A low-power operation adjusting method for a tire pressure monitoring system based on user driving habits

Abstract

The application relates to a low-power operation adjustment method for a tire pressure monitoring system based on user driving habits, which comprises the following steps: obtaining navigation planning information and real-time road condition information; determining an actual driving scene according to real-time vehicle speed and road traffic data, and generating conflict event information when a preset road type is inconsistent with the actual driving scene; obtaining a user driving habit image containing a conflict response habit sub-image; predicting a personalized behavior mode based on the conflict event information and the user habit image; and dynamically generating and executing a corresponding tire pressure monitoring power consumption adjustment strategy according to the predicted behavior mode. Through deep integration of scene conflict detection and user conflict response habits, the application solves the problem of strategy mismatch when a preset scene is inconsistent with an actual scene in the prior art, and realizes intelligent power consumption management according to roads and users.

Description

Technical Field

[0001] This application relates to the field of tire pressure monitoring, and in particular to a low-power operation adjustment method for a tire pressure monitoring system based on user driving habits. Background Technology

[0002] Currently, low-power operation adjustment methods for tire pressure monitoring systems (TPMS) mainly rely on passive adjustments based on single parameters of vehicle driving status. For example, some solutions detect changes in vehicle speed or vehicle stationary time, increasing sensor acquisition and transmission frequencies at high speeds to ensure real-time monitoring, and reducing frequencies when the vehicle is stationary or at low speeds to save energy. Other improved solutions attempt to combine navigation-planned path information and preset power consumption strategies based on the type of road ahead (e.g., highway, national road) to achieve proactive adjustment based on road conditions. These methods, to some extent, achieve a balance between power consumption and monitoring requirements, forming the technical foundation for low-power operation adjustment of existing TPMS systems.

[0003] However, existing technologies suffer from the following fundamental flaws: First, the adjustment logic relies entirely on preset static rules or the road type planned by the navigation system, failing to perceive and respond to dynamic changes in real-time traffic conditions. When severe congestion actually occurs on the "highway" planned by the navigation system, the vehicle is in a low-speed crawling state with frequent starts and stops, significantly increasing the risk of tire pressure fluctuations. However, the system still uses low-frequency data collection in "high-speed cruise" mode, resulting in delayed tire pressure anomaly monitoring and posing a safety hazard. In other words, existing technologies cannot effectively address the conflict between preset scenarios and actual scenarios. In such cases, they can only revert to a conservative strategy and cannot achieve fine-grained adjustment. Conversely, if the planned road is in an urban area but the actual traffic is smooth, it may cause unnecessary power consumption waste. Second, existing solutions adopt a uniform adjustment strategy, failing to consider the individual driving behavior differences of different users facing the same road conditions—aggressive users frequently brake and change lanes suddenly in congestion, causing drastic tire pressure changes; moderate users drive gently, resulting in relatively stable tire pressure. This "one-size-fits-all" adjustment method cannot achieve a precise match between power consumption and safety.

[0004] To address this, we propose a low-power operation adjustment method for tire pressure monitoring systems (TPMS) based on user driving habits. This method accurately identifies conflict events between preset road types and actual driving scenarios by fusing navigation planning information and real-time traffic data. Simultaneously, it introduces a pre-constructed user driving habit profile, particularly a conflict response habit sub-profile that includes personalized responses to historical conflict events. Based on these personalized responses, the method predicts the user's behavior pattern in the current conflict event and dynamically generates an appropriate TPMS power consumption adjustment strategy. This solution aims to solve the power consumption and safety imbalance problem caused by scenario misjudgment in existing technologies, achieving truly intelligent power consumption management tailored to specific road conditions and individual user needs. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a low-power operation adjustment method for a tire pressure monitoring system based on user driving habits, employing the following technical solution:

[0006] A method for adjusting the low-power operation of a tire pressure monitoring system based on user driving habits includes the following steps:

[0007] S1. Obtain vehicle navigation planning information and real-time traffic information. The navigation planning information includes preset road types, and the preset road types include at least two different levels of road types. The real-time traffic information includes at least real-time vehicle speed and road traffic flow data.

[0008] S2. Determine the actual driving scenario of the vehicle based on the real-time vehicle speed and road traffic flow data. When it is detected that the preset road type is inconsistent with the actual driving scenario, generate conflict event information.

[0009] S3. Obtain a pre-built user driving habit profile, which includes at least the user's historical driving behavior characteristics in different driving scenarios such as highways, urban main roads, and suburban roads.

[0010] S4. Based on the conflict event information and the user driving habit profile, predict the user's personalized behavior pattern in this conflict event using a behavioral feature matching algorithm;

[0011] S5. Based on the predicted personalized behavior pattern, dynamically generate and execute the corresponding tire pressure monitoring power consumption adjustment strategy. The power consumption adjustment strategy includes at least the graded adjustment of the tire pressure sensor acquisition frequency and the switching of the wireless data transmission mode.

[0012] Preferably, the user driving habit profile further includes a conflict response habit sub-profile, which is constructed based on the user's driving response behavior data each time in history a conflict between a preset road type and an actual driving scenario.

[0013] Preferably, the conflict response habit profile includes an aggressive driving mode under congested road conditions. The criteria for determining the aggressive driving mode are: during a continuous 10-minute period of congested driving, the number of emergency braking events exceeds 5 or the number of lane change events exceeds 3.

[0014] Preferably, the conflict response habit sub-profile includes a detour driving mode under congested road conditions, and the criterion for determining the detour driving mode is: in historical conflict events, the proportion of users choosing to leave the current congested road exceeds 80%.

[0015] Preferably, it also includes habit mutation detection and emergency correction steps:

[0016] S61. Continuously monitor the user's real-time driving behavior and collect real-time behavioral data on emergency braking, lane changing, and vehicle speed changes.

[0017] S62. Compare the real-time driving behavior data with the feature data of the predicted personalized behavior pattern, and calculate the degree of deviation between the two.

[0018] S63. If the deviation exceeds the preset deviation threshold and the duration of the deviation exceeds the preset deviation duration, it is determined to be a habit change event, and the tire pressure monitoring system is switched to the preset conservative safety mode.

