Energy-saving optimization control method for operation condition of vehicle-mounted terminal

Abstract

The invention discloses an energy-saving optimization control method for the operation condition of a vehicle-mounted terminal, and particularly relates to the technical field of energy-saving optimization control. The method comprises the following steps: acquiring real-time operation condition data and real-time power consumption state data of a vehicle-mounted terminal, and performing preprocessing and feature extraction to generate condition switching feature data and terminal real-time power consumption feature data; the switching speed characteristics of the vehicle operation condition are analyzed, and the switching speed grade of the vehicle operation condition is generated; analyzing response delay characteristics of different energy-saving control strategies, and generating response delay levels of the energy-saving control strategies; performing conjoint analysis on the switching speed grade of the vehicle operation condition and the response delay grade of the energy-saving control strategy to generate time sequence mismatch data; analyzing occurrence conditions and duration of an energy efficiency upside-down phenomenon, generating time domain risk data of energy efficiency upside-down, and dynamically adjusting pre-triggering opportunity and response speed of an energy-saving control strategy; and the energy-saving response accuracy and the control robustness in the vehicle running process are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving optimization control technology, and more specifically, to an energy-saving optimization control method for vehicle-mounted terminal operating conditions. Background Art

[0002] As vehicles become increasingly intelligent and networked, the functions of on-board terminals are becoming increasingly rich, and the complexity of their operating conditions is also significantly increasing.

[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 speed of operating condition changes during vehicle operation and the response delay of the energy-saving control strategy. This leads to frequent short-term energy efficiency inversion between the actual power consumption state of the vehicle terminal and the operating condition requirements, which reduces the overall energy-saving effect of the vehicle terminal and makes it difficult to achieve efficient matching between the vehicle operating state and the terminal power consumption strategy, 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, an embodiment of the present invention provides an energy-saving optimization control method for the operating conditions of a vehicle-mounted terminal to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An energy-saving optimization control method for an on-vehicle terminal operating condition comprises the following steps: S1: Collect the real-time operating condition data and real-time power consumption status data of the vehicle terminal, perform preprocessing and feature extraction, and generate operating condition switching feature data and terminal real-time power consumption feature data; S2: Analyze the switching speed characteristics of the vehicle operating condition based on the operating condition switching characteristic data and generate a switching speed level of the vehicle operating condition; S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the terminal's real-time power consumption characteristic data, and generate the response delay level of the energy-saving control strategy; S4: Based on the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy, the timing mismatch degree between the operating condition switching speed and the control strategy response delay is analyzed, and the timing mismatch data between the vehicle operating condition and the energy-saving control strategy is generated; S5: Based on the time series mismatch data, analyze the occurrence conditions and duration of the energy efficiency inversion phenomenon and generate the time domain risk data of the energy efficiency inversion; S6: Based on the time-domain risk data of energy efficiency inversion, dynamically adjust the pre-trigger timing and response speed of the energy-saving control strategy to generate an adjustment plan for the energy-saving optimization control strategy.

[0006] In a preferred embodiment, S1 is specifically: Collect real-time operating condition data and real-time power consumption status data generated by the vehicle terminal during the actual operation of the vehicle; Preprocessing real-time operating condition data and real-time power consumption status data; Extracting the time interval characteristics of vehicle operating condition switching and the frequency characteristics of vehicle operating condition changes from the pre-processed real-time operating condition data to generate vehicle operating condition switching characteristic data; The fluctuation characteristics of the vehicle terminal power consumption and the frequency characteristics of the vehicle terminal power consumption mode switching are extracted from the preprocessed real-time power consumption status data to generate the terminal real-time power consumption characteristic data of the vehicle terminal.

[0007] In a preferred embodiment, S2 is specifically: Determining the number of times the vehicle operating condition changes within a unit time based on operating condition switching characteristic data of the vehicle operating condition; The number of times the vehicle operating condition changes within a unit time is compared with a preset threshold value of the vehicle operating condition switching speed to determine the switching speed level of the vehicle operating condition.

[0008] In a preferred embodiment, S3 is specifically: Based on the real-time power consumption characteristic data of the vehicle terminal, determine the response delay time caused by different energy-saving control strategies when the vehicle operating conditions change; The response delay time of different energy-saving control strategies when the vehicle operating conditions change is compared with the preset threshold value of the energy-saving control strategy response delay to determine the response delay level of the energy-saving control strategy.

[0009] In a preferred embodiment, S4 is specifically: Mapping and pairing the switching speed level of the vehicle operating condition with the response delay level of the energy-saving control strategy; Querying the temporal matching matrix based on the mapping pairing relationship to determine the matching score of each mapping pairing combination; Comparing the matching score with a first timing matching threshold and a second timing matching threshold to determine a timing mismatch between the vehicle operating condition and the energy-saving control strategy; The degree of timing mismatch, the occurrence time and duration of timing mismatch are recorded to generate timing mismatch data of vehicle operating conditions and energy-saving control strategies.

