Control system for two-wheeled vehicle communication equipment

Abstract

The invention discloses a control system for two-wheeled vehicle communication equipment, which relates to the technical field of communication equipment control and comprises a weak signal detection module, a signal fluctuation analysis module, a connection interruption evaluation module, a dynamic control optimization module and a self-adaptive adjustment module. The signal fluctuation analysis module is used for analyzing the network signal intensity fluctuation degree of the communication equipment in a weak signal environment based on the acquired network signal fluctuation information, and acquiring connection interruption risk information of the communication equipment in real time if the fluctuation degree exceeds a preset threshold value; and the connection interruption evaluation module is used for evaluating the connection interruption risk of the communication equipment under the condition of network signal fluctuation when the two-wheeled vehicle enters the weak signal environment. The problems of unstable connection, frequent disconnection and high power consumption of two-wheeled vehicle communication equipment in a weak signal environment are solved, intelligent detection, accurate evaluation, dynamic optimization and adaptive adjustment are realized, and the communication stability and the equipment cruising ability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication equipment control, and in particular to a control system for two-wheeled vehicle communication equipment. Background Art

[0002] Two-wheeled vehicle communication equipment refers to wireless communication devices installed on two-wheeled vehicles such as motorcycles and electric vehicles, such as GPS, Bluetooth, cellular network and other modules, which are used for intelligent functions such as data collection, remote monitoring, and navigation optimization. However, due to the small size, complex environment and battery power supply of two-wheeled vehicles, communication equipment may face problems such as signal attenuation, data loss, and excessive power consumption, so effective control is required. By optimizing data transmission, enhancing signal stability, and reducing energy consumption, the reliability and security of communication equipment can be improved, thereby improving user experience and improving the intelligence level of two-wheeled vehicles.

[0003] The existing control technology for two-wheeled vehicle communication equipment is mainly controlled by means of intelligent scheduling, network optimization and remote management. First, in terms of data transmission, an adaptive transmission strategy is adopted to dynamically adjust the data reporting frequency according to the network signal strength and vehicle speed to reduce power consumption and reduce data loss. Secondly, in order to enhance communication stability, the equipment usually supports multiple communication modes (such as 4G / 5G, Bluetooth, Wi-Fi), and uses signal switching algorithms to automatically switch between different network environments to ensure uninterrupted data transmission. In addition, through the coordinated optimization of on-board sensors, the communication equipment can adjust the signal gain in combination with the vehicle status (such as acceleration, tilt angle) to improve the communication quality in harsh environments. In terms of security management, remote control technology allows users to remotely switch, locate and track the equipment, and alarm abnormalities through mobile applications or cloud servers, and ensure the security of data transmission through encryption algorithms. In general, the existing control technology mainly revolves around data optimization, signal enhancement, and remote management to improve the stability, reliability and security of two-wheeled vehicle communication equipment.

[0004] The prior art has the following deficiencies: When a two-wheeled vehicle travels to a weak signal environment (such as a tunnel, underground parking lot, mountain road, or the edge of base station coverage), the network connection of the communication device may be temporarily disconnected due to large fluctuations in signal strength. Since the base station signal coverage in these environments is weak, the signal will show intermittent recovery and re-attenuation fluctuation characteristics, causing the device to experience repeated disconnection and reconnection in a short period of time. Since the device uses a fixed interval reconnection mechanism by default (such as automatically trying to reconnect once every 10 seconds), in an extremely weak signal environment, this mechanism cannot identify the pattern of signal fluctuations and the duration of disconnection, which may cause the device to repeatedly attempt to connect to the server while the signal is still in an extremely unstable state. Each reconnection consumes additional power, which in turn causes the device battery to consume faster and shorten the battery life. In addition, frequent reconnection requests will occupy the device's computing resources, affect other background tasks (such as data caching, log storage), and may even cause the device to freeze or crash. However, the existing control technology for two-wheeled vehicle communication equipment cannot evaluate the risk of connection interruption of communication equipment when the two-wheeled vehicle enters a weak signal environment under network signal fluctuations, and cannot dynamically optimize the disconnection recovery strategy based on the evaluation results, resulting in the device blindly reconnecting even during a short disconnection, causing the server to receive a large number of invalid connection requests, and ultimately causing the server to incorrectly record the online status of the device, resulting in frequent "online-offline" switching, affecting the accuracy of remote monitoring, and may mislead users or background management systems to make incorrect device status judgments, further reducing the reliability and stability of communication equipment.

[0005] 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 constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] An object of the present invention is to provide a control system for a two-wheeled vehicle communication device to solve the above-mentioned problems in the background art.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a control system for a two-wheeled vehicle communication device, comprising a weak signal detection module, a signal fluctuation analysis module, a connection interruption evaluation module, a dynamic control optimization module and an adaptive adjustment module; A weak signal detection module detects in real time whether the two-wheeled vehicle enters a weak signal environment during the driving process of the two-wheeled vehicle, and obtains network signal fluctuation information of the communication equipment on the two-wheeled vehicle in real time when the two-wheeled vehicle enters a weak signal environment; The signal fluctuation analysis module analyzes the fluctuation degree of the network signal strength of the communication device in a weak signal environment based on the obtained network signal fluctuation information. If the fluctuation degree exceeds the preset threshold, the connection interruption risk information of the communication device is obtained in real time; The connection interruption assessment module analyzes the acquired connection interruption risk information, assesses the connection interruption risk of the communication device under the condition of network signal fluctuation when the two-wheeled vehicle enters a weak signal environment, and divides it into three categories: low risk, medium risk and high risk; Dynamic control optimization module, which takes corresponding control optimization measures for communication equipment under different risk conditions according to the evaluation results; The adaptive adjustment module continuously monitors the communication status of the communication device during its operation, adjusts the control parameters based on the historical operation data of the communication device, and updates the control strategy of the communication device.

[0008] Preferably, in the signal fluctuation analysis module, the obtained network signal fluctuation information of the communication device on the two-wheeled vehicle is preprocessed to extract the signal strength change rate data, that is, the change rate of the network signal strength of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment; the standard deviation of the extracted data is calculated to reflect the degree of fluctuation of the network signal strength of the communication device in the weak signal environment, and the calculated standard deviation is compared with a preset threshold value. If the standard deviation is greater than the preset threshold value, the connection interruption risk information of the communication device is obtained in real time.

[0009] Preferably, in the connection interruption assessment module, the acquired connection interruption risk information of the communication device is preprocessed; the signal fluctuation information, connection stability information and recovery performance information are extracted from the preprocessed connection interruption risk information, and analyzed after extraction to generate a signal fluctuation index, a connection stability coefficient and a connection recovery coefficient, respectively; a connection interruption risk assessment model is constructed for the generated signal fluctuation index, connection stability coefficient and connection recovery coefficient, and an interruption risk coefficient is generated by weighted summation, and analyzed after generation to evaluate the connection interruption risk of the communication device under network signal fluctuation conditions when the two-wheeled vehicle enters a weak signal environment, and divide it into three categories: low risk, medium risk and high risk.

[0010] Preferably, the logic for obtaining the signal fluctuation index is as follows: The signal fluctuation information is extracted from the pre-processed connection interruption risk information, including the change speed of the network signal strength of the communication equipment at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the fluctuation of the signal power to the interference plus noise power ratio, and the base station switching frequency, and is calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The speed at which the network signal strength changes at each moment. Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The fluctuation of the signal power to interference plus noise power ratio at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The base station switching frequency at time, , is a positive integer; Calculate the signal volatility index. The specific calculation formula is as follows: ; In the formula, is the signal volatility index.

