WO2023241388A1

WO2023241388A1 - Model training method and apparatus, energy replenishment intention recognition method and apparatus, device, and medium

Info

Publication number: WO2023241388A1
Application number: PCT/CN2023/098229
Authority: WO
Inventors: 袁鲁峰; 付振; 王明月; 刘拼拼; 贾振坤
Original assignee: 中国第一汽车股份有限公司
Priority date: 2022-06-14
Filing date: 2023-06-05
Publication date: 2023-12-21
Also published as: CN114896482A

Abstract

Disclosed in the present application are a model training method and apparatus, an energy replenishment intention recognition method and apparatus, a device, and a medium. The model training method comprises: extracting sample energy replenishment reference data from sample vehicle data; determining energy replenishment behavior data according to the sample energy replenishment reference data; constructing image data of a sample vehicle according to the energy replenishment behavior data; and training an energy replenishment intention recognition model according to the energy replenishment behavior data and the image data.

Description

Model training method and device, energy replenishment intention recognition method and device, equipment, media

This application claims priority to the Chinese patent application with application number 202210670153.1, which was submitted to the China Patent Office on June 14, 2022. The entire content of this application is incorporated into this application by reference.

Technical field

This application relates to the field of intelligent transportation technology, such as model training methods and device replenishment intention identification methods and devices, equipment, and media.

Background technique

With the rapid development of intelligent transportation technology, the demand for segmentation of intelligent service scenarios and predictive proactive services is increasing. For example, by predicting users' energy replenishment intentions, we can provide users with services such as energy replenishment reminders or energy replenishment service recommendations.

Related technologies usually set rules based on the user's current vehicle fuel level or battery power. When the set threshold is triggered, it is considered that there is an intention to replenish energy. However, the prediction accuracy of this method is low and needs improvement urgently.

Contents of the invention

This application provides model training methods and device energy replenishment intention identification methods and devices, equipment, and media to solve defects in related technologies and improve the accuracy of predictions.

In the first aspect, this application provides a model training method, which includes:

Extract sample energy supplement reference data from sample vehicle data;

Determine the energy replenishment behavior data based on the sample energy replenishment reference data;

Based on the energy replenishment behavior data, the portrait data of the sample vehicle is constructed;

Based on the energy replenishment behavior data and portrait data, the energy replenishment intention recognition model is trained.

In the second aspect, this application also provides a method for identifying energy supplementation intentions, which includes:

Obtain the current energy replenishment reference data from the current vehicle data;

Input the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment;

Among them, the energy-replenishing intention recognition model is trained through the above-mentioned model training method.

In a third aspect, this application also provides a model training device, which includes:

The sample data extraction module is configured to extract sample energy replenishment reference data from sample vehicle data;

The behavior data determination module is configured to determine the energy replenishment behavior data based on the sample energy replenishment reference data;

The portrait data construction module is configured to construct portrait data of the sample vehicle based on energy replenishment behavior data;

The model training module is set to train the energy replenishment intention recognition model based on the energy replenishment behavior data and portrait data.

In a fourth aspect, this application also provides an energy supplement intention recognition device, which includes:

The current data acquisition module is set to obtain the current energy replenishment reference data from the current vehicle data;

The energy replenishment intention recognition module is configured to input the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment;

Among them, the energy-replenishing intention recognition model is trained through the model training device.

In a fifth aspect, this application also provides an electronic device, which includes:

one or more processors;

memory configured to store one or more programs;

When one or more programs are executed by one or more processors, the one or more processors implement the above-mentioned model training method or energizing intention identification method.

In a sixth aspect, this application example also provides a computer-readable storage medium on which a computer program is stored, wherein when the program is executed by a processor, the above-mentioned model training method or energy-enhancing intention identification method is implemented.

Description of the drawings

Figure 1 is a flow chart of a model training method provided in Embodiment 1 of the present application;

Figure 2 is a schematic diagram of the energy replenishment intention data selection principle provided by Embodiment 1 of the present application;

Figure 3 is a flow chart of a model training method provided in Embodiment 2 of the present application;

Figure 4 is a flow chart of a model training method provided in Embodiment 3 of the present application;

Figure 5 is a statistical distribution diagram of refueling time intervals provided in Embodiment 3 of the present application;

Figure 6 is a statistical distribution diagram of a single refueling amount provided in Embodiment 3 of the present application;

Figure 7 is a flow chart of a method for identifying energy replenishment intentions provided in Embodiment 4 of the present application;

Figure 8 is a schematic diagram of the training and application scenarios of a supplementary intention recognition model provided in Embodiment 5 of the present application;

Figure 9 is a system framework diagram for energy replenishment intention recognition provided in Embodiment 5 of the present application;

Figure 10 is a schematic structural diagram of a model training device provided in Embodiment 6 of the present application;

Figure 11 is a schematic structural diagram of an energy replenishment intention recognition device provided in Embodiment 7 of the present application;

FIG. 12 is a schematic structural diagram of an electronic device provided in Embodiment 8 of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments are only part of the embodiments of the present application.

Embodiment 1

Figure 1 is a flow chart of a model training method provided in Embodiment 1 of the present application. This embodiment can be applied to the situation of vehicle energy replenishment intention model training. The method can be executed by a model training device. The model training device can use Implemented in the form of hardware and/or software, the model training device can be integrated into an electronic device with model training.

As shown in Figure 1, the method includes:

S110. Extract the sample energy replenishment reference data from the sample vehicle data.

Sample vehicles can be vehicles that provide data for model training. In order to ensure the accuracy of model training, the number of sample vehicles in this embodiment is usually multiple.

In the model training method of any embodiment of this application, the sample vehicle data includes: vehicle terminal log data and vehicle sensor data.

The vehicle-mounted terminal log data can be hidden log data generated by vehicle-machine user behavior. For example, the vehicle-mounted terminal log data may include: record data of user navigation searches or screen clicks, etc. Vehicle terminal log data can also be called color data. Car console can be the abbreviation of in-vehicle infotainment products installed in vehicles. Car console can functionally realize information communication between people and vehicles, vehicles and the outside world, or vehicles and vehicles.

Vehicle sensor data may be data generated by multiple types of sensors of the vehicle. For example, the vehicle sensor data may include data such as vehicle speed or remaining fuel level of the vehicle. Vehicle sensor data can also be called gray data.

Through vehicle terminal log data and vehicle sensor data, preliminary data preparation can be made for determining data related to vehicle energy replenishment, determining vehicle energy replenishment behavior, and identifying vehicle energy replenishment intentions.

The sample energy replenishment reference data may be data related to energy replenishment in the sample vehicle data. Sample energy replenishment reference data may include but is not limited to: engine status data, motor status data, vehicle speed data, vehicle energy data, vehicle mileage data, vehicle information data (such as vehicle identification code), time data and location data (such as longitude and latitude information) ) and so on. Among them, the engine status data can be It is the engine starting or stalling status data; the motor status data can be the motor is not powered on or the motor is powered on; the vehicle speed data can include the current vehicle speed and vehicle speed changes; the vehicle energy data can be the vehicle's remaining energy and remaining energy percentage and other data; vehicle mileage data can be the total mileage of the vehicle or the number of miles that the vehicle can still travel with remaining energy.

The sample energy replenishment reference data in this embodiment includes both data when energy is replenished and data when energy is not replenished.

Extracting the sample energy replenishment reference data from the sample vehicle data may be a process of initially screening out data related to energy replenishment from all data of the sample vehicle. For example, the relevant script code can be called to read the fields of the sample energy replenishment reference data from the database to extract the sample energy replenishment reference data.