[0010] In a preferred embodiment, S5 is specifically: Arrange the time series mismatch data in chronological order to construct the energy efficiency inversion time domain series; The area value and interval length of the continuous positive value interval of the energy efficiency inversion time domain series are calculated to obtain the energy efficiency inversion time domain feature vector; A two-dimensional clustering method based on interval length and area value is used to divide the time-domain feature vectors of energy efficiency inversion into high-risk clusters, medium-risk clusters, and low-risk clusters, and the conditions for the occurrence of energy efficiency inversion are obtained. The starting time and duration of energy efficiency inversion are extracted within each risk cluster to generate energy efficiency inversion time domain risk data.

[0011] In a preferred embodiment, S6 is specifically: Based on the starting time and duration of energy efficiency inversion, the pre-trigger adjustment time window of the energy-saving control strategy is determined; According to the pre-trigger adjustment time window of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy is determined in combination with the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy; Based on the pre-trigger timing of the energy-saving control strategy, the execution process of the energy-saving control strategy is re-planned in terms of time scale, and a response speed adjustment plan for the energy-saving control strategy is generated; The pre-triggering timing and response speed adjustment scheme of the energy-saving control strategy are combined to form an adjustment scheme of the energy-saving optimization control strategy.

[0012] The technical effects and advantages of the energy-saving optimization control method for the operating conditions of a vehicle-mounted terminal of the present invention are as follows: By collecting real-time operating condition data and power consumption status data from the vehicle terminal, extracting operating condition switching characteristics and real-time terminal power consumption characteristics, this system achieves precise perception of the vehicle's changing operating conditions and power consumption dynamics. Based on these operating condition switching characteristics and terminal real-time power consumption characteristics, the system analyzes the switching speed characteristics of the vehicle's operating conditions and the response delay characteristics of different control strategies in the vehicle terminal. This system derives the switching speed level of the vehicle's operating conditions and the response delay level of the energy-saving control strategy, achieving a quantitative assessment of the speed of operating condition changes and control responsiveness. By jointly analyzing the switching speed level and response delay level, the system effectively identifies the timing mismatch between operating condition changes and control responses, dynamically identifies energy efficiency inversion risk zones, and adaptively adjusts the pre-trigger timing and response speed of the energy-saving control strategy 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 during sudden operating condition changes, and enables efficient adaptation of the vehicle terminal's energy-saving management strategy, improving the vehicle's energy consumption control performance and overall energy efficiency under complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of an energy-saving optimization control method for vehicle-mounted terminal operating conditions according to the present invention. DETAILED DESCRIPTION

[0014] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] Example Figure 1 The present invention provides an energy-saving optimization control method for vehicle-mounted terminal operating conditions, which includes the following steps: S1: Collect the real-time operating condition data and real-time power consumption status data of the vehicle terminal, perform preprocessing and feature extraction, and generate operating condition switching feature data and terminal real-time power consumption feature data; S2: Analyze the switching speed characteristics of the vehicle operating condition based on the operating condition switching characteristic data and generate a switching speed level of the vehicle operating condition; S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the terminal's real-time power consumption characteristic data, and generate the response delay level of the energy-saving control strategy; S4: Based on the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy, the timing mismatch degree between the operating condition switching speed and the control strategy response delay is analyzed, and the timing mismatch data between the vehicle operating condition and the energy-saving control strategy is generated; S5: Based on the time series mismatch data, analyze the occurrence conditions and duration of the energy efficiency inversion phenomenon and generate the time domain risk data of the energy efficiency inversion; S6: Based on the time-domain risk data of energy efficiency inversion, dynamically adjust the pre-trigger timing and response speed of the energy-saving control strategy to generate an adjustment plan for the energy-saving optimization control strategy.

[0016] S1: Collects real-time operating condition data and real-time power consumption status data of the vehicle terminal, performs preprocessing and feature extraction, and generates operating condition switching feature data and terminal real-time power consumption feature data, including: Collect real-time operating condition data and real-time power consumption status data generated by the vehicle terminal during the actual operation of the vehicle; The vehicle terminal is installed inside the vehicle. During actual vehicle operation, real-time operating condition data is acquired through a data acquisition device installed on the vehicle. This real-time operating condition data includes various data categories, such as the vehicle's current speed, engine speed, the type of road the vehicle is currently on, and the vehicle's current driving mode. For example, the vehicle's current speed is 65 kilometers per hour, the engine speed is 2800 revolutions per minute, the vehicle is currently on a highway, and the vehicle's current driving mode is cruise mode. Simultaneously, real-time power consumption status data is synchronously acquired through a current sensor and a voltage sensor installed on the output port of the vehicle terminal's power supply module. The real-time power consumption status data is calculated based on the current and voltage measured simultaneously by the current and voltage sensors. For example, when the vehicle terminal is in cruise mode during actual operation, the voltage sensor measures a supply voltage of 12.5 volts and the current sensor measures a current of 1.5 amperes. The real-time power consumption status data then corresponds to a power consumption of 18.75 watts. Furthermore, the real-time power consumption status data also includes the vehicle terminal's current power consumption mode, for example, the vehicle terminal's current power consumption mode is energy-saving mode.