[0011] Preferably, the logic for obtaining the connection stability coefficient is as follows: The connection stability information is extracted from the preprocessed connection interruption risk information, including the network signal strength value of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the proportion of lost data packets, and the ratio of signal power to interference plus noise power, and they are calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The network signal strength value at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The proportion of lost packets at the time, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The ratio of the signal power to the interference plus noise power at the moment, , is a positive integer; Calculate the connection stability coefficient. The specific calculation formula is as follows: ; In the formula, is the connection stability factor.

[0012] Preferably, the logic for obtaining the connection recovery coefficient is as follows: The recovery performance information is extracted from the pre-processed connection interruption risk information, including the average speed of signal recovery after the communication device is disconnected in the historical time period and the frequency of disconnection events experienced by the communication device, and is marked as and , Indicates the average speed at which communication equipment recovers signals after a connection interruption during a historical period. Indicates the frequency of disconnection events experienced by the communication device during the historical time period; Calculate the connection recovery coefficient. The specific calculation formula is as follows: ; In the formula, is the connection restoration coefficient.

[0013] Preferably, the generated signal fluctuation index , Connection stability coefficient and the connection recovery coefficient A connection interruption risk assessment model is constructed, and the interruption risk coefficient is generated by weighted summation. The specific calculation formula is as follows: ; In the formula, is the interruption risk factor, , and Signal Volatility Index , Connection stability coefficient and the connection recovery coefficient The non-zero weight coefficient of .

[0014] Preferably, a preset interruption risk coefficient threshold interval is determined , and after determination, the generated interruption risk factor The comparison is carried out, and the risk of connection interruption of communication equipment under network signal fluctuation when the two-wheeled vehicle enters a weak signal environment is evaluated based on the comparison results, and it is divided into three categories: low risk, medium risk and high risk. The specific comparison analysis is as follows: like ,When a two-wheeled vehicle enters a weak signal environment, the risk of connection interruption of the communication equipment under network signal fluctuations is low; like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,communication equipment being disconnected when the network signal fluctuates is medium risk; like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,connection interruption of the communication equipment when the network signal fluctuates is high.

[0015] Preferably, in the dynamic control optimization module, corresponding control optimization measures are taken for communication devices under different risk conditions according to the evaluation results, specifically: If the assessment result is high risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: reduce the reconnection frequency of the communication equipment to avoid high power consumption due to frequent connection attempts; switch to low power mode to reduce the number of network scans and data synchronization; optimize the network selection strategy to give priority to connecting to available networks with stable signal quality; send high-priority alarm information to the background management system and record the data of this high-risk event; If the assessment result is a medium risk, the control optimization measures taken for the communication equipment under this risk situation are as follows: adjust the reconnection strategy, dynamically optimize the reconnection interval, so that the device can quickly restore the connection after a short disconnection but avoid invalid reconnection; optimize the data transmission mechanism, reduce the frequency of data transmission and increase the cache mechanism to reduce data loss during disconnection; send medium priority warning information to the background management system and record the risk assessment data; If the assessment result is low risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: maintain the current communication strategy unchanged and keep the communication equipment operating normally; continuously monitor the communication status and store historical communication data to analyze long-term trends; and periodically adjust the optimization strategy based on changes in historical communication data.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention uses a weak signal detection module to monitor the network environment of the two-wheeled vehicle communication equipment in real time, and obtains signal fluctuation information when the equipment enters a weak signal environment. This mechanism breaks through the limitation of the existing technology that it cannot accurately judge the signal degradation situation. Traditional methods usually rely on a fixed signal strength threshold for judgment, which may lead to misjudgment of weak signal status in complex environments. This solution uses multi-dimensional data such as signal strength change speed, base station switching frequency, signal power and interference plus noise power ratio to build a more comprehensive signal detection mechanism, so that the device can accurately identify when it enters a weak signal environment, and then trigger the optimization strategy at the right time. In addition, based on the signal fluctuation analysis module, the device can calculate the degree of fluctuation of the network signal, and quantify the network fluctuation characteristics through the standard deviation calculation method, avoiding false triggering caused by short-term signal drops, thereby achieving more intelligent communication management and improving the adaptability and operation stability of the equipment.

[0017] 2. The present invention introduces a signal fluctuation index, a connection stability coefficient and a connection recovery coefficient, and constructs a connection interruption risk assessment model based on these parameters. The interruption risk coefficient is calculated by weighted summation, and the risk is divided into three categories: low, medium and high, so that the device can adjust the control strategy according to different risk levels. This method breaks through the limitations of the fixed interval reconnection mechanism adopted by traditional communication equipment, and can dynamically optimize the connection management strategy of the device based on the actual signal fluctuation situation. For example, when the device is evaluated as a high-risk state, the system will reduce the reconnection frequency, switch to a low-power mode, and reduce invalid data transmission, thereby avoiding high power consumption and computing resource occupation caused by frequent reconnection; in a medium-risk state, the system can adjust the reconnection interval, optimize the data caching mechanism, and improve the connection recovery success rate. Compared with the prior art, this solution uses a data-driven intelligent evaluation method to make the device connection recovery more accurate, significantly reduce the problems of battery consumption, increased server load and device overheating caused by blind reconnection, and improve the stability and reliability of communication equipment.

[0018] 3. The present invention can not only optimize the current network status in real time, but also has the ability of adaptive adjustment. By continuously monitoring the communication status of the device and combining historical operation data, it can dynamically optimize the control parameters and communication strategies. For example, the device can record the long-term signal fluctuation trend, the distribution of disconnection events, and the reconnection success rate under different signal environments, and adjust the network switching strategy, signal threshold setting, reconnection mechanism, etc. based on these historical data, so that the device can gradually adapt to different driving environments and improve long-term communication stability. In addition, combined with machine learning or adaptive algorithms, the system can identify key factors that affect the quality of the connection, such as high disconnection rates at specific locations or abnormal signal fluctuations in specific time periods, and automatically optimize the parameter configuration of the device to reduce the impact of weak signal environments on the communication performance of the device. This feature makes this solution not only suitable for single connection optimization, but also can continuously optimize its own control strategy over time, thereby improving the self-learning ability of the system, achieving long-term stable network connections, and improving the availability of communication equipment in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced 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.

[0020] Figure 1 The module diagram of the control system of the present invention for a two-wheeled vehicle communication device. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of 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 the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0022] The present invention provides Figure 1 The control system for a two-wheeled vehicle communication device shown includes a weak signal detection module, a signal fluctuation analysis module, a connection interruption evaluation module, a dynamic control optimization module, and an adaptive adjustment module; A weak signal detection module detects in real time whether the two-wheeled vehicle enters a weak signal environment during the driving process of the two-wheeled vehicle, and obtains network signal fluctuation information of the communication equipment on the two-wheeled vehicle in real time when the two-wheeled vehicle enters a weak signal environment; During the driving process of the two-wheeled vehicle, the network status of the communication equipment can be continuously monitored, combined with the signal change trend, base station connection status and data transmission quality to determine whether it has entered a weak signal environment. First, the communication equipment can regularly detect the current network signal strength and record the signal changes over a period of time. If the signal strength shows a continuous downward trend, it means that it may have entered a weak signal environment. Secondly, the communication equipment can monitor the stability of the base station connection. For example, frequent switching of base stations in a short period of time or an increase in the number of connection failures indicates that the device may be in an area with poor network coverage. In addition, the communication equipment can detect whether there is data loss, increased response delay or network connection interruption during data transmission. If these situations continue to occur, they can also be used as a basis for judging whether it has entered a weak signal environment. When multiple weak signal characteristics are met, the communication equipment can determine that the two-wheeled vehicle has entered a weak signal environment, providing a basis for subsequent optimization of the control strategy.

