Power consumption behavior determination method, system and device and nonvolatile storage medium

Abstract

The invention discloses a power consumption behavior determination method, system and device and a nonvolatile storage medium. The method comprises the steps that charging and discharging data corresponding to a plurality of electric vehicles in a preset time period are collected; fragmenting the charging and discharging data corresponding to the plurality of electric vehicles to obtain a plurality of fragment data corresponding to the plurality of electric vehicles and storing the fragment data in a Hive database; adopting a preset query statement to query charging and discharging information corresponding to the plurality of electric vehicles in a Hive database; and processing the charging and discharging information corresponding to the plurality of electric vehicles, and determining power utilization behavior results of the plurality of electric vehicles. According to the method and the device, the technical problems that the obtained power utilization behavior result is not accurate enough and the effect is poor due to the fact that the data involved in charging and discharging of the electric vehicle is relatively large are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method, system, device and non-volatile storage medium for determining power usage behavior. Background Art

[0002] The charging and discharging data of electric vehicles is key information that reflects their electricity consumption behavior, but this type of data is usually massive, high-dimensional, and complex. Traditional data analysis methods cannot effectively extract valuable information when processing this type of data. Specifically, the charging and discharging data of electric vehicles may include multiple dimensions such as charging time, charging location, charging power, battery status, etc., and the amount of data is huge. Traditional data processing and analysis tools directly process this data, but the effect is not good enough, and the speed is slow and the efficiency is low.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] Embodiments of the present invention provide a method, system, device and non-volatile storage medium for determining power usage behavior, so as to at least solve the technical problem that a large amount of data involved in charging and discharging of electric vehicles leads to inaccurate power usage behavior results and poor effects.

[0005] According to one aspect of an embodiment of the present invention, a method for determining electricity usage behavior is provided, comprising: collecting charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period; slicing the charging and discharging data corresponding to each of the plurality of electric vehicles to obtain a plurality of fragmented data corresponding to each of the plurality of electric vehicles and storing them in a Hive database; using a preset query statement to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database; processing the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the electricity usage behavior results of the plurality of electric vehicles.

[0006] Optionally, before using a pre-set query statement to query the charging and discharging information corresponding to multiple electric vehicles in the Hive database, it includes: receiving a query request; setting a query statement based on the query request, wherein the query statement includes at least one of the following: a statement for querying the charging frequency of the electric vehicle, a statement for querying the charging time of the electric vehicle, and a statement for querying the charging power of the electric vehicle.

[0007] Optionally, the charging and discharging information corresponding to each of the multiple electric vehicles is processed to determine the electricity usage behavior results of the multiple electric vehicles, including: obtaining weather data within a preset time period; determining association rules between the weather data and the charging and discharging information based on the weather data within the preset time period and the charging and discharging information corresponding to each of the multiple electric vehicles; and determining the electricity usage behavior results of the multiple electric vehicles based on the association rules.

[0008] Optionally, the charging and discharging information corresponding to each of the multiple electric vehicles is processed to determine the power usage behavior results of the multiple electric vehicles, including: clustering the charging and discharging information corresponding to each of the multiple electric vehicles based on the vehicle types corresponding to each of the multiple electric vehicles to determine the charging and discharging characteristics corresponding to the electric vehicle types; determining the power usage behavior results of the multiple electric vehicles according to the charging and discharging characteristics corresponding to the electric vehicle types.

[0009] Optionally, the electricity usage behavior results are visualized to obtain a target image; and the target image is displayed.

[0010] Optionally, the electricity usage behavior results of the multiple electric vehicles include at least one of the following: the charging period preferences corresponding to each of the multiple electric vehicles, the charging efficiencies corresponding to each of the multiple electric vehicles, and the battery health conditions corresponding to each of the multiple electric vehicles.

[0011] According to another aspect of an embodiment of the present invention, a power usage behavior determination system is also provided, which adopts any one of the above-mentioned power usage behavior determination methods, including: a data acquisition module, used to collect charging and discharging data corresponding to each of the multiple electric vehicles; a data slicing module, used to slice the charging and discharging data corresponding to each of the multiple electric vehicles, obtain multiple fragment data corresponding to each of the multiple electric vehicles and store them in a Hive database; a data query module, used to use a preset query statement to query the charging and discharging information corresponding to each of the multiple electric vehicles in the Hive database; a data determination module, used to process the charging and discharging information corresponding to each of the multiple electric vehicles, and determine the power usage behavior results of the multiple electric vehicles.

[0012] Optionally, a result display module is used to visualize the electricity consumption behavior results, obtain a target image, and display the target image.

