An energy-saving optimization control method for vehicle-mounted terminal operating conditions

Abstract

This invention discloses an energy-saving optimization control method for vehicle-mounted terminals, specifically relating to the field of energy-saving optimization control technology. It involves collecting real-time operating condition data and real-time power consumption status data of the vehicle-mounted terminal, preprocessing and extracting features to generate operating condition switching feature data and real-time power consumption feature data of the terminal; analyzing the switching speed characteristics of vehicle operating conditions to generate switching speed levels for vehicle operating conditions; analyzing the response delay characteristics of different energy-saving control strategies to generate response delay levels for energy-saving control strategies; jointly analyzing the switching speed levels of vehicle operating conditions and the response delay levels of energy-saving control strategies to generate time-series mismatch data; analyzing the occurrence conditions and duration of energy efficiency inversion phenomena to generate time-domain risk data of energy efficiency inversion, and dynamically adjusting the pre-triggering timing and response speed of energy-saving control strategies; thereby improving the accuracy of energy-saving response and control robustness during vehicle operation.

Description

Technical Field

[0001] This invention relates to the field of energy-saving optimization control technology, and more specifically, to an energy-saving optimization control method for the operating conditions of an on-board terminal. Background Technology

[0002] As vehicles become increasingly intelligent and connected, the functions of in-vehicle terminals are becoming richer, and the complexity of their operating conditions is also increasing significantly.

[0003] Existing energy-saving control strategies for vehicle terminals mostly rely on fixed thresholds or static adjustment methods, ignoring the dynamic timing mismatch between the rate of change of operating conditions during vehicle operation and the response delay of energy-saving control strategies. This often leads to a temporary energy efficiency inversion between the actual power consumption of the vehicle terminal and the operating condition requirements, resulting in a decrease in the overall energy-saving effect of the vehicle terminal. It is difficult to achieve efficient matching between the vehicle operating state and the terminal power consumption strategy, thus restricting the energy-saving control performance of the vehicle terminal under variable operating conditions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an energy-saving optimization control method for the operating conditions of an in-vehicle terminal to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An energy-saving optimization control method for vehicle-mounted terminal operation includes the following steps:

[0007] S1: Collect real-time operating condition data and real-time power consumption status data of the vehicle terminal, and perform preprocessing and feature extraction to generate operating condition switching feature data and terminal real-time power consumption feature data.

[0008] S2: Analyze the switching speed characteristics of vehicle operating conditions based on operating condition switching feature data, and generate the switching speed level of vehicle operating conditions;

[0009] S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the real-time power consumption characteristic data of the terminal, and generate the response delay level of the energy-saving control strategy;

[0010] S4: Based on the switching speed level of vehicle operating conditions and the response delay level of energy-saving control strategy, analyze the degree of timing mismatch between operating condition switching speed and control strategy response delay, and generate timing mismatch data between vehicle operating conditions and energy-saving control strategy.

[0011] S5: Based on time-series mismatch data, analyze the conditions and duration of the energy efficiency inversion phenomenon, and generate time-domain risk data of energy efficiency inversion;

[0012] S6: Based on time-domain risk data of energy efficiency inversion, dynamically adjust the pre-triggering timing and response speed of energy-saving control strategies to generate adjustment schemes for energy-saving optimization control strategies.

[0013] In a preferred embodiment, S1 specifically refers to:

[0014] Collect real-time operating condition data and real-time power consumption status data generated by the vehicle-mounted terminal during actual vehicle operation;

[0015] Preprocess the real-time operating condition data and real-time power consumption status data;

[0016] Extract the time interval features of vehicle operating condition switching and the frequency features of vehicle operating condition changes from the preprocessed real-time operating condition data to generate operating condition switching feature data of vehicle operating conditions.

[0017] The fluctuation characteristics of the vehicle terminal's power consumption and the frequency characteristics of the vehicle terminal's power consumption mode switching are extracted from the preprocessed real-time power consumption status data to generate the real-time power consumption characteristic data of the vehicle terminal.

[0018] In a preferred embodiment, S2 specifically refers to:

[0019] Based on the characteristic data of vehicle operating conditions switching, determine the number of times the vehicle operating conditions change per unit time.

[0020] The number of times the vehicle's operating conditions change per unit time is compared with a preset threshold for the vehicle's operating condition switching speed to determine the vehicle's operating condition switching speed level.

[0021] In a preferred embodiment, S3 specifically refers to:

[0022] Based on the real-time power consumption characteristics of the vehicle terminal, the response delay time of different energy-saving control strategies when the vehicle operating conditions change is determined.

[0023] The response delay time of different energy-saving control strategies when the vehicle operating conditions change is compared with the preset threshold of the response delay of the energy-saving control strategy to determine the response delay level of the energy-saving control strategy.

[0024] In a preferred embodiment, S4 specifically refers to:

[0025] Map and pair the vehicle operating condition switching speed level with the energy-saving control strategy response delay level;

[0026] Based on the mapping pairing relationship, query the time-series matching matrix to determine the matching score of each mapping pairing combination;

[0027] The matching score is compared with the first time-series matching threshold and the second time-series matching threshold to determine the degree of time-series mismatch between the vehicle's operating conditions and the energy-saving control strategy.

[0028] Record the degree of timing mismatch, the time of occurrence of timing mismatch, and the duration of timing mismatch to generate timing mismatch data between vehicle operating conditions and energy-saving control strategies.

