Information Management System

Abstract

To provide an information management system that can efficiently promote utilization of data.SOLUTION: An information management system 5 disclosed herein acquires a plurality of pieces of vehicle-related information transmitted from a vehicle 10 capable of EV traveling, and manages the vehicle-related information. The information management system 5 comprises: information processing means 6 that creates a plurality of tables in which part of the vehicle-related information is recorded; and storage means 8 that stores the tables. The information processing means 6 stores, in separate tables, first information used for calculating an index related to the history of the EV traveling and the other pieces of information, of the vehicle-related information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This matter concerns an information management system for managing vehicle-related information transmitted from vehicles. [Background technology]

[0002] Conventionally, information management systems have been proposed that collect diverse vehicle-related information transmitted from numerous vehicles as big data and provide a portion of this big data to various customers. For example, information management systems are known that generate individual aggregated results showing the driving trends of each user, as well as overall aggregated results showing the driving trends of many users, and provide this aggregated information to maintenance companies, insurance companies, etc. (See Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2011-198334 [Overview of the project] [Problems that the invention aims to solve]

[0004] In recent years, the uses and analytical purposes of big data have become diverse and increasingly varied. Therefore, it is desirable to extract and process a portion of the big data so that various types of information can be provided in a form that matches the needs of each customer, rather than just aggregated results as described in Patent Document 1. On the other hand, existing information management systems do not fully recognize the importance of such pre-processing of big data, and instead, a portion of the big data is extracted, processed, and formatted each time a request is received from a customer. Consequently, it is difficult to provide various types of information requested by customers quickly and in large quantities, and there is a challenge in promoting the utilization of data.

[0005] For example, when analyzing the operational status of a hybrid vehicle, it may be necessary to distinguish between driving conditions using the motor and driving conditions using the engine to understand the driving history. However, extracting the necessary information from a vast amount of big data while distinguishing between these driving conditions takes a great deal of time, and the required computing power and computational costs are enormous. The same applies to the analysis of the operational status of electric vehicles; the time, computing power, and costs required to calculate indicators related to EV driving history tend to be high, making it difficult to promote the utilization of the data.

[0006] One of the objectives of this project is to provide an information management system that was devised in light of the above-mentioned challenges and that can efficiently promote the utilization of data. Furthermore, in addition to this objective, another objective of this project is to achieve effects and benefits that cannot be obtained with conventional technology, derived from the various configurations shown in the "Modes for Carrying Out the Invention" described later. [Means for solving the problem]

[0007] The disclosure information management system can be implemented in the manner (examples of application) disclosed below, and will solve at least some of the above-mentioned problems. Each of the manners from Manifest 2 onward is an additional manner that can be selected as appropriate, and each of them is an optional manner. None of the manners from Manifest 2 onward disclose any manner or configuration that is essential to this case.

[0008] Appearance 1. The disclosed information management system is for EV driving, which is driving using the power of a motor. And non-EV driving, which uses the power of an internal combustion engine. It is possible hybrid This is an information management system that acquires multiple vehicle-related information transmitted from a vehicle and manages said vehicle-related information. Record The system comprises an information processing means for generating multiple tables and a storage means for storing the tables. The information processing means stores first information and other information from the vehicle-related information used to calculate an index related to the history of EV driving in separate tables. The information processing means calculates the average power consumption rate of the EV driving based on the first information. The information processing means further calculates the first power consumption amount in a trip that includes the EV driving and the non-EV driving, and calculates the index based on the first power consumption amount and the average power consumption rate.

[0009] Aspect 2. In the above Aspect 1, Preferably, the aforementioned index is the distance traveled by adding the motor-contributed distance of the non-EV driving to the cumulative distance of the EV driving. Embodiment 3. In Embodiment 2 described above, it is preferable that the information processing means excludes the vehicle whose total travel distance is less than the first predetermined distance from the target for calculating the index. hybrid Aspect 4 . In the above Aspect 3 , the information processing means calculates the average power consumption rate of the EV travel based on the first information in a trip in which the EV travel of a second predetermined distance or more shorter than the first predetermined distance is continued. The aforementioned It is preferable to calculate Ruko .

[0010] Embodiment 5. In any of embodiments 1 to 4 described above, the first information is preferably information relating to the charging and discharging of the drive battery installed in the hybrid vehicle.

