Method and system for extracting time-sharing energy consumption map based on electric vehicle driving data

Abstract

The present invention discloses a time-sharing energy consumption map extraction method and system based on electric vehicle driving data, which relates to the technical field of time-sharing energy consumption map extraction. The method includes obtaining vehicle-mounted signal data during vehicle driving and obtaining the vehicle's itinerary, and dividing the vehicle's itinerary into itinerary segments according to preset rules; converting the trajectory of each itinerary segment into an ordered set containing only fork codes; based on the fork codes and set rules in the ordered set corresponding to the itinerary segment, converting the itinerary segment into a road section, and calculating the power consumption of the vehicle in each road section; obtaining the vehicle startup status label of the road section based on relevant temperature information, and obtaining the time label of the road section based on the collection time of the vehicle-mounted signal data; and writing the power consumption of the road section, the vehicle startup status label and the time label into a distributed database. The present invention has simple logic, is easy to implement, has strong practicality, good effect, good real-time performance, and can ensure support for application scenarios related to electric vehicle energy consumption analysis.

Description

Technical Field

[0001] The invention relates to the technical field of time-sharing energy consumption map extraction, and in particular to a time-sharing energy consumption map extraction method and system based on electric vehicle driving data. Background Art

[0002] New energy vehicles, especially pure electric vehicles, have obvious advantages over traditional fuel vehicles in terms of energy saving, emission reduction and stronger power. Leading regions in the automotive industry and major automakers have reached a high degree of consensus on the future development of automotive electrification, and have released strategic electrification goals to accelerate the transformation of automotive electrification. At the corporate level, almost all major auto companies have released new electrification goals and product plans. Based on the dual-carbon strategy, new energy vehicles, especially pure electric vehicles, will also be vigorously promoted to gradually replace traditional fuel vehicles.

[0003] However, when it comes to electric vehicles, the topic that cannot be avoided is range anxiety. Therefore, compared with fuel vehicles, electric vehicle owners are more concerned about the vehicle's energy consumption and endurance. The factors that affect the energy consumption and endurance of electric vehicles mainly include: battery energy density, appearance design / drag coefficient, body weight, ambient temperature, driving conditions, driving habits, charging habits, etc. The first three items are related to vehicle design, and the last four items can be used to reasonably avoid high energy consumption during the vehicle's use phase.

[0004] Among them, different driving road conditions lead to different energy consumption levels for electric vehicles. Traditional navigation functions mainly plan routes based on driving time and driving mileage, and lack route planning solutions based on energy consumption prediction. At the same time, the research and development of electric vehicle-related technologies is in an urgent breakthrough stage, and the acquisition and analysis of relevant test data is particularly important. How to obtain road sections with different energy consumption levels in different seasons is of great significance to automobile road tests. R&D personnel can select different real driving road conditions and driving environment tests in a targeted manner to collect relevant performance data to guide vehicle energy consumption optimization.

[0005] Whether it is path planning based on energy consumption prediction or guiding vehicle road tests to optimize vehicle energy consumption, data support is required. At present, new energy vehicles are equipped with on-board driving signal collection devices, which can collect rich Internet of Vehicles data for big data analysis and mining. Based on the on-board GPS (Global Positioning System), motor signals, battery signals and other data, the "energy consumption map" of different models can be extracted. This "energy consumption map" can reflect the energy consumption level of different sections of roads in different seasons and time periods, and can support application scenarios such as low-energy driving route recommendations and road test section selection guidance. It can be seen that the "energy consumption map" is the foundation and core of the application. How to accurately extract the "energy consumption map" directly determines the effect of these applications. Summary of the invention

[0006] In view of the defects existing in the prior art, the purpose of the present invention is to provide a method and system for extracting a time-sharing energy consumption map based on electric vehicle driving data, which has simple logic, is easy to implement, has strong practicality, good effect, good real-time performance, and can ensure support for application scenarios related to electric vehicle energy consumption analysis.

