Positioning optimization method and system based on distributed long-range radio frequency identification

Abstract

The application relates to the technical field of radio frequency identification positioning optimization, and discloses a positioning optimization method and system based on distributed long-distance wireless radio frequency identification, which comprises the following steps: acquiring a radio frequency signal, carrying out data processing and feature extraction, and obtaining a multipath feature vector; matching the multipath feature vector with a feature template, extracting a path loss index, adopting a logarithmic distance path loss model, and calculating a theoretical device distance; carrying out path separation on the radio frequency signal, obtaining a path time delay interval of each path, determining a signal angle range according to the path time delay interval and the theoretical device distance, generating signal processing parameters according to environmental noise data; carrying out distance calculation on the radio frequency signal according to the signal angle range and the signal processing parameters, obtaining an actual device distance, and calculating a transmitting end device coordinate according to the actual device distance and a receiving end device coordinate. The application realizes accurate and efficient device positioning in a complex environment by suppressing multipath interference and dynamically compensating for the environment.

Description

Technical Field

[0001] This invention relates to the field of radio frequency identification (RFID) positioning optimization technology, and in particular to a positioning optimization method and system based on distributed long-range wireless RFID. Background Technology

[0002] With the development of radio frequency identification (RFID) technology, distributed long-range RFID systems with communication distances reaching hundreds of meters can be used in the power industry to achieve functions such as equipment positioning and asset tracking. RFID technology achieves precise positioning through the propagation of radio frequency signals. However, in environments with dense power equipment, traditional RFID positioning technology has certain limitations. On the one hand, in complex environments with dense metal structures, traditional positioning algorithms mostly rely on idealized radio frequency signal propagation models, which cannot adapt to interference caused by environmental changes, resulting in severe multipath effects and thus large positioning deviations and poor stability. On the other hand, environmental changes such as temperature and humidity affect the propagation speed of electromagnetic waves. In environments with dense power equipment, this effect exacerbates positioning drift, further affecting the accuracy and stability of power equipment positioning. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a positioning optimization method and system based on distributed long-range radio frequency identification. By suppressing multipath interference and dynamic environmental compensation, it improves the accuracy and stability of power equipment positioning in complex environments with dense equipment layouts.

[0004] In a first aspect, the present invention provides a positioning optimization method based on distributed long-range radio frequency identification, the method comprising:

[0005] The radio frequency signal in a densely packed equipment environment is acquired, and the radio frequency signal is processed and feature extracted to obtain a multipath feature vector, which includes path length, number of reflections and attenuation coefficient.

[0006] The multipath feature vector is matched with the feature template in the preset database. Based on the matching result, the corresponding path loss index is extracted. The logarithmic distance path loss model is used to calculate the theoretical device spacing, which is the theoretical distance between the transmitting and receiving devices of the radio frequency signal.

[0007] The radio frequency signal is path-separated based on path delay to obtain the path delay interval of each path. The signal angle range is determined according to the path delay interval and the theoretical device spacing. Signal processing parameters are generated based on environmental noise data. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio.

[0008] Based on the signal angle range and the signal processing parameters, the distance of the received radio frequency signal is calculated to obtain the actual device distance, and the coordinates of the transmitting device are calculated based on the actual device spacing and the coordinates of the receiving device.

[0009] Furthermore, the step of performing data processing and feature extraction on the radio frequency signal to obtain a multipath feature vector includes:

[0010] Calculate the first path delay and amplitude attenuation coefficient of the radio frequency signal, calculate the path length based on the first path delay, and calculate the number of reflections based on the amplitude attenuation coefficient;

[0011] The path length, the number of reflections, and the amplitude attenuation coefficient are combined to obtain a multipath feature vector.

[0012] Furthermore, after the step of calculating the theoretical equipment spacing using the logarithmic distance path loss model, the method further includes:

[0013] Acquire real-time environmental parameters, including real-time temperature and real-time relative humidity;

[0014] Based on the real-time environmental parameters, calculate the environmental compensation factor, and extract the corresponding reflection coefficient based on the matching result between the multipath feature vector and the feature template;

[0015] The theoretical equipment spacing is calibrated based on the environmental compensation factor and the reflection coefficient to obtain the calibrated theoretical equipment spacing.

[0016] Furthermore, the step of calculating the environmental compensation factor based on the real-time environmental parameters includes:

[0017] Calculate the temperature compensation coefficient based on the difference between the real-time temperature value and the reference temperature value;

[0018] Calculate the humidity influence coefficient based on the real-time relative humidity;

[0019] The product of the temperature compensation coefficient and the humidity influence coefficient is used as the environmental compensation factor.

[0020] Further, the step of determining the signal angle range based on the path delay interval and the theoretical device spacing, and generating signal processing parameters based on environmental noise data, includes:

[0021] Based on the path delay interval, calculate the average delay of each path, and based on the average delay and the theoretical device spacing, calculate the deviation angle of each path, and take the minimum value of each deviation angle as the main path angle.