[0023] When the communication device detects that a two-wheeled vehicle has entered a weak signal environment, it is necessary to further collect detailed network status information to evaluate the fluctuation of the network signal. This can be achieved by shortening the data collection interval so that the device can record the signal status multiple times in a shorter period of time to obtain more accurate network changes. In addition, the device can compare the signal strength changes at the current moment with those in the past period of time to calculate the degree of signal fluctuation. If the signal changes too quickly, it means that the network is unstable and further optimization strategies need to be adopted. At the same time, the communication device can monitor the connection status, such as whether frequent attempts to re-establish the connection, whether data transmission failures occur, and other information, and summarize these data to form complete network signal fluctuation information as the basis for subsequent risk assessment.

[0024] The reason why we first detect whether we have entered a weak signal environment and then further obtain network signal fluctuation information is that a weak signal environment does not necessarily lead to a serious decline in communication quality, but the instability of the signal is the key factor affecting the normal operation of communication equipment. If we rely solely on signal strength to judge the communication status, we may misjudge, causing the device to execute unnecessary optimization strategies in a weak signal environment that is still available, affecting the continuity of communication. Therefore, we first make a comprehensive judgment on the signal change trend, base station connection status and data transmission quality to ensure that the device has indeed entered a weak signal environment, and then further obtain detailed signal fluctuation information, so as to accurately assess the risk of connection interruption and take appropriate control strategies. This can prevent the device from reducing the connection frequency or entering a low-power mode when it is not necessary, while ensuring that the strategy can be adjusted quickly in a truly unstable environment, thereby improving the reliability and operating efficiency of communication equipment.

[0025] The signal fluctuation analysis module analyzes the fluctuation degree of the network signal strength of the communication device in a weak signal environment based on the obtained network signal fluctuation information. If the fluctuation degree exceeds the preset threshold, the connection interruption risk information of the communication device is obtained in real time; In this embodiment, in the signal fluctuation analysis module, the network signal fluctuation information of the communication device on the two-wheeled vehicle is preprocessed to extract the signal strength change rate data, that is, the change rate of the network signal strength of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment; the standard deviation of the extracted data is calculated to reflect the degree of fluctuation of the network signal strength of the communication device in the weak signal environment, and the calculated standard deviation is compared with the preset threshold value. If the standard deviation is greater than the preset threshold value, the connection interruption risk information of the communication device is obtained in real time.

[0026] In the signal fluctuation analysis module, the network signal fluctuation information of the communication device on the two-wheeled vehicle can be obtained by continuously monitoring and collecting relevant data of the network signal through the communication device. Specifically, the communication device can obtain network status indicators such as signal strength (RSSI), signal quality (SINR), packet loss rate (PLR) and latency in real time through the network interface (such as LTE, 5G) API or the interface provided by the operating system. These data can be sampled at fixed time intervals (such as every second or shorter) to form a series of signal quality data points. After obtaining the original signal data, preprocessing is necessary because the original signal data usually contains noise, outliers or fluctuations caused by external interference. Therefore, the first step of preprocessing is denoising, which can remove frequent small fluctuations through filtering algorithms (such as low-pass filters) to obtain a smoother and more realistic signal fluctuation trend. Then, in order to improve the accuracy of subsequent analysis, the data can be standardized to unify the signal strength data in different time periods into a standard range to avoid errors caused by differences in data magnitude. In addition, missing data filling is also required. For example, if the signal data is lost in certain time periods, these missing values ​​can be filled by interpolation methods (such as linear interpolation). After these preprocessing steps, the data obtained will be more stable and reliable, and can provide a more accurate basis for subsequent signal fluctuation analysis, help further identify changes in the stability of network signals, and assess possible risks of connection interruption.

[0027] To extract the signal strength change rate data, that is, the change rate of the network signal strength of the communication device at different times when the two-wheeled vehicle enters a weak signal environment, it can be achieved by calculating the rate of change of the signal strength. Specifically, first obtain continuous signal strength data (such as RSSI values) from the communication device, which are collected regularly at specific time intervals (for example, every second or shorter). Then, the change rate is obtained by calculating the change in signal strength between consecutive time points. The change rate is usually expressed by calculating the ratio of the signal strength difference (ΔRSSI) between the current moment and the previous moment to the time interval (Δt), that is, the signal strength change rate = ΔRSSI / Δt. This method can effectively capture the change trend of the signal, including the speed of the rise or fall of the signal strength. By continuously calculating and recording these change rate data, the signal changes over a period of time can be obtained, thereby providing data support for subsequent signal fluctuation analysis and interruption risk assessment. This method can reflect the change rate of signal strength in real time, thereby providing an accurate basis for judging the severity of signal fluctuations and assessing the risk of connection interruption.

[0028] The main purpose of calculating the standard deviation of the extracted signal strength change rate data is to measure the severity of signal fluctuations in a weak signal environment. By calculating the standard deviation of the signal change rate data over a period of time, the fluctuation range of the signal strength can be quantified. A data set with a large standard deviation indicates that the signal fluctuates violently, while a small standard deviation indicates that the signal changes relatively smoothly. In actual operation, the signal change rate at each moment in a certain time window (such as the past 1 minute or 10 seconds) can be obtained, and then the standard deviation of these change rates can be calculated. This process can be done by accumulating the signal change rate data and updating the calculated standard deviation every time new data arrives. The calculation result of the standard deviation will be compared with the preset threshold set in advance. If the calculated standard deviation is greater than the threshold, it means that the signal fluctuation exceeds the acceptable range. At this time, the system will determine that the stability of the network connection is poor and obtain the connection interruption risk information in real time. In this way, the system can promptly identify situations where the signal fluctuation is too large and the connection may be interrupted, so as to take corresponding optimization measures, such as adjusting the reconnection frequency, entering low power mode, or further analyzing the risk of device connection. This method is implemented in software, which helps to dynamically adjust the behavior of the device in an unstable signal environment and reduce invalid reconnections and power consumption waste.

[0029] To obtain the connection interruption risk information of communication devices in real time, it is first necessary to continuously monitor multiple key indicators related to network connection on the device side, such as signal strength (RSSI), signal quality (SINR), packet loss rate (PLR), and connection status (such as RRC connection status). These data can be collected at a certain time interval (such as every 1 second) through the device's network interface or the API interface provided by the operating system, and updated in real time. Specifically, when the standard deviation exceeds the preset threshold, it means that the signal fluctuation is large. At this time, based on these collected signal quality parameters, the possibility of current connection interruption can be comprehensively evaluated through a weighted algorithm or a decision tree model. For high-risk signal fluctuations, changes in connection status (such as frequent disconnection or reconnection) can be used as the basis for risk assessment. When the connection quality drops to a certain threshold, the system will automatically mark it as a high risk of connection interruption, thereby triggering relevant strategies, such as reducing the reconnection frequency or notifying the system for further processing. In addition, through the anomaly detection algorithm, the system can identify significant anomalies in the real-time signal fluctuation data stream to further determine the risk of connection interruption. In this way, the system can respond and generate risk information in real time, and take timely measures to prevent the impact of unstable or frequent disconnections of network connections on device performance and user experience.