[0019] Preferably, the deviation is calculated using a distance measurement algorithm, the preset deviation threshold is 30%, the preset deviation duration is 5 minutes, and the conservative safety mode is an operating mode that combines the highest level of tire pressure sensor acquisition frequency with real-time transmission at a low compression ratio.

[0020] Preferably, before dynamically generating and executing the corresponding tire pressure monitoring power consumption adjustment strategy, a cross-scenario preloading step is also included:

[0021] The actual distance between the current vehicle position and the potential scene switching point ahead is determined based on navigation planning information;

[0022] If the actual distance is less than a preset distance threshold, and the scenario switching probability predicted based on the user's driving habit profile exceeds a preset probability threshold, then the next scenario tire pressure monitoring power consumption adjustment strategy corresponding to the potential scenario switching point will be preloaded.

[0023] Preferably, the potential scene switching points include at least highway exits, highway service areas, tunnel entrances and exits, and urban ring road entrances and exits; the preset distance threshold is dynamically adjusted according to the road type, wherein the preset distance threshold for highway sections is 2km, and the preset distance threshold for urban roads is 500m; the preset probability threshold is 80%.

[0024] Preferably, if the predicted personalized behavior pattern is a detour driving mode, the generated power consumption adjustment strategy is as follows: in the current congested road segment, a combination of medium- and high-frequency data acquisition and event-triggered transmission is used, and when the vehicle is detected turning away from the current congested road, the power consumption adjustment strategy corresponding to the pre-loaded next road type is seamlessly switched to.

[0025] Preferably, if the predicted personalized behavior pattern is an aggressive driving mode, the generated power consumption adjustment strategy is as follows: increase the sampling frequency of the tire pressure sensor to the highest level, and adopt a real-time priority wireless transmission method with low compression ratio, and turn off unnecessary data compression and batch transmission mechanisms.

[0026] In summary, this application includes at least one of the following beneficial technical effects:

[0027] 1. This application identifies inconsistencies between preset road types and actual driving scenarios through a scenario conflict detection mechanism; it captures personalized response characteristics of users in unexpected road conditions by constructing a user driving habit profile that includes a sub-profile of conflict response habits; based on this, it predicts the user's behavioral patterns in conflict events based on the user's conflict response habits and dynamically generates an adapted power consumption adjustment strategy. This application overcomes the limitation of existing technologies that can only revert to conservative strategies in scenario conflicts, achieving a technological leap from "adaptation to normal road conditions" to "prediction of sudden conflicts," fundamentally solving the problem of power consumption and safety imbalance.

[0028] 2. This application determines the actual driving scenario by using real-time vehicle speed and road traffic flow data. When a discrepancy is detected between the preset road type and the actual scenario, conflict event information is generated. Based on the user's historical conflict response habits, personalized behavior patterns are predicted, achieving dynamic response and precise adaptation of the power consumption adjustment strategy. For aggressive driving modes, safety monitoring density is prioritized; for detour-type driving modes, power consumption is optimized while ensuring safety. This approach takes into account the differentiated needs of different users and in different scenarios, avoiding safety risks or energy waste caused by a one-size-fits-all strategy.

[0029] 3. This application prepares the power consumption adjustment strategy for the next road type in advance through a cross-scenario preloading mechanism and achieves seamless switching when the user actually leaves the current road. At the same time, through habit mutation detection and emergency correction mechanism, it quickly switches to conservative safety mode when the user behavior deviates significantly from the prediction mode, thus constructing a complete closed loop of "prediction-execution-feedback-correction". This significantly improves the system's foresight and robustness, ensuring that tire pressure monitoring always maintains the best balance between safety and power consumption in complex and ever-changing driving environments. Attached Figure Description

[0030] Figure 1 This is a flowchart of a method for adjusting the low-power operation of a tire pressure monitoring system based on user driving habits, as described in an embodiment of this application.

[0031] Figure 2 This is a flowchart of the habit mutation detection and emergency correction steps in the embodiments of this application. Detailed Implementation

[0032] The following is in conjunction with the appendix Figure 1 and Figure 2 This application will be described in further detail.

[0033] Existing low-power adjustment methods for tire pressure monitoring systems typically collect user habits and generate strategies based on preset road types. However, in actual driving, changes in road conditions often lead to discrepancies between preset scenarios and real-world scenarios (e.g., planning a highway but encountering congestion). Existing technologies cannot effectively handle such conflicts and often have to revert to conservative strategies, resulting in wasted power or insufficient monitoring. To address this issue, this application's embodiments perform scenario conflict detection by fusing navigation planning and real-time road conditions, combine user driving habit profiles (especially conflict response habit sub-profiles) to predict personalized behavior patterns, and dynamically generate adaptive power adjustment strategies. It also incorporates cross-scenario preloading and habit mutation emergency mechanisms, aiming to achieve intelligent, personalized, and low-power operation of the tire pressure monitoring system in complex and ever-changing driving environments.

[0034] This application discloses a low-power operation adjustment method for a tire pressure monitoring system based on user driving habits. (Refer to...) Figure 1 A method for adjusting the low-power operation of a tire pressure monitoring system based on user driving habits includes the following steps:

[0035] S1. Multi-source data acquisition: Acquire vehicle navigation planning information and real-time traffic information. The navigation planning information includes preset road types, which include at least two different levels of road types. The real-time traffic information includes at least real-time vehicle speed and road traffic flow data.

[0036] In this step, the vehicle's navigation planning information can be obtained through the in-vehicle navigation system or by interconnecting with a mobile navigation application. The preset road types include at least two different levels of road types, which refer to categories divided according to road design standards, traffic capacity, or functional positioning. For example, roads can be divided into expressways, Class I highways, Class II highways, Class III highways, and different levels of urban roads such as expressways, arterial roads, secondary arterial roads, and local roads, according to the "Highway Engineering Technical Standards" (JTG B01-2014). In specific implementation, the preset road types should select at least two or more road types with different design speeds and traffic characteristics for differentiation, such as expressways and ordinary urban roads, or urban expressways and ordinary arterial roads, to facilitate subsequent scene conflict detection.

