Methods, devices, computer equipment, and storage media for determining fuel-saving operating conditions

Abstract

This application relates to a method, apparatus, computer equipment, storage medium, and computer program product for determining fuel-saving operating conditions. The method includes: acquiring the operating data, latitude, longitude, and heading angle of each vehicle under each of multiple market segments at various collection times; obtaining the road condition type corresponding to each vehicle through road network matching based on the latitude, longitude, and heading angle corresponding to each vehicle under each type at the current collection time; identifying target vehicles with the same road condition type under the current market segment; determining simulated operating condition data based on the operating data of the target vehicles; determining simulated operating condition data corresponding to the current market segment based on the simulated operating condition data of the target vehicle's road condition type; obtaining the target torque range changing over time by looking up a table; and determining the fuel-saving operating condition corresponding to the corresponding market segment based on the vehicle speed changing over time and the target torque range changing over time. Using this method can save vehicle energy consumption.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for determining fuel-saving operating conditions. Background Technology

[0002] Domestic and foreign OEMs are conducting relevant research in the fields of engine combustion process, fuel injection control strategy and electrification accessories, in order to reduce engine minimum specific fuel consumption, improve thermal efficiency and achieve energy conservation and emission reduction.

[0003] Currently, engines are exhibiting increasingly lower overall fuel consumption and higher thermal efficiency under specific operating conditions. However, even excluding the impact of regulatory upgrades on fuel consumption, the average fuel consumption of the entire vehicle has not decreased. Although many energy-saving optimization measures are available, the typical operating conditions of vehicles do not match the lowest specific fuel consumption range and are significantly different from the operating point with the highest thermal efficiency. This results in no reduction in overall vehicle fuel consumption under real-world road conditions. Therefore, existing methods for controlling vehicles based on their typical operating conditions suffer from relatively high fuel consumption. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can save fuel consumption in order to address the aforementioned technical problems.

[0005] Firstly, this application provides a method for determining fuel-saving operating conditions. The method includes:

[0006] Acquire operational data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each data collection time; vehicles in the various market segments have the same engine type;

[0007] For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching;

[0008] For any market segment, identify target vehicles with the same road condition type under the current market segment; based on the target vehicle's operating data, determine the simulated operating condition data for the road condition type to which the target vehicle belongs; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment, including vehicle speed changing over time.

[0009] Based on the simulated operating condition data corresponding to each market segment type, the target torque range changing over time is obtained by looking up the table; based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the corresponding market segment type are determined. The fuel-saving operating conditions are used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0010] In one embodiment, the operating data includes operating speed, engine speed, and torque; based on the operating data of the target vehicle, simulated operating condition data for the road condition type to which the target vehicle belongs is determined, including:

[0011] Based on the vehicle speed at each data collection time, determine the speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the vehicle speed at each data collection time, determine the speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the vehicle speed and torque at each data collection time, determine the speed and torque distribution corresponding to the road condition type to which the target vehicle belongs.

[0012] Based on the vehicle speed distribution, rotational speed distribution, and rotational speed-torque distribution, the simulated operating condition data of the road condition type to which the target vehicle belongs is determined through a simulated operating condition model.

[0013] In one embodiment, the simulated operating condition data corresponding to the current market segment is determined based on the simulated operating condition data for each road condition type, including:

[0014] The vehicle speed distributions corresponding to each road condition type are weighted and summed to obtain the summed vehicle speed distribution; the engine speed distributions corresponding to each road condition type are weighted and summed to obtain the summed engine speed distribution; the engine speed and torque distributions corresponding to each road condition type are weighted and summed to obtain the summed engine speed and torque distribution.

[0015] The summed vehicle speed distribution, summed engine speed distribution, and summed engine speed-torque distribution are input into the simulation operating condition model to obtain the simulation operating condition data corresponding to the current market segment type.

[0016] In one embodiment, based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the relevant market segment are determined, including:

[0017] Based on the summed speed-torque distribution, density clustering is used to obtain the speed-torque cluster distribution;

[0018] Based on the vehicle speed at each time point and the corresponding target torque range, the fuel-saving area is determined in the speed-torque cluster distribution.

[0019] Determine fuel-saving operating conditions based on fuel-saving areas.

[0020] In one embodiment, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, road network matching is used to obtain the road condition type corresponding to each vehicle under each type at the current data collection time, including:

[0021] Based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road type and slope type corresponding to each vehicle under each type at the current data collection time are obtained through road network matching.

[0022] Based on road type and slope type, determine the road condition type corresponding to each vehicle under each type at the current data collection time.

[0023] In one embodiment, the fuel-saving operating condition determination method further includes:

[0024] Determine the specific fuel consumption and power output for each fuel-saving operating condition corresponding to each market segment type;

[0025] The actual fuel consumption is determined based on the ratio of fuel consumption, power, and fuel-saving operating conditions.

[0026] Secondly, this application also provides a fuel-saving operating condition determination device. The device includes:

[0027] The data acquisition module is used to acquire the operating data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each collection time; the vehicles in the various market segments have the same engine type;

[0028] The road condition determination module is used to determine the road condition type for each vehicle under each type at any given time by matching the road network based on the latitude, longitude and heading angle of each vehicle under each type at the current time of data collection.

[0029] The simulated operating condition data determination module is used to identify target vehicles with the same road operating condition type under any given market segment type; determine the simulated operating condition data of the road operating condition type to which the target vehicle belongs based on the target vehicle's operating data; and determine the simulated operating condition data corresponding to the current market segment type based on the simulated operating condition data of each road operating condition type. The simulated operating condition data includes vehicle speed that changes over time.