[0022] Based on the real-time signal-to-noise ratio of the radio frequency signal, the angle spread factor is determined, and based on the angle spread factor and the main path angle, the signal angle range is obtained;

[0023] The dynamic sampling frequency is determined based on the background noise power, and the filter type is determined based on the dynamic sampling frequency.

[0024] The sampling time window is determined based on the dynamic sampling frequency and the real-time signal-to-noise ratio.

[0025] Further, the step of determining the sampling time window based on the dynamic sampling frequency and the real-time signal-to-noise ratio includes:

[0026] The ratio of the preset sensitivity coefficient to the real-time signal-to-noise ratio is used as the time resolution increment;

[0027] The sampling period is determined by taking the reciprocal of the dynamic sampling frequency as the sampling period and by determining the sampling time window based on the sampling period and the time resolution increment.

[0028] Further, the step of calculating the distance of the received radio frequency signal based on the signal angle range and the signal processing parameters to obtain the actual device distance includes:

[0029] Select the radio frequency signal within the signal angle range, sample the signal based on the dynamic sampling frequency, perform dynamic filtering according to the determined filter type, and truncate the filtered radio frequency signal according to the sampling time window.

[0030] Within the captured time window, the cross-correlation peak of the filtered RF signal is detected to obtain the second path delay. Based on the second path delay and the signal propagation speed, the actual device spacing is calculated.

[0031] Furthermore, after the step of calculating the actual device spacing based on the second path delay and signal propagation speed, the method further includes:

[0032] The actual equipment spacing is calibrated based on the environmental compensation factor and the reflection coefficient to obtain the calibrated actual equipment spacing.

[0033] Furthermore, the step of calculating the coordinates of the transmitting device based on the actual device spacing and the coordinates of the receiving device includes:

[0034] The coordinates of the transmitting equipment are calculated using the standard coordinate calculation method based on the coordinates of the receiving equipment, the elevation angle of the receiving equipment, the azimuth angle of the receiving equipment, and the actual equipment spacing.

[0035] Secondly, the present invention provides a positioning optimization system based on distributed long-range radio frequency identification, the system comprising:

[0036] The feature extraction module is used to acquire radio frequency signals in a densely arranged equipment environment, perform data processing and feature extraction on the radio frequency signals, and obtain a multipath feature vector, which includes path length, number of reflections and attenuation coefficient.

[0037] The spacing calculation module is used to match the multipath feature vector with the feature template in the preset database, extract the corresponding path loss index according to the matching result, and use the logarithmic distance path loss model to calculate the theoretical device spacing, which is the theoretical distance between the transmitting end device and the receiving end device of the radio frequency signal.

[0038] The parameter generation module is used to perform path separation on the radio frequency signal based on path delay to obtain the path delay interval of each path, determine the signal angle range according to the path delay interval and the theoretical device spacing, and generate signal processing parameters according to environmental noise data. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio.

[0039] The position calculation module is used to calculate the distance of the received radio frequency signal according to the signal angle range and the signal processing parameters to obtain the actual device distance, and to calculate the coordinates of the transmitting device according to the actual device spacing and the coordinates of the receiving device.

[0040] This invention provides a positioning optimization method and system based on distributed long-range radio frequency identification (RFID). By analyzing the propagation characteristics of radio frequency signals in densely populated environments, and optimizing the signal reception angle range and signal processing parameters, this invention suppresses multipath interference and environmental interference, improves the accuracy of device distance calculation, and, combined with a three-dimensional coordinate calculation method, achieves accurate and efficient positioning of transmitting devices. This invention effectively improves the accuracy and stability of device positioning in complex environments, providing a reliable solution for precise positioning in densely populated scenarios. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the positioning optimization method based on distributed long-range radio frequency identification in an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the positioning optimization system based on distributed long-range radio frequency identification in an embodiment of the present invention;

[0043] Figure label:

[0044] 10. Feature extraction module; 20. Spacing calculation module; 30. Parameter generation module; 40. Position calculation module. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. 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.

[0046] Please see Figure 1 The first embodiment of the present invention proposes a positioning optimization method based on distributed long-range radio frequency identification, including steps S10 to S40:

[0047] Step S10: Obtain radio frequency signals in a densely packed equipment environment, perform data processing and feature extraction on the radio frequency signals to obtain a multipath feature vector, wherein the multipath feature vector includes path length, number of reflections and attenuation coefficient;

[0048] Step S20: Match the multipath feature vector with the feature template in the preset database. Based on the matching result, extract the corresponding path loss index and use the logarithmic distance path loss model to calculate the theoretical device spacing, where the theoretical device spacing is the theoretical distance between the transmitting end device and the receiving end device of the radio frequency signal.

