Working condition cycle extraction method, device, electronic equipment and storage medium

Abstract

The present invention relates to a method, device, electronic device, and storage medium for extracting a working cycle, and belongs to the field of vehicle technology. The method comprises: identifying hotspots in a vehicle's GPS data based on a moving window heat map recognition method; identifying stop points in the GPS data based on a distance matrix; constructing the distance matrix based on the distances between hotspots; segmenting the vehicle's operating data into multiple segments of cycle data based on hotspots and stop points; and extracting working cycles based on the operating characteristics corresponding to the cycle data. The working cycle extraction method provided by the present invention extracts working cycles using a hotspot and stop point segmentation algorithm from vehicle operation data collected within a fixed period by the Internet of Vehicles, thereby shortening the extraction cycle and improving efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method, device, electronic equipment and storage medium for extracting a working cycle. Background Art

[0002] With increasingly stringent emissions regulations and fierce market competition, automakers are prioritizing energy conservation and emission reduction as a key means of enhancing product competitiveness, and the development of fuel-saving technologies is becoming increasingly urgent. The operating cycle is a critical common technology in the automotive industry and forms the basis for the development of test methods and limit standards for vehicle energy consumption and emissions. Vehicle driving cycles are crucial for the design, development, and verification of standard test conditions for vehicle power and economy parameters. Standard vehicle test conditions are a series of standardized test conditions established to evaluate and compare vehicle power, economy, fuel efficiency, and emissions. They aim to provide a standardized environment so that manufacturers, organizations, and consumers can evaluate the performance of different vehicle models against the same benchmark. However, with the increasing sophistication of product development, standard test conditions can no longer meet the market's increasingly refined product needs, and the development of tailored test conditions for specific scenarios is becoming increasingly important.

[0003] Currently, the only way to extract working conditions for large customers or typical users is to install data collection equipment offline and then manually process it into a single cycle. The extraction period of the working condition cycle (the working condition cycle refers to the general term for the various working states or load conditions experienced by a device or system during actual use) is long, and additional costs are required for installing equipment and signing agreements with users. Since data cannot be collected for a long time, the coverage of user operating conditions is lacking, and the extraction results may be biased. Summary of the Invention

[0004] In view of this, it is necessary to provide a working condition cycle extraction method, device, electronic device and storage medium to solve the problems of low efficiency and accuracy of existing working condition cycle extraction methods.

[0005] In order to solve the above problems, the present invention provides a working condition cycle extraction method, comprising:

[0006] Based on the moving window heat map recognition method, heat spots are identified in the vehicle's GPS data;

[0007] identifying stop points in the GPS data based on a distance matrix constructed based on distances between the thermal points;

[0008] Based on the thermal points and the stop points, the vehicle operation data is divided into a plurality of segments of cycle data;

[0009] Based on the operating characteristics corresponding to the cycle data, an operating cycle is extracted.

[0010] In a possible implementation, identifying a stop point in the GPS data based on a distance matrix includes:

[0011] Based on the distance matrix, construct a distance marker matrix;

[0012] The stop points are identified based on the distance marker matrix.

[0013] In a possible implementation, identifying the stop point based on the distance marker matrix includes:

[0014] Based on the sum of each row or column in the distance marker matrix, the row or column corresponding to the maximum value is saved in a list in sequence;

[0015] A point in the list where the time jump exceeds a preset value is determined as the stop point.

[0016] In a possible implementation, extracting the operating cycle based on the operating characteristics corresponding to the cycle data includes:

[0017] Determine the average value of the operating characteristics corresponding to each cycle data;

[0018] Based on the error between the operating characteristic and the average value, the operating characteristic corresponding to the value with the smallest error is used as the operating cycle.

[0019] In one possible implementation, the operating characteristics include at least one of the following:

[0020] Average driving speed, mileage, driving time, idling time percentage, braking mileage percentage, economic speed time percentage, gear shifting frequency, start-stop times, or fuel consumption per 100 kilometers.

[0021] In one possible implementation, the moving window heat map recognition method, after identifying heat points in the vehicle's GPS data, further includes:

[0022] Based on the time at which any two adjacent points of the thermal points are located, the thermal points are cleaned up.

