differential and wavelet basis function to generate multi-scale components, extract the abnormal signal pattern, and calculate the initial abnormal probability by combining the sensor spatial distance and electrical signal difference.

[0035] Furthermore, in step S3, for calculating the initial abnormal probability, its structure is as follows: Figure 3 As shown, the specific steps for calculating the initial abnormality probability are:

[0036] S31. Decompose the disease prevention environmental electrical signal into multi-scale tensors and directly extract potential abnormal patterns. The mathematical model is: ; In the formula, is the disease prevention environmental electrical signal component after multi-scale decomposition, is a fractional differential operator, , controls the decomposition of disease prevention environmental electrical signals at different scales, s is the scale parameter, Corresponding to high frequency, medium frequency and low frequency components respectively, It is an adaptive wavelet basis function, and the frequency band component is controlled by the scale parameter s; The mathematical model of the fractional-order differential operator is: ; In the formula, is the gamma function, which normalizes the result of fractional differentiation. To obtain The integer part of , a is the historical time, and t is the current time point; The mathematical model of adaptive wavelet basis function is: .

[0037] S32. Combine multi-scale components with spatial correlation to generate initial anomaly probability. The mathematical model is: ; In the formula, is the initial abnormal probability, is the Euclidean space distance between sensor nodes i and j, is the smoothing constant, , to prevent the denominator from being zero, Variance of environmental electrical signals for disease prevention under scale parameter s.

[0038] S4. Map the disease prevention environmental electrical signals to the mixed Gaussian-Riemann manifold space, enhance the disease prevention environmental electrical signals, and inversely enhance the abnormal components through manifold geometric gradient and fractional differential.

[0039] Further, in step S4, for enhancing the abnormal disease prevention environment electrical signal, its structure is as follows Figure 4 As shown, the specific steps for enhancing the disease prevention environmental electrical signals are as follows.

[0040] S41. Mapping the disease prevention environmental electrical signal to a mixed Gaussian-Riemann manifold to enhance the abnormal components. The mathematical model is: ; In the formula, For enhanced disease prevention environmental electrical signals, is the number of Gaussian components. In this embodiment, K =3, indicating three risk modes: low, medium and high. is the uniform mixing coefficient, is the center of the manifold, The covariance and manifold center are updated every 10 seconds through the online K-means clustering algorithm to adapt to the dynamic changes of the environment. is the adaptive fractional order parameter, , is the information entropy, is a stabilizing constant to prevent the denominator from being zero. .

[0041] S42, the abnormal signal is enhanced by reverse enhancement of fractional gradient, and the mathematical model is: ; In the formula, is the geometric gradient of the manifold computed via the Riemann logarithmic map, , is a logarithmic mapping on the Riemann manifold, mapping the disease prevention environmental electrical signal from the manifold space to the tangent space, To increase strength, , For In A value equal to 0.5, is the symbolic function, Is an assignment symbol, indicating that the signal is updated through gradient enhancement.

[0042] S5. Generate an adaptive threshold based on the mean, standard deviation and spatial consistency measurement of the local disease prevention environment electrical signal, and generate an abnormality confidence by combining the enhanced disease prevention environment electrical signal with the initial abnormality probability.

[0043] Furthermore, in step S5, for calculating the anomaly confidence, its structure is as follows: Figure 5 As shown, the specific steps for calculating the anomaly confidence are:

[0044] S51, generating an adaptive threshold based on signal statistical characteristics and spatial consistency, the mathematical model is: ; In the formula, is the adaptive threshold, is the local signal mean, , is the local standard deviation, , half width of the time window 2, the total length of the time window is 5, the calculation window is 5 seconds, and the mean and standard deviation of the local signal are calculated every 5 seconds. is the spatial consistency measure, is the adaptive threshold amplitude adjustment coefficient, is the spatial consistency adjustment coefficient. In this embodiment, Set it to 2.0 to amplify the effect of the standard deviation on the threshold. Set it to 0.5 to adjust the spatial consistency weight to avoid misjudgment of local anomalies; The mathematical model of spatial consistency measurement is: ; In the formula, For the mutual correlation coefficient, the correlation of the disease prevention environmental electrical signals was calculated; The mathematical model of the mutual correlation coefficient is: ; In the formula, for The average value of for The average value of is the sum of all moments in time t.