[0017] Preprocessing real-time operating condition data and real-time power consumption status data; Real-time operating condition data contains noise signals or errors during the acquisition process. For example, brief jitters caused by vehicle inertia during speed measurement, or brief abnormal fluctuations in engine speed due to mechanical vibration, can cause interference. Therefore, data cleaning is performed on the real-time operating condition data, including removing outliers and filtering and smoothing the abnormal jitters to improve the accuracy of the data. For example, after smoothing and filtering, the vehicle's current stable speed is determined to be 68 kilometers per hour, instead of the brief abnormal jitters of 65-75 kilometers per hour found in the original data. Real-time power consumption status data also contains noise or outliers, such as transient 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, if the real-time power consumption status data contains transient voltage fluctuations of 11.5-13.5 volts during the original measurement, smoothing can determine that the corresponding stable supply voltage is 12.5 volts. The final output is the pre-processed real-time power consumption status data.

[0018] Extracting the time interval characteristics of vehicle operating condition switching and the frequency characteristics of vehicle operating condition changes from the pre-processed real-time operating condition data to generate vehicle operating condition switching characteristic data; The time interval characteristic of vehicle operating condition switching is the length of time between two consecutive vehicle operating condition changes. For example, if the vehicle's operating condition changes from urban road cruising mode to congestion mode at 2:30 p.m., and the vehicle's operating condition changes from congestion mode to fast driving mode at 2:45 p.m., then the time interval characteristic of vehicle operating condition switching is 15 minutes. The frequency characteristic of vehicle operating condition change is the number of times the vehicle operating condition changes within a specified time range. For example, in the actual operation of the vehicle, if the vehicle operating condition changes three times per 60 minutes, then the frequency characteristic of vehicle operating condition change is 3 times per hour. By extracting the time interval characteristic of vehicle operating condition switching and the frequency characteristic of vehicle operating condition change, the operating condition switching characteristic data of the vehicle operating condition is generated.

[0019] Extracting the fluctuation characteristics of the vehicle terminal power consumption and the frequency characteristics of the vehicle terminal power consumption mode switching from the pre-processed real-time power consumption status data to generate the terminal real-time power consumption characteristic data of the vehicle terminal; The fluctuation characteristics of the vehicle terminal power consumption refer to the amplitude of the change in the vehicle terminal power consumption within a specific time window during the operation of the vehicle. For example, within the 10-minute time window after the vehicle enters the congestion mode, the vehicle terminal power consumption increases from 15 watts to 20 watts, then the fluctuation characteristics of the vehicle terminal power consumption are that the power consumption fluctuation amplitude is 5 watts. At the same time, the frequency characteristics of the vehicle terminal power consumption mode switching refer to the number of times the vehicle terminal power consumption mode switching occurs within a given time range. For example, during the actual operation of the vehicle, within a given time range of each hour, the vehicle terminal power consumption mode switches from normal mode to energy-saving mode, and then switches from energy-saving mode to high-performance mode, with a total of two switches. The frequency characteristics of the vehicle terminal power consumption mode switching are 2 times per hour. Based on the fluctuation characteristics of the vehicle terminal power consumption and the frequency characteristics of the vehicle terminal power consumption mode switching, the real-time power consumption characteristic data of the vehicle terminal is generated.

[0020] S2: Analyze the switching speed characteristics of the vehicle operating condition based on the operating condition switching characteristic data and generate the switching speed level of the vehicle operating condition, including: Determining the number of times the vehicle operating condition changes within a unit time based on operating condition switching characteristic data of the vehicle operating condition; The number of vehicle operating condition changes per unit time refers to the number of times the vehicle's operating condition transitions from one mode or state to another within a set time period. For example, in actual operation, a vehicle's transition from city cruising mode to congestion mode is one operating condition change, a transition from congestion mode to highway driving mode is a second operating condition change, and a transition from highway driving mode back to city cruising mode is a third operating condition change. Therefore, within a given unit time period, the vehicle experiences a total of three operating condition changes.