[0030] The connection interruption assessment module analyzes the acquired connection interruption risk information, assesses the connection interruption risk of the communication device under the condition of network signal fluctuation when the two-wheeled vehicle enters a weak signal environment, and divides it into three categories: low risk, medium risk and high risk; In this embodiment, in the connection interruption assessment module, the acquired connection interruption risk information of the communication device is preprocessed; the signal fluctuation information, the connection stability information and the recovery performance information are extracted from the preprocessed connection interruption risk information, and the information is analyzed after extraction to generate a signal fluctuation index, a connection stability coefficient and a connection recovery coefficient, respectively; a connection interruption risk assessment model is constructed for the generated signal fluctuation index, the connection stability coefficient and the connection recovery coefficient, and the interruption risk coefficient is generated by weighted summation, and the information is analyzed after generation to evaluate the connection interruption risk of the communication device under the condition of network signal fluctuation when the two-wheeled vehicle enters a weak signal environment, and the risk is divided into three categories: low risk, medium risk and high risk.

[0031] In the connection interruption assessment module, it is necessary to preprocess the connection interruption risk information of the acquired communication equipment, because the original connection interruption risk information usually contains noise, outliers, missing data, and inconsistent numerical ranges. These problems will affect the calculation accuracy of the subsequent signal fluctuation index, connection stability coefficient, and connection recovery coefficient. Therefore, preprocessing operations such as data cleaning, smoothing, and standardization are required. First, use an outlier detection algorithm (such as the Z score method or the IQR quartile method based on historical statistical distribution) to identify and remove extreme outliers in data such as signal quality, packet loss rate, and base station switching frequency to avoid individual extreme fluctuations from affecting the overall assessment results. Secondly, in the case of missing data, interpolation methods (such as linear interpolation or predictive interpolation based on time series models) can be used to fill in the missing data to ensure that all data points are evenly distributed and improve the continuity and stability of the calculation. Then, smooth the data of different dimensions, such as using the sliding average or exponentially weighted moving average (EWMA) method for data such as RSSI change rate and packet loss rate to reduce the impact of short-term fluctuations and make the trend clearer. Finally, the data is standardized and mapped to the same numerical range (such as normalization to the [0,1] interval or Z-score standardization) to prevent some data from having an excessive impact on the calculation results due to a large numerical range. These preprocessing steps are implemented in software, making the extracted signal fluctuation information, connection stability information, and recovery performance information more accurate, improving the reliability of the evaluation model, and ensuring that the calculated interruption risk coefficient can accurately reflect the connection status of communication equipment in a weak signal environment.

[0032] In the connection interruption assessment module, signal fluctuation information, connection stability information and recovery performance information are extracted from the preprocessed connection interruption risk information, which can be achieved through feature extraction and data aggregation analysis. First, for signal fluctuation information, by calculating the key features such as the signal strength change rate, signal quality fluctuation, base station switching frequency (handover frequency) over a period of time, the fluctuation trend can be extracted from the signal data of multiple continuous time points using the time window sliding method, and it is encapsulated as signal fluctuation information. Secondly, for connection stability information, by analyzing the signal strength, data packet loss rate, and signal quality index of the device over a period of time, the mean, variance, and trend changes are calculated, and the key features affecting the connection stability are further extracted, and these data are integrated into connection stability information. Finally, for recovery performance information, the signal recovery rate, signal quality after recovery, and disconnection event frequency can be extracted from the disconnected data, and these data can be used to construct the recovery capability characteristics of the device in a weak signal environment, and finally form recovery performance information. The entire process is implemented through software, using methods such as sliding time windows, statistical analysis, and feature calculation to extract information of different dimensions from continuous network status data, so that the subsequent calculation of the signal fluctuation index, connection stability coefficient, and connection recovery coefficient can be based on accurate data, thereby improving the accuracy and reliability of risk assessment.

[0033] In this embodiment, the logic for obtaining the signal fluctuation index is as follows: The signal fluctuation information is extracted from the pre-processed connection interruption risk information, including the change speed of the network signal strength of the communication equipment at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the fluctuation of the signal power to the interference plus noise power ratio, and the base station switching frequency, and is calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The speed at which the network signal strength changes at each moment. Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The fluctuation of the signal power to interference plus noise power ratio at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The base station switching frequency at time, , is a positive integer; To obtain the speed of change of network signal strength of communication equipment at different times within a period of time, the fluctuation of signal power to interference plus noise power ratio, and base station switching frequency in real time when a two-wheeled vehicle enters a weak signal environment, regular sampling can be performed through the network interface API of the communication equipment or the network monitoring service provided by the operating system, and the acquired data can be processed using the time series analysis method. First, the speed of change of network signal strength can be obtained by continuously reading the received signal strength indicator (RSSI) value of the device, calculating the RSSI change rate within each time interval (such as every 200ms), that is, the current RSSI value minus the RSSI value at the previous moment, and dividing it by the time interval, so as to obtain the speed of change of signal strength, reflecting the signal stability. Secondly, the fluctuation of signal power to interference plus noise power ratio (SINR fluctuation) can be obtained by collecting historical data of signal power to interference plus noise power ratio (SINR) and calculating its standard deviation. The larger the standard deviation, the more serious the signal is affected by interference and the higher the degree of fluctuation. Finally, the base station switching frequency can be obtained by recording the base station switching events (Cell Reselection or Handover) of the communication device per unit time. When the device frequently switches base stations in a short period of time, it means that the device is in the boundary area covered by the base station or the network is unstable. By counting the number of switches within a certain time window (such as 10 seconds), the base station switching frequency can be obtained. The real-time acquisition of these data depends on the underlying network management interface of the device. By regularly polling, calculating the change trend and storing historical data, it can ensure that the signal fluctuation state of the communication device in a weak signal environment can be monitored in real time, and provide accurate data support for the subsequent signal fluctuation index calculation.

[0034] Calculate the signal volatility index. The specific calculation formula is as follows: ; In the formula, is the signal volatility index.