[0011] Embodiment 6. The second information management system of the disclosure acquires a plurality of vehicle-related information transmitted from a vehicle capable of EV driving, which is driving using the power of an electric motor, and manages the vehicle-related information, comprising: an information processing means for generating a plurality of tables in which a portion of the vehicle-related information is recorded; and a storage means for storing the tables. The information processing means stores first information and other information from the vehicle-related information used to calculate an index related to the history of EV driving in separate tables. The information processing means excludes vehicles whose total driving distance is less than a first predetermined distance from the calculation of the index. The information processing means calculates the average power consumption rate of the EV driving based on the first information in a trip in which the EV driving continued for a second predetermined distance or more, which is shorter than the first predetermined distance, and calculates the index based on the average power consumption rate. The vehicle is a hybrid vehicle capable of not only EV driving but also non-EV driving, which is driving using the power of an internal combustion engine. The information processing means calculates a first power consumption amount in a trip including the EV driving and the non-EV driving, and calculates the index based on the first power consumption amount and the average power consumption rate. Aspect 7. In the above Aspect 6 , it is preferable that the first information is information regarding charging and discharging of a drive battery mounted on the vehicle. hybrid

Advantages of the Invention

[0012] According to the disclosed information management system, when managing a plurality of vehicle-related information (big data of automobiles), by separately generating a table including the first information and a table including other information, it is possible to improve the calculation efficiency and data analysis efficiency of an index related to the history of EV travel. That is, vehicle-related information can be organized in a form suitable for the purpose and application of its utilization, and the convenience of information can be improved. Therefore, it is possible to efficiently promote the utilization of data.

Brief Description of the Drawings

[0013] [Figure 1] It is a schematic diagram illustrating the configuration of a network to which the information management system according to the embodiment is applied. [Figure 2] It is a block diagram illustrating the hardware configuration of a server according to the information management system. [Figure 3] This table illustrates the structure of data managed by an information management system. [Figure 4] This diagram explains the calculation method for the indicator (total EV driving distance). [Modes for carrying out the invention]

[0014] The following describes an information management system according to an embodiment of the present invention. The information management system to which the present invention is applied is an information management system that manages multiple vehicle-related pieces of information transmitted from an in-vehicle communication device installed in a vehicle. The term "vehicle" here includes engine vehicles that run using an engine (internal combustion engine) as a power source, electric vehicles that run using a motor (electric motor) as a power source, and hybrid vehicles (HEV, Hybrid Electric Vehicle) that run using both an engine and a motor as power sources.

[0015] The above-mentioned hybrid vehicles include plug-in hybrid electric vehicles (PHEVs) that can be charged or supplied with power externally. A plug-in hybrid vehicle is a hybrid vehicle equipped with an engine and motor as power sources, a generator as a power generation device, and a drive battery as an energy storage device, and which can be charged externally to the drive battery or supplied with power externally from the drive battery.

[0016] The former type of plug-in hybrid vehicle is equipped with a charging port (inlet) for inserting a charging cable that receives power from an external charging facility, as well as a contactless power receiving device. The latter type of plug-in hybrid vehicle is equipped with an outlet for external power supply, as well as a contactless power supply device. It is also possible to install both the above-mentioned charging port and outlet on a single plug-in hybrid vehicle. [Examples]

[0017] [1. System Configuration] Figure 1 is a schematic diagram illustrating the configuration of network 1 to which the connected data platform 5, as an information management system according to the embodiment, is applied. The device platform 2 and the connected data platform 5 are provided on network 1.

[0018] Device platform 2 forms the basis of a car telematics service targeting a vehicle 10 equipped with an in-vehicle communication device 11. Device platform 2 is capable of connecting to multiple external computers via network 1. Specific examples of these external computers include the in-vehicle communication device 11, mobile terminals 12 carried by the vehicle 10's users and occupants, the vehicle 10's factory 13 (a computer installed within the factory 13's facilities), and the supplier 14 (a computer managed by supplier 14), which manufactures the in-vehicle equipment installed in the vehicle 10. Information held by factory 13 and supplier 14 may be pre-recorded in vehicle 10. Therefore, there may be cases where factory 13 and supplier 14 are not connected to device platform 2.

[0019] Specific examples of the in-vehicle communication device 11 include computers installed in the vehicle 10, such as IVC (In-Vehicle Communication module), TCU (Telematics Control Unit), and IVI (In-Vehicle Infotainment module) with communication functions. Specific examples of the mobile terminal 12 include computers not installed in the vehicle 10, such as smartphones and laptop computers.

[0020] The large amount of vehicle-related information transmitted from the vehicles 10 that are subject to the telematics service is managed as big data on the device platform 2. In addition, information transmitted from the factories 13 and suppliers 14 is managed on the device platform 2 in a format that allows the correspondence with each vehicle 10 to be understood.

[0021] The device platform 2 is provided with a first storage 3 and a second storage 4. The first storage 3 is a storage device (e.g., physical storage or cloud storage) that stores information transmitted from the in-vehicle communication device 11 of the vehicle 10. The information stored in the first storage 3 is managed by, for example, a telematics server (not shown). The telematics server is a management device (e.g., a physical server or cloud server) that is the entity that provides various car telematics services, and has the function of storing information transmitted from the in-vehicle communication device 11 in the first storage 3.