[0021] Comparing the number of times the vehicle operating condition changes per unit time with a preset threshold value of the vehicle operating condition switching speed to determine the switching speed level of the vehicle operating condition; The preset threshold value for the vehicle operating condition switching speed is a judgment standard pre-set based on the characteristics of the vehicle's actual operating condition changes, and is used to distinguish the switching speed levels of the vehicle's operating conditions. The vehicle operating condition switching speed levels include a high-frequency switching level, a medium-frequency switching level, and a low-frequency switching level. The preset threshold value for the vehicle operating condition switching speed includes a preset high-frequency threshold value and a preset medium-frequency threshold value. The preset high-frequency threshold value is greater than the preset medium-frequency threshold value, and both are standard values ​​for the vehicle operating condition switching speed level classification. For example, the preset high-frequency threshold value is greater than 5 times the number of operating condition changes per hour, and the preset medium-frequency threshold value is 2 times the number of operating condition changes per hour.

[0022] When the number of times the vehicle operating condition changes within a unit time is greater than a preset high-frequency threshold, determining that the switching speed level of the vehicle operating condition is a high-frequency switching level; For example, during actual vehicle operation, statistical analysis was performed every hour. Within that unit time period, the vehicle's operating condition changed eight times, specifically from urban cruising mode to congestion mode, then to fast driving mode, and then back to low-speed mode. This eight operating condition changes per hour exceeded the preset high-frequency threshold of five per hour, so the vehicle's operating condition switching speed level was determined to be high-frequency.

[0023] When the number of times the vehicle operating condition changes per unit time is greater than a preset medium frequency threshold and less than or equal to a preset high frequency threshold, determining that the switching speed level of the vehicle operating condition is the medium frequency switching level; For example, if the unit time period for statistical analysis is hourly, the vehicle operating condition changes three times: the vehicle switches from urban road cruising mode to congestion mode, from congestion mode to fast driving mode, and from fast driving mode to highway cruising mode, for a total of three operating condition changes. Since the number of operating condition changes of three times per hour is greater than the preset medium-frequency threshold of two times per hour, but less than or equal to the preset high-frequency threshold of five times per hour, the vehicle operating condition switching speed level is determined to be the medium-frequency switching level.

[0024] When the number of times the vehicle operating condition changes within a unit time is less than or equal to a preset medium frequency threshold, the switching speed level of the vehicle operating condition is determined to be a low frequency switching level; For example, within a given unit time period of one hour, the vehicle's operating condition changes once, meaning the vehicle switches from city cruising mode to highway cruising mode, and remains in highway cruising mode for the rest of the time. This one operating condition change per hour is less than or equal to the preset medium-frequency threshold of two per hour, so the vehicle's operating condition switching speed level is determined to be a low-frequency switching level.

[0025] S3: Analyze the response delay characteristics of different control strategies of the vehicle terminal based on the terminal's real-time power consumption characteristic data, and generate the response delay level of the energy-saving control strategy, including: Based on the real-time power consumption characteristic data of the vehicle terminal, determine the response delay time caused by different energy-saving control strategies when the vehicle operating conditions change; When the vehicle's operating conditions change, the various energy-saving control strategies used for energy-saving control in the on-board terminal cannot complete the power consumption mode adjustment at the moment the vehicle's operating conditions change due to reasons such as algorithm execution speed, hardware response performance, and instruction execution process, resulting in a certain time delay. The response delay time is defined as the length of the time interval between the moment the vehicle's operating conditions change and the moment the energy-saving control strategy in the on-board terminal completes the corresponding power consumption mode switching adjustment. For example, when the vehicle's operating conditions change from urban road cruising mode to highway cruising mode at 15:00:00, the moment the energy-saving control strategy in the on-board terminal completes the power consumption mode change from normal mode to energy-saving mode at 15:00:03, then the response delay time generated by the energy-saving control strategy is 3 seconds; through the above method, the response delay time corresponding to all different energy-saving control strategies is determined in turn.

[0026] Comparing the response delay times of different energy-saving control strategies when the vehicle operating conditions change with a preset threshold value of the energy-saving control strategy response delay to determine the response delay level of the energy-saving control strategy; The preset thresholds for the energy-saving control strategy's response delay are pre-set based on the actual usage needs and performance requirements of the vehicle terminal and are divided into a preset high delay threshold and a preset medium delay threshold. The preset high delay threshold represents the maximum tolerable time limit for the vehicle terminal's energy-saving control strategy response delay, for example, set to 6 seconds; the preset medium delay threshold represents the generally acceptable medium time limit for the vehicle terminal's energy-saving control strategy response delay, for example, set to 4 seconds. The response delay level of different energy-saving control strategies is determined by comparing the actual energy-saving control strategy response delay time with the preset high delay threshold and the preset medium delay threshold, respectively.

[0027] When the response delay time generated by the energy-saving control strategy is greater than a preset high delay threshold, determining the response delay level of the energy-saving control strategy to be a high delay level; For example, when the vehicle's operating conditions change at 15:20:00, the energy-saving control strategy generates a response delay of 7 seconds, exceeding the preset high delay threshold of 6 seconds. Therefore, the energy-saving control strategy's response delay level for the vehicle's operating condition change is determined to be high. This high delay level indicates that the energy-saving control strategy's response to changes in vehicle operating conditions is slow during actual operation on the vehicle terminal, requiring special attention and optimization to reduce energy efficiency losses.

