Intelligent wireless sensing and communication system for rotating part

Abstract

The invention discloses an intelligent wireless sensing and communication system for a rotating part, and relates to the technical field of sensors. The method comprises the following steps: receiving state data of a torque sensor at a rotating part end, and performing modulation operation according to a preset pseudo-random sequence and the state data to obtain target state data; calculating the target state data to obtain a transmission abnormal coefficient; judging whether signal transmission abnormity occurs in the rotating process of the rotating part or not; and performing early warning processing according to the rotating part corresponding to the abnormal signal transmission. According to the invention, the information leakage risk is reduced by improving the security of state data transmission; modulation operation is carried out through a preset pseudo-random sequence and state data, so that the information processing capability is improved, and a foundation is laid for subsequent calculation; dynamic comparison can be carried out based on the transmission abnormal coefficient and the preset threshold value, tiny fluctuation in the data transmission process is accurately captured, the flatness and efficiency of the transmission spectrum are improved, early warning processing can be carried out in time, and then the equipment fault risk is reduced.

Description

Technical Field

[0001] This invention belongs to the field of sensor technology, specifically relating to an intelligent wireless sensing and communication system for rotating components. Background Technology

[0002] With the development of sensors, this technology is responsible for sensing and exchanging information about the operating status of rotating components, ensuring that their working conditions can be accurately and in real time. This system uses a series of intelligent sensing and wireless communication facilities, such as miniature intelligent sensors, wireless communication modules, and energy harvesting units, to convert the physical signals of rotating components into transmittable electrical signals and achieve wireless transmission, thus constructing an integrated intelligent monitoring network. This provides efficient and reliable technical support for fault early warning, condition assessment, and intelligent operation and maintenance of rotating machinery.

[0003] However, in practical applications, the detection is often performed by rotating components during operation, which can lead to loss of detection information, such as external electromagnetic interference or loss of energy transmission; this reduces the flatness and efficiency of sensor transmission. Summary of the Invention

[0004] The purpose of this invention is to solve the problem of loss of detection information when rotating components are in operation, such as due to external electromagnetic interference or loss of energy transmission; and to address the issues of reduced flatness and efficiency of sensor transmission. Therefore, this invention proposes an intelligent wireless sensing and communication system for rotating components.

[0005] In a first aspect of this invention, an intelligent wireless sensing and communication system for rotating components is first proposed, the intelligent wireless sensing and communication system for rotating components comprising: Data acquisition module: Receives state data from torque sensor at the rotating component end, and modulates the state data according to a preset pseudo-random sequence to obtain target state data; Transmission anomaly coefficient module: Calculates the transmission anomaly coefficient from the target state data; Judgment module: Determines whether signal transmission abnormalities will occur during the rotation of the rotating component based on the transmission anomaly coefficient; Early warning module: performs early warning processing based on the rotating component corresponding to the abnormal signal transmission.

[0006] Optionally, the transmission anomaly coefficient module is further configured to calculate the transmission anomaly coefficient from the target state data: Target state data includes energy efficiency coefficient, mutual information spectrum attenuation coefficient, and transmission stability efficiency coefficient; Substituting the energy efficiency coefficient, mutual information spectrum attenuation coefficient, and transmission stability efficiency coefficient into the transmission anomaly function yields the transmission anomaly coefficient. The specific formula for the transmission exception function is: Where CS represents the transmission anomaly coefficient, Xs represents the energy efficiency coefficient, Sa represents the mutual information spectrum attenuation coefficient, Ln represents the transmission stability efficiency coefficient, and β1 and β2 represent the weighting coefficients of transmission anomalies.

[0007] Optionally, the calculation process of the energy efficiency coefficient includes: Within a preset time period, the energy value sequence received at the end of the rotating component is acquired, and the energy value sequence is smoothed to obtain a smoothed energy value sequence. The maximum and minimum smoothed energy values ​​are obtained by querying the smoothed energy value sequence. The average smoothed energy value is obtained by averaging the smoothed energy value sequence. The energy decay coefficient is calculated by multiplying the ratio of the smoothed maximum energy value to the smoothed minimum energy value by the smoothed average energy value.

