A method for monitoring electric vehicle violations based on radio frequency wireless transmission technology

Abstract

The present invention discloses a method for monitoring violations of electric vehicles based on radio frequency wireless transmission technology, which relates to the technical field of monitoring violations of electric vehicles and includes the following steps: First, the radio frequency sensor on the electric vehicle transmits a wireless signal at a preset power to ensure that the signal of each electric vehicle is stably and reliably transmitted within the monitoring area. By combining radio frequency wireless transmission technology with a machine learning model, the present invention effectively solves the problems of signal interference and collision of electric vehicles in traffic-dense areas. Real-time acquisition of on-board radio frequency sensor signals and adjustment of transmission power through intelligent evaluation can reduce interference and improve data transmission accuracy and monitoring accuracy. This solution ensures accurate identification of violations of electric vehicles in high-density traffic environments, optimizes the intelligence level of traffic management systems, and enhances the ability to respond to dynamic traffic conditions, thereby improving traffic safety and flow management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring illegal behaviors of electric vehicles, and in particular to a method for monitoring illegal behaviors of electric vehicles based on radio frequency wireless transmission technology. Background Art

[0002] Electric vehicle violation monitoring based on radio frequency (RF) wireless transmission technology refers to the use of radio frequency (RF) technology to monitor and transmit data about electric vehicles in real time via wireless signals. Specifically, RF technology can install RF sensors on electric vehicles to communicate with receiving equipment on the road or in traffic management systems, obtaining real-time information such as the electric vehicle's speed, location, driving trajectory, and parking status. When an electric vehicle violates traffic regulations, such as running a red light, illegally parking, or driving on an overland route, the system can analyze the transmitted data to make a judgment and promptly issue an alarm or record the violation. RF wireless technology is characterized by high efficiency, low power consumption, and long distance, making it widely used in urban traffic management to achieve automated monitoring, rapid response, and processing of electric vehicle violations.

[0003] Existing technologies have the following shortcomings: In existing technologies, radio frequency sensors on electric vehicles typically transmit signals at a fixed power level to ensure stable and reliable signal transmission, conserve energy, and extend the sensor's lifespan. This fixed-power signal ensures effective communication between the RF sensor and the receiving device within a certain distance, preventing transmission attenuation or loss of reception due to low signal power. However, in densely trafficked areas, when multiple electric vehicles pass through the monitoring area simultaneously, radio frequency signal interference may occur, preventing the monitoring system from accurately identifying each vehicle's behavior. This situation can have serious consequences: signal interference can cause signals from some vehicles to be lost or confused, making it impossible for the traffic management system to distinguish signals from different electric vehicles or to mix and match data from different vehicles. This information loss or confusion creates monitoring blind spots, preventing the traffic management system from fully and accurately tracking and recording the movements of each vehicle, thereby impacting traffic flow control and safety warnings. It can even prevent certain violations from being promptly detected and addressed, increasing the risk of traffic accidents.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for monitoring electric vehicle violations based on radio frequency wireless transmission technology. By combining radio frequency wireless transmission technology with machine learning models, this method effectively solves the problems of signal interference and collision of electric vehicles in traffic-dense areas. Real-time acquisition of on-board radio frequency sensor signals and adjustment of transmission power through intelligent evaluation can reduce interference and improve data transmission accuracy and monitoring accuracy. This solution ensures accurate identification of electric vehicle violations in high-density traffic environments, optimizes the intelligence level of traffic management systems, and enhances the ability to respond to dynamic traffic conditions, thereby improving traffic safety and flow management efficiency to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for monitoring electric vehicle violations based on radio frequency wireless transmission technology, comprising the following steps:

[0007] First, the electric vehicle's onboard radio frequency sensor transmits wireless signals at a preset power to ensure that each electric vehicle's signal is stably and reliably transmitted within the monitoring area;

[0008] The RF receiver acquires the signal data sent by the RF sensors on vehicles passing through the monitoring area in real time. By capturing the wireless signals, the receiver obtains the real-time dynamic information of each vehicle;

[0009] The acquired wireless signal data is constructed into a data set and preprocessed to lay the foundation for subsequent data analysis. Key features reflecting signal conflicts are extracted from the preprocessed signal data. The extracted key features are then analyzed in depth within the detection window to evaluate the current signal status.

[0010] The analyzed key features are input into a pre-trained machine learning model, which then performs an intelligent assessment of the wireless signal status.

[0011] Based on the evaluation results of the machine learning model, wireless signals are divided into two categories: RF signal collision and RF signal interference-free propagation;

[0012] To ensure interference-free propagation of RF signals, the RF sensors on electric vehicles continue to send wireless signals at a preset power to ensure stable and reliable signal coverage;

[0013] In the event of signal collisions, the signal transmission power of different electric vehicle onboard RF sensors is dynamically adjusted based on the evaluation results of the machine learning model to ensure optimized signal transmission quality in high-density traffic environments, reduce interference, and improve the accuracy and reliability of data transmission.

