Vehicle energy consumption data processing method and system and computer equipment

Abstract

The invention provides a vehicle energy consumption data processing method and system and computer equipment. The method comprises the following steps: acquiring energy consumption data of a vehicle; the energy consumption standard deviation of the vehicle is determined based on the energy consumption data of the vehicle, and an energy consumption stability index value used for representing the energy consumption state of the vehicle is determined according to the energy consumption standard deviation; and performing weight distribution on the energy consumption data of the vehicle according to the energy consumption stability index value, and determining the comprehensive average energy consumption of the vehicle according to a weight distribution result. According to the method, the comprehensive average energy consumption of the vehicle is determined through the weight distribution result, and then the comprehensive average power consumption is used as the driving energy consumption reflecting the real environment, so that the problem that the real energy consumption data cannot be correctly represented according to the static fixed weight in a part of road sections in the related technology can be solved or relieved; and the problem that the comprehensive average power consumption is remarkably increased or decreased when the comprehensive average power consumption is calculated according to the static fixed weight in a part of road sections in the related technology can be solved or relieved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and system for processing vehicle energy consumption data, and a computer device. Background Technology

[0002] When calculating the comprehensive driving range of new energy vehicles, related technologies may use a weighted average of multi-source energy consumption data. However, when the corresponding weights are static fixed weights (such as static calibration values), they may not accurately represent the true energy consumption data for some road sections. For example, in actual use, if a user has just experienced special conditions (such as congested roads, frequent start-stop operations, or long downhill energy recovery), the energy consumption per kilometer on some road sections may fluctuate drastically or even become negative, making the data for that section unrepresentative. If the comprehensive average energy consumption is still calculated using static fixed weights, it will significantly inflate or deflate the comprehensive average energy consumption, causing sudden changes in the vehicle's range display, making it appear too high or too low, thus affecting the user's judgment of the range's reliability. Summary of the Invention

[0003] This application provides a vehicle energy consumption data processing method and system, and a computer device, to solve or alleviate the problems described above.

[0004] In a first aspect, this application provides a vehicle energy consumption data processing method, comprising the following steps: acquiring vehicle energy consumption data; determining the energy consumption standard deviation of the vehicle based on the vehicle energy consumption data, and determining an energy consumption stability index value to represent the energy consumption status of the vehicle based on the energy consumption standard deviation; assigning weights to the vehicle energy consumption data based on the energy consumption stability index value, and determining the comprehensive average energy consumption of the vehicle based on the weight assignment result.

[0005] Compared with related technologies, this vehicle energy consumption data processing method has at least the following beneficial effects: By introducing an energy consumption stability index to weight vehicle energy consumption data, not only can the static fixed weights be dynamically adjusted, but a weight mapping mechanism between the energy consumption stability index value and the vehicle's energy consumption data can also be constructed. Furthermore, the comprehensive average energy consumption of the vehicle is determined through the weight allocation result, and then the comprehensive average energy consumption is used as a reflection of driving energy consumption in the real environment. This can not only solve or alleviate the problem that related technologies cannot accurately represent real energy consumption data on some road sections using static fixed weights, but also solve or alleviate the problem that related technologies significantly raise or lower the comprehensive average energy consumption when calculating the comprehensive average energy consumption on some road sections using static fixed weights. This reduces the probability of sudden changes, inflated or excessively low vehicle range display, and increases the credibility of the range display.

[0006] In a first possible implementation of the first aspect, if the energy consumption data includes energy consumption data per unit kilometer within a preset mileage, then the process of determining the energy consumption standard deviation of the vehicle based on the vehicle's energy consumption data includes: calculating the average energy consumption of the vehicle within the preset mileage based on the energy consumption data per unit kilometer within the preset mileage; and calculating the energy consumption standard deviation of the vehicle based on the energy consumption data per unit kilometer within the preset mileage, the number of kilometers of the preset mileage, and the average energy consumption of the vehicle within the preset mileage.

[0007] In a first possible implementation of the first aspect, the standard deviation of vehicle energy consumption can be calculated based on the vehicle's energy consumption data per kilometer within a preset mileage.

[0008] In a second possible implementation of the first aspect, the process of determining the energy consumption stability index value representing the energy consumption status of the vehicle based on the energy consumption standard deviation includes: obtaining a protection energy consumption for distortion protection of the energy consumption stability index value; comparing the protection energy consumption with the average energy consumption of the vehicle within a preset mileage, and determining an energy consumption fluctuation ratio value based on the corresponding comparison result and the energy consumption standard deviation; and determining the energy consumption stability index value based on the energy consumption fluctuation ratio value.

[0009] In a second possible implementation of the first aspect, the energy consumption stability index value is determined based on the energy consumption standard deviation and the protection energy consumption, and weight allocation can be performed under the distortion protection of the energy consumption stability index value.

[0010] In a third possible implementation of the first aspect, the process of determining the energy consumption stability index value for representing the vehicle's energy consumption status based on the energy consumption standard deviation further includes: obtaining the maximum and minimum values ​​of the energy consumption stability index value; determining the energy consumption fluctuation ratio value based on the maximum value of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage, and the energy consumption standard deviation of the vehicle; wherein the maximum value of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage is obtained based on a comparison result of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage; calculating the difference between the maximum value of the energy consumption stability index value and the energy consumption fluctuation ratio value, comparing the difference with the minimum value of the energy consumption stability index value, and using the maximum value between the difference and the minimum value of the energy consumption stability index value as the energy consumption stability index value for representing the vehicle's energy consumption status.

[0011] In a third possible implementation of the first aspect, the energy consumption stability index value is determined based on the energy consumption standard deviation, the protection energy consumption, the maximum value and the minimum value of the energy consumption stability index value, and weight allocation can be performed under the distortion protection of the energy consumption stability index value.