[0037] Real-time traffic condition information can be obtained through the vehicle-to-everything (V2X) module, traffic radio or the Internet, and at least includes real-time vehicle speed and road traffic flow data. The real-time vehicle speed can include the average vehicle speed, instantaneous vehicle speed or section vehicle speed of the current road section; the road traffic flow data can include traffic density (the number of vehicles on a unit length of road), congestion index or traffic efficiency index. Preferably, traffic efficiency data such as travel time ratio, delay time, etc. can also be obtained as an auxiliary determination basis. The sampling frequency of the real-time vehicle speed and road traffic flow data can be set according to the communication conditions and system resources, usually within the range of once every 10 seconds to once every 30 seconds, and preferably once every 15 seconds in this embodiment.

[0038] S2. Scene conflict detection: Determine the actual driving scene of the vehicle according to the real-time vehicle speed and road traffic flow data. When it is detected that the preset road type is inconsistent with the actual driving scene, generate conflict event information.

[0039] In this step, the determination of the actual driving scene of the vehicle can adopt a multi-index comprehensive evaluation method. Set the real-time vehicle speed as V (unit: km / h), and the road traffic flow density as D (which can be quantified as a normalized value between 0 and 1, 0 means no vehicles, and 1 means completely congested). According to the combination of V and D, the actual driving scene is divided into several categories. Exemplarily, the following determination logic can be adopted:

[0040] If V≥80km / h and D≤0.3, it is determined as the "smooth highway scene";

[0041] If 60km / h≤V<80km / h and 0.3<D≤0.6, it is determined as the "conventional traffic scene";

[0042] If V<40km / h and D>0.7, it is determined as the "congested urban area scene";

[0043] If 40km / h≤V<60km / h and 0.6<D≤0.8, it is determined as the "slow-moving scene".

[0044] It should be noted that the above vehicle speed threshold and traffic flow density threshold can be adjusted according to actual application requirements. The reasonable range of the vehicle speed threshold can be 30 - 100km / h, and the reasonable range of the traffic flow density threshold can be 0.2 - 0.9. The values given in this embodiment are only for exemplary classification, and those skilled in the art can set different threshold ranges and scene classifications according to actual road conditions, vehicle type characteristics and application requirements. For example, a fuzzy logic method can be introduced to model the membership functions of vehicle speed and traffic flow, and the scene category can be obtained through fuzzy inference.

[0045] When the preset road type (e.g., navigation-planned highway) is inconsistent with the actual driving scenario (e.g., a congested urban scenario determined based on real-time vehicle speed and traffic flow), a scenario conflict is identified. Conflict event information is then generated, which includes at least: conflict type (e.g., highway congestion), conflict intensity (e.g., severe congestion), occurrence time, geographical location, and estimated duration. Conflict types can be categorized and coded based on the combination of the preset road type and the actual scenario, such as "highway-congestion" or "urban-smooth traffic." Conflict intensity can be quantified and graded based on the degree of speed deviation or congestion index, such as mild (speed deviation 20-40%), moderate (40-60%), and severe (>60%).

[0046] S3. User habit profile acquisition: Acquire a pre-built user driving habit profile, which includes at least the user's historical driving behavior characteristics in different driving scenarios such as highways, urban main roads, and suburban roads.

[0047] In this step, the user driving habit profile is pre-built through long-term data collection and machine learning modeling. The construction process includes the following stages:

[0048] (1) Data Acquisition Stage: During normal vehicle use, user driving behavior data is continuously collected. The data acquisition frequency can be in the range of 10Hz to 50Hz, and in this embodiment, 20Hz is preferred. The collected data includes at least: vehicle speed (instantaneous value, average value), acceleration (longitudinal acceleration, lateral acceleration), steering behavior (steering wheel angle, angular velocity), braking behavior (brake pedal travel, deceleration), lane change behavior (lateral acceleration change, turn signal), driving time period, travel patterns, etc. The raw data is stored in local storage or cloud database after preprocessing such as noise reduction and normalization.

[0049] (2) Scene labeling stage: Combine navigation planning information and real-time traffic conditions to label driving behavior data in different scenarios. Specifically, based on the navigation road type and real-time traffic conditions when the vehicle is driving, the data is divided into behavioral data subsets under different scenarios such as highways, urban arterial roads, and suburban roads. The granularity of scene labeling can be refined as needed. For example, highways can be further subdivided into sub-scenarios such as smooth highways and congested highways.

[0050] (3) Feature extraction stage: Extract statistical features from the behavioral data for each scenario. The extracted features include at least: average vehicle speed, standard deviation of vehicle speed, vehicle speed percentile (e.g., 85th percentile vehicle speed); frequency of emergency braking events (defined as events with deceleration exceeding 0.2g-0.4g, preferably 0.3g in this embodiment); frequency of rapid acceleration events (defined as events with acceleration exceeding 0.2g-0.4g, preferably 0.3g in this embodiment); frequency of lane change events (defined as events with lateral acceleration exceeding 0.1g and duration exceeding 1-3 seconds, preferably 2 seconds in this embodiment); distribution of parking frequency and parking duration; proportion of nighttime driving, etc.

[0051] (4) Clustering Modeling Stage: Clustering algorithms are used to classify users into different driving habit types. Available clustering algorithms include K-means, DBSCAN, Gaussian Mixture Model (GMM), etc. The number of clusters can be selected from 3 to 8, and in this embodiment, 5 clusters are preferred, corresponding to typical driving styles such as "stable", "normal", "aggressive", "long-distance cruising", and "urban commuting". The clustering model can be updated regularly, with an update cycle ranging from 1 week to 3 months. In this embodiment, it is preferred to update monthly to adapt to the gradual changes in driving habits.

[0052] The user driving habit profile obtained in this step is the modeling result mentioned above, which includes typical behavioral feature vectors of users in different driving scenarios (such as statistical values ​​of features like average speed and frequency of emergency braking) and type labels.

[0053] In addition, the user driving habit profile further includes a conflict response habit sub-profile, which is constructed based on the user's driving response behavior data each time in history a conflict between a preset road type and an actual driving scenario.

[0054] The construction of conflict response habit sub-profiles is similar to that of regular driving habit profiles, but focuses on the specific context of scenario-based conflict events. When the system detects a scenario-based conflict event (such as highway congestion), it automatically triggers dedicated data recording for that event, collecting complete response behavior data of the user in that event. The collected data includes at least: initial reaction time (time from entering the conflict area to the first deceleration / lane change), subsequent behavior sequence (whether to decelerate, change lanes, or leave the highway), behavior intensity (frequency of sudden braking, frequency of lane changes, magnitude of acceleration change), and final choice (continue waiting, detour, leave, etc.). By statistically analyzing behavioral patterns in historical conflict events, a user's conflict response habit sub-profile can be obtained, such as detour tendency, aggressive tendency, and calm waiting tendency.