[0014] Preferably, key features reflecting signal conflicts are extracted from the preprocessed signal data, and the extracted features include the error rate of symbols during data transmission and the consistency of the same signal at different time points. Within the detection window, the error rate of symbols during data transmission and the consistency of the same signal at different time points are analyzed, and a symbol error rate factor and a signal autocorrelation coefficient factor are generated respectively. The symbol error rate factor is used to quantify the degree of mismatch between the received symbol and the original symbol during signal transmission, and the signal autocorrelation coefficient factor is used to quantify the consistency of the same signal at different time points, that is, the stability of the signal over time.

[0015] Preferably, the specific steps of analyzing the error rate of symbols in the data transmission process within the detection window to generate a symbol error rate factor are as follows:

[0016] First, the received signal symbols are compared with the original symbols sent during data transmission to calculate the bit error rate. Suppose the received signal symbol set is , and the original symbol set is ,in, It is the first symbols, The first symbols, is the total number of symbols, and the bit error rate for each symbol position is calculated using the following formula:

[0017] , where It is Error marking of symbols;

[0018] Next, calculate the symbol error rate within the entire detection window. The calculation expression is as follows:

[0019] , where is the symbol error rate;

[0020] In order to accurately reflect the performance of the signal in a complex environment, the characteristics of the signal in the time domain and frequency domain are considered, and the interference influence coefficient is introduced. The calculation expression is as follows:

[0021] , where Is the power exponent, which is used to adjust the influence of the error part. Is an index used to adjust the impact of signal amplitude on the overall calculation. It is an index that adjusts the influence of the signal amplitude part. is the interference influence coefficient;

[0022] The final symbol error rate factor is calculated using the following formula:

[0023] , where is the symbol error rate factor.

[0024] Preferably, the specific steps of analyzing the consistency of the same signal at different time points in the detection window to generate the signal autocorrelation coefficient factor are as follows:

[0025] First, we need to calculate the signal autocorrelation function to quantify the consistency of the signal at different time points. In the time interval The signal autocorrelation function is calculated as follows:

[0026] , where It's time The signal value at time is a measure of the time delay of the signal, is the detection window length, It's a signal In passing The value of the position after the time delay, is the autocorrelation function, which is used to quantify the consistency of the signal between different time points;

[0027] Next, based on the calculation results of the autocorrelation function, the autocorrelation coefficient factor of the signal is generated, and the enhanced nonlinear metric is used to describe the periodic consistency of the signal. The calculation expression of the signal autocorrelation coefficient factor is as follows:

[0028] , where is the maximum value of the autocorrelation function within the detection window, is the parameter that controls the exponential decay, It is the amplitude of the change in signal autocorrelation, which measures the degree of signal fluctuation at different delays. is the signal autocorrelation coefficient factor.

[0029] Preferably, the analyzed symbol error rate factor and signal autocorrelation coefficient factor are input into a pre-learned machine learning model, a signal conflict coefficient is generated by the machine learning model, and the signal conflict coefficient is used to intelligently evaluate the wireless signal conflict status during wireless signal propagation in the current detection area.

[0030] Preferably, a signal conflict coefficient generated when a pre-learned machine learning model is used to intelligently evaluate the wireless signal conflict state during wireless signal propagation in the current detection area is compared with a pre-set signal conflict coefficient reference threshold value to divide the radio frequency wireless signal state in the current detection area. The division steps are as follows:

[0031] If the signal conflict coefficient is greater than a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as a radio frequency signal collision;

[0032] If the signal conflict coefficient is less than or equal to a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as radio frequency signal non-interference propagation.

[0033] Preferably, in the case of RF signal collision, based on the evaluation results of the machine learning model, the signal transmission power of different electric vehicle onboard RF sensors is dynamically adjusted to ensure the optimization of signal transmission quality in a high-density traffic environment, reduce interference, and improve the accuracy and reliability of data transmission. The specific steps are as follows:

[0034] After confirming that there is a radio frequency signal collision in the current area, the signal transmission power of each electric vehicle's onboard radio frequency sensor is dynamically adjusted based on the results of the machine learning model evaluation. The transmission power of each vehicle is adjusted based on the preset signal transmission power and the machine learning model evaluation results. The specific adjustment formula is as follows:

[0035] , where is the adjusted signal transmission power, is the preset signal transmission power, is the power adjustment factor, is the signal conflict coefficient, is the signal conflict coefficient reference threshold, is an adjustment parameter used to control the nonlinear relationship between the conflict level and power adjustment;

[0036] After adjusting the signal transmission power of the electric vehicle's onboard RF sensor, the following optimization strategy is adopted to accurately control the signal transmission quality by continuously optimizing the transmission power, reducing interference caused by signal collisions, minimizing data loss and transmission errors, and ensuring the reliability of the communication link:

[0037] , where is the optimized signal transmission quality, It is the proportional coefficient of signal optimization, which indicates the influence of power adjustment on signal quality. is the interference factor calculated from the current environmental interference level, It is an exponential adjustment parameter used to control the impact of power adjustment on signal quality.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] By combining radio frequency wireless transmission technology with machine learning models, this invention can effectively address the signal interference and collision issues of electric vehicles in densely trafficked areas. By acquiring the wireless signals transmitted by on-board radio frequency sensors in real time, utilizing machine learning models to intelligently assess the signal status, and dynamically adjusting the signal transmission power based on the assessment results, it is possible to reduce signal interference, improve the accuracy of data transmission, and enhance the precision of the monitoring system in high-density traffic environments, thereby enabling accurate identification and timely handling of electric vehicle violations. This method effectively enhances the intelligence and reliability of the traffic management system, strengthens the system's ability to respond to dynamic traffic conditions, and thereby improves the efficiency of traffic safety and flow management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 This is a flow chart of a method for monitoring electric vehicle violations based on radio frequency wireless transmission technology of the present invention. DETAILED DESCRIPTION

[0042] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0043] The present invention provides Figure 1 The method for monitoring electric vehicle traffic violations based on radio frequency wireless transmission technology includes the following steps:

[0044] First, the electric vehicle's onboard radio frequency sensor transmits wireless signals at a preset power to ensure that each electric vehicle's signal is stably and reliably transmitted within the monitoring area;

[0045] In traffic environments, electric vehicle RF sensors communicate with surrounding receiving devices via wireless signals. These RF sensors typically transmit signals at a fixed power level to ensure that each vehicle's signal is effectively transmitted within the monitoring area. Transmitting signals at a preset power level ensures stable signal strength between the vehicle and the receiving device, preventing signal attenuation or loss of reception, thereby providing a reliable data foundation for subsequent signal acquisition and analysis. This fixed power setting also helps conserve battery power, extend sensor life, and ensure stable operation in varying traffic conditions.

[0046] The RF receiver acquires the signal data sent by the RF sensors on vehicles passing through the monitoring area in real time. By capturing the wireless signals, the receiver obtains the real-time dynamic information of each vehicle;

[0047] Within the monitoring area, receiving devices receive real-time signals transmitted by RF sensors on electric vehicles. These receivers continuously scan for signals and collect corresponding data. By capturing wireless signals, they acquire real-time dynamic information about each vehicle, such as its location, speed, and signal strength. This process ensures the monitoring system can promptly monitor the status of electric vehicles and provides data support for subsequent data analysis. Through real-time data collection, the system can rapidly respond to changes in traffic flow and provide real-time information for traffic management.

[0048] The acquired wireless signal data is constructed into a data set and preprocessed to lay the foundation for subsequent data analysis. Key features reflecting signal conflicts are extracted from the preprocessed signal data. The extracted key features are then analyzed in depth within the detection window to evaluate the current signal status.

[0049] Once the receiver acquires signal data from the electric vehicle, it is organized into data sets for processing. The preprocessing process includes steps such as denoising, calibration, and standardization. Denoising removes stray information from the signal and improves signal clarity; calibration synchronizes the signal time, ensuring that all signal data is compared on the same time scale; and standardization unifies data from different sources, enabling analysis based on the same standards. These preprocessing steps are crucial to ensuring data accuracy and validity, ensuring that subsequent analysis is based on high-quality data.

[0050] Key features reflecting signal conflicts are extracted from the preprocessed signal data. The extracted features include the symbol error rate during data transmission and the consistency of the same signal at different time points. Within the detection window, the symbol error rate during data transmission and the consistency of the same signal at different time points are analyzed to generate a symbol error rate factor and a signal autocorrelation coefficient factor, respectively. The symbol error rate factor is used to quantify the degree of mismatch between the received symbol and the original symbol during signal transmission. The signal autocorrelation coefficient factor is used to quantify the consistency of the same signal at different time points, that is, the stability of the signal over time.

[0051] An abnormally high symbol error rate during data transmission typically indicates interference with the radio frequency signal in the monitoring area. The symbol error rate (BER) is a key metric that measures the proportion of erroneous symbols in data transmission. Under normal circumstances, it should be kept low to ensure accurate signal decoding. When radio frequency signals encounter interference, the interference can come from multiple sources, including signal collisions from other devices, reflections, and noise. These factors can affect the decoding process at the receiving device. Interference causes the received signal to contain more errors, which directly lead to incorrect symbol decoding and, in turn, an increase in the BER. If the BER increases abnormally over a short period of time, it typically indicates significant signal quality interference or signal collisions, preventing accurate data transmission. Furthermore, an increased BER can prevent the monitoring system from accurately capturing electric vehicle behavior data, impacting the normal operation of the traffic management system and potentially leading to missed identification of traffic violations. Therefore, an abnormally high BER is a key indicator of interference with the radio frequency signal in the monitoring area.