[0028] When the response delay time generated by the energy-saving control strategy is greater than the preset medium delay threshold and less than or equal to the preset high delay threshold, determining the response delay level of the energy-saving control strategy to be the medium delay level; For example, when the vehicle's operating conditions change at 16:00:00, the energy-saving control strategy actually generates a response delay of 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 energy-saving control strategy's response delay level for the change in vehicle operating conditions is determined to be medium. A medium delay level indicates that the energy-saving control strategy has average responsiveness to changes in vehicle operating conditions during actual application on an onboard terminal, and can be moderately optimized during adjustment and optimization.

[0029] When the response delay time generated by the energy-saving control strategy is less than or equal to the preset medium delay threshold, determining the response delay level of the energy-saving control strategy as a low delay level; For example, when the vehicle's operating conditions change at 17:10:00, the energy-saving control strategy's actual response delay is 3 seconds, which is less than or equal to the preset delay threshold of 4 seconds. Therefore, the energy-saving control strategy's response delay level for changes in vehicle operating conditions is determined to be low. A low delay level means that the energy-saving control strategy can quickly respond to changes in vehicle operating conditions during actual use of the vehicle terminal, meeting energy-saving control performance requirements.

[0030] By using the above classification method, the response delay levels of all energy-saving control strategies are determined, and a classification result of the response delay levels of the energy-saving control strategies is formed.

[0031] S4: Based on the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy, analyze the timing mismatch between the operating condition switching speed and the control strategy response delay, and generate timing mismatch data between the vehicle operating condition and the energy-saving control strategy, including: Mapping and pairing the switching speed level of the vehicle operating condition with the response delay level of the energy-saving control strategy; During actual vehicle operation, the switching speed levels of the vehicle operating conditions are paired with the response delay levels of the energy-saving control strategy to form a level mapping pairing relationship. For example, a high-frequency switching speed level is paired with a high-delay response delay level of the energy-saving control strategy to form the first mapping pairing combination; a medium-frequency switching speed level is paired with a low-delay response delay level of the energy-saving control strategy to form 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, thereby establishing a level mapping pairing set.

[0032] Querying the temporal matching matrix based on the mapping pairing relationship to determine the matching score of each mapping pairing combination; 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 switching levels, medium-frequency switching levels, and low-frequency switching levels, respectively. The vertical axis represents the response delay level of the energy-saving control strategy, corresponding to high-latency levels, medium-latency levels, and low-latency levels, respectively. Each coordinate position in the matrix stores a value, which represents the timing matching score for the corresponding combination of the vehicle's operating condition switching speed level and the energy-saving control strategy's response delay level. In actual operation, the corresponding value in the timing matching matrix is ​​searched for each mapping pairing, and this value is used as the matching score. For example, when the vehicle operating condition switching speed level is a high-frequency switching level and the energy-saving control strategy response delay level is a high-delay level, the value of the corresponding position of the timing matching matrix is ​​15 points, and the matching score of this mapping pairing combination is determined to be 15 points; if the vehicle operating condition switching speed level is a low-frequency switching level and the energy-saving control strategy response delay level is a low-delay level, the value of the corresponding position of the timing matching matrix is ​​95 points, and the matching score of this mapping pairing combination is 95 points; the matching scores of all mapping pairing combinations are determined in the same way.

[0033] Comparing the matching score with a first timing matching threshold and a second timing matching threshold to determine a timing mismatch between the vehicle operating condition and the energy-saving control strategy; The first timing matching threshold and the second timing matching threshold are predetermined within the vehicle terminal and are used to classify the matching score into three timing mismatch levels: severe mismatch level, medium mismatch level, and slight mismatch level, wherein the first timing matching threshold is less 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 mapping pairing combination is less than 40 points, the timing mismatch level between the vehicle operating condition and the energy-saving control strategy is severe; when the matching score of the mapping pairing combination is greater than or equal to 40 points and less than or equal to 70 points, the timing mismatch level between the vehicle operating condition and the energy-saving control strategy is medium; and when the matching score of the mapping pairing combination is greater than 70 points, the timing mismatch level between the vehicle operating condition and the energy-saving control strategy is slight. For example, a combination with a matching score of 15 points belongs to a severe mismatch level, a combination with a matching score of 60 points belongs to a moderate mismatch level, and a combination with a matching score of 95 points belongs to a slight mismatch level. The above method is used to determine the degree of temporal mismatch of all mapping pairing combinations.