[0030] The fuel-saving operating condition determination module is used to look up the target torque range over time by referring to the simulated operating condition data corresponding to each market segment type; and to determine the fuel-saving operating condition corresponding to the corresponding market segment type based on the vehicle speed and the target torque range over time. The fuel-saving operating condition is used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0032] Acquire operational data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each data collection time; vehicles in the various market segments have the same engine type;

[0033] For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching;

[0034] For any market segment, identify target vehicles with the same road condition type under the current market segment; based on the target vehicle's operating data, determine the simulated operating condition data for the road condition type to which the target vehicle belongs; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment, including vehicle speed changing over time.

[0035] Based on the simulated operating condition data corresponding to each market segment type, the target torque range changing over time is obtained by looking up the table; based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the corresponding market segment type are determined. The fuel-saving operating conditions are used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0036] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0037] Acquire operational data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each data collection time; vehicles in the various market segments have the same engine type;

[0038] For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching;

[0039] For any market segment, identify target vehicles with the same road condition type under the current market segment; based on the target vehicle's operating data, determine the simulated operating condition data for the road condition type to which the target vehicle belongs; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment, including vehicle speed changing over time.

[0040] Based on the simulated operating condition data corresponding to each market segment type, the target torque range changing over time is obtained by looking up the table; based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the corresponding market segment type are determined. The fuel-saving operating conditions are used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0041] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0042] Acquire operational data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each data collection time; vehicles in the various market segments have the same engine type;

[0043] For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching;

[0044] For any market segment, identify target vehicles with the same road condition type under the current market segment; based on the target vehicle's operating data, determine the simulated operating condition data for the road condition type to which the target vehicle belongs; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment, including vehicle speed changing over time.

[0045] Based on the simulated operating condition data corresponding to each market segment type, the target torque range changing over time is obtained by looking up the table; based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the corresponding market segment type are determined. The fuel-saving operating conditions are used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0046] The aforementioned method, device, computer equipment, storage medium, and computer program products for determining fuel-saving operating conditions acquire the operating data, latitude, longitude, and heading angle of each vehicle under each of multiple market segments at various collection times. Through road network matching, they obtain the corresponding road condition type for each vehicle under each type at each collection time. Based on the operating data of target vehicles with the same road condition type under the current market segment, they determine the simulated operating condition data of the target vehicle's road condition type, and then determine the simulated operating condition data corresponding to each market segment. This method of determining the simulated operating condition data corresponding to vehicles under multiple market segments based on road condition type combines the actual road conditions of the vehicles, improves the accuracy of simulated operating condition data, and is conducive to determining fuel-saving operating conditions. By looking up tables to obtain the target torque range and vehicle speed that change over time, the fuel-saving operating conditions corresponding to the corresponding market segment are determined. This method of determining fuel-saving operating conditions through simulated operating condition data can control vehicles of different market segments under the same engine type using the corresponding speed and torque range for fuel-saving operating conditions, which is beneficial to saving vehicle fuel consumption. Attached Figure Description

[0047] Figure 1 This is an application environment diagram of the fuel-saving operating condition determination method in one embodiment;

[0048] Figure 2 This is a flowchart illustrating a method for determining fuel-saving operating conditions in one embodiment;

[0049] Figure 3 This is a schematic diagram of a sub-process of S206 in one embodiment;

[0050] Figure 4 This is a schematic diagram of vehicle speed distribution in one embodiment;

[0051] Figure 5 This is a schematic diagram of the rotational speed distribution in one embodiment;

[0052] Figure 6 This is a schematic diagram of the rotational speed and torque distribution in one embodiment;

[0053] Figure 7 This is a schematic diagram of a sub-process of S206 in another embodiment;

[0054] Figure 8 This is a schematic diagram of a sub-process of S208 in one embodiment;

[0055] Figure 9 This is a schematic diagram of the speed torque clustering distribution in one embodiment;

[0056] Figure 10 This is a schematic diagram illustrating the determination of a fuel-saving region in the speed-torque clustering distribution in one embodiment;

[0057] Figure 11 This is a schematic diagram of a sub-process of S204 in one embodiment;

[0058] Figure 12 This is a schematic diagram of the overall process for determining fuel-saving operating conditions in one embodiment;

[0059] Figure 13 This is a schematic diagram of the universal characteristic curve of an engine in one embodiment;

[0060] Figure 14 This is a three-dimensional fitting plot of the isofuel consumption rate curve and isopower curve in one embodiment;

[0061] Figure 15 Here is a friction torque curve in one embodiment;

[0062] Figure 16 This is a three-dimensional fitting diagram of the constant friction torque curve and the constant power curve in one embodiment;

[0063] Figure 17 Here is a vehicle resistance curve in one embodiment;

[0064] Figure 18 This is a driving force curve for each gear in one embodiment;

[0065] Figure 19 This is a structural block diagram of the fuel-saving condition determination device in one embodiment;

[0066] Figure 20 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0068] The fuel-saving operating condition determination method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. The fuel-saving operating condition determination method provided in this application embodiment can be executed by terminal 102 or server 104 alone, or by terminal 102 and server 104 collaboratively. Taking execution by terminal 102 alone as an example: The method acquires the operating data, latitude, longitude, and heading angle of each vehicle under each of multiple market segments at each collection time; vehicles in multiple market segments have the same engine type; for any collection time, based on the latitude, longitude, and heading angle corresponding to each vehicle under each type at the current collection time, road network matching is used to obtain the road condition type corresponding to each vehicle under each type at the current collection time; for any market segment, the current market segment is determined. The system identifies target vehicles with the same road condition type under the same category. Based on the target vehicle's operational data, it determines the simulated operating condition data for the target vehicle's road condition type. Based on the simulated operating condition data for each road condition type, it determines the simulated operating condition data corresponding to the current market segment type, including vehicle speed over time. Based on the simulated operating condition data for each market segment type, it looks up the target torque range over time using a table. Based on the vehicle speed and target torque range over time, it determines the fuel-saving operating condition for the corresponding market segment type, characterizing the vehicle speed and corresponding torque range for fuel-saving control of vehicles within that market segment type. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0069] In one embodiment, such as Figure 2 As shown, a method for determining fuel-saving operating conditions is provided, which can be applied to computer equipment (the computer equipment can be...) Figure 1 Taking terminal 102 or server 104 as an example, the following steps are included:

[0070] S202, acquires the operating data, latitude, longitude and heading angle of each vehicle in each of the various market segments at each collection time; the vehicles in the various market segments have the same engine type.

