Vehicle energy consumption monitoring method, device, equipment, medium, program product and vehicle

Abstract

The embodiment of the invention provides a vehicle energy consumption monitoring method and device, equipment, a medium, a program product and a vehicle. The method comprises the steps that a current driving scene is determined by obtaining vehicle basic data, when it is determined that a surface high energy consumption factor exists in the vehicle basic data based on the driving scene, the surface energy consumption factor is disassembled layer by layer, and a first layer potential influence element and a second layer potential influence element corresponding to the surface energy consumption factor are obtained; and calculating an actual influence value of the surface high energy consumption factor according to the real-time monitoring data of the first-layer potential influence element and the second-layer potential influence element, and when the actual influence value exceeds a preset abnormal threshold value corresponding to the surface high energy consumption factor, generating early warning information of the surface high energy consumption factor and sending the early warning information to a user. The method is used for achieving the effects of improving the timeliness, accuracy and reliability of vehicle energy consumption monitoring and regulation, and improving the driving experience of a user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, and in particular to a vehicle energy consumption monitoring method, device, equipment, medium, program product and vehicle. BACKGROUND

[0002] Under the background of rapid development of the new energy vehicle industry, the difference in user driving habits (such as sudden acceleration and deceleration, and idling time) and the rationality of vehicle function settings (such as power saving mode and air conditioner temperature) have an increasingly significant impact on vehicle energy consumption, and therefore, vehicle energy consumption optimization has become a core direction for improving user experience.

[0003] The prior art divides vehicle function settings into multiple scene modules according to different driving scenes, and users create driving scenes and set the priority of function start according to their own needs through the multiple scene modules, so as to achieve energy saving in the driving scene.

[0004] However, the prior art relies on user active configuration of vehicle function modes, and it is difficult to accurately identify vehicle energy consumption during vehicle driving, so it is difficult to prompt the user in time to make adjustments to driving behavior when the vehicle has high energy consumption, thereby reducing the user's driving experience. SUMMARY

[0005] The vehicle energy consumption monitoring method, device, equipment, medium, program product and vehicle provided by the embodiments of the present application can improve the timeliness, accuracy and reliability of vehicle energy consumption monitoring and control, and improve the user's vehicle experience.

[0006] In a first aspect, the embodiments of the present application provide a vehicle energy consumption monitoring method, comprising:

[0007] Obtaining vehicle basic data, the vehicle basic data comprising real-time monitoring data of multiple factors;

[0008] Determining a driving scene according to the vehicle basic data, and determining whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene;

[0009] If there is, determining a first layer of potential impact elements corresponding to the surface high energy consumption factor and a second layer of potential impact elements corresponding to the first layer of potential impact elements based on an energy consumption mapping relationship, and determining an actual impact value of the surface high energy consumption factor according to real-time monitoring data of the first layer of potential impact elements and real-time monitoring data of the second layer of potential impact elements;

[0010] Determining whether the actual impact value of the surface high energy consumption factor exceeds a preset abnormal threshold value; if the actual impact value exceeds the preset abnormal threshold value, generating a warning information of the surface high energy consumption factor and sending it to the user.

[0011] Optionally, the actual influence value of the surface high energy consumption factor is determined according to the real-time monitoring data of the first layer potential influence element and the real-time monitoring data of the second layer potential influence element, and specifically includes:

[0012] Based on the coefficient mapping relationship, the energy consumption influence coefficient corresponding to the real-time monitoring data of the second layer potential influence element is determined, and based on the preset energy consumption weight mapping relationship, the energy consumption weight corresponding to the second layer potential influence element is determined.

[0013] Based on the real-time monitoring data of the first layer potential influence element and the energy consumption influence coefficient and the energy consumption weight of the second layer potential influence element, the actual influence value of the surface high energy consumption factor is calculated.

[0014] Optionally, if the preset abnormal threshold is exceeded, the warning information of the surface high energy consumption factor is generated, and specifically includes:

[0015] If the preset abnormal threshold is exceeded, the abnormal state of the surface high energy consumption factor is triggered.

[0016] Determine whether the number of times of triggering the abnormal state of the surface high energy consumption factor in the preset time period exceeds the preset frequency; if the preset frequency is exceeded, the warning information of the surface high energy consumption factor is generated.

[0017] Optionally, the warning information of the surface high energy consumption factor is generated, and specifically includes:

[0018] Based on the first layer potential influence element and the second layer potential influence element corresponding to the surface high energy consumption factor, a driving adjustment scheme of the vehicle is generated.

[0019] According to the driving adjustment scheme, the warning information of the surface high energy consumption factor is generated.

[0020] Optionally, the warning information of the surface high energy consumption factor is generated according to the driving adjustment scheme, and specifically includes:

[0021] Determine the estimated influence value of the surface high energy consumption factor based on the driving adjustment scheme, and generate the estimated energy saving amount of the vehicle based on the estimated influence value and the actual influence value.

[0022] According to the driving adjustment scheme and the estimated energy saving amount, the warning information of the surface high energy consumption factor is generated.

[0023] Optionally, the preset abnormal threshold corresponding to the surface high energy consumption factor is determined by calculating the average influence value of a plurality of vehicles in the preset range in the driving scene for the surface high energy consumption factor; wherein the plurality of vehicles refer to vehicles of the same model as the user vehicle.

[0024] In a second aspect, an embodiment of the present application provides a vehicle energy consumption monitoring device, which includes:

[0025] An acquisition module is configured to acquire vehicle basic data, the vehicle basic data including real-time monitoring data of a plurality of factors;

[0026] A processing module is configured to determine a driving scene according to the vehicle basic data, and determine whether a surface high-energy-consumption factor exists in the vehicle basic data based on the driving scene;

[0027] The processing module is further configured to, when it is determined that the surface high-energy-consumption factor exists, determine a first layer of potential impact elements corresponding to the surface high-energy-consumption factor and a second layer of potential impact elements corresponding to the first layer of potential impact elements based on an energy consumption mapping relationship, and determine an actual impact value of the surface high-energy-consumption factor according to real-time monitoring data of the first layer of potential impact elements and real-time monitoring data of the second layer of potential impact elements.