[0029] In a preferred embodiment, S5 specifically refers to:

[0030] Arrange the time-series mismatched data in chronological order to construct an energy efficiency inverted time-domain sequence;

[0031] The area and length of the continuous positive intervals in the time-domain sequence of energy efficiency inversion are calculated to obtain the time-domain feature vector of energy efficiency inversion.

[0032] A two-dimensional clustering method based on interval length and area value is used to divide the time-domain feature vector of energy efficiency inversion into high-risk, medium-risk, and low-risk clusters, thereby obtaining the conditions for energy efficiency inversion to occur.

[0033] Extract the start time and duration of energy efficiency inversion within each risk cluster to generate time-domain risk data for energy efficiency inversion.

[0034] In a preferred embodiment, S6 specifically refers to:

[0035] Based on the start time and duration of energy efficiency inversion, the pre-trigger adjustment time window of the energy-saving control strategy is determined;

[0036] Based on the pre-trigger adjustment time window of the energy-saving control strategy, and combined with the switching speed level of the vehicle operating conditions and the response delay level of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy is determined.

[0037] Based on the pre-triggering timing of the energy-saving control strategy, the execution process of the energy-saving control strategy is re-planned on a time scale to generate an adjustment scheme for the response speed of the energy-saving control strategy.

[0038] The pre-triggering timing and response speed adjustment schemes of energy-saving control strategies are combined to form an adjustment scheme for energy-saving optimization control strategies.

[0039] The technical effects and advantages of the energy-saving optimization control method for vehicle-mounted terminal operating conditions of the present invention are as follows:

[0040] By collecting real-time operating condition data and real-time power consumption status data from the vehicle terminal, and extracting operating condition switching characteristic data and terminal real-time power consumption characteristic data, accurate perception of the vehicle's changing operating conditions and power consumption dynamics is achieved. Based on the operating condition switching characteristic data and terminal real-time power consumption characteristic data, the switching speed characteristics of the vehicle's operating conditions and the response delay characteristics of different control strategies of the vehicle terminal are analyzed respectively. This yields the switching speed level of the vehicle's operating conditions and the response delay level of the energy-saving control strategy, enabling quantitative discrimination of the speed of operating condition changes and control response capability. By jointly analyzing the switching speed level and response delay level, the timing mismatch between operating condition changes and control response is effectively identified, the energy efficiency inversion risk range is dynamically discovered, and the pre-triggering timing and response speed of the energy-saving control strategy are adaptively adjusted based on the time-domain risk data of energy efficiency inversion. This improves the timing adaptability and accuracy of the vehicle terminal's energy-saving control, reduces the occurrence of energy efficiency inversion when operating conditions change abruptly, achieves efficient self-adaptation of the vehicle terminal's energy-saving management strategy, and improves the vehicle's energy consumption control performance and overall vehicle energy utilization efficiency under complex operating conditions. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of an energy-saving optimization control method for vehicle-mounted terminal operation conditions according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] Example

[0044] Figure 1 This invention provides an energy-saving optimization control method for vehicle-mounted terminal operation, comprising the following steps:

[0045] S1: Collect real-time operating condition data and real-time power consumption status data of the vehicle terminal, and perform preprocessing and feature extraction to generate operating condition switching feature data and terminal real-time power consumption feature data.

[0046] S2: Analyze the switching speed characteristics of vehicle operating conditions based on operating condition switching feature data, and generate the switching speed level of vehicle operating conditions;

[0047] S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the real-time power consumption characteristic data of the terminal, and generate the response delay level of the energy-saving control strategy;

[0048] S4: Based on the switching speed level of vehicle operating conditions and the response delay level of energy-saving control strategy, analyze the degree of timing mismatch between operating condition switching speed and control strategy response delay, and generate timing mismatch data between vehicle operating conditions and energy-saving control strategy.

[0049] S5: Based on time-series mismatch data, analyze the conditions and duration of the energy efficiency inversion phenomenon, and generate time-domain risk data of energy efficiency inversion;

[0050] S6: Based on time-domain risk data of energy efficiency inversion, dynamically adjust the pre-triggering timing and response speed of energy-saving control strategies to generate adjustment schemes for energy-saving optimization control strategies.

[0051] S1: Collect real-time operating condition data and real-time power consumption status data of the vehicle terminal, and perform preprocessing and feature extraction to generate operating condition switching feature data and terminal real-time power consumption feature data, including:

[0052] Collect real-time operating condition data and real-time power consumption status data generated by the vehicle-mounted terminal during actual vehicle operation;

[0053] The vehicle-mounted terminal is installed inside the vehicle. During actual vehicle operation, real-time operating condition data is acquired through data acquisition devices installed on the vehicle. This real-time operating condition data includes various categories such as the vehicle's current speed, engine speed, road type, and driving mode. For example, if the vehicle's current speed is 65 km / h, engine speed is 2800 rpm, the vehicle is on a highway, and the driving mode is cruise control. Simultaneously, real-time power consumption data is acquired synchronously through current and voltage sensors installed on the output port of the vehicle-mounted terminal's power supply module. The real-time power consumption data is calculated based on the current and voltage measurements taken by both sensors. For example, when the vehicle-mounted terminal is in cruise control mode, if the voltage sensor measures a supply voltage of 12.5 volts and the current sensor measures a current of 1.5 amps, the corresponding real-time power consumption data would be 18.75 watts. The real-time power consumption data also includes the current power consumption mode of the vehicle-mounted terminal, such as energy-saving mode.