[0023] The present invention also provides a working condition cycle extraction device, comprising:

[0024] A first recognition module is used to identify heat points in the vehicle's GPS data based on a moving window heat map recognition method;

[0025] a second identification module for identifying a stop point in the GPS data based on a distance matrix constructed based on the distances between the thermal points;

[0026] a segmentation module, configured to segment the vehicle's operating data into multiple segments of cyclic data based on the thermal points and the stop points;

[0027] The extraction module is used to extract the operating cycle based on the operating characteristics corresponding to the cycle data.

[0028] On the other hand, the present invention also provides an electronic device, comprising a memory and a processor, wherein:

[0029] The memory is used to store programs;

[0030] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the operating cycle extraction method described in any of the above implementations.

[0031] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the operating cycle extraction method described in any of the above implementations is implemented.

[0032] On the other hand, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the operating cycle extraction method described in any of the above implementations.

[0033] The beneficial effects of the present invention are: the operating cycle extraction method, device, electronic device and storage medium provided by the present invention use a moving window heat map recognition method to analyze the vehicle's GPS data to identify hot spots, which are areas where vehicles often stay or move, and construct a distance matrix based on the distance between the hot spots, thereby identifying the vehicle's stop points near the hot spots, that is, the actual location where the vehicle stops. Based on the identified hot spots and stop points, the continuous vehicle operation data can be divided into multiple independent cycle data segments, and each cycle data segment represents a data segment of the vehicle in different working cycles or operating modes. Therefore, for each cycle data segment, its corresponding operating characteristics, such as driving speed, driving time, stop time, etc., can be analyzed to obtain the vehicle's operating cycle. The vehicle operation data within a fixed period is collected through the Internet of Vehicles, and the operating cycle is extracted using a hot point and stop point segmentation algorithm, which shortens the extraction cycle and improves efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 One of the method flow charts of an embodiment of the working condition cycle extraction method provided by the present invention;

[0036] Figure 2 A schematic diagram of the thermal point provided by the present invention;

[0037] Figure 3 A schematic diagram of the moving window heat map recognition method provided by the present invention;

[0038] Figure 4 A schematic diagram of the actual distances between the thermal points provided by the present invention;

[0039] Figure 5 A schematic diagram of the values ​​of the distance marker matrix provided by the present invention;

[0040] Figure 6 This is a second method flow chart of an embodiment of the working condition cycle extraction method provided by the present invention;

[0041] Figure 7 A schematic structural diagram of an embodiment of the operating condition cycle extraction device provided by the present invention;

[0042] Figure 8 This is a schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] In the description of the embodiments of the present invention, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0045] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, technical features designated as "first" or "second" may explicitly or implicitly include at least one such feature.

[0046] Figure 1 This is a flow chart of one embodiment of the working cycle extraction method provided by the present invention, as shown in FIG. Figure 1As shown, the operating cycle extraction method includes:

[0047] S101, identifying heat points in the vehicle's GPS data based on a moving window heat map recognition method;

[0048] S102, identifying a stop point in the GPS data based on a distance matrix, wherein the distance matrix is ​​constructed based on the distances between the thermal points;

[0049] S103, dividing the vehicle's operating data into multiple segments of cyclic data based on the thermal points and the stop points;

[0050] S104: Extracting operating cycles based on the operating characteristics corresponding to the cycle data.

[0051] It should be noted that in the embodiment of the present invention, the operating cycle refers to the general term for various operating states or load conditions experienced by a vehicle during actual use.

[0052] Compared with the prior art, the operating cycle extraction method provided by the embodiment of the present invention uses a moving window heat map recognition method to analyze the vehicle's GPS data to identify hot spots, which are areas where vehicles often stay or move. A distance matrix is ​​constructed based on the distance between the hot spots, and then the vehicle's stop points near the hot spots are identified, that is, the actual location where the vehicle stops. Therefore, based on the identified hot spots and stop points, the continuous vehicle operation data can be divided into multiple independent cycle data segments. Each cycle data segment represents a data segment of the vehicle in different working cycles or operating modes. Therefore, for each cycle data segment, its corresponding operating characteristics, such as driving speed, driving time, stop time, etc., can be analyzed to obtain the vehicle's operating cycle. The vehicle operation data within a fixed period is collected through the Internet of Vehicles, and the operating cycle is extracted using a hot point and stop point segmentation algorithm, which shortens the extraction cycle and improves efficiency and accuracy.