[0049] Step S30: Based on the path delay, the radio frequency signal is path-separated to obtain the path delay interval of each path. According to the path delay interval and the theoretical device spacing, the signal angle range is determined. And according to the environmental noise data, signal processing parameters are generated. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio.

[0050] Step S40: Based on the signal angle range and the signal processing parameters, perform distance calculation on the received radio frequency signal to obtain the actual device distance, and calculate the coordinates of the transmitting device based on the actual device spacing and the coordinates of the receiving device.

[0051] This invention addresses the application of distributed long-range radio frequency identification (RFID) systems for equipment positioning in the power industry, providing a method for optimizing the positioning of transmitting devices. In this embodiment, the RFID system includes a transmitting device, a receiving device, and RFID tags. The RFID tags are installed on the power equipment to be monitored for equipment identification. The transmitting device is positioned near the equipment's sensors to ensure accurate transmission of signal commands. The receiving device can be deployed at a distance, up to hundreds of meters from the RFID tag, effectively capturing and processing weak signals transmitted from the sensors. In practical applications, an inspection robot equipped with the receiving device can perform inspections to achieve data acquisition based on RFID positioning, such as for radio frequency temperature measurement.

[0052] In IoT environments with dense power equipment deployments, sensor nodes are concentrated in a limited space, resulting in a relatively dense layout of transmitting equipment. The metal casings between these power devices cause strong reflections and scattering of radio frequency (RF) signals, leading to complex multipath effects on RF signal propagation. To overcome multipath interference, this embodiment analyzes the RF signal to extract multipath feature vectors. These feature vectors describe the signal propagation characteristics under specific conditions, providing a basis for subsequent distance correction. The specific steps for constructing the multipath feature vectors include:

[0053] Calculate the first path delay and amplitude attenuation coefficient of the radio frequency signal, calculate the path length based on the first path delay, and calculate the number of reflections based on the amplitude attenuation coefficient;

[0054] The path length, the number of reflections, and the amplitude attenuation coefficient are combined to obtain a multipath feature vector.

[0055] In this embodiment, the radio frequency signal transmitted by the transmitter is reflected when it passes through a metal surface during propagation. The receiver receives the direct signal and the reflected signals after reflection through different paths; that is, it receives the superposition of signals from different paths. For the received radio frequency signal, its path delay difference and amplitude attenuation coefficient are calculated. The path delay difference can be calculated using cross-correlation, a statistical method used to measure the similarity between two time series. In wireless communication, cross-correlation is often used to estimate the time delay between a reference signal and a received signal. The maximum value of the cross-correlation function corresponds to the estimated integer-order time delay between the two signals. Specifically, the transmitter uses a preset pseudo-random sequence as the signal carrier, and the corresponding receiver stores an identical sequence as the reference signal. By performing cross-correlation calculations on the received radio frequency signal and the stored reference signal, the path delay can be calculated. The propagation time is the delay of the signal; the amplitude attenuation coefficient reflects the degree of energy loss after the signal is reflected. It is calculated by the ratio of the amplitude of the direct signal to the amplitude of the reflected signal. Specifically, cross-correlation is performed on the received RF signal and a preset reference signal, and the first peak value is separated by a time window. Since the first peak value corresponds to the direct path, the amplitude of the direct signal can be obtained based on the first peak value. The amplitude of the reflected signal can be obtained by performing a discrete wavelet transform on the received signal to separate the multipath components. Furthermore, the signal strength attenuation has an exponential rather than a nonlinear relationship with the propagation distance. Therefore, by performing a logarithmic transformation on the ratio of the direct signal amplitude to the reflected signal amplitude, the exponential relationship can be linearized, and the amplitude attenuation coefficient α is obtained. att It can be represented as:

[0056] ‌

[0057] In the formula, A di A represents the amplitude of the direct signal. re This indicates the amplitude of the reflected signal.

[0058] After obtaining the first path delay, the path delay is multiplied by the signal propagation speed to obtain the path length. The amplitude attenuation coefficient obtained based on waveform analysis can be understood as the total attenuation value. Each reflection will cause the signal strength to attenuate by a certain proportion. For example, a single reflection on a metal surface usually causes an attenuation of 3-6dB. Therefore, dividing the total attenuation value by the single attenuation value can give the number of reflections.

[0059] By combining path length, number of reflections, and amplitude attenuation coefficient, a multipath feature vector is obtained. Path length characterizes the physical distance of signal propagation, the number of reflections reflects the degree of interaction between the signal and the environment, and the amplitude attenuation coefficient quantifies the degradation of signal quality. These three parameters collectively describe the signal propagation characteristics under specific environmental conditions, thus providing a basis for subsequent distance correction.