[0054] Preprocess the real-time operating condition data and real-time power consumption status data;

[0055] Real-time operating condition data contains noise or errors during acquisition. For example, brief fluctuations in vehicle speed due to vehicle inertia or brief abnormal fluctuations in engine speed due to mechanical vibration can cause interference. Therefore, data cleaning of real-time operating condition data is performed, including removing outliers and filtering and smoothing abnormal fluctuations, to improve the accuracy of the data. For instance, after smoothing and filtering, the vehicle speed fluctuations from the original data (65-75 km / h) are smoothed to determine a stable current speed of 68 km / h. Similarly, real-time power consumption status data also contains noise or outliers, such as instantaneous abnormal fluctuations measured by current or voltage sensors. Therefore, data smoothing and filtering are performed on the real-time power consumption status data. For example, the original measurement of real-time power consumption status data showed instantaneous voltage fluctuations of 11.5-13.5 volts; after smoothing, the corresponding stable supply voltage is determined to be 12.5 volts, resulting in the final pre-processed real-time power consumption status data output.

[0056] Extract the time interval features of vehicle operating condition switching and the frequency features of vehicle operating condition changes from the preprocessed real-time operating condition data to generate operating condition switching feature data of vehicle operating conditions.

[0057] The time interval characteristic of vehicle operating condition switching is the length of time between two consecutive changes in vehicle operating condition. For example, if the vehicle changes from urban cruise mode to congestion mode at 14:30, and then changes from congestion mode to high-speed driving mode at 14:45, the time interval characteristic of vehicle operating condition switching is 15 minutes. The frequency characteristic of vehicle operating condition changes is the number of times the vehicle operating condition changes within a specified time range. For example, in actual vehicle operation, if the vehicle operating condition changes three times within a specified time range of 60 minutes, namely urban cruise mode, congestion mode, and high-speed mode, then the frequency characteristic of vehicle operating condition changes is 3 times per hour. By extracting the time interval characteristic and the frequency characteristic of vehicle operating condition switching, operating condition switching characteristic data of vehicle operating conditions is generated.

[0058] Extract the fluctuation characteristics of the vehicle terminal power consumption and the frequency characteristics of the vehicle terminal power consumption mode switching from the preprocessed real-time power consumption status data to generate the real-time power consumption characteristic data of the vehicle terminal.

[0059] The fluctuation characteristic of vehicle terminal power consumption refers to the magnitude of power consumption change of the vehicle terminal within a specific time window during vehicle operation. For example, if the vehicle terminal power consumption increases from 15 watts to 20 watts within a 10-minute time window after entering congestion mode, the fluctuation characteristic of the vehicle terminal power consumption is a power consumption fluctuation amplitude of 5 watts. Simultaneously, the frequency characteristic of vehicle terminal power consumption mode switching refers to the number of times the vehicle terminal switches power consumption modes within a given time range. For example, during actual vehicle operation, if the vehicle terminal power consumption mode switches from normal mode to energy-saving mode and then back to high-performance mode within a given time range of one hour, the frequency characteristic of vehicle terminal power consumption mode switching is 2 times per hour. Based on the fluctuation characteristic and the frequency characteristic of vehicle terminal power consumption mode switching, real-time power consumption characteristic data of the vehicle terminal is generated.

[0060] S2: Based on the analysis of operating condition switching characteristic data, analyze the switching speed characteristics of vehicle operating conditions and generate the switching speed level of vehicle operating conditions, including:

[0061] Based on the characteristic data of vehicle operating conditions switching, determine the number of times the vehicle operating conditions change per unit time.

[0062] The number of times a vehicle's operating conditions change within a unit of time refers to the number of times the vehicle's operating conditions transition from one mode or state to another within a set time period. For example, in actual operation, a vehicle switching from city cruising mode to congestion mode is one operating condition change; switching from congestion mode to highway driving mode is the second operating condition change; and switching back from highway driving mode to city cruising mode is the third operating condition change. Therefore, the vehicle experiences a total of three changes in its operating conditions within a given unit of time.

[0063] The number of times the vehicle's operating conditions change per unit time is compared with a preset threshold for the vehicle's operating condition switching speed to determine the vehicle's operating condition switching speed level.

[0064] The preset threshold for vehicle operating condition switching speed is a pre-set judgment standard based on the actual operating condition changes of the vehicle, used to distinguish the switching speed levels of vehicle operating conditions. Vehicle operating condition switching speed levels include high-frequency switching level, medium-frequency switching level, and low-frequency switching level. The preset thresholds for vehicle operating condition switching speed include preset high-frequency thresholds and preset medium-frequency thresholds. The preset high-frequency threshold is greater than the preset medium-frequency threshold; both are standard values ​​for classifying vehicle operating condition switching speed levels. For example, the preset high-frequency threshold is greater than 5 times per hour, and the preset medium-frequency threshold is 2 times per hour.

[0065] When the number of times the vehicle's operating conditions change within a unit of time exceeds a preset high-frequency threshold, the switching speed level of the vehicle's operating conditions is determined to be a high-frequency switching level.

[0066] For example, during actual vehicle operation, statistical analysis is performed on a time unit of hour. Within this time unit, the vehicle's operating conditions change 8 times, specifically from urban cruise mode to congestion mode, from congestion mode to fast driving mode, and from fast driving mode back to low-speed driving mode, totaling 8 mode transitions. Since this number of 8 operating condition changes per hour exceeds the preset high-frequency threshold of 5 times per hour, the vehicle's operating condition switching speed level is determined to be a high-frequency switching level.