[0053] In some embodiments of the present invention, the moving window heat map recognition method, after identifying heat points in the vehicle's GPS data, further includes:

[0054] Based on the time at which any two adjacent points of the thermal points are located, the thermal points are cleaned up.

[0055] Optionally, extract GPS data for a vehicle, divide the longitude and latitude into intervals (longitude by 0.0001, latitude by 0.00001), cross-count the number of sample trajectory points in different intervals, and then sort them in reverse order. Take the group of points with the largest number of sample points and mark their positions on the curve as shown below: Figure 2 As shown, Figure 2 Schematic diagram of the thermal point provided by the present invention.

[0056] Because the route may not be fixed, the global hotspot cannot fully capture the hotspot of each section of the route. The present invention uses a moving window heat map recognition method to identify the hotspot of each section of data, and then aggregates them together for deduplication and sorting. Figure 2 As can be seen, the hotspot identification algorithm identifies the starting and ending points (the minimum and maximum latitude values). Only one-way data is included between the two points, and a complete operating cycle includes both the outbound and return trips. This method uses a distance algorithm to group locations with smaller distances, selecting either the starting or ending point group. Stops near the starting or ending points are then identified as the final segmentation points. The operating data is segmented according to the segmentation points to form multiple operating cycles. The cycle characteristics are then calculated, and the cycle with the smallest error is selected as the representative cycle.

[0057] For example, a moving window heat map recognition method can be used to identify hotspots in the vehicle's GPS data. Hotspots are areas where vehicles frequently stop or move, and can be determined by analyzing data density.

[0058] Extract the vehicle's GPS data and divide the longitude and latitude into intervals, with longitude being 0.0001 and latitude being 0.00001. Figure 3 A schematic diagram of the moving window heat map recognition method provided by the present invention is shown as follows: Figure 3 As shown in the figure, set the time window W and the sliding step size S, cross-count the number of sample trajectory points in different intervals within the time window W, and then sort them in reverse order. The data sequence number of the group of points with the largest number of sample points is saved in the list L middle.

[0059] Then move the time window back S steps, count the heat points in the next time window, and count the indexes of the heat points in all windows in turn, and save them all to L In the above example, L is deduplicated and sorted, as shown in Table 1.

[0060] Table 1

[0061]

[0062] After identifying the hot spots, since the time spent on loading and unloading the vehicle is long, a large number of points will be generated near a location. L The data contains a large number of points that are continuous or close in time. Therefore, the hot spots can be cleaned up according to the time when any two adjacent points in the hot spots are located.

[0063] calculate L The difference between the data sequence numbers of two adjacent data at the time, retaining the difference greater than the preset threshold H The second point.

[0064] Preset thresholdH The calculation formula is: H =Complete data duration / max (sum of longitude peaks and troughs, sum of latitude peaks and troughs).

[0065] The operating cycle extraction method provided in an embodiment of the present invention identifies thermal points in the vehicle's GPS data through a moving window thermal map recognition method, and then cleans the thermal points based on the time at which any two adjacent points in the thermal points are located, further optimizing the recognition results of the thermal points and improving the accuracy of subsequent analysis.

[0066] In some embodiments of the present invention, identifying the stop points in the GPS data based on the distance matrix includes:

[0067] Based on the distance matrix, construct a distance marker matrix;

[0068] The stop points are identified based on the distance marker matrix.

[0069] In some embodiments of the present invention, identifying the stop point based on the distance marker matrix includes:

[0070] Based on the sum of each row or column in the distance marker matrix, the row or column corresponding to the maximum value is saved in a list in sequence;

[0071] A point in the list where the time jump exceeds a preset value is determined as the stop point.

[0072] According to the cleaned hot spot list L , calculate the actual distance between all thermal points and get the distance matrix M d , Figure 4 A schematic diagram of the actual distance between thermal points provided by the present invention.

[0073] Compare the distance between each two points with the maximum (average distance of all points, 20), if it is greater than, mark it as 0, and if it is less than, mark it as 1, and get the distance marking matrix M f , Figure 5 This is a schematic diagram of the values ​​of the distance marker matrix provided by the present invention.

[0074] Distance labeling matrix M f Sum by row or column. Take a row as an example and find the row with the largest sum and the corresponding row value as 1. L Get the list by number L p , the distance label matrix M f Corresponding L pThe rows and columns in are assigned the value 0.