[0034] Record the degree of timing mismatch, the time of occurrence and duration of timing mismatch, and generate timing mismatch data of vehicle operating conditions and energy-saving control strategies; The time of occurrence of the timing mismatch is the starting moment when the vehicle's operating conditions change and the energy-saving control strategy fails to respond in time, resulting in a timing mismatch. For example, when the vehicle's operating conditions change from low-speed cruise mode to high-speed cruise mode at 18:00:00, the corresponding energy-saving control strategy is not adjusted in time, and the power consumption mode switching adjustment is delayed until 18:00:05. The time of occurrence of the timing mismatch is recorded as 18:00:00. The duration of the timing mismatch is the time interval between the actual adjustment moment of the energy-saving control strategy and the time of the timing mismatch, which is 5 seconds. The time of occurrence and duration of each timing mismatch are recorded in the same way, and combined with the degree of timing mismatch, the timing mismatch data of the vehicle operating conditions and the energy-saving control strategy are formed in chronological order, including but not limited to the specific time and duration information when the timing mismatch degree is severe mismatch level, medium mismatch level, and slight mismatch level, which is used to dynamically adjust the vehicle operating conditions and the energy-saving control strategy.

[0035] S5: Based on the time series mismatch data, analyze the occurrence conditions and duration of the energy efficiency inversion phenomenon and generate time domain risk data of the energy efficiency inversion, including: Arrange the time series mismatch data in chronological order to construct the energy efficiency inversion time domain series; Energy efficiency inversion occurs when the energy-saving control strategy fails to respond promptly to changes in vehicle operating conditions, resulting in a temporary mismatch between the vehicle's power consumption configuration and actual power requirements during actual operation. This manifests as a delayed response from the energy-saving control strategy, causing actual power consumption to exceed the ideal power consumption. This results in a temporary, non-ideal increase in power consumption, creating an energy efficiency inversion. The timing mismatch data between the vehicle's operating conditions and the energy-saving control strategy includes the degree of timing mismatch, the time of occurrence, and the duration of the timing mismatch. The timing mismatch occurs when the vehicle's operating conditions change, but the corresponding energy-saving control strategy fails to synchronize its response, resulting in a timing mismatch. The duration of the timing mismatch is the duration between the time the energy-saving control strategy actually completes its response and the time the timing mismatch occurs. All timing mismatch data between the vehicle's operating conditions and the energy-saving control strategy are sorted in chronological order, from earliest to latest. For example, the moment when the vehicle operating condition changes from urban cruising mode to congestion mode is 8:15:00, and the energy-saving control strategy does not complete the energy-saving mode response until 8:15:06, then the timing mismatch data is arranged in the first place in the timing sequence; the second time the vehicle operating condition changes from congestion mode to fast driving mode is 8:30:00, and the energy-saving control strategy completes the response at 8:30:04, then the timing mismatch data is arranged in the second place; through the above method, all timing mismatch data are arranged in sequence to form an energy efficiency inverted time domain sequence.

[0036] The area value and interval length of the continuous positive value interval of the energy efficiency inversion time domain series are calculated to obtain the energy efficiency inversion time domain feature vector; Each timing mismatch event in the energy efficiency inversion time domain sequence has a duration and a timing mismatch degree. The timing mismatch degree is represented by the difference between the determined matching score and the ideal matching score, thus taking a positive value. The continuous positive value interval is the time interval consisting of consecutive timing mismatch events in the energy efficiency inversion time domain sequence. First, determine the start and end times of each continuous 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. Secondly, calculate the area value for each positive interval, that is, multiply the timing mismatch degree by the corresponding duration length. For example, the timing mismatch degree difference in the first positive interval is 20 minutes, and the duration length is 6 seconds. Then, the area value of the first positive interval is 120 (20×6) minutes and seconds. The timing mismatch degree difference in the second positive interval is 15 minutes, and the duration length 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 continuous positive intervals to form a set of energy efficiency inversion time domain feature vectors.

[0037] A two-dimensional clustering method based on interval length and area value is used to divide the time-domain feature vectors of energy efficiency inversion into high-risk clusters, medium-risk clusters, and low-risk clusters, and the conditions for the occurrence of energy efficiency inversion are obtained. The two-dimensional clustering method uses the area value of each energy-efficiency inversion time-domain feature vector in the energy-efficiency inversion time-domain feature vector set as the ordinate and the interval length as the abscissa to construct a two-dimensional spatial coordinate system consisting of the area value and the interval length. Then, based on the distance between the energy-efficiency inversion time-domain feature vectors, an iterative clustering method is used to divide the energy-efficiency inversion time-domain feature vector set into different clusters to reflect the risk level of the energy-efficiency inversion phenomenon in different time-domain intervals. For example, energy-efficiency inversion time-domain feature vectors with large area values ​​and long interval lengths correspond to situations where vehicle operating conditions change frequently and the energy-saving control strategy has severe 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 small area values ​​and short interval lengths are classified as low-risk clusters. For example, a time-domain feature vector with an area value of 120 minutes and seconds and an interval length of 6 seconds for energy efficiency inversion is classified as a high-risk cluster; a feature vector with an area value of 60 minutes and seconds and an interval length of 4 seconds is classified as a medium-risk cluster; and a feature vector with an area value of 15 minutes and seconds and an interval length of 1 second is classified as a low-risk cluster. Through the above method, the conditions for energy efficiency inversion to occur are divided into three categories: high-risk cluster, medium-risk cluster, and low-risk cluster.