By extracting the sample energy replenishment reference data from the sample vehicle data, the sample vehicle data was initially screened, the data irrelevant to energy replenishment was removed, and the data related to energy replenishment was preliminarily screened out, which facilitated the subsequent rapid and accurate determination of replenishment. Preliminary preparations were made for the behavioral data.

S120. Determine the energy replenishment behavior data based on the sample energy replenishment reference data.

The energy replenishment behavior data may be data corresponding to the time of energy replenishment behavior filtered out from the sample energy replenishment reference data. The energy-replenishing behavior data can be a set of sample energy-replenishing reference data combined with the corresponding moments of the energy-replenishing behavior; the energy-replenishing behavior data can be matched one-to-one by determining the corresponding moments of the energy-replenishing behavior. For example, the refueling behavior data may include: "Vehicle Identifier (ID)", "Engine Status", "Remaining Gasoline Amount", "Remaining Gasoline Amount Percentage", "Kilometers that the vehicle can still travel with the remaining gasoline amount" "Number", "Total Mileage", "Current Speed", "Season of the Day", "Month of the Day", "Week of the Day", "Time Period of the Day", "Hour of the Day", "Day of the Day" "minute range" and "whether it is a special holiday or public holiday" and other data.

On the basis of the above technical solution, the energy replenishment behavior data is determined based on the sample energy replenishment reference data, including: directly locating the energy replenishment moment based on the sample energy replenishment reference data, and then determining the energy replenishment behavior data.

The energy replenishment time may be a time when the vehicle performs energy replenishment behavior. For example, it may be a time when the vehicle performs refueling behavior or charging behavior.

Through changes in vehicle energy data, the energy replenishment moments during and before and after the data change can be extracted, and then the energy replenishment behavior data corresponding to the energy replenishment moments can be determined. For example, when the sample vehicle replenishes energy, the vehicle's remaining energy, remaining energy percentage, and the mileage that the vehicle can still travel with the remaining energy in the sample energy replenishment reference data will increase. This can be determined by changes in the sample energy replenishment reference data. It is always at the energy replenishment moment, and then the energy replenishment behavior data corresponding to the energy replenishment moment is determined.

It is also possible to extract the energy replenishment time corresponding to the data at the energy replenishment position of the vehicle through the vehicle position information, and then determine the energy replenishment behavior data corresponding to the energy replenishment time.

It can also be used to extract the energy replenishment position of the vehicle through changes in vehicle energy data and vehicle location information. position and vehicle energy data changes corresponding to the energy replenishment time, and then determine the energy replenishment behavior data corresponding to the energy replenishment time.

On the basis of the above technical solution, the energy replenishment behavior data is determined based on the sample energy replenishment reference data, including: extracting the energy replenishment intention data related to the energy replenishment intention from the sample energy replenishment reference data; from the energy replenishment intention data, Extract energy-replenishing behavior data related to energy-replenishing behavior.

The energy replenishment intention data may be the sample energy replenishment reference data corresponding to the moment containing the energy replenishment intention. The energy-replenishing intention data may correspond to at least one moment containing energy-replenishing intention. Illustratively, FIG. 2 is a schematic diagram of the energy-replenishing intention data selection principle provided in Embodiment 1 of the present application. As shown in Figure 2, the energy replenishment behavior is set to "refuel", the horizontal axis represents time, and the circular dot represents the time node when the user has the intention to refuel. Assume that between the time of "intention t" and the time of "come on t", the user will always maintain the intention of "come on", and the sample energy replenishment reference data in this interval is a positive sample; between the time of "go on t-1" and "go on" Between the "intention t" moment, the user will always maintain the intention of "not refueling", and the sample energy replenishment reference data in this interval is a negative sample. The energy replenishment intention data may be the sample energy replenishment reference data corresponding between the "intention t" time and the "refueling t" time, that is, the positive sample data.

From the sample energy replenishment reference data, the energy replenishment intention data related to the energy replenishment intention can be extracted by judging the moment when the energy replenishment intention is generated or the operation when the energy replenishment intention is generated, and judging the point at which the energy replenishment intention is generated, thereby determining The process of generating energy-replenishing intention data from energy-replenishing intention to energy-replenishing behavior. The moment when the intention to replenish energy is judged can be the midpoint value in the process of two replenishment behaviors, or it can be the value at other time in the process of two replenishment behaviors. The operation when the intention to replenish energy is generated may be related to going to a gas station or a battery swap station. Once the point where the energy-replenishment intention is generated is determined, the interval of the energy-replenishment intention behavior data can be confirmed, and then the energy-replenishment intention behavior data can be extracted.

Extracting energy-replenishing behavior data related to energy-replenishing behavior from the energy-replenishing intention data may be a process of determining whether the vehicle is in energy-replenishing behavior based on changes in the data of the sample vehicle. For example, when the remaining fuel capacity of the fuel vehicle increases and the fuel vehicle is in a stalled state, it can be considered that the fuel vehicle has replenished energy.

On the basis of the above scheme, by extracting the energy replenishment intention data related to the energy replenishment intention from the sample energy replenishment reference data, the sample energy replenishment reference data screening is realized; at the same time, the user's energy replenishment intention is converted into an obtainable The energy replenishment intention data allows the user's energy replenishment intention to be visualized. By extracting energy-replenishing behavior data related to energy-replenishing behavior from the energy-replenishing intention data, and filtering the energy-replenishing intention data, the obtained energy-replenishing behavior data is more accurate.

S130. Construct portrait data of the sample vehicle based on the energy replenishment behavior data.

The portrait data can be generated through the behavioral data of the sample vehicle to generate label data describing the sample vehicle. The portrait data may include at least one of the following: parking location, energy replenishment location, driving route and energy data. wait. For example, the portrait data may include: "distance from the last gas station" and "distance from frequently visited gas stations" and other data.

On the basis of the above technical solution, portrait data is constructed based on the energy replenishment behavior data, including: determining the energy replenishment location based on the energy replenishment behavior data; and constructing portrait data of the sample vehicle based on the energy replenishment location.

Energy replenishment locations can include: gas stations, charging stations, charging piles or battery swap stations, etc.

Determining the energy replenishment location based on the energy replenishment behavior data may involve filtering out the longitude and latitude coordinates of the energy replenishment location from the energy replenishment behavior data. For example, based on the refueling and charging behavior discovery algorithm, the latitude and longitude coordinates of each refueling and charging of the sample vehicle can be identified from the energy replenishment behavior data.

Constructing the portrait data of the sample vehicle based on the energy replenishment location may be based on the longitude and latitude coordinates of the energy replenishment location, and determining the energy replenishment location that the sample vehicle frequently visits and/or the last energy replenishment location that the sample vehicle visited from the energy replenishment behavior data. For example, according to the longitude and latitude coordinates of refueling and charging, use the "reverse geographical analysis" service to obtain the gas stations or charging stations near the current coordinate point, and store the results in the database to obtain the location of each refueling and charging of the sample vehicle. and name. Among them, the energy-replenishing location that appears most often is the "frequently visited gas station"; the energy-replenishing location closest to the current moment is the "last gas station visited".

By determining the energy replenishment location and constructing the portrait data of the sample vehicle based on the energy replenishment location, the sample vehicle's frequent energy replenishment location or the last energy replenishment location visited is clarified, and the distance replenishment distance of the sample vehicle can be determined based on the energy replenishment location. The distance between the energy positions makes the auxiliary judgment of the energy replenishment intention of the sample vehicle more targeted.