[0075] like Figure 5 As shown, the distance marker matrix M f The sum of rows (columns) 0, 2, 5, 7, 9, 12, 13, 15, 16, 17 is 4, the largest of all columns. Save 0, 2, 5, 7, 9, 12, 13, 15, 16, 17 to a list. L p At the same time, the matrix M f All values ​​in rows and columns 0, 2, 5, 7, 9, 12, 13, 15, 16, and 17 are set to 0.

[0076] Repeat the above steps until the distance marker matrix M f All values ​​are 0, get the complete list L p .

[0077] Traversal L p For all points in the range of 3600s before and after, find the point with a time jump exceeding the preset value (for example, 1800s) as the stop point, and save the index of the corresponding point to L s ,according to L s Split the original data into multiple loop data.

[0078] The operating cycle extraction method provided in an embodiment of the present invention divides the vehicle's operating data into multiple cycle data by combining the identified thermal points and stop points. Each cycle data segment represents the data of the vehicle in different working cycles or operating modes, providing data support for the subsequent extraction of the operating cycle.

[0079] In some embodiments of the present invention, extracting the operating cycle based on the operating characteristics corresponding to the cycle data includes:

[0080] Determine the average value of the operating characteristics corresponding to each cycle data;

[0081] Based on the error between the operating characteristic and the average value, the operating characteristic corresponding to the value with the smallest error is used as the operating cycle.

[0082] In some embodiments of the present invention, the operating characteristics include at least one of the following:

[0083] Average driving speed, mileage, driving time, idling time percentage, braking mileage percentage, economic speed time percentage, gear shifting frequency, start-stop times, or fuel consumption per 100 kilometers.

[0084] Based on the segmented cycle data, the operating characteristics corresponding to each segment of cycle data are extracted. The operating characteristics may include: average vehicle speed, mileage, driving time, idling time percentage, braking mileage percentage, economic speed time percentage, gear shifting frequency, start-stop number, fuel consumption per 100 kilometers, etc., and the average value of each operating characteristic is calculated.

[0085] For example, if it is divided into 10 cycles, the average vehicle speed, mileage, driving time, idling time ratio, braking mileage ratio, economic speed time ratio, gear shifting frequency, start-stop number, and fuel consumption per 100 kilometers of the 10 cycles are calculated respectively.

[0086] The error between each operating characteristic and the average value is calculated separately (absolute error is used for proportion and relative error is used for real value), and the operating characteristic corresponding to the value with the smallest error is taken as the typical cycle, that is, the operating condition cycle.

[0087] The operating cycle extraction method provided in the embodiment of the present invention effectively converts the vehicle's GPS data into practical operating cycle data by combining thermal point identification and distance matrix analysis, providing a basis and convenience for subsequent vehicle operation management and data analysis.

[0088] Figure 6 The second method flow chart of an embodiment of the working condition cycle extraction method provided by the present invention is as follows: Figure 6 As shown, the operating cycle extraction method includes:

[0089] S601. Identify hot spots.

[0090] First, extract GPS data for a car and divide the longitude and latitude into intervals. The longitude is divided into intervals of 0.0001 and the latitude is divided into intervals of 0.00001. Set the time window W and the sliding step size S. Cross-count the number of sample trajectory points in different intervals within the window W, and then sort them in reverse order. Take the data sequence number of the group with the largest number of sample points and save it in the list. L In the process, the window moves back S steps, and the hottest point in the next window is counted. Then the hottest point indexes in all windows are counted and saved in L In the above example, L is deduplicated and sorted, as shown in Table 1.

[0091] S602, cleaning of hot spots.

[0092] The time it takes for a vehicle to load and unload is long, and a large number of points will be generated near a location. Lcontains a large number of points that are continuous or close in time, and the calculation L The difference between the data sequence numbers of the two adjacent data at the time is entered, and the data with a difference greater than H The second point.

[0093] H =Complete data duration / max (sum of longitude peaks and troughs, sum of latitude peaks and troughs).

[0094] S603: Calculate the distance matrix.

[0095] Cleaned hotspot list L , calculate the actual distance between all points and get the distance matrix M d , the heat map is represented as Figure 4 Compare the distance between each two points with the max (average distance of all points, 20) value, if it is greater than, mark it as 0, if it is less than, mark it as 1, and get the distance marking matrix M f .