[0013] According to another aspect of an embodiment of the present invention, a device for determining electricity consumption behavior is also provided, including: a collection module, used to collect charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period; a sharding module, used to shard the charging and discharging data corresponding to each of the plurality of electric vehicles, obtain a plurality of fragmented data corresponding to each of the plurality of electric vehicles and store them in a Hive database; a query module, used to use a preset query statement to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database; and a processing module, used to process the charging and discharging information corresponding to each of the plurality of electric vehicles, and determine the electricity consumption behavior results of the plurality of electric vehicles.

[0014] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned methods for determining power consumption behavior.

[0015] According to another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned methods for determining power consumption behavior is executed.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above-mentioned methods for determining power usage behavior is implemented.

[0017] In an embodiment of the present invention, a method for determining electricity usage behavior is adopted, by collecting charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period; slicing the charging and discharging data corresponding to each of the plurality of electric vehicles to obtain a plurality of fragmented data corresponding to each of the plurality of electric vehicles and storing them in a Hive database; using a preset query statement to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database; processing the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the electricity usage behavior results of the plurality of electric vehicles, thereby achieving the purpose of slicing the data and using the Hive database to store and query to determine the electricity usage behavior results, thereby achieving the technical effect of improving processing efficiency, and further solving the technical problem that the electricity usage behavior results obtained due to the large amount of data involved in the charging and discharging of electric vehicles are not accurate enough and the effect is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for determining power consumption behavior is shown;

[0020] Figure 2 is a flow chart of a method for determining electricity usage behavior according to an embodiment of the present invention;

[0021] Figure 3 is a specific flow chart of a method for determining power usage behavior according to an optional embodiment of the present invention;

[0022] Figure 4 is a system framework diagram of a power consumption behavior determination system provided according to an embodiment of the present invention;

[0023] Figure 5 4 is a structural block diagram of a device for determining power usage behavior provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present invention, a method embodiment of a method for determining electricity usage behavior is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for determining power consumption behavior. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1Different configurations are shown.

[0028] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power consumption behavior determination method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the power consumption behavior determination method of the above-mentioned application program is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0031] Figure 2 is a flow chart of a method for determining power usage behavior according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0032] Step S202 , collecting charging and discharging data corresponding to a plurality of electric vehicles within a preset time period.

[0033] In this step, the charge and discharge data corresponding to each of the multiple electric vehicles within the preset period can be collected, wherein the preset period can be one year, and the charge and discharge data can include the charging time, single charging duration, charging power, charging frequency, etc. of the electric vehicle. The charge and discharge data can be collected from the vehicle forwarding platform or other data sources, such as the vehicle-mounted system of the electric vehicle, the monitoring system of the charging station, the smart meter data of the power grid, or the data reported by the user. The collected data can also be cleaned, converted, or formatted to ensure the accuracy and consistency of the data.

[0034] Step S204, sharding the charging and discharging data corresponding to each of the multiple electric vehicles to obtain multiple fragmented data corresponding to each of the multiple electric vehicles and storing them in a Hive database.

[0035] In this step, the charging and discharging data corresponding to each of the multiple electric vehicles can be sliced ​​to obtain multiple fragment data corresponding to each of the multiple electric vehicles, wherein each fragment data represents a complete charging and discharging process, which is convenient for subsequent analysis of electricity consumption behavior. Slicing can be based on Spark technology. Spark is an open source large-scale data processing framework that provides a unified solution for big data processing, including batch processing, stream processing, machine learning, graph computing, etc. Spark technology takes into account the speed and efficiency of data processing. It performs calculations in memory, greatly improving the speed of data processing. The core component of Spark is Spark Core, on which functional modules such as SQL (Spark SQL), stream processing (Spark Streaming), machine learning (MLlib), and graph processing (GraphX) are built. In Spark, data is distributed on multiple nodes for processing, and the data set is divided into multiple partitions (also called slices or shards), and each partition can be processed independently on different nodes of the cluster. This segmentation mechanism is the basis of Spark parallel computing, which allows large-scale data processing tasks to be parallelized and improve computing efficiency. Multiple fragments of data corresponding to multiple electric vehicles are stored in the Hive database, where the Hive database supports the storage and query of massive data and provides a variety of data formats and query methods to meet the needs of electric vehicle charging and discharging data storage.

[0036] Step S206, using a preset query statement to query the Hive database for the charging and discharging information corresponding to each of the multiple electric vehicles.