[0071] Here, "vehicles in different market segments" refers to vehicles applied to different market needs. For example, various market segment types include vehicles for transporting food, vehicles for engineering operations, or tractor-trailers. Vehicles in multiple market segments may share the same engine type. Computer equipment retrieves operational data, latitude, longitude, and heading angles for each vehicle within each market segment at various data collection times from a pre-defined set of vehicle-to-everything (V2X) big data.

[0072] S204: For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, road network matching is used to obtain the road condition type corresponding to each vehicle under each type at the current data collection time.

[0073] Road network matching is a process of converting the vehicle's latitude, longitude, and heading angle into a sequence. Optionally, road network matching can employ a Markov model-based algorithm. Road condition type refers to the type of road-related conditions. For example, road condition types include road type and slope type. For any given data collection time, the computer device, based on the latitude, longitude, and heading angle of each vehicle under each type at that time, uses road network matching to obtain the corresponding road condition type for each vehicle under each type at the current data collection time.

[0074] S206, for any market segment type, identify target vehicles with the same road condition type under the current market segment type; based on the target vehicle's operating data, determine the simulated operating condition data for the road condition type to which the target vehicle belongs; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment type, the simulated operating condition data including vehicle speed changing over time.

[0075] Here, "target vehicle" refers to vehicles with the same road condition type within the same market segment. For any given market segment, the computer equipment identifies target vehicles with the same road condition type within that market segment.

[0076] Simulated operating condition data refers to the vehicle's operating condition data under each road condition type. For example, simulated operating condition data includes fields such as timestamp, vehicle speed, gradient, and coolant temperature. Simulated operating condition data can be represented as a vehicle speed curve over time, a gradient curve over time, and a coolant temperature curve over time. Each market segment includes at least one road condition type. The computer equipment determines the simulated operating condition data for the target vehicle's road condition type based on the target vehicle's operating data, and then obtains the simulated operating condition data corresponding to the respective market segment by weighted summation of the simulated operating condition data for each road condition type.

[0077] S208: Based on the simulated operating condition data corresponding to each market segment type, the target torque range that changes over time is obtained by looking up a table; based on the vehicle speed that changes over time and the target torque range that changes over time, the fuel-saving operating conditions corresponding to the corresponding market segment type are determined. The fuel-saving operating conditions are used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0078] The computer system uses simulated operating data for each market segment to look up the target torque range for each vehicle speed in a table, thus obtaining the target torque range over time. Fuel-saving operating conditions refer to conditions designed to reduce vehicle fuel consumption. These conditions characterize the vehicle speed and corresponding torque range for fuel-saving control within a specific market segment. Fuel-saving operating conditions include engine speed and the target torque range corresponding to that speed. The computer system determines the engine speed over time based on the vehicle speed. Based on each time point within the engine speed variation, it determines the target torque range for that current time. Based on the engine speed and the corresponding target torque range at each time point, it looks up the corresponding specific fuel consumption in a table. The vehicle speed and target torque range corresponding to the lowest specific fuel consumption for each market segment are then determined as the fuel-saving operating condition.

[0079] The aforementioned method for determining fuel-saving operating conditions involves acquiring the operating data, latitude, longitude, and heading angle of each vehicle under each of multiple market segments at various collection times. Through road network matching, the corresponding road condition type for each vehicle under each market segment at each collection time is obtained. Based on the operating data of target vehicles with the same road condition type under the current market segment, simulated operating condition data for the target vehicle's road condition type is determined, thereby determining the simulated operating condition data corresponding to each market segment. This method, which determines the simulated operating condition data for vehicles under multiple market segments based on road condition type, combines the actual road conditions of the vehicles, improving the accuracy of the simulated operating condition data and facilitating the determination of fuel-saving operating conditions. Furthermore, by looking up tables to obtain the target torque range and vehicle speed that change over time, the corresponding fuel-saving operating conditions for the corresponding market segment are determined. This method, which determines fuel-saving operating conditions through simulated operating condition data, allows for the control of vehicles with the same engine type but different market segments using the corresponding speed and torque ranges for fuel-saving operating conditions, thus helping to save vehicle fuel consumption.

[0080] In one embodiment, such as Figure 3 As shown, the operating data includes vehicle speed, engine speed, and torque; based on the target vehicle's operating data, the simulated operating condition data for the target vehicle's road condition type is determined, including:

[0081] S302, based on the vehicle speed of the target vehicle at each collection time, determine the speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the rotational speed of the target vehicle at each collection time, determine the rotational speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the rotational speed and torque of the target vehicle at each collection time, determine the rotational speed and torque distribution corresponding to the road condition type to which the target vehicle belongs.

[0082] Among them, vehicle speed distribution is used to characterize the speed distribution corresponding to the road condition type to which the target vehicle belongs. The computer calculates the percentage of operating speeds corresponding to the road condition type to which the target vehicle belongs, based on mileage or time, to obtain the vehicle speed distribution corresponding to the road condition type to which the target vehicle belongs. Specifically, the speed segments of the target vehicle's operating speed are determined, and the mileage and operating time of the target vehicle in each speed segment are calculated to obtain the mileage percentage or time percentage of each speed segment. Based on the mileage percentage or time percentage of each speed segment, the vehicle speed distribution corresponding to the road condition type to which the target vehicle belongs is determined. For example... Figure 4 The diagram shown is a schematic representation of vehicle speed distribution.