[0052] The specific steps for analyzing the symbol error rate during data transmission within the detection window to generate the symbol error rate factor are as follows:

[0053] First, the received signal symbols are compared with the original symbols sent during data transmission to calculate the bit error rate. To accurately evaluate the signal quality, the symbol error rate factor is not only based on the number of bit errors, but also takes into account the time-varying nature of the signal and the environmental impact. Suppose the received signal symbol set is , and the original symbol set is ,in, It is the first symbols, The first symbols, should be received by the receiving end, is the total number of symbols, and the bit error rate for each symbol position is calculated using the following formula:

[0054] , where It is Error marking of symbols;

[0055] The above formula is used to determine the Specifically, if the received symbol Symbols with original send If they are not the same, it means that an error occurred during the transmission, so The value of 1 indicates that the symbol is wrong; if and If the received symbols are the same, it means that the received symbols are correct. A value of 0 indicates that the symbol has no errors. Using the above formula, we can calculate whether an error has occurred for each symbol and further calculate the bit error rate during the entire data transmission process by counting the bit errors of all symbols.

[0056] Next, calculate the symbol error rate within the entire detection window. The calculation expression is as follows:

[0057] , where is the symbol error rate, which means The proportion of symbols with bit errors among the symbols;

[0058] The purpose of the above steps is to quantify the error situation in the transmission of each signal symbol and provide a basis for the generation of the subsequent symbol error rate factor. It reflects the frequency of errors occurring during signal transmission, and the change in the symbol error rate factor will be directly related to the size of this value.

[0059] To accurately reflect the performance of signals in complex environments, the interference impact coefficient is introduced by considering the characteristics of the signal in the time domain and frequency domain. This coefficient is obtained by quantitatively calculating different interference sources in the transmission environment. The calculation expression is as follows:

[0060] , where Is the power exponent, which is used to adjust the influence of the error part. Is an index used to adjust the impact of signal amplitude on the overall calculation. It is an index that adjusts the influence of the signal amplitude part, which is used to control the proportion of the signal amplitude in the final calculation. is the interference influence coefficient;

[0061] This step comprehensively considers the impact of the square of the symbol error (reflecting the degree of error) and the symbol strength (reflecting the signal strength) on the signal quality, and then introduces the interference influence coefficient To adjust the bit error rate index.

[0062] The final symbol error rate factor is calculated using the following formula:

[0063] , where is the symbol error rate factor.

[0064] The above steps combine the basic symbol error rate with the interference coefficient to generate a comprehensive bit error rate index, which effectively reflects the quality and interference status of the wireless signal. A larger value indicates that the current signal is subject to strong interference during transmission and the signal quality is poor; conversely, a smaller value indicates that the signal transmission process is relatively stable and the interference is small.

[0065] The larger the symbol error rate factor performance value, which is generated after analyzing the error rate of symbols during data transmission under the detection window, the more erroneous symbols occurred during the transmission process. This is usually due to the signal being affected by factors such as interference, collisions, or noise. When the signal quality decreases and the bit error rate increases, it means that the wireless RF signal encountered obstacles or interference during the transmission process, resulting in the inaccurate transmission of data, thereby increasing the error rate. Therefore, the larger the performance value of the symbol error rate factor, it usually indicates that the wireless RF signal in the current detection area is in an interference state, which may be caused by a conflict between multiple electric vehicle signals or environmental interference. Conversely, when the symbol error rate factor is low, it indicates that the signal maintains a high quality during transmission and the bit error rate is low, which means that the signal is in an interference-free propagation state and the data transmission is relatively stable and reliable.

[0066] Dramatic changes in the consistency of the same signal at different points in time typically indicate interference with the wireless RF signal in the monitoring area. This is because the stability and consistency of wireless signals are often directly related to their transmission quality. Under normal circumstances, signal consistency should be relatively stable, especially for signals transmitted from the same source. The signal waveform should change smoothly and predictably over time. Dramatic changes in signal consistency at different points in time may be due to external interference, signal collisions from other devices, or environmental changes (such as weather or electromagnetic interference). When RF signals from multiple electric vehicles are transmitted simultaneously in the same monitoring area, they interfere with each other, causing significant signal fluctuations at the receiver. This can even lead to phase distortion, frequency drift, or data loss. This dramatic change in consistency indicates that the signal is significantly affected by external factors, causing its stability to deteriorate, which in turn affects signal transmission quality and data decoding accuracy. Therefore, dramatic changes in signal consistency are a key indicator of signal interference, helping the system promptly identify interference and take appropriate measures, such as adjusting signal transmission power or optimizing signal scheduling strategies, to ensure the accuracy and reliability of the monitoring system.