By constructing the portrait data of the sample vehicle, the energy replenishment behavior data of the sample vehicle is more specific, making the identification data of the energy replenishment intention of the sample vehicle more comprehensive.

S140. Based on the energy replenishment behavior data and portrait data, train the energy replenishment intention recognition model.

The energy replenishment intention recognition model may be a model that performs the energy replenishment intention recognition task based on energy replenishment behavior data and portrait data. Input the energy-replenishing behavior data and portrait data into the energy-replenishing intention recognition model. The model can give the result of whether the sample vehicle has the intention of replenishing energy.

The energy replenishment behavior data and portrait data in this embodiment correspond to the data of multiple sample vehicles. For the data of each sample vehicle, a relationship between the vehicle ID and the vehicle's energy replenishment behavior data and portrait data can be established. Mapping relationship, then the corresponding energy replenishment behavior data and portrait data of each vehicle ID can be input into the energy replenishment intention recognition model as a set of training data to train the model.

The process of training the energy-replenishing intention recognition model can be: input the energy-replenishing behavior data and portrait data into the energy-replenishing intention recognition model. The energy-replenishing intention recognition model can predict whether the set of data contains energy-replenishing intentions through the intention prediction algorithm, and Compare the prediction results of the energy replenishment intention identification model with the actual energy replenishment intention results of the sample vehicles, calculate the loss function, and then use the parameter configuration of the mediation model based on the loss function to perform multiple iterations of the energy replenishment intention identification model according to the above scheme. The trained energy-replenishing intention recognition model can be obtained.

The intent prediction algorithm can be Linear Support Vector Classification (LinearSVC), Logistic Regression (LR), Decision Tree (DecisionTree, DT), Random Forest (RandomForest, RF) or Gradient Boosting Decision Tree (Gradient Boosting Decision) Tree, GBDT), etc. You can choose the GBDT algorithm.

Before training the energy replenishment intention recognition model, the energy replenishment behavior data and portrait data can also be processed. For example, it can be based on feature engineering processing, including feature extraction, aggregation, formatting, etc., to improve the significance of model feature recognition.

For example, assuming that there are 39,000 pieces of energy-replenishing behavior data, first randomly scramble the 39,000 pieces of data, and the random number seed can be set to 42; then the "training set: verification set: test set" can be equal to "7:1 :2" ratio, select the training set, verification set and test set; then use the training set to train the energy-supplementing intention recognition model, use the verification set to verify the energy-supplementing intention recognition model, and then continuously adjust the energy-supplementing intention recognition model according to the situation, and get After adjustment and optimization, the energy replenishment intention recognition model is finally used to evaluate the final energy replenishment intention recognition model using the test set. If the evaluation passes, the training process of the energy replenishment intention recognition model is completed.

The technical solution of the embodiment of the present application extracts sample energy replenishment reference data from sample vehicle data; determines energy replenishment behavior data based on the sample energy replenishment reference data; constructs portrait data of the sample vehicle based on the energy replenishment behavior data; Behavioral data and portrait data are used to train the replenishment intention recognition model. The technical solution of the embodiment of the present application adopts a two-stage data screening operation, that is, preliminary screening of data related to energy replenishment, and secondary screening of energy replenishment behavior data corresponding to the energy replenishment moment, which improves the accuracy and accuracy of vehicle energy replenishment behavior data extraction. Efficiency. The portrait data of the sample vehicle and the energy replenishment behavior data are used as model training data to make the model training data more comprehensive, thereby improving the accuracy and generalization of the energy replenishment intention recognition model training.

Embodiment 2

Figure 3 is a flow chart of a model training method provided in Embodiment 2 of the present application. Based on the above embodiment, this embodiment will extract energy replenishment intention data related to energy replenishment intention from the sample energy replenishment reference data. optimization. As shown in Figure 3, the method includes:

S310. Extract the sample energy supplement reference data from the sample vehicle data.

S320. Determine the energy replenishment time based on the vehicle sensor data in the sample energy replenishment reference data.

Determining the energy replenishment time based on the vehicle sensor data in the sample energy replenishment reference data may include identifying the energy replenishment time with energy replenishment behavior through the vehicle sensor data in the sample energy replenishment reference data. For example, the refueling moment of the vehicle's refueling/charging behavior can be identified through data such as "remaining fuel level/power" and "current speed" in the vehicle sensor data.

By determining the energy replenishment time based on the vehicle sensor data in the sample energy replenishment reference data, the vehicle The sensor data corresponds to the energy replenishment time. By confirming the energy replenishment time, the vehicle sensor data corresponding to that time can be found.

S330. Determine the energy replenishment intention period based on the energy replenishment time and the reference driving time.

The energy replenishment intention period may be a time period in which there is an energy replenishment intention. It can be the time period between generating the intention to replenish energy and executing the replenishment behavior.

Based on the above technical solution, after the model training method determines the energy replenishment moment, it also includes:

If there are at least two energy replenishment moments, the average driving time is determined based on at least two energy replenishing moments; the reference driving time is determined based on the average driving time and the preset interval percentage.

The average driving time may be the average of the time intervals between adjacent energy replenishment moments. Different sample vehicles have different energy replenishment frequencies, and the average driving time can be different. For example, the replenishment energies are set to t ₁ , t ₂ , t ₃ , and t ₄ , and the time intervals between adjacent replenishment moments are (t ₂ -t ₁ ), (t ₃ –t ₂ ), (t ₄ –t ₃ ), the average driving time is the average of the above time intervals.

The preset interval percentage may be the percentage of the driving time interval from when the energy replenishment intention is generated until the vehicle starts to perform the energy replenishment behavior. The preset interval percentage may be a value based on empirical assumptions.

The reference driving time may be the driving time interval from when the intention to replenish energy is generated until the vehicle starts to perform energy replenishment behavior. The reference driving time can be an empirically assumed value or a fixed time interval. The reference driving time can also be a value determined by a preset interval percentage and the average driving time. For example, the average driving time is set to t, the preset interval percentage is φ, and the reference driving time is t*φ.

By determining the reference driving time by preset interval percentage and average driving time, the average driving time of different sample vehicles can be counted, making the average driving time data more accurate. At the same time, the reference driving time data can also be made more accurate. The reference driving time can be obtained through statistics based on the data of different sample vehicles, which can make the obtained reference driving time data more targeted.

Determining the energy replenishment intention period can be by setting the energy replenishment moment as the time point when the energy replenishment behavior is performed, and setting the time point corresponding to the reference driving time from the energy replenishment moment back to the energy replenishment intention as the time point when the energy replenishment intention is generated. Two The time period between time points is the energy replenishment intention period. For example, the energy replenishment time is set to T, the reference driving time is ΔT, the time when the energy replenishment intention is generated is (T-ΔT), and the energy replenishment intention period is the time from (T-ΔT) time to T time. part.

By determining the energy replenishment intention period based on the energy replenishment time and the reference driving time, the energy replenishment intention time period is specified to facilitate data extraction.

S340. Extract the sample energy replenishment reference data located in the energy replenishment intention period as the energy replenishment intention data.

The sample energy replenishment reference data contains vehicle information data and time data. Through the energy replenishment intention period, based on the vehicle information data and time data, the sample energy replenishment reference data of the corresponding vehicle can be found. This energy replenishment reference data can be used as energy replenishment intention data. For example, the sample energy replenishment data of a single sample vehicle at a single time can be used as a data set, and the data set includes the sample energy replenishment reference data of the vehicle at that time. By confirming the vehicle ID and the corresponding time of the vehicle, you can find the corresponding sample energy replenishment reference data.