[0083] Rotational speed distribution is used to characterize the rotational speed distribution corresponding to the road condition type of the target vehicle. Computer equipment statistically analyzes the rotational speed distribution corresponding to the road condition type of the target vehicle based on mileage or time. Specifically, it determines the rotational speed segments of the target vehicle, calculates the mileage and runtime of the target vehicle in each rotational speed segment, obtains the mileage or time percentage of each rotational speed segment, and determines the vehicle speed distribution corresponding to the road condition type of the target vehicle based on the mileage or time percentage of each rotational speed segment. For example... Figure 5 The diagram shows the rotational speed distribution.

[0084] Speed-torque distribution is used to characterize the speed and torque distribution corresponding to the road condition type of the target vehicle. The computer statistically analyzes the speed-torque distribution corresponding to the road condition type of the target vehicle based on mileage or time. Specifically, it determines the speed segments and torque segments of the target vehicle, calculates the mileage and runtime of the target vehicle in each speed and torque segment, and obtains the mileage or time percentage of the vehicle simultaneously in both speed and torque segments. Based on these percentages, the speed-torque distribution corresponding to the road condition type of the target vehicle is determined. For example... Figure 6 The diagram shows the torque distribution at various speeds.

[0085] S304, based on vehicle speed distribution, rotational speed distribution, and rotational speed-torque distribution, uses a simulated operating condition model to determine the simulated operating condition data of the road condition type to which the target vehicle belongs.

[0086] The simulated operating condition model can employ a logical model based on a Markov chain model. This model uses Markov chain computation to perform Markov analysis, primarily aiming to predict potential changes within a specific future range based on the distribution of vehicle speed, engine speed, and torque, and their trends, thus providing a basis for decision-making. The computer equipment uses the simulated operating condition model to obtain simulated operating condition data for the target vehicle's road condition type. This simulated operating condition data includes time-varying vehicle speed, time-varying gradient, and time-varying coolant temperature, among other things.

[0087] In this embodiment, by determining the vehicle speed distribution, rotational speed distribution, and rotational speed-torque distribution corresponding to the road condition type of the target vehicle based on the vehicle's operating data at each collection time, and obtaining the simulated operating condition data of the road condition type of the target vehicle through a simulated operating condition model, the method combines the vehicle's operating data with the actual road conditions, improves the accuracy of the simulated operating condition data, and helps save vehicle fuel consumption.

[0088] In one embodiment, such as Figure 7 As shown, based on the simulated operating condition data for each road condition type, the simulated operating condition data corresponding to the current market segment is determined, including:

[0089] S702, the vehicle speed distributions corresponding to each road condition type are weighted and summed to obtain the summed vehicle speed distribution; the speed distributions corresponding to each road condition type are weighted and summed to obtain the summed speed distribution; the speed and torque distributions corresponding to each road condition type are weighted and summed to obtain the summed speed and torque distribution.

[0090] For any given market segment, the computer equipment performs a weighted summation of the vehicle speed distributions corresponding to various road conditions, resulting in a summed vehicle speed distribution. Specifically, this can be achieved by weighting and summing the proportions of mileage or time within the same speed segment in the vehicle speed distributions for each road condition type. Similarly, the computer equipment performs a weighted summation of the proportions of mileage or time within the same speed segment in the engine speed distributions for each road condition type. Finally, the computer equipment performs a weighted summation of the proportions of mileage or time within the same speed and torque segments in the engine speed and torque distributions for each road condition type, resulting in a summed engine speed and torque distribution.

[0091] S704 inputs the summed vehicle speed distribution, summed engine speed distribution, and summed engine speed-torque distribution into the simulation operating condition model to obtain the simulation operating condition data corresponding to the current market segment type.

[0092] The computer equipment inputs all the operating data of the target vehicle under the current market segment, the summed vehicle speed distribution, the summed speed distribution, and the summed speed-torque distribution into the simulation operating condition model to obtain the simulation operating condition data corresponding to the current market segment.

[0093] In this embodiment, by weighted summing of the vehicle speed distribution, engine speed distribution, and engine speed-torque distribution corresponding to each road condition type under any market segment, the summed result can reflect the vehicle speed, engine speed, and engine speed-torque characteristics of the corresponding market segment. Based on the summed result, simulated operating condition data corresponding to each market segment is obtained, which helps to improve the accuracy of fuel-saving operating condition determination.

[0094] In one embodiment, such as Figure 8 As shown, based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating conditions corresponding to the respective market segment are determined, including:

[0095] S802, based on the summed speed and torque distribution, density clustering is used to obtain the speed and torque cluster distribution.

[0096] Density clustering is an algorithm that clusters samples based on the density of their distribution. Common density clustering algorithms include DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points To Identify the Clustering Structure), and DENCLUE (Density-based Clustering). Computer equipment uses density clustering to obtain the speed-torque cluster distribution after summing the speed-torque distribution. The speed-torque cluster distribution can more intuitively reflect the distribution of speed-torque. Figure 9 The diagram shows the distribution of speed and torque clusters. The horizontal axis of the speed and torque cluster distribution represents speed, and the vertical axis represents torque. Points of different depths in the diagram represent the proportion of mileage or time at the corresponding speed and torque. Points with higher depths represent a larger proportion, while points with lower depths represent a smaller proportion.

[0097] S804 determines the fuel-saving region in the speed-torque cluster distribution based on the vehicle speed at each time point and the corresponding target torque range, and then determines the fuel-saving operating condition based on the fuel-saving region.

[0098] Specifically, for each vehicle speed within a given time period, the corresponding engine speed is determined. Based on the engine speed, combined with the gradient and coolant temperature, a target torque range is determined. The target locations of the vehicle speed and corresponding target torque range for each time period are identified within the engine speed and torque cluster distribution. The region encompassing these target locations is designated as the fuel-saving zone. The computer system interprets this fuel-saving zone as the fuel-saving operating condition, representing the vehicle speed and corresponding target torque range for each time period as the fuel-saving operating condition. For example... Figure 10 The diagram shows how to determine the fuel-saving region in the speed and torque clustering distribution. The area pointed to by the arrow represents the fuel-saving region. The target vehicle is updated from the speed and torque range corresponding to the preset operating conditions to the speed and target torque range corresponding to the fuel-saving operating conditions. Using the fuel-saving operating conditions for vehicle control is beneficial to saving fuel consumption.