[0060] Then, the multipath feature vectors are matched with feature templates in a preset database, and the feature template with the highest matching degree is selected. In this embodiment, through experimental measurement and data acquisition, signal propagation characteristics are systematically recorded under different device spacing and metal interference conditions to form a standard reference feature template. Based on the standard reference feature template, a feature database is established. Each feature template in the database corresponds to a specific environmental configuration and contains typical signal feature parameters under that configuration. Therefore, by performing similarity matching calculations between the multipath feature vectors and feature templates in the database, such as matching based on cosine similarity, the corresponding feature template is selected from the database as a reference template according to the similarity. Since each feature template corresponds to a specific environmental configuration, after selecting the reference template with the highest similarity, the environmental configuration parameters corresponding to that reference template can be obtained. Among them, the environmental configuration parameters include the path loss index.

[0061] In this embodiment, the logarithmic distance path loss model is used to calculate the theoretical device spacing. This theoretical device spacing can be understood as the direct distance between the RF signal transmitter and receiver. The logarithmic distance path loss model describes the attenuation of signal strength with propagation distance. The standard formula for the logarithmic distance path loss model is:

[0062]

[0063] In the formula, PL(d) represents the path loss based on the device spacing d, d0 represents the preset reference spacing, and n represents the path loss exponent. This indicates a random item.

[0064] Received signal power P rx Transmitted signal power P tx The relationship between path loss PL(d) and path loss is expressed as follows:

[0065]

[0066] Substituting the formula for path loss into the above relational expression, and ignoring the random term, we get:

[0067]

[0068] By deriving the above formula, we can obtain the expression for the theoretical equipment spacing d:

[0069]

[0070] In a preferred embodiment, since the ranging deviation caused by metal reflection in complex environments with dense metal layouts has a significant impact, and the temperature and humidity changes brought about by power equipment also affect the accuracy of positioning, this invention calibrates the device spacing through reflection coefficients and environmental compensation factors to ensure the accuracy of subsequent positioning. The specific calibration steps include:

[0071] Acquire real-time environmental parameters, including real-time temperature and real-time relative humidity;

[0072] Based on the real-time environmental parameters, calculate the environmental compensation factor, and extract the corresponding reflection coefficient based on the matching result between the multipath feature vector and the feature template;

[0073] The theoretical equipment spacing is calibrated based on the environmental compensation factor and the reflection coefficient to obtain the calibrated theoretical equipment spacing.

[0074] In this embodiment, in the preset database, in addition to setting the corresponding path loss index for each feature template, a corresponding reflection coefficient is also set based on the layout environment. The reflection coefficient is used to compensate for the ranging deviation caused by metal reflection. By multiplying the initial device spacing by the reflection coefficient, a more accurate device spacing is obtained. This correction method can effectively improve the ranging accuracy in dense layout environments and provide reliable reference parameters for device positioning.

[0075] In addition to metal reflection compensation, this embodiment also considers the impact of temperature and humidity on signal propagation speed. An environmental compensation factor is calculated based on real-time temperature parameters and real-time relative humidity. The specific calculation steps include:

[0076] Calculate the temperature compensation coefficient based on the difference between the real-time temperature value and the reference temperature value;

[0077] Calculate the humidity influence coefficient based on the real-time relative humidity;

[0078] The product of the temperature compensation coefficient and the humidity influence coefficient is used as the environmental compensation factor.

[0079] In this embodiment, a temperature compensation coefficient is first calculated based on the difference between the real-time temperature parameter and the reference temperature value under the current environment. The temperature compensation coefficient is used to correct the temperature drift of the electromagnetic wave velocity. Preferably, the temperature compensation coefficient K... T The expression is:

[0080]

[0081] In the formula, a represents the temperature coefficient, T represents the real-time temperature parameter, and T0 represents the reference temperature value, which is preferably set to 25 degrees.

[0082] Based on the real-time relative humidity, a humidity influence coefficient is calculated. This coefficient is used to correct for changes in the dielectric constant caused by humidity. Preferably, the humidity influence coefficient K is... H The expression is:

[0083]

[0084] In the formula, b represents the humidity coefficient, and RH represents the real-time relative humidity.

[0085] After obtaining the temperature compensation coefficient and humidity influence coefficient, their product is used as the environmental compensation factor to correct the equipment spacing. The equipment spacing after metal reflection compensation and environmental compensation can then be expressed as:

[0086]

[0087] In the formula, d cal Γ represents the theoretical equipment spacing after calibration, and Γ represents the reflection coefficient.

[0088] After obtaining the calibrated device spacing, the RF signal is path-separated based on the first path delay. When constructing the multipath feature vector, the path length is calculated using the path delay. To find the main path from multiple paths, this embodiment employs a clustering analysis algorithm, such as the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. Based on the first path delay, multiple paths of the RF signal are clustered, distinguishing between effective paths and occasional reflections to avoid misclassifying random noise as a propagation path. Several effective paths are extracted, and the path delay interval of each path is output. Then, based on the path delay interval and the theoretical device spacing, the signal angle range is calculated, and combined with environmental noise data, signal processing parameters are generated. Specific steps include:

[0089] Based on the path delay interval, calculate the average delay of each path, and based on the average delay and the theoretical device spacing, calculate the deviation angle of each path, and take the minimum value of each deviation angle as the main path angle.