[0022] The telematics server has the function of providing information stored in the first storage 3 to the in-vehicle communication device 11 and mobile terminal 12, and the function of outputting control commands and alarms to the vehicle 10 and various related service providers in response to requests from the mobile terminal 12. Specific examples of functions of the telematics server include remote air conditioning function, remote charging management function, remote autonomous driving function, automatic notification function, remote monitoring function, remote locking / unlocking function, vehicle status confirmation function, vehicle location confirmation function, driving history confirmation function, customer support referral function, and online update function (map information update, system update, security update).

[0023] The second storage 4 is a storage device (e.g., physical storage or cloud storage) that stores information transmitted from the vehicle 10's factory 13 and supplier 14. The information stored in the second storage 4 is managed by, for example, an FTP (File Transfer Protocol) server (not shown). The FTP server has the function of storing information about the vehicle 10 transmitted from the factory 13 and information about the vehicle's in-vehicle equipment transmitted from the supplier 14 in the second storage 4.

[0024] The Connected Data Platform 5 is a platform for organizing diverse vehicle-related information (big data) acquired through telematics services and providing various functions for mobility services, insurance services, public transportation services, etc. Specific examples of big data recipients (customers) include car leasing companies, taxi companies, insurance companies, police, fire departments, hospitals, data analytics companies, vehicle dealerships, repair shops, and vehicle manufacturers. Vehicle manufacturers may also receive feedback on big data from their own vehicles, not just from customers. Figure 1 illustrates one of the customer servers 9 managed by such a customer. The Connected Data Platform 5 in this embodiment is configured to efficiently transmit the information requested by the customer to the customer server 9 in a format suitable for that request, from among the diverse vehicle-related information.

[0025] The connected data platform 5 includes a server 6 (information processing means), a data lake 7, and a data warehouse 8 (storage means). Server 6 is a management device (e.g., a physical server or cloud server) that oversees the computational processing in the connected data platform 5. The data lake 7 and data warehouse 8 are storage devices (e.g., physical storage or cloud storage) managed by Server 6. Various types of information stored in the data lake 7 and data warehouse 8 can be transmitted to the customer server 9 upon customer request.

[0026] Data Lake 7 is a storage device that stores the same vehicle-related information as that stored in the first storage 3 and the second storage 4. Raw data of vehicle-related information transmitted from the telematics server is stored in Data Lake 7 in its original form. On the other hand, processed data is stored in Data Warehouse 8. Processed data here includes extracted, modified, and formatted portions of the raw data.

[0027] In other words, while Data Lake 7 is responsible for storing the big data itself, Data Warehouse 8 is responsible for storing the big data after it has been pre-processed. Thus, the processed data is stored separately from the unprocessed vehicle-related information. Data Lake 7 and Data Warehouse 8 may be independently located within a single physical storage unit, or they may be located in separate physical storage units.

[0028] Figure 2 is a block diagram illustrating the hardware configuration of a server 6 provided on the connected data platform 5. Server 6 is equipped with a processing unit 20 having a processor 21 and memory 22, a communication device 23, and a storage device 24. The processing unit 20 is the main device for performing calculations on server 6, and the communication device 23 is a device for exchanging information with other computers via network 1. The storage device 24 is a device that stores the contents of the calculations performed on server 6 as a processing program. The contents of the processing program are appropriately read into the processor 21 and memory 22 and executed. Note that the storage device 24 may be provided separately from server 6. Also, server 6 may be one of several virtual servers included in a single physical server, or it may be a combination of several physical servers functioning as a single virtual server.

[0029] Server 6 has the function of receiving information stored in the first storage 3 and the second storage 4 from a telematics server (not shown) and storing the received information in the data lake 7 and data warehouse 8. The information storage function of Server 6 can be broadly divided into two types. The first function is to store all received vehicle-related information as raw data in the data lake 7. The second function is to extract and process a portion of the received vehicle-related information and store it as processed data in the data warehouse 8. The processed data stored in the data warehouse 8 will be described in detail below.

[0030] The processed data in data warehouse 8 is stored in a table format where multiple information elements form a single row, and multiple rows form columns. Data warehouse 8 stores multiple such tables. Server 6 generates tables for each theme, extracting vehicle-related information from the vehicle-related data according to each theme. Data warehouse 8 also individually stores the multiple tables generated by server 6.

[0031] The types and number of information elements stored in each table differ from table to table. Furthermore, the update frequency (number of updates per hour) and update timing (update timing) of each table also differ from table to table. On the other hand, the information on update frequency and update timing, which differ from table to table, is not included in the information transmitted from device platform 2, but is information that should be determined according to the purpose and use of each table. Therefore, in this embodiment, server 6 adds attribute information representing the update frequency and update timing to each table and then stores each table in data warehouse 8. In other words, each of the multiple tables generated by server 6 is a collection of vehicle-related information accumulated according to a unique theme, and each of the multiple themes is classified based on the update frequency or update timing of the vehicle-related information extracted for that theme.