[0012] In a fourth possible implementation of the first aspect, the process of weighting the energy consumption data of the vehicle according to the energy consumption stability index value includes: comparing the energy consumption stability index value with a preset weight allocation coefficient, wherein the preset weight allocation coefficient is used to distinguish the driving conditions of the vehicle; and weighting the energy consumption data of the vehicle according to the comparison result of the energy consumption stability index value and the preset weight allocation coefficient.

[0013] In the fourth possible implementation of the first aspect, the energy consumption data of the vehicle is weighted according to a preset weighting coefficient, which can be used to weight the vehicle under more driving conditions, thereby reflecting the real driving energy consumption of the vehicle under more driving conditions.

[0014] In a fifth possible implementation of the first aspect, if the preset weight allocation coefficient includes a first weight allocation coefficient, a second weight allocation coefficient, and a third weight allocation coefficient, then the process of weighting the vehicle's energy consumption data according to the comparison result of the energy consumption stability index value and the preset weight allocation coefficient includes: allocating a first weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the first weight allocation coefficient; allocating a second weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the second weight allocation coefficient and the energy consumption stability index value is less than the first weight allocation coefficient; allocating a third weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the third weight allocation coefficient and the energy consumption stability index value is less than the second weight allocation coefficient; and allocating a fourth weight to the vehicle's energy consumption data when the energy consumption stability index value is less than the third weight allocation coefficient; wherein, the first weight allocation coefficient is greater than the second weight allocation coefficient, the second weight allocation coefficient is greater than the third weight allocation coefficient; the first weight is greater than the second weight, the second weight is greater than the third weight, and the third weight is greater than the fourth weight.

[0015] In the fifth possible implementation of the first aspect, the energy consumption data of the vehicle is segmented and weighted according to the comparison result of the energy consumption stability index value and the preset weight allocation coefficient. This not only allows for dynamic adjustment of static fixed weights under different driving environments, but also enables weighted calculation of energy consumption data under different driving environments, thereby increasing the credibility of the range display.

[0016] In a sixth possible implementation of the first aspect, the process of determining the comprehensive average energy consumption of the vehicle based on the weight allocation result includes: recording the weight corresponding to the weight allocation result as a dynamic weight, and recording the weight of the vehicle's energy consumption data before weight allocation as a static fixed weight; adjusting the static fixed weight according to the dynamic weight, and calculating the comprehensive average energy consumption of the vehicle based on the weight adjustment result.

[0017] In the sixth possible implementation of the first aspect, by adjusting the static fixed weights with dynamic weights, the problem that related technologies cannot correctly represent recent energy consumption data according to static fixed weights can be solved or alleviated. It can also solve or alleviate the problem that related technologies significantly raise or lower the comprehensive average energy consumption when calculating the comprehensive average energy consumption according to static fixed weights, thereby reducing the probability of sudden changes, false highs or lows in vehicle range display and increasing the credibility of range display.

[0018] In a seventh possible implementation of the first aspect, if the energy consumption data includes energy consumption data per unit kilometer within a first preset mileage, energy consumption data per unit kilometer within a second preset mileage, and theoretical energy consumption data, then the process of adjusting the static fixed weights according to the dynamic weights and calculating the comprehensive average energy consumption of the vehicle based on the weight adjustment results further includes: replacing the static fixed weights of the energy consumption data per unit kilometer within the first preset mileage with the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage; adjusting the static fixed weights of the energy consumption data per unit kilometer within the second preset mileage according to the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage, to obtain the second... The dynamic weight of energy consumption data per unit kilometer within a preset mileage; and, based on the dynamic weight of energy consumption data per unit kilometer within the first preset mileage, adjusting the static fixed weight of the theoretical energy consumption data to obtain the dynamic weight of the theoretical energy consumption data; calculating the comprehensive average energy consumption of the vehicle based on the energy consumption data per unit kilometer within the first preset mileage, the dynamic weight of energy consumption data per unit kilometer within the first preset mileage, the energy consumption data per unit kilometer within the second preset mileage, the dynamic weight of energy consumption data per unit kilometer within the second preset mileage, the theoretical energy consumption data, and the dynamic weight of the theoretical energy consumption data; wherein, the first preset mileage is less than the second preset mileage.

[0019] In the seventh possible implementation of the first aspect, the credibility of multi-source energy consumption data can be automatically determined by replacing the static fixed weight of the unit kilometer energy consumption data of a certain mileage in different mileages with a dynamic weight, and then calculating the comprehensive average energy consumption of the vehicle.

[0020] Secondly, this application provides a vehicle energy consumption data processing system, comprising: a data acquisition module for acquiring vehicle energy consumption data; an energy consumption stability index module for determining the energy consumption standard deviation of the vehicle based on the vehicle energy consumption data, and determining an energy consumption stability index value representing the vehicle energy consumption status based on the energy consumption standard deviation; and an average energy consumption module for weighting the vehicle energy consumption data based on the energy consumption stability index value, and determining the vehicle's comprehensive average energy consumption based on the weighting result.

[0021] Compared with related technologies, this vehicle energy consumption data processing system has at least the following beneficial effects: By introducing an energy consumption stability index to weight vehicle energy consumption data, it can not only dynamically adjust static fixed weights, but also construct a weight mapping mechanism between the energy consumption stability index value and the vehicle's energy consumption data; and by determining the vehicle's comprehensive average energy consumption through the weight allocation result, and then using the comprehensive average energy consumption as a reflection of driving energy consumption in the real environment, it can not only solve or alleviate the problem that related technologies cannot accurately represent real energy consumption data on some road sections using static fixed weights, but also solve or alleviate the problem that related technologies significantly inflate or deflate the comprehensive average energy consumption when calculating it on some road sections using static fixed weights, thereby reducing the probability of sudden changes, inflated or excessively low vehicle range display, and increasing the credibility of the range display.