[0090] Based on the real-time signal-to-noise ratio of the radio frequency signal, the angle spread factor is determined, and based on the angle spread factor and the main path angle, the signal angle range is obtained;

[0091] The dynamic sampling frequency is determined based on the background noise power, and the filter type is determined based on the dynamic sampling frequency.

[0092] The sampling time window is determined based on the dynamic sampling frequency and the real-time signal-to-noise ratio.

[0093] In this embodiment, for each path, the mean delay is calculated based on the path delay interval. The mean delay is the center value of the path delay fluctuation range, representing the closest possible delay time for that path. The actual path length is obtained by multiplying the mean delay by the signal propagation speed. In the above embodiment, a logarithmic distance path loss model was used to calculate the theoretical device spacing, i.e., the theoretical direct path length. Assuming the angle between the actual path and the theoretical path is θ, then according to the projection relationship, the projection of the actual path in the direct direction is equal to the direct path length. Its projection formula can be approximately equivalent to:

[0094]

[0095] Where, d direct This represents the length of the direct path, and its value is equal to d mentioned above. cal d actual The actual path length is calculated based on the mean delay, and θ represents the deviation angle. By transforming the projection formula, we can obtain the angle by which the actual propagation direction deviates from the direct direction, i.e., the deviation angle θ:

[0096]

[0097] Then, the minimum deviation angle is selected from the set of deviation angles for each path and taken as the main path angle. The minimum deviation angle is chosen as the main path angle because the path corresponding to this deviation angle has the highest signal strength, thus exhibiting higher reliability.

[0098] The environmental noise data of the current radio frequency signal is acquired. This environmental noise data includes background noise power and real-time signal-to-noise ratio (SNR). Based on the ratio of a preset constant to the real-time SNR, the angle spread factor Δθ is determined, expressed as: Δθ = k / SNR, where k represents the preset constant and SNR represents the real-time SNR. Then, based on the angle spread factor, the angle range of the signal is determined, which can be expressed as:

[0099]

[0100] In the formula, θ min and θ max θ represents the minimum and maximum angle values, respectively. main Δθ represents the main path angle, and Δθ represents the angle expansion coefficient.

[0101] The lower the current signal-to-noise ratio, the wider the angle range, and the stronger the ability to capture weak signals. In this embodiment, the signal angle range is set based on the main path angle. Selecting signals within this angle range ensures that the received signal is focused on the main path signal, suppresses positioning drift caused by multipath interference, and dynamically and adaptively adjusts the angle range through real-time signal-to-noise ratio, thus balancing accuracy and robustness.

[0102] The sampling frequency of the signal is determined by the background noise power. If the background noise power is less than a preset noise threshold, the base sampling rate is used as the dynamic sampling frequency; otherwise, the product of the base sampling rate and a preset multiple is used as the dynamic sampling frequency. The preset multiple is preferably set to 2. The sampling period can be determined based on the reciprocal of the dynamic sampling frequency, and its formula is as follows:

[0103]

[0104] In the formula, f s f represents the dynamic sampling frequency. base P represents the base sampling rate, σ represents the preset factor, and P represents the base sampling rate. noise P represents the background noise power, and P represents the noise threshold.

[0105] The selection strategy for signal processing filters is determined based on the relationship between signal bandwidth and sampling frequency. The Nyquist frequency is the minimum sampling frequency that needs to be defined to prevent signal aliasing, and its value is half of the dynamic sampling frequency. The main lobe width is extracted by analyzing the received signal spectrum through Fast Fourier Transform. The product of the Nyquist frequency and a frequency multiple is used as the width threshold, with the frequency multiple preferably set to 0.4. Then, it is determined whether the main lobe width is greater than the width threshold. If it is, it means that the signal bandwidth has occupied more than 40% of the Nyquist frequency, that is, the high-frequency components of the signal are more and closer to the Nyquist frequency. If the flat passband characteristic of the Butterworth filter is used, it may cause distortion of the high-frequency components. Therefore, a steeper transition band is needed to prevent aliasing and retain the high-frequency components of the signal. In this case, an 8th-order Chebyshev filter is preferred to provide a steeper cutoff characteristic, and the passband ripple is set to 0.5dB to ensure a balance between filtering effect and signal fidelity. Conversely, a 5th-order Butterworth filter is selected, and the transition band width is preferably set to 0.2 times the Nyquist frequency.