[0045] S52. The enhanced disease prevention environment electrical signal is combined with the adaptive threshold to generate confidence. The mathematical model is: ; In the formula, is the abnormal confidence level, is the normalization constant, , max is the maximum value.

[0046] S6. The confidences of different sensors are fused through the Riemann manifold distance to verify the abnormal pattern, and the fusion results are fed back to the initial step S2. The fractional-order differential parameters are dynamically adjusted to perform feedback optimization on the disease prevention environment electrical signal. The iterative execution is performed until convergence to obtain the disease prevention environment electrical signal after the final processing and the final fusion confidence.

[0047] Furthermore, in step S6, for different disease prevention environment electrical signal fusion and feedback optimization, its structure is as follows Figure 6 As shown, the specific steps for feedback optimization of disease prevention environmental electrical signals are as follows.

[0048] S61. Based on the information geometry distance, different disease prevention environmental electrical signals are fused to verify the abnormal pattern. The mathematical model is: ; In the formula, is the fusion confidence after fusing the confidences of different sensors. is the Riemann manifold distance, , is the fusion attenuation coefficient, , Var is the variance.

[0049] S62, Fusion Confidence Feedback to S2 to adjust the fractional order differential parameters , the mathematical model is: ; The iteration termination condition is The variance is less than the preset threshold ,Setting the preset threshold indicates that the obtained confidence is in a stable state.

[0050] S7. Construct a disease prevention environment electrical signal processing model, input a disease prevention environment electrical signal data set, go through steps S2 to S6 in sequence, and perform iterative training until convergence to complete the optimization processing of the disease prevention environment electrical signal.

[0051] Furthermore, in step S7, for the disease prevention environment electrical signal processing model, the disease prevention environment electrical signal data set is first input, and the processes in steps S2 to S6 are executed in sequence, the specific steps are: the disease prevention environment electrical signals are fused by introducing gain factors and graph gain methods; then multi-scale fractional differentials and wavelet basis function decomposition are used to extract abnormal signal patterns; the disease prevention environment electrical signals are enhanced by combining manifold space mapping and fractional differentials; then, an adaptive threshold is generated by calculating local statistical characteristics and the abnormal confidence is calculated; the confidence of different sensors is fused through the Riemann manifold distance, the abnormal pattern is verified and optimized; the disease prevention environment electrical signal processing model is continuously adjusted and optimized through iterative training until the processing results converge, so as to ensure the accuracy and stability of the disease prevention environment electrical signal optimization; the disease prevention environment electrical signal processing model can efficiently and accurately optimize the disease prevention environment electrical signals, improve the abnormality detection capability, and provide more reliable data support for disease prevention.

[0052] Furthermore, in step S7, for the disease prevention environmental electrical signal processing model, it is developed based on the Python programming language, takes the disease prevention environmental electrical signal as input, sets the batch size to 64, uses the Adam optimizer, sets the learning rate to 0.001, and trains 500 times. After the iterative training is completed, the disease prevention environmental electrical signal processing model outputs the final disease prevention environmental electrical signal processing result.

[0053] Furthermore, in step S7, the disease prevention environment electrical signal is input into the disease prevention environment electrical signal processing model for processing, and the effects before and after the processing are as follows: Figure 7 As shown in the figure, the horizontal axis represents time in seconds, and the vertical axis represents the amplitude of the signal without units. The input original disease prevention environmental electrical signal shows obvious random noise fluctuations, while the disease prevention environmental electrical signal after enhancing the abnormal component suppresses high-frequency noise and amplifies key abnormal events while retaining the trend of the original disease prevention environmental electrical signal. The peak area in the figure is the key abnormal event. The effect shown in the figure proves that the disease prevention environmental electrical signal processing model can effectively distinguish between noise and real abnormalities, improve the signal-to-noise ratio and abnormality detection sensitivity of the disease prevention environmental electrical signal, provide reliable technical support for the processing of the disease prevention environmental electrical signal, and verify the effectiveness of the model proposed in this paper.