[0067] When the number of times the vehicle's operating conditions change per unit time is greater than the preset intermediate frequency threshold but less than or equal to the preset high frequency threshold, the vehicle's operating condition switching speed level is determined as the intermediate frequency switching level.

[0068] For example, if statistical analysis is performed every hour, and the vehicle's operating conditions change three times: from city cruise mode to congestion mode, from congestion mode to high-speed mode, and from high-speed mode back to highway cruise mode, totaling three changes, then the number of changes per hour is greater than the preset mid-frequency threshold of two times per hour, but less than or equal to the preset high-frequency threshold of five times per hour. Therefore, the vehicle's operating condition switching speed level is determined to be the mid-frequency switching level.

[0069] When the number of times the vehicle's operating conditions change per unit time is less than or equal to the preset intermediate frequency threshold, the switching speed level of the vehicle's operating conditions is determined to be the low frequency switching level.

[0070] For example, if a vehicle's operating conditions change only once per hour within a given time period (i.e., from city cruise mode to highway cruise mode, and remain in highway cruise mode for the rest of the time without any change in operating conditions), and this number of operating condition changes (once per hour) is less than or equal to a preset mid-frequency threshold of twice per hour, then the vehicle's operating condition switching speed level is determined to be a low-frequency switching level.

[0071] S3: Based on real-time power consumption characteristic data analysis of the terminal, the response delay characteristics of different control strategies of the vehicle terminal are analyzed to generate the response delay level of the energy-saving control strategy, including:

[0072] Based on the real-time power consumption characteristics of the vehicle terminal, the response delay time of different energy-saving control strategies when the vehicle operating conditions change is determined.

[0073] When vehicle operating conditions change, various energy-saving control strategies within the onboard terminal cannot instantly adjust their power consumption modes due to limitations in algorithm execution speed, hardware response performance, and instruction execution processes; a certain time delay exists. The response delay is defined as the time interval between the moment the vehicle's operating conditions change and the moment the onboard terminal's energy-saving control strategy completes the corresponding power consumption mode switch. For example, if the vehicle's operating conditions change from urban cruise mode to highway cruise mode at 15:00:00, and the onboard terminal's energy-saving control strategy completes the power consumption mode switch from normal mode to energy-saving mode at 15:00:03, then the response delay generated by the energy-saving control strategy is 3 seconds. The response delay for each different energy-saving control strategy is determined sequentially using the above method.

[0074] The response delay time of different energy-saving control strategies when the vehicle operating conditions change is compared with the preset threshold of the response delay of the energy-saving control strategy to determine the response delay level of the energy-saving control strategy.

[0075] The preset thresholds for the response latency of the energy-saving control strategy are pre-set based on the actual usage needs and performance requirements of the vehicle terminal, and are divided into preset high latency thresholds and preset medium latency thresholds. The preset high latency threshold represents the highest tolerable time limit for the response latency of the vehicle terminal's energy-saving control strategy, for example, set to 6 seconds; the preset medium latency threshold represents the generally acceptable medium time limit for the response latency of the vehicle terminal's energy-saving control strategy, for example, set to 4 seconds. By comparing the actual response latency of the energy-saving control strategy with the preset high latency threshold and preset medium latency threshold, the response latency level of different energy-saving control strategies is determined.

[0076] When the response delay time generated by the energy-saving control strategy is greater than the preset high delay threshold, the response delay level of the energy-saving control strategy is determined to be high delay level;

[0077] For example, when the vehicle's operating conditions change at 15:20:00, the response delay of the energy-saving control strategy is 7 seconds, exceeding the preset high latency threshold of 6 seconds. Therefore, the response delay level of the energy-saving control strategy when the vehicle's operating conditions change is determined to be high latency. The determination of high latency means that the energy-saving control strategy responds slowly to changes in vehicle operating conditions during actual operation of the on-board terminal, requiring special attention and optimization to reduce energy consumption.

[0078] When the response delay time generated by the energy-saving control strategy is greater than the preset medium delay threshold but less than or equal to the preset high delay threshold, the response delay level of the energy-saving control strategy is determined to be the medium delay level.

[0079] For example, when the vehicle's operating conditions change at 16:00:00, the actual response delay of the energy-saving control strategy is 5 seconds, which is greater than the preset medium delay threshold of 4 seconds, but less than or equal to the preset high delay threshold of 6 seconds. Therefore, the response delay level of the energy-saving control strategy when the vehicle's operating conditions change is determined to be medium delay. Medium delay indicates that the energy-saving control strategy has a general response capability to changes in vehicle operating conditions during actual application at the in-vehicle terminal, and can be moderately optimized during adjustment and optimization.

[0080] When the response delay time generated by the energy-saving control strategy is less than or equal to the preset delay threshold, the response delay level of the energy-saving control strategy is determined to be a low delay level.

[0081] For example, when the vehicle's operating conditions change at 17:10:00, the actual response delay of the energy-saving control strategy is 3 seconds, which is less than or equal to the preset delay threshold of 4 seconds. Therefore, the response delay level of the energy-saving control strategy when the vehicle's operating conditions change is determined to be low latency. Low latency means that the energy-saving control strategy can quickly respond to changes in vehicle operating conditions during actual use by the onboard terminal, meeting the energy-saving control performance requirements.

[0082] Using the above classification method, the response delay level of all energy-saving control strategies is determined, and the classification result of the response delay level of energy-saving control strategies is formed.