[0007] To achieve the above purpose, the present invention provides a method for extracting a time-sharing energy consumption map based on electric vehicle driving data, which specifically includes the following steps:

[0008] Acquire vehicle-borne signal data during the vehicle's travel and obtain the vehicle's itinerary, and divide the vehicle's itinerary into itinerary segments according to preset rules;

[0009] Based on the intersection data after geohash encoding, the trajectory of each trip segment is converted into an ordered set containing only the intersection codes;

[0010] Based on the fork codes and setting rules in the ordered set corresponding to the travel segments, the travel segments are converted into road segments, and the power consumption of the vehicle in each road segment is calculated;

[0011] The vehicle startup status label of the road section is obtained based on the relevant temperature information, and the time label of the road section is obtained based on the collection time of the vehicle signal data;

[0012] The power consumption of the road section, the vehicle startup status tag and the time tag are written into the distributed database in the form of fields.

[0013] On the basis of the above technical solution, the vehicle-mounted signal data includes GPS data, motor temperature, battery temperature, power battery output voltage and current and data collection time.

[0014] On the basis of the above technical solution, the specific steps of obtaining the vehicle-borne signal data during vehicle driving include:

[0015] Collect vehicle signal data in real time during driving, parse the vehicle signal data according to the data protocol, and push it to Kafka;

[0016] Use Flink to pull vehicle signal data, filter the vehicle signal data, and perform data cleaning and conversion operations.

[0017] Among them, the data fields after the screening operation include longitude, latitude, motor temperature, battery temperature, battery output voltage, battery output current, and data acquisition timestamp; the data cleaning and conversion operation is to remove out-of-bounds data and perform data conversion and restoration on the original data with scaling or offset.

[0018] On the basis of the above technical solution, the vehicle's journey is divided into journey segments according to preset rules, specifically:

[0019] The vehicle signal data is grouped according to the vehicle frame number, and the vehicle's journey is divided into journey segments according to the vehicle signal data of each vehicle and the preset rules;

[0020] The division rule for each trip segment is as follows: the vehicle starts and runs for a preset time and then stops, and the change in the vehicle's geographical location exceeds a threshold, and the mileage of the vehicle after starting is not less than a preset kilometer.

[0021] On the basis of the above technical solution, the intersection data after geohash encoding is used to convert the trajectory of each trip segment into an ordered set containing only the intersection code. The specific steps include:

[0022] Based on the intersection data after geohash encoding, the trajectory points of each journey segment of the vehicle are compared with the intersection data in turn. If the current trajectory point falls into the area represented by the intersection geohash, the current trajectory point is replaced with the geohash code. If the current trajectory point does not fall into the area represented by the intersection geohash, the current trajectory point is discarded, thereby obtaining an ordered set of fork codes for each journey segment.

[0023] On the basis of the above technical solution, the trip segments are converted into road segments based on the fork codes and setting rules in the ordered set corresponding to the trip segments, wherein the setting rules are: in the ordered set corresponding to the trip segments, two adjacent fork codes represent a road segment.

[0024] On the basis of the above technical solution, the power consumption of the vehicle in each road section is calculated, specifically: based on the power battery output voltage and current time series data of each road section, the power consumption of each road section is calculated, and the calculation method is:

[0025]

[0026] Among them, V i Indicates the voltage reported for the i-th time in the current section, A i represents the i-th reported current in the current section, T represents the reporting time interval, and n represents the number of voltage and current reports in the current section.

[0027] Based on the above technical solutions,

[0028] The relevant temperature information includes motor temperature and battery temperature;

[0029] The vehicle startup status label includes the initial cold start and the non-initial cold start;

[0030] The time labels include driving time, season, working day, non-working day, holiday, hour segment and hour segment fraction;

[0031] The division rule of the cold start initial stage is: based on the ideal operating motor temperature range and battery temperature range of the vehicle and the statistical results of actual vehicle networking data, the temperature range of the vehicle motor temperature and battery temperature during normal driving is determined, and the time period when the motor temperature or battery temperature of the vehicle is lower than the minimum value of the range at the initial stage of driving is the cold start initial stage;

[0032] If the ratio of the time the vehicle is in the early stage of cold start in the current section to the entire time of the section is greater than a preset value, the vehicle startup status label of the section is the early stage of cold start, otherwise it is not the early stage of cold start.

[0033] On the basis of the above technical solution, the power consumption of the road section, the vehicle start status label and the time label are written into the distributed database in the form of fields, wherein the fields of the road section include the trip ID, the road section code, the vehicle model, the power consumption of this road section, whether it belongs to the initial cold start, the driving time of the road section, the driving season, the driving week, whether it is a weekday, whether it is a holiday, the driving hour section and the driving hour percentile.