[0067] The specific steps for analyzing the consistency of the same signal at different time points in the detection window to generate the signal autocorrelation coefficient factor are as follows:

[0068] First, we need to calculate the signal autocorrelation function to quantify the consistency of the signal at different time points. In the time interval The signal autocorrelation function is calculated as follows:

[0069] , where It's time The signal value at time It is a measure of the time delay of a signal, reflecting the time delay difference between the current signal and the previous signal. is the detection window length, It's a signal In passing The value of the position after the time delay, is the autocorrelation function, which is used to quantify the consistency of the signal between different time points;

[0070] Autocorrelation function Reflects the signal at different delays By comparing the similarity of the signal itself at different time points, we can determine whether the signal is stable and whether there are periodic or repetitive components. For example, when the signal has high autocorrelation at certain delay values, it indicates that the signal exhibits periodicity or stability, while low autocorrelation indicates that the signal is highly volatile and may lack periodicity or regularity.

[0071] Next, based on the calculation results of the autocorrelation function, the autocorrelation coefficient factor of the signal is generated, and the enhanced nonlinear metric is used to describe the periodic consistency of the signal. The calculation expression of the signal autocorrelation coefficient factor is as follows:

[0072] , where is the maximum value of the autocorrelation function within the detection window, is the parameter that controls the exponential decay, usually , used to emphasize the part where the signal similarity changes greatly, by adjusting , can capture signal irregularities or interference more finely, It is the amplitude of the change in signal autocorrelation, which measures the degree of signal fluctuation at different delays. is the signal autocorrelation coefficient factor.

[0073] By nonlinear measurement, the autocorrelation coefficient factor of the signal It can more precisely reflect the stability and interference level of the signal. A larger autocorrelation coefficient factor indicates a stronger temporal consistency of the signal, which may indicate the presence of periodic or overlapping interference sources in the signal. A smaller autocorrelation coefficient factor indicates larger signal fluctuations, less interference, or more free signal propagation.

[0074] The signal autocorrelation coefficient, generated by analyzing the consistency of the same signal at different time points within the detection window, indicates greater temporal consistency of the signal. This may indicate the presence of periodic or repetitive components in the signal, which is often associated with interference sources, such as overlapping signal sources or external electromagnetic interference. In this case, signal transmission may be disrupted, resulting in inaccurate or lost data. On the other hand, a smaller autocorrelation coefficient indicates greater temporal fluctuations and a lack of strong periodicity, which may indicate that the signal propagates more freely without significant interference.

[0075] The analyzed key features are input into a pre-trained machine learning model, which then performs an intelligent assessment of the wireless signal status.

[0076] The analyzed symbol error rate factor and signal autocorrelation coefficient factor are input into a pre-learned machine learning model, and a signal conflict coefficient is generated through the machine learning model. The signal conflict coefficient is used to intelligently evaluate the wireless signal conflict status during wireless signal propagation in the current detection area.

[0077] The machine learning model is not limited here and can achieve the symbol error rate factor Sum signal autocorrelation coefficient factor Generate signal conflict coefficient after comprehensive analysis The machine learning model can be used. In order to realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0078] Signal conflict coefficient The generation formula is as follows: , where 、 Symbol Error Rate Factor Sum signal autocorrelation coefficient factor The preset scaling factor, 、 And both are greater than 0.

[0079] "Preset scaling factor" refers to a specific scaling constant set in a machine learning model to adjust the importance or weight of different features in signal generation. In this formula, the preset scaling factor and and signal error rate factor Sum signal autocorrelation coefficient factor Related proportional coefficients, which affect the final signal conflict coefficient by adjusting different features in the signal generation model In short, the role of the preset proportional coefficient is to optimize the signal generation process by controlling the influence of different input features on the model output, ensuring that the final result has appropriate accuracy and responsiveness.

[0080] It can be seen from the signal conflict coefficient that the larger the symbol error rate factor performance value generated after analyzing the error rate of symbols in the data transmission process under the detection window, the larger the signal autocorrelation coefficient factor performance value generated after analyzing the consistency of the same signal at different time points under the detection window, and the larger the signal conflict coefficient performance value generated when the wireless signal status is intelligently evaluated by the pre-learned machine learning model, it indicates that the wireless RF signal in the current detection area is in an interference state, otherwise it indicates that the RF signal in the current detection area is in interference-free propagation.

[0081] A pre-learned machine learning model typically refers to a model that has been trained and optimized on a dataset containing a large amount of representative historical signal data and its relationship to interference conditions and signal propagation quality. By learning the characteristics, patterns, and regularities of this data, a machine learning model can provide intelligent predictions or classifications for new, unseen data. In this context, "pre-learning" means that the model has been trained with historical signal data and labels (such as signal interference, bit error rate, and consistency) before being deployed in real-world applications, enabling it to automatically recognize and process new signal inputs and assess signal conditions. Common machine learning algorithms include decision trees, support vector machines (SVMs), and neural networks. These algorithms use feature extraction and selection to learn underlying patterns from complex data.