[0038] Extract the starting time and duration of energy efficiency inversion within each risk cluster to generate energy efficiency inversion time domain risk data; The energy efficiency inversion start time is the time when the first timing mismatch event occurs within the continuous positive interval contained in each risk cluster. For example, the start time of the first timing mismatch event in the high-risk cluster is 8:15:00. The energy efficiency inversion duration is the total time length of all continuous positive intervals within each risk cluster. For example, the total duration in the high-risk cluster is 6 seconds. The energy efficiency inversion start time and duration of all continuous positive intervals in the medium-risk and low-risk clusters are extracted in the same way. The medium-risk cluster has a start time of 8:30:00 and a duration of 4 seconds, and the low-risk cluster has a start time of 9:00:00 and a duration of 1 second. The extracted information is aggregated to form the energy efficiency inversion time domain risk data, including the start time and duration of each cluster in the high-risk cluster, medium-risk cluster, and low-risk cluster, providing a basis for dynamic adjustment of the energy-saving control strategy under vehicle operating conditions.

[0039] S6: Based on the time-domain risk data of energy efficiency inversion, dynamically adjust the pre-trigger timing and response speed of the energy-saving control strategy, and generate an adjustment plan for the energy-saving optimization control strategy, including: Based on the starting time and duration of energy efficiency inversion, the pre-trigger adjustment time window of the energy-saving control strategy is determined; 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 the 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 the starting time and duration of the energy efficiency inversion of each risk cluster, the corresponding pre-trigger adjustment time window of the energy-saving control strategy is set respectively. For example, for the high-risk cluster, when the starting time of the energy efficiency inversion is 8:15:00 and the duration is 6 seconds, the pre-trigger adjustment time window of the energy-saving control strategy is set to the time range of 10 to 15 seconds before the vehicle operating condition changes, so as to ensure that the energy-saving control strategy enters the preparation state in advance before the vehicle operating condition actually changes; for the medium-risk cluster, when the starting time of the energy efficiency inversion is 8:30:00 and the duration is 4 seconds, the pre-trigger adjustment time window of the energy-saving control strategy is set to the time range of 6 to 10 seconds before the vehicle operating condition changes; for the low-risk cluster, when the starting time of the energy efficiency inversion is 9:00:00 and the duration is 1 second, the pre-trigger adjustment time window of the energy-saving control strategy is set to the time range of 3 to 6 seconds before the vehicle operating condition changes.

[0040] According to the pre-trigger adjustment time window of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy is determined in combination with the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy; After 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 the vehicle operating condition changes is determined in conjunction with the switching speed level of the vehicle operating condition and the response delay level of the energy-saving control strategy. The switching speed level of the vehicle operating condition is divided into high-frequency switching level, medium-frequency switching level, and low-frequency switching level; the response delay level of the energy-saving control strategy is divided into high-delay level, medium-delay level, and low-delay level. The higher the switching speed level of the vehicle operating condition and the higher the response delay level of the energy-saving control strategy, the more necessary it is to determine the pre-trigger timing of the energy-saving control strategy in advance; conversely, the lower the switching speed level of the vehicle operating condition and the lower the response delay level of the energy-saving control strategy, the pre-trigger timing of the energy-saving control strategy can be relatively close to the actual time when the vehicle operating condition occurs. For example, when the vehicle operating condition switching speed level is a high-frequency switching level and the energy-saving control strategy response delay level is a high-delay level, within the pre-trigger adjustment time window of 10 to 15 seconds, the maximum value, that is, 15 seconds before the vehicle operating condition occurs, is taken as the pre-trigger timing of the energy-saving control strategy. When the vehicle operating condition switching speed level is a medium-frequency switching level and the energy-saving control strategy response delay level is a medium-delay level, within the pre-trigger adjustment time window of 6 to 10 seconds, the middle value of 8 seconds is taken as the pre-trigger timing of the energy-saving control strategy. When the vehicle operating condition switching speed level is a low-frequency switching level and the energy-saving control strategy response delay level is a low-delay level, the minimum value of 3 seconds within the pre-trigger adjustment time window is taken as the pre-trigger timing of the energy-saving control strategy. Through the above method, the pre-trigger timing of the energy-saving control strategy for all combinations of vehicle operating condition switching speed levels and energy-saving control strategy response delay levels is determined.