[0099] In this embodiment, the summed speed-torque distribution is used to obtain the speed-torque cluster distribution through density clustering. Based on the vehicle speed and the corresponding target torque range at each time point in the time-varying vehicle speed, the fuel-saving area and fuel-saving operating conditions are determined in the speed-torque cluster distribution. Using the fuel-saving operating conditions for vehicle control is more beneficial to saving fuel consumption compared to using preset operating conditions for vehicle control.

[0100] In one embodiment, such as Figure 11 As shown, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, road network matching is used to obtain the road condition type corresponding to each vehicle under each type at the current data collection time, including:

[0101] S1102. Based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road type and slope type corresponding to each vehicle under each type at the current data collection time are obtained through road network matching.

[0102] Specifically, for each data collection time, the latitude, longitude, and heading angle of each vehicle under each market segment are matched with the road network to obtain the road type and slope type corresponding to each vehicle under each market segment at the current data collection time. For example, road types include highways, urban areas, and suburban areas, and slope types include flat roads, uphill, steep uphill, downhill, and steep downhill.

[0103] S1104, based on road type and slope type, determine the road condition type corresponding to each vehicle under each type at the current data collection time.

[0104] The road condition types include: highway-flat road, highway-uphill, highway-sharp uphill, highway-downhill, highway-sharp downhill, city-flat road, city-uphill, city-sharp uphill, city-downhill, city-sharp downhill, suburban-flat road, suburban-uphill, suburban-sharp uphill, suburban-downhill, and suburban-sharp downhill. The computer equipment determines the corresponding road condition type for each vehicle under each type at the current data collection time based on the road type and slope type. For example, if the road type is highway and the slope type is uphill, then the road condition type is highway-uphill.

[0105] In this embodiment, the method of determining the road condition type of each vehicle by matching the road network with the road type and slope type based on the latitude, longitude and heading angle of each vehicle can accurately determine the road condition type of the vehicle, which is beneficial for determining the fuel-saving condition.

[0106] In one embodiment, the method for determining fuel-saving operating conditions further includes: determining the specific fuel consumption and power of each fuel-saving operating condition corresponding to each market segment; and determining the actual fuel consumption based on the specific fuel consumption, power, and fuel-saving operating conditions.

[0107] Specifically, based on the engine speed and target torque range corresponding to the fuel-saving operating conditions for each market segment, the specific fuel consumption and power corresponding to the fuel-saving operating conditions are quickly obtained by looking up tables. The average specific fuel consumption is obtained by averaging the specific fuel consumption at various vehicle speeds within the fuel-saving operating conditions. The computer equipment then calculates the vehicle's actual fuel consumption using the average specific fuel consumption, power, and fuel-saving operating conditions.

[0108] In this embodiment, the actual fuel consumption is determined by the ratio of fuel consumption under fuel-saving conditions and the operating conditions. The actual fuel consumption under fuel-saving conditions can reflect the fuel consumption of the vehicle under fuel-saving conditions.

[0109] To explain in detail the method for determining fuel-saving operating conditions and its effects in this solution, a detailed embodiment is provided below:

[0110] The method for determining fuel-efficient operating conditions includes functional modules such as engine curve plotting, vehicle network data preprocessing, precise operating condition identification, identification of common operating condition areas, and determination of the area with the lowest specific fuel consumption. For example... Figure 12 The diagram shows the overall process flow for determining fuel-saving operating conditions.

[0111] In terms of engine curve plotting, the computer equipment uses MATLAB's GUI (Graphical User Interface) module to automatically identify the data, classify and store the data in memory, and build in relevant data preprocessing and plotting programs to output the characteristic curves of each engine.

[0112] Step 1: Based on the engine universal characteristic test data, form as follows Figure 13 The engine universal characteristic curve shown, and Figure 14 The three-dimensional fitting diagram of the isofuel consumption rate curve and the isopower curve is shown. Figure 13 In the diagram, the horizontal axis represents engine speed, the vertical axis represents torque, the top horizontal line represents engine external characteristics, the series of diagonal lines in the upper left and lower right are constant power lines, and the curves in the diagram are constant fuel consumption lines. Figure 14 In the diagram, the horizontal axis represents rotational speed, and the vertical axis represents torque. Figure 14 for Figure 13 The 3D fitting graph is primarily used to quickly query the specific fuel consumption value and the corresponding engine output power based on engine speed and torque. The engine's universal characteristic data mainly includes three fields: engine speed (r / min), actual output torque (Nm), and specific fuel consumption (g / kWh). For example, the engine speed range can be 600r / min-2000r / min, with a step size of 100r / min; at each engine speed, the output torque is controlled within a range of maximum actual output torque - 200N.m, with a step size of 200N.m; when the engine speed and actual output torque are stable, the specific fuel consumption value is calculated.

[0113] Step 2: Based on the friction work test data, generate the following... Figure 15 The friction torque curve shown, and as shown Figure 16 The three-dimensional fitting diagram of the constant friction torque curve and constant power curve is shown. Figure 15 In the diagram: the horizontal axis represents rotational speed, and the vertical axis represents frictional torque. The thick curve represents the constant frictional power curve, and the thin curve represents the constant frictional torque curve. Figure 16 In the center: the horizontal axis represents rotational speed, the vertical axis represents coolant temperature, and the center axis represents frictional power and frictional torque. Figure 16 for Figure 15 The 3D fitting graph is mainly used to quickly query friction torque and friction power based on rotational speed and coolant temperature. The experimental data for friction work mainly includes three fields: rotational speed (r / min), coolant temperature (°C), and friction torque (Nm); the rotational speed range is 600 r / min to 2000 r / min, with a step size of 100 r / min; at each rotational speed, the coolant temperature is controlled within the range of -30°C to 120°C, with a step size of 10°C; the friction torque value is measured when the rotational speed and coolant temperature are stable.