[0083] S4: Based on the switching speed level of vehicle operating conditions and the response delay level of energy-saving control strategies, analyze the degree of timing mismatch between the switching speed of operating conditions and the response delay of control strategies, and generate timing mismatch data between vehicle operating conditions and energy-saving control strategies, including:

[0084] Map and pair the vehicle operating condition switching speed level with the energy-saving control strategy response delay level;

[0085] During actual vehicle operation, the switching speed levels of vehicle operating conditions are paired with the response delay levels of energy-saving control strategies to form level mapping pairing relationships. For example, a high-frequency switching speed level of the vehicle operating condition is paired with a high-delay response delay level of the energy-saving control strategy, forming the first mapping pairing combination; a medium-frequency switching speed level of the vehicle operating condition is paired with a low-delay response delay level of the energy-saving control strategy, forming the second mapping pairing combination; through the above method, all possible combinations of vehicle operating condition switching speed levels and energy-saving control strategy response delay levels are mapped and paired to establish a level mapping pairing set.

[0086] Based on the mapping pairing relationship, query the time-series matching matrix to determine the matching score of each mapping pairing combination;

[0087] The timing matching matrix is ​​pre-set within the vehicle terminal and is a two-dimensional matrix. The horizontal axis represents the switching speed level of the vehicle's operating conditions, corresponding to high-frequency, medium-frequency, and low-frequency switching levels. The vertical axis represents the response delay level of the energy-saving control strategy, corresponding to high-latency, medium-latency, and low-latency levels. Each coordinate position in the matrix stores a numerical value, which is the timing matching score for the corresponding combination of the vehicle's operating condition switching speed level and the energy-saving control strategy response delay level. In actual operation, for each mapping pairing, the corresponding value is searched in the timing matching matrix, and the numerical value is used as the matching score. For example, if the vehicle operating condition switching speed level is high-frequency switching and the energy-saving control strategy response delay level is high-delay, the value at the corresponding position in the timing matching matrix is ​​15 points, then the matching score of this mapping pairing combination is determined to be 15 points; if the vehicle operating condition switching speed level is low-frequency switching and the energy-saving control strategy response delay level is low-delay, the value at the corresponding position in the timing matching matrix is ​​95 points, then the matching score of this mapping pairing combination is 95 points; the matching score of all mapping pairing combinations is determined using the same method.

[0088] The matching score is compared with the first time-series matching threshold and the second time-series matching threshold to determine the degree of time-series mismatch between the vehicle's operating conditions and the energy-saving control strategy.

[0089] The first and second timing matching thresholds are predetermined within the vehicle terminal and are used as criteria to classify the matching score into three levels of timing mismatch: severe mismatch, moderate mismatch, and slight mismatch. The first timing matching threshold is lower than the second timing matching threshold. For example, the first timing matching threshold is set to 40 points, and the second timing matching threshold is set to 70 points. When the matching score of the mapped pairing combination is below 40 points, the timing mismatch between the vehicle operating condition and the energy-saving control strategy is at the severe mismatch level. When the matching score of the mapped pairing combination is greater than or equal to 40 points and less than or equal to 70 points, the timing mismatch between the vehicle operating condition and the energy-saving control strategy is at the moderate mismatch level. When the matching score of the mapped pairing combination is higher than 70 points, the timing mismatch between the vehicle operating condition and the energy-saving control strategy is at the slight mismatch level. For example, a combination with a matching score of 15 is considered a severe mismatch, a combination with a matching score of 60 is considered a moderate mismatch, and a combination with a matching score of 95 is considered a slight mismatch; the temporal mismatch degree of all mapped pairings is determined using the above method.

[0090] Record the degree of timing mismatch, the time of occurrence of timing mismatch, and the duration of timing mismatch to generate timing mismatch data between vehicle operating conditions and energy-saving control strategies;

[0091] The timing mismatch occurs when the vehicle's operating conditions change and the energy-saving control strategy fails to respond in time, thus initiating the timing mismatch. For example, if the vehicle's operating conditions change from low-speed cruise mode to high-speed cruise mode at 18:00:00, and the corresponding energy-saving control strategy does not adjust in time, completing the power consumption mode switch adjustment only at 18:00:05, then the timing mismatch occurrence time is recorded as 18:00:00. The duration of the timing mismatch is the time interval between the actual adjustment time of the energy-saving control strategy and the timing mismatch occurrence time, which is 5 seconds. The occurrence time and duration of each timing mismatch are recorded in the same way, and combined with the degree of timing mismatch, time-series mismatch data between vehicle operating conditions and energy-saving control strategies is formed, including but not limited to the specific time and duration information for severe, moderate, and slight mismatch levels, for dynamic adjustment of vehicle operating conditions and energy-saving control strategies.

[0092] S5: Based on time-series mismatch data, analyze the conditions and duration of energy efficiency inversion, and generate time-domain risk data for energy efficiency inversion, including:

[0093] Arrange the time-series mismatched data in chronological order to construct an energy efficiency inverted time-domain sequence;

[0094] The phenomenon of energy efficiency inversion occurs when vehicle operating conditions change, and the energy-saving control strategy fails to respond promptly to these changes. This leads to a short-term mismatch between the power consumption configuration and actual power demand at the on-board terminal, manifesting as a delayed response from the energy-saving control strategy and a temporary increase in power consumption compared to the ideal consumption. This is known as an undesirable temporary increase in power consumption, resulting in energy efficiency inversion. The timing mismatch data between vehicle operating conditions and energy-saving control strategies includes the degree of mismatch, the occurrence time of the mismatch, and its duration. The occurrence time refers to the moment when the vehicle operating conditions change, and the corresponding energy-saving control strategy fails to adjust its response synchronously at that moment. The duration of the mismatch is the time between when the energy-saving control strategy actually completes its response adjustment and the occurrence time of the mismatch. All timing mismatch data between vehicle operating conditions and energy-saving control strategies are arranged sequentially from earliest to latest occurrence time. For example, if the vehicle's operating condition changes from city cruise mode to congestion mode at 8:15:00, but the energy-saving control strategy doesn't respond until 8:15:06, then the time-mismatched data is placed at the first position in the time sequence. If the vehicle's operating condition changes from congestion mode to fast driving mode at 8:30:00, but the energy-saving control strategy responds at 8:30:04, then the time-mismatched data is placed at the second position. By arranging all the time-mismatched data in this way, an energy efficiency inverted time domain sequence is formed.