[0008] Optionally, the calculation process of the energy efficiency coefficient further includes: Obtain a preset discrete signal sequence, calculate the total signal energy based on the preset discrete signal sequence, and record each discrete point in the discrete signal sequence as an energy probability; The energy entropy is obtained by calculating the various energy probabilities. The formula for calculating energy entropy is: ; Where Ea represents energy entropy, N represents the total number of preset discrete signal sequences, and P i Represents the energy probability of the i-th discrete point; i = 1, 2, ..., N; The average distribution energy value is defined as the ratio of the total energy of the signal to the total number of discrete signal sequences. The energy distribution coefficient is obtained by weighted summation of the energy entropy and the average distributed energy value. The energy efficiency coefficient is obtained by summing the energy efficiency coefficient and the energy decay coefficient.

[0009] Optionally, the calculation process of the mutual information spectrum attenuation coefficient includes: Obtain the transmission signal sequence, perform a Fourier transform on the transmission signal sequence to obtain the spectrum; query the spectrum to obtain the maximum and minimum frequency bands; The frequency band difference is obtained based on the maximum and minimum frequency bands; The frequency band mutual information entropy is calculated based on the spectrum and the frequency band difference. The formula for calculating the frequency band mutual information entropy is: ; Where Ia represents the band mutual information entropy, Dc represents the band difference, and PD... j Let J represent the j-th frequency band; M represents the total number of transmitted signal sequences, j = 1, 2, ..., M.

[0010] Optionally, the calculation process of the mutual information spectrum attenuation coefficient further includes: Within the target time period, subcarrier occupancy lists are collected at each time point, and the spectral sparsity at the target time point is calculated based on the subcarrier occupancy lists; the target time point is any time point within the target time period. The spectral sparsity calculation process is as follows: the ratio of the number of idle subcarriers to the total number of subcarriers is denoted as the spectral sparsity; the spectral sparsity corresponding to all time points is obtained, and the average value is calculated to obtain the spectral sparsity stability coefficient; The mutual information spectrum attenuation coefficient is obtained by taking the reciprocals of the frequency band mutual information entropy and the spectral sparsity stability coefficient and then summing them.

[0011] Optionally, the calculation process for the transmission stability effective coefficient includes: Obtain the real-time SNR value from the receiver and integrate the SNR values ​​to obtain the dataset; The standard deviation of the SNR is calculated for the dataset. The calculation process for the standard deviation is as follows: Where σ represents the standard deviation of SNR, H represents the total number of data collections, and SNR k This represents the SNR value of the k-th sample, where k = 1, 2, ..., H; The ratio of the standard deviation to the real-time SNR value is denoted as the signal-to-noise ratio fluctuation coefficient.

[0012] Optionally, the calculation process for the transmission stability effective coefficient further includes: Obtain the preset characteristics of the useful signal, and separate the transmitted information into useful signal power and interference signal power according to the preset adjustment method; The ratio of the interference signal power to the useful signal power is denoted as the electromagnetic interference ratio. The effective transmission stability coefficient is obtained by taking the reciprocals of the signal-to-noise ratio fluctuation coefficient and the electromagnetic interference ratio, and then summing them by weight.

[0013] Optionally, the judgment module is further configured to determine whether a signal transmission abnormality will occur during the rotation of the rotating component based on the transmission abnormality coefficient: If the transmission anomaly coefficient is greater than the preset transmission anomaly threshold, then the signal transmission is abnormal. If the transmission anomaly coefficient is less than or equal to the preset transmission anomaly threshold, then the signal transmission is normal.