[0028] The processing module is further configured to determine whether the actual impact value of the surface high-energy-consumption factor exceeds a preset abnormal threshold, and generate an early warning information of the surface high-energy-consumption factor and send the early warning information to a user if the actual impact value exceeds the preset abnormal threshold.

[0029] Optionally, the processing module is further configured to determine an energy consumption impact coefficient corresponding to the real-time monitoring data of the second layer of potential impact elements based on a coefficient mapping relationship, and determine an energy consumption weight corresponding to the second layer of potential impact elements based on a preset energy consumption weight mapping relationship.

[0030] The actual impact value of the surface high-energy-consumption factor is calculated based on the real-time monitoring data of the first layer of potential impact elements, and the energy consumption impact coefficient and the energy consumption weight of the second layer of potential impact elements.

[0031] Optionally, the processing module is further configured to trigger an abnormal state of the surface high-energy-consumption factor when it is determined that the actual impact value exceeds the preset abnormal threshold.

[0032] The processing module is further configured to determine whether a number of times of triggering the abnormal state of the surface high-energy-consumption factor in a preset time period exceeds a preset frequency, and generate the early warning information of the surface high-energy-consumption factor if the number of times of triggering the abnormal state exceeds the preset frequency.

[0033] Optionally, the processing module is further configured to generate a driving adjustment scheme of the vehicle based on the first layer of potential impact elements and the second layer of potential impact elements corresponding to the surface high-energy-consumption factor.

[0034] The early warning information of the surface high-energy-consumption factor is generated according to the driving adjustment scheme.

[0035] Optionally, the processing module is further configured to determine an estimated impact value of the surface high-energy-consumption factor based on the driving adjustment scheme, and generate an estimated energy saving amount of the vehicle based on the estimated impact value and the actual impact value.

[0036] The early warning information of the surface high-energy-consumption factor is generated according to the driving adjustment scheme and the estimated energy saving amount.

[0037] Optionally, the preset abnormal threshold corresponding to the surface high energy consumption factor is determined by processing the average influence value of the plurality of vehicles in the preset range in the driving scene for the surface high energy consumption factor; wherein the plurality of vehicles refer to vehicles of the same model as the user vehicle.

[0038] In a third aspect, the embodiments of the present application provide a vehicle, comprising:

[0039] a sensor module, configured to acquire vehicle basic data, the vehicle basic data comprising real-time monitoring data of a plurality of factors;

[0040] a control unit, configured to determine a driving scene according to the vehicle basic data, determine whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene, if there is, determine a first layer of potential influence elements corresponding to the surface high energy consumption factor and a second layer of potential influence elements corresponding to the first layer of potential influence elements based on an energy consumption mapping relationship, and determine an actual influence value of the surface high energy consumption factor according to real-time monitoring data of the first layer of potential influence elements and real-time monitoring data of the second layer of potential influence elements.

[0041] The control unit is further configured to determine whether the actual influence value of the surface high energy consumption factor exceeds a preset abnormal threshold, and if it exceeds the preset abnormal threshold, generate a warning information of the surface high energy consumption factor and send it to the user through the information output module.

[0042] In a fourth aspect, the embodiments of the present application provide an electronic device, comprising a memory and a processor.

[0043] The memory stores computer execution instructions.

[0044] The processor executes the computer execution instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect.

[0045] In a fifth aspect, the embodiments of the present application provide a computer readable storage medium, which stores computer execution instructions, and the computer execution instructions are executed by the processor to realize the first aspect and / or various possible implementation manners of the first aspect.

[0046] In a sixth aspect, the embodiments of the present application provide a computer program product, comprising a computer program, which is executed by the processor to realize the first aspect and / or various possible implementation manners of the first aspect.

[0047] The vehicle energy consumption monitoring method, device, equipment, medium, program product and vehicle provided by the embodiment of the application, by obtaining vehicle basic data, determining the current driving scene according to the vehicle basic data, determining whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene, if there is a surface high energy consumption factor, the first layer potential impact element and the second layer potential impact element corresponding to the surface energy consumption factor are obtained by layer-by-layer disassembly of the surface energy consumption factor, so as to calculate the actual impact value of the surface high energy consumption factor according to the real-time monitoring data of the first layer potential impact element and the second layer potential impact element, and when the actual impact value exceeds the preset abnormal threshold value corresponding to the surface high energy consumption factor, the warning information of the surface high energy consumption factor is generated and sent to the user. The timeliness, accuracy and reliability of vehicle energy consumption monitoring and regulation are improved, and the effect of improving the user driving experience is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0049] Figure 1 The flowchart of the vehicle energy consumption monitoring method provided by the application is shown in the figure.

[0050] Figure 2 The multi-level disassembly schematic diagram of the surface high energy consumption factor provided by the application is shown in the figure.

[0051] Figure 3 The structure schematic diagram of the vehicle energy consumption monitoring system provided by the application is shown in the figure.

[0052] Figure 4 The structure schematic diagram of the vehicle energy consumption monitoring device provided by the application is shown in the figure.

[0053] Figure 5 The structure schematic diagram of the electronic device provided by the application is shown in the figure.