[0082] In electric vehicle onboard RF sensor systems, pre-learned models are trained using annotated signal conflict cases from historical data. This data may include characteristics such as signal strength, symbol error rate, and signal autocorrelation coefficient under different traffic conditions, as well as labels indicating whether signals are conflicting or experiencing interference. Using this data, the machine learning model can identify potential patterns of signal conflict and interference and form a predictive model. Based on new signal data input and combined with existing knowledge, the model automatically determines whether the signal is in a conflict state, providing a basis for subsequent decisions (such as dynamic power adjustment or maintaining the current signal power). In this way, the machine learning model provides intelligent decision support for the system, enabling it to quickly and accurately handle signal conflict issues in actual operation.

[0083] In practical applications, a pre-trained machine learning model receives characteristic parameters such as the symbol error rate factor and signal autocorrelation coefficient from the monitored area as input, generating a "signal conflict coefficient" that quantifies whether the current signal is experiencing conflict and assesses signal quality. A higher signal conflict coefficient indicates a greater likelihood of signal conflict or interference; conversely, a lower conflict coefficient indicates better signal transmission quality and less interference. Key to this process lies in the model's "intelligence"—the machine learning model's ability to analyze new signal characteristics and make judgments in real time based on patterns and experience learned from historical data, without the need for human intervention.

[0084] The advantage of machine learning models lies in their ability to process complex, high-dimensional data and automatically discover nonlinear relationships between signals and between signals and interference. When the input data changes or new types of interference are introduced, the model gradually optimizes its predictive capabilities through continuous learning (such as incremental learning or online learning). This adaptability enables the system to cope with dynamically changing traffic environments and maintain efficient and stable performance even in conditions of dense traffic and frequent signal conflicts. Furthermore, pre-learned models can further improve the accuracy of signal conflict identification by continuously accumulating data feedback from actual operations, thereby achieving more precise interference control and signal optimization, ensuring the stable operation of the electric vehicle monitoring system in complex environments.

[0085] Based on the evaluation results of the machine learning model, wireless signals are divided into two categories: RF signal collision and RF signal interference-free propagation;

[0086] The signal conflict coefficient generated by the pre-learned machine learning model during the intelligent evaluation of the wireless signal conflict status in the current detection area during wireless signal propagation is compared and analyzed with the pre-set signal conflict coefficient reference threshold to classify the RF wireless signal status in the current detection area. The classification steps are as follows:

[0087] If the signal conflict coefficient is greater than a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as a radio frequency signal collision;

[0088] If the signal conflict coefficient is less than or equal to a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as radio frequency signal non-interference propagation;

[0089] RF signal collision occurs when multiple EV RF sensors or other wireless devices transmit simultaneously within the same monitoring area, causing interference or overlap, which can lead to signal data loss, corruption, or transmission errors. Interference-free RF signal propagation means that within the monitoring area, signals from EV RF sensors can be stably and unimpededly transmitted to the receiving device, and the received signal is clear and reliable, unaffected by any external interference or other signal sources.

[0090] To ensure interference-free propagation of RF signals, the RF sensors on electric vehicles continue to send wireless signals at a preset power to ensure stable and reliable signal coverage;

[0091] When RF signals are transmitting without interference, the electric vehicle's onboard RF sensors continue to transmit wireless signals at a preset power level. This ensures signal stability and reliability, allowing the monitoring system to continuously and accurately receive signals from the vehicle without interference. This approach maintains signal coverage within the monitoring area, preventing signal attenuation or loss due to low signal power. This ensures that the traffic management system can effectively track vehicle behavior in real time, enabling dynamic monitoring and data collection. By maintaining constant signal power, the system operates efficiently and provides reliable data support for subsequent traffic scheduling and safety monitoring.

[0092] In the event of RF signal collisions, the system dynamically adjusts the signal transmission power of different EV onboard RF sensors based on machine learning model evaluation results to ensure optimized signal transmission quality in high-density traffic environments, reduce interference, and improve data transmission accuracy and reliability.

[0093] In the event of RF signal collisions, the signal transmission power of different EV onboard RF sensors is dynamically adjusted based on the machine learning model evaluation results to ensure optimized signal transmission quality in high-density traffic environments, reduce interference, and improve data transmission accuracy and reliability. The specific steps are as follows:

[0094] After confirming that there is a radio frequency signal collision in the current area, the signal transmission power of each electric vehicle's onboard radio frequency sensor is dynamically adjusted based on the results of the machine learning model evaluation. The adjustment process aims to optimize signal quality, reduce interference, and ensure the accuracy and reliability of data transmission. The transmission power of each vehicle is adjusted based on the preset signal transmission power and the machine learning model evaluation results. The specific adjustment formula is as follows:

[0095] , where It is the adjusted signal transmission power. By adjusting the power of different electric vehicle-mounted RF sensors, the signal transmission quality can be optimized, interference can be reduced, and the signal stability and reliability can be ensured. is the preset signal transmission power, It is the power adjustment factor, which is used to control the power adjustment range. It determines the impact of the change in the ratio of the signal conflict coefficient to the reference threshold on the transmit power. is the signal conflict coefficient, is the signal conflict coefficient reference threshold, is an adjustment parameter used to control the nonlinear relationship between the conflict level and power adjustment;

[0096] This step dynamically adjusts the signal transmission power of the electric vehicle's onboard RF sensor based on the results of signal conflict detection to optimize the signal transmission quality. and preset reference thresholds , capable of adjusting signal transmission power in real time to address signal interference and conflicts in high-density traffic environments. The adjusted transmission power reduces interference, improves signal reliability and accuracy, and ensures stable data transmission and efficient operation of the traffic monitoring system.