[0032] [2. Table] Figure 3 is a table illustrating specific examples of tables stored in data warehouse 8 and the structure of each table. Each row in the table represents one table stored in data warehouse 8, and a total of 31 tables are illustrated. Each table stores multiple information elements, which differ from table to table, in a matrix format. The information stored in each table can be considered as pre-formatted data of vehicle-related information. In addition, each table is assigned a table type that represents the main type of information stored in that table.

[0033] Table types include master tables, summary tables, history tables, and transaction tables. A master table is a type of table that primarily stores master data. Master data is basically unchanging information that is unique to each individual vehicle 10 or in-vehicle equipment. Master data is used to identify vehicles 10, in-vehicle equipment, and the users of vehicles 10. Specific examples of master data include vehicle identification number, model, in-vehicle equipment serial number, and user ID.

[0034] Individual master data is determined, for example, during the manufacturing of vehicle 10 or when major in-vehicle equipment (e.g., in-vehicle communication device 11) is replaced during maintenance. Therefore, the master table is generated, for example, during the manufacturing of vehicle 10, and subsequently updated during maintenance of major in-vehicle equipment. In all other circumstances, the master table is basically not updated or modified.

[0035] A summary table is a type of table that primarily stores summary data. Summary data refers to information that summarizes or aggregates information (summary aggregate information) that represents the state of vehicle 10 (operating state, driving state, operation state, etc.) for a predetermined period of time. For example, summary data includes numerical information at the end of a predetermined period, total information for a predetermined period, and average information for a predetermined period. For example, information on the daily mileage and remaining fuel of vehicle 10 is included in the summary data.

[0036] Summary data is calculated at predetermined intervals defined for each table. Therefore, the summary tables are updated at predetermined intervals defined for each table. Specific examples of these predetermined intervals include relatively long intervals such as one trip (an operating state defined as the period from when the main switch or ignition switch is turned on until it is turned off), one day, one week, or one month. Vehicle identification information is recorded in each summary table in this embodiment. This makes it possible to identify the correspondence between all the information contained in the summary table and each individual vehicle 10.

[0037] A history table is a type of table that primarily stores history data. History data refers to information representing the past state of vehicle 10 among vehicle-related information. For example, the history data includes the past location information and remaining fuel history information of vehicle 10. In addition, vehicle identification information is recorded in each history table in this embodiment. This makes it possible to identify the correspondence between all the information contained in the history table and each individual vehicle 10. Furthermore, there is one transaction table corresponding to each history table, and some (or all) of the information previously stored in the transaction table is appended to and accumulated in the history table.

[0038] A transaction table is a type of table that primarily stores transaction data. Transaction data refers to information representing the current state or most recent (latest) state of vehicle 10 among vehicle-related information. For example, the current location information and remaining fuel information of vehicle 10 are included in the transaction data. In addition, vehicle identification information is recorded in each transaction table in this embodiment. This makes it possible to identify the correspondence between all the information contained in the transaction table and each individual vehicle 10.

[0039] Transaction tables are updated at predetermined intervals for each table. These predetermined intervals are shorter than the predetermined intervals during which summary tables are updated (for example, relatively short intervals of a few seconds to a few minutes). Furthermore, the update frequency of history tables is set to be at least less than or equal to the update frequency of the corresponding transaction table. For example, every time a transaction table is updated several times, its corresponding history table is updated.

[0040] Table 1 shows the relationship between table type, update frequency, and computational load (system resources). The update frequency of history tables is set higher than that of summary tables. Furthermore, the update frequency of transaction tables is set higher than or the same as that of history tables. In other words, history tables and transaction tables, which have relatively low computational loads, are set to have higher update frequencies, while summary tables, which have relatively high computational loads, are set to have lower update frequencies. Master tables have a low computational load and a lower update frequency than summary tables.

[0041] [Table 1]

[0042] The contents of the table shown in Figure 3 will be described in detail. The first column of the table represents the table type, and the second column represents the table name (the name of the table, which is its theme). Hereafter, for convenience, the table name will be enclosed in parentheses. The master table includes "Vehicle Basic," "Vehicle Registration," "TCU," and "Head Unit."

[0043] The "Vehicle Basics" table stores basic information about vehicle 10. Examples of information stored in this table include vehicle identification number and model. The "Vehicle Registration" table stores information to identify the user of the vehicle 10 to which the telematics service is provided. Examples of information stored in this table include the vehicle identification number and user ID.

[0044] "TCU" is a table that stores information about the TCU installed in vehicle 10. Examples of information stored in this table include the vehicle identification number and the TCU serial number. The "head unit" table stores information about the head unit installed in vehicle 10. Examples of information stored in this table include the vehicle identification number and the serial number of the head unit.

[0045] The summary table includes "Daily Parent", "Daily Vehicle", "Daily Tire", "Daily Warning", and "Trip-by-Trip Metric Value". The "Daily Parent" is the parent table for the three daily summary tables described below. A daily summary table is a table that stores summarized and aggregated information about vehicles on a daily basis. Examples of information stored in this table include the vehicle identification number and the date the history was updated.