[0037] In this step, the charging and discharging information corresponding to multiple electric vehicles can be queried in the Hive database according to the pre-set query statement. For example, through the SQL query statement, various useful information can be extracted, such as the charging frequency, charging time, and charging power of the electric vehicle. Specifically, when querying the charging and discharging information of the electric vehicle in the Hive database, the main focus is on the data fields that reflect the charging and discharging behavior of the electric vehicle. These fields can include:

[0038] Vehicle ID: This is a unique identifier for each electric vehicle and is used to distinguish between different vehicles. In the data table, it may be named vehicle_id, ev_id, etc.

[0039] Time Information: includes charge start time (charge_start_time), charge end time (charge_end_time), discharge start time (discharge_start_time) and discharge end time (discharge_end_time). This time information is used to analyze the periodicity and peak hours of charging behavior.

[0040] Charge Energy: Records the amount of electrical energy received by the electric vehicle during each charging process, usually in kilowatt-hours (kWh). The field name may be charge_energy, charge_amount, etc.

[0041] Discharge Energy: records the amount of electric energy consumed by the electric vehicle during each discharge process, also in kilowatt-hours (kWh). The field name may be discharge_energy, discharge_amount, etc.

[0042] State Of Charge: Usually the percentage of the remaining battery capacity to the total capacity.

[0043] Charge Location: Information describing the location where the vehicle is charged, which can be the ID or location information of the charging station. This helps to understand the usage of charging facilities and user charging preferences.

[0044] Charge Type: Distinguish between fast charging (fast_charge), slow charging (slow_charge), or other types of charging methods. This helps analyze the frequency of use and preference of different charging methods.

[0045] Charge Frequency: Calculates the number of times each electric vehicle is charged in a specific time period. This can reveal the user's charging habits and needs.

[0046] Step S208: Process the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power usage behavior results of the multiple electric vehicles.

[0047] In this step, the charging and discharging information corresponding to each of the multiple electric vehicles can be processed to determine the results of the electricity consumption behavior, which usually involves the application of data analysis, data mining and machine learning technologies. For example, statistical indicators of each electric vehicle, such as average charging amount, charging times, charging time preference, etc., can be calculated based on the charging and discharging information. The time trend of the charging and discharging information can be analyzed based on time series analysis to identify seasonal or periodic patterns. Cluster analysis can also be used to group the electricity consumption behavior of electric vehicles and identify different types of users or charging patterns. Association rule learning can also be used to find the association between different charging and discharging behaviors, such as the relationship between "night charging" and "weekday charging". Prediction models, such as random forests or neural networks, can also be constructed to predict the future charging demand or battery status of electric vehicles. Based on the above analysis, different electricity consumption behavior patterns such as "frequent fast charging users", "daytime charging preference users", and "low power warning users" can be identified.

[0048] Through the above steps, the characteristics and patterns of electricity consumption behavior can be extracted from the charging and discharging information of electric vehicles, providing valuable insights for electric vehicle users, grid operators and policy makers. For example, the identified charging patterns can be used to predict grid load and optimize the layout of charging facilities; the prediction model can help users plan charging strategies and extend battery life; and the clustering results can guide charging service providers to provide personalized services for different types of users.

[0049] The above steps can effectively process massive, high-dimensional, and complex electric vehicle charging and discharging data, extract valuable electricity consumption behavior information, provide electricity optimization suggestions for electric vehicle users, and provide data support for charging facility planning, power grid load forecasting, etc., thereby promoting the improvement of power grid operation efficiency.

[0050] Through the above steps, the purpose of sharding the data and using the Hive database to store and query to determine the electricity usage behavior results is achieved, thereby achieving the technical effect of improving processing efficiency, and further solving the technical problem that the electricity usage behavior results obtained due to the large amount of data involved in the charging and discharging of electric vehicles are not accurate and the effect is poor.

[0051] As an optional embodiment, before using a pre-set query statement to query the charging and discharging information corresponding to multiple electric vehicles in the Hive database, it includes: receiving a query request; setting a query statement based on the query request, wherein the query statement includes at least one of the following: a statement for querying the charging frequency of the electric vehicle, a statement for querying the charging time of the electric vehicle, and a statement for querying the charging power of the electric vehicle.