[0035] Calculate the signal volatility index The formula is to quantify the signal instability of two-wheeled vehicle communication equipment in a weak signal environment, and comprehensively considers signal strength changes, signal quality fluctuations and base station switching. Each calculation step in the formula has a specific role to ensure that the calculated It can accurately reflect the stability of communication equipment in a complex network environment. First, perform a square operation on the speed of signal strength change, that is, The purpose of this operation is to amplify the larger signal fluctuations so that they occupy a higher weight in the index calculation. Because when the signal strength fluctuates violently, the communication equipment is more likely to have unstable connection. The square operation can ensure that the violent signal strength changes dominate the final index evaluation. Secondly, the exponential operation is performed on the fluctuation of the signal power and the interference plus noise power ratio, that is, ,Since the higher the SINR value, the better the signal quality is. When the SINR fluctuates greatly, it means that the device may experience frequent interference or noise changes. Therefore, the exponential decay operation can more intuitively reflect the fluctuation of signal quality. The characteristic of exponential operation is that when When a larger value is taken (indicating a dramatic fluctuation in signal quality), will approach zero, and when When a smaller value is taken (indicating a more stable signal quality), will be close to 1, thus effectively reducing the impact of drastic fluctuations in signal quality on device stability and making the calculation results more in line with actual conditions; then, the base station switching frequency is logarithmically calculated, that is, A high base station switching frequency means that the communication device may be in the boundary area covered by the base station or in an unstable network area, which may easily lead to signal switching failure or temporary disconnection. However, the impact of base station switching does not change linearly, but presents a decreasing effect, that is, when the base station switching frequency increases from 0 times to 1 times, the impact is much greater than the impact from 10 times to 11 times. Therefore, the use of logarithmic operations can smooth the growth trend of the base station switching frequency, making its impact on SFI more practical and avoiding distortion of results due to extreme situations. Finally, the entire calculation formula uses summation and averaging to statistically normalize the signal fluctuations at all time points, so that the final signal fluctuation index can fully reflect the instability of the device signal over a period of time, and will not cause the calculation results to deviate excessively from the actual situation due to abnormal data at a certain moment. This method ensures that the calculated SFI can balance the signal fluctuations in different dimensions, and ultimately provide an accurate quantitative indicator to provide data support for subsequent connection interruption risk assessment.

[0036] The size of the signal fluctuation index (SFI) directly affects the assessment of the risk of connection interruption of communication equipment in a weak signal environment. The larger the value, the more drastic the signal fluctuation, the worse the connection stability of the device, and the higher the risk of interruption. Specifically, SFI is composed of the speed of signal strength change. , signal quality fluctuation Switching frequency with base station Together, when SFI is large, it usually means that the signal strength of the communication device changes dramatically (signal instability), SINR fluctuates greatly (signal is seriously interfered), and base station switching is frequent (the device is at the edge of network coverage). These factors will cause the network connection status of the communication device to become more unstable, increasing the probability of short-term disconnection, reconnection failure or communication delay. Therefore, when evaluating the risk of connection interruption, SFI is used as a core indicator. If its value is high, it means that the current device is in an extremely unstable network environment, and the risk of connection interruption is high. The device may experience more frequent disconnections, reconnection failures, or even complete loss of connection; conversely, if the SFI value is low, it means that the signal strength changes less, the signal quality is relatively stable, the base station switching frequency is low, the device's communication connection is relatively stable, and the risk of connection interruption is low.

[0037] In this embodiment, the logic for obtaining the connection stability coefficient is as follows: The connection stability information is extracted from the preprocessed connection interruption risk information, including the network signal strength value of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the proportion of lost data packets, and the ratio of signal power to interference plus noise power, and they are calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The network signal strength value at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The proportion of lost packets at the time, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The ratio of the signal power to the interference plus noise power at the moment, , is a positive integer; To obtain the network signal strength value, the proportion of lost data packets, and the ratio of signal power to interference plus noise power of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment in real time, regular sampling can be performed through the network interface API of the communication device or the network monitoring service provided by the operating system, and the data can be processed using the time series analysis method. First, the network signal strength value (RSSI) can be obtained in real time through the wireless communication module of the device (such as LTE / 5G modem), and the current RSSI value is recorded at regular intervals (such as 200ms) to form a signal strength data sequence within a period of time. Secondly, the proportion of lost data packets (PL) can be calculated by the ratio between the number of data packets sent by the communication protocol stack monitoring device during data transmission and the number of confirmation packets successfully received. The device can count the packet loss rate within a specified time window (such as 10 seconds) and continuously update the value through a sliding window. Finally, the ratio of signal power to interference plus noise power (SI, i.e. SINR) can read the current signal power (dBm) and interference plus noise power (dBm) of the device through the signal quality monitoring function of the wireless communication module, and calculate the ratio of the two. This value can reflect the change of the device's anti-interference ability in a weak signal environment. All of this data can be regularly acquired by the software and stored in a buffer for subsequent calculation of the connection stability coefficient and further assessment of the risk of connection interruption of the communication equipment.

[0038] Calculate the connection stability coefficient. The specific calculation formula is as follows: ; In the formula, is the connection stability factor.

[0039] Calculate the connection stability factor The formula uses exponential, logarithmic and normalized operations to accurately measure the connection stability of communication equipment in a weak signal environment. First, the signal strength value Perform exponential decay operation , which is used to amplify the impact of low signal strength on connection stability, making it more important when the signal is weak and less important when the signal is strong. Take the logarithm And normalize it , where the logarithmic operation is used to smooth the fluctuations of low packet loss rate, and the signal power to interference plus noise power ratio in the normalized denominator Ensure that the impact of packet loss rate is reduced when the signal quality is good, and the impact of packet loss rate is more significant when the signal quality is poor. Finally, by summing and taking the average, calculate the overall connection stability over a period of time to avoid misjudgment caused by single point data anomalies. More accurately reflect the connection status of communication equipment and provide a reliable basis for subsequent connection interruption risk assessment.

[0040] Connection stability factor The size directly reflects the connection stability of the communication equipment in a weak signal environment. The larger the value, the more stable the connection and the lower the risk of interruption. The smaller the value, the weaker the signal, the higher the packet loss rate, or the serious interference of the signal, and the higher the risk of connection interruption. By signal strength , Packet loss rate and signal quality The calculation shows that the lower the signal strength, the greater the exponential decay part. The larger the value, the stronger the negative impact of weak signal status on connection stability; when the packet loss rate is high, the logarithmic term Increase, while the signal quality When it is low, the normalized ratio of this item becomes larger, which means that the device is more likely to lose data when the network is poor, further increasing the risk of connection interruption. A low value indicates that the device is in an extremely unstable network environment and is prone to connection interruption, while a high value indicates that the device is in an extremely unstable network environment and is prone to connection interruption. A value of 0 indicates that the device connection stability is good and the risk of interruption is low.

[0041] In this embodiment, the logic for obtaining the connection restoration coefficient is as follows: The recovery performance information is extracted from the pre-processed connection interruption risk information, including the average speed of signal recovery after the communication device is disconnected in the historical time period and the frequency of disconnection events experienced by the communication device, and is marked as and , Indicates the average speed at which communication equipment recovers signals after a connection interruption during a historical period. Indicates the frequency of disconnection events experienced by the communication device during the historical time period; To obtain the average speed of signal recovery after the communication device is disconnected in the historical time period and the frequency of disconnection events experienced by the device in real time, data can be collected and calculated through the network monitoring module and log recording mechanism of the communication device. First, the average speed of signal recovery can be calculated by monitoring the time interval between the device going from the disconnected state (disconnection) to re-establishing a stable connection (restoring normal communication). Specifically, the communication device can monitor its network state change events (RRC connection failure → re-establishing connection successfully), record the occurrence time and connection recovery time each time a disconnection event occurs, and then calculate the recovery time interval. In order to obtain the recovery speed, all recovery times are averaged in the historical time period to obtain the average recovery speed, that is, the ability of the communication device to restore network connection in the past period of time. Secondly, the frequency of disconnection events can be detected by the device's log management module or communication protocol stack. The number of disconnections that occurred within a certain time window (such as the past 10 minutes or 1 hour). The device can record the occurrence time of each connection interruption and use the sliding window counting method to calculate the number of disconnections in the past period of time to reflect the disconnection frequency of the device in a weak signal environment. All of this data can be collected and updated regularly through software and stored in a buffer for subsequent calculation of the connection recovery coefficient (CRC), thereby evaluating the device's connection recovery capability in a weak signal environment, and further optimizing the network connection strategy to improve communication stability.