[0046] The "Daily Vehicles" table is one of the daily summary tables and stores summarized information about the vehicle's driving status. Examples of information stored in this table include vehicle identification number and total mileage (daily report). The "Daily Tires" table is one of the daily summary tables and stores summarized aggregate information about tires. Examples of information stored in this table include vehicle identification number and tire pressure (daily report).

[0047] The "Daily Warnings" table is one of the daily summary tables and stores information (diagnosis information) related to various warnings issued by in-vehicle equipment. Examples of information stored in this table include the vehicle identification number and daily reports of warning types. The "Trip-Specific Indicator Values" table stores indicators related to the history of EV driving. This table stores indicator data calculated for each trip, targeting at least 10 vehicles capable of EV driving. These indicators include total EV driving distance (cumulative driving distance using the motor), electricity consumption, and electricity-to-fuel ratio (for example, the ratio of electricity consumption [km / kWh] to fuel consumption [km / l]). Other information stored in this table may include vehicle identification number, total driving distance, and fuel consumption. The method for calculating the indicators will be described later.

[0048] The history table includes "Vehicle Location History," "Vehicle Status History," "Vehicle Tire History," "Vehicle Warning History," "EV Data History," "Door Status History," "Light Status History," "Seatbelt Status History," "Trip History," and "Charge / Discharge Event History." The "Vehicle Location History" table stores historical information about the location of vehicle 10. Examples of information stored in this table include vehicle identification number, latitude and longitude (history), and vehicle speed (history).

[0049] The "Vehicle Status History" table stores historical information related to the vehicle's driving status. Examples of information stored in this table include the vehicle identification number and total mileage (history). The "Vehicle Tire History" table stores historical information related to tires. Examples of information stored in this table include vehicle identification number and tire pressure (history).

[0050] The "Vehicle Warning History" is a table that stores the vehicle identification number and historical information regarding various warnings issued by the in-vehicle equipment. The "EV Data History" table stores historical information related to EV (Electric Vehicle) driving for electric vehicles (electric cars, hybrid vehicles). Examples of information stored in this table include the vehicle identification number and the remaining charge level of the drive battery (history).

[0051] The "Door Status History" table stores historical information about the status of the doors. Examples of information stored in this table include the vehicle identification number and the open / closed status (history) of the doors. The "Light Status History" table stores historical information about the status of the lights. Examples of information stored in this table include the vehicle identification number and the status (history) of the lights.

[0052] The "Seatbelt Status History" table stores historical information regarding the status of seatbelts. Examples of information stored in this table include the vehicle identification number and seatbelt usage information (most recent) indicating whether or not a seatbelt was fastened. The "Trip History" table stores historical information related to the trips of vehicle 10. Examples of information stored in this table include the vehicle identification number and the distance traveled from the start to the end of the trip (history). The "Charge / Discharge Event History" is a table for electric vehicles that stores the vehicle identification number and historical information regarding the charging and discharging of the drive battery related to power exchange between the electric vehicle and the outside world.

[0053] The transaction table includes "Vehicle Location", "Vehicle Status", "Vehicle Tires", "Vehicle Warnings", "EV Data", "Door Status", "Light Status", "Seatbelt Status", "Trip Event List", "Charge / Discharge Event List", "Charge Reservation Synchronization Confirmation", and "Charge Reservation Details".

[0054] The "Vehicle Location" table stores the latest information regarding the location of vehicle 10. Examples of information stored in this table include the vehicle identification number, latitude and longitude (latest), and vehicle speed (latest). The "Vehicle Status" table stores the latest information regarding the vehicle's condition. Examples of information stored in this table include the vehicle identification number and the most recent total mileage.

[0055] The "Vehicle Tires" table stores the latest information regarding tires. Examples of information stored in this table include the vehicle identification number and tire pressure (latest). The "Vehicle Warnings" table stores the vehicle identification number and the latest information regarding various warnings issued by the in-vehicle equipment.

[0056] The "EV Data" table stores the latest information regarding EV driving (motor-driven driving) for electric vehicles. Examples of information stored in this table include the vehicle identification number and the remaining charge level of the drive battery (latest information). The "Door Status" table stores the latest information regarding the door status. Examples of information stored in this table include the vehicle identification number and the door's open / closed status (most recent).

[0057] The "Light Status" table stores the latest information regarding the status of the lights. Examples of information stored in this table include the vehicle identification number and the current status of the lights (most recent). The "Seatbelt Status" table stores the latest information regarding the status of seatbelts. Examples of information stored in this table include seatbelt usage information (most recent), such as whether or not a seatbelt is fastened.

[0058] The "Trip Event List" is a table that stores the latest information regarding the trips of vehicle 10. Examples of information stored in this table include the vehicle identification number and the latest mileage from the start to the end of the trip. The "Charge / Discharge Event List" is a table for electric vehicles that stores the vehicle identification number and charge / discharge information of the drive battery related to power exchange between the electric vehicle and the outside world.