[0052] Optionally, a query request from a user can be received, and a query statement can be set based on the query request. Specifically, query requests from users or systems are received, and these requests may include specific query parameters, such as query time period, vehicle ID list, query type (charging frequency, charging time, charging power), etc. After receiving the request, it is necessary to parse the parameters in the request, such as the type of query, time range, vehicle ID, etc., and convert them into elements that can be used to build Hive SQL queries. According to the request parameters, a corresponding Hive SQL query statement is constructed. The query statement may include at least one of the following statements: query the charging frequency of electric vehicles, query the charging time of electric vehicles, and query the charging power of electric vehicles. Alternatively, if the query request contains multiple conditions (such as querying the charging frequency and charging power of a specific vehicle ID), a more complex query can be constructed by combining the above basic query statements. The constructed Hive SQL query statement can be submitted to the Hive server for execution. After the query is executed, the returned result data is processed, and the result can also be converted into a specific data structure or format for display on the front end or further data analysis. In addition, for frequently executed queries, you can consider pre-computing and storing partial results in Hive to reduce the response time of real-time queries.

[0053] When querying the charging and discharging information of electric vehicles in the Hive database, in addition to querying the above data, you can also query basic charging information, such as the unique identifier of the charging record, the unique identifier of the electric vehicle, the timestamp of the start of charging, the timestamp of the end of charging, the amount of electric energy received by the electric vehicle during charging, the amount of electric energy consumed by the electric vehicle during discharging, the location where charging occurs or the ID of the charging station, the type of charging, such as fast charging, slow charging, etc. You can also query the battery status information, which can include the battery status before charging, the battery status after charging, the battery temperature during charging, the battery voltage during charging, etc.

[0054] Through query, we can deeply analyze the charging and discharging behavior of electric vehicles, identify user preferences, predict demand patterns, and evaluate the performance and efficiency of charging infrastructure. This information is of great significance in promoting the popularization of electric vehicles, optimizing the layout of charging stations, and improving the user charging experience.

[0055] As an optional embodiment, the charging and discharging information corresponding to each of the multiple electric vehicles is processed to determine the electricity usage behavior results of the multiple electric vehicles, including: obtaining weather data within a preset time period; determining an association rule between the weather data and the charging and discharging information based on the weather data within the preset time period and the charging and discharging information corresponding to each of the multiple electric vehicles; and determining the electricity usage behavior results of the multiple electric vehicles based on the association rule.

[0056] Optionally, the charging and discharging information corresponding to each of the multiple electric vehicles is processed, combined with the weather data within a preset period of time to determine the association rules between the weather data and the charging and discharging information, and the electricity consumption behavior results are determined based on these rules. The weather data within the preset period of time can be first obtained, for example, the weather data within the preset period of time can be obtained through a weather APP or a weather data provider. These data may include temperature, humidity, wind speed, rainfall, etc. The weather data and the charging and discharging information of the electric vehicle are time-aligned and integrated to ensure that each charging and discharging record is associated with the corresponding weather data. Then, based on the weather data and the charging and discharging information, the association rules are determined, wherein the data can be pre-processed to ensure data quality, and the weather data and the charging and discharging information are integrated into a format suitable for analysis. It may be necessary to convert the data into a transaction data format to be suitable for the association rule learning algorithm. Apply an association rule learning algorithm, such as Apriori, ECLAT or FP-growth, to mine the potential association rules between weather factors and charging and discharging behaviors from the integrated data. For example, it may be found that "the charging frequency of electric vehicles increases under low temperature conditions" or "the utilization rate of charging stations in specific areas decreases on rainy days". The mined association rules can be evaluated, including support, confidence, and lift, to determine the reliability and practical significance of the rules. Based on the mined association rules, explain how weather conditions affect the electricity consumption behavior of electric vehicles. For example, low temperatures may lead to reduced battery efficiency, requiring more frequent charging; while in sunny weather, the use of solar charging facilities may increase. More complex models, such as predictive models, can also be built based on association rules to predict the electricity demand of electric vehicles under specific weather conditions.

[0057] Through the above process, we can deeply understand the impact of weather conditions on the charging and discharging behavior of electric vehicles, provide a scientific basis for the formulation of charging strategies, and also help improve the charging experience of electric vehicle users and the operational efficiency of charging facilities.

[0058] As an optional embodiment, the charging and discharging information corresponding to each of the multiple electric vehicles is processed to determine the power usage behavior results of the multiple electric vehicles, including: clustering the charging and discharging information corresponding to each of the multiple electric vehicles based on the vehicle types corresponding to each of the multiple electric vehicles to determine the charging and discharging characteristics corresponding to the electric vehicle types; determining the power usage behavior results of the multiple electric vehicles according to the charging and discharging characteristics corresponding to the electric vehicle types.