[0034] The present invention provides a time-sharing energy consumption map extraction system based on electric vehicle driving data, comprising:

[0035] A division unit, which is used to obtain the vehicle-borne signal data during the vehicle's travel and obtain the vehicle's itinerary, and divide the vehicle's itinerary into itinerary segments according to a preset rule;

[0036] A conversion unit, which is used to convert the trajectory of each trip segment into an ordered set containing only intersection codes based on the intersection data after geohash encoding;

[0037] A calculation unit, which is used to convert the travel segment into a road segment based on the fork codes and setting rules in the ordered set corresponding to the travel segment, and calculate the power consumption of the vehicle in each road segment;

[0038] An execution unit, which is used to obtain a vehicle startup status label of a road section based on relevant temperature information, and obtain a time label of the road section based on a collection time of vehicle-borne signal data;

[0039] The writing unit is used to write the power consumption of the road section, the vehicle startup status tag and the time tag into the distributed database in the form of fields.

[0040] Compared with the prior art, the advantages of the present invention are: by acquiring the power consumption of the road section, the vehicle startup status label and the time label, the time-sharing energy consumption map of the electric vehicle driving data can be extracted; the method has simple logic, is easy to implement, has strong practicality, good effect, good real-time performance, and can ensure support for application scenarios related to electric vehicle energy consumption analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 The present invention is a flowchart of a method for extracting a time-sharing energy consumption map based on electric vehicle driving data in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0044] In actual application, the energy consumption levels of the same electric vehicle model in different seasons, time periods and road sections will be different, and it is not easy to quantify the degree of this difference. In order to accurately quantify the energy consumption levels under different conditions, the present invention proposes an "energy consumption map" extraction method based on massive electric vehicle driving data, which can obtain the driving energy consumption of each road section in different combinations including seasons, working days, non-working days, holidays, time periods, etc. The work results are core data support for application scenarios related to energy consumption levels, including but not limited to low-energy driving route recommendations, road test section selection guidance, etc., and therefore have important significance for upper-level related applications.

[0045] See also Figure 1 As shown, an embodiment of the present invention provides a method for extracting a time-sharing energy consumption map based on electric vehicle driving data. Since the data collected by the Internet of Vehicles is large, the present invention is based on a Hadoop cluster, collects data in real time and aggregates it in units of trips through flink (a distributed processing engine and framework for state calculation of bounded and unbounded data streams), and pushes the data related to the completed trip to Kafka (an open source stream processing platform), and then uses sparkstreaming (a stream processing framework) to perform quasi-real-time processing in batches at a certain frequency. The time-sharing energy consumption map extraction method of the present invention specifically includes the following steps:

[0046] S1: Obtain the vehicle-borne signal data during the vehicle's driving process and obtain the vehicle's itinerary, and divide the vehicle's itinerary into travel segments according to preset rules; the vehicle-borne signal data includes GPS data, motor temperature, battery temperature, power battery output voltage and current, and data collection time.

[0047] In the embodiment of the present invention, the vehicle-borne signal data during the vehicle driving process is obtained, and the specific steps include:

[0048] S101: collect vehicle signal data in real time during vehicle driving, parse the vehicle signal data according to the data protocol, and push it to Kafka;

[0049] S102: Pull the vehicle signal data through flink and filter the vehicle signal data, and perform data cleaning and conversion operations at the same time; pull the vehicle signal data through flink to perform the next step of data processing on the vehicle signal data, thereby filtering the data fields and retaining only the fields required for subsequent calculations.

[0050] Among them, the data fields after the screening operation include longitude, latitude, motor temperature, battery temperature, battery output voltage, battery output current, and data acquisition timestamp; the data cleaning and conversion operation is to remove out-of-bounds data and perform data conversion and restoration on the original data with scaling or offset.

[0051] In the embodiment of the present invention, the vehicle's trip is divided into trip segments according to a preset rule, specifically: the vehicle signal data is grouped according to the frame number, and the vehicle's trip is divided into trip segments according to the vehicle signal data of each vehicle according to the preset rule; wherein the division rule of each trip segment is: the vehicle is started and the vehicle stops after running for a preset time, and the geographical location of the vehicle changes by more than a threshold, and the mileage of the vehicle after starting is not less than a preset kilometer. The preset kilometer can be 3 kilometers, that is, the trips that run less than 3 kilometers after starting are eliminated.