[0059] The "Charging Reservation Synchronization Confirmation" table stores information related to a synchronization operation that shares the vehicle identification number and the charging reservation information for the drive battery (for example, a pre-reservation of charging using nighttime electricity supplied from a home outlet) with the server 6, for electric vehicles. In this embodiment, the history table corresponding to "Charging Reservation Synchronization Confirmation" is omitted, but a history table corresponding to "Charging Reservation Synchronization Confirmation" may be provided.

[0060] The "Charging Reservation Details" table stores the vehicle identification number and the details of the drive battery charging reservation confirmed in "Charging Reservation Synchronization Confirmation". In this embodiment, the history table corresponding to "Charging Reservation Details" is omitted, but a history table corresponding to "Charging Reservation Details" may be provided.

[0061] [3. Method for Calculating Indicators] This section details the calculation method for indicators related to the history of EV driving (motor driving). The indicators are calculated by server 6 based on primary information regarding the motor driving history of vehicle 10 and stored in the "Trip-Specific Indicator Values" table in data warehouse 8. Specific examples of indicators include total EV driving distance, energy consumption, and energy consumption-to-fuel ratio. Specific examples of primary information include history information regarding the charging and discharging of the drive battery related to power exchange between the electric vehicle and the outside world, total driving distance, and driving distance from the start to the end of a trip.

[0062] The primary information used to calculate the index is stored separately from other information (information other than the primary information). In this embodiment, the primary information is stored in the "Charge / Discharge Event History" and "Trip History" tables. These "Charge / Discharge Event History" and "Trip History" tables are separate from the tables that store information other than the primary information. The table that stores the calculated index is the "Trip-Specific Index Value" table. This "Trip-Specific Index Value" table is also separate from the "Charge / Discharge Event History" and "Trip History" tables and other tables.

[0063] figure 4 This graph shows the relationship between the distance traveled and the charge level of the drive battery for a single hybrid vehicle (vehicle 10). This graph can be created based on information stored, for example, in the "charge / discharge event history" and "trip history". The thick solid line shows the relationship between distance and charge level in one trip where EV driving using only the motor's power was performed. The thin dashed line shows the relationship between distance and charge level in one trip for hybrid driving (mixed driving) which includes not only EV driving but also non-EV driving using the engine's power.

[0064] figure 4 Point P1 in the middle corresponds to the start point of a trip, and point P2 corresponds to the end point of that trip. In this trip, the entire journey is by electric vehicle (thick solid line), and the engine (i.e., engine fuel) is not used at all. In other words, the trip distance D in this trip E This is the trip power consumption C E This was achieved by consuming only [amount]. Therefore, the power consumption rate (reciprocal of the energy consumption) for this trip is C E / D E This is the result. Power consumption rate (C E / D E The value of ) corresponds to the slope of the thick dashed line connecting points P1 and P2. This thick dashed line is a linear approximation of the charge fluctuation graph shown by the thick solid line. Server 6 calculates the power consumption rate for trips where the entire journey is EV driving in this manner.

[0065] Furthermore, if multiple trips are repeated where the entire journey is in EV mode, Server 6 calculates the average of the power consumption rates calculated for each trip. This average power consumption rate is called the average power consumption rate R. The formula for calculating the average power consumption rate R is R = avg(C E / D E ) can be expressed as follows. For example, if a vehicle 10 has recorded 10 trips where the entire journey was in EV mode, the average of the power consumption rates for those 10 trips can be calculated as the average power consumption rate R.

[0066] However, when the total driving distance of the vehicle 10 is less than the first predetermined distance (for example, when the total driving distance is less than 百公里), there is a risk that the calculation accuracy of the power consumption rate and the average power consumption rate R will decrease. Therefore, the vehicle 10 with a total driving distance less than the first predetermined distance is excluded from the calculation target of the average power consumption rate R, and is also excluded from the calculation target for the calculation of the index.

[0067] Also, for the trip distance D of EV driving E When it is short, there is also a risk that the calculation accuracy of the power consumption rate and the average power consumption rate R will decrease. Therefore, when the trip distance D E is less than the second predetermined distance shorter than the first predetermined distance (for example, when the trip distance D [[ID=Z]] E is less than 5 km), the power consumption rate of that trip is excluded and the average power consumption rate R is calculated. For example, in a certain vehicle 10, 10 trips in which the entire journey is EV driving are recorded, and among them, in 2 trips, the trip distance D E is less than 5 km. In this case, the average value of the power consumption rates of the other 8 trips is calculated as the average power consumption rate R.

[0068] Figure 4 The dashed line between the points P2 and P3 in the figure corresponds to the state where the charge amount is restored by external charging. Point P3 corresponds to the start point of the trip after external charging, and point P4 corresponds to the end point of that trip. Since this trip is a hybrid driving (dotted line) in which EV driving and non-EV driving are mixed, it is difficult to distinguish between the contribution of driving by motor power and the contribution of driving by engine power in the trip distance D H among them.