[0106] In practical signal processing, the real-time signal-to-noise ratio (SNR) affects the sensitivity of signal reception, which in turn affects the timing accuracy of signal detection. Higher sensitivity allows for earlier detection of the rising edge of the signal, effectively improving the time resolution. Therefore, this embodiment converts the reception sensitivity based on the real-time SNR into a time resolution increment, which is then added to the basic sampling period to obtain the sampling time window. The sampling time window characterizes the time span of the entire signal processing process. Specifically, the quotient of the preset sensitivity coefficient divided by the real-time SNR is used as the time resolution increment Δt. res Δt res =γ / SNR, where γ is the sensitivity coefficient (default 0.1), and SNR is the real-time signal-to-noise ratio. The product of the time resolution increment and the increment coefficient is then used as the resolution compensation term, with the increment coefficient preferably set to 0.3. The resolution compensation term is added to the sampling period to obtain the sampling time window, expressed by the formula:

[0107]

[0108] In the formula, T w The sampling time window is represented by μ, the increment coefficient is represented by Δt. res This indicates the increment in time resolution.

[0109] The reason for adding a resolution compensation term to the sampling period is that in low signal-to-noise ratio (SNR) environments, relying solely on the sampling period is insufficient to distinguish between real signals and noise. Extending the time window through the resolution compensation term ensures complete capture of signal characteristics. Under this time window formula, in high SNR environments, the resolution compensation term is small, and the duration of the sampling time window approximates the sampling period, maintaining high sampling efficiency. In low SNR environments, the increased sampling time window compensates for resolution loss, ensuring sampling accuracy.

[0110] Based on the aforementioned signal angle range and signal processing parameters, the distance to the received radio frequency signal can be calculated to obtain the actual device distance. Specific steps include:

[0111] Select the radio frequency signal within the signal angle range, sample the signal based on the dynamic sampling frequency, perform dynamic filtering according to the determined filter type, and truncate the filtered radio frequency signal according to the sampling time window.

[0112] Within the captured time window, the cross-correlation peak of the filtered RF signal is detected to obtain the second path delay. Based on the second path delay and the signal propagation speed, the actual device spacing is calculated.

[0113] In this embodiment, the actual device distance is directly calculated by the propagation delay of the radio frequency signal. Therefore, the accuracy of the actual device distance is directly related to the accuracy of the propagation delay. To ensure the accuracy of the propagation delay calculation, the radio frequency signal is first filtered according to the signal angle range. Since the signal angle range focuses on the main path signal, it can suppress the positioning drift caused by multipath interference. Then, the signal is sampled according to the set dynamic sampling frequency and dynamically filtered according to the set filter. Finally, the filtered signal is truncated according to the sampling time window, that is, the continuous signal within the sampling time window is extracted to ensure that the signal features can be completely captured. For signals within a time window, cross-correlation peak detection is performed based on the cross-correlation function. By finding the main peak position, the path delay of the RF signal is determined. When calculating the cross-correlation function, the cross-correlation function of the RF signal received by the receiving device and the pre-stored transmitted signal is used. This is because the cross-correlation calculation yields the time offset between the two signals, i.e., the time delay. If the cross-correlation calculation is performed on the received and transmitted signals, the resulting delay is actually the actual propagation time of the signal. Therefore, the actual device distance between the transmitter and receiver can be calculated based on the signal propagation time and the signal propagation speed. Thus, in this embodiment, the transmitting device uses a preset pseudo-random sequence as the signal carrier, and the corresponding receiving device pre-stores an identical sequence as a reference signal. By performing cross-correlation calculation on the received RF signal and the pre-stored reference signal, the path delay, i.e., the signal propagation time, can be calculated. Finally, the actual device distance is obtained by multiplying the path delay by the signal propagation speed. The cross-correlation peak detection based on the cross-correlation function can refer to the conventional cross-correlation function calculation steps, which will not be elaborated here. This embodiment can effectively improve the accuracy and efficiency of calculating actual device distance by optimizing the signal angle range and signal processing parameters.

[0114] As can be seen from the above embodiments, there are environmental factors and metal reflection interference during signal propagation. In order to further improve the accuracy of the calculation of the actual device distance, after calculating the actual device distance based on the path delay and signal propagation speed, the actual device distance can also be calibrated according to the environmental compensation factor and reflection coefficient determined in the above embodiments, so as to obtain the calibrated actual device distance.

[0115] In this embodiment, since the receiving device is mounted on the inspection robot, the coordinates and attitude angles of the receiving device can be determined based on the real-time pose of the inspection robot. The attitude angles include pitch and azimuth. Therefore, after the above steps, the actual device distance between the transmitting and receiving devices and the pose of the receiving device can be determined. Then, using the standard coordinate calculation method in the wireless positioning system, the coordinates of the transmitting device can be calculated. Its three-dimensional coordinate calculation expression is:

[0116]

[0117] In the formula, (X) r Y r Z r () represents the three-dimensional coordinates of the receiving device. Indicates the receiver's pitch angle. Indicates the azimuth angle of the receiving end. Indicates the actual distance to the device, (X) t Y t Z t () represents the three-dimensional coordinates of the transmitting device.