[0052] That is, the original vehicle signal data is aggregated into trips, and the original collected data of each vehicle is divided into trip segments according to the rules. No signal data is reported during the vehicle shutdown process, so the division rule is that there is no data when the threshold time is exceeded.

[0053] S2: Based on the intersection data encoded by geohash (a kind of address code), the trajectory of each journey segment is converted into an ordered set containing only fork codes; this step is specifically as follows: based on the intersection data encoded by geohash, the trajectory points of each journey segment of the vehicle are compared with the intersection data in turn. If the current trajectory point falls within the area represented by the intersection geohash, the current trajectory point is replaced with the geohash code. If the current trajectory point does not fall within the area represented by the intersection geohash, the current trajectory point is discarded, thereby obtaining an ordered set of fork codes for each journey segment.

[0054] It should be noted that when encoding intersections, different intersections will have different coverage sizes. Therefore, it is necessary to set a reasonable geohash code length according to the actual situation so that the entire intersection can be covered with the smallest rectangular range. When consecutive trajectory points fall into the area represented by the same intersection geohash, only one code is retained, that is, there will be no consecutive identical codes in the final ordered trajectory code.

[0055] S3: Based on the fork codes and setting rules in the ordered set corresponding to the travel segments, the travel segments are converted into road sections, and the power consumption of the vehicle in each road section is calculated; the setting rule is: in the ordered set corresponding to the travel segments, two adjacent fork codes represent a road section.

[0056] In the embodiment of the present invention, the power consumption of the vehicle in each road section is calculated, specifically: based on the power battery output voltage and current time series data of each road section, the power consumption of each road section is calculated, and the calculation method is:

[0057]

[0058] Among them, V i Indicates the voltage reported for the i-th time in the current section, A i It represents the i-th reported current in the current section, T represents the reporting time interval, which can be 10s, and n represents the number of voltage and current reports in the current section. When the voltage and current are reported, the voltage and current are reported at the same time.

[0059] S4: obtaining a vehicle start-up status label of the road section based on the relevant temperature information, and obtaining a time label of the road section based on the collection time of the vehicle signal data;

[0060] In the embodiment of the present invention, the relevant temperature information includes the motor temperature and the battery temperature; the vehicle startup status label includes the cold start initial stage and the non-cold start initial stage; the time label includes the driving time, season, working day, non-working day, holiday, hour segment and hour segment fraction (each 10 minutes is one interval);

[0061] The division rule for the early cold start period is as follows: based on the ideal operating motor temperature range and battery temperature range of the vehicle, as well as the statistical results of actual Internet of Vehicles data, the temperature range of the vehicle motor temperature and battery temperature during normal driving is determined. The period when the motor temperature or battery temperature is lower than the minimum value of the range at the beginning of vehicle driving is the early cold start period.

[0062] If the ratio of the time the vehicle is in the early stage of cold start in the current section to the entire time of the section is greater than a preset value, the vehicle startup status label of the section is the early stage of cold start, otherwise it is not the early stage of cold start.

[0063] When labeling each road section as "Whether it is in the early stage of cold start", there may be a section that belongs to the early stage of cold start for part of the time and not in the early stage of cold start for part of the time. For this case, the proportion of the time belonging to the early stage of cold start in this section is calculated. If it exceeds the preset value (such as 50%), the vehicle startup status label of the current section is the early stage of cold start, otherwise it is not the early stage of cold start.

[0064] It should be noted that the time label of a road section can be obtained by collecting the time and converting or calculating it. In some individual cases, there may be multiple time labels converted from a certain driving section. In this case, it is necessary to select the label with the largest number of conversions in this section. For example, for section ab, 30% of the time belongs to the hourly period of 7:00-8:00, and the remaining 70% of the time belongs to 8:00-9:00, then the hourly period corresponding to this section is 8:00-9:00.

[0065] S5: Write the power consumption of the road section, the vehicle startup status tag and the time tag into the distributed database in the form of fields.

[0066] In an embodiment of the present invention, the power consumption of a road section, a vehicle startup status tag and a time tag are written into a distributed database in the form of fields, wherein the fields of the road section include a trip ID (Identity document, code), a road section code, a vehicle model, power consumption of this road section, whether it is an initial cold start, road section driving time, driving season, driving week, whether it is a weekday, whether it is a holiday, driving hour section and driving hour percentile.

[0067] Furthermore, the newly added vehicle signal data is processed according to the logic of the above steps, and then the obtained road section results are written into the distributed database.