[0022] Thirdly, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle energy consumption data processing method described in any one of the above.

[0023] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle energy consumption data processing method described in any one of the above.

[0024] The beneficial effects of this application are as follows: This application proposes a vehicle energy consumption data processing method and system, and a computer device. By introducing an energy consumption stability index to assign weights to vehicle energy consumption data, it is possible not only to dynamically adjust static fixed weights, but also to construct a weight mapping mechanism between the energy consumption stability index value and the vehicle's energy consumption data. Furthermore, the comprehensive average energy consumption of the vehicle is determined through the weight allocation result, and then the comprehensive average energy consumption is used as a reflection of driving energy consumption in the real environment. This not only solves or alleviates the problem that related technologies cannot accurately represent real energy consumption data on some road sections using static fixed weights, but also solves or alleviates the problem that related technologies significantly inflate or deflate the comprehensive average energy consumption when calculating it on some road sections using static fixed weights. This reduces the probability of sudden changes, inflated or excessively low vehicle range display, and increases the credibility of the range display. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0026] In the attached diagram:

[0027] Figure 1 This is a flowchart illustrating a vehicle energy consumption data processing method provided in one embodiment of this application. Figure 2 This is a schematic diagram of the hardware structure of a vehicle energy consumption data processing system provided in an embodiment of this application. Figure 3 This is a data interaction diagram of a vehicle energy consumption data processing system provided in another embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of a computer device suitable for implementing one or more embodiments of this application. Detailed Implementation

[0028] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0029] It is understood that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0030] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0031] When calculating the comprehensive driving range of new energy vehicles, related technologies may employ a weighted average of multi-source energy consumption data. For example, this involves merging WLTC (Worldwide Harmonized Light Vehicles Test Cycle) energy consumption, the average energy consumption over the most recent 100km, and the average energy consumption over the most recent 10km with static fixed weights (e.g., 0.7:0.2:0.1) to calculate the comprehensive average energy consumption, aiming to balance nominal values ​​with actual user driving habits. However, this method has a significant drawback: the weights are statically calibrated values ​​and may not accurately represent the true energy consumption data over the most recent 10km. For instance, in actual use, if a user has just experienced special driving conditions (such as congested roads, frequent start-stop cycles, or long downhill energy recovery), the energy consumption per kilometer over the most recent 10km may fluctuate drastically or even become negative, making this data unrepresentative. If the data is still calculated using static fixed weights, it will significantly inflate or deflate the comprehensive average energy consumption, causing sudden changes in the displayed range, resulting in inflated or excessively low figures, severely impacting the user's judgment of the range's reliability. Furthermore, the relevant technologies do not quantitatively assess the stability of energy consumption data, lack an intelligent judgment mechanism for fluctuating data, and are difficult to adaptively adjust the reliability of data under different driving scenarios.

[0032] Based on the above description Figure 1 A flowchart illustrating a method for processing vehicle energy consumption data is shown. Specifically, in an exemplary embodiment, as... Figure 1 As shown, this embodiment provides a vehicle energy consumption data processing method, including the following steps: S110, Obtain vehicle energy consumption data. In some examples, the vehicle includes, but is not limited to, new energy vehicles, and the energy consumption data includes, but is not limited to, electricity consumption data.

[0033] S120, determine the standard deviation of vehicle energy consumption based on vehicle energy consumption data, and determine the energy consumption stability index value to represent the energy consumption status of the vehicle based on the standard deviation of energy consumption. S130 assigns weights to vehicle energy consumption data based on energy consumption stability index values ​​and determines the vehicle's overall average energy consumption based on the weighting results.

[0034] Compared with related technologies, this vehicle energy consumption data processing method has at least the following beneficial effects: By introducing an energy consumption stability index to weight vehicle energy consumption data, not only can the static fixed weights be dynamically adjusted, but a weight mapping mechanism between the energy consumption stability index value and the vehicle's energy consumption data can also be constructed. Furthermore, the comprehensive average energy consumption of the vehicle is determined through the weight allocation result, and then the comprehensive average energy consumption is used as a reflection of driving energy consumption in the real environment. This can not only solve or alleviate the problem that related technologies cannot accurately represent real energy consumption data on some road sections using static fixed weights, but also solve or alleviate the problem that related technologies significantly raise or lower the comprehensive average energy consumption when calculating the comprehensive average energy consumption on some road sections using static fixed weights. This reduces the probability of sudden changes, inflated or excessively low vehicle range display, and increases the credibility of the range display.

[0035] In some exemplary embodiments, if the energy consumption data includes energy consumption data per unit kilometer within a preset mileage, the process of determining the vehicle's energy consumption standard deviation based on the vehicle's energy consumption data includes: calculating the vehicle's average energy consumption within the preset mileage based on the energy consumption data per unit kilometer within the preset mileage; and calculating the vehicle's energy consumption standard deviation based on the energy consumption data per unit kilometer within the preset mileage, the number of kilometers in the preset mileage, and the average energy consumption within the preset mileage. Therefore, this vehicle energy consumption data processing method can calculate the vehicle's energy consumption standard deviation based on the energy consumption data per unit kilometer within the preset mileage. The preset mileage can be selected or set according to the actual scenario; the specific value of the preset mileage is not limited here, for example, the preset mileage could be 10km. In some examples, the energy consumption data per unit kilometer can also be referred to as energy consumption per kilometer.