[0055] To facilitate storage and retrieval, the conflict response habit sub-profile can be stored in a structured data format. An example data table structure is as follows:

[0056]

[0057] Statistical analysis of historical events can reveal the distribution of user behavior tendencies under different conflict types. For example, in the case of highway congestion events, if a user's historical detour rate exceeds 75%, they can be classified as a detour-type user; if the frequency of sudden braking and lane changing is significantly higher than the average, they can be classified as an aggressive user.

[0058] The user driving habit profile constructed using the above method quantifies the user's behavioral characteristics in different driving scenarios into a computable data model. In particular, the introduction of the conflict response habit sub-profile enables the system to capture the user's personalized response patterns when facing conflicts between preset scenarios and actual scenarios. Compared to traditional methods that only adjust based on general thresholds or fixed rules, this method achieves a leap from "one-size-fits-all" to "personalized" through user habit profiles, providing accurate data support for subsequent behavior pattern prediction and laying the foundation for personalized power consumption adjustment.

[0059] S4. Behavior pattern prediction: Based on the conflict event information and the user's driving habit profile, predict the user's personalized behavior pattern in this conflict event through a behavior feature matching algorithm.

[0060] In this step, the core of the behavioral feature matching algorithm is to match the features of the current conflict event with the user's historical conflict coping habits to predict the user's most likely behavioral pattern. The following is a concrete example of an implementable algorithm—the nearest neighbor matching method based on distance metrics:

[0061] (1) Construct the feature vector of the current conflict event: Quantize the information of the current conflict event into a feature vector X=[x1,x2,…,x n The dimension n of the feature vector can be selected from 3 to 10. In this embodiment, 5 dimensions are preferred. Specifically, it includes: x1 is the conflict type encoding, which is encoded according to the combination of preset road type and actual driving scenario, such as highway congestion = 1, highway slow traffic = 2, urban congestion = 3, urban smooth traffic = 4, suburban congestion = 5, suburban smooth traffic = 6, etc.; x2 is the congestion intensity, normalized to the 0-1 interval, where 0 represents completely smooth traffic and 1 represents completely congested traffic; x3 is the expected duration, in minutes and normalized; for example, using the min-max normalization method to map to the 0-1 interval, for example, the reasonable range of the expected duration is 0-120 minutes, then the normalized value = actual number of minutes / 120; x4 is the current time encoding, which can be encoded by time period division or hourly normalized value; x5 is the weather condition encoding, encoded according to real-time weather information, such as sunny = 1, rainy = 2, snowy = 3, etc. Each feature needs to be normalized to eliminate the influence of units.

[0062] (2) Obtain the user conflict coping pattern library: Extract the feature vector center Y corresponding to various behavioral patterns in the user's history from the conflict coping habit sub-profile. k =[y1,y2,…,y n Let k = 1, ..., K, where K is the number of behavioral pattern categories (e.g., detour, aggressive, stable, etc.). The pattern center can be obtained by averaging the feature vectors of all historical events under that category.

[0063] (3) Calculate the feature vector distance: Use a distance metric algorithm to calculate the distance between the current event feature vector X and the center Y of each type of pattern. k Distance D k Available distance metric algorithms include, but are not limited to:

[0064] European distance: It is suitable for continuous numerical features;

[0065] Manhattan distance: Suitable for sparse feature scenarios;

[0066] Cosine similarity: Suitable for orientation-sensitive features;

[0067] Mahalanobis distance: This is suitable for situations where the features are highly correlated;

[0068] The range of distance metric algorithms to be selected includes the algorithms mentioned above and their combinations. In this embodiment, Euclidean distance is preferred because it is simple to calculate and has a clear physical meaning.

[0069] (4) Matching and prediction: Select the pattern category with the smallest distance as the prediction result, that is, predict behavior pattern = argminD k To improve reliability, a minimum distance threshold can be set. A reasonable range for the minimum distance threshold is 0.2 to 0.5, and in this embodiment, 0.3 is preferred. When all distances are greater than this threshold, it is determined to be a novel event, and a default conservative strategy (such as intermediate frequency acquisition + batch transmission) is adopted.

[0070] It should be noted that the above algorithm is only an exemplary implementation, and those skilled in the art can also use other machine learning methods for prediction, including but not limited to decision trees, random forests, support vector machines, and Naive Bayes classifiers. The input of the prediction model is the conflict event features, and the output is the behavior pattern category or probability distribution.

[0071] Through the aforementioned behavioral feature matching algorithm, this method can accurately predict the most likely behavioral pattern of a user when a scenario conflict occurs, based on the similarity between the characteristics of the current conflict event and the user's historical coping habits. This prediction mechanism deeply integrates the user's personalized driving habits with the real-time driving environment, so that the generation of power consumption adjustment strategies no longer depends on static rules, but is based on dynamic prediction of user behavior, providing a reliable decision-making basis for the accurate adaptation of subsequent strategies.

[0072] S5. Strategy Generation and Execution: Based on the predicted personalized behavior pattern, dynamically generate and execute the corresponding tire pressure monitoring power consumption adjustment strategy. The power consumption adjustment strategy includes at least the graded adjustment of the tire pressure sensor acquisition frequency and the switching of the wireless data transmission mode.

[0073] In this step, the system generates a corresponding power consumption adjustment strategy based on the predicted behavior pattern. The sampling frequency and transmission method adopt a hierarchical adjustable design, with an exemplary hierarchical scheme as follows:

[0074] Acquisition frequency classification: The acquisition frequency can be adjusted from 0.5 times / minute to 20 times / minute, and is divided into several levels according to security requirements and power consumption constraints:

[0075] Low power consumption level: 0.5-2 times / minute, suitable for vehicles that are stationary or in stable motion;

[0076] Standard level: 2-5 times / minute, suitable for daily travel;

[0077] Enhanced level: 5-10 times / minute, suitable for high-risk scenarios;

[0078] Safety level: 10-20 times / minute, suitable for emergency situations.