[0042] Calculate the connection recovery coefficient. The specific calculation formula is as follows: ; In the formula, is the connection restoration coefficient.

[0043] Calculate the joint recovery factor The formula comprehensively evaluates the connection recovery capability of communication equipment in a weak signal environment through exponential and logarithmic operations. First, the average speed of signal recovery is Perform exponential decay operation , which is used to amplify the adverse effect of slow recovery on the connection recovery capability, so that it contributes more when the recovery is faster, and has a more significant impact when the recovery is slower, ensuring that the recovery speed has an impact on the connection recovery capability. The calculated impact is consistent with the actual network situation. Take the logarithm and as a negative term in the formula, where the logarithmic operation is used to smooth the impact of low frequency of disconnection events and ensure that when the frequency of disconnection events is high, this term has no effect on The calculated impact is gradually increased so that it can accurately reflect the severity of the device being offline. The larger the calculation result, the stronger the connection recovery capability is. Perform exponential operations on Performing logarithmic operations and taking negative values ​​can ensure that devices with fast recovery speed and low disconnection frequency are Devices with high values, slow recovery and high disconnection frequency The value is low, which can accurately evaluate the connection recovery capability of communication equipment in a weak signal environment and provide a quantitative basis for subsequent optimization strategies.

[0044] Connection recovery coefficient The size of directly reflects the connection recovery capability of the communication equipment in a weak signal environment and is closely related to the risk assessment of connection interruption. A larger value means that the device's signal recovery speed is faster and the frequency of disconnection is lower. The device can re-establish a stable connection in a short time, so the risk of connection interruption is lower; The smaller the value, the slower the device recovery speed and the more frequent disconnections. It is more difficult for the device to maintain a stable connection in a weak signal environment, and the risk of connection interruption is higher. Specifically, Calculated from the average speed of signal recovery and the frequency of line drop events, where Amplify the slower recovery speed by exponential operation negative impact, ensuring that the recovery speed is low Significantly reduced, The impact of the disconnection frequency is smoothed by logarithmic operation, making the devices with high disconnection frequency Therefore, when assessing the risk of connection interruption, Devices with low network availability are more likely to experience long periods of network unavailability, while Devices with high bandwidth can quickly recover after a brief disconnection, ensuring continuity of communication.

[0045] In this embodiment, the generated signal fluctuation index , Connection stability coefficient and the connection recovery coefficient A connection interruption risk assessment model is constructed, and the interruption risk coefficient is generated by weighted summation. The specific calculation formula is as follows: ; In the formula, is the interruption risk factor, , and Signal Volatility Index , Connection stability coefficient and the connection recovery coefficient The non-zero weight coefficient of .

[0046] Calculating the interruption risk factor Signal Volatility Index , Connection stability coefficient and the connection recovery coefficient The weighted sum of is used to evaluate the risk of connection interruption of communication equipment in a weak signal environment. In the specific implementation, the system first calculates , and , and according to the preset weight coefficient , and Calculate, where Reflects the impact of signal fluctuations on interruption risk. A larger value indicates that signal fluctuations play a greater role in the assessment. This is applicable to environments with severe signal fluctuations. Reflects the impact of connection stability on risk. A larger value means that when the device connection is stable, even if the signal fluctuates greatly, it will not be mistakenly judged as high risk; Reflects the device's ability to recover. A higher weight means that when assessing the risk of interruption, the device is more dependent on its ability to recover from the network. This is suitable for devices with strong fast reconnection capabilities. The total weight coefficient must satisfy , and can be set through historical data analysis, machine learning optimization or expert experience to make the calculated It can accurately predict the risk of connection interruption and optimize the reconnection strategy accordingly to improve communication stability.

[0047] In this embodiment, a preset interruption risk coefficient threshold interval is determined. , and after determination, the generated interruption risk factor The comparison is carried out, and the risk of connection interruption of communication equipment under network signal fluctuation when the two-wheeled vehicle enters a weak signal environment is evaluated based on the comparison results, and it is divided into three categories: low risk, medium risk and high risk. The specific comparison analysis is as follows: like ,When a two-wheeled vehicle enters a weak signal environment, the risk of connection interruption of the communication equipment under network signal fluctuations is low; This situation indicates that the device's communication status in a weak signal environment is relatively stable, the signal fluctuation amplitude is small, the device connection reliability is high, and the probability of disconnection in a short period of time is low. At this time, the device can maintain normal communication functions, data transmission will not be significantly affected, and there is no need to optimize the reconnection strategy. In a low-risk state, communication equipment can continue to operate according to the normal connection management strategy without additional intervention measures, thereby reducing unnecessary power consumption and system resource usage, while ensuring the smoothness and stability of communication.

[0048] like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,communication equipment being disconnected when the network signal fluctuates is medium risk; This situation indicates that the device's connection stability in a weak signal environment is at risk, with large signal fluctuations or high packet loss rates, but not to the extent that it seriously affects communication. At this point, the device may occasionally experience a short disconnection or a drop in connection quality, resulting in delayed or partial data loss, affecting the real-time nature of communication. Devices in a medium-risk state need to adjust network management policies appropriately, such as optimizing data retransmission mechanisms, adjusting reconnection intervals, or switching to backup network modes, to reduce the probability of disconnection and improve communication stability.

[0049] like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,connection interruption of the communication equipment when the network signal fluctuates is high.

[0050] This situation indicates that the device's connection is extremely unstable in a weak signal environment, with severe signal fluctuations, high packet loss rates, and frequent disconnections, making it difficult for the device to maintain a continuous connection, and may even be disconnected for a long time. At this point, the network function of the communication device is severely affected, which may cause remote control failure, data loss, or communication interruption, causing a significant impact on real-time communications or critical business. For devices in high-risk states, optimization measures should be taken immediately, such as reducing the reconnection frequency, switching to low-power mode, adjusting wireless parameters, or enabling a more robust network switching strategy to reduce the waste of resources caused by frequent disconnections of devices and improve the reliability and availability of the system.

[0051] The predetermined threshold interval of the interruption risk coefficient can be set by historical data analysis, statistical modeling, and machine learning optimization, and can be optimized through dynamic adjustment mechanisms to make it more accurately adapt to the communication status in different environments. First, through historical data analysis, a large number of interruption risk coefficient (IRC) calculation results of communication equipment in different signal environments can be collected, and the corresponding communication status (such as disconnection frequency, packet loss rate, signal fluctuation) can be classified, and the low, medium, and high risk intervals can be divided by setting percentile thresholds (such as 10% as a low risk threshold and 90% as a high risk threshold). Secondly, statistical modeling methods (such as K-means clustering and standard deviation analysis) are used to perform cluster analysis on the interruption risk coefficient in historical data to find the inflection point of data distribution, and accordingly set a reasonable threshold interval so that the threshold can adapt to changes in different environments. Furthermore, combined with machine learning optimization, a supervised learning-based model can be trained, multiple parameters that affect communication stability (such as SFI, CSC, CRC) and the disconnection status of the equipment can be input, and the optimal threshold division can be learned using training data to make the evaluation results more accurate. In addition, a dynamic adjustment mechanism can be used to regularly monitor the actual offline status of the device and adaptively optimize the threshold range. For example, when the interruption rate is too high or too low for a long time, the system can automatically adjust the threshold range to more accurately reflect the communication status under different signal environments, thereby improving the accuracy of the device's risk assessment.