[0054] Through the above-mentioned drawings, the specific embodiments of the application have been shown, and more detailed descriptions will be given in the following. These drawings and textual descriptions are not intended to limit the scope of the concept of the application by any means, but to illustrate the concept of the application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0055] The exemplary embodiments will be described in detail hereinbelow with reference to the drawings. Unless otherwise indicated, the same numbers on different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0056] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards in the relevant region, necessary security measures are taken, and the public order is not violated, and appropriate operation portal is provided for user to choose authorization or refusal.

[0057] In the process of rapid development of new energy automobile industry, vehicle energy consumption optimization has become a key link to improve user experience. At present, the difference of user driving behavior (such as sudden acceleration and deceleration, idling time) and the rationality of vehicle function setting (such as power saving mode, air conditioning temperature) have an increasingly significant impact on the vehicle energy consumption.

[0058] In the prior art, in order to save the vehicle energy consumption, the user usually needs to set multiple scene modules divided in the vehicle in advance to save the vehicle energy consumption in the corresponding driving scene. However, the prior art relies on the user to actively configure the vehicle function mode, and the user cannot accurately quantify the vehicle energy consumption in the configured scene, so the accuracy and reliability of vehicle energy consumption identification are low, which makes it difficult to realize timely and effective high energy consumption intervention, further affecting the user's vehicle experience.

[0059] The vehicle energy consumption monitoring method provided by the present application determines the current driving scene by collecting vehicle basic data in the vehicle running process in real time, identifies whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene, if there is a surface high energy consumption factor, the surface high energy consumption factor is decomposed layer by layer based on the energy consumption mapping relationship, to obtain the first layer potential impact element and the second layer potential impact element corresponding to the first layer potential impact element, determine the corresponding energy consumption influence coefficient according to the real-time monitoring data of the second layer potential impact element, and obtain the energy consumption weight corresponding to the potential impact element, calculate the actual influence value of the surface high energy consumption factor according to the energy consumption influence coefficient and energy consumption weight corresponding to the second layer potential impact element and the real-time monitoring data of the first layer potential impact element, when the actual influence value exceeds the preset abnormal threshold value of the surface high energy consumption factor based on the driving scene, generate the warning information corresponding to the surface high energy consumption factor and push it to the user, improve the accuracy and reliability of energy consumption monitoring, realize accurate early warning of high energy consumption state, make the user adjust the driving scheme in time according to the warning information, realize timely and effective energy consumption reduction, and improve the accuracy of energy consumption control.

[0060] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0061] Figure 1 The flowchart of the vehicle energy consumption monitoring method provided by the present application is shown in FIG. 1, which comprises the following steps. Figure 1

[0062] S101, real-time acquisition of vehicle basic data.

[0063] More specifically, real-time acquisition of vehicle basic data, the vehicle basic data comprising real-time monitoring data of a plurality of factors.

[0064] Optionally, real-time acquisition of vehicle travel information, obtaining vehicle basic data, the vehicle basic data comprising but not limited to real-time monitoring data of a plurality of factors such as road conditions, vehicle speed, driving mileage, energy consumption, air conditioning settings, driving mode and charging and discharging data.

[0065] S102, determining the driving scene according to the vehicle basic data, and determining whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene.

[0066] More specifically, determining the driving scene according to the vehicle basic data, classifying the vehicle basic data based on the driving scene, and obtaining real-time monitoring data of factors under the driving scene. Comparing the real-time monitoring data of any one factor under the driving scene with the standard data of each factor under the driving scene, if the comparison result indicates that the real-time monitoring data exceeds the standard data, it is determined that the factor is a surface high energy consumption factor, otherwise, it is determined that the factor is not a surface high energy consumption factor. Repeat the above steps to identify high energy consumption for each factor under the driving scene, and determine whether there is at least one surface high energy consumption factor in the vehicle basic data.

[0067] For example, if there is one surface high energy consumption factor, steps S103-S104 are performed in turn for the surface high energy consumption factor.

[0068] For example, if there are n surface high energy consumption factors, steps S103-S104 are performed in turn for each surface high energy consumption factor.

[0069] ​In one possible embodiment, real-time monitoring data of factors such as driving time (e.g., weekdays / weekends), road conditions (e.g., city / highway / slope), driving purpose (e.g., commuting / long-distance), and environmental conditions (e.g., temperature / humidity) are used to label and group various factors in the vehicle's basic data. Each group corresponds to a driving scenario, and the criteria for judging high energy consumption differ for each driving scenario. For example, the real-time monitoring data of multiple factors involved in the scenarios of "high-temperature city commuting" and "low-temperature high-speed long-distance" are processed separately to calculate the vehicle's index category in the driving scenario (e.g., percentage of rapid acceleration / deceleration, idling time, battery protection setting status, and ratio of air conditioning setting temperature to ambient temperature). The obtained percentage of rapid acceleration / deceleration, idling time, battery protection setting status, and the difference between air conditioning setting temperature and ambient temperature are compared with the average values ​​of the percentage of rapid acceleration / deceleration, idling time, battery protection setting status, and the ratio of air conditioning setting temperature to ambient temperature for multiple vehicles in the driving scenario within a preset range. Index categories that exceed the average value are filtered out, and the factors related to the index category are determined as surface high energy consumption factors. In this context, "multiple vehicles within the preset range" refers to vehicles of the same model as the user of this application. This embodiment avoids the identification bias of surface high-energy-consumption factors caused by mixed analysis in different scenarios.

[0070] S103. If it exists, determine the actual impact value of the surface high energy consumption factor.

[0071] More specifically, if they exist, the first layer of potential influencing elements corresponding to the high energy consumption factor on the surface is determined based on the energy consumption mapping relationship, and the second layer of potential influencing elements corresponding to the first layer of potential influencing elements are determined; the actual influence value of the high energy consumption factor on the surface is determined based on the real-time monitoring data of the first layer of potential influencing elements and the real-time monitoring data of the second layer of potential influencing elements.