[0095] The area and length of the continuous positive intervals in the time-domain sequence of energy efficiency inversion are calculated to obtain the time-domain feature vector of energy efficiency inversion.

[0096] Each time-series mismatch event in the energy efficiency inversion time-domain sequence has a duration and a degree of time-series mismatch; the degree of time-series mismatch is represented by the difference between the determined matching score and the ideal matching score, and is therefore a positive value. The continuous positive value interval is the time interval formed by the consecutively occurring time-series mismatch events in the energy efficiency inversion time-domain sequence. First, determine the start and end times of each consecutive positive interval. For example, the first positive interval is from 8:15:00 to 8:15:06, and the second positive interval is from 8:30:00 to 8:30:04. Second, calculate the area value for each positive interval, which is the degree of time mismatch multiplied by the corresponding duration. For example, if the difference in time mismatch in the first positive interval is 20 minutes and the duration is 6 seconds, then the area value of the first positive interval is 120 (20 × 6) minutes and seconds. If the difference in time mismatch in the second positive interval is 15 minutes and the duration is 4 seconds, then the area value of the second positive interval is 60 (15 × 4) minutes and seconds. At the same time, record the length of each positive interval. The length of the first positive interval is 6 seconds, and the length of the second positive interval is 4 seconds. Then, use the area values ​​and interval lengths of all consecutive positive intervals to construct a set of time-domain feature vectors for energy efficiency inversion.

[0097] A two-dimensional clustering method based on interval length and area value is used to divide the time-domain feature vector of energy efficiency inversion into high-risk, medium-risk, and low-risk clusters, thereby obtaining the conditions for energy efficiency inversion to occur.

[0098] The two-dimensional clustering method constructs a two-dimensional coordinate system using the area value of each energy efficiency inversion time-domain feature vector in the set as the ordinate and the interval length as the abscissa. Then, based on the distance between the energy efficiency inversion time-domain feature vectors, an iterative clustering method divides the set of energy efficiency inversion time-domain feature vectors into different clusters to reflect the risk level of energy efficiency inversion phenomena in different time-domain intervals. For example, energy efficiency inversion time-domain feature vectors with larger area values ​​and longer interval lengths correspond to situations where vehicle operating conditions change frequently and energy-saving control strategies experience significant response delays, and are therefore classified as high-risk clusters; energy efficiency inversion time-domain feature vectors with medium area values ​​and medium interval lengths are classified as medium-risk clusters; and energy efficiency inversion time-domain feature vectors with smaller area values ​​and shorter interval lengths are classified as low-risk clusters. For example, the time-domain feature vector of energy efficiency inversion with an area of ​​120 minutes and a length of 6 seconds is classified as a high-risk cluster; the feature vector with an area of ​​60 minutes and a length of 4 seconds is classified as a medium-risk cluster; and the feature vector with an area of ​​15 minutes and a length of 1 second is classified as a low-risk cluster. Using this method, three categories of energy efficiency inversion conditions—high-risk, medium-risk, and low-risk clusters—are identified.

[0099] Extract the start time and duration of energy efficiency inversion within each risk cluster to generate time-domain risk data of energy efficiency inversion.

[0100] The start time of energy efficiency inversion is the occurrence time of the first time-series mismatch event within a continuous positive value interval contained in each risk cluster. For example, the start time of the first time-series mismatch event in the high-risk cluster is 8:15:00. The duration of energy efficiency inversion is the total duration of all continuous positive value intervals within each risk cluster. For example, the total duration in the high-risk cluster is 6 seconds. The start time and duration of energy efficiency inversion for all continuous positive value intervals in the medium-risk and low-risk clusters are extracted in the same way. The start time for the medium-risk cluster is recorded as 8:30:00, and the duration as 4 seconds, while the start time for the low-risk cluster is recorded as 9:00:00, and the duration as 1 second. The extracted information is combined to form energy efficiency inversion time-domain risk data, including the start time and duration of each cluster in the high-risk, medium-risk, and low-risk clusters, providing a basis for the dynamic adjustment of energy-saving control strategies for vehicle operating conditions.