[0036] As an example, if the energy consumption data includes energy consumption per kilometer within the most recent 10 km when the vehicle is stationary, then the process of determining the standard deviation of the vehicle's energy consumption based on the energy consumption per kilometer within the most recent 10 km when the vehicle is stationary involves: calculating the average energy consumption of the vehicle within the most recent 10 km when the vehicle is stationary based on the energy consumption per kilometer within the most recent 10 km when the vehicle is stationary, as follows: In the formula, This indicates the average energy consumption of the vehicle over the most recent 10km when it is stationary. This represents the energy consumption per kilometer within the nearest 10km from the parking point when the vehicle is stopped. `i` is an integer ranging from 1 to 10. When `i=1`, it represents the energy consumption per kilometer at the 1st kilometer from the parking point when the vehicle is stopped; when `i=2`, it represents the energy consumption per kilometer at the 2nd kilometer from the parking point when the vehicle is stopped. The same logic applies to other values ​​of `i`, which will not be elaborated further here. ≤0 means that the net energy within the nearest 10km of the parking point when the vehicle is stopped is feedback, with no actual discharge. This indicates that this data segment cannot reflect the true energy consumption level and can be ignored to avoid the phenomenon of inflated range. Based on the energy consumption per kilometer within the nearest 10km of the vehicle being stopped, the distance traveled within the nearest 10km of the vehicle being stopped, and the average energy consumption within the nearest 10km of the vehicle being stopped, the standard deviation of the vehicle's energy consumption is calculated as follows: In the formula, This represents the standard deviation of a vehicle's energy consumption, used to measure the dispersion of energy consumption data.

[0037] In some exemplary embodiments, the process of determining the energy consumption stability index value representing the energy consumption status of a vehicle based on the energy consumption standard deviation includes: obtaining a protection energy consumption for distortion protection of the energy consumption stability index value; comparing the protection energy consumption with the average energy consumption of the vehicle within a preset mileage, and determining the energy consumption fluctuation ratio value based on the corresponding comparison result and the energy consumption standard deviation; and determining the energy consumption stability index value based on the energy consumption fluctuation ratio value. Specifically, the process of determining the energy consumption stability index value representing the vehicle's energy consumption status based on the energy consumption standard deviation may include: obtaining the protection energy consumption used for distortion protection of the energy consumption stability index value, the maximum value of the energy consumption stability index value, and the minimum value of the energy consumption stability index value; comparing the protection energy consumption with the vehicle's average energy consumption within a preset mileage to determine the maximum value between the protection energy consumption and the vehicle's average energy consumption within the preset mileage; determining the energy consumption fluctuation ratio value based on the maximum value between the protection energy consumption and the vehicle's average energy consumption within the preset mileage, and the vehicle's energy consumption standard deviation; calculating the difference between the maximum value of the energy consumption stability index value and the energy consumption fluctuation ratio value, comparing the difference with the minimum value of the energy consumption stability index value, and using the maximum value between the difference and the minimum value of the energy consumption stability index value as the energy consumption stability index value representing the vehicle's energy consumption status. Therefore, this vehicle energy consumption data processing method can determine the energy consumption stability index value based on the energy consumption standard deviation, protection energy consumption, and the maximum and minimum values ​​of the energy consumption stability index value, and can perform weight allocation under distortion protection of the energy consumption stability index value. In some examples, the maximum and minimum values ​​of the energy consumption stability index can be selected or set according to the actual scenario, and no specific numerical limit is specified here. For example, the maximum value of the energy consumption stability index can be 1, and the minimum value can be 0. The closer the energy consumption stability index value is to the maximum value, the more stable the energy consumption and the more reliable the data; the closer the energy consumption stability index value is to the minimum value, the greater the energy consumption fluctuation; if the energy consumption stability index value is negative, it indicates abnormal energy consumption.

[0038] As an example, if the energy consumption data includes energy consumption per kilometer within the most recent 10 km when the vehicle is stationary, the process of determining the energy consumption stability index value based on the energy consumption standard deviation may include: obtaining the protection energy consumption used to protect the energy consumption stability index value from distortion, denoted as... The protection energy consumption and the vehicle's average energy consumption over the most recent 10km when the vehicle is stationary are compared. The maximum value between these two values ​​is recorded as follows: Based on the maximum value of the energy consumption protection and the average energy consumption of the vehicle within the most recent 10km when it is stationary. Vehicle energy consumption standard deviation To determine the energy consumption fluctuation ratio, we have: The difference between the maximum value of the energy consumption stability index and the energy consumption fluctuation ratio is calculated as follows: The difference is compared with the minimum value of the energy consumption stability index, and the maximum value between the difference and the minimum value of the energy consumption stability index is used as the energy consumption stability index value to represent the vehicle's energy consumption status. Thus: In the formula, This represents the energy consumption stability index value. This represents the minimum value of the energy consumption stability index. This indicates the maximum value of the energy consumption stability index. This represents the energy consumption fluctuation ratio. This represents the standard deviation of a vehicle's energy consumption. This indicates the average energy consumption of the vehicle over the most recent 10km when it is stationary. This indicates the need to protect energy consumption.

[0039] In some exemplary embodiments, the process of weighting vehicle energy consumption data based on the energy consumption stability index value includes: comparing the energy consumption stability index value with a preset weighting coefficient, wherein the preset weighting coefficient is used to distinguish the vehicle's driving conditions; and weighting the vehicle's energy consumption data based on the comparison result between the energy consumption stability index value and the preset weighting coefficient. Therefore, this vehicle energy consumption data processing method can weight vehicle energy consumption data according to the preset weighting coefficient, and can perform weighting under more driving conditions, thereby reflecting the vehicle's true driving energy consumption under more driving conditions.