[0096] S604: Matrix assignment.

[0097] Distance labeling matrix M f Sum by row or column. Take a row as an example and find the row with the largest sum and the corresponding row value as 1. L Get the list by number L p , the distance label matrix M f Corresponding L p The rows and columns in are assigned the value 0.

[0098] like Figure 5 As shown, the distance marker matrix M f The sum of rows (columns) 0, 2, 5, 7, 9, 12, 13, 15, 16, 17 is 4, the largest of all columns. Save 0, 2, 5, 7, 9, 12, 13, 15, 16, 17 to a list. L p At the same time, the matrix M f All values ​​in rows and columns 0, 2, 5, 7, 9, 12, 13, 15, 16, and 17 are set to 0.

[0099] Repeat the above steps until the distance marker matrix M f All values ​​are 0, get the complete list Lp .

[0100] S605: Calculate the stop point.

[0101] Traversal L p For all points in , find the points with a time jump of more than 1800s within the range of 3600s before and after as the stop points, and save the index of the corresponding point to L s ,according to L s Split the original data into multiple loop data.

[0102] S606, cyclic screening.

[0103] Calculate the following characteristics of all cycles obtained in S605: average driving speed, mileage, driving time, idle time percentage, braking mileage percentage, economy speed time percentage, gear shifting frequency, number of starts and stops, and fuel consumption per 100 kilometers. Calculate the average of the characteristics of the cycles. For example, if the cycles are divided into 10 cycles, calculate the average of the average driving speed, mileage, driving time, idle time percentage, braking mileage percentage, economy speed time percentage, gear shifting frequency, number of starts and stops, and fuel consumption per 100 kilometers for each of the 10 cycles. Calculate the error between each cycle and the average value (absolute error is used for proportions and relative error is used for actual values). Take the cycle with the smallest error as the typical cycle.

[0104] The operating cycle extraction method provided by the embodiment of the present invention aims to divide multiple cycles within a period of time into a single cycle. Due to the periodicity of the vehicle's running trajectory, the operational GPS data of a single vehicle or multiple vehicles within a period of time collected by the Internet of Vehicles can be used to extract typical cycles through the itinerary. The hottest driving spots are identified according to the distribution density of GPS location points, and the hottest driving spots are used as the starting point or end point. The parking points near the driving hot spots are then identified as segmentation points. The operating data for a period of time is divided into multiple operating cycle, and the cycle closest to the average value is selected as a typical cycle using an error algorithm. Because some vehicles have unfixed routes, driving hot spots need to be identified in segments. Because this solution is deployed in the cloud, it can avoid the steps of communicating with users to install equipment offline, and dismantling equipment after collecting data, effectively saving time and cost. At the same time, it can be completed based on historical data, effectively shortening the R&D cycle.

[0105] In order to better implement the working condition cycle extraction method in the embodiment of the present invention, based on the working condition cycle extraction method, the embodiment of the present invention further provides a working condition cycle extraction device. Figure 7 A schematic structural diagram of an embodiment of the working condition cycle extraction device provided by the present invention is shown as follows: Figure 7 As shown, the operating cycle extraction device 700 includes:

[0106] A first recognition module 710 is configured to identify heat points in the vehicle's GPS data based on a moving window heat map recognition method;

[0107] A second identification module 720 is configured to identify a stop point in the GPS data based on a distance matrix constructed based on the distances between the thermal points;

[0108] A segmentation module 730 is configured to segment the vehicle's operating data into multiple segments of cyclic data based on the thermal points and the stop points;

[0109] The extraction module 740 is configured to extract the operating cycle based on the operating characteristics corresponding to the cycle data.

[0110] The operating cycle extraction device 700 provided in the above embodiment can implement the technical solution described in the above operating cycle extraction method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the operating cycle extraction method embodiment, which will not be repeated here.

[0111] like Figure 8 As shown, the present invention also provides an electronic device 800. The electronic device 800 includes a processor 801, a memory 802 and a display 803. Figure 8 Only some of the components of the electronic device 800 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0112] In some embodiments, the processor 801 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 802 , such as the operating cycle extraction method of the present invention.