By extracting the sample energy replenishment reference data in the energy replenishment intention period as the energy replenishment intention data, the energy replenishment intention data is extracted, which provides a guarantee for the subsequent rapid and accurate extraction of energy replenishment behavior data.

S350. Extract energy-replenishing behavior data related to energy-replenishing behavior from the energy-replenishing intention data.

S360. Construct portrait data of the sample vehicle based on the energy replenishment behavior data.

S370. Train the energy replenishment intention recognition model based on the energy replenishment behavior data and portrait data.

The technical solution of the embodiment of the present application extracts sample energy replenishment reference data from sample vehicle data; determines the energy replenishment time based on the vehicle sensor data in the sample energy replenishment reference data; determines the energy replenishment time based on the energy replenishment time and the reference driving time Intention period; extract sample energy replenishment reference data located in the energy replenishment intention period as energy replenishment intention data; extract energy replenishment behavior data related to energy replenishment behavior from the energy replenishment intention data; construct a sample vehicle based on the energy replenishment behavior data portrait data; train the energy replenishment intention recognition model based on the energy replenishment behavior data and portrait data. Through the energy replenishment moment and the reference driving time, the energy replenishment intention period is confirmed in time, and the energy replenishment intention data is confirmed and extracted through the energy replenishment intention period, which improves the accuracy of energy replenishment intention identification.

Based on the above embodiment, another way of extracting energy-replenishing intention data related to energy-replenishing intention from the sample energy-replenishing reference data may include the following steps:

Step A: Screen the energy replenishment intention keywords from the vehicle terminal log data in the sample energy replenishment reference data.

The energy-replenishing intention keyword may be keyword information related to the energy-replenishing behavior. For example, it can be information about energy replenishment positions. For example, the address of the battery swap station or the name of the battery swap station, etc. It can also be the general information of energy replenishment positions. For example, battery swap stations, charging piles or gas stations, etc.

Screening the energy replenishment intention keywords from the vehicle terminal log data in the sample energy replenishment reference data may be a process of screening out keywords related to the energy replenishment intention from the search records in the vehicle terminal log data. For example, you can check the historical search records in the log data of the vehicle terminal, and check the keywords corresponding to "gas station", "battery swap station", the address of the gas station or the address of the battery swap station, which can be the key to screening energy replenishment intentions. word.

Step B: Screen the associated sensor data of the energy replenishment intention keywords from the vehicle sensor data in the sample energy replenishment reference data.

The associated sensing data may be related to the vehicle sensor data containing the keyword of energy replenishment intention. data. For example, the energy replenishment intention keyword is set to "gas station". The vehicle sensor data includes the location data of the gas station and the number of other vehicle sensors corresponding to the location data, such as engine status data, vehicle speed data, and vehicle energy. Data, vehicle mileage data, vehicle information data, time data, etc. are all associated sensing data.

Step C. From the vehicle sensor data in the sample energy replenishment reference data, filtering the associated sensor data of the energy replenishment intention keyword can be through the field of the energy replenishment intention keyword, and determining the field containing the keyword in the vehicle sensor data. By determining the vehicle sensor data corresponding to the data, the associated sensing data is confirmed.

According to the energy-replenishing intention keywords and associated sensing data, the energy-replenishing intention data is determined.

Determine the energy replenishment intention data based on the energy replenishment intention keywords and associated sensing data. This may be by determining the vehicle information data or time data corresponding to the keywords in the vehicle terminal log data through the energy replenishment intention keywords, and then from the associated transmission data. The associated sensing data corresponding to the vehicle information data or the time data is found in the sensory data, thereby determining the energy replenishment intention data when the energy replenishment intention is generated. Combined with the energy replenishment behavior, the energy replenishment intention data can be determined.

The advantage of this embodiment is that by associating the energy replenishment intention keywords in the vehicle terminal log data with the vehicle sensor data, the moment when the energy replenishment intention is generated can be determined more accurately, thereby improving the accuracy of determining the energy replenishment intention.

Embodiment 3

Figure 4 is a flow chart of a model training method provided in Embodiment 3 of the present application. Based on the above embodiment, this embodiment will extract energy-replenishing behavior data related to energy-replenishing behavior from the energy-replenishing intention data and optimize it. . As shown in Figure 4, the method includes:

S410. Extract the sample energy supplement reference data from the sample vehicle data.

S420. Extract energy replenishment intention data related to energy replenishment intention from the sample energy replenishment reference data.

S430: Traverse the energy replenishment intention data at each moment in sequence, and determine whether the push condition is satisfied at that moment. If the push condition is satisfied at this moment, S440 is executed. If the push condition is not satisfied at this moment, S430 is returned to execution.

The condition for pushing onto the stack may be that the energy replenishment intention data satisfies the conditions for the energy replenishment behavior. When the vehicle is a fuel vehicle, the push conditions can be engine status data, vehicle speed data, and event types; when the vehicle is an electric vehicle, the push conditions can be motor status data, vehicle speed data, and event types. Among them, the event type can be "vehicle stalled" or "vehicle started"; the event type can also be "vehicle powered off" or "vehicle powered on". For example, assuming that the vehicle is a fuel vehicle, the stacking conditions can be that the vehicle speed is 0, the engine is turned off, and the event If the event type is "vehicle stalled", this scenario may be to stop and prepare to replenish energy; the stacking condition may also be that the vehicle speed is 0, the engine is started and the event type is "vehicle start", this scenario may be to drive out of the gas station after replenishing energy. .

This embodiment can sequentially determine whether the energy replenishment intention data at each moment meets the stacking condition. If it satisfies the stacking condition, perform the subsequent operation of S440. If the energy replenishment intention data for the next moment does not meet, continue to perform this step.

S440. If the push condition is met at this moment, then after pushing the energy replenishment intention data at this moment into the stack, it is judged whether the number of data items in the stack meets the preset number. If the number of data items in the stack meets the preset number, execute S450, if the number of data items in the stack does not meet the preset number, return to S430.

The number of data items in the stack can be the number of energy replenishment intention data items that have been pushed into the stack. The number of data items in the stack does not exceed the preset number. For example, the default number of items is set to 2. If after the energy replenishment intention data is pushed into the stack, the number of data items in the stack is 1, then return to the operation of S430 and continue to extract the energy replenishment intention data that meets the conditions for being pushed into the stack. . If after the replenishment intention data is pushed into the stack, the number of data items in the stack is 2, it will no longer be pushed into the stack and the subsequent operation of S450 will be performed.

S450. If the number of data items in the stack meets the preset number, determine whether there is an energy growth event based on the energy replenishment intention data in the stack. If there is an energy growth event, execute S460. If there is no energy growth event, clear it. Compensation intention data in the stack, and returns to execution S430.

Energy growth events are events in which vehicle energy data increases. It can be increased by the remaining fuel volume or the remaining electric power.

Determine whether there is an energy growth event, screen the energy replenishment intention data in the stack, and confirm whether there is energy replenishment behavior when the vehicle status data changes, ensuring the accuracy of energy replenishment behavior acquisition.

Based on the above technical solution, determining whether there is an energy growth event based on the energy replenishment intention data in the stack may include: determining the energy replenishment increase amount and/or energy replenishment time interval based on the energy replenishment intention data in the stack; The energy replenishment increase amount and/or the energy replenishment time interval determine whether there is an energy increase event.