[0059] Optionally, the charging and discharging information corresponding to each of the multiple electric vehicles is processed to determine the power consumption behavior results of the multiple electric vehicles. This process can be achieved by clustering analysis based on vehicle type. Features related to the vehicle type can be selected from the charging and discharging information, including but not limited to charging frequency, charging time, charging power, charging location, battery state (SoC) changes, etc. Then, according to business needs and analysis objectives, new features are created, such as average charging amount, charging time preference, charging efficiency (ratio of charging amount to charging time), etc. Based on the charging and discharging characteristics and vehicle type, a suitable clustering algorithm can be selected, such as K-means, DBSCAN, hierarchical clustering, or density-based clustering algorithm. The K-means algorithm is suitable for scenarios with relatively uniform data distribution, while DBSCAN is more suitable for processing clusters and outliers with different densities. The selected features are normalized to ensure fairness in clustering analysis. Commonly used methods include minimum-maximum normalization (MinMaxScaler) and Z-score normalization. Then the selected clustering algorithm is applied to cluster the features to obtain the charging and discharging features corresponding to different types of electric vehicles. For example, there may be different vehicle charging and discharging behavior patterns such as "fast charging preference type", "night charging type", and "low battery warning type". Appropriate evaluation indicators (such as silhouette coefficient, Calinski-Harabasz index) can be used to evaluate the quality of clustering results to ensure that the clustering is reasonable and meaningful. After clustering, the charging and discharging characteristics of each cluster can be analyzed in detail to explain the differences in charging and discharging behaviors of different types of electric vehicles. For example, fast charging preference vehicles may use fast charging stations more frequently, while night charging vehicles tend to charge at night when electricity prices are lower.

[0060] Through cluster analysis based on vehicle type, we can deeply understand the differences in charging and discharging behaviors of different types of electric vehicles, providing valuable insights for charging service providers, grid operators and policymakers to optimize charging infrastructure, improve user charging experience and promote the popularization of electric vehicles.

[0061] As an optional embodiment, the electricity usage behavior results are visualized to obtain a target image; and the target image is displayed.

[0062] Optionally, the results can be displayed in a visual form to help users intuitively understand the electricity consumption behavior of electric vehicles. The display content can include charging trend charts, power load distribution charts, power consumption behavior clustering results, etc., to provide users with clear power consumption behavior analysis and optimization suggestions. The platform also supports an evaluation system to collect user feedback and update design routes and indicators.

[0063] Specifically, visualizing the results of electricity consumption behavior into target images and displaying these images in the user interface or report helps to transform complex data into intuitive information for easy understanding and decision-making. You can choose the appropriate visualization type according to the type of information displayed. For example, a time series graph is suitable for showing the trend of charging volume over time, a bar graph is suitable for comparing the charging frequency of different vehicle types, and a heat map is suitable for showing the usage of charging locations. Among them, the generated target image can also include an electricity consumption behavior prediction graph, which can build a prediction model based on historical electricity consumption data to show the charging demand forecast for a period of time in the future. This helps charging stations and power grid operators plan resources and scheduling in advance. Among them, time series prediction models (such as ARIMA, LSTM) can be used to predict data, and then line graphs or area graphs can be used to display the prediction results. Charging cost analysis graphs are used to analyze charging costs at different times, locations or vehicle types, display the distribution and changing trends of costs, and help users identify the best time and place for charging. The charging volume and real-time electricity price data can be combined to display the charging cost using bar graphs, heat maps or scatter plots. Charging facility utilization graphs can display the utilization of charging stations or charging piles, helping operators evaluate the efficiency of facilities and optimize layout. Heat maps or stacked area maps can be used to show the usage of each charging facility in different time periods. Battery state (SoC) change graphs can show the changes in the battery state of electric vehicles over time or mileage, helping to understand the battery usage pattern and health status. Line graphs or scatter plots can be used to show the relationship between battery state (SoC) and time or mileage. The time series analysis graph of charging behavior can help understand the seasonal and daily periodic patterns of charging behavior by decomposing time series data to show seasonal, trend and periodic components. Visual images can help to more fully understand the charging needs, behavior patterns and related factors of electric vehicles, thereby providing data support for optimizing charging strategies, improving user experience and promoting the construction of smart grids.

[0064] As an optional embodiment, the electricity usage behavior results of multiple electric vehicles include at least one of the following: the charging period preferences corresponding to each of the multiple electric vehicles, the charging efficiency corresponding to each of the multiple electric vehicles, and the battery health status corresponding to each of the multiple electric vehicles.