[0079] Based on the frequency classification of data acquisition, the system also switches data transmission methods according to security requirements. Data transmission methods include at least the following three, which can be switched or combined as needed:

[0080] Real-time transmission: Data is sent immediately after each acquisition, resulting in minimal latency but higher power consumption;

[0081] Event-triggered transmission: Data is transmitted in real time only when a specific event (such as emergency braking or sudden tire pressure change) is detected, and temporarily stored when the system is stable. The triggering events include at least: emergency braking events (deceleration exceeding 0.3g), rapid acceleration events (acceleration exceeding 0.3g), sudden tire pressure changes (tire pressure change rate exceeding 0.1 bar / s), and sudden vehicle speed changes (vehicle speed change exceeding 20 km / h within 1 second). When no event is triggered, the data is temporarily stored in a local cache.

[0082] Batch transmission: Packs multiple data collections into a single package and sends them at set intervals (e.g., 30 seconds), resulting in lower power consumption;

[0083] Data compression ratio: can be adjusted from 1:1 (no compression) to 20:1. The higher the compression ratio, the smaller the amount of data transmitted, but some detailed information may be lost. In this embodiment, the corresponding strategy combination can be selected according to the predicted behavior pattern.

[0084] Through steps S1 to S5 described above, this embodiment of the application constructs a complete closed-loop control mechanism encompassing scene perception, habit recognition, and strategy generation. First, steps S1-S2, by integrating navigation planning and real-time traffic conditions, overcome the limitations of traditional methods that rely solely on a single preset road type. This allows for accurate identification of conflicts between preset scenarios and actual driving scenarios, providing precise triggering conditions for subsequent adjustments. Second, step S3, by constructing a user driving habit profile, particularly a conflict response habit sub-profile, incorporates the user's personalized driving behavior characteristics into power consumption adjustment decisions, enabling the system to possess intelligent adjustment capabilities tailored to individual users. Finally, steps S4-S5 predict behavioral patterns based on conflict event information and user habit profiles, and dynamically generate appropriate power consumption adjustment strategies, achieving a leap from passive response to proactive prediction.

[0085] This method, through the coordinated steps described above, solves the problems of power consumption and safety imbalance caused by conflicts between preset scenarios and actual scenarios in existing technologies, as well as the problem of insufficient adaptability of adjustment strategies due to neglecting users' personalized driving habits. In typical scenario conflict situations (such as highway congestion during the Spring Festival travel rush), this method can accurately identify conflicts and switch to the appropriate strategy, avoiding both safety hazards caused by insufficient monitoring and power waste caused by excessive monitoring, thus achieving a dynamic balance between power consumption optimization and safety monitoring.

[0086] The conflict response habit sub-profile includes an aggressive driving mode in congested traffic conditions. The criteria for determining an aggressive driving mode are: more than 5 sudden braking events or more than 3 lane change events within a continuous 10-minute period of congested driving. Specifically:

[0087] The range for determining continuous congestion driving time can be from 5 minutes to 20 minutes, and in this embodiment, 10 minutes is preferred. The selection of this time window needs to balance statistical significance and responsiveness: if it is too short (<5 minutes), it is easily affected by instantaneous fluctuations and has a high misjudgment rate; if it is too long (>20 minutes), the response will be delayed and may miss the opportunity for real-time adjustment.

[0088] The threshold range for the number of emergency braking events can be from 3 to 8 times, and in this embodiment, 5 times is preferred. An emergency braking event is defined as an event with a deceleration exceeding 0.2g-0.4g, and in this embodiment, 0.3g is uniformly used as the judgment standard. This threshold is based on statistical analysis of the behavior of 1000 drivers in congested road conditions: drivers who brake more than 5 times within 10 consecutive minutes have tire pressure fluctuation amplitude (standard deviation) that is significantly higher than the average level (p<0.01).

[0089] The threshold range for the number of lane change events can be from 2 to 5 times, and in this embodiment, 3 times is preferred. A lane change event is defined as a lateral acceleration exceeding 0.1g and a duration exceeding 1-3 seconds; in this embodiment, 2 seconds is uniformly used as the criterion.

[0090] It should be noted that the above values ​​can be adaptively adjusted based on factors such as different vehicle models (e.g., the handling differences between sedans and SUVs), different regional driving habits, and different seasonal road conditions. The system provides a parameter configuration interface, allowing manufacturers or users to fine-tune the values ​​according to actual conditions. For example, for large SUVs, the emergency braking threshold can be appropriately lowered (e.g., 4 times) to suit the driving characteristics of this type of vehicle; for regions where driving styles are generally aggressive, the threshold can be appropriately increased (e.g., 6 times) to avoid oversensitivity.

[0091] Based on the aforementioned criteria for determining aggressive driving modes, this method can identify high-risk driving characteristics such as frequent sudden braking and lane changes in congested traffic. These users have a significantly higher risk of tire pressure fluctuations in congested environments than ordinary users. Therefore, classifying them separately and assigning them higher monitoring priority avoids applying a uniform conservative strategy to all users while ensuring safety, achieving a refined balance between safety requirements and power consumption constraints.

[0092] In addition, the conflict response habit sub-profile also includes detour driving modes in congested traffic conditions. The criterion for determining detour driving modes is: in historical conflict events, the proportion of users choosing to leave the currently congested road exceeds 80%. Specifically:

[0093] The threshold for determining the proportion of departure behavior can range from 70% to 90%, and is preferably 80% in this embodiment. This threshold is set based on the principle of statistical significance: when a user chooses to leave in more than 80% of historical conflict events, it can be considered that detouring is a stable behavior pattern of the user and has statistical significance.

[0094] The statistical baseline for historical conflict events must be at least 5 times to ensure statistical reliability. For new users or cases with fewer than 5 historical events, one of the following strategies can be adopted: ① Perform a cold start based on statistical data from similar user groups; ② Use the default strategy (e.g., do not determine the bypass type, directly use the general strategy); ③ Determine the pattern after accumulating sufficient data.

[0095] The definition of "leaving the current congested road" includes: exiting from a highway exit, exiting from an urban expressway exit, turning onto a side road or branch road before a congested section, or choosing an alternative route outside the original route planned by the navigation system. The departure behavior can be detected through navigation route change events, vehicle turning to an exit, or the deviation of the actual driving trajectory from the planned route exceeding a threshold.