The energy replenishment increment may be the change in vehicle energy obtained by comparing the energy replenishment intention data in the stack. For example, it may be an increase in the remaining fuel amount or an increase in the remaining electric power.

The energy replenishment time interval may be the time interval data obtained by comparing the energy replenishment intention data in the stack. For example, if there are two pieces of data in the stack, the replenishment time interval can be the time interval between the two pieces of data.

For example, Table 1 below is a historical data statistics table of energy replenishment intention data in the stack. As shown in Table 1 below, the time from "2020-12-01 18:16:49" to the time "2020-12-01 18:19:19" corresponds to refueling event 1. Refueling event 1 is a normal refueling event. The current fuel volume Stable changes. The time "2020-12-01 18:19:59" corresponds to refueling event 2. Refueling event 2 is an abnormal refueling event, and the current oil volume fluctuates briefly. Limited by the accuracy of the sensor, the refueling data sequence has brief fluctuations. In order to ensure the accuracy of the data, the refueling data needs to be cleaned.

According to the technical solution of the embodiment of the present application, the energy replenishment increase amount and/or the energy replenishment time interval are determined based on the energy replenishment intention data in the stack; based on the energy replenishment increase amount and/or the energy replenishment time interval, it is determined whether the energy replenishment increase amount is Reach the growth amount threshold and/or whether the energy replenishment time interval reaches the energy replenishment time interval threshold, and then determine whether there is an energy growth event, thereby realizing the cleaning of energy replenishment intention data.

The growth threshold is the minimum set value for energy supplement growth.

The energy replenishment time interval threshold is the minimum setting value of the energy replenishment time interval.

The replenishment time interval threshold can be used to smooth data by setting a fluctuation tolerance time window. The selection method of the energy replenishment time interval threshold is as follows:

Calculate the time interval between two refuelings and select the time interval as T ₁ -T _n .

Draw a cumulative distribution proportion chart: determine the time interval when the cumulative proportion reaches more than 90%.

Select this time interval as the energy replenishment time interval threshold.

For example, FIG. 5 is a statistical distribution diagram of refueling time intervals provided in Embodiment 3 of the present application.

As shown in Figure 5, the energy replenishment time interval is set as the refueling time interval. The horizontal axis is the time interval between two refuelings. The vertical axis is the cumulative proportion of normal data. The energy replenishment time interval threshold is selected as follows:

Calculate the time interval between two refuelings and select the time interval as (10-600s).

Draw the cumulative distribution proportion chart: when the time interval between two refuelings is 300s, the cumulative proportion reaches 92%.

Therefore, the threshold value of the time interval between two refuelings is selected as 300s.

By setting the energy replenishment time interval threshold, short-term abnormal fluctuations in energy replenishment intention data are removed, ensuring the accuracy of the energy replenishment intention reference data.

To select the growth threshold, the data processing process is as follows:

Calculate the vehicle energy data of the energy replenishment behavior data, and keep only the vehicle energy data >1; draw the vehicle energy data frequency chart: determine the minimum value of the vehicle energy data that accounts for an abnormally large proportion; the vehicle energy data is the growth threshold.

For example, FIG. 6 is a statistical distribution diagram of a single refueling amount provided in Embodiment 3 of the present application.

As shown in Figure 6, the energy replenishment growth amount is set to the refueling amount of the refueling event, the horizontal axis is the refueling amount of each refueling event, and the vertical axis is the frequency of the refueling amount. The cleaning process of outliers in the single refueling volume during a single refueling process involves the selection of the single refueling volume threshold. The data processing process is as follows:

Calculate the refueling volume for each refueling event, and only retain the refueling volume >1L.

Draw a frequency chart of refueling volume: when the refueling volume is 2L, the proportion is abnormally high.

Therefore, the effective refueling volume threshold is selected to be >2L.

By setting the growth threshold, the interference of abnormal data in a single energy replenishment is avoided and the accuracy of the data is ensured.

S460. If there is an energy growth event, use the energy replenishment intention data in the stack as energy replenishment behavior data, and clear the energy replenishment intention data in the stack.

In this embodiment, the energy replenishment intention data in the stack is used as the energy replenishment behavior data by extracting the data as the energy replenishment behavior data.

Clearing the energy replenishment intention data in the stack may be a process of clearing the energy replenishment intention data in the stack so that the energy replenishment intention data in the stack becomes 0.

By using the energy replenishment intention data in the stack as energy replenishment behavior data, and clearing the energy replenishment intention data in the stack, the energy replenishment behavior data is determined, and the extraction of the energy replenishment behavior data is completed. At the same time, the energy replenishment intention data in the stack is cleared. The energy replenishment intention data provides conditions for the subsequent judgment of the energy replenishment intention data, realizing the circular judgment of the data.

S470. Construct portrait data of the sample vehicle based on the energy replenishment behavior data.

S480. Train the energy replenishment intention recognition model based on the energy replenishment behavior data and portrait data.

The technical solution of the embodiment of the present application extracts sample energy replenishment reference data from sample vehicle data; extracts energy replenishment intention data related to energy replenishment intention from the sample energy replenishment reference data; and sequentially traverses the energy replenishment intention data at each moment. , determine whether the push condition is met at this moment; if it is satisfied, then after pushing the replenishment intention data at that moment into the stack, it will be judged whether the number of data items in the stack meets the preset number; if it is satisfied, then according to the replenishment intention data in the stack data to determine whether there is an energy growth event; if so, use the energy replenishment intention data in the stack as energy replenishment behavior data, and clear the energy replenishment intention data in the stack; construct the portrait data of the sample vehicle based on the energy replenishment behavior data; Based on the energy replenishment behavior data and portrait data, the energy replenishment intention recognition model is trained. The energy replenishment intention data were gradually filtered to achieve accurate judgment on the energy replenishment behavior data. At the same time, by clearing the stack and replenishing the reference data, the cyclic judgment of the data is realized.

Embodiment 4

Figure 7 is a flow chart of a method for identifying energy replenishment intentions provided in Embodiment 4 of the present application. This embodiment can be applied to the situation of vehicle replenishment intention identification. The method can be executed by an energy replenishment intention identification device. The energy replenishment intention The intention recognition device can be implemented in the form of hardware and/or software. The power-supply intention recognition device can be integrated into an electronic device configured with a power-supplement intention recognition model. The electronic device can be a vehicle-mounted terminal.

As shown in Figure 7, the method includes:

S710. Obtain the current energy replenishment reference data from the current vehicle data.

The current vehicle data can be all real-time data generated by the current vehicle at the current moment.

In the method for identifying energy replenishment intentions in any embodiment of the present application, the current vehicle data includes: vehicle terminal log data and vehicle sensor data.

Through the vehicle terminal log data and vehicle sensor data, the vehicle terminal log data and vehicle sensor data are mapped one-to-one to prepare for the energy replenishment intention identification process.

The current energy replenishment reference data may be all associated data related to energy replenishment in the current vehicle data.

Obtaining the current energy replenishment reference data from the current vehicle data is similar to the process of extracting the sample energy replenishment reference data from the sample vehicle data introduced in the above embodiment, and will not be described in detail here.

By obtaining the current energy replenishment reference data from the current vehicle data, a preliminary screening of the data is achieved, and data irrelevant to energy replenishment is removed.

S720. Input the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment.

The energy-replenishing intention recognition model can be trained through the model training method of any embodiment of the present application. The energy replenishment intention identification model is used to analyze the input energy replenishment reference data and provide the identification result of whether there is an energy replenishment intention at the current moment.