[0065] Optionally, the electricity behavior results in the electric vehicle field usually cover multiple aspects of electric vehicle charging and electricity usage patterns. These results are essential for optimizing charging strategies, improving charging facility utilization, predicting grid loads, and maintaining battery health. The following are several key aspects that electricity behavior results may include: Charging frequency, which indicates the number of times an electric vehicle is charged in a certain period of time. A high frequency may mean that the vehicle is used frequently or the battery capacity is small. Charging time preference, which reflects when electric vehicle users prefer to charge (such as early morning, evening, weekdays or weekends). This helps charging facility operators arrange the opening hours of charging stations to improve utilization. Charging location preference, analyzes where electric vehicle users prefer to charge, such as home, workplace, and public charging stations. This helps in the planning and investment decisions of charging infrastructure. Charging amount distribution, which shows the distribution of the amount of electricity charged during a charging process, can be a histogram or density map, which helps to understand the average charging demand of electric vehicles. Charging efficiency, which calculates the efficiency of energy conversion during charging, reflects the charging speed and the performance of the charging system. Battery health, which evaluates the health of the battery based on data such as battery state of system (SoC), battery temperature, and charging cycle. This helps predict battery life and optimize battery maintenance strategies. Power load distribution: Analyze the impact of electric vehicle charging on grid load, usually displayed in the form of a time series graph to help grid operators predict and manage load. Power consumption behavior patterns: Use data mining techniques such as cluster analysis to identify different power consumption behavior patterns, such as "fast charging preference" and "night charging type".

[0066] Figure 3 is a specific flow chart of a method for determining power usage behavior according to an optional embodiment of the present invention. Figure 3 As shown, a specific implementation method is given:

[0067] First, you need to build JDK, Spark, and Hive environments on the server, and configure the database address according to the set port and host IP; second, develop a Spark program according to business needs. The Spark program will read the files in the specified directory according to the configuration, and segment the data in the electric vehicle according to the corresponding code logic; then, store the read results in Hive (DWS layer), and write corresponding SQL statements to calculate the segmented data fragments; finally, write the calculated results to Hive (ADS layer), and develop the corresponding indicator display page in the platform to provide travel suggestions for users. In addition, users can evaluate the indicators through the feedback system and update the indicator library. At this point, the entire electricity consumption behavior analysis process is completed.

[0068] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0069] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method for determining the electricity consumption behavior according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0070] According to an embodiment of the present invention, a power usage behavior determination system is further provided, which adopts any one of the above power usage behavior determination methods. Figure 4 is a system framework diagram of a power consumption behavior determination system provided according to an embodiment of the present invention. Figure 4 As shown, it includes: a data acquisition module, which is used to collect the charging and discharging data corresponding to each of the multiple electric vehicles; a data slicing module, which is used to slice the charging and discharging data corresponding to each of the multiple electric vehicles, obtain multiple fragment data corresponding to each of the multiple electric vehicles and store them in a Hive database; a data query module, which is used to use a preset query statement to query the charging and discharging information corresponding to each of the multiple electric vehicles in the Hive database; a data determination module, which is used to process the charging and discharging information corresponding to each of the multiple electric vehicles and determine the power consumption behavior results of the multiple electric vehicles.

[0071] As an optional embodiment, the result display module is used to visualize the electricity consumption behavior results, obtain a target image, and display the target image.

[0072] According to an embodiment of the present invention, there is also provided a power usage behavior determination device for implementing the above power usage behavior determination method. Figure 5 is a structural block diagram of a device for determining power usage behavior according to an embodiment of the present invention. Figure 5 As shown, the power usage behavior determination device includes: a collection module 52, a slicing module 54, a query module 56 and a processing module 58. The power usage behavior determination device is described below.

[0073] The collection module 52 is used to collect the charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period.

[0074] The slicing module 54 is connected to the acquisition module 52 and is used to slice the charging and discharging data corresponding to each of the multiple electric vehicles to obtain multiple fragment data corresponding to each of the multiple electric vehicles and store them in the Hive database.

[0075] The query module 56 is connected to the sharding module 54 and is used to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database by using a preset query statement.

[0076] The processing module 58 is connected to the query module 56 and is used to process the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power consumption behavior results of the multiple electric vehicles.

[0077] It should be noted that the above-mentioned acquisition module 52, slicing module 54, query module 56 and processing module 58 correspond to steps S202 to S208 in the embodiment, and the examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment. It should be noted that the above-mentioned modules as part of the device can be run in the computer terminal 10 provided in the embodiment.

[0078] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0079] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power consumption behavior determination method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned power consumption behavior determination method is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0080] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: collect the charging and discharging data corresponding to each of the multiple electric vehicles within a preset time period; segment the charging and discharging data corresponding to each of the multiple electric vehicles to obtain multiple fragmented data corresponding to each of the multiple electric vehicles and store them in the Hive database; use a preset query statement to query the charging and discharging information corresponding to each of the multiple electric vehicles in the Hive database; process the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power consumption behavior results of the multiple electric vehicles.