[0072] For example, such as Figure 2 As shown, if step S102 determines that the surface high energy consumption factor is the driving mileage, then based on the energy consumption mapping relationship, the first layer of potential influencing elements corresponding to the driving mileage is determined, namely the fuel driving mileage, and the second layer of potential influencing elements of the fuel driving mileage is determined, namely, the accelerator pedal change rate, temperature, load, power protection setting status and power system maintenance status.

[0073] Optionally, the actual influence value of the surface high energy consumption factor is determined according to the real-time monitoring data of the first layer potential influence element and the real-time monitoring data of the second layer potential influence element, and specifically comprises: determining the energy consumption influence coefficient corresponding to the real-time monitoring data of the second layer potential influence element based on the coefficient mapping relationship; and determining the energy consumption weight corresponding to the second layer potential influence element based on the preset energy consumption weight mapping relationship; and calculating the actual influence value of the surface high energy consumption factor based on the real-time monitoring data of the first layer potential influence element and the energy consumption influence coefficient and the energy consumption weight of the second layer potential influence element.

[0074] For example, if there is at least one factor that is a surface high energy consumption factor, the energy consumption influence coefficient corresponding to the real-time data of the second layer potential influence element is determined based on the coefficient mapping relationship; and the energy consumption weight corresponding to the second layer potential influence element is determined based on the preset energy consumption weight mapping relationship. The actual influence value of the high energy consumption influence factor is calculated by the formula: actual influence value of high energy consumption influence factor = ∑ (energy consumption influence coefficient of second layer potential influence element x corresponding energy consumption weight x real-time monitoring data of first layer potential influence element), to realize accurate quantitative calculation of the actual influence value of the surface high energy consumption factor.

[0075] Optionally, the sum of the energy consumption weights corresponding to each potential influence element of each factor / surface high energy consumption influence factor in the vehicle is 100%, and the sum of the corresponding energy consumption influence coefficients is 100%.

[0076] According to the real-time monitoring data of the first layer potential influence element and the energy consumption influence coefficient and the corresponding energy consumption weight of the second layer potential influence element, the actual influence value of the surface high energy consumption factor is calculated, the contribution degree of each potential influence element to energy consumption is accurately quantified, the accuracy and reliability of energy consumption monitoring are improved, and timely identification of high energy consumption factors is realized.

[0077] S104, determining whether the actual influence value of the surface high energy consumption factor exceeds a preset abnormal threshold value; if the actual influence value of the surface high energy consumption factor exceeds the preset abnormal threshold value, generating a warning information of the surface high energy consumption factor and sending the warning information to the user.

[0078] More specifically, it is determined whether the actual influence value of the surface high energy consumption factor exceeds a preset abnormal threshold value; if the actual influence value of the surface high energy consumption factor exceeds the preset abnormal threshold value, an abnormal state of the surface high energy consumption factor is triggered; a warning information of the surface high energy consumption factor is generated based on the abnormal state, and the warning information is sent to the user.

[0079] Optionally, the preset abnormal threshold value corresponding to the surface high energy consumption factor is determined by calculating the average influence value of a plurality of vehicles in a preset range with respect to the surface high energy consumption factor in a driving scene, and the plurality of vehicles refer to vehicles of the same model as the user's vehicle.

[0080] For example, according to the travel information of a plurality of vehicles of the same model as the user's vehicle in the same scenario within a preset range, the influence values of a plurality of factors corresponding to the plurality of vehicles are determined. Taking any one factor as an example, the average influence value of the factor is calculated according to the influence values of the factor in the plurality of vehicles, and the average influence value is taken as the preset abnormal threshold value corresponding to the factor when the factor is determined as a surface high energy consumption factor.

[0081] In this embodiment, the average influence value of each factor is determined according to the travel information of a plurality of vehicles of the same model as the user's vehicle in the same scenario within a preset range, so that the average influence value of each factor is used as the preset abnormal threshold value when the factor is determined as a surface high energy consumption factor, and a high energy consumption determination criterion is provided for the factor, thereby improving the reliability and accuracy of the whole vehicle energy consumption state recognition.

[0082] Optionally, if the preset abnormal threshold value is exceeded, a warning information of the surface high energy consumption factor is generated, specifically including: if the preset abnormal threshold value is exceeded, an abnormal state of the surface high energy consumption factor is triggered; it is determined whether the number of times of triggering the abnormal state of the surface high energy consumption factor within a preset time period exceeds a preset frequency; if the preset frequency is exceeded, the warning information of the surface high energy consumption factor is generated.

[0083] For example, when the actual influence value of the high energy consumption influencing factor exceeds the preset abnormal threshold value, the abnormal state is triggered, and it is further determined whether the number of times of triggering the abnormal state within a preset time period exceeds a preset frequency. If it is exceeded, a warning information is generated to ensure the timeliness and reliability of the warning. If it is not exceeded, the generation of the warning information is not triggered.

[0084] The embodiments of the present application compare whether the frequency of the abnormal state within a preset time period exceeds a preset frequency, avoid misjudgment of high energy consumption / energy consumption abnormality, improve the accuracy of energy consumption monitoring, and further enhance the reliability of accurate control of the whole vehicle energy consumption.

[0085] Optionally, the warning information of the surface high energy consumption factor is generated, specifically including: generating a driving adjustment scheme of the vehicle based on the first layer potential influencing element and the second layer potential influencing element corresponding to the surface high energy consumption factor; and generating the warning information of the surface high energy consumption factor according to the driving adjustment scheme.

[0086] Optionally, the warning information of the surface high energy consumption factor is generated according to the driving adjustment scheme, specifically including: determining an estimated influence value of the surface high energy consumption factor based on the driving adjustment scheme, generating an estimated energy saving amount of the vehicle based on the estimated influence value and the actual influence value; and generating the warning information of the surface high energy consumption factor according to the driving adjustment scheme and the estimated energy saving amount.

