New energy heavy truck energy consumption map construction method and electronic equipment

Abstract

The invention relates to the technical field of new energy, and particularly discloses a new energy heavy truck energy consumption map construction method and electronic equipment, and the new energy heavy truck energy consumption map construction method comprises the steps: cleaning invalid data in vehicle driving data; the effective data are clustered according to the driving vehicle weight, a data structure of {vehicle weight intervals: {driving data segment sets}} is formed, and each vehicle weight interval corresponds to a set of clustered driving data segments; associating the obtained data structure with a geographic road network, and matching the vehicle energy consumption information with the actual driving road section of the vehicle; and based on the matched data, establishing and training a road section level energy consumption model, and integrating output results of the model into a global energy consumption map. According to the new energy heavy truck energy consumption map construction method, the geographic information and the vehicle energy consumption data are combined, the new energy heavy truck energy consumption map is constructed, and the driving safety and efficiency are improved; and a low-energy-consumption route is accurately planned.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method for constructing an energy consumption map of a new energy heavy truck and an electronic device. Background Art

[0002] With the development of new energy vehicles, how to reduce the energy consumption of new energy vehicles and increase the driving range has become a key challenge. During the operation of new energy heavy trucks, due to the significant changes in vehicle weight and high dependence on energy replenishment facilities, and the existing research has not fully considered its driving characteristics, the energy utilization efficiency is low and the energy consumption remains high, which in turn affects the driving range.

[0003] Current energy consumption map research fails to fully reflect the specific operating characteristics of new energy heavy-duty trucks. Existing solutions mostly focus on improving battery technology and drive system efficiency, but ignore the use of geographic information to optimize route selection, and are unable to provide users with the most economical and efficient driving routes. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention aims to provide a method and electronic device for constructing an energy consumption map of a new energy heavy truck, combining geographic information with vehicle energy consumption data to reduce vehicle energy consumption.

[0005] To achieve the above-mentioned purpose, a first embodiment of the present invention proposes a method for constructing an energy consumption map of a new energy heavy truck, the method comprising: Clean invalid data in vehicle driving data to obtain valid data; Clustering the above valid data according to the vehicle weight to form a data structure of {vehicle weight interval:{driving data segment set}}, wherein each vehicle weight interval corresponds to a set of clustered driving data segments; According to the above-obtained data structure, the data is associated with the geographical road network, the vehicle energy consumption information is matched with the road section where the vehicle actually travels, and a mapping relationship of "road section-energy consumption" is established; Based on the above matched data, a section-level energy consumption model is established and trained, and the output results of the model are integrated into a global energy consumption map.

[0006] In some embodiments of the present invention, the invalid data in the cleaning vehicle driving data includes the following: For any vehicle driving trajectory point, if the following conditions are met, it is invalid driving data: ① Data that is marked as invalid; ② Abnormal position; ③ Abnormal speed; ④Abnormal current or voltage; The clearing condition of the track point is as follows: In the above formula, It is the data valid flag. is the lower left longitude of China’s geographical area, is the upper right longitude of China’s geographical area, is the lower left latitude of China’s geographical area, is the upper right latitude of China’s geographical area, is the theoretical maximum speed, is the maximum current threshold, is the minimum current threshold, is the maximum voltage threshold, is the minimum voltage threshold.

[0007] In some embodiments of the present invention, clustering the valid data according to the vehicle weight includes the following: Firstly, the vehicle's driving trajectory is divided into several driving data segments according to the actual vehicle weight during driving, and each driving data segment represents the continuous driving process of the vehicle under a certain driving weight; Then, for each defined driving vehicle weight interval, all driving data segments belonging to the interval are collected to form a data structure of {vehicle weight interval: {driving data segment set}}.