The identification result of the energy replenishment intention may be the result of determining whether the current vehicle has the energy replenishment intention. For example, there may or may not be a supplementary intention.

The data can be processed before being input into the energizing intent recognition model. For example, it can be based on feature engineering processing, including feature extraction, aggregation, formatting, etc., to improve the significance of model feature recognition.

Through the energy replenishment intention recognition model, the problem of being unable to predict the vehicle's energy replenishment intention in advance is solved, and the current vehicle's energy replenishment intention can be identified and predicted through the current energy replenishment reference data.

Based on the above technical solution, after the energy replenishment intention identification method obtains the current energy replenishment reference data from the current vehicle data, it also includes:

Obtain the supplementary reference data of the current energy supplement reference data from the historical energy supplement reference data.

The historical energy replenishment reference data may be all associated data related to energy replenishment in the current vehicle's historical data. The historical energy replenishment reference data is the historical energy replenishment experience value of the vehicle, which records the relevant data of the vehicle's historical energy replenishment record.

The supplementary reference data may be historical energy replenishment reference data through the common correlation between the historical energy replenishment reference data and the current energy replenishment reference data, or it may directly use the historical energy replenishment reference data of the current vehicle as the energy replenishment reference data.

Correspondingly, the current energy replenishment reference data is input into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition results at the current moment, including:

Input the current energy replenishment reference data and supplementary reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment.

Input the current energy replenishment reference data and the supplementary reference data into the energy replenishment intention identification model. The current energy replenishment reference data and the supplementary reference data may be input into the energy replenishment intention identification model as a whole data group to obtain the current energy replenishment intention identification model. Complementary intention identification results.

By obtaining supplementary reference data, the current energy replenishment reference data is supplemented from historical data, providing more sufficient data support for subsequent predictions. The current energy replenishment reference data and supplementary reference data are input into the energy replenishment intention identification model to obtain the current moment. The identification results of energy replenishment intentions and the input data are more comprehensive, ensuring the accuracy of predictions.

Based on the above technical solution, the energy replenishment intention identification method inputs the current energy replenishment reference data into the energy replenishment intention identification model, and obtains the energy replenishment intention identification results at the current moment, including:

Obtain the portrait data of the current vehicle; input the portrait data of the current vehicle and the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment.

The portrait data of the current vehicle may be generated in advance through the historical behavior data of the current vehicle to generate label data describing the current vehicle.

The portrait data of the current vehicle and the current energy replenishment reference data can be input into the energy replenishment intention recognition model as a data group, and the model determines whether there is an energy replenishment intention at the current moment through calculation.

By obtaining portrait data, the input data of the energy replenishment intention recognition model is more comprehensive, ensuring the accuracy of energy replenishment intention recognition.

The technical solution of the embodiment of the present application obtains the current energy replenishment reference data from the current vehicle data; inputs the current energy replenishment reference data into the energy replenishment intention identification model to obtain the energy replenishment intention identification result at the current moment. By applying the energy replenishment intention recognition model, the function of directly predicting and identifying energy replenishment intentions through the current energy replenishment reference data is realized, which improves the efficiency and accuracy of energy replenishment intention identification.

Embodiment 5

Figure 8 is a schematic diagram of the training and application scenarios of a supplementary intention recognition model provided in Embodiment 5 of the present application. This embodiment can implement the model training method and energy replenishment intention identification method provided in the above embodiments of this application. As shown in Figure 8, model training is performed first, and algorithm evaluation/optimization is repeatedly performed to obtain an intent recognition model. Then the model is applied to realize the identification process of energy replenishment intention.

The process of model training includes: source data collection, energy replenishment intention sample selection, portrait extraction, training data set construction, modeling and algorithm evaluation/optimization.

Source data collection is equivalent to the process of extracting sample energy reference data.

The selection of energy-replenishing intention samples is equivalent to extracting the energy-replenishing intention data related to the energy-replenishing intention.

Portrait extraction is equivalent to constructing portrait data.

The construction of training data set is equivalent to extracting energy-replenishing behavior data and constructing portrait data.

Modeling can be building an energy-replenishing intention recognition model, which is equivalent to training an energy-replenishing intention recognition model.

The energy-replenishing intention identification process includes: real-time data sources, portrait extraction, feature set construction, models and intentions. The energy-replenishing intention identification process is the process of identifying energy-replenishing intentions using the energy-replenishing intention recognition model.

The real-time data source is equivalent to obtaining the current energy replenishment reference data from the current vehicle data.

The portrait data is equivalent to obtaining the portrait data of the current vehicle.

The construction of the feature set is equivalent to obtaining the current energy supplement reference data and the portrait data of the current vehicle.

The model is equivalent to inputting the current vehicle's portrait data and current energy replenishment reference data into the energy replenishment intention recognition model.

Intention is equivalent to getting the recognition result of energy-supplementing intention at the current moment.

Figure 9 is a system framework diagram for energy replenishment intention recognition provided in Embodiment 5 of the present application. The system framework diagram can be integrated into the vehicle terminal. This embodiment can implement the energy replenishment intention identification method provided in the above embodiments of this application. As shown in Figure 9, the system framework consists of the basic layer of data access and storage, the real-time/offline computing engine layer, the technology implementation layer and the backward service application layer from bottom to top.

The data source can be vehicle terminal log data and vehicle sensor data collected in real time. Data sources are transmitted through a data bus. For example, the data bus may be a distributed publish/subscribe based messaging system. Data storage can be implemented through a database, and can be implemented through multiple databases. For example, the type of database may include: a database management system based on a distributed file system, an open source non-relational distributed database management system, or a relational database management system, etc.

Real-time computing can be implemented based on the distributed processing engine (Flink) for streaming data and batch data. Vehicle terminal log data and vehicle sensor data can be calculated in real time through the real-time calculation engine, and the calculated data can be stored in the database. Offline computing can calculate the vehicle terminal log data and vehicle sensor data through the offline computing engine, and calculate the vehicle's portrait data, which can be stored in the database.

Feature engineering is used to process data before inputting it into the model, including feature extraction, aggregation and formatting, to help the model improve the significance of feature recognition. Energy replenishment identification can be used to obtain current energy replenishment reference data from current vehicle data. Intention prediction can be based on vehicle terminal log data and sensor data, and through the energy replenishment intention recognition model, the energy replenishment intention can be predicted and the energy replenishment intention prediction result can be output.

Corresponding services can also be provided for supplementary image recognition. For example, activity recommendations can be made, and suitable preferential activities can be recommended for the purpose of replenishing energy. Points of interest (POI) can also be recommended to provide personalized demand recommendations based on geographical information, public service sites and information about buildings such as bus stations or service sites that can provide services. Refueling reminders can also be carried out. When it is determined that the user has the intention to replenish energy, the refueling reminder can be carried out through the vehicle machine. Among them, the refueling reminder can include: sound, pop-up window or image information reminder, etc.

By building a system framework for energy replenishment intention recognition, the application of the energy replenishment intention recognition model is realized, and the energy replenishment intention recognition service is expanded to achieve more personalized performance.

Embodiment 6

Figure 10 is a schematic structural diagram of a model training device provided in Embodiment 6 of the present application. This device can implement the model training method provided in the above embodiments of this application. This embodiment can be applied to the situation of vehicle replenishment intention model training. The device can be implemented by software and/or hardware, and can be integrated into an electronic device with model training. As shown in Figure 10, the device includes:

The sample data extraction module 1010 is configured to extract the sample energy replenishment reference data from the sample vehicle data; the behavior data determination module 1020 is configured to determine the energy replenishment behavior data based on the sample energy replenishment reference data; the portrait data construction module 1030 is configured to determine the energy replenishment behavior data based on the sample energy replenishment reference data. The energy replenishment behavior data is used to construct portrait data of the sample vehicle; the model training module 1040 is configured to train the energy replenishment intention recognition model based on the energy replenishment behavior data and portrait data.