[0097] After adjusting the signal transmission power of the electric vehicle's onboard RF sensor, the following optimization strategy is adopted to accurately control the signal transmission quality by continuously optimizing the transmission power, reducing interference caused by signal collisions, minimizing data loss and transmission errors, and ensuring the reliability of the communication link:

[0098] , where is the optimized signal transmission quality, It is the proportional coefficient of signal optimization, which indicates the influence of power adjustment on signal quality. is the interference factor calculated from the current environmental interference level, It is an exponential adjustment parameter used to control the impact of power adjustment on signal quality.

[0099] This step dynamically adjusts the transmit power of the electric vehicle's onboard RF sensors to optimize signal transmission quality, reduce interference, and improve the accuracy and reliability of data transmission. Through precise power adjustment, the system can address signal conflicts in high-density traffic environments, ensuring stable and interference-free signal transmission. This improves the stability of the communication link and ensures that the traffic management system can accurately and promptly receive and process data. This process maintains efficient signal transmission quality in complex environments, preventing data loss or transmission errors.

[0100] By dynamically adjusting the signal transmission power of EVs' onboard RF sensors based on machine learning model evaluation results, the system addresses RF signal collisions in high-density traffic environments. With the increasing number of EVs and denser traffic, RF signals from multiple EVs may interfere with each other, leading to signal collisions and impacting data transmission quality and monitoring system accuracy. In such situations, traditional fixed-power signal transmission methods are ineffective in addressing signal interference, often resulting in signal loss or misinterpretation, impacting the real-time monitoring of violations and traffic management.

[0101] By dynamically adjusting signal transmission power, the system can flexibly adjust the signal strength of each electric vehicle's onboard RF sensor based on real-time assessment of signal conflicts, ensuring that signals from different vehicles do not interfere with each other and are transmitted at optimal strength. This dynamic adjustment not only achieves better signal isolation between different vehicles, but also optimizes signal coverage and quality, avoiding signal attenuation caused by too low power or excessive interference caused by too high power.

[0102] Furthermore, machine learning models play a key role in this process. By analyzing and evaluating collected signal data in real time, they can accurately identify patterns and trends in signal conflicts, providing a scientific basis for adjusting transmit power. This intelligent, real-time adjustment method can effectively reduce interference in complex traffic environments, improve the stability and reliability of signal transmission, ensure the monitoring system's accurate identification of electric vehicle behavior, and enhance the responsiveness and decision-making efficiency of traffic management systems. Ultimately, the application of this technology can improve overall traffic safety, reduce the occurrence of traffic accidents, and improve the efficiency of traffic flow management.

[0103] By combining radio frequency wireless transmission technology with machine learning models, this invention can effectively address the signal interference and collision issues of electric vehicles in densely trafficked areas. By acquiring the wireless signals transmitted by on-board radio frequency sensors in real time, utilizing machine learning models to intelligently assess the signal status, and dynamically adjusting the signal transmission power based on the assessment results, it is possible to reduce signal interference, improve the accuracy of data transmission, and enhance the precision of the monitoring system in high-density traffic environments, thereby enabling accurate identification and timely handling of electric vehicle violations. This method effectively enhances the intelligence and reliability of the traffic management system, strengthens the system's ability to respond to dynamic traffic conditions, and thereby improves the efficiency of traffic safety and flow management.