[0008] In some embodiments of the present invention, matching the vehicle energy consumption information with the road section on which the vehicle actually travels includes the following: Obtain matching road sections and select different acquisition methods according to different situations of vehicle trajectory points; If the last track point of the vehicle successfully matches a road segment in the geographic road network, and the distance between the current track point and the matching point corresponding to the last successful match is less than the vertical distance, the matching road segment is obtained using the road network topology method; If the vehicle's last trajectory point fails to match a road section in the geographic road network, a geographic rectangular box is constructed based on the current trajectory point, and the matching road section is obtained by geographic box selection.

[0009] In some embodiments of the present invention, the method of obtaining the matching road segment by using the road network topology is expressed as follows: In the above formula, is the road network topology segment set, is a topological segment, It is a secondary topological section.

[0010] In some embodiments of the present invention, when the matching road segment is obtained by adopting the geographic frame selection method, the frame selection area should be dynamically adjusted according to the vehicle motion state, including the following contents: First, dynamically calculate the length of the frame selection: Based on the current vehicle speed v and direction change rate at the trajectory point Adjust the selection side length L. The selection side length L is expressed as follows: In the above formula, is the base side length, and is the adjustment coefficient, is the maximum direction change angle; Then constrain the fan-shaped area in the direction of the box selection:

[0011] Defines the angle range of the fan-shaped search area , only matching candidate road segments with the same direction.

[0012] In some embodiments of the present invention, the matching road segments obtained by the road network topology method and the geographical frame selection method are used as candidates, and the best matching road segment is found from the candidate road segment set, which specifically includes the following contents: set up is the angular distance between the trajectory point and the road segment, set is the vertical distance between the trajectory point and the road segment; It can be expressed as: In the above formula, is the angle influence coefficient threshold, is the angle between the trajectory point direction and the road section direction; for and , first normalize the two, and the normalized vertical distance can be expressed as: In the above formula, is the maximum value between the trajectory point and the road segment, is the minimum value between the trajectory point and the road segment; The normalized angular distance can be expressed as: For each road segment, its comprehensive score The calculation formula is: In the above formula, is the weight of the directional factor, is the weight of the distance factor, and + =1,0≤ ≤1,0≤ ≤1; By comparing the comprehensive scores of adjacent sections of the trajectory point, the optimal matching section is obtained.

[0013] In some embodiments of the present invention, when establishing the road section level energy consumption model, a weighted truncated mean algorithm is used to remove extreme values ​​in the energy consumption data, and an interval index storage structure is used to store the energy consumption data according to the vehicle weight interval classification, specifically including: For a certain vehicle weight interval on a road section, there are n observed energy consumption values ; First, sort these energy consumption values, set k = 0.05n, remove the highest k values ​​and the lowest k values, and then calculate the weighted average of the remaining data as the road section energy consumption of the vehicle weight range. The formula is expressed as: In the above formula, is the weight of each energy consumption value, and p is the index in the sorted energy consumption value sequence.

[0014] In some embodiments of the present invention, when constructing the global energy consumption map, the road sections in the geographical road network are layered, including low-level road sections and high-level road sections, wherein the relationship between the high-level road sections and the low-level road sections is a one-to-many relationship, and the energy consumption corresponding to the high-level road sections can be expressed as: In the above formula, is the average energy consumption per kilometer of the ith road section in the low-level road section sequence, is the weighted value of the road segment attribute, is the length of the i-th road segment in the low-level road segment sequence, is the number of high-level sections corresponding to low-level sections, is the length of the high-rise section.

[0015] To achieve the above-mentioned purpose, the second aspect of the present invention proposes an electronic device, including a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the above-mentioned method for constructing an energy consumption map of a new energy heavy truck is implemented.