[0101] S6: Based on time-domain risk data of energy efficiency inversion, dynamically adjust the pre-triggering timing and response speed of energy-saving control strategies to generate adjustment schemes for energy-saving optimization control strategies, including:

[0102] Based on the start time and duration of energy efficiency inversion, the pre-trigger adjustment time window of the energy-saving control strategy is determined;

[0103] The pre-trigger adjustment time window of the energy-saving control strategy is defined as the advance warning time interval before the energy-saving control strategy in the vehicle terminal responds to changes in vehicle operating conditions. By determining the advance warning time interval, the vehicle terminal can activate the execution process of the energy-saving control strategy in advance, thereby avoiding the recurrence of timing mismatch. For each risk cluster, the start time and duration of the energy efficiency inversion are set with corresponding pre-trigger adjustment time windows for energy-saving control strategies. For example, for high-risk clusters, when the energy efficiency inversion starts at 8:15:00 and lasts for 6 seconds, the pre-trigger adjustment time window for energy-saving control strategies is set to be within the range of 10 to 15 seconds before the change in vehicle operating conditions, to ensure that the energy-saving control strategies enter the preparation state in advance before the actual change in vehicle operating conditions. For medium-risk clusters, when the energy efficiency inversion starts at 8:30:00 and lasts for 4 seconds, the pre-trigger adjustment time window for energy-saving control strategies is set to be within the range of 6 to 10 seconds before the change in vehicle operating conditions. For low-risk clusters, when the energy efficiency inversion starts at 9:00:00 and lasts for 1 second, the pre-trigger adjustment time window for energy-saving control strategies is set to be within the range of 3 to 6 seconds before the change in vehicle operating conditions.

[0104] Based on the pre-trigger adjustment time window of the energy-saving control strategy, and combined with the switching speed level of the vehicle operating conditions and the response delay level of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy is determined.

[0105] Once the pre-trigger adjustment time window for the energy-saving control strategy is determined, the pre-trigger timing of the energy-saving control strategy before changes in vehicle operating conditions is jointly determined by combining the vehicle operating condition switching speed level and the energy-saving control strategy response delay level. The vehicle operating condition switching speed levels are categorized into high-frequency, medium-frequency, and low-frequency switching levels; the energy-saving control strategy response delay levels are categorized into high-delay, medium-delay, and low-delay levels. The higher the vehicle operating condition switching speed level and the higher the energy-saving control strategy response delay level, the earlier the pre-trigger timing of the energy-saving control strategy needs to be determined; conversely, the lower the vehicle operating condition switching speed level and the lower the energy-saving control strategy response delay level, the earlier the pre-trigger timing of the energy-saving control strategy can be. For example, when the vehicle operating condition switching speed level is high-frequency switching and the energy-saving control strategy response delay level is high-delay, the maximum value within the pre-trigger adjustment time window of 10 to 15 seconds, i.e., 15 seconds before the occurrence of the vehicle operating condition, is taken as the pre-trigger timing for the energy-saving control strategy. When the vehicle operating condition switching speed level is medium-frequency switching and the energy-saving control strategy response delay level is medium-delay, the median value of 8 seconds within the pre-trigger adjustment time window of 6 to 10 seconds is taken as the pre-trigger timing for the energy-saving control strategy. When the vehicle operating condition switching speed level is low-frequency switching and the energy-saving control strategy response delay level is low-delay, the minimum value of the pre-trigger adjustment time window of 3 seconds is taken as the pre-trigger timing for the energy-saving control strategy. Using the above methods, the pre-trigger timing for the energy-saving control strategy is determined for all combinations of vehicle operating condition switching speed levels and energy-saving control strategy response delay levels.

[0106] Based on the pre-triggering timing of the energy-saving control strategy, the execution process of the energy-saving control strategy is re-planned on a time scale to generate an adjustment scheme for the response speed of the energy-saving control strategy.

[0107] The execution process of an energy-saving control strategy includes three stages: strategy activation, strategy execution, and strategy effect realization. In its original state, the execution process may have a fixed time cycle, lacking flexibility to adapt to different vehicle operating conditions. After determining the pre-triggering timing of the energy-saving control strategy, the execution process is re-planned on a time scale based on this timing. The energy-saving control strategy enters the activation stage before changes in vehicle operating conditions. By shortening the waiting time of the activation stage or initiating energy-saving strategy preparation actions earlier, the speed and efficiency of the strategy execution stage are improved. For example, if the original energy-saving control strategy had a fixed cycle of 3 seconds for activation, 4 seconds for execution, and 1 second for effect realization, totaling 8 seconds, with the pre-triggering timing set 15 seconds earlier, the activation stage is adjusted to 2 seconds, the strategy execution stage to 3 seconds, and the strategy effect realization stage is advanced to the 6th second after activation. The total duration is compressed from 8 seconds to 6 seconds, ensuring that the energy-saving control strategy is ready and takes effect quickly when vehicle operating conditions change, thus avoiding response delays. By using the above methods, the execution process of all energy-saving control strategies is adjusted and planned on a time scale, and all adjusted execution stages and time allocations are recorded to form an energy-saving control strategy response speed adjustment plan.

[0108] The pre-triggering timing and response speed adjustment schemes of energy-saving control strategies are combined to form an adjustment scheme for energy-saving optimization control strategies;

[0109] After determining the pre-triggering timing and planning the response speed adjustment scheme for the energy-saving control strategy, the pre-triggering timing and response speed adjustment scheme are integrated and combined to ultimately form an optimized energy-saving control strategy adjustment scheme. This scheme specifies the energy-saving control actions that the on-board terminal should take before changes in vehicle operating conditions, under different vehicle operating condition switching speed levels and energy-saving control strategy response delay levels. For example, for a high-risk cluster with a combination of high-frequency switching and high-delay levels, the energy-saving control strategy is pre-triggered 15 seconds in advance, and the total execution time is adjusted to 6 seconds; for a medium-risk cluster with a combination of medium-frequency switching and medium-delay levels, the energy-saving control strategy is pre-triggered 8 seconds in advance, and the execution time is adjusted to 5 seconds; for a low-risk cluster with a combination of low-frequency switching and low-delay levels, the energy-saving control strategy is pre-triggered 3 seconds in advance, and the execution time is adjusted to 4 seconds. The above energy-saving optimization control strategy adjustment scheme can provide the steps and time scale planning for the implementation of the vehicle terminal energy-saving control strategy under each combination, and execute it in the vehicle terminal controller, thereby solving the timing mismatch between changes in vehicle operating conditions and the response of energy-saving control strategy, and ensuring that the vehicle terminal achieves efficient energy-saving effect in complex actual operating environment.