[0096] It should be noted that the determination of detour driving patterns not only focuses on the departure rate, but also takes into account auxiliary characteristics such as waiting time before departure and route selection after departure for comprehensive judgment. For example, users who leave after a very short waiting time (<2 minutes) can be classified as "sensitive detour type"; users who leave after a longer waiting time can be classified as "patient detour type", thus further refining the behavior pattern.

[0097] Based on the aforementioned criteria for determining detour driving modes, this method can identify users who tend to actively leave congested areas and seek alternative routes. These users typically have shorter driving times in congested environments and enter new road types after leaving, requiring the system to prepare power consumption strategies for the next scenario in advance. Categorizing them separately and assigning them appropriate adjustment strategies avoids excessive data collection in congested areas, preventing wasted power, while also preparing for upcoming scenario changes, achieving a balance between power consumption optimization and forward-looking prediction.

[0098] Based on the aforementioned criteria for determining aggressive and evasive driving modes, the system will implement corresponding power consumption adjustment strategies when it predicts the corresponding behavior mode. Specifically:

[0099] If the predicted personalized behavior pattern is a detour driving mode, the generated power consumption adjustment strategy is as follows: in the current congested road segment, a combination of medium and high frequency acquisition and event-triggered transmission is used, and when the vehicle is detected turning away from the current congested road, the power consumption adjustment strategy corresponding to the pre-loaded next road type is seamlessly switched to.

[0100] When the predicted detour is expected, the system can select a sampling frequency of 3-8 times / minute for the current road segment. In this embodiment, 5 times / minute (medium-high frequency) is preferred. The transmission method combines event triggering and batch transmission: during smooth driving, batch transmission is performed every 30 seconds; when sudden braking (deceleration > 0.3g), rapid acceleration (acceleration > 0.3g), or tire pressure change rate exceeds 0.1 bar / s is detected, real-time transmission is triggered immediately.

[0101] Simultaneously, the system continuously monitors vehicle turning signals. Turn signal detection can be achieved through any of the following methods: turn signal activation, steering wheel angle exceeding 15° for more than 2 seconds, or vehicle heading angle change rate exceeding 10° / s. When a vehicle is detected turning away from the current congested road, the system immediately switches to the power consumption strategy corresponding to the pre-loaded next road type. The "seamless switching" mechanism includes: employing a double-buffer design to ensure that the current data acquisition and transmission are not affected during the loading of new strategy parameters; or employing a state saving and recovery mechanism to ensure no data loss during the switching process. The switching time can be controlled within 100ms, and in this embodiment, it is preferably within 50ms, so that the user is unaware of it.

[0102] If the predicted personalized behavior pattern is an aggressive driving mode, the generated power consumption adjustment strategy is as follows: increase the sampling frequency of the tire pressure sensor to the highest level, and adopt a real-time priority wireless transmission method with low compression ratio, while turning off unnecessary data compression and batch transmission mechanisms.

[0103] When the prediction is aggressive, the system increases the sampling frequency to the highest level. The highest sampling frequency can be selected in the range of 8-15 times / minute, and in this embodiment, 10 times / minute is preferred. The data compression ratio adopts a low compression ratio, which ranges from 1:1 to 3:1. In this embodiment, 1:1 (no compression) is preferred to ensure the original accuracy of the tire pressure data.

[0104] The transmission mode is switched to real-time priority mode, meaning that transmission is triggered immediately after each data acquisition, with the maximum transmission delay controlled within 1 second, preferably within 0.5 seconds in this embodiment. Simultaneously, batch transmission mechanisms and unnecessary data compression functions are disabled, sacrificing some power consumption for maximum security redundancy.

[0105] In addition to adjusting the strategy in real time based on the current conflict events, this method also has a proactive adjustment capability, preparing for upcoming scenario changes through a cross-scenario preloading mechanism. Before dynamically generating and executing the corresponding tire pressure monitoring power consumption adjustment strategy, a cross-scenario preloading step is included to prepare the strategy parameters for the next scenario in advance. The specific implementation of this step is as follows:

[0106] First, the actual distance between the current vehicle position and the potential scene switching point ahead is determined based on navigation planning information;

[0107] If the actual distance is less than a preset distance threshold, and the scenario switching probability predicted based on the user's driving habit profile exceeds a preset probability threshold, then the next scenario tire pressure monitoring power consumption adjustment strategy corresponding to the potential scenario switching point will be preloaded.

[0108] The specific implementation of the cross-scenario preloading mechanism is as follows:

[0109] (1) Identification of potential scene switching points: Based on navigation planning information, identify locations where scene switching may occur ahead. Potential scene switching points include at least highway exits, highway service areas, tunnel entrances and exits, and urban ring road entrances and exits. In addition, they may also include nodes that may change the driving status, such as large overpasses, toll stations, and provincial border checkpoints. The identification of switching points can be achieved through point of interest (POI) data and route planning information in the map database.

[0110] (2) Distance Calculation: The actual path distance L between the current vehicle position and the potential scene switching point ahead is calculated in real time. The distance calculation can be performed using GPS positioning combined with a map matching algorithm, with a positioning accuracy requirement of within 10 meters. The distance calculation update frequency can be in the range of 1 second to 5 seconds, and in this embodiment, it is preferably updated once per second.

[0111] (3) Distance threshold determination: The preset distance threshold is dynamically adjusted according to the road type. The reasonable range of the distance threshold is as follows:

[0112] Highway section: 1km to 3km, preferably 2km in this embodiment.

[0113] Urban expressway: 800m to 1.5km, preferably 1km in this embodiment.

[0114] Urban ordinary roads: 300m to 800m, preferably 500m in this embodiment.

[0115] If the actual distance L is less than the corresponding preset distance threshold, proceed to the next step. The distance threshold is determined by taking into account the average vehicle speed on different roads and the time required for system preloading, ensuring sufficient time for strategy loading and parameter initialization.

[0116] (4) Switching Probability Prediction: Based on the user's driving habit profile, predict the probability P of the user entering a potential scene switching point at the current time, current location, and current driving state. The prediction method can adopt the historical statistical method: count the frequency of the user entering the switching point under similar conditions (same time period, same road type, same weather, same date type such as weekday / weekend). The reasonable range of the switching probability is 0-100%, and it is expressed as a percentage in this embodiment.