[0040] In some exemplary embodiments, if the preset weight allocation coefficients include a first weight allocation coefficient, a second weight allocation coefficient, and a third weight allocation coefficient, then the process of weighting the vehicle's energy consumption data based on the comparison result between the energy consumption stability index value and the preset weight allocation coefficients includes: allocating a first weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the first weight allocation coefficient; allocating a second weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the second weight allocation coefficient and less than the first weight allocation coefficient; allocating a third weight to the vehicle's energy consumption data when the energy consumption stability index value is greater than or equal to the third weight allocation coefficient and less than the second weight allocation coefficient; and allocating a fourth weight to the vehicle's energy consumption data when the energy consumption stability index value is less than the third weight allocation coefficient; wherein the first weight allocation coefficient is greater than the second weight allocation coefficient, the second weight allocation coefficient is greater than the third weight allocation coefficient, the first weight is greater than the second weight, the second weight is greater than the third weight, and the third weight is greater than the fourth weight. Therefore, this vehicle energy consumption data processing method can segment and weight vehicle energy consumption data based on the comparison between the energy consumption stability index value and the preset weight allocation coefficient. This not only allows for dynamic adjustment of static fixed weights under different driving environments but also enables weighted calculation of energy consumption data under different driving environments, increasing the reliability of the displayed range. The first, second, and third weight allocation coefficients, as well as the first, second, third, and fourth weights, can be set or selected according to the actual scenario; no specific numerical limitations are imposed here. For example, the first weight allocation coefficient could be 0.8, the second weight allocation coefficient could be 0.6, the third weight allocation coefficient could be 0.4, the first weight could be 0.3, the second weight could be 0.2, the third weight could be 0.1, and the fourth weight could be 0.05.

[0041] As an example, if the energy consumption data includes the energy consumption data of the vehicle within the most recent 10km when it is stopped from driving, the process of weighting the vehicle's energy consumption data may include: denoting the energy consumption stability index value as SI, and denoting the weight of the energy consumption data within the most recent 10km when the vehicle is stopped as... If SI ≥ 0.8, then =0.30, where SI ≥ 0.8 indicates that the energy consumption data within the most recent 10km when the vehicle is stationary is very stable, such as during highway cruising. The more stable the driving conditions, the more reliable the data. In this case, we can place more trust in the 10km data to make the range more closely reflect reality. SI ≥ 0.8 represents high confidence, in which case the original static fixed weights need to be increased. If 0.6 ≤ SI < 0.8, then... A score of 0.20 indicates that the vehicle's energy consumption data within the most recent 10km was moderately stable when the vehicle was stationary, e.g., under suburban driving conditions. A score of 0.6 ≤ SI < 0.8 represents a high confidence level, in which case the original static fixed weights can be appropriately increased. If 0.4 ≤ SI < 0.6, then... A score of 0.10 indicates that the vehicle's energy consumption data is generally stable within the last 10km when the vehicle is stationary, e.g., during daily urban commuting. A score of 0.4 ≤ SI < 0.6 represents a medium confidence level, in which case the original static fixed weights are maintained. If SI < 0.4, then... A score of 0.05 indicates that the vehicle's energy consumption data within the last 10km is unstable when the vehicle is stationary, such as during rush hour traffic or frequent stop-and-go driving conditions. An SI score of <0.4 indicates low confidence, in which case the original static fixed weights need to be significantly reduced.

[0042] As an example, in a multi-source fusion strategy, even if the energy consumption data within the most recent 10km when the vehicle is stopped is highly reliable, the weight of WLTC (Worldwide Harmonized Light Vehicles Test Cycle) energy consumption should not be excessively reduced to maintain stability. For example, setting... =0.30 to ensure that the WLTC power consumption weight reaches at least 0.58. Here, WLTC power consumption can also be referred to as theoretical power consumption.

[0043] In some exemplary embodiments, the process of determining the vehicle's overall average energy consumption based on the weight allocation result includes: recording the weights corresponding to the weight allocation result as dynamic weights, and recording the weights of the vehicle's energy consumption data before weight allocation as static fixed weights; adjusting the static fixed weights according to the dynamic weights, and calculating the vehicle's overall average energy consumption based on the weight adjustment result. Therefore, this vehicle energy consumption data processing method can perform segmented weight allocation on the vehicle's energy consumption data based on the comparison result between the energy consumption stability index value and the preset weight allocation coefficient. This not only allows for dynamic adjustment of the static fixed weights under different driving environments, but also enables weighted calculation of energy consumption data under different driving environments, increasing the reliability of the range display.

[0044] In some exemplary embodiments, if the energy consumption data includes energy consumption data per unit kilometer within a first preset mileage, energy consumption data per unit kilometer within a second preset mileage, and theoretical energy consumption data, the process of adjusting the static fixed weights according to the dynamic weights and calculating the vehicle's comprehensive average energy consumption based on the weight adjustment results further includes: replacing the static fixed weights of the energy consumption data per unit kilometer within the first preset mileage with the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage; adjusting the static fixed weights of the energy consumption data per unit kilometer within the second preset mileage according to the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage to obtain the dynamic weights of the energy consumption data per unit kilometer within the second preset mileage; and adjusting the static fixed weights of the theoretical energy consumption data according to the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage to obtain the dynamic weights of the theoretical energy consumption data; and calculating the vehicle's comprehensive average energy consumption based on the energy consumption data per unit kilometer within the first preset mileage, the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage, the energy consumption data per unit kilometer within the second preset mileage, the dynamic weights of the energy consumption data per unit kilometer within the second preset mileage, the theoretical energy consumption data, and the dynamic weights of the theoretical energy consumption data; wherein the first preset mileage is less than the second preset mileage. Therefore, this vehicle energy consumption data processing method can automatically determine the reliability of multi-source energy consumption data by replacing the static fixed weights of unit-kilometer energy consumption data at a certain mileage with dynamic weights, and then calculating the vehicle's comprehensive average energy consumption. The specific values ​​of the first and second preset mileages can be selected or set according to actual conditions, and no specific numerical limit is specified here. For example, the first preset mileage can be 10km, and the second preset mileage can be 100km.