[0118] This embodiment improves the accuracy of path delay calculation by setting signal processing parameters, thereby improving the accuracy of actual device path calculation. Based on the accurate actual device path and combined with the receiver device pose, physical drive is used to directly calculate the three-dimensional coordinates, which can achieve accurate and efficient positioning of the transmitter device. Since the transmitter device is installed at a fixed position near the power equipment to be monitored, this invention achieves accurate positioning of the power equipment.

[0119] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes a positioning optimization system based on distributed long-range radio frequency identification, comprising:

[0120] Feature extraction module 10 is used to acquire radio frequency signals in a densely arranged equipment environment, perform data processing and feature extraction on the radio frequency signals to obtain a multipath feature vector, wherein the multipath feature vector includes path length, number of reflections and attenuation coefficient;

[0121] The spacing calculation module 20 is used to match the multipath feature vector with the feature template in the preset database, extract the corresponding path loss index according to the matching result, and use the logarithmic distance path loss model to calculate the theoretical device spacing, which is the theoretical distance between the transmitting end device and the receiving end device of the radio frequency signal.

[0122] The parameter generation module 30 is used to perform path separation on the radio frequency signal based on path delay to obtain the path delay interval of each path, determine the signal angle range according to the path delay interval and the theoretical device spacing, and generate signal processing parameters according to environmental noise data. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio.

[0123] The position calculation module 40 is used to calculate the distance of the received radio frequency signal according to the signal angle range and the signal processing parameters to obtain the actual device distance, and to calculate the coordinates of the transmitting device according to the actual device spacing and the coordinates of the receiving device.

[0124] The technical features and effects of the positioning optimization system based on distributed long-range radio frequency identification proposed in this invention are the same as those of the method proposed in this invention, and will not be repeated here. Each module in the above-mentioned positioning optimization system based on distributed long-range radio frequency identification can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0125] In summary, the present invention proposes a positioning optimization method and system based on distributed long-range radio frequency identification (RFID). The method acquires radio frequency (RF) signals in a densely populated environment, processes and extracts features from the RF signals to obtain a multipath feature vector, which includes path length, number of reflections, and attenuation coefficient. The multipath feature vector is then matched with feature templates in a preset database. Based on the matching results, the corresponding path loss index is extracted, and a logarithmic distance path loss model is used to calculate the theoretical device spacing, which is the theoretical distance between the transmitting and receiving devices of the RF signal. The RF signal is then path-separated based on path delay to obtain the path delay intervals for each path. Based on the path delay intervals and the theoretical device spacing, a signal angle range is determined. Signal processing parameters are generated based on environmental noise data, including dynamic sampling frequency, sampling time window, and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio. Finally, based on the signal angle range and the signal processing parameters, the distance to the received RF signal is calculated to obtain the actual device distance. The coordinates of the transmitting device are then calculated based on the actual device spacing and the coordinates of the receiving device. This invention analyzes the propagation characteristics of radio frequency signals in densely populated environments, optimizing the signal reception angle range and signal processing parameters. This suppresses multipath interference and environmental interference, improves the accuracy of device distance calculation, and, combined with a three-dimensional coordinate calculation method, achieves accurate and efficient positioning of transmitting devices. Thus, it optimizes the positioning of radio frequency identification. This invention effectively improves the accuracy and stability of device positioning in complex environments, providing a reliable solution for precise positioning in densely populated scenarios.

[0126] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0127] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A positioning optimization method based on distributed long-range radio frequency identification, characterized in that, include: The radio frequency signal in a densely packed equipment environment is acquired, and the radio frequency signal is processed and feature extracted to obtain a multipath feature vector, which includes path length, number of reflections and attenuation coefficient. The multipath feature vector is matched with the feature template in the preset database. Based on the matching result, the corresponding path loss index is extracted. The logarithmic distance path loss model is used to calculate the theoretical device spacing, which is the theoretical distance between the transmitting and receiving devices of the radio frequency signal. The radio frequency signal is path-separated based on path delay to obtain the path delay interval of each path. The signal angle range is determined according to the path delay interval and the theoretical device spacing. Signal processing parameters are generated based on environmental noise data. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio. Based on the signal angle range and the signal processing parameters, the distance of the received radio frequency signal is calculated to obtain the actual device distance, and the coordinates of the transmitting device are calculated based on the actual device spacing and the coordinates of the receiving device.

2. The positioning optimization method based on distributed long-range radio frequency identification according to claim 1, characterized in that, The steps of performing data processing and feature extraction on the radio frequency signal to obtain a multipath feature vector include: Calculate the first path delay and amplitude attenuation coefficient of the radio frequency signal, calculate the path length based on the first path delay, and calculate the number of reflections based on the amplitude attenuation coefficient; The path length, the number of reflections, and the amplitude attenuation coefficient are combined to obtain a multipath feature vector.