[0104] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0105] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0106] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0107] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0110] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0113] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for monitoring electric vehicle violations based on radio frequency wireless transmission technology, characterized in that: The following steps are involved: First, the electric vehicle's onboard radio frequency sensor transmits wireless signals at a preset power to ensure that each electric vehicle's signal is stably and reliably transmitted within the monitoring area; The RF receiver acquires the signal data sent by the RF sensors on vehicles passing through the monitoring area in real time. By capturing the wireless signals, the receiver obtains the real-time dynamic information of each vehicle; The acquired wireless signal data is constructed into a data set and preprocessed to lay the foundation for subsequent data analysis. Key features reflecting signal conflicts are extracted from the preprocessed signal data. The extracted key features are then analyzed in depth within the detection window to evaluate the current signal status. The analyzed key features are input into a pre-trained machine learning model, which then performs an intelligent assessment of the wireless signal status. Based on the evaluation results of the machine learning model, wireless signals are divided into two categories: RF signal collision and RF signal interference-free propagation; To ensure interference-free propagation of RF signals, the RF sensors on electric vehicles continue to send wireless signals at a preset power to ensure stable and reliable signal coverage; In the event of signal collisions, the signal transmission power of different electric vehicle onboard RF sensors is dynamically adjusted based on the evaluation results of the machine learning model; Extract key features reflecting signal conflicts from preprocessed signal data. The extracted features include the symbol error rate during data transmission and the consistency of the same signal at different time points. Within the detection window, analyze the symbol error rate during data transmission and the consistency of the same signal at different time points to generate a symbol error rate factor and a signal autocorrelation coefficient factor, respectively. The symbol error rate factor quantifies the degree of mismatch between the received symbol and the original symbol during signal transmission. The signal autocorrelation coefficient factor quantifies the consistency of the same signal at different time points, that is, the stability of the signal over time. The analyzed symbol error rate factor and signal autocorrelation coefficient factor are input into a pre-learned machine learning model. The signal conflict coefficient is generated by the machine learning model, and the signal conflict status of the wireless signal during the wireless signal propagation process in the current detection area is intelligently evaluated by the signal conflict coefficient. If the signal conflict coefficient is greater than a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as a radio frequency signal collision; If the signal conflict coefficient is less than or equal to a preset signal conflict coefficient reference threshold, the radio frequency wireless signal in the current detection area is classified as radio frequency signal non-interference propagation; In the case of RF signal collision, the specific steps for dynamically adjusting the signal transmission power of different electric vehicle onboard RF sensors based on the machine learning model evaluation results are as follows: After confirming that there is a radio frequency signal collision in the current area, the signal transmission power of each electric vehicle's onboard radio frequency sensor is dynamically adjusted based on the results of the machine learning model evaluation. The transmission power of each vehicle is adjusted based on the preset signal transmission power and the machine learning model evaluation results. The specific adjustment formula is as follows: , where is the adjusted signal transmission power, is the preset signal transmission power, is the power adjustment factor, is the signal conflict coefficient, is the signal conflict coefficient reference threshold, is an adjustment parameter used to control the nonlinear relationship between the conflict level and power adjustment; After adjusting the signal transmission power of the electric vehicle's onboard RF sensor, the following optimization strategy is adopted to accurately control the signal transmission quality by continuously optimizing the transmission power, reducing interference caused by signal collisions, minimizing data loss and transmission errors, and ensuring the reliability of the communication link: , where is the optimized signal transmission quality, It is the proportional coefficient of signal optimization, which indicates the influence of power adjustment on signal quality. is the interference factor calculated from the current environmental interference level, It is an exponential adjustment parameter used to control the impact of power adjustment on signal quality.

2. The method for monitoring electric vehicle violations based on radio frequency wireless transmission technology according to claim 1, characterized in that: The specific steps for analyzing the symbol error rate during data transmission within the detection window to generate the symbol error rate factor are as follows: First, the signal symbols received during data transmission are compared with the original symbols sent, and the bit error rate is calculated. The set of received signal symbols is , and the original symbol set is ,in, It is the first symbols, The first symbols, is the total number of symbols, and the bit error rate for each symbol position is calculated using the following formula: , where It is Error marking of symbols; Next, calculate the symbol error rate within the entire detection window. The calculation expression is as follows: , where is the symbol error rate; In order to accurately reflect the performance of the signal in a complex environment, the characteristics of the signal in the time domain and frequency domain are considered, and the interference influence coefficient is introduced. The calculation expression is as follows: , where Is the power exponent, which is used to adjust the influence of the error part. Is an index used to adjust the impact of signal amplitude on the overall calculation. It is an index that adjusts the influence of the signal amplitude part. is the interference influence coefficient; The final symbol error rate factor is calculated using the following formula: , where is the symbol error rate factor.

3. The method for monitoring electric vehicle violations based on radio frequency wireless transmission technology according to claim 1, characterized in that: The specific steps for analyzing the consistency of the same signal at different time points in the detection window to generate the signal autocorrelation coefficient factor are as follows: First, the signal autocorrelation function needs to be calculated to quantify the consistency of the signal at different time points. In the time interval The signal autocorrelation function is calculated as follows: , where It's time The signal value at time is a measure of the time delay of the signal, is the detection window length, It's a signal In passing The value of the position after the time delay, is the autocorrelation function, which is used to quantify the consistency of the signal between different time points; Next, based on the calculation results of the autocorrelation function, the autocorrelation coefficient factor of the signal is generated, and the enhanced nonlinear metric is used to describe the periodic consistency of the signal. The calculation expression of the signal autocorrelation coefficient factor is as follows: , where is the maximum value of the autocorrelation function within the detection window, is the parameter that controls the exponential decay, It is the amplitude of the change in signal autocorrelation, which measures the degree of signal fluctuation at different delays. is the signal autocorrelation coefficient factor.

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