[0045] As an example, if the original fusion strategy uses a fixed-weight combination: static fixed weights of theoretical energy consumption =0.7, Static fixed weight of energy consumption per kilometer within the most recent 100km when the vehicle is stationary. =0.2 (reflecting medium- to long-term driving trends), static fixed weight of energy consumption per kilometer within the most recent 10km when the vehicle is stationary. =0.1; When assigning weights to energy consumption data within the most recent 10km when the vehicle is stationary, the adjusted dynamic weight is... The weight margin originally allocated to the vehicle within the nearest 10km when it stops driving is then... Need to be reallocated to and To ensure that the total weight sums to 1. For example, proportionally. Will The weight allocated to theoretical energy consumption, the remainder Weights are assigned to the energy consumption per kilometer of the vehicle within the most recent 100 km when it is stationary, where... The core purpose of these allocation coefficients is to prioritize the stability of theoretical energy consumption when dynamically adjusting recent weights, ensuring that the system does not deviate from a reasonable benchmark due to over-reliance on short-term data. In other words: , In the formula, The dynamic weight of theoretical energy consumption, Dynamic weights are applied to the energy consumption data per kilometer within the most recent 100km when the vehicle is stationary. The final comprehensive average energy consumption is calculated as follows: ,in, The comprehensive average energy consumption calculated from theoretical energy consumption, energy consumption data within the most recent 100km when the vehicle is not driving, and energy consumption data within the most recent 10km when the vehicle is not driving can be used as the unit energy consumption benchmark for current range calculation.

[0046] In summary, this application proposes a vehicle energy consumption data processing method. By introducing an energy consumption stability index to weight vehicle energy consumption data, it not only allows for dynamic adjustment of static fixed weights but also constructs a weighted mapping mechanism between the energy consumption stability index value and vehicle energy consumption data. Furthermore, the method determines the vehicle's comprehensive average energy consumption based on the weight allocation results, and then uses this comprehensive average energy consumption as a reflection of real-world driving energy consumption. This not only solves or alleviates the problem of related technologies failing to accurately represent true energy consumption data on certain road sections using static fixed weights but also addresses the issue of significantly inflating or deflating the comprehensive average energy consumption when calculating it on certain road sections using static fixed weights. This reduces the probability of sudden, inflated, or excessively low vehicle range displays, increasing the reliability of the range display. In addition, this method effectively suppresses range jumps caused by abnormal operating conditions such as congestion, slopes, and energy recovery. During smooth high-speed driving, it automatically increases the weight of recent data, making the range response more sensitive; in complex urban road conditions, it automatically reduces the influence of unreliable data, making the range display more stable.

[0047] In an exemplary embodiment of this application, as Figure 2 As shown, a vehicle energy consumption data processing system is provided, including: The data acquisition module 210 is used to acquire the vehicle's energy consumption data; The energy consumption stability index module 220 is used to determine the energy consumption standard deviation of the vehicle based on the vehicle's energy consumption data, and to determine the energy consumption stability index value used to represent the energy consumption status of the vehicle based on the energy consumption standard deviation. The average energy consumption module 230 is used to assign weights to the vehicle's energy consumption data based on the energy consumption stability index value, and to determine the vehicle's comprehensive average energy consumption based on the weight assignment results.

[0048] It is understood that the vehicle energy consumption data processing system and the vehicle energy consumption data processing method provided in the above embodiments belong to the same concept. The specific way in which the vehicle energy consumption data processing method performs its operations has been described in detail in the above method embodiments and will not be repeated here. In practical applications, the vehicle energy consumption data processing system provided in the above embodiments can allocate the above functions to different functional modules as needed. That is, the internal structure of the vehicle energy consumption data processing system is divided into different functional modules, and then all or part of the functions of the corresponding functional modules are implemented by the vehicle energy consumption data processing method described in the above embodiments. For example, all or part of the functions of the image acquisition module 210 can be implemented through the relevant execution process of step S110, all or part of the functions of the prediction module 220 can be implemented through the relevant execution process of step S120, and all or part of the functions of the adaptive adjustment module 230 can be implemented through the relevant execution process of step S130. No specific limitations are imposed here.

[0049] In summary, this application proposes a vehicle energy consumption data processing system. By introducing an energy consumption stability index to weight vehicle energy consumption data, it not only dynamically adjusts static fixed weights but also constructs a weighted mapping mechanism between the energy consumption stability index value and vehicle energy consumption data. Furthermore, it determines the vehicle's comprehensive average energy consumption based on the weight allocation results, and then uses this comprehensive average energy consumption as a reflection of driving energy consumption in real-world conditions. This not only solves or alleviates the problem of related technologies failing to accurately represent true energy consumption data on certain road sections using static fixed weights, but also addresses the issue of significantly inflating or deflating the comprehensive average energy consumption when calculating it on certain road sections using static fixed weights. This reduces the probability of sudden, inflated, or excessively low vehicle range displays, increasing the reliability of the range display. In addition, this system effectively suppresses range jumps caused by abnormal operating conditions such as congestion, inclines, and energy recovery. During smooth high-speed driving, it automatically increases the weight of recent data, making the range response more sensitive; in complex urban road conditions, it automatically reduces the influence of unreliable data, making the range display more stable.

[0050] In an exemplary embodiment of this application, as Figure 3As shown, a vehicle energy consumption data processing system is provided. Specifically, the vehicle's Battery Management System (BMS) can collect battery parameters and send battery signals to the Vehicle Control Unit (VCU) via the CAN (Controller Area Network) bus. These battery signals can carry the battery's real-time state of charge (SOC). The VCU can include an Application Software (ASW) layer and a Basic Software (BSW) layer. The ASW and BSW can interact by writing to and reading from storage based on the Runtime Environment (RTE) to jointly calculate vehicle energy consumption data. The VCU can synchronize the calculated vehicle energy consumption data to multiple terminals via the CAN bus, enabling full-scene display updates, including both in-vehicle and external, hardware and software aspects. For example, the vehicle controller can send the calculated vehicle energy consumption data to the in-vehicle infotainment system (IVI) via the CAN bus. The IVI then transmits the corresponding vehicle range to the display device (Instrument Cluster, IC) via the Low-Voltage Differential Signaling (LVDS) bus for displaying the vehicle range. Alternatively, the IVI can also transmit the calculated vehicle range to the Telematics Box (TBOX) via the CAN bus. The TBOX then pushes the data to the user's application (APP) via a wireless network, displaying the vehicle range on the APP.