[0114] Step 3: Based on the coasting test data under different loads, and according to the relationship between vehicle speed and gear:

[0115]

[0116] Where: n represents engine speed, v represents vehicle speed, and i o Indicates the drive axle speed ratio, i g T represents the gear ratio of the transmission, r represents the tire rolling radius, and T represents the gear ratio of the transmission.r η represents the resistance torque, f represents the overall vehicle resistance, and η represents the transmission efficiency.

[0117] Calculate the vehicle speed and the corresponding rolling resistance and torque, then map the speed-torque data to a speed-torque data matrix to facilitate the generation of... Figure 17 The vehicle drag curve is shown. The coasting test data mainly includes vehicle speed (km / h), timestamp, engine speed (r / min), engine output torque (Nm), and distance (m). Figure 17 In the diagram, the horizontal axis represents vehicle speed, and the vertical axis represents resistance torque. Among the clustered curves, the top curve represents the engine's external torque characteristic at the corresponding vehicle speed, while the lower curves represent different torque values ​​at different vehicle speeds. The curves sloping upwards to the right represent the vehicle's resistance at different gradients. Its main function is to quickly obtain, through a lookup table, the suitable torque value for a vehicle at a given speed and gradient.

[0118] Step Four: Based on the engine universal characteristic test results and the vehicle's basic configuration information, such as transmission gear ratios, drive axle final reduction ratio, effective tire rolling radius, and efficiency coefficient, generate parameters like... Figure 18 The driving force curves for each gear position are shown. Figure 18 The horizontal axis represents vehicle speed, and the vertical axis represents traction or resistance. In the diagram, the solid line represents the maximum driving force at different gears, and the dashed line represents the slope resistance at different gradients. The calculation principle is the same. Figure 17 Its main function is to quickly determine the most suitable speed and driving force for a vehicle when driving in different gears and on different slopes.

[0119] For vehicle-to-everything (V2X) data preprocessing, the target vehicle model and time period are selected based on requirements to determine the V2X data set to be preprocessed. A list of matching Vehicle Identification Numbers (VINs) is then filtered from the vehicle basic configuration information database. Further, based on the VIN list and time period, relevant data is extracted from the Hive (data warehouse tool) database, primarily including longitude, latitude, altitude, heading angle, vehicle speed, engine speed, throttle position, torque, and instantaneous fuel consumption. Then, the data is grouped according to the VINs, and road network data matching is performed on the grouped data. The integrated data with matched road network information is then rewritten into the Hive database.

[0120] Precise operating condition identification categorizes road types in the comprehensive data into three types: highway, urban, and suburban. Gradient is binned into five types: flat, uphill, steep uphill, downhill, and steep downhill. Two fields, road type and slope type, are added to the comprehensive data. Further, the comprehensive data is categorized into 15 precise operating conditions based on the three road types and five slope types: highway-flat, highway-uphill, highway-steep uphill, highway-downhill, highway-steep downhill, urban-flat, urban-uphill, urban-steep uphill, urban-downhill, urban-steep downhill, suburban-flat, suburban-uphill, suburban-steep uphill, suburban-downhill, and suburban-steep downhill. Data for each precise operating condition is filtered, and a vehicle operating condition analysis program analyzes data such as vehicle speed distribution ratio, engine speed distribution ratio, and engine speed-torque distribution ratio. The results are then tagged with precise operating conditions and stored in a MongoDB (distributed file storage database). The vehicle condition analysis program is a distributed big data processing program based on a Spark (big data analytics engine) and Hadoop (distributed file management system) cluster.

[0121] Common operating condition area identification involves weighted summation of the speed-torque distribution percentages based on the proportion of each precise operating condition within the vehicle segment market. This yields new speed-torque distribution percentage data, representing the characteristics of a specific segment market type. Then, density clustering is performed on this new speed-torque distribution percentage data to identify several regions of common operating conditions. The region overlapping with the speed range where the continuous 300 r / min percentage is highest is defined as the common operating condition area for that segment market. For example, the precise operating condition percentages for the vehicle segment market are: highway-flat road 12%, highway-uphill 3%, highway-sharp uphill 11%, ..., with a total percentage of 100%. Simultaneously, weighted summation is performed on the vehicle speed distribution percentage data and the speed distribution percentage data to calculate new vehicle speed distribution percentage data and speed distribution percentage data, representing the vehicle speed and speed distribution characteristics of that segment market. These vehicle speed distribution percentage data, speed distribution percentage data, speed-torque distribution percentage data, and all operating data of vehicles in that segment market are then input into a simulated operating condition model, which outputs a simulated operating condition data. The simulated operating condition data includes fields such as timestamp, vehicle speed, gradient, and coolant temperature.

[0122] The region with the lowest fuel consumption was determined by appropriately migrating it to the commonly used operating conditions of this market segment, based on empirical models of vehicle transmission matching and engine combustion control, while meeting relevant regulations. Multiple universal characteristic data sets for the engine were developed. Furthermore, based on simulated operating condition data and engine characteristic curves, the fuel consumption performance of this market segment was simulated and verified. After multiple rounds of verification and comparison, an optimal universal characteristic data set was selected that satisfies both vehicle power requirements and energy conservation and emission reduction requirements.