[0014] The beneficial effects of this invention are: This invention proposes an intelligent wireless sensing and communication system for rotating components. By receiving state data from a torque sensor at the rotating component end, the system obtains target state data through modulation operations based on a preset pseudo-random sequence and the state data; and calculates a transmission anomaly coefficient from the target state data. This invention determines whether a signal transmission anomaly will occur during the rotation of a rotating component based on a transmission anomaly coefficient; and provides early warning processing based on the rotating component corresponding to the signal transmission anomaly. This invention improves the security of state data transmission, reducing the risk of information leakage; enhances information processing capabilities by modulating state data using a preset pseudo-random sequence, laying the foundation for subsequent calculations; and enables dynamic comparison based on the transmission anomaly coefficient and preset thresholds to accurately capture minute fluctuations during data transmission, improving the flatness and efficiency of the transmission spectrum, and allowing for timely early warning processing, thereby reducing the risk of equipment failure. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a framework diagram of an intelligent wireless sensing and communication system for rotating components provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and B can represent: A alone, A and B simultaneously, and B alone. Furthermore, descriptions involving "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" can explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0018] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides an intelligent wireless sensing and communication system for rotating components. See also... Figure 1 , Figure 1A flowchart illustrating an intelligent wireless sensing and communication system for rotating components provided in an embodiment of the present invention. The system includes the following steps: Data acquisition module: Receives state data from torque sensor at the rotating component end, and modulates the state data according to a preset pseudo-random sequence to obtain target state data; Transmission anomaly coefficient module: Calculates the transmission anomaly coefficient from the target state data; Judgment module: Determines whether signal transmission abnormalities will occur during the rotation of the rotating component based on the transmission anomaly coefficient; Early warning module: Provides early warning based on the rotating component corresponding to the abnormal signal transmission.

[0020] Based on the intelligent wireless sensing and communication system for rotating components provided in this invention, the security of state data transmission is improved, reducing the risk of information leakage; modulation operations are performed using preset pseudo-random sequences and state data, improving information processing capabilities and laying the foundation for subsequent calculations; dynamic comparisons are performed based on transmission anomaly coefficients and preset thresholds to accurately capture minute fluctuations in the data transmission process, improving the flatness and efficiency of the transmission spectrum, enabling timely early warning processing, and thus reducing the risk of equipment failure.

[0021] Specifically, the modulation operation is a basic XOR operation, which involves XORing the preset pseudo-random sequence with the state data. This algorithm is simple, efficient, and improves its timeliness. The preset pseudo-random sequence is set by staff based on historical experience. The state data includes the energy efficiency coefficient, mutual information spectrum attenuation coefficient, and transmission stability efficiency coefficient before modulation.

[0022] In one implementation, the transmission anomaly coefficient module is further used to calculate the transmission anomaly coefficient from the target state data: Target state data includes energy efficiency coefficient, mutual information spectrum attenuation coefficient, and transmission stability efficiency coefficient; Substituting the energy efficiency coefficient, mutual information spectrum attenuation coefficient, and transmission stability efficiency coefficient into the transmission anomaly function yields the transmission anomaly coefficient. The specific formula for the transmission exception function is: Where CS represents the transmission anomaly coefficient, Xs represents the energy efficiency coefficient, Sa represents the mutual information spectrum attenuation coefficient, Ln represents the transmission stability efficiency coefficient, and β1 and β2 represent the weighting coefficients of transmission anomalies.

[0023] Specifically, the sum of β1 and β2 is 1, which is determined based on historical experience. In one implementation, a transmission anomaly coefficient is calculated from the target state data, enabling precise and comprehensive quantification of anomalies during transmission. The energy efficiency coefficient reflects the effective utilization of transmission energy, the mutual information spectrum attenuation coefficient reflects the attenuation characteristics during information transmission, and the transmission stability efficiency coefficient characterizes transmission stability. Combining these three coefficients and calculating them using a transmission anomaly function with weighted coefficients allows for a comprehensive evaluation of the transmission status from multiple dimensions, avoiding the limitations of single-indicator assessments. This enables the system to identify transmission anomalies promptly and accurately, providing a reliable basis for subsequent anomaly handling and transmission optimization, thus improving the reliability, stability, and efficiency of the entire transmission system and ensuring the quality and continuity of data transmission.

[0024] In one implementation, the calculation process for the energy efficiency coefficient includes: Within a preset time period, acquire the energy value sequence received at the end of the rotating component, and smooth the energy value sequence to obtain a smoothed energy value sequence. The maximum and minimum smoothed energy values ​​can be obtained by querying the smoothed energy value sequence. The average smoothed energy value is obtained by averaging the smoothed energy value sequence. The energy decay coefficient is calculated by multiplying the ratio of the smoothed maximum energy value to the smoothed minimum energy value by the smoothed average energy value.