[0054] The above are only preferred embodiments of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A disease prevention environmental sensor electrical signal processing method, characterized in that: The specific steps include: S1, collect electrical signals collected by environmental sensors and create a disease prevention environmental electrical signal dataset; S2. According to the spatiotemporal relationship of the disease prevention environment electrical signals, a gain factor is introduced, and the disease prevention environment electrical signals are fused by combining the graph gain method and the spatial distance; S3, decompose the signal by multi-scale fractional differential and wavelet basis function to generate multi-scale components, extract the abnormal signal pattern, and calculate the initial abnormal probability by combining the sensor spatial distance and electrical signal difference; S4, mapping the disease prevention environment electrical signal to the mixed Gaussian-Riemann manifold space, enhancing the disease prevention environment electrical signal, and inversely enhancing the abnormal component through the manifold geometric gradient and fractional differential; S5, generating an adaptive threshold based on the mean, standard deviation and spatial consistency measurement of the local disease prevention environment electrical signal, and generating an abnormality confidence by combining the enhanced disease prevention environment electrical signal with the initial abnormality probability; S6, fusing the confidences of different sensors through the Riemann manifold distance, verifying the abnormal mode, and feeding back the fusion results to the initial step S2, dynamically adjusting the fractional-order differential parameters, performing feedback optimization on the disease prevention environment electrical signal, iterating until convergence, and obtaining the disease prevention environment electrical signal after the final processing and the final fusion confidence; S7. Construct a disease prevention environment electrical signal processing model, input a disease prevention environment electrical signal data set, go through steps S2 to S6 in sequence, and perform iterative training until convergence to complete the optimization of the disease prevention environment electrical signal.

2. The disease prevention environmental sensor electrical signal processing method according to claim 1, characterized in that: In the S1 step, for the preparation of the disease prevention environmental electrical signal data set, environmental sensors suitable for disease prevention monitoring are selected. The environmental sensors include temperature and humidity sensors, air quality sensors, and gas concentration sensors, which are used to monitor environmental parameters such as temperature and humidity, air quality, and gas concentration. Nursing homes are selected as the collection sites for the disease prevention environmental electrical signal data sets, and sensors are installed at different monitoring locations. Environmental sensors are used to continuously collect real-time data and record electrical signals under different time and space conditions. All collected disease prevention environmental electrical signals will be stored together with the corresponding sensor sampling values ​​and spatial coordinate information, and data annotation will be performed. The disease prevention environmental electrical signals and the annotation information will be organized into a unified format to complete the preparation of the disease prevention environmental electrical signal data set.

3. The method for processing electrical signals of a disease prevention environmental sensor according to claim 2, characterized in that: In the step S2, the specific steps of fusing the disease prevention environment electrical signal are as follows: S21. Input original disease prevention environment electrical signal and sensor space coordinates , represents the sampling value of the i-th sensor node at time t, is the total number of sensor nodes, is the three-dimensional spatial coordinate of sensor i, calculate the spatial and temporal relationship between the disease prevention environment electrical signals, establish the signal association model, and obtain the gain factor between the disease prevention environment electrical signals. The mathematical model of the gain factor is: ； In the formula, is the gain factor, and are the signal values ​​of sensors i and j at time t, is an exponential function, and is the adjustment coefficient; S22. Use the graph gain method to perform signal fusion, construct the graph structure between the disease prevention environment electrical signals and optimize the signal fusion process. The mathematical model of the disease prevention environment electrical signal fusion is: ； In the formula, is the fusion signal matrix after graph gain optimization, is the eigenvector matrix, is the graph gain matrix, A relationship matrix of environmental electrical signals for disease prevention, for The transposed matrix of is element-wise multiplication; S23. By combining the gain factor, the graph gain and the spatial distance and processing the signal using an exponential function, the optimized disease prevention environmental electrical signal is obtained. The mathematical model is: ； In the formula, For optimized disease prevention environmental electrical signals, represents the position of sensor i, is the adjustment factor for controlling the spatial distance attenuation term, is the average value of the sensor positions.