[0087] Exemplarily, based on the first layer and the second layer potential influence elements corresponding to the surface high energy consumption factor, a driving adjustment scheme for adjusting the whole vehicle high energy consumption caused by the surface high energy consumption factor is generated. Through linear regression model fitting of the energy consumption data under the driving adjustment scheme, the energy consumption difference between the driving adjustment scheme and the current setting scheme of the vehicle is calculated, and the estimated energy saving amount is generated. Finally, the driving adjustment scheme and the estimated energy saving amount are combined to push the visual warning information to the user.

[0088] The embodiment of the present application generates a driving adjustment scheme according to a plurality of potential influencing factors corresponding to the surface high energy consumption factor, thereby improving the effectiveness and reliability of the driving adjustment scheme for reducing energy consumption. By converting the abstract energy saving suggestion into visual energy consumption benefit, the acceptance and execution of the recommended driving adjustment scheme by the user are improved, and the user's vehicle experience is further enhanced.

[0089] The vehicle energy consumption monitoring method provided by the embodiment of the present application, when it is determined that the collected vehicle basic data contains a surface high energy consumption factor, hierarchically disassembles a plurality of hierarchical potential influencing factors corresponding to the surface high energy consumption factor, to deeply excavate the bottom layer root cause of energy consumption anomaly, calculates the actual influence value of the surface high energy consumption factor according to the plurality of hierarchical potential influencing factors, realizes the accurate positioning and dynamic early warning of the energy consumption anomaly, improves the identification accuracy and reliability of the high energy consumption, thereby further timely reminding the user to accurately adjust the driving behavior, and enhancing the effectiveness and reliability of the energy consumption reduction.

[0090] Figure 3 The structure diagram of the vehicle energy consumption monitoring system provided by the present application is shown in Figure 3 The vehicle energy consumption monitoring system includes a high energy consumption identification module, a potential influence analysis module, an influence value calculation module, a threshold analysis module, a warning triggering module, and an energy saving recommendation module.

[0091] In a possible embodiment, the embodiment shown in Figure 3 The vehicle energy consumption system is based on the vehicle energy consumption monitoring method, which includes:

[0092] The vehicle basic data is obtained through the high energy consumption identification module, and the vehicle basic data is as follows: road condition (urban road), driving time (morning peak period on weekdays), driving duration (1 hour), number of sudden acceleration and deceleration (20 times), number of acceleration and deceleration operations (30 times), idling time (15 minutes), air conditioner temperature (26°C), environment temperature (30°C), and driving mileage (10 km). According to the driving time and road condition in the vehicle basic data, the driving scene is determined to be an “early morning peak urban commuting scene”. The above vehicle basic data is classified according to the “early morning peak urban commuting scene”, and the real-time monitoring data of each factor in the “early morning peak urban commuting scene” classification is as follows: number of sudden acceleration and deceleration (20 times), number of acceleration and deceleration operations (30 times), idling time (15 minutes), driving duration (1 hour), air conditioner temperature (26°C), environment temperature (30°C), and driving mileage (10 km). Based on the real-time monitoring data of each factor in the “early morning peak urban commuting scene” classification, a plurality of index types under the “early morning peak urban commuting scene” classification are calculated: sudden acceleration and deceleration ratio (i.e., number of sudden acceleration and deceleration 20 times / number of acceleration and deceleration operations 30 times=66.7%), idling time ratio (i.e., idling time 15 minutes / driving duration 60 minutes=25%), air conditioner temperature difference (i.e., |environment temperature 30°C-air conditioner temperature 26°C|=4°C), and driving mileage (20 km). The data of each index type is compared with the standard data corresponding to the index type, respectively.

[0093] The “early morning peak urban commuting scene” and the index types of sudden acceleration and deceleration ratio, idling time ratio, air conditioner temperature difference, and driving mileage are synchronized to the threshold analysis module through the high energy consumption identification module. The standard data of sudden acceleration and deceleration ratio (e.g., 19.5%), idling time ratio (e.g., 30%), air conditioner temperature difference (e.g., 6.5°C), and driving mileage (10 km) corresponding to the “early morning peak urban commuting scene” are determined by the threshold analysis module according to the pre-calculated threshold table and fed back to the high energy consumption identification module. In the above threshold table, the standard data of sudden acceleration and deceleration ratio is determined by determining the average value of the sudden acceleration and deceleration ratio of a plurality of vehicles of the same model as the user's vehicle under the “early morning peak urban commuting scene” within a preset range; the standard data of idling time ratio is determined by determining the average value of the idling time ratio of a plurality of vehicles of the same model as the user's vehicle under the “early morning peak urban commuting scene” within a preset range; the standard data of air conditioner temperature difference is determined by determining the average value of the air conditioner temperature difference of a plurality of vehicles of the same model as the user's vehicle under the “early morning peak urban commuting scene” within a preset range, and the standard data of driving mileage is determined by determining the average value of the driving mileage of a plurality of vehicles of the same model as the user's vehicle under the “early morning peak urban commuting scene” within a preset range.

[0094] The high-energy consumption identification module determines that the user vehicle in the "morning peak urban commuting scenario" category has a sudden acceleration and deceleration ratio that does not exceed the standard data of the sudden acceleration and deceleration ratio, an idling time ratio that does not exceed the standard data of the idling time ratio, an air conditioner temperature difference that does not exceed the standard data of the air conditioner temperature difference, and a driving mileage that exceeds the standard data of the driving mileage, thereby determining the driving mileage as the surface high-energy consumption factor, and synchronously transmitting the surface high-energy consumption factor to the potential influence analysis module and the threshold analysis module.