[0025] Specifically, the energy value refers to the strength of its information-carrying capacity; the energy value sequence contains multiple energy values; the smoothing process specifically involves using a moving average filter or a low-pass filter. In one implementation, the calculation process for the energy efficiency coefficient further includes: Obtain a preset discrete signal sequence, calculate the total signal energy based on the preset discrete signal sequence, and record each discrete point in the discrete signal sequence as an energy probability; The energy entropy is obtained by calculating the probability of each energy. The formula for calculating energy entropy is: ; Where Ea represents energy entropy, N represents the total number of preset discrete signal sequences, and P i Represents the energy probability of the i-th discrete point; i = 1, 2, ..., N; The average distribution energy value is defined as the ratio of the total energy of the signal to the total number of discrete signal sequences. The energy distribution coefficient is obtained by weighted summation of the energy entropy and the average distributed energy value. The energy efficiency coefficient is obtained by summing the energy efficiency coefficient and the energy decay coefficient.

[0026] In one implementation, the energy efficiency coefficient is used to quantify the effectiveness of signal energy in the time-frequency domain; the higher the energy efficiency coefficient value, the higher the effectiveness of the energy, the more stable the transmitted signal, and the more accurate the corresponding data.

[0027] In one implementation, the calculation process of the mutual information spectrum attenuation coefficient includes: Obtain the transmitted signal sequence, perform a Fourier transform on the transmitted signal sequence to obtain the spectrum; query the spectrum to obtain the maximum and minimum frequency bands; The frequency band difference is obtained from the maximum and minimum frequency bands; The frequency band mutual information entropy is calculated based on the spectrum and the frequency band difference. The formula for calculating the cross-band mutual information entropy is: ; Where Ia represents the band mutual information entropy, Dc represents the band difference, and PD... j Let J represent the j-th frequency band; M represents the total number of transmitted signal sequences, j = 1, 2, ..., M.

[0028] In one implementation, the calculation process for the mutual information spectrum attenuation coefficient further includes: Within the target time period, the subcarrier occupancy list for each moment is collected, and the spectral sparsity at the target moment is calculated based on the subcarrier occupancy list; the target moment is any moment within the target time period. The spectral sparsity calculation process is as follows: the ratio of the number of idle subcarriers to the total number of subcarriers is denoted as the spectral sparsity; the spectral sparsity corresponding to all time points is obtained, and the average value is calculated to obtain the spectral sparsity stability coefficient; The mutual information spectrum attenuation coefficient is obtained by taking the reciprocals of the frequency band mutual information entropy and the spectral sparsity stability coefficient and then summing them.

[0029] In one implementation, spectral sparsity is calculated by collecting subcarrier occupancy lists at various times within the target time period, and a spectral sparsity stability coefficient is obtained. Finally, the mutual information spectral attenuation coefficient is calculated by combining the frequency band mutual information entropy. This approach can describe the changes in subcarrier occupancy over time and the attenuation characteristics of frequency band information. Spectral sparsity is represented by the ratio of idle subcarriers to total subcarriers, and its stability coefficient reflects the temporal stability of this sparsity characteristic. This quantifies the transmission spectrum characteristics from both temporal and subcarrier occupancy dimensions. Furthermore, by combining the reciprocal of the frequency band mutual information entropy, the change in information entropy is further correlated with the stability of spectral sparsity, achieving a comprehensive evaluation of mutual information attenuation in the spectral domain.

[0030] In one implementation, the calculation process for the transmission stability efficiency coefficient includes: Obtain the real-time SNR value from the receiver and integrate the SNR values ​​to obtain the dataset; Calculate the standard deviation of SNR for the dataset. The calculation process for the standard deviation is as follows: Where σ represents the standard deviation of SNR, H represents the total number of data collections, and SNR k This represents the SNR value of the k-th sample, where k = 1, 2, ..., H; The ratio of the standard deviation to the real-time SNR value is denoted as the signal-to-noise ratio fluctuation coefficient.