[0016] The energy consumption map construction method and electronic device of the new energy heavy-duty truck embodiment of the present invention combines geographic information with vehicle energy consumption data to construct an energy consumption map for new energy heavy-duty trucks, which can cultivate good driving habits of drivers in real time and improve driving safety and efficiency; accurately plan low-energy consumption routes and reduce the cost of inducing drivers; alleviate congestion and improve road use efficiency; and accurately reflect vehicle operating characteristics, optimize route selection, increase cruising range, and reduce dependence on energy replenishment facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for constructing an energy consumption map of a new energy heavy truck according to an embodiment of the present invention; Figure 2 is a schematic diagram of obtaining matching road sections using a road network topology method in one embodiment of the present invention; Figure 3 is a schematic diagram of obtaining matching road sections by using a box selection method in one embodiment of the present invention; Figure 4 It is a schematic diagram of the correspondence between the high-level road section and the low-level road section in one embodiment of the present invention; Figure 5 It is a schematic structural diagram of an electronic device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0018] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0019] The following describes a method for constructing an energy consumption map for a new energy heavy truck and an electronic device according to an embodiment of the present invention with reference to the accompanying drawings.

[0020] Figure 1 It is a flow chart of a method for constructing an energy consumption map of a new energy heavy truck according to an embodiment of the present invention.

[0021] like Figure 1 As shown, the method for constructing the energy consumption map of a new energy heavy truck includes: S1. Clean invalid data in vehicle driving data to obtain valid data.

[0022] This step aims to eliminate interfering data and ensure the accuracy of subsequent analysis. First, the invalid data is removed based on the data itself. Then, the location abnormality is determined by the longitude and latitude range. Finally, it is compared with the maximum theoretical vehicle speed and the maximum current threshold, minimum current threshold, maximum voltage threshold, and minimum voltage threshold in turn. Any data beyond the normal range is considered abnormal.

[0023] As an example, for any vehicle driving trajectory point, if the following conditions are met, it is invalid driving data: ① Data that is marked as invalid; ② Abnormal position; ③ Abnormal speed; ④Abnormal current or voltage; The clearing condition of the track point is as follows: In the above formula, It is the data valid flag. is the lower left longitude of China’s geographical area, is the upper right longitude of China’s geographical area, is the lower left latitude of China's geographical scope, is the upper right latitude of China's geographical scope, is the theoretical maximum speed, is the maximum current threshold, is the minimum current threshold, is the maximum voltage threshold, is the minimum voltage threshold.

[0024] S2. Cluster the valid data according to the vehicle weight to form a data structure of {vehicle weight interval: {driving data segment set}}, wherein each vehicle weight interval corresponds to a set of clustered driving data segments.

[0025] Since vehicle weight significantly affects the energy consumption of new energy heavy trucks, this step first divides the vehicle's driving trajectory according to the actual driving weight. For example, the vehicle weight changes during loading and unloading, and the driving data segments are divided based on the vehicle weight change nodes. Each data segment represents a continuous driving process under a specific vehicle weight. After that, different vehicle weight intervals are set, such as [10-15]t, [15-20]t, etc., and each data segment is classified into the corresponding interval to form a data structure of {vehicle weight interval: {driving data segment set}}, which is convenient for subsequent analysis of energy consumption characteristics for different vehicle weight intervals.

[0026] S3. Associating the above-obtained data structure with the geographical road network, matching the vehicle energy consumption information with the road section where the vehicle actually travels, and establishing a “road section-energy consumption” mapping relationship.

[0027] As an example, matching the vehicle energy consumption information with the road section the vehicle actually travels on includes the following: Obtain matching road sections and select different acquisition methods according to different situations of vehicle trajectory points; If the last track point of the vehicle successfully matches a road segment in the geographic road network, and the distance between the current track point and the matching point corresponding to the last successful match is less than the vertical distance, the matching road segment is obtained using the road network topology method; If the vehicle's last trajectory point fails to match a road section in the geographic road network, a geographic rectangular box is constructed based on the current trajectory point, and the matching road section is obtained by geographic box selection.

[0028] S4. Based on the above matched data, a road section-level energy consumption model is established and trained, and the output results of the model are integrated into a global energy consumption map.