[0123] A brief description of the simulation verification process: Step 1: Determine the total weight and basic configuration parameters of the simulated vehicle; Step 2: Analyze each point of the simulated operating condition. First, calculate the engine speed and gear corresponding to that speed. Then, using vehicle speed, engine speed, gradient, gear, and coolant temperature, combined with the engine characteristic curve, quickly look up the range of torque, specific fuel consumption, and power required to pass that operating point. Filter out the torque and specific fuel consumption values ​​corresponding to the lowest specific fuel consumption that can pass that operating point, and record the specific fuel consumption and power values. Step 3: Calculate the actual fuel consumption corresponding to the simulated operating condition data.

[0124] The aforementioned method for determining fuel-saving operating conditions acquires the operating data, latitude, longitude, and heading angle of each vehicle in each of multiple market segments at various collection times. Through road network matching, it obtains the corresponding road condition type for each vehicle in each market segment at each collection time. Based on the operating data of target vehicles with the same road condition type in the current market segment, it determines the simulated operating condition data for the target vehicle's road condition type, and then determines the simulated operating condition data corresponding to each market segment. This method of determining the simulated operating condition data for vehicles in multiple market segments based on road condition type combines the actual road conditions of the vehicles, improves the accuracy of the simulated operating condition data, and is conducive to determining fuel-saving operating conditions. By looking up the target torque range and vehicle speed that change over time through a table, the fuel-saving operating conditions corresponding to the corresponding market segment are determined. This method of determining fuel-saving operating conditions through simulated operating condition data can control the vehicle speed and torque range corresponding to the corresponding fuel-saving operating conditions for vehicles of the same engine type in different market segments, which is conducive to saving vehicle fuel consumption. At the same time, it can guide the engine to migrate from the region with the lowest specific fuel consumption to the region of commonly used operating conditions, support the development of multiple maps for universal characteristics of multiple engines, and ultimately enable users to run in the engine's optimal energy-saving region under various operating conditions, thereby reducing fuel consumption and achieving energy conservation and emission reduction, while shortening the development cycle of customized development.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides a fuel-saving operating condition determination device for implementing the fuel-saving operating condition determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the fuel-saving operating condition determination device provided below can be found in the limitations of the fuel-saving operating condition determination method described above, and will not be repeated here.

[0127] In one embodiment, such as Figure 19 As shown, a fuel-saving condition determination device 100 is provided, including: a data acquisition module 120, a road condition determination module 140, a simulated condition data determination module 160, and a fuel-saving condition determination module 180, wherein:

[0128] The data acquisition module 120 is used to acquire the operating data, latitude, longitude and heading angle of each vehicle in each of the various market segments at each collection time; the vehicles in the various market segments have the same engine type;

[0129] The road condition determination module 140 is used to determine the road condition type for each vehicle under each type at any given time by matching the road network based on the latitude, longitude and heading angle of each vehicle under each type at the current time of data collection.

[0130] The simulated operating condition data determination module 160 is used to determine, for any given market segment type, target vehicles with the same road operating condition type under the current market segment type; determine the simulated operating condition data of the road operating condition type to which the target vehicle belongs based on the target vehicle's operating data; and determine the simulated operating condition data corresponding to the current market segment type based on the simulated operating condition data of each road operating condition type. The simulated operating condition data includes vehicle speed that changes over time.

[0131] The fuel-saving operating condition determination module 180 is used to look up the target torque range over time by referring to the simulated operating condition data corresponding to each market segment type; and to determine the fuel-saving operating condition corresponding to the corresponding market segment type based on the vehicle speed and the target torque range over time. The fuel-saving operating condition is used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

[0132] The aforementioned fuel-saving operating condition determination device acquires the operating data, latitude, longitude, and heading angle of each vehicle in each of multiple market segments at various collection times. Through road network matching, it obtains the corresponding road condition type for each vehicle in each market segment at each collection time. Based on the operating data of target vehicles with the same road condition type in the current market segment, it determines the simulated operating condition data of the target vehicle's road condition type, and then determines the simulated operating condition data corresponding to each market segment. This method of determining the simulated operating condition data corresponding to vehicles in multiple market segments based on road condition type combines the actual road conditions of the vehicles, improves the accuracy of the simulated operating condition data, and is conducive to determining fuel-saving operating conditions. By looking up the target torque range and vehicle speed that change over time through a table, the fuel-saving operating condition corresponding to the corresponding market segment is determined. This method of determining fuel-saving operating conditions through simulated operating condition data can control the vehicle speed and torque range corresponding to the corresponding fuel-saving operating conditions for vehicles of the same engine type but different market segments, which is conducive to saving vehicle fuel consumption.

[0133] In one embodiment, the operating data includes vehicle speed, engine speed, and torque. Based on the operating data of the target vehicle, simulated operating condition data for the road condition type to which the target vehicle belongs is determined. The simulated operating condition data determination module 160 is further configured to: determine the speed distribution corresponding to the road condition type to which the target vehicle belongs based on the vehicle speed at each collection time; determine the speed distribution corresponding to the road condition type to which the target vehicle belongs based on the engine speed at each collection time; determine the speed-torque distribution corresponding to the road condition type to which the target vehicle belongs based on the speed-torque and torque of the target vehicle at each collection time; and determine the simulated operating condition data for the road condition type to which the target vehicle belongs based on the speed distribution, engine speed distribution, and speed-torque distribution, using a simulated operating condition model.

[0134] In one embodiment, based on the simulated operating condition data for each road condition type, the simulated operating condition data corresponding to the current market segment type is determined. The simulated operating condition data determination module 160 is further configured to: perform a weighted summation of the vehicle speed distributions corresponding to each road condition type to obtain a summed vehicle speed distribution; perform a weighted summation of the speed distributions corresponding to each road condition type to obtain a summed speed distribution; perform a weighted summation of the speed and torque distributions corresponding to each road condition type to obtain a summed speed and torque distribution; and input the summed vehicle speed distribution, the summed speed distribution, and the summed speed and torque distribution into the simulated operating condition model to obtain the simulated operating condition data corresponding to the current market segment type.