[0117] (5) Probability threshold determination: The preset probability threshold can be selected in the range of 60% to 90%, and is preferably 80% in this embodiment. The setting of this threshold is based on cost-benefit analysis: too low a probability will lead to frequent invalid preloading, increasing the system burden; too high a probability may miss the opportunity for effective preloading. If the switching probability P exceeds the preset probability threshold, preloading is triggered.

[0118] (6) Strategy preloading: Preload the tire pressure monitoring power consumption adjustment strategy corresponding to the potential scenario switching point. For example, if it is predicted that the user will drive from the highway to the city road, the parameter set of "city road mode" is preloaded, including the acquisition frequency, compression ratio, transmission method, wake-up cycle, etc., and the parameter initialization is completed. The preloaded content includes at least the strategy identifier and complete parameter configuration to ensure that the new strategy can be applied seamlessly at the moment of switching.

[0119] It should be noted that the specific values ​​of the distance threshold and probability threshold mentioned above can be adjusted according to the actual application scenario. For example, in rainy or snowy weather or at night when visibility is low, the distance threshold can be appropriately increased (e.g., 3km for highways and 800m for urban roads); for users with strong driving behavior patterns, the probability threshold requirement can be appropriately increased (e.g., 85%); for new users with variable driving behavior, the probability threshold can be appropriately decreased (e.g., 70%), adopting a more conservative preloading strategy.

[0120] Through the aforementioned cross-scenario preloading mechanism, this method expands the scope of power consumption adjustment from the current moment to future scenarios, enabling proactive adjustment capabilities. Before the user actually reaches the scenario switching point, the system has already predicted the probability of entering the scenario based on user habits and completed the loading preparation of the next scenario policy parameters in advance. This mechanism effectively avoids the monitoring delay or power consumption fluctuation caused by the lag in policy switching in traditional methods, making the policy switching process completely imperceptible to the user, thus improving the system's intelligence level and user experience.

[0121] Reference Figure 2 After executing the power consumption regulation strategy in step S5, the system runs habit mutation detection and emergency correction steps in parallel to monitor the effectiveness of the strategy in real time. Specifically, this includes:

[0122] S61. Continuously monitor the user's real-time driving behavior and collect real-time behavioral data on emergency braking, lane changing, and vehicle speed changes.

[0123] S62. Compare the real-time driving behavior data with the feature data of the predicted personalized behavior pattern, and calculate the degree of deviation between the two.

[0124] S63. If the deviation exceeds the preset deviation threshold and the duration of the deviation exceeds the preset deviation duration, it is determined to be a habit change event, and the tire pressure monitoring system is switched to the preset conservative safety mode.

[0125] Habitual mutation detection and emergency correction mechanisms are crucial for ensuring system robustness, and are implemented as follows:

[0126] (1) Real-time behavior monitoring: After the power consumption adjustment strategy is executed in step S5, the system continuously monitors the user's real-time driving behavior at a high frequency. The monitoring frequency can be in the range of 1Hz to 20Hz, and is preferably 5Hz in this embodiment. The collected data includes at least: emergency braking events (defined as deceleration exceeding 0.2g-0.4g, preferably 0.3g in this embodiment), lane change events (defined as lateral acceleration exceeding 0.1g and duration exceeding 1-3 seconds, preferably 2 seconds in this embodiment), and vehicle speed change rate (vehicle speed change per second). These data constitute the real-time behavior feature vector X. real The dimension is consistent with the feature vector of the prediction pattern.

[0127] (2) Feature data comparison: Extract typical feature data from the predicted personalized behavior pattern to form the predicted feature vector X. pred The predicted feature vector can be obtained from the user habit profile, which includes the expected value or threshold range of features such as the frequency of emergency braking, the frequency of lane changes, and the rate of change of vehicle speed in this mode.

[0128] (3) Deviation calculation: Calculate the real-time behavior feature vector X real With the predicted feature vector X pred Deviation D between dev The deviation can be calculated using a distance metric algorithm, including Euclidean distance, Manhattan distance, and cosine similarity transformed difference. In this embodiment, the deviation is calculated using a distance metric algorithm, and the formula for calculating the deviation is:

[0129] D dev =||X real -X pred || / ||X pred ||×100%

[0130] Where ||·|| represents the L2 norm of the vector, and the calculated D devThis represents the normalized percentage deviation.

[0131] (4) Threshold determination: The reasonable range of the preset deviation threshold can be 20% to 40%, and in this embodiment, it is preferably 30%. The reasonable range of the preset deviation duration can be 3 minutes to 8 minutes, and in this embodiment, it is preferably 5 minutes. If D dev If the deviation exceeds 30% and lasts for more than 5 minutes, it is considered a habit mutation event. The duration is determined using a sliding window method, meaning that each monitoring point within a consecutive 5 minutes must meet the deviation exceeding the threshold condition.

[0132] (5) Emergency Switching: Once a habit change event is detected, the system immediately switches the tire pressure monitoring system to the preset conservative safety mode. The conservative safety mode is an operating mode that combines the highest level of tire pressure sensor acquisition frequency with real-time transmission at a low compression ratio. The highest level acquisition frequency can be in the range of 8-15 times / minute, and in this embodiment, it is preferably 10 times / minute; the compression ratio is 1:1 (no compression); the transmission method adopts real-time forced transmission to ensure that any tire pressure abnormality can be reported immediately.

[0133] Common scenarios for habit abrupt change events include, but are not limited to: a detour-oriented user failing to leave a congested area for 15 consecutive minutes due to special reasons (such as a patient with an acute illness in the car); an aggressive user suddenly becoming more relaxed while driving due to answering a phone call; or the vehicle being taken over by different drivers. Through this emergency mechanism, the system can respond quickly when predictions fail, avoiding insufficient monitoring due to policy mismatch.

[0134] It should be noted that the above-mentioned 30% deviation threshold and 5-minute duration are based on experimental verification: experimental data shows that when the deviation between real-time behavior and the predicted pattern exceeds 30% and lasts for more than 5 minutes, the probability of the original prediction strategy failing exceeds 85%. This value can be calibrated according to different user groups or vehicle models.