[0029] The solution deeply combines geographic information with vehicle energy consumption data to build a global energy consumption map based on the energy consumption of different road sections. With the help of this map, drivers can intuitively compare the energy consumption differences of different routes and accurately choose the route with the lowest energy consumption. In urban scenarios, new energy heavy trucks can avoid congested sections with high energy consumption or sections with large slopes, and choose flat and light-traffic routes with low energy consumption, thereby reducing vehicle energy consumption and the cost of inducing drivers to choose high-energy consumption routes.

[0030] In some embodiments of the present invention, the geographical range is dynamically adjusted according to the actual operating area of ​​the vehicle, and the adjustment formula can be expressed as: =min(track longitude)- x, =max(track longitude)+ x in, x is the extended buffer value (such as 0.1 degrees).

[0031] As an example, assuming that the longitude of a trajectory point is 116.40°, the latitude is 39.90°, the vehicle speed is 150km / h (exceeding the theoretical maximum value of 120km / h), and the current is 300A (exceeding the threshold [50A, 250A]), then the point is marked as invalid data.

[0032] In some embodiments of the present invention, clustering the valid data according to the vehicle weight includes the following: Firstly, the vehicle's driving trajectory is divided into several driving data segments according to the actual vehicle weight during driving, and each driving data segment represents the continuous driving process of the vehicle under a certain driving weight; Then, for each defined driving vehicle weight interval, all driving data segments belonging to the interval are collected to form a data structure of {vehicle weight interval: {driving data segment set}}.

[0033] As an example, the vehicle weight changes during loading and unloading. The driving data segments are divided based on the vehicle weight change nodes. Each data segment represents a continuous driving process under a specific vehicle weight. After that, different vehicle weight intervals are set, such as [10-15]t, [15-20]t, etc., and each data segment is classified into the corresponding interval to form a data structure of {vehicle weight interval: {driving data segment set}}, which is convenient for subsequent analysis of energy consumption characteristics for different vehicle weight intervals.

[0034] For example, a new energy heavy truck transports goods. When it starts, it weighs 13 tons. After driving a certain distance, it reaches the unloading point and its weight becomes 8 tons. The driving data segment from the start to the unloading is classified as the [10-15]t vehicle weight interval, and the driving data segment after unloading is reclassified according to the subsequent vehicle weight. If the vehicle weight is stable at 8 tons after unloading, the data segment can be classified into the [5-10]t interval (assuming this interval exists).

[0035] It should be noted that, considering the nonlinear relationship between vehicle weight and energy consumption, based on the existing clustering method, improvements can be made in terms of vehicle weight interval division, data processing and model construction, for example: The traditional fixed vehicle weight interval division method cannot adapt to the nonlinear relationship between vehicle weight and energy consumption. The density-based spatial clustering algorithm (DBSCAN) is used to dynamically determine the vehicle weight interval. The algorithm can automatically identify the core area and boundary area of ​​the data according to the distribution density of the data points, and divide the density-connected data points into the same category. In the driving data of new energy heavy trucks, the distribution of vehicle weight and energy consumption data points can reflect different vehicle weight-energy consumption relationship clusters. Through the DBSCAN algorithm, more reasonable vehicle weight intervals can be automatically divided in areas with dense vehicle weight data. If in a specific transportation scenario, the vehicle weight is very different when fully loaded and unloaded, and the energy consumption performance is different, the algorithm can accurately identify the vehicle weight intervals corresponding to different states such as full load, half load, and unloaded, avoiding the problem of excessive data differences within the interval caused by fixed interval division.

[0036] In addition, if the amount of data in a certain interval is insufficient (e.g. <100), it will be merged with the adjacent interval to ensure statistical significance.

[0037] For example, if the vehicle weight data of a certain road section is densely distributed in the range of 18-22 tons, DBSCAN will automatically divide the data into the interval [18,22), and merge the remaining data into adjacent intervals.