[0052] Dynamic control optimization module, which takes corresponding control optimization measures for communication equipment under different risk conditions according to the evaluation results; In this embodiment, in the dynamic control optimization module, corresponding control optimization measures are taken for communication devices under different risk conditions according to the evaluation results, specifically: If the assessment result is high risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: reduce the reconnection frequency of the communication equipment to avoid high power consumption due to frequent connection attempts; switch to low power mode to reduce the number of network scans and data synchronization; optimize the network selection strategy to give priority to connecting to available networks with stable signal quality, such as switching to backup networks or reducing communication rates to enhance connection stability; send high-priority alarm information to the background management system and record the data of this high-risk event; When the assessment result is high risk, the device's network connection is extremely unstable, disconnections are frequent, and the signal fluctuates violently. Strict optimization measures need to be taken to reduce power consumption and improve connection recovery capabilities. First, reducing the reconnection frequency can be achieved through the dynamic reconnection strategy adjustment algorithm. The device can record the number of recent reconnection failures and time intervals. If multiple reconnection failures occur in a short period of time, the interval for the next reconnection will be automatically extended (such as using an exponential backoff algorithm) to avoid invalid reconnections of the device in an extremely weak signal environment, thereby reducing power consumption. Secondly, switching to low-power mode can be achieved by adjusting the working state of the communication module. The device can reduce active network scanning requests in high-risk situations and only try to connect when the signal is significantly enhanced, thereby reducing power consumption and avoiding excessive occupation of system resources. Thirdly, optimizing the network selection strategy can dynamically determine the availability of the current network through the device's signal quality analysis algorithm, and prioritize connecting to networks with more stable signal quality when available, such as 4G / 5G network priority adjustment, Wi-Fi assisted switching, etc., to ensure that the device can choose the best connection method even in a weak signal environment. In addition, the device can send high-priority alarm information to the background management system, and record the number of connection failures and signal quality fluctuation data of the device through the software log system for the background analysis to optimize the communication strategy. At the same time, the device can adjust its own connection method accordingly to adapt to changes in the long-term weak signal environment.

[0053] If the assessment result is a medium risk, the control optimization measures taken for the communication equipment under this risk situation are as follows: adjust the reconnection strategy, dynamically optimize the reconnection interval, so that the device can quickly restore the connection after a short disconnection but avoid invalid reconnection; optimize the data transmission mechanism, reduce the frequency of data transmission and increase the cache mechanism to reduce data loss during disconnection; send medium priority warning information to the background management system, and record the risk assessment data for subsequent optimization strategy adjustment; When the assessment result is medium risk, the device may occasionally drop offline, but it can still restore the connection. Therefore, it is necessary to optimize the reconnection strategy and data transmission method to improve the connection recovery efficiency. First, the optimization of the reconnection strategy can be achieved through an adaptive reconnection interval adjustment algorithm. The device can dynamically adjust the reconnection interval according to the recent signal strength change trend (such as when the signal quality fluctuates but is not completely lost, the reconnection interval can be appropriately shortened; if the signal continues to be poor for a short period of time, the reconnection interval can be appropriately extended), thereby improving the reconnection success rate and reducing invalid attempts. Secondly, the optimization of the data transmission mechanism can be achieved through a cache and batch transmission mechanism. When the device detects that the current signal quality is unstable, it can temporarily store data and send it in batches after the signal is restored to reduce data loss during disconnection and ensure the integrity of key data. At the same time, the device can send medium priority warning information to the background management system to record the communication status of the device under medium risk conditions for subsequent analysis and optimization. In addition, the device can enable intelligent signal enhancement strategies, such as dynamically adjusting the device's transmission power or switching to a more stable frequency band when the signal quality fluctuates greatly, to reduce the volatility of network connections and improve the continuity of data transmission.

[0054] If the assessment result is low risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: maintain the current communication strategy unchanged and keep the communication equipment operating normally; continuously monitor the communication status and store historical communication data to analyze long-term trends; periodically adjust the optimization strategy based on changes in historical communication data so that the equipment can adapt to the communication stability requirements in different environments.

[0055] When the assessment result is low risk, the overall network connection of the device is stable and no immediate intervention measures are required, but continuous monitoring and long-term optimization are still required. First, maintain the current communication strategy to ensure that the device operates in normal mode in a stable signal environment without additional reconnection or optimization to avoid waste of resources. Secondly, continuous monitoring of communication status can be achieved through time series data recording and trend analysis. The device can regularly record communication quality data (such as signal strength, packet loss rate, etc.) and establish long-term operating trends to identify potential signal quality degradation trends or environmental changes. In addition, the device can store historical communication data and perform periodic optimization. The background system can regularly analyze the long-term data of low-risk devices. If it is found that the signal fluctuation of some devices in a specific time period or specific location increases, the communication strategy of the device can be adjusted in a targeted manner, such as adjusting the base station switching threshold, optimizing the network selection logic, or early warning of potential network coverage blind spots, to ensure that the communication equipment maintains high stability during long-term operation.

[0056] The adaptive adjustment module continuously monitors the communication status of the communication device during its operation, adjusts the control parameters based on the historical operation data of the communication device, and updates the control strategy of the communication device.

[0057] To implement the adaptive adjustment module, it is first necessary to continuously monitor the operating status of the communication equipment, which can be achieved through real-time data collection and historical data storage and analysis. The device can regularly record key communication indicators such as network signal strength (RSSI), signal quality (SINR), packet loss rate, base station switching frequency, connection success rate, and disconnection event frequency, and store these data in local storage or upload them to a remote server for processing. Through time series analysis, the long-term change trend of the communication status can be extracted, such as the downward trend of signal strength, the increase in disconnection frequency, or the change in reconnection success rate within a specific time period. If the communication performance of the device is found to fluctuate continuously, the system can automatically trigger the adjustment logic to optimize the connection strategy of the device. In addition, the device can use anomaly detection algorithms (such as Z-score or moving average algorithms) to identify sudden anomalies, such as high disconnection rates or sudden signal attenuation in specific locations, and dynamically adjust the communication parameters of the device to adapt to changes in the current environment and improve the stability of communication.

[0058] In terms of adjusting control parameters and updating control strategies based on the historical operation data of communication equipment, this can be achieved through machine learning prediction models or dynamic parameter optimization algorithms. The device can analyze the optimal reconnection interval, optimal signal switching threshold, data transmission rate adjustment strategy, etc. under different network conditions based on historical data, and optimize these parameters using adaptive algorithms (such as reinforcement learning or adaptive filters). For example, if historical data shows that a device frequently drops offline within a specific signal range, the system can automatically lower the signal switching threshold of the device to reduce unnecessary network switching and improve connection stability. At the same time, if the device exhibits poor network quality in a certain area for a long time, the system can give priority to enabling the backup network or adjusting the data sending strategy, such as reducing the frequency of unnecessary signal reporting to reduce communication loss. This dynamic adaptive adjustment method ensures that the device can intelligently optimize its own communication strategy in different environments, improve communication stability, and reduce power consumption and unnecessary network load.