[0068] The present invention can generate an energy consumption map based on the output road section related result data. In the application scenario of low-energy consumption route recommendation, by calculating the power consumption average value under the current corresponding time label (the small-granularity time label of the road section behind the route is accumulated by the average driving time of the previous road section to successively calculate the approximate time of driving to different road sections, and then determine the time label, and at the same time, according to the average time of the initial cold start in different seasons and time periods and the power consumption under the corresponding label of the corresponding road section), the total power consumption of each optional route can be further obtained, and the low-energy consumption route recommendation is made based on the total power consumption. In addition, different drivers driving the same model will have different driving energy consumption levels. The total power consumption of each optional route can be calculated by combining the quantile of the driver's personal driving energy consumption level in the same model, and the power consumption corresponding to the quantile of each road section is used to calculate the total power consumption of each optional route, instead of uniformly using the average power consumption. The total power consumption of the route calculated in this way is more personalized.

[0069] For road test developers, they can conduct preliminary multi-dimensional statistical analysis based on the output road section-related result data to find out whether the energy consumption level is different from the design or points that the developers think are unreasonable. Then, based on these situations, they can use the "energy consumption map" to select the environment (road, time, etc.) where real vehicle testing is required, and collect vehicle test signal data for the next step of in-depth analysis.

[0070] In the actual application of road section related result data, the time weight of the data can be considered. The older the time, the smaller the corresponding weight.

[0071] An embodiment of the present invention provides a time-sharing energy consumption map extraction system based on electric vehicle driving data, comprising a division unit, a conversion unit, a calculation unit, an execution unit and a writing unit.

[0072] The division unit is used to obtain the on-board signal data of the vehicle during its travel and obtain the vehicle's itinerary, and divide the vehicle's itinerary into itinerary segments according to preset rules; the conversion unit is used to convert the trajectory of each itinerary segment into an ordered set containing only fork codes based on the intersection data after geohash encoding; the calculation unit is used to convert the itinerary segment into a road section based on the fork codes in the ordered set corresponding to the itinerary segment and the set rules, and calculate the power consumption of the vehicle in each road section; the execution unit is used to obtain the vehicle startup status label of the road section based on the relevant temperature information, and obtain the time label of the road section based on the collection time of the on-board signal data; the writing unit is used to write the power consumption of the road section, the vehicle startup status label and the time label into the distributed database in the form of fields.

[0073] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A method for extracting a time-sharing energy consumption map based on electric vehicle driving data, characterized in that: The specific steps include: Acquire vehicle-borne signal data during the vehicle's travel and obtain the vehicle's itinerary, and divide the vehicle's itinerary into itinerary segments according to preset rules; Based on the intersection data after geohash encoding, the trajectory of each trip segment is converted into an ordered set containing only the intersection codes; Based on the fork codes and setting rules in the ordered set corresponding to the travel segments, the travel segments are converted into road segments, and the power consumption of the vehicle in each road segment is calculated; The vehicle startup status label of the road section is obtained based on the relevant temperature information, and the time label of the road section is obtained based on the collection time of the vehicle signal data; The power consumption, vehicle start status tag and time tag of the road section are written into the distributed database in the form of fields; The geohash-coded intersection data is used to convert the trajectory of each trip segment into an ordered set containing only intersection codes. The specific steps include: Based on the intersection data after geohash coding, the trajectory points of each trip segment of the vehicle are compared with the intersection data in turn. If the current trajectory point falls into the area represented by the intersection geohash, the current trajectory point is replaced with the geohash code. If the current trajectory point does not fall into the area represented by the intersection geohash, the current trajectory point is discarded, thereby obtaining an ordered set of intersection codes for each trip segment. The travel segments are converted into road segments based on the fork codes and setting rules in the ordered set corresponding to the travel segments, wherein the setting rule is: in the ordered set corresponding to the travel segments, two adjacent fork codes represent a road segment.

2. A method for extracting a time-sharing energy consumption map based on electric vehicle driving data as claimed in claim 1, characterized in that: The vehicle-mounted signal data includes GPS data, motor temperature, battery temperature, power battery output voltage and current, and data collection time.