[0095] The potential influence analysis module determines the first layer potential influence element corresponding to the driving mileage, i.e. the fuel driving mileage, based on the energy consumption mapping relationship, and determines the second layer potential influence element of the fuel driving mileage, i.e. the acceleration pedal change rate, the temperature, and the load. The real-time monitoring data of the first layer potential influence element and the real-time monitoring data of the second layer potential influence element are synchronously transmitted to the influence value calculation module.

[0096] The influence value calculation module determines the energy consumption coefficient curve corresponding to each element in the second layer potential influence element based on the pre-established coefficient mapping relationship, thereby determining the energy consumption influence coefficient corresponding to the real-time monitoring data of each element according to the energy consumption coefficient curve of each element, and simultaneously determining the energy consumption weight corresponding to the element. The actual influence value of the driving mileage is calculated by the formula: actual influence value of driving mileage = (energy consumption influence coefficient of acceleration pedal change rate x energy consumption weight of acceleration pedal change rate + energy consumption influence coefficient of temperature x energy consumption weight of temperature + energy consumption influence coefficient of load x energy consumption weight of load) x real-time monitoring data of fuel driving mileage, and the actual influence value is sent to the early warning triggering module.

[0097] The threshold analysis module determines the preset abnormal threshold corresponding to the driving mileage surface high-energy consumption factor based on the threshold table, and sends the preset abnormal threshold to the early warning triggering module. The preset abnormal threshold corresponding to the driving mileage surface high-energy consumption factor is determined by determining the average influence value corresponding to the driving mileage of a plurality of vehicles of the same model as the user vehicle in the "morning peak urban commuting scenario" within a preset range.

[0098] The early warning triggering module receives the actual influence value of the driving mileage and the preset abnormal threshold, determines whether the actual influence value exceeds the preset influence threshold, and if it does, determines that the current driving mileage of the user vehicle is in an abnormal state, and then further determines the number of times the abnormal state of the driving mileage is triggered within a preset time period. If the number of times exceeds the preset frequency, a signal triggering energy saving recommendation is sent to the energy saving recommendation module.

[0099] The energy saving recommendation module generates a driving adjustment scheme according to the first layer potential influence element and the second layer potential influence element, and determines the estimated influence value corresponding to the driving adjustment scheme, and generates early warning information according to the driving adjustment scheme and the estimated influence value and pushes it to the user.

[0100] Optionally, the vehicle energy consumption monitoring system displays high energy consumption factors and potential influencing elements on the surface, and shows driving adjustment schemes for the current scenario, as well as the energy savings that can be achieved by using the driving adjustment schemes.

[0101] The vehicle energy consumption monitoring method provided in this application collects basic vehicle data in real time to determine the current driving scenario. Based on the driving scenario, it identifies whether there are surface high-energy-consumption factors in the basic vehicle data. If at least one surface high-energy-consumption factor exists, it decomposes the surface high-energy-consumption factor layer by layer to obtain a first-layer potential influencing element and a second-layer potential influencing element. Based on the real-time monitoring data of the first-layer potential influencing element and the second-layer potential influencing element, it determines the actual influence value of the surface high-energy-consumption factor. When the actual influence value exceeds the preset abnormal threshold of the surface high-energy-consumption factor based on the driving scenario, it generates a warning message corresponding to the surface high-energy-consumption factor and pushes it to the user. This improves the accuracy and reliability of energy consumption monitoring, realizes accurate warning of high-energy-consumption state, and enables users to adjust their driving plans in a timely manner based on the warning message, thereby achieving timely and effective energy consumption reduction and improving the accuracy of energy consumption control.

[0102] The vehicle energy consumption monitoring method of this application is applicable to new energy vehicles such as pure electric vehicles and hybrid vehicles. Application scenarios of this method include, but are not limited to: commuting in congested urban traffic, long-distance driving on highways, and vehicle use in extreme temperature environments. By monitoring vehicle energy consumption in these application scenarios in real time, it provides users with effective guidance on vehicle energy conservation.

[0103] The vehicle energy consumption monitoring method of this application can be executed by a backend server connected to the vehicle, or by the vehicle and a cloud server working together. The vehicle energy consumption monitoring method of this application can also be implemented collaboratively by control units, sensor modules, and information output modules within the vehicle. The control units include, but are not limited to, electronic control units (ECUs), vehicle control units (VCUs), battery monitoring and management systems (BMSs), intelligent cockpit domain controllers, and powertrain domain controllers. Information output modules include, but are not limited to, in-vehicle infotainment systems, instrument panels, vehicle networking modules, and in-vehicle voice assistants.

[0104] The application provides a vehicle, comprising: a sensor module configured to obtain vehicle basic data, the vehicle basic data comprising real-time monitoring data of a plurality of factors; a control unit configured to determine a driving scene based on the vehicle basic data, determine whether a surface high energy consumption factor exists in the vehicle basic data based on the driving scene, if the surface high energy consumption factor exists, determine a first layer of potential impact elements corresponding to the surface high energy consumption factor and a second layer of potential impact elements corresponding to the first layer of potential impact elements based on an energy consumption mapping relationship, determine an actual impact value of the surface high energy consumption factor based on real-time monitoring data of the first layer of potential impact elements and real-time monitoring data of the second layer of potential impact elements, and determine whether the actual impact value of the surface high energy consumption factor exceeds a preset abnormal threshold value, and if the actual impact value exceeds the preset abnormal threshold value, generate a warning information of the surface high energy consumption factor and send the warning information to a user through an information output module.

[0105] Optionally, the sensor module comprises, but is not limited to, a vehicle speed sensor, a temperature sensor, an inertial measurement unit, a wheel speed sensor, an acceleration sensor, and the like.