[0041] Based on the pre-trigger timing of the energy-saving control strategy, the execution process of the energy-saving control strategy is re-planned in terms of time scale, and a response speed adjustment plan for the energy-saving control strategy is generated; The execution process of an energy-saving control strategy consists of three phases: strategy activation, strategy execution, and strategy effect realization. In its original state, this execution process may have a fixed time period and lacks flexibility under varying vehicle operating conditions. After determining the pre-trigger timing for the energy-saving control strategy, the execution process is time-scaled accordingly. The energy-saving control strategy enters the activation phase before vehicle operating conditions change. This improves the speed and efficiency of the strategy execution phase by shortening the activation phase's waiting time or initiating energy-saving strategy preparation actions in advance. For example, if the energy-saving control strategy originally had a fixed activation phase of 3 seconds, an execution phase of 4 seconds, and an effect realization phase of 1 second, for a total of 8 seconds, then if the pre-trigger timing is determined to be 15 seconds in advance, the activation phase is adjusted to 2 seconds, the strategy execution phase to 3 seconds, and the effect realization phase is brought forward to the sixth second after the energy-saving control strategy is activated, shortening the total duration from 8 seconds to 6 seconds. This ensures that the energy-saving control strategy is ready and takes effect quickly when vehicle operating conditions change, thus avoiding response delays. Through the above methods, the time scale of the execution process of all energy-saving control strategies is adjusted and planned, and all adjusted execution stages and time allocations are recorded to form an energy-saving control strategy response speed adjustment plan.

[0042] Combine the pre-trigger timing and response speed adjustment scheme of the energy-saving control strategy to form an adjustment scheme for the energy-saving optimization control strategy; After determining the pre-trigger timing of the energy-saving control strategy and planning the response speed adjustment plan, the pre-trigger timing of the energy-saving control strategy and the response speed adjustment plan are integrated and combined to ultimately form an energy-saving optimization control strategy adjustment plan. The energy-saving optimization control strategy adjustment plan specifies the energy-saving control actions that the on-board terminal should take before changes in the vehicle's operating conditions, given different vehicle operating condition switching speed levels and energy-saving control strategy response delay levels. For example, for high-risk clusters with a high-frequency switching level and a high-latency level combination, the energy-saving control strategy is pre-triggered 15 seconds in advance and the total execution time of the response speed is adjusted to 6 seconds; for medium-risk clusters with a medium-frequency switching level and a medium-latency level combination, the energy-saving control strategy is pre-triggered 8 seconds in advance and the execution time is adjusted to 5 seconds; for low-risk clusters with a low-frequency switching level and a low-latency level combination, 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 situation, and execute it within the vehicle terminal controller, thereby solving the timing mismatch between the changes in vehicle operating conditions and the response of the energy-saving control strategy, and ensuring that the vehicle terminal achieves efficient energy-saving effects in a complex actual operating environment.

[0043] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0044] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. 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 means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0045] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0046] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0048] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0050] If the functions are implemented in the form of software function 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0051] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0052] Finally: 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 in the scope of protection of the present invention.

Claims

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

2. The energy-saving optimization control method for vehicle terminal operating conditions according to claim 1 is characterized in that: S1, specifically: Collect real-time operating condition data and real-time power consumption status data generated by the vehicle terminal during the actual operation of the vehicle; Preprocessing real-time operating condition data and real-time power consumption status data; Extracting the time interval characteristics of vehicle operating condition switching and the frequency characteristics of vehicle operating condition changes from the pre-processed real-time operating condition data to generate vehicle operating condition switching characteristic data; The fluctuation characteristics of the vehicle terminal power consumption and the frequency characteristics of the vehicle terminal power consumption mode switching are extracted from the preprocessed real-time power consumption status data to generate the terminal real-time power consumption characteristic data of the vehicle terminal.

3. The energy-saving optimization control method for vehicle terminal operating conditions according to claim 2 is characterized in that: S2, specifically: Determining the number of times the vehicle operating condition changes within a unit time based on operating condition switching characteristic data of the vehicle operating condition; The number of times the vehicle operating condition changes within a unit time is compared with a preset threshold value of the vehicle operating condition switching speed to determine the switching speed level of the vehicle operating condition.

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

5. The energy-saving optimization control method for vehicle terminal operating conditions according to claim 4 is characterized in that: S4, specifically: Mapping and pairing the switching speed level of the vehicle operating condition with the response delay level of the energy-saving control strategy; Querying the temporal matching matrix based on the mapping pairing relationship to determine the matching score of each mapping pairing combination; Comparing the matching score with a first timing matching threshold and a second timing matching threshold to determine a timing mismatch between the vehicle operating condition and the energy-saving control strategy; The degree of timing mismatch, the occurrence time and duration of timing mismatch are recorded to generate timing mismatch data of vehicle operating conditions and energy-saving control strategies.

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

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

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