3. A method for extracting a time-sharing energy consumption map based on electric vehicle driving data as claimed in claim 2, characterized in that: The specific steps of obtaining the vehicle-borne signal data during the vehicle driving process include: Collect vehicle signal data in real time during driving, parse the vehicle signal data according to the data protocol, and push it to Kafka; Use Flink to pull vehicle signal data, filter the vehicle signal data, and perform data cleaning and conversion operations. Among them, the data fields after the screening operation include longitude, latitude, motor temperature, battery temperature, battery output voltage, battery output current, and data acquisition timestamp; the data cleaning and conversion operation is to remove out-of-bounds data and perform data conversion and restoration on the original data with scaling or offset.

4. A method for extracting a time-sharing energy consumption map based on electric vehicle driving data as claimed in claim 1, characterized in that: The vehicle's journey is divided into journey segments according to a preset rule, specifically: The vehicle signal data is grouped according to the vehicle frame number, and the vehicle's journey is divided into journey segments according to the vehicle signal data of each vehicle and the preset rules; The division rule of each trip segment is as follows: the vehicle starts and runs for a preset time and then stops, and the change in the vehicle's geographical location exceeds a threshold, and the mileage of the vehicle after starting is not less than a preset kilometer.

5. The method for extracting a time-sharing energy consumption map based on electric vehicle driving data according to claim 1, characterized in that: The power consumption of the vehicle in each road section is calculated by: based on the power battery output voltage and current time series data of each road section, the power consumption of each road section is calculated by: Among them, V i Indicates the voltage reported for the i-th time in the current section, A i represents the i-th reported current in the current section, T represents the reporting time interval, and n represents the number of voltage and current reports in the current section.

6. The method for extracting a time-sharing energy consumption map based on electric vehicle driving data according to claim 1, characterized in that: The relevant temperature information includes motor temperature and battery temperature; The vehicle startup status label includes the initial cold start and the non-initial cold start; The time labels include driving time, season, working day, non-working day, holiday, hour segment and hour segment fraction; The division rule of the cold start initial stage is: based on the ideal operating motor temperature range and battery temperature range of the vehicle and the statistical results of actual vehicle networking data, the temperature range of the vehicle motor temperature and battery temperature during normal driving is determined, and the time period when the motor temperature or battery temperature of the vehicle is lower than the minimum value of the range at the initial stage of driving is the cold start initial stage; If the ratio of the time the vehicle is in the early stage of cold start in the current section to the entire time of the section is greater than a preset value, the vehicle startup status label of the section is the early stage of cold start, otherwise it is not the early stage of cold start.

7. A method for extracting a time-sharing energy consumption map based on electric vehicle driving data as claimed in claim 1, characterized in that: The power consumption of the road section, the vehicle startup status label and the time label are written into the distributed database in the form of fields, wherein the fields of the road section include the trip ID, the road section code, the vehicle model, the power consumption of this road section, whether it is the initial cold start, the road section driving time, the driving season, the driving week, whether it is a working day, whether it is a holiday, the driving hour section and the driving hour percentile.

8. A time-sharing energy consumption map extraction system based on electric vehicle driving data, characterized in that: include: A division unit, which is used to obtain the vehicle-borne signal data during the vehicle's travel and obtain the vehicle's itinerary, and divide the vehicle's itinerary into itinerary segments according to a preset rule; A conversion unit, which is used to convert the trajectory of each trip segment into an ordered set containing only intersection codes based on the intersection data after geohash encoding; A calculation unit, which is used to convert the travel segment into a road segment based on the fork codes and setting rules in the ordered set corresponding to the travel segment, and calculate the power consumption of the vehicle in each road segment; An execution unit, which is used to obtain a vehicle startup status label of a road section based on relevant temperature information, and obtain a time label of the road section based on a collection time of vehicle-borne signal data; A writing unit, which is used to write the power consumption of the road section, the vehicle start status tag and the time tag into the distributed database in the form of fields; The geohash-coded intersection data is used to convert the trajectory of each trip segment into an ordered set containing only intersection codes. The specific steps include: Based on the intersection data after geohash coding, the trajectory points of each trip segment of the vehicle are compared with the intersection data in turn. If the current trajectory point falls into the area represented by the intersection geohash, the current trajectory point is replaced with the geohash code. If the current trajectory point does not fall into the area represented by the intersection geohash, the current trajectory point is discarded, thereby obtaining an ordered set of intersection codes for each trip segment. The travel segments are converted into road segments based on the fork codes and setting rules in the ordered set corresponding to the travel segments, wherein the setting rule is: in the ordered set corresponding to the travel segments, two adjacent fork codes represent a road segment.

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