[0135] In one embodiment, the fuel-saving operating conditions corresponding to the corresponding market segment are determined based on the vehicle speed changing over time and the target torque range changing over time. The fuel-saving operating condition determination module 180 is further configured to: obtain a speed-torque cluster distribution by density clustering based on the summed speed-torque distribution; determine a fuel-saving region in the speed-torque cluster distribution based on the vehicle speed corresponding to each time and the corresponding target torque range changing over time; and determine the fuel-saving operating conditions based on the fuel-saving regions.

[0136] In one embodiment, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching. The road condition determination module 140 is further configured to: based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, obtain the road type and slope type corresponding to each vehicle under each type at the current data collection time through road network matching; and determine the road condition type corresponding to each vehicle under each type at the current data collection time based on the road type and slope type.

[0137] In one embodiment, the fuel-saving condition determination device 100 is further configured to: determine the specific fuel consumption and power of the fuel-saving condition corresponding to each market segment; and determine the actual fuel consumption based on the specific fuel consumption, power, and fuel-saving condition.

[0138] The various modules in the aforementioned fuel-saving operating condition determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0139] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 20As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a fuel-saving operating condition determination method.

[0140] Those skilled in the art will understand that Figure 20 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0143] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0145] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0146] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining fuel-saving operating conditions, characterized in that, The method includes: The system acquires the operating data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each data collection time; the vehicles in these various market segments have the same engine type. For any given data collection time, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road condition type corresponding to each vehicle under each type at the current data collection time is obtained through road network matching. For any given market segment, identify target vehicles with the same road condition type within that market segment; based on the target vehicles' operational data, determine simulated operating condition data for the road condition type to which the target vehicles belong; based on the simulated operating condition data for each road condition type, determine the simulated operating condition data corresponding to the current market segment, wherein the simulated operating condition data includes vehicle speed changing over time. Based on the simulated operating condition data corresponding to each market segment type, the target torque range changing over time is obtained by looking up a table; based on the vehicle speed changing over time and the target torque range changing over time, the fuel-saving operating condition corresponding to the corresponding market segment type is determined, and the fuel-saving operating condition is used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

2. The method according to claim 1, characterized in that, The operating data includes vehicle speed, engine speed, and torque; the simulated operating condition data for determining the road condition type of the target vehicle based on the operating data of the target vehicle includes: Based on the vehicle speed of the target vehicle at each collection time, determine the speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the rotational speed of the target vehicle at each collection time, determine the rotational speed distribution corresponding to the road condition type to which the target vehicle belongs; based on the rotational speed and torque of the target vehicle at each collection time, determine the rotational speed and torque distribution corresponding to the road condition type to which the target vehicle belongs. Based on the vehicle speed distribution, the rotational speed distribution, and the rotational speed-torque distribution, the simulated operating condition data of the road condition type to which the target vehicle belongs is determined through a simulated operating condition model.

3. The method according to claim 2, characterized in that, The step of determining the simulated operating condition data corresponding to the current market segment type based on the simulated operating condition data of each road condition type includes: The vehicle speed distributions corresponding to each road condition type are weighted and summed to obtain the summed vehicle speed distribution; the engine speed distributions corresponding to each road condition type are weighted and summed to obtain the summed engine speed distribution; the engine speed and torque distributions corresponding to each road condition type are weighted and summed to obtain the summed engine speed and torque distribution. The summed vehicle speed distribution, the summed engine speed distribution, and the summed engine speed-torque distribution are input into the simulation operating condition model to obtain the simulation operating condition data corresponding to the current market segment type.

4. The method according to claim 3, characterized in that, The step of determining the fuel-saving operating conditions corresponding to the relevant market segment based on the vehicle speed changing over time and the target torque range changing over time includes: Based on the summed speed-torque distribution, density clustering is used to obtain the speed-torque cluster distribution; Based on the vehicle speed at each time point and the corresponding target torque range, a fuel-saving region is determined in the speed-torque clustering distribution. Based on the described fuel-saving area, determine the fuel-saving operating conditions.

5. The method according to claim 1, wherein The process of obtaining the road condition type corresponding to each vehicle under each type at the current data collection time through road network matching, based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, includes: Based on the latitude, longitude, and heading angle of each vehicle under each type at the current data collection time, the road type and slope type corresponding to each vehicle under each type at the current data collection time are obtained through road network matching. Based on the road type and the slope type, determine the road condition type corresponding to each vehicle under each type at the current data collection time.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Determine the specific fuel consumption and power output for each fuel-saving operating condition corresponding to each market segment type; The actual fuel consumption is determined based on the specific fuel consumption, the power, and the fuel-saving operating conditions.

7. A fuel-saving operating condition determination device, characterized in that, The device includes: The data acquisition module is used to acquire the operating data, latitude, longitude, and heading angle of each vehicle in each of the various market segments at each collection time; the vehicles in the various market segments have the same engine type; The road condition determination module is used to determine the road condition type corresponding to each vehicle under each type at any given time by matching the road network based on the latitude, longitude and heading angle of each vehicle under each type at the current time of data collection. The simulated operating condition data determination module is used to determine, for any given market segment, a target vehicle with the same road operating condition type under the current market segment; determine the simulated operating condition data of the road operating condition type to which the target vehicle belongs based on the operating data of the target vehicle; and determine the simulated operating condition data corresponding to the current market segment based on the simulated operating condition data of each road operating condition type, wherein the simulated operating condition data includes vehicle speed changing over time. The fuel-saving operating condition determination module is used to look up the target torque range that changes over time based on the simulated operating condition data corresponding to each market segment type; and to determine the fuel-saving operating condition corresponding to the corresponding market segment type based on the vehicle speed that changes over time and the target torque range that changes over time. The fuel-saving operating condition is used to characterize the vehicle speed and the corresponding torque range for fuel-saving control of vehicles under the corresponding market segment type.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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