In one embodiment, the behavior data determination module 1020 includes:

The intention data extraction unit is configured to extract the energy replenishment intention data related to the energy replenishment intention from the sample energy replenishment reference data; the behavior data extraction unit is configured to extract the energy replenishment intention data related to the energy replenishment behavior from the energy replenishment intention data. behavioral data.

In an embodiment, the intent data extraction unit may include:

The keyword filtering subunit is set to filter energy replenishment intention keywords from the vehicle terminal log data in the sample energy replenishment reference data; the associated data filtering subunit is set to filter the energy replenishment intention keywords from the vehicle sensor data in the sample energy replenishment reference data. The associated sensing data of the energy-replenishing intention keywords is screened; the intention data determination subunit is configured to determine the energy-replenishing intention data based on the energy-replenishing intention keywords and the associated sensing data.

In an embodiment, the intent data extraction unit may also include:

The energy replenishment time determination subunit is set to determine the energy replenishment time based on the vehicle sensor data in the sample energy replenishment reference data; the intention period determination subunit is set to determine the energy replenishment intention period based on the energy replenishment time and the reference driving time; intention The data determination subunit is configured to extract sample energy replenishment reference data located in the energy replenishment intention period as energy replenishment intention data.

In one embodiment, after determining the energy replenishment time, the energy replenishment time determination subunit further includes:

The average duration determination subunit is set so that if the energy replenishment time is at least two, it will be determined based on at least two replenishment moments. At the available time, the average driving time is determined; the reference duration determination subunit is set to determine the reference driving time based on the average driving time and the preset interval percentage.

In one embodiment, the energy-replenishing behavior data extraction unit includes:

The condition judgment subunit is set to traverse the energy replenishment intention data at each moment in sequence to determine whether the stacking condition is met at that moment; the number judgment subunit is set to push the energy replenishment intention data at that moment into the stack if it is met. , determine whether the number of data items in the stack meets the preset number; the growth determination subunit is set to If it is satisfied, determine whether there is an energy growth event based on the energy replenishment intention data in the stack; the data determination subunit is set to If it exists , then the energy replenishment intention data in the stack will be used as the energy replenishment behavior data, and the energy replenishment intention data in the stack will be cleared.

In one embodiment, the growth determination subunit may be set to:

According to the energy replenishment intention data in the stack, the energy replenishment increase amount and/or the energy replenishment time interval is determined; based on the energy replenishment increase amount and/or the energy replenishment time interval, it is determined whether there is an energy increase event.

In one embodiment, the portrait data building module includes:

The energy replenishment position determination unit is configured to determine the energy replenishment position based on the energy replenishment behavior data; the portrait data construction unit is configured to construct portrait data of the sample vehicle based on the energy replenishment location.

In any model training device in the embodiment of this application, the sample vehicle data includes: vehicle terminal log data and vehicle sensor data.

The model training device provided by the embodiments of this application can execute the model training method provided by any embodiment of this application, and has functional modules and effects corresponding to the execution method.

Embodiment 7

Figure 11 is a schematic structural diagram of an energy replenishment intention recognition device provided in Embodiment 7 of the present application. This device can implement the energy replenishment intention identification method provided by the above embodiments of the present application. This embodiment can be applied to the situation of vehicle energy replenishment intention recognition. The device can be implemented by software and/or hardware, and can be integrated into an electronic device equipped with an energy replenishment intention recognition model. The electronic device can be a vehicle-mounted device. terminal. As shown in Figure 11, the device includes:

The current data acquisition module 1110 is configured to obtain the current energy replenishment reference data from the current vehicle data; the energy replenishment intention identification module 1120 is configured to input the current energy replenishment reference data into the energy replenishment intention identification model to obtain the energy replenishment at the current moment. Intention recognition results; among them, the energy-supplementing intention recognition model is trained by the model training device.

The technical solution of the embodiment of the present application obtains the current energy replenishment reference data from the current vehicle data; inputs the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention awareness at the current moment. Don't get results. By applying the energy replenishment intention recognition model, the function of directly predicting and identifying energy replenishment intentions through the current energy replenishment reference data is realized, which improves the efficiency and accuracy of energy replenishment intention identification.

Correspondingly, after the current energy replenishment data acquisition module 1110 obtains the current energy replenishment reference data from the current vehicle data, it also includes:

The supplementary data acquisition module is configured to obtain the supplementary reference data of the current energy supplement reference data from the historical energy supplement reference data; accordingly, the energy supplement intention identification module 1120 includes:

Correspondingly, the current data acquisition module 1110 includes:

The portrait data acquisition unit is configured to acquire the portrait data of the current vehicle; the recognition result acquisition unit is configured to input the portrait data of the current vehicle and the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition at the current moment. result.

In any device for energy replenishment intention recognition in the embodiment of the present application, the current vehicle data includes: vehicle terminal log data and vehicle sensor data.

The energy replenishment intention identification device provided by the embodiments of the present application can execute the energy replenishment intention identification method provided by any embodiment of the present application, and has corresponding functional modules and effects of the execution method.

Embodiment 8

FIG. 12 is a schematic structural diagram of an electronic device provided in Embodiment 8 of the present application. FIG. 12 shows a block diagram of an exemplary device suitable for implementing the implementation of the embodiment of the present application. The device shown in Figure 12 is only an example and should not impose any restrictions on the functions and usage scope of the embodiments of the present application.

As shown in Figure 12, electronic device 1200 is embodied in the form of a general computing device. The components of the electronic device 1200 may include, but are not limited to: one or more processors or processing units 1210, a system memory 1220, and a bus 1230 connecting different system components (including the system memory 1220 and the processing unit 1210).

Bus 1230 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics accelerated port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards) Association, VESA) local bus and Peripheral Component Interconnect (PCI) bus.

Electronic device 1200 includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 1200, including volatile and nonvolatile media, removable and non-removable media.

System memory 1220 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 1221 and/or cache memory (cache 1222). Electronic device 1200 may include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 1223 may be configured to read and write to non-removable, non-volatile magnetic media (not shown in Figure 12, commonly referred to as a "hard drive"). Although not shown in FIG. 12, a disk drive configured to read and write to a removable non-volatile disk (e.g., a "floppy disk") and a removable non-volatile optical disk (e.g., a Compact Disc) may be provided. Read-Only Memory (CD-ROM), Digital Video Disk Read-Only Memory (DVD-ROM) or other optical media) that reads and writes optical disc drives. In these cases, each drive may be connected to bus 1230 through one or more data media interfaces. The system memory 1220 may include at least one program product having a set of (eg, at least one) program modules configured to perform the functions of embodiments of the present application.

A program/utility 1225 having a set of (at least one) program modules 1224 , which may be stored, for example, in system memory 1220 , such program modules 1224 including, but not limited to, an operating system, one or more applications, other program modules, and programs Data, each of these examples or a combination may include the implementation of a network environment. Program modules 1224 generally perform functions and/or methods in the embodiments described in the embodiments of this application.