[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0060] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0061] 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.

[0062] Those of ordinary skill 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 to be beyond the scope of this application.

[0063] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0064] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] 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.

[0066] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control system for a two-wheeled vehicle communication device, characterized in that: It includes weak signal detection module, signal fluctuation analysis module, connection interruption assessment module, dynamic control optimization module and adaptive adjustment module; A weak signal detection module detects in real time whether the two-wheeled vehicle enters a weak signal environment during the driving process of the two-wheeled vehicle, and obtains network signal fluctuation information of the communication equipment on the two-wheeled vehicle in real time when the two-wheeled vehicle enters a weak signal environment; The signal fluctuation analysis module analyzes the fluctuation degree of the network signal strength of the communication device in a weak signal environment based on the obtained network signal fluctuation information. If the fluctuation degree exceeds the preset threshold, the connection interruption risk information of the communication device is obtained in real time; The connection interruption assessment module analyzes the acquired connection interruption risk information, assesses the connection interruption risk of the communication device under the condition of network signal fluctuation when the two-wheeled vehicle enters a weak signal environment, and divides it into three categories: low risk, medium risk and high risk; Dynamic control optimization module, which takes corresponding control optimization measures for communication equipment under different risk conditions according to the evaluation results; The adaptive adjustment module continuously monitors the communication status of the communication device during its operation, adjusts the control parameters based on the historical operation data of the communication device, and updates the control strategy of the communication device.

2. The control system for a two-wheeled vehicle communication device according to claim 1, characterized in that: In the signal fluctuation analysis module, the network signal fluctuation information of the communication device on the two-wheeled vehicle is preprocessed to extract the signal strength change speed data, that is, the change speed of the network signal strength of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment; the standard deviation of the extracted data is calculated to reflect the degree of fluctuation of the network signal strength of the communication device in the weak signal environment, and the calculated standard deviation is compared with the preset threshold. If the standard deviation is greater than the preset threshold, the connection interruption risk information of the communication device is obtained in real time.

3. The control system for a two-wheeled vehicle communication device according to claim 2, characterized in that: In the connection interruption assessment module, the acquired connection interruption risk information of the communication equipment is preprocessed; the signal fluctuation information, connection stability information and recovery performance information are extracted from the preprocessed connection interruption risk information, and the information is analyzed after extraction to generate a signal fluctuation index, a connection stability coefficient and a connection recovery coefficient respectively; a connection interruption risk assessment model is constructed for the generated signal fluctuation index, connection stability coefficient and connection recovery coefficient, and the interruption risk coefficient is generated by weighted summation, and the information is analyzed after generation to evaluate the connection interruption risk of the communication equipment under the condition of network signal fluctuation when the two-wheeled vehicle enters a weak signal environment, and the risk is divided into three categories: low risk, medium risk and high risk.

4. The control system for a two-wheeled vehicle communication device according to claim 3, characterized in that: The logic for obtaining the signal fluctuation index is as follows: The signal fluctuation information is extracted from the pre-processed connection interruption risk information, including the change speed of the network signal strength of the communication equipment at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the fluctuation of the signal power to the interference plus noise power ratio, and the base station switching frequency, and is calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The speed at which the network signal strength changes at each moment. Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The fluctuation of the signal power to interference plus noise power ratio at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will be The base station switching frequency at time, , is a positive integer; Calculate the signal volatility index. The specific calculation formula is as follows: In the formula, is the signal volatility index.

5. The control system for a two-wheeled vehicle communication device according to claim 4, characterized in that: The logic for obtaining the connection stability coefficient is as follows: The connection stability information is extracted from the preprocessed connection interruption risk information, including the network signal strength value of the communication device at different times within a period of time when the two-wheeled vehicle enters a weak signal environment, the proportion of lost data packets, and the ratio of signal power to interference plus noise power, and they are calibrated as , and , Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The network signal strength value at the moment, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The proportion of lost packets at the time, Indicates that when a two-wheeled vehicle enters a weak signal environment, the communication device will The ratio of the signal power to the interference plus noise power at the moment, , is a positive integer; Calculate the connection stability coefficient. The specific calculation formula is as follows: In the formula, is the connection stability factor.

6. The control system for a two-wheeled vehicle communication device according to claim 5, characterized in that: The logic for obtaining the connection recovery coefficient is as follows: The recovery performance information is extracted from the pre-processed connection interruption risk information, including the average speed of signal recovery after the communication device is disconnected in the historical time period and the frequency of disconnection events experienced by the communication device, and is marked as and , Indicates the average speed at which communication equipment recovers signals after a connection interruption during a historical period. Indicates the frequency of disconnection events experienced by the communication device during the historical time period; Calculate the connection recovery coefficient. The specific calculation formula is as follows: In the formula, is the connection restoration coefficient.

7. The control system for a two-wheeled vehicle communication device according to claim 6, characterized in that: The volatility index of the generated signal , Connection stability coefficient and the connection recovery coefficient A connection interruption risk assessment model is constructed, and the interruption risk coefficient is generated by weighted summation. The specific calculation formula is as follows: In the formula, is the interruption risk factor, , and Signal Volatility Index , Connection stability coefficient and the connection recovery coefficient The non-zero weight coefficient of .

8. The control system for a two-wheeled vehicle communication device according to claim 7, characterized in that: Determine the pre-set interruption risk factor threshold range , and after determination, the generated interruption risk factor The comparison is carried out, and the risk of connection interruption of communication equipment under network signal fluctuation when the two-wheeled vehicle enters a weak signal environment is evaluated based on the comparison results, and it is divided into three categories: low risk, medium risk and high risk. The specific comparison analysis is as follows: like ,When a two-wheeled vehicle enters a weak signal environment, the risk of connection interruption of the communication equipment under network signal fluctuations is low; like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,communication equipment being disconnected when the network signal fluctuates is medium risk; like ,When a two-wheeled vehicle enters a weak signal environment, the risk of ,connection interruption of the communication equipment when the network signal fluctuates is high.

9. The control system for a two-wheeled vehicle communication device according to claim 8, characterized in that: In the dynamic control optimization module, according to the evaluation results, corresponding control optimization measures are taken for communication equipment under different risk conditions, specifically: If the assessment result is high risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: reduce the reconnection frequency of the communication equipment to avoid high power consumption due to frequent connection attempts; switch to low power mode to reduce the number of network scans and data synchronization; optimize the network selection strategy to give priority to connecting to available networks with stable signal quality; send high-priority alarm information to the background management system and record the data of this high-risk event; If the assessment result is a medium risk, the control optimization measures taken for the communication equipment under this risk situation are as follows: adjust the reconnection strategy, dynamically optimize the reconnection interval, so that the device can quickly restore the connection after a short disconnection but avoid invalid reconnection; optimize the data transmission mechanism, reduce the frequency of data transmission and increase the cache mechanism to reduce data loss during disconnection; send medium priority warning information to the background management system and record the risk assessment data; If the assessment result is low risk, the control optimization measures taken for the communication equipment in this risk situation are as follows: maintain the current communication strategy unchanged and keep the communication equipment operating normally; continuously monitor the communication status and store historical communication data to analyze long-term trends; and periodically adjust the optimization strategy based on changes in historical communication data.

Images