[0106] Optionally, the control unit is further configured to determine an energy consumption impact coefficient corresponding to the real-time monitoring data of the second layer of potential impact elements based on a coefficient mapping relationship, and determine an energy consumption weight corresponding to the second layer of potential impact elements based on a preset energy consumption weight mapping relationship.

[0107] The actual impact value of the surface high energy consumption factor is calculated based on the real-time monitoring data of the first layer of potential impact elements, and the energy consumption impact coefficient and the energy consumption weight of the second layer of potential impact elements.

[0108] Optionally, the control unit is further configured to trigger an abnormal state of the surface high energy consumption factor when the actual impact value exceeds the preset abnormal threshold value.

[0109] The control unit is further configured to determine whether the number of times of triggering the abnormal state of the surface high energy consumption factor in a preset time period exceeds a preset frequency, and if the number of times of triggering the abnormal state of the surface high energy consumption factor in the preset time period exceeds the preset frequency, generate a warning information of the surface high energy consumption factor.

[0110] Optionally, the control unit is further configured to generate a driving adjustment scheme of the vehicle based on the first layer of potential impact elements and the second layer of potential impact elements corresponding to the surface high energy consumption factor.

[0111] The control unit is further configured to generate the warning information of the surface high energy consumption factor based on the driving adjustment scheme.

[0112] Optionally, the control unit is further configured to determine an estimated impact value of the surface high energy consumption factor based on the driving adjustment scheme, and generate an estimated energy saving amount of the vehicle based on the estimated impact value and the actual impact value.

[0113] The control unit is further configured to generate the warning information of the surface high energy consumption factor based on the driving adjustment scheme and the estimated energy saving amount.

[0114] Optionally, the control unit is further configured to determine a preset abnormal threshold corresponding to the surface high energy consumption factor by calculating an average influence value of the surface high energy consumption factor in the preset range under the driving scene of a plurality of vehicles; wherein the plurality of vehicles refer to vehicles of the same vehicle model as the user vehicle.

[0115] The control unit provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects. Details are not described herein.

[0116] Figure 4 A structural schematic diagram of a vehicle energy consumption monitoring device provided in the present application is shown in FIG. 1. Figure 4 As shown in FIG. 1, the vehicle energy consumption monitoring device 40 provided in the embodiment includes:

[0117] The acquisition module 401 is configured to acquire vehicle basic data, and the vehicle basic data includes real-time monitoring data of a plurality of factors.

[0118] The processing module 402 is configured to determine a driving scene according to the vehicle basic data, and determine whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scene.

[0119] The processing module 402 is further configured to, when it is determined that there is a surface high energy consumption factor, determine a first layer of potential influence elements corresponding to the surface high energy consumption factor and a second layer of potential influence elements corresponding to the first layer of potential influence elements based on an energy consumption mapping relationship; and determine an actual influence value of the surface high energy consumption factor according to real-time monitoring data of the first layer of potential influence elements and real-time monitoring data of the second layer of potential influence elements.

[0120] The processing module 402 is further configured to determine whether the actual influence value of the surface high energy consumption factor exceeds a preset abnormal threshold; and if the actual influence value exceeds the preset abnormal threshold, generate a warning information of the surface high energy consumption factor and send the warning information to a user.

[0121] Optionally, the processing module 402 is further configured to determine an energy consumption influence coefficient corresponding to the real-time monitoring data of the second layer of potential influence elements based on a coefficient mapping relationship; and determine an energy consumption weight corresponding to the second layer of potential influence elements based on a preset energy consumption weight mapping relationship.

[0122] The actual influence value of the surface high energy consumption factor is calculated based on the real-time monitoring data of the first layer of potential influence elements, and the energy consumption influence coefficient and the energy consumption weight of the second layer of potential influence elements.

[0123] Optionally, the processing module 402 is further configured to, when it is determined that the actual influence value exceeds the preset abnormal threshold, trigger an abnormal state of the surface high energy consumption factor.

[0124] Determine whether the number of abnormal state triggers of the surface high energy consumption factor within the preset time period exceeds a preset frequency; if the number of abnormal state triggers exceeds the preset frequency, generate a warning information of the surface high energy consumption factor.

[0125] Optionally, the processing module 402 is further configured to generate a driving adjustment scheme of the vehicle based on the first layer potential impact element and the second layer potential impact element corresponding to the surface high energy consumption factor.

[0126] Generate the warning information of the surface high energy consumption factor according to the driving adjustment scheme.

[0127] Optionally, the processing module 402 is further configured to determine an estimated impact value of the surface high energy consumption factor based on the driving adjustment scheme, and generate an estimated energy saving amount of the vehicle based on the estimated impact value and the actual impact value.

[0128] Generate the warning information of the surface high energy consumption factor according to the driving adjustment scheme and the estimated energy saving amount.

[0129] Optionally, the preset abnormal threshold value corresponding to the surface high energy consumption factor is determined by the processing module 402 calculating an average impact value of a plurality of vehicles in the preset range in the driving scene for the surface high energy consumption factor; wherein the plurality of vehicles refer to vehicles of the same model as the user's vehicle.

[0130] The vehicle energy consumption monitoring device provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects, which will not be described here.

[0131] Figure 5 The structure of the electronic device provided in the present application is shown in FIG. 1. Figure 5 As shown in FIG. 1, the electronic device 50 provided in the embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected through a bus 504.

[0132] In the specific implementation process, the at least one processor 501 executes the computer execution instructions stored in the memory 502, so that the at least one processor 501 executes the method described above.

[0133] The specific implementation process of the processor 501 can refer to the method embodiment described above, which has similar implementation principles and technical effects, and will not be described here.

[0134] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0135] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0136] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0137] The present application also provides a computer program product, comprising a computer program, which is executed by a processor to implement the above method.

[0138] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when the processor executes the computer execution instructions, the above method is implemented.