Electronic device 1200 may also communicate with one or more external devices 1300 (e.g., keyboard, pointing device, display 1310, etc.), may also communicate with one or more devices that enable a user to interact with electronic device 1200, and/or with Any device (eg, network card, modem, etc.) that enables the electronic device 1200 to communicate with one or more other computing devices. This communication may occur through an input/output (I/O) interface 1240. Moreover, the electronic device 1200 can also communicate with one or more networks (such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network, such as the Internet) through the network adapter 1250. As shown, network adapter 1250 communicates with other modules of electronic device 1200 via bus 1230. It should be understood that, although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant arrays of independent disks (Redundant Arrays of Independent Disks, RAID) systems, tape drives and data backup storage systems, etc.

The processing unit 1210 executes a variety of functional applications and data processing by running programs stored in the system memory 1220, such as implementing the model training method and energy supplement provided by the embodiments of the present application. Intent identification methods.

Embodiment 8

Embodiment 8 of the present application also provides a computer-readable storage medium on which a computer program (also known as computer executable instructions) is stored. When the program is executed by a processor, it is used to perform model training provided by the embodiment of the present application. Methods and empowering intention identification methods.

The computer storage medium in the embodiment of the present application may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. Examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more conductors, portable computer disks, hard drives, RAM, ROM, Erasable Programmable Read-Only Memory, EPROM or flash memory), optical fiber, CD-ROM, optical storage device, magnetic storage device, or any suitable combination of the above. As used herein, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .

Program code embodied on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.

Computer program code for performing operations of embodiments of the present application may be written in one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional A procedural programming language such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. Where a remote computer is involved, the remote computer may be connected to the user's computer through any kind of network, including a LAN or WAN, or may be connected to an external computer (such as through the Internet using an Internet service provider).

In the technical solution of the present disclosure, the involved sample vehicle data, sample energy replenishment reference data, energy replenishment behavior data, sample vehicle portrait data, vehicle terminal log data, vehicle sensor data, associated sensing data, energy replenishment intention data, The acquisition, storage and application of current vehicle data, current energy replenishment reference data, historical energy replenishment reference data, supplementary reference data, current vehicle portrait data, etc. are in compliance with relevant laws and regulations and do not violate public order and good customs.

The energizing intention recognition model in this embodiment is not targeted at a specific user and cannot reflect the personal information of a specific user.

Claims

A model training method including:

Extract sample energy supplement reference data from sample vehicle data;

Determine the energy replenishment behavior data according to the sample energy replenishment reference data;

Construct portrait data of the sample vehicle based on the energy replenishment behavior data;

According to the energy-replenishing behavior data and the portrait data, an energy-replenishing intention recognition model is trained.
The method according to claim 1, wherein determining the energy replenishment behavior data according to the sample energy replenishment reference data includes:

From the sample energy replenishment reference data, extract energy replenishment intention data related to energy replenishment intention;

From the energy-replenishing intention data, the energy-replenishing behavior data related to the energy-replenishing behavior is extracted.
The method according to claim 2, wherein extracting energy-replenishing intention data related to energy-replenishing intention from the sample energy-replenishing reference data includes:

Screen energy replenishment intention keywords from the vehicle terminal log data in the sample energy replenishment reference data;

From the vehicle sensor data in the sample energy replenishment reference data, filter the associated sensor data of the energy replenishment intention keyword;

The energy-replenishing intention data is determined according to the energy-replenishing intention keyword and the associated sensing data.
The method according to claim 2, wherein extracting energy-replenishing intention data related to energy-replenishing intention from the sample energy-replenishing reference data includes:

Determine the energy replenishment time according to the vehicle sensor data in the sample energy replenishment reference data;

According to the energy replenishment time and the reference driving time, determine the energy replenishment intention period;

Extract sample energy replenishment reference data located in the energy replenishment intention period as the energy replenishment intention data.
The method according to claim 4, after determining the energy replenishment moment, further comprising:

In the case where there are at least two energy replenishment moments, determine the average driving time based on the at least two energy replenishment moments;

The reference driving time is determined based on the average driving time and a preset interval percentage.
The method according to claim 2, wherein extracting the energy-replenishing behavior data related to the energy-replenishing behavior from the energy-replenishing intention data includes:

Traverse the energy replenishment intention data at each moment in turn to determine whether the moment meets the stacking conditions;

In response to the time meeting the time, after pushing the energy replenishment intention data at the time into the stack, it is determined whether the number of data items in the stack meets the preset number;

In response to the number of data items in the stack meeting the preset number, determining whether there is an energy growth event based on the energy replenishment intention data in the stack;

When the energy growth event exists, the energy replenishment intention data in the stack is used as the energy replenishment behavior data, and the energy replenishment intention data in the stack is cleared.
The method according to claim 6, wherein determining whether there is an energy growth event according to the energy replenishment intention data in the stack includes:

Determine at least one of the energy replenishment increment amount and the energy replenishment time interval according to the energy replenishment intention data in the stack;

Whether the energy increase event exists is determined based on at least one of the energy supplement increase amount and the energy supplement time interval.
The method according to claim 1, wherein said constructing the portrait data of the sample vehicle based on the energy replenishment behavior data includes:

Determine the energy replenishment location based on the energy replenishment behavior data;

According to the energy replenishment position, the portrait data of the sample vehicle is constructed.
The method according to any one of claims 1 to 8, wherein the sample vehicle data includes: vehicle terminal log data and vehicle sensor data.
A method for identifying energy-replenishing intentions, including:

Obtain the current energy replenishment reference data from the current vehicle data;

Input the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment;

Wherein, the energy-replenishing intention recognition model is trained by the method described in any one of claims 1-9.
The method according to claim 10, after obtaining the current energy supplement reference data from the current vehicle data, further comprising:

Obtain the supplementary reference data of the current energy supplement reference data from the historical energy supplement reference data;

Inputting the current energy replenishment reference data into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment includes:

The current energy replenishment reference data and the supplementary reference data are input into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment.
The method according to claim 10, wherein said inputting said current energy replenishment reference data Enter the energy replenishment intention recognition model to obtain the energy replenishment intention recognition results at the current moment, including:

Get the portrait data of the current vehicle;

The portrait data of the current vehicle and the current energy replenishment reference data are input into the energy replenishment intention recognition model to obtain the energy replenishment intention recognition result at the current moment.
The method according to any one of claims 10-12, wherein the current vehicle data includes: vehicle terminal log data and vehicle sensor data.
A model training device including:

The sample data extraction module is configured to extract sample energy replenishment reference data from sample vehicle data;

The behavior data determination module is configured to determine the energy replenishment behavior data based on the sample energy replenishment reference data;

A portrait data construction module configured to construct portrait data of the sample vehicle based on the energy replenishment behavior data;

A model training module is configured to train an energy-replenishing intention recognition model based on the energy-replenishing behavior data and the portrait data.
An energy-replenishing intention recognition device, including:

The current data acquisition module is set to obtain the current energy replenishment reference data from the current vehicle data;

The energy replenishment intention identification module is configured to input the current energy replenishment reference data into the energy replenishment intention identification model to obtain the energy replenishment intention identification result at the current moment;

Wherein, the energy-replenishing intention recognition model is trained by the method described in any one of claims 1-9.
An electronic device including:

at least one processor;

a memory configured to store at least one program;

When the at least one program is executed by the at least one processor, the at least one processor implements the model training method according to any one of claims 1-9, or any one of claims 10-13 The energy-replenishing intention identification method.
A computer-readable storage medium storing a computer program, which when executed by a processor implements the model training method as described in any one of claims 1-9, or the method as described in any one of claims 10-13 Energy-replenishing intention identification method.