[0135] Through the aforementioned habit mutation detection and emergency correction mechanisms, this method constructs a complete closed loop of "prediction-execution-feedback-correction," significantly improving the system's robustness and reliability. When user behavior deviates significantly from the predicted pattern due to special reasons (such as driver change or unexpected situations), the system can promptly identify this abnormal state and proactively switch to a conservative safety mode, ensuring that tire pressure monitoring maintains basic safety monitoring capabilities under all circumstances. This mechanism not only compensates for the inherent limitations of the predictive model but also provides a final line of defense for the system's safe operation.

[0136] In summary, the method provided in this application has the following beneficial effects:

[0137] (i) Adapting to local conditions and responding dynamically: By integrating navigation planning information with real-time traffic data, this method can perceive the conflict between the preset road type and the actual driving scenario in real time, and adjust the power consumption adjustment strategy in a timely manner. This solves the problem of strategy mismatch in scenarios such as highway congestion and sudden road conditions in traditional methods, and achieves a high degree of adaptation to the real driving environment.

[0138] (ii) Personalized and Precise Adaptation: By using pre-built user driving habit profiles, this method can identify the personalized driving behavior characteristics of different users and predict user behavior patterns when there are conflicting scenarios, thereby generating personalized power consumption adjustment strategies. For aggressive users, safety is prioritized, while for detour users, power consumption is optimized under the premise of ensuring safety, achieving a personalized balance between safety requirements and power consumption constraints.

[0139] (III) Proactive prediction and seamless experience: Through the cross-scenario preloading mechanism, this method can prepare the strategy parameters of the next scenario in advance before the user reaches the scenario switching point, avoiding the monitoring delay or power consumption fluctuation caused by the lag in strategy switching in traditional methods, and improving the user experience.

[0140] (iv) Safety redundancy, robust and reliable: Through habit mutation detection and emergency correction mechanism, this method can quickly switch to conservative safety mode when user behavior deviates significantly from the prediction mode, ensuring that tire pressure monitoring can maintain basic safety monitoring capability under any abnormal situation, and constructing a complete closed loop of "prediction-execution-feedback-correction", which significantly improves the robustness of the system.

[0141] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. A low-power operation adjustment method for a tire pressure monitoring system based on user driving habits, characterized in that, Includes the following steps: S1. Obtain vehicle navigation planning information and real-time traffic information. The navigation planning information includes preset road types, and the preset road types include at least two different levels of road types. The real-time traffic information includes at least real-time vehicle speed and road traffic flow data. S2. Determine the actual driving scenario of the vehicle based on the real-time vehicle speed and road traffic flow data. When it is detected that the preset road type is inconsistent with the actual driving scenario, generate conflict event information. S3. Obtain a pre-built user driving habit profile, which includes at least the user's historical driving behavior characteristics in different driving scenarios such as highways, urban main roads, and suburban roads. The user driving habit profile further includes a conflict response habit sub-profile, which is constructed based on the user's driving response behavior data each time in history a conflict between a preset road type and an actual driving scenario; S4. Based on the conflict event information and the user driving habit profile, predict the user's personalized behavior pattern in this conflict event using a behavioral feature matching algorithm; S5. Based on the predicted personalized behavior pattern, dynamically generate and execute the corresponding tire pressure monitoring power consumption adjustment strategy. The power consumption adjustment strategy includes at least the graded adjustment of the tire pressure sensor acquisition frequency and the switching of the wireless data transmission mode.

2. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 1, characterized in that, The conflict response habit sub-profile includes an aggressive driving mode in congested traffic conditions. The criteria for determining the aggressive driving mode are: more than 5 emergency braking events or more than 3 lane change events during a continuous 10-minute period of congested driving.

3. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 1, characterized in that, The conflict response habit sub-profile includes detour driving mode in congested road conditions. The criterion for determining the detour driving mode is: in historical conflict events, the proportion of users choosing to leave the current congested road exceeds 80%.

4. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 1, characterized in that, It also includes habit mutation detection and emergency correction steps: S61. Continuously monitor the user's real-time driving behavior and collect real-time behavioral data on emergency braking, lane changing, and vehicle speed changes. S62. Compare the real-time driving behavior data with the feature data of the predicted personalized behavior pattern, and calculate the degree of deviation between the two. S63. If the deviation exceeds the preset deviation threshold and the duration of the deviation exceeds the preset deviation duration, it is determined to be a habit change event, and the tire pressure monitoring system is switched to the preset conservative safety mode.

5. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 4, characterized in that, The deviation is calculated using a distance measurement algorithm, the preset deviation threshold is 30%, the preset deviation duration is 5 minutes, and the conservative safety mode is an operating mode that combines the highest level of tire pressure sensor acquisition frequency with real-time transmission at a low compression ratio.

6. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 1, characterized in that, Before dynamically generating and executing the corresponding tire pressure monitoring power consumption adjustment strategy, a cross-scenario preloading step is also included: The actual distance between the current vehicle position and the potential scene switching point ahead is determined based on navigation planning information; If the actual distance is less than a preset distance threshold, and the scenario switching probability predicted based on the user's driving habit profile exceeds a preset probability threshold, then the next scenario tire pressure monitoring power consumption adjustment strategy corresponding to the potential scenario switching point will be preloaded.

7. A low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 6, characterized in that, The potential scene switching points include at least highway exits, highway service areas, tunnel entrances and exits, and urban ring road entrances and exits; the preset distance threshold is dynamically adjusted according to the road type, wherein the preset distance threshold for highway sections is 2km, and the preset distance threshold for urban roads is 500m; the preset probability threshold is 80%.

8. The low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 3, characterized in that, If the predicted personalized behavior pattern is a detour driving mode, the generated power consumption adjustment strategy is as follows: in the current congested road segment, a combination of medium and high frequency acquisition and event-triggered transmission is used, and when the vehicle is detected turning away from the current congested road, the power consumption adjustment strategy corresponding to the pre-loaded next road type is seamlessly switched to.

9. A low-power operation adjustment method for a tire pressure monitoring system based on user driving habits according to claim 2, characterized in that, If the predicted personalized behavior pattern is an aggressive driving mode, the generated power consumption adjustment strategy is as follows: increase the sampling frequency of the tire pressure sensor to the highest level, and adopt a real-time priority wireless transmission method with low compression ratio, while turning off unnecessary data compression and batch transmission mechanisms.

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