[0081] Optionally, the processor may also execute program code for the following steps: before using a pre-set query statement to query the charging and discharging information corresponding to multiple electric vehicles in the Hive database, the steps include: receiving a query request; and setting a query statement based on the query request, wherein the query statement includes at least one of the following: a statement for querying the charging frequency of the electric vehicle, a statement for querying the charging time of the electric vehicle, and a statement for querying the charging power of the electric vehicle.

[0082] Optionally, the processor may also execute program code for the following steps: processing the charging and discharging information corresponding to each of the multiple electric vehicles to determine the electricity usage behavior results of the multiple electric vehicles, including: obtaining weather data within a preset time period; determining association rules between weather data and charging and discharging information based on the weather data within the preset time period and the charging and discharging information corresponding to each of the multiple electric vehicles; and determining the electricity usage behavior results of the multiple electric vehicles based on the association rules.

[0083] Optionally, the processor may also execute program code for the following steps: processing the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power usage behavior results of the multiple electric vehicles, including: clustering the charging and discharging information corresponding to each of the multiple electric vehicles based on the vehicle types corresponding to each of the multiple electric vehicles to determine the charging and discharging characteristics corresponding to the electric vehicle types; determining the power usage behavior results of the multiple electric vehicles based on the charging and discharging characteristics corresponding to the electric vehicle types.

[0084] Optionally, the processor may also execute program codes of the following steps: visualizing the electricity usage behavior results to obtain a target image; and displaying the target image.

[0085] Optionally, the processor may also execute program code of the following steps: the power usage behavior results of multiple electric vehicles include at least one of the following: the charging period preferences corresponding to each of the multiple electric vehicles, the charging efficiencies corresponding to each of the multiple electric vehicles, and the battery health conditions corresponding to each of the multiple electric vehicles.

[0086] By adopting an embodiment of the present invention, a method for determining electricity usage behavior is provided, which collects charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period; segments the charging and discharging data corresponding to each of the plurality of electric vehicles to obtain a plurality of fragmented data corresponding to each of the plurality of electric vehicles and stores them in a Hive database; uses a preset query statement to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database; processes the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the electricity usage behavior results of the plurality of electric vehicles, thereby achieving the purpose of segmenting the data and using the Hive database to store and query to determine the electricity usage behavior results, thereby realizing the technical effect of improving processing efficiency, and further solving the technical problem that the electricity usage behavior results obtained due to the large amount of data involved in the charging and discharging of electric vehicles are not accurate enough and the effect is poor.

[0087] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0088] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the power usage behavior determination method provided in the above embodiment.

[0089] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0090] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: collecting charging and discharging data corresponding to each of the multiple electric vehicles within a preset time period; segmenting the charging and discharging data corresponding to each of the multiple electric vehicles to obtain multiple fragmented data corresponding to each of the multiple electric vehicles and storing them in a Hive database; using a preset query statement to query the charging and discharging information corresponding to each of the multiple electric vehicles in the Hive database; processing the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power usage behavior results of the multiple electric vehicles.

[0091] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: before using a pre-set query statement to query the charging and discharging information corresponding to multiple electric vehicles in the Hive database, it includes: receiving a query request; based on the query request, setting a query statement, wherein the query statement includes at least one of the following: a statement for querying the charging frequency of the electric vehicle, a statement for querying the charging time of the electric vehicle, and a statement for querying the charging power of the electric vehicle.

[0092] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: processing the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power usage behavior results of the multiple electric vehicles, including: obtaining weather data within a preset time period; based on the weather data within the preset time period and the charging and discharging information corresponding to each of the multiple electric vehicles, determining the association rules between the weather data and the charging and discharging information; based on the association rules, determining the power usage behavior results of the multiple electric vehicles.

[0093] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: processing the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power usage behavior results of the multiple electric vehicles, including: clustering the charging and discharging information corresponding to each of the multiple electric vehicles based on the vehicle types corresponding to each of the multiple electric vehicles to determine the charging and discharging characteristics corresponding to the electric vehicle types; determining the power usage behavior results of the multiple electric vehicles based on the charging and discharging characteristics corresponding to the electric vehicle types.

[0094] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: visualizing the power usage behavior results to obtain a target image; and displaying the target image.