[0051] In an exemplary embodiment of the present invention, a computer device is also provided. The computer device may include a memory, a processor, and a computer program stored in the memory. The processor can execute the computer program to cause the computer device to perform actions such as... Figure 1 The steps of the vehicle energy consumption data processing method shown are as follows. Figure 4 A schematic diagram of the structure of a computer device 1000 is shown. (See attached diagram.) Figure 4 As shown, the computer device 1000 includes: a processor 1010, a memory 1020, a power supply 1030, a display unit 1040, and an input unit 1060.

[0052] The processor 1010 is the control center of the computer device 1000. It connects various components via interfaces and lines, and performs various functions of the computer device 1000 by running or executing computer programs / instructions stored in the memory 1020, thereby providing overall monitoring of the computer device 1000. In some embodiments, when the processor 1010 calls a computer program stored in the memory 1020, it can execute, for example... Figure 1 The steps of the vehicle energy consumption data processing method shown are as follows. Optionally, the processor 1010 may include one or more processing units; preferably, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. In some embodiments, the processor 1010 and the memory 1020 may be implemented on a single chip; in other embodiments, they may be implemented on separate chips.

[0053] The memory 1020 mainly includes a program storage area and a data storage area. The program storage area can store the operating system, various applications, etc.; the data storage area can store instruction data created according to the use of the computer device 1000. In addition, the memory 1020 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0054] The computer device 1000 also includes a power supply 1030 (such as a battery) that supplies power to various components. The power supply can be logically connected to the processor 1010 through a power management system, thereby enabling the management of functions such as charging, discharging, and power consumption through the power management system.

[0055] The display unit 1040 can be used to display information input by the user or information provided to the user, and can also be used to display various menus of the computer device 1000, etc. In this embodiment of the invention, it is mainly used to display the display interfaces of various applications in the computer device 1000, as well as text, pictures, and other objects displayed in the display interfaces. The display unit 1040 may include a display panel 1050. The display panel 1050 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0056] The input unit 1060 can be used to receive information such as numbers or characters input by the user. The input unit 1060 may include a touch panel 1070 and other input devices 1080. The touch panel 1070 can also be referred to as a touch screen, and the touch panel 1070 can collect touch operations on or near the user (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 1070).

[0057] Specifically, the touch panel 1070 can detect user touch operations and the signals generated by these operations, convert these signals into touch point coordinates and send them to the processor 1010, and receive and execute commands transmitted by the processor 1010. Furthermore, the touch panel 1070 can employ various input methods such as resistive, capacitive, infrared, and surface acoustic waves to achieve interaction. Other input devices 1080 include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, and joystick.

[0058] Of course, the touch panel 1070 can also cover the display panel 1050. When the touch panel 1070 detects a touch operation on or near it, it can transmit the information to the processor 1010 to determine the type of touch event. Subsequently, the processor 1010 provides corresponding visual output on the display panel 1050 based on the type of touch event. Although in Figure 4 In this embodiment, the touch panel 1070 and the display panel 1050 are two separate components to realize the input and output functions of the computer device 1000. However, in some embodiments, the touch panel 1070 and the display panel 1050 can be integrated to realize the input and output functions of the computer device 1000.

[0059] The computer device 1000 may also include one or more sensors, such as pressure sensors, gravity acceleration sensors, proximity sensors, etc. Of course, depending on the specific application scenario, the computer device 1000 may also include other components such as cameras.

[0060] In an exemplary embodiment of the present invention, a computer-readable storage medium is also provided, which stores a computer program / instructions. When executed by a processor, the computer program / instructions enable the computer device to perform the functions described in the present invention. Figure 1 The steps of the vehicle energy consumption data processing method shown are as follows.

[0061] It will be understood by those skilled in the art that Figure 4The examples of computer devices are merely illustrative and do not constitute a limitation on the device. The device may include more or fewer components than illustrated, or a combination of certain components, or different components. For ease of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, in implementing this invention, the functions of each module (or unit) can be implemented in one or more software or hardware components. For example, as some examples, the aforementioned computer device may be a vehicle, an in-vehicle system, etc.

[0062] Those skilled in the art will understand that the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described in accordance with flowcharts and / or block diagrams of vehicle energy consumption data processing methods, vehicle energy consumption data processing systems, and computer program products according to some embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be applied to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] It is understood that although terms such as first, second, third, etc., may be used in this application to describe weights, these terms are only used to distinguish weights from each other. For example, without departing from the scope of the embodiments of this application, a first weight may also be referred to as a second weight, and similarly, a second weight may also be referred to as a first weight.

[0064] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for processing vehicle energy consumption data, characterized in that, The method includes the following steps: Obtain vehicle energy consumption data; The energy consumption standard deviation of the vehicle is determined based on the energy consumption data of the vehicle, and an energy consumption stability index value is determined based on the energy consumption standard deviation to represent the energy consumption status of the vehicle. The energy consumption data of the vehicle is weighted according to the energy consumption stability index value, and the overall average energy consumption of the vehicle is determined according to the weighting result.

2. The vehicle energy consumption data processing method according to claim 1, characterized in that, If the energy consumption data includes energy consumption data per unit kilometer within a preset mileage, then the process of determining the energy consumption standard deviation of the vehicle based on the vehicle's energy consumption data includes: Calculate the average energy consumption of the vehicle within the preset mileage based on the energy consumption data per kilometer of the vehicle within the preset mileage. The standard deviation of the vehicle's energy consumption is calculated based on the vehicle's energy consumption per kilometer within a preset mileage, the number of kilometers within the preset mileage, and the vehicle's average energy consumption within the preset mileage.