[0110] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0111] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as 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, all or part of the processes or functions described in the embodiments of this application are generated. 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0112] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0113] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0115] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0117] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0119] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy-saving optimization control method for vehicle-mounted terminal operation, characterized in that, Includes the following steps: S1: Collect real-time operating condition data and real-time power consumption status data of the vehicle terminal, and perform preprocessing and feature extraction to generate operating condition switching feature data and terminal real-time power consumption feature data. S2: Analyze the switching speed characteristics of vehicle operating conditions based on operating condition switching feature data, and generate the switching speed level of vehicle operating conditions; S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the real-time power consumption characteristic data of the terminal, and generate the response delay level of the energy-saving control strategy; S4: Based on the switching speed level of vehicle operating conditions and the response delay level of energy-saving control strategy, analyze the degree of timing mismatch between operating condition switching speed and control strategy response delay, and generate timing mismatch data between vehicle operating conditions and energy-saving control strategy. S5: Based on time-series mismatch data, analyze the conditions and duration of the energy efficiency inversion phenomenon, and generate time-domain risk data of energy efficiency inversion; S6: Based on time-domain risk data of energy efficiency inversion, dynamically adjust the pre-triggering timing and response speed of energy-saving control strategies to generate adjustment schemes for energy-saving optimization control strategies.

2. The energy-saving optimization control method for vehicle-mounted terminal operation as described in claim 1, characterized in that, S1, specifically: Collect real-time operating condition data and real-time power consumption status data generated by the vehicle-mounted terminal during actual vehicle operation; Preprocess the real-time operating condition data and real-time power consumption status data; Extract the time interval features of vehicle operating condition switching and the frequency features of vehicle operating condition changes from the preprocessed real-time operating condition data to generate operating condition switching feature data of vehicle operating conditions. The fluctuation characteristics of the vehicle terminal's power consumption and the frequency characteristics of the vehicle terminal's power consumption mode switching are extracted from the preprocessed real-time power consumption status data to generate the real-time power consumption characteristic data of the vehicle terminal.

3. The energy-saving optimization control method for vehicle-mounted terminal operating conditions according to claim 2, characterized in that, S2, specifically: Based on the characteristic data of vehicle operating conditions switching, determine the number of times the vehicle operating conditions change per unit time. The number of times the vehicle's operating conditions change per unit time is compared with a preset threshold for the vehicle's operating condition switching speed to determine the vehicle's operating condition switching speed level.

4. The energy-saving optimization control method for vehicle-mounted terminal operating conditions according to claim 3, characterized in that, S3, specifically: Based on the real-time power consumption characteristics of the vehicle terminal, the response delay time of different energy-saving control strategies when the vehicle operating conditions change is determined. The response delay time of different energy-saving control strategies when the vehicle operating conditions change is compared with the preset threshold of the response delay of the energy-saving control strategy to determine the response delay level of the energy-saving control strategy.

5. The energy-saving optimization control method for vehicle-mounted terminal operating conditions according to claim 4, characterized in that, S4, specifically: Map and pair the vehicle operating condition switching speed level with the energy-saving control strategy response delay level; Based on the mapping pairing relationship, query the time-series matching matrix to determine the matching score of each mapping pairing combination; The matching score is compared with the first time-series matching threshold and the second time-series matching threshold to determine the degree of time-series mismatch between the vehicle's operating conditions and the energy-saving control strategy. Record the degree of timing mismatch, the time of occurrence of timing mismatch, and the duration of timing mismatch to generate timing mismatch data between vehicle operating conditions and energy-saving control strategies.

6. The energy-saving optimization control method for vehicle-mounted terminal operating conditions according to claim 5, characterized in that, S5, specifically: Arrange the time-series mismatched data in chronological order to construct an energy efficiency inverted time-domain sequence; The area and length of the continuous positive intervals in the time-domain sequence of energy efficiency inversion are calculated to obtain the time-domain feature vector of energy efficiency inversion. A two-dimensional clustering method based on interval length and area value is used to divide the time-domain feature vector of energy efficiency inversion into high-risk, medium-risk, and low-risk clusters, thereby obtaining the conditions for energy efficiency inversion to occur. Extract the start time and duration of energy efficiency inversion within each risk cluster to generate time-domain risk data for energy efficiency inversion.

7. The energy-saving optimization control method for vehicle-mounted terminal operating conditions according to claim 6, characterized in that, S6, specifically: Based on the start time and duration of energy efficiency inversion, the pre-trigger adjustment time window of the energy-saving control strategy is determined; Based on the pre-trigger adjustment time window of the energy-saving control strategy, and combined with the switching speed level of the vehicle operating conditions and the response delay level of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy is determined. Based on the pre-triggering timing of the energy-saving control strategy, the execution process of the energy-saving control strategy is re-planned on a time scale to generate an adjustment scheme for the response speed of the energy-saving control strategy. The pre-triggering timing and response speed adjustment schemes of energy-saving control strategies are combined to form an adjustment scheme for energy-saving optimization control strategies.

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