[0095] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: the power usage behavior results of multiple electric vehicles include at least one of the following: the charging period preferences corresponding to each of the multiple electric vehicles, the charging efficiencies corresponding to each of the multiple electric vehicles, and the battery health conditions corresponding to each of the multiple electric vehicles.

[0096] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: collecting charging and discharging data corresponding to each of a plurality of electric vehicles within a preset time period; segmenting the charging and discharging data corresponding to each of the plurality of electric vehicles to obtain a plurality of fragmented data corresponding to each of the plurality of electric vehicles and storing them in a Hive database; using a preset query statement to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database; processing the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the electricity usage behavior results of the plurality of electric vehicles.

[0097] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0098] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0101] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0103] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining electricity usage behavior, characterized in that: include: Collecting charging and discharging data corresponding to a plurality of electric vehicles within a preset time period; Slice the charging and discharging data corresponding to each of the plurality of electric vehicles to obtain a plurality of fragment data corresponding to each of the plurality of electric vehicles and store the fragment data in a Hive database; Using a preset query statement, query the Hive database for the charging and discharging information corresponding to each of the plurality of electric vehicles; The charging and discharging information corresponding to each of the plurality of electric vehicles is processed to determine the power usage behavior results of the plurality of electric vehicles.

2. The method according to claim 1, characterized in that Before using the preset query statement to query the Hive database for the charging and discharging information corresponding to each of the plurality of electric vehicles, the method includes: receiving a query request; Based on the query request, the query statement is set, wherein the query statement includes at least one of the following: a statement for querying the charging frequency of the electric vehicle, a statement for querying the charging time of the electric vehicle, and a statement for querying the charging power of the electric vehicle.

3. The method according to claim 1, characterized in that The processing of the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the power usage behavior results of the plurality of electric vehicles includes: Obtaining weather data within the preset time period; Determining an association rule between weather data and charging and discharging information based on weather data in the preset time period and charging and discharging information corresponding to each of the plurality of electric vehicles; Based on the association rule, power usage behavior results of the plurality of electric vehicles are determined.

4. The method according to claim 1, characterized in that: The processing of the charging and discharging information corresponding to each of the plurality of electric vehicles to determine the power usage behavior results of the plurality of electric vehicles includes: Based on the vehicle types corresponding to the multiple electric vehicles, respectively, clustering the charging and discharging information corresponding to the multiple electric vehicles, and determining the charging and discharging characteristics corresponding to the electric vehicle types; The power usage behavior results of the plurality of electric vehicles are determined according to the charging and discharging characteristics corresponding to the electric vehicle type.

5. The method according to any one of claims 1 to 4, characterized in that: Also includes: Visualizing the electricity usage behavior results to obtain a target image; The target image is displayed.

6. The method according to any one of claims 1 to 4, characterized in that: The power usage behavior results of the multiple electric vehicles include at least one of the following: charging time period preferences corresponding to the multiple electric vehicles, charging efficiencies corresponding to the multiple electric vehicles, and battery health conditions corresponding to the multiple electric vehicles.

7. A system for determining electricity usage behavior, characterized in that: The method for analyzing power consumption behavior according to any one of claims 1 to 6 above comprises: A data acquisition module, used to collect charging and discharging data corresponding to a plurality of electric vehicles; A data slicing module, used for slicing the charging and discharging data corresponding to each of the plurality of electric vehicles, obtaining a plurality of fragment data corresponding to each of the plurality of electric vehicles and storing them in a Hive database; A data query module, used to query the charging and discharging information corresponding to each of the plurality of electric vehicles in the Hive database by using a preset query statement; The data determination module is used to process the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power consumption behavior results of the multiple electric vehicles.

8. The system according to claim 7, characterized in that Also includes: The result display module is used to visualize the electricity consumption behavior results, obtain a target image, and display the target image.

9. A device for determining electricity usage behavior, characterized in that: include: A collection module, used to collect charging and discharging data corresponding to a plurality of electric vehicles within a preset time period; A sharding module, used for sharding the charging and discharging data corresponding to each of the plurality of electric vehicles, obtaining a plurality of fragment data corresponding to each of the plurality of electric vehicles and storing them in a Hive database; A query module, used to query the Hive database for the charge and discharge information corresponding to each of the plurality of electric vehicles using a preset query statement; The processing module is used to process the charging and discharging information corresponding to each of the multiple electric vehicles to determine the power consumption behavior results of the multiple electric vehicles.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method for determining power usage behavior described in any one of claims 1 to 6.

11. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the method for determining electricity usage behavior as described in any one of claims 1 to 6.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining electricity usage behavior described in any one of claims 1 to 6 is implemented.

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