[0113] In some embodiments, processor 801 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 801 may be local or remote. In some embodiments, processor 801 may be implemented on a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0114] In some embodiments, the memory 802 may be an internal storage unit of the electronic device 800, such as a hard disk or memory of the electronic device 800. In other embodiments, the memory 802 may also be an external storage device of the electronic device 800, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 800.

[0115] Furthermore, the memory 802 may include both an internal storage unit of the electronic device 800 and an external storage device. The memory 802 is used to store application software installed in the electronic device 800 and various data.

[0116] In some embodiments, display 803 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. Display 803 is used to display information on electronic device 800 and to display a visual user interface. Components 801-803 of electronic device 800 communicate with each other via a system bus.

[0117] In one embodiment, when the processor 801 executes the operating cycle extraction program in the memory 802, the following steps may be implemented:

[0118] Based on the moving window heat map recognition method, heat spots are identified in the vehicle's GPS data;

[0119] identifying stop points in the GPS data based on a distance matrix constructed based on distances between the thermal points;

[0120] Based on the thermal points and the stop points, the vehicle operation data is divided into a plurality of segments of cycle data;

[0121] Based on the operating characteristics corresponding to the cycle data, an operating cycle is extracted.

[0122] It should be understood that, when the processor 801 executes the operating cycle extraction program in the memory 802 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0123] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 800 mentioned. The electronic device 800 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 800 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0124] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the operating cycle extraction method provided in the above-mentioned method embodiments can be implemented.

[0125] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the steps or functions in the operating cycle extraction method provided in the above-mentioned method embodiments.

[0126] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0127] The above is a detailed introduction to the operating cycle extraction method, device, electronic device and storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A working cycle extraction method, characterized in that: include: Based on the moving window heat map recognition method, heat spots are identified in the vehicle's GPS data; identifying stop points in the GPS data based on a distance matrix; The distance matrix is ​​constructed based on the distances between the heat points; Based on the thermal points and the stop points, the vehicle operation data is divided into a plurality of segments of cycle data; extracting an operating cycle based on operating characteristics corresponding to the cycle data; The step of identifying a stop point in the GPS data based on a distance matrix includes: Based on the distance matrix, construct a distance marker matrix; identifying the stop points based on the distance marker matrix; The step of identifying the stop point based on the distance marker matrix includes: Based on the sum of each row or column in the distance marker matrix, the row or column corresponding to the maximum value is saved in a list in sequence; A point in the list where the time jump exceeds a preset value is determined as the stop point.

2. The operating cycle extraction method according to claim 1, characterized in that: The extracting of the operating cycle based on the operating characteristics corresponding to the cycle data includes: Determine the average value of the operating characteristics corresponding to each cycle data; Based on the error between the operating characteristic and the average value, the operating characteristic corresponding to the value with the smallest error is used as the operating cycle.

3. The operating cycle extraction method according to claim 1, characterized in that: The operational characteristics include at least one of the following: Average driving speed, mileage, driving time, idling time percentage, braking mileage percentage, economic speed time percentage, gear shifting frequency, start-stop times, or fuel consumption per 100 kilometers.

4. The operating cycle extraction method according to claim 1, characterized in that: The moving window heat map recognition method, after identifying heat points in the vehicle's GPS data, further includes: Based on the time at which any two adjacent points of the thermal points are located, the thermal points are cleaned up.

5. A working condition cycle extraction device, characterized in that: include: A first recognition module is used to identify heat points in the vehicle's GPS data based on a moving window heat map recognition method; a second identification module for identifying a stop point in the GPS data based on a distance matrix constructed based on the distances between the thermal points; a segmentation module, configured to segment the vehicle's operating data into multiple segments of cyclic data based on the thermal points and the stop points; an extraction module, configured to extract an operating cycle based on operating characteristics corresponding to the cycle data; The step of identifying a stop point in the GPS data based on a distance matrix includes: Based on the distance matrix, construct a distance marker matrix; identifying the stop points based on the distance marker matrix; The step of identifying the stop point based on the distance marker matrix includes: Based on the sum of each row or column in the distance marker matrix, the row or column corresponding to the maximum value is saved in a list in sequence; A point in the list where the time jump exceeds a preset value is determined as the stop point.

6. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the operating cycle extraction method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the operating cycle extraction method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the operating cycle extraction method according to any one of claims 1 to 4 is implemented.

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