4. The disease prevention environmental sensor electrical signal processing method according to claim 3, characterized in that: In the step S3, the specific steps of calculating the initial abnormal probability are: S31. Decompose the disease prevention environmental electrical signal into multi-scale tensors and directly extract potential abnormal patterns. The mathematical model is: ； In the formula, is the disease prevention environmental electrical signal component after multi-scale decomposition, is a fractional differential operator, Control the decomposition of disease prevention environmental electrical signals at different scales, s is the scale parameter, Corresponding to high frequency, medium frequency and low frequency components respectively, is the adaptive wavelet basis function; The mathematical model of the fractional-order differential operator is: ； In the formula, is the gamma function, To obtain The integer part of , a is the historical time, and t is the current time point; The mathematical model of adaptive wavelet basis function is: ； S32. Combine multi-scale components with spatial correlation to generate initial anomaly probability. The mathematical model is: ； In the formula, is the initial abnormal probability, is the Euclidean space distance between sensor nodes i and j, is the smoothing constant, Variance of environmental electrical signals for disease prevention under scale parameter s.

5. The disease prevention environmental sensor electrical signal processing method according to claim 4, characterized in that: In the step S4, the specific steps of enhancing the disease prevention environment electrical signal are as follows: S41. Mapping the disease prevention environmental electrical signal to a mixed Gaussian-Riemann manifold to enhance the abnormal components. The mathematical model is: ； In the formula, For enhanced disease prevention environmental electrical signals, is the number of Gaussian components, is the uniform mixing coefficient, is the center of the manifold, is the covariance, is the adaptive fractional order parameter, , is the information entropy, is the stability constant; S42, the abnormal signal is enhanced by reverse enhancement of fractional gradient, and the mathematical model is: ； In the formula, is the geometric gradient of the manifold computed via the Riemann logarithmic map, , is a logarithmic mapping on the Riemann manifold, mapping the disease prevention environmental electrical signal from the manifold space to the tangent space, To increase strength, , For In A value equal to 0.5, is the symbolic function, Is an assignment symbol, indicating that the signal is updated through gradient enhancement.

6. The disease prevention environmental sensor electrical signal processing method according to claim 5, characterized in that: In step S5, the specific steps of calculating the abnormality confidence are: S51, generating an adaptive threshold based on signal statistical characteristics and spatial consistency, the mathematical model is: ； In the formula, is the adaptive threshold, is the local signal mean, is the local standard deviation, with a calculation window of 5 seconds, is the spatial consistency measure, is the adaptive threshold amplitude adjustment coefficient, is the spatial consistency adjustment coefficient; The mathematical model of spatial consistency measurement is: ； In the formula, is the mutual correlation coefficient; The mathematical model of the mutual correlation coefficient is: ； In the formula, for The average value of for The average value of is the sum of all moments in time t; S52. The enhanced disease prevention environment electrical signal is combined with the adaptive threshold to generate abnormal confidence. The mathematical model is: ； In the formula, is the abnormal confidence level, is the normalization constant, , max is the maximum value.

7. The method for processing electrical signals of a disease prevention environment sensor according to claim 6, characterized in that: In the step S6, the specific steps of performing feedback optimization on the disease prevention environment electrical signal are as follows: S61. Based on the information geometry distance, different disease prevention environmental electrical signals are fused to verify the abnormal pattern. The mathematical model is: ； In the formula, is the fusion confidence after fusing the confidences of different sensors. is the Riemann manifold distance, , is the fusion attenuation coefficient, , Var is the variance; S62, Fusion Confidence Feedback to S2 to adjust the fractional order differential parameters , the mathematical model is: ； The iteration termination condition is The variance is less than the preset threshold.

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