[0069] Therefore, the server 6 calculates the motor contribution distance D2, which is the distance achieved by the contribution of motor power among the trip distance D H using the aforementioned average power consumption rate R. The motor contribution distance D2 is the trip power consumption C HIt is calculated by dividing (first power consumption) by the average power consumption rate R. That is, when a straight line (thick dashed line) is drawn from point P3 with the same gradient as the thick dashed line in EV driving, and extended until the same charge level as point P4 is reached, the distance traveled by vehicle 10 (horizontal dimension of the thick dashed line) is calculated. Also, the trip distance D H Of the distances achieved through the contribution of engine power, engine contribution distance D1 is equal to trip distance D H It can be calculated by subtracting the motor contribution distance D2 from this.

[0070] Subsequently, Server 6 calculates the total engine contribution distance D1 as the total engine contribution distance D SUM1 The motor contribution distance D2 and the trip distance D for EV driving only are calculated as follows: E The total is the total EV driving distance D SUM2 The total engine contribution distance D calculated here is calculated as follows. SUM1 Total EV driving range D SUM2 This information, along with the vehicle identification information and total mileage information for vehicle 10, is stored in the "Trip-Specific Index Values" table.

[0071] [4. Effects] (1) The connected data platform 5 described above includes a server 6 (information processing means) that generates a table containing formatted data to be provided to the customer by performing predetermined calculation processing on vehicle-related information, and a data warehouse 8 (storage means) that stores the table. The server 6 stores primary information and other information used to calculate an index related to the history of EV driving (for example, total EV driving distance) from the vehicle-related information in separate tables.

[0072] For example, as shown in Figure 3, the "Charge / Discharge Event History" and "Trip History" tables, which contain primary information, are generated separately from other tables and stored individually in data warehouse 8. By separating primary information from other information and storing them in separate tables in this way, the efficiency of calculating indicators related to EV driving history and the efficiency of data analysis can be improved. In other words, vehicle-related information can be organized in a form suitable for its purpose and use, improving the usability of the information. Therefore, the efficient utilization of data can be promoted.

[0073] Furthermore, because the volume of vehicle-related information transmitted and received as big data on device platform 2 is enormous, if, for example, a customer requests information on EV driving history, the server 6's computational load becomes enormous, making it difficult to provide the information quickly. To address this challenge, tables containing "charge / discharge event history" and "trip history," which include primary information, are pre-generated and stored in data warehouse 8, enabling the provision of appropriate information in a short time.

[0074] (2) In this embodiment, vehicles 10 whose total mileage is less than a first predetermined distance (for example, 100 km) are excluded from the calculation of the index. For example, for vehicles 10 whose total mileage is less than the first predetermined distance, the average power consumption rate R is not calculated, and the index is not calculated either. This configuration makes it possible to improve the accuracy of the index calculation. Furthermore, when it is desired to analyze the index for multiple vehicles 10, the reliability of the analysis results can be improved.

[0075] (3) The server 6 of this embodiment calculates the average power consumption rate R of EV driving based on first information for trips in which EV driving continued for a second predetermined distance (e.g., 5 km) or more, which is shorter than the first predetermined distance, and also calculates an index based on the average power consumption rate R. In other words, trips in which EV driving is less than the second predetermined distance are excluded from the calculation of the average power consumption rate R and the index. With this configuration, the accuracy of calculating the average power consumption rate R and the index can be improved. Furthermore, when it is desired to analyze the index for multiple vehicles 10, the reliability of the analysis results can be further improved.

[0076] (4) The server 6 of this embodiment is shown in Figure 4 As shown, the trip power consumption C in a trip that includes EV driving and non-EV driving H (First power consumption) is calculated, and trip power consumption C H The motor contribution distance D2 for calculating the indicator is calculated based on the average power consumption rate R. With this configuration, the contribution of motor power in a trip where EV driving and non-EV driving are mixed can be accurately determined. Therefore, the accuracy of calculating the indicator (e.g., total EV driving distance) can be improved. In addition, when it is desired to analyze the indicator for multiple vehicles 10, the reliability of the analysis results can be further improved.

[0077] (5) In this embodiment, the above indicator is the total EV driving distance (total EV driving distance D2) obtained by adding the cumulative value of the motor contribution distance D2 to the cumulative value of the EV driving distance. SUM2 This configuration makes it easier to analyze the operational status of the vehicle 10 (for example, calculating the ratio of driving powered by the motor to driving powered by the engine), improves the usability of the indicators, and promotes the utilization of the data.

[0078] (6) In this embodiment, the first information used is information regarding the charging and discharging of the drive battery mounted on the vehicle 10. This configuration improves the accuracy of calculating the average power consumption rate R and the index, and consequently improves the accuracy of calculating the index. Furthermore, when it is desired to analyze the index for multiple vehicles 10, the reliability of the analysis results can be further improved.