[0139] The above readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0140] An example readable storage medium is coupled to the processor such that the processor can read information from the readable storage medium and can write information to the readable storage medium. Of course, the readable storage medium can also be a part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0141] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0142] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0143] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0144] If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0145] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes various media capable of storing program codes, such as ROM, RAM, magnetic disk, or optical disk.

[0146] Finally, it should be noted that other embodiments of the present application will readily occur to those skilled in the art upon consideration of the specification and practice of the present application disclosed herein. The present application is intended to include all such variations, uses, or adaptations of the application in which the general principles of the application are used to best advantage and encompassed within its scope. The present application is not limited to the precise structures described and shown in the accompanying drawings and figures, and can be practiced with variation of modifications and alterations without departing from the scope of the present application. The scope of the present application is limited only by the claims appended hereto.

Claims

1. A method for monitoring vehicle energy consumption, characterized in that, include: Acquire basic vehicle data, which includes real-time monitoring data of multiple factors; Based on the vehicle's basic data, a driving scenario is determined, and based on the driving scenario, it is determined whether a surface high energy consumption factor exists in the vehicle's basic data. If they exist, then based on the energy consumption mapping relationship, determine the first layer of potential influencing elements corresponding to the surface high energy consumption factor, and the second layer of potential influencing elements corresponding to the first layer of potential influencing elements; determine the actual influence value of the surface high energy consumption factor based on the real-time monitoring data of the first layer of potential influencing elements and the real-time monitoring data of the second layer of potential influencing elements. Determine whether the actual impact value of the surface high energy consumption factor exceeds a preset abnormal threshold; If the preset abnormal threshold is exceeded, a warning message for the surface high energy consumption factor is generated and sent to the user.

2. The method according to claim 1, characterized in that, The actual impact value of the surface high energy consumption factor is determined based on the real-time monitoring data of the first layer of potential influencing elements and the real-time monitoring data of the second layer of potential influencing elements, specifically including: Based on the coefficient mapping relationship, the energy consumption impact coefficient corresponding to the real-time monitoring data of the second layer of potential impact elements is determined; and based on the preset energy consumption weight mapping relationship, the energy consumption weight corresponding to the second layer of potential impact elements is determined. Based on the real-time monitoring data of the first layer of potential influencing elements and the energy consumption influence coefficient and energy consumption weight of the second layer of potential influencing elements, the actual influence value of the surface high energy consumption factor is calculated.

3. The method according to claim 1, characterized in that, If the preset abnormal threshold is exceeded, a warning message for the surface high energy consumption factor is generated, specifically including: If the preset abnormal threshold is exceeded, the abnormal state of the surface high energy consumption factor is triggered. Determine whether the number of abnormal state triggers of the surface high energy consumption factor within a preset time period exceeds a preset frequency; if it exceeds the preset frequency, generate a warning message for the surface high energy consumption factor.

4. The method according to claim 1 or 3, characterized in that, Generating early warning information for the surface high energy consumption factor specifically includes: Based on the first and second layer potential influence elements corresponding to the surface high energy consumption factor, a driving adjustment scheme for the vehicle is generated. Early warning information about the surface high energy consumption factor is generated based on the driving adjustment scheme.

5. The method according to claim 4, characterized in that, The warning information for the high energy consumption factor of the surface is generated based on the driving adjustment scheme, specifically including: Determine the estimated impact value of the surface high energy consumption factor based on the driving adjustment scheme, and generate the estimated energy saving of the vehicle based on the estimated impact value and the actual impact value; The warning information for the surface high energy consumption factor is generated based on the driving adjustment scheme and the estimated energy saving.

6. The method according to claim 1, characterized in that, The preset abnormal threshold corresponding to the surface high energy consumption factor is determined by calculating the average impact value of multiple vehicles within a preset range on the surface high energy consumption factor in the driving scenario; wherein, the multiple vehicles refer to vehicles of the same model as the user's vehicle.

7. A vehicle energy consumption monitoring device, characterized in that, include: The acquisition module is used to acquire basic vehicle data, which includes real-time monitoring data of multiple factors. The processing module is used to determine the driving scenario based on the vehicle basic data, and to determine whether there is a surface high energy consumption factor in the vehicle basic data based on the driving scenario. The processing module is further configured to, upon determining the existence of the surface high energy consumption factor, determine the first layer of potential influencing elements corresponding to the surface high energy consumption factor and the second layer of potential influencing elements corresponding to the first layer of potential influencing elements based on the energy consumption mapping relationship; and determine the actual influence value of the surface high energy consumption factor based on the real-time monitoring data of the first layer of potential influencing elements and the real-time monitoring data of the second layer of potential influencing elements. The processing module is also used to determine whether the actual impact value of the surface high energy consumption factor exceeds a preset abnormal threshold; if it exceeds the preset abnormal threshold, an early warning message for the surface high energy consumption factor is generated and sent to the user.

8. A vehicle, characterized in that, include: A sensor module is used to acquire basic vehicle data, which includes real-time monitoring data of multiple factors. The control unit is configured to determine a driving scenario based on the vehicle's basic data, and determine whether a surface high-energy-consumption factor exists in the vehicle's basic data based on the driving scenario; if it exists, it determines a first-layer potential influencing element corresponding to the surface high-energy-consumption factor and a second-layer potential influencing element corresponding to the first-layer potential influencing element based on the energy consumption mapping relationship; and determines the actual influence value of the surface high-energy-consumption factor based on the real-time monitoring data of the first-layer potential influencing element and the real-time monitoring data of the second-layer potential influencing element. The control unit is also used to determine whether the actual impact value of the surface high energy consumption factor exceeds a preset abnormal threshold; if it exceeds the preset abnormal threshold, it generates a warning message for the surface high energy consumption factor and sends it to the user through the information output module.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

Images