3. The vehicle energy consumption data processing method according to claim 2, characterized in that, The process of determining the energy consumption stability index value, which represents the energy consumption status of a vehicle, based on the energy consumption standard deviation includes: Obtain the protection energy consumption used to protect against distortion of the energy consumption stability index value; The protection energy consumption and the average energy consumption of the vehicle within a preset mileage are compared, and the energy consumption fluctuation ratio is determined based on the corresponding comparison results and the energy consumption standard deviation. The energy consumption stability index value is determined based on the energy consumption fluctuation ratio value.

4. The vehicle energy consumption data processing method according to claim 3, characterized in that, The process of determining the energy consumption stability index value, which represents the energy consumption status of a vehicle, based on the energy consumption standard deviation also includes: Obtain the maximum and minimum values ​​of the energy consumption stability index; The energy consumption fluctuation ratio is determined based on the maximum value of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage, and the standard deviation of the vehicle's energy consumption; wherein, the maximum value of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage is obtained based on the comparison result of the protection energy consumption and the average energy consumption of the vehicle within a preset mileage. The difference between the maximum value of the energy consumption stability index and the energy consumption fluctuation ratio is calculated, and the difference is compared with the minimum value of the energy consumption stability index. The maximum value between the difference and the minimum value of the energy consumption stability index is used as the energy consumption stability index value to represent the energy consumption status of the vehicle.

5. The vehicle energy consumption data processing method according to any one of claims 1 to 4, characterized in that, The process of weighting the vehicle's energy consumption data based on the energy consumption stability index value includes: The energy consumption stability index value is compared with a preset weight allocation coefficient, wherein the preset weight allocation coefficient is used to distinguish the driving conditions of the vehicle. Based on the comparison between the energy consumption stability index value and the preset weight allocation coefficient, the energy consumption data of the vehicle is weighted.

6. The vehicle energy consumption data processing method according to claim 5, characterized in that, If the preset weight allocation coefficients include a first weight allocation coefficient, a second weight allocation coefficient, and a third weight allocation coefficient, then the process of weighting the vehicle's energy consumption data based on the comparison result between the energy consumption stability index value and the preset weight allocation coefficients includes: Under the condition that the energy consumption stability index value is greater than or equal to the first weight allocation coefficient, the energy consumption data of the vehicle is assigned a first weight. Under the condition that the energy consumption stability index value is greater than or equal to the second weight allocation coefficient and the energy consumption stability index value is less than the first weight allocation coefficient, the energy consumption data of the vehicle is assigned a second weight. Under the condition that the energy consumption stability index value is greater than or equal to the third weight allocation coefficient and the energy consumption stability index value is less than the second weight allocation coefficient, the energy consumption data of the vehicle is assigned a third weight. Under the condition that the energy consumption stability index value is less than the third weight allocation coefficient, a fourth weight is assigned to the energy consumption data of the vehicle. Wherein, the first weight allocation coefficient is greater than the second weight allocation coefficient, the second weight allocation coefficient is greater than the third weight allocation coefficient; the first weight is greater than the second weight, the second weight is greater than the third weight, and the third weight is greater than the fourth weight.

7. The vehicle energy consumption data processing method according to any one of claims 1 to 4, characterized in that, The process of determining the overall average energy consumption of the vehicle based on the weighting allocation results includes: The weights corresponding to the weight allocation results are recorded as dynamic weights, and the weights of the vehicle's energy consumption data before weight allocation are recorded as static fixed weights. The static fixed weights are adjusted according to the dynamic weights, and the overall average energy consumption of the vehicle is calculated based on the weight adjustment results.

8. The vehicle energy consumption data processing method according to claim 7, characterized in that, If the energy consumption data includes energy consumption data per unit kilometer within a first preset mileage, energy consumption data per unit kilometer within a second preset mileage, and theoretical energy consumption data, then the process of adjusting the static fixed weights according to the dynamic weights and calculating the vehicle's comprehensive average energy consumption based on the weight adjustment results further includes: The static fixed weights of the energy consumption data per unit kilometer within the first preset mileage are replaced by the dynamic weights of the energy consumption data per unit kilometer within the first preset mileage. Based on the dynamic weight of the energy consumption data per unit kilometer within the first preset mileage, the static fixed weight of the energy consumption data per unit kilometer within the second preset mileage is adjusted to obtain the dynamic weight of the energy consumption data per unit kilometer within the second preset mileage; and based on the dynamic weight of the energy consumption data per unit kilometer within the first preset mileage, the static fixed weight of the theoretical energy consumption data is adjusted to obtain the dynamic weight of the theoretical energy consumption data. The comprehensive average energy consumption of the vehicle is calculated based on the energy consumption data per unit kilometer within the first preset mileage, the dynamic weight of the energy consumption data per unit kilometer within the first preset mileage, the energy consumption data per unit kilometer within the second preset mileage, the dynamic weight of the energy consumption data per unit kilometer within the second preset mileage, the theoretical energy consumption data, and the dynamic weight of the theoretical energy consumption data. Wherein, the first preset mileage is less than the second preset mileage.

9. A vehicle energy consumption data processing system, characterized in that, The system includes: The data acquisition module is used to acquire vehicle energy consumption data; The energy consumption stability index module is used to determine the energy consumption standard deviation of the vehicle based on the vehicle's energy consumption data, and to determine the energy consumption stability index value representing the vehicle's energy consumption status based on the energy consumption standard deviation. The average energy consumption module is used to assign weights to the energy consumption data of the vehicle based on the energy consumption stability index value, and to determine the overall average energy consumption of the vehicle based on the weight assignment result.

10. A computer device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle energy consumption data processing method according to any one of claims 1 to 8.

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