[0079] [5. Others] The above embodiments are merely illustrative examples, and there is no intention to exclude various modifications or applications of techniques not explicitly stated in these embodiments. Each configuration of these embodiments can be modified in various ways without departing from their intended purpose. Furthermore, each configuration of these embodiments can be selected or combined as needed.

[0080] For example, in the above embodiment, a connected data platform 5 is provided, which includes a server 6, a data lake 7, and a data warehouse 8. However, the data lake 7 is not a mandatory element and can be omitted as appropriate. Also, the server 6 may or may not be located on the connected data platform 5. The server 6 only needs to exist physically or virtually on the network 1, with at least the ability to access the data warehouse 8.

[0081] In the above embodiment, as shown in Figure 3, the "charge / discharge event history" table containing the primary information and the "trip history" table are separate, but these may be combined into a single table. At the very least, by storing the primary information used to calculate the index and other information in separate tables, the same effects as in the above embodiment can be obtained.

[0082] Furthermore, in the above embodiment, a table of "Trip-Specific Index Values" containing index information calculated by server 6 is provided, but such a table can be omitted. At a minimum, if tables of "Charge / Discharge Event History" and "Trip History" containing the first information are provided, the index (for example, total EV driving distance D) can be easily calculated in a relatively short time. SUM2 The following can be calculated, and the same effects as in the above embodiment can be obtained. In other words, the server 6 may be configured to calculate the indicator each time in response to a customer request and provide the customer with information on that indicator. [Industrial applicability]

[0083] This technology is widely applicable to the manufacturing and service industries that utilize information management systems for managing multiple vehicle-related pieces of information transmitted from in-vehicle communication devices installed in vehicles. Furthermore, it can be integrated with vehicle operation management systems, autonomous driving systems, car navigation systems, automatic charging management systems, traffic management systems, driving skill evaluation systems, etc., and has industrial applicability to a wide variety of vehicle systems. [Explanation of Symbols]

[0084] 1 Network 2 Device Platforms 3. First Storage 4. Second storage 5. Connected Data Platform (Information Management System) 6. Server (information processing means) 7 Data Lakes 8. Data Warehouse (Storage Method) 9 Customer Server 10 vehicles 11. In-vehicle communication device 12 Mobile devices 13 factories 14 Suppliers 20 Processing Units 21 processors 22 memory 23 Communication equipment 24 Memory devices

Claims

1. An information management system that acquires multiple vehicle-related information transmitted from a hybrid vehicle capable of both EV driving (driving using the power of an electric motor) and non-EV driving (driving using the power of an internal combustion engine), and manages said vehicle-related information, Information processing means for generating multiple tables in which a portion of the aforementioned vehicle-related information is recorded, The system comprises a storage means for storing the aforementioned table, The information processing means stores the first information and other information used to calculate an index related to the EV driving history from the vehicle-related information into separate tables. The information processing means calculates the average power consumption rate of the EV driving based on the first information, The information processing means further calculates the first power consumption during the trip, which includes both EV driving and non-EV driving, and calculates the index based on the first power consumption and the average power consumption rate. An information management system characterized by the following features.

2. The index is the distance traveled by adding the motor-contributed distance of the non-EV driving to the cumulative distance of the EV driving. The information management system according to claim 1, characterized in that

3. The information processing means excludes hybrid vehicles whose total mileage is less than the first predetermined distance from the calculation of the index. The information management system according to claim 2, characterized in that

4. The information processing means calculates the average power consumption rate of the EV driving based on the first information for the trip in which the EV driving continued for a second predetermined distance or longer, which is shorter than the first predetermined distance. The information management system according to claim 3, characterized in that

5. The aforementioned first information is information relating to the charging and discharging of the drive battery installed in the hybrid vehicle. An information management system according to any one of claims 1 to 4, characterized in that

6. An information management system that acquires a plurality of vehicle-related pieces of information transmitted from a vehicle capable of EV driving, which is driving using the power of a motor, and manages the said vehicle-related pieces of information, Information processing means for generating multiple tables in which a portion of the aforementioned vehicle-related information is recorded, The system comprises a storage means for storing the aforementioned table, The information processing means stores the first information and other information used to calculate an index related to the EV driving history from the vehicle-related information into separate tables. The information processing means excludes vehicles whose total mileage is less than the first predetermined distance from the calculation of the index. The information processing means calculates the average power consumption rate of the EV driving based on the first information in a trip in which the EV driving continued for a second predetermined distance or longer, which is shorter than the first predetermined distance, and calculates the index based on the average power consumption rate. The aforementioned vehicle is a hybrid vehicle capable of not only EV driving but also non-EV driving, which is driving using the power of an internal combustion engine. The information processing means calculates the first power consumption amount during a trip that includes both EV driving and non-EV driving, and calculates the index based on the first power consumption amount and the average power consumption rate. An information management system characterized by the following features.

7. The aforementioned first information is information relating to the charging and discharging of the drive battery installed in the hybrid vehicle. The information management system according to claim 6, characterized in that

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