3. The positioning optimization method based on distributed long-range radio frequency identification according to claim 1, characterized in that, After the step of calculating the theoretical equipment spacing using the logarithmic distance path loss model, the method further includes: Acquire real-time environmental parameters, including real-time temperature and real-time relative humidity; Based on the real-time environmental parameters, calculate the environmental compensation factor, and extract the corresponding reflection coefficient based on the matching result between the multipath feature vector and the feature template; The theoretical equipment spacing is calibrated based on the environmental compensation factor and the reflection coefficient to obtain the calibrated theoretical equipment spacing.

4. The positioning optimization method based on distributed long-range radio frequency identification according to claim 3, characterized in that, The step of calculating the environmental compensation factor based on the real-time environmental parameters includes: Calculate the temperature compensation coefficient based on the difference between the real-time temperature value and the reference temperature value; Calculate the humidity influence coefficient based on the real-time relative humidity; The product of the temperature compensation coefficient and the humidity influence coefficient is used as the environmental compensation factor.

5. The positioning optimization method based on distributed long-range radio frequency identification according to claim 1, characterized in that, The steps of determining the signal angle range based on the path delay interval and the theoretical device spacing, and generating signal processing parameters based on environmental noise data, include: Based on the path delay interval, calculate the average delay of each path, and based on the average delay and the theoretical device spacing, calculate the deviation angle of each path, and take the minimum value of each deviation angle as the main path angle. Based on the real-time signal-to-noise ratio of the radio frequency signal, the angle spread factor is determined, and based on the angle spread factor and the main path angle, the signal angle range is obtained; The dynamic sampling frequency is determined based on the background noise power, and the filter type is determined based on the dynamic sampling frequency. The sampling time window is determined based on the dynamic sampling frequency and the real-time signal-to-noise ratio.

6. The positioning optimization method based on distributed long-range radio frequency identification according to claim 5, characterized in that, The step of determining the sampling time window based on the dynamic sampling frequency and the real-time signal-to-noise ratio includes: The ratio of the preset sensitivity coefficient to the real-time signal-to-noise ratio is used as the time resolution increment; The sampling period is determined by taking the reciprocal of the dynamic sampling frequency as the sampling period, and by determining the sampling time window based on the sampling period and the time resolution increment.

7. The positioning optimization method based on distributed long-range radio frequency identification according to claim 3, characterized in that, The step of calculating the distance to the received radio frequency signal based on the signal angle range and the signal processing parameters to obtain the actual device distance includes: Select the radio frequency signal within the signal angle range, sample the signal based on the dynamic sampling frequency, perform dynamic filtering according to the determined filter type, and truncate the filtered radio frequency signal according to the sampling time window. Within the captured time window, the cross-correlation peak of the filtered RF signal is detected to obtain the second path delay. Based on the second path delay and the signal propagation speed, the actual device spacing is calculated.

8. The positioning optimization method based on distributed long-range radio frequency identification according to claim 7, characterized in that, After the step of calculating the actual device spacing based on the second path delay and signal propagation speed, the method further includes: The actual equipment spacing is calibrated based on the environmental compensation factor and the reflection coefficient to obtain the calibrated actual equipment spacing.

9. The positioning optimization method based on distributed long-range radio frequency identification according to claim 1, characterized in that, The step of calculating the coordinates of the transmitting device based on the actual device spacing and the coordinates of the receiving device includes: The coordinates of the transmitting equipment are calculated using the standard coordinate calculation method based on the coordinates of the receiving equipment, the elevation angle of the receiving equipment, the azimuth angle of the receiving equipment, and the actual equipment spacing.

10. A positioning optimization system based on distributed long-range radio frequency identification, characterized in that, include: The feature extraction module is used to acquire radio frequency signals in a densely arranged equipment environment, perform data processing and feature extraction on the radio frequency signals, and obtain a multipath feature vector, which includes path length, number of reflections and attenuation coefficient. The spacing calculation module is used to match the multipath feature vector with the feature template in the preset database, extract the corresponding path loss index according to the matching result, and use the logarithmic distance path loss model to calculate the theoretical device spacing, which is the theoretical distance between the transmitting end device and the receiving end device of the radio frequency signal. The parameter generation module is used to perform path separation on the radio frequency signal based on path delay to obtain the path delay interval of each path, determine the signal angle range according to the path delay interval and the theoretical device spacing, and generate signal processing parameters according to environmental noise data. The signal processing parameters include dynamic sampling frequency, sampling time window and filter type. The environmental noise data includes background noise power and real-time signal-to-noise ratio. The position calculation module is used to calculate the distance of the received radio frequency signal according to the signal angle range and the signal processing parameters to obtain the actual device distance, and to calculate the coordinates of the transmitting device according to the actual device spacing and the coordinates of the receiving device.

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