[0031] Specifically, SNR is an abbreviation for signal-to-noise ratio; In one implementation, the calculation process for the transmission stability efficiency coefficient further includes: Obtain the preset characteristics of the useful signal, and separate the transmitted information into useful signal power and interference signal power according to the preset adjustment method; The ratio of interference signal power to useful signal power is denoted as the electromagnetic interference ratio. The effective transmission stability coefficient is obtained by taking the reciprocals of the signal-to-noise ratio fluctuation coefficient and the electromagnetic interference ratio, and then summing them by weight.

[0032] Specifically, preset adjustment methods may include frequency separation and modulation / demodulation separation, etc. In one implementation, the useful signal power and interference signal power are obtained by acquiring preset characteristics of the useful signal and separating the transmitted information. The electromagnetic interference ratio is then calculated, and combined with the signal-to-noise ratio (SNR) fluctuation coefficient, a transmission stability effective coefficient is calculated. This allows for multi-dimensional and precise quantification of transmission stability. Specifically, separating the useful and interference signal power and calculating the electromagnetic interference ratio directly reflects the impact of interference on the useful signal in the transmission environment from the perspective of electromagnetic interference. Combining this with the reciprocal calculation of the SNR fluctuation coefficient further supplements the characterization of transmission stability from the perspective of signal quality fluctuation. Integrating these two key factors—electromagnetic interference and SNR fluctuation—achieves a comprehensive assessment of transmission stability, providing a reliable foundation for the accurate calculation of subsequent transmission anomaly coefficients and the precise judgment of signal transmission anomalies, thus reducing equipment failures or data transmission errors caused by transmission instability.

[0033] In one implementation, the judgment module is further used to determine whether a signal transmission abnormality will occur during the rotation of the rotating component based on the transmission abnormality coefficient. If the transmission anomaly coefficient is greater than the preset transmission anomaly threshold, then the signal transmission is abnormal. If the transmission anomaly coefficient is less than or equal to the preset transmission anomaly threshold, then the signal transmission is normal.

[0034] Specifically, the preset transmission anomaly threshold was set by staff based on historical data; In one implementation, a signal transmission anomaly is determined by comparing a transmission anomaly coefficient with a preset transmission anomaly threshold, enabling rapid and accurate assessment of the signal transmission status. This method, based on explicit numerical comparison logic, is intuitive and easy to operate, allowing the system to quickly determine whether signal transmission is normal or abnormal. When an anomaly is detected, the system can promptly trigger the corresponding anomaly handling mechanism to prevent malfunctions or data loss in the rotating component caused by the signal transmission anomaly, ensuring the stable operation of the rotating component-related systems and the reliability of data transmission. Conversely, when a normal operation is detected, the system can confidently continue data transmission and component control according to the normal process, improving the overall system efficiency and stability.

[0035] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An intelligent wireless sensing and communication system for rotating components, characterized by, The method comprises: The data acquisition module receives the state data of the torque sensor at the end of the rotating component, and obtains target state data by modulating and operating the state data according to a preset pseudo-random sequence; The transmission abnormality coefficient module calculates the target state data to obtain a transmission abnormality coefficient; The judgment module judges whether the rotating component will appear signal transmission abnormality in the rotating process according to the transmission abnormality coefficient; The early warning module performs early warning processing on the rotating component corresponding to the signal transmission abnormality.

2. The rotating component oriented smart wireless sensing and communication system of claim 1, wherein, The transmission abnormality coefficient module is also used for calculating the target state data to obtain a transmission abnormality coefficient: The target state data comprises an energy effective coefficient, a mutual information spectrum attenuation coefficient and a transmission stable effective coefficient; The transmission abnormality coefficient is obtained by substituting the energy effective coefficient, the mutual information spectrum attenuation coefficient and the transmission stable effective coefficient into a transmission abnormality function; The specific formula of the transmission abnormality function is: Wherein, CS represents the transmission abnormality coefficient, Xs represents the energy effective coefficient, Sa represents the mutual information spectrum attenuation coefficient, Ln represents the transmission stable effective coefficient, and β1 and β2 represent the weight coefficients of the transmission abnormality.

3. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the energy effective coefficient comprises: In a preset time period, the energy value sequence received at the end of the rotating component is obtained, and the energy value sequence is smoothed to obtain a smoothed energy value sequence; The smoothed energy maximum value and the smoothed energy minimum value are obtained by querying the smoothed energy value sequence; The smoothed energy average value is obtained by averaging the smoothed energy value sequence; The energy decay coefficient is calculated by multiplying the ratio of the smoothed energy maximum value to the smoothed energy minimum value by the smoothed energy average value.

4. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the energy effective coefficient further comprises: A preset discrete signal sequence is obtained, and the total signal energy is calculated according to the preset discrete signal sequence; each discrete point in the discrete signal sequence is recorded as an energy probability; The energy entropy is calculated by using the energy probabilities; The calculation formula of energy entropy is: ; wherein, Ea represents energy entropy, N represents total number of preset discrete signal sequences, P i represents energy probability of the i-th discrete point; i = 1, 2, …, N; The average distribution energy value is recorded as the ratio of the total signal energy to the total number of the discrete signal sequence; The energy distribution coefficient is obtained by weighting and summing the energy entropy and the average distribution energy value; The energy effective coefficient is obtained by summing the energy effective coefficient and the energy decay coefficient.

5. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the mutual information spectrum attenuation coefficient comprises: The transmission signal sequence is obtained, and the frequency spectrum is obtained by performing Fourier transform on the transmission signal sequence; the maximum frequency band and the minimum frequency band are obtained by querying the frequency spectrum; The frequency band difference value is obtained according to the maximum frequency band and the minimum frequency band; The frequency band mutual information entropy is calculated according to the frequency spectrum and the frequency band difference value; The frequency band mutual information entropy calculation formula is: ; wherein Ia represents a band mutual information entropy, Dc represents a band difference, PD j represents the jth frequency band; M represents the total number of sequences of the transmission signal sequence, j = 1, 2, …, M.

6. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the mutual information spectrum attenuation coefficient further comprises: In a target time period, the subcarrier occupation list at each time is collected, and the spectrum sparsity at the target time is calculated according to the subcarrier occupation list; the target time is any time in the target time period; The spectrum sparsity calculation process is that the ratio of the number of idle subcarriers to the total number of subcarriers is recorded as the spectrum sparsity; the spectrum sparsities corresponding to all times are obtained, and the average value is obtained to obtain the spectrum sparsity stability coefficient; The reciprocal of the frequency band mutual information entropy and the spectrum sparsity stability coefficient is calculated, and the sum is obtained to obtain the mutual information spectrum attenuation coefficient.

7. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the transmission stability effective coefficient comprises: obtaining the real-time SNR value of the receiving end, and integrating the SNR value to obtain a data set; calculating the SNR standard deviation of the data set, and the calculation process of the standard deviation comprising: wherein σ represents the SNR standard deviation, H represents the total number of acquisitions, SNR k represents the SNR value of the kth acquisition, k = 1, 2, …, H; taking the ratio of the standard deviation to the real-time SNR value as a signal-to-noise ratio fluctuation coefficient.

8. The rotating component oriented smart wireless sensing and communication system of claim 2, wherein, The calculation process of the transmission stability effective coefficient further comprises: obtaining preset features of the useful signal, separating the transmission information into useful signal power and interference signal power according to a preset adjustment mode; taking the ratio of the interference signal power to the useful signal power as an electromagnetic interference ratio; respectively taking the inverse of the signal-to-noise ratio fluctuation coefficient and the electromagnetic interference ratio, and then weighting and summing to obtain the transmission stability effective coefficient.

9. The rotating component oriented smart wireless sensing and communication system of claim 1, wherein, The judging module is further configured to judge whether the signal transmission is abnormal during the rotation of the rotating component according to the transmission abnormality coefficient: if the transmission abnormality coefficient is greater than a preset transmission abnormality threshold, the signal transmission is abnormal; if the transmission abnormality coefficient is less than or equal to the preset transmission abnormality threshold, the signal transmission is normal.

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