[0038] like Figure 2 As shown, in some embodiments of the present invention, the matching road segments are obtained by adopting the road network topology method as shown in the following formula: In the above formula, is the road network topology segment set, is a topological segment, It is a secondary topological section.

[0039] It should be noted that in actual complex road networks, such as urban roads that include main roads, secondary roads, and branch roads, the primary topological road segment can be understood as the road segment directly connected to the current matching point, and the secondary topological road segment is the surrounding road segment connected to the primary topological road segment. Through comprehensive analysis of these road segments, the matching road segment set that best matches the vehicle's driving path is determined.

[0040] In actual applications, we can further distinguish between elevated roads and ground roads to avoid cross-layer mismatching. The matching conditions are set as follows: Segment level ( ) = current track point level As an example, if the vehicle is traveling on an elevated road, only the elevated topology road sections are matched. , excluding surface roads.

[0041] like Figure 3 As shown, in some embodiments of the present invention, when a matching road segment is obtained by adopting a geographic frame selection method, the frame selection area should be dynamically adjusted according to the vehicle motion state, including the following contents: First, dynamically calculate the length of the frame selection: Based on the current vehicle speed v and direction change rate at the trajectory point Adjust the selection side length L. The selection side length L is expressed as follows: In the above formula, is the base side length, and is the adjustment coefficient, used to adjust the vehicle speed v and direction change rate The degree of influence on the frame selection side length L, is the maximum direction change angle; It should be noted that the faster the speed, the higher the v / The larger the value, the longer the frame length will be, so as to cover the possible driving range of the vehicle; the larger the direction change rate, | ∣ / The larger the value, the smaller the frame length, ensuring that the frame area is more in line with the vehicle's driving direction. Then constrain the fan-shaped area in the direction of the box selection: Defines the angle range of the fan-shaped search area , only candidate sections with the same direction are matched; this formula calculates the angle range of the fan-shaped search area through the proportional relationship between the vehicle speed and the maximum vehicle speed, combined with a fixed angle, and only matches candidate sections with the same direction within this range, reducing invalid matches and improving matching efficiency.

[0042] Here, the significance of dynamically adjusting the selection area according to the vehicle's motion state is to significantly reduce the mismatch rate through a hybrid search strategy combining dynamic selection with directional fan-shaped search. The algorithm steps can be summarized as follows: First, calculate L and ; Then construct the current trajectory point A rectangular frame with a center and a side length of L, and superimposed fan-shaped direction filtering; Finally, candidate road sections are extracted from the selected range and filtered again based on road attributes (height limit, weight limit).

[0043] It should be noted here that when obtaining matching sections, for example, in a certain urban road scene, the vehicle is traveling along the main road, the last track point is successfully matched with a section of the main road, and the current track point is close to the last matching point. At this time, the road network topology method is used to obtain the matching section. Assuming that there are secondary roads and branches around the main road, the primary topological section includes the main road part directly connected to the current matching point, and the secondary topological section includes the adjacent secondary roads and branches. The final matching section is determined by screening these sections. If the vehicle enters a new area and the last track point is not successfully matched, the geographic box selection method is used.

[0044] As an example, suppose the current vehicle speed v = 60 km / h and the reference side length =200m, =0.6, =0.4, =100km / h, =20°, =80°, and use this to calculate the side length of the frame: L = 200 × (1 + 0.6 × )×(1-0.4× )=200×1.36×0.9=244.8m; Then calculate the angle range of the fan-shaped search area =max(30°,90°- ×45°)=63°; Therefore, it is concluded that within a rectangular box with a side length of 244.8m and a 63° fan-shaped area centered on the current trajectory point, candidate road sections with the same direction are searched for matching.

[0045] To further refine the above solution, the matching road section obtained by the frame selection method can be optimized, and the angle range can be reduced when the speed is low to improve the matching accuracy, for example: When v=10m / s, =75°; v=30m / s, =45°.

[0046] In some embodiments of the present invention, the matching road segments obtained by the road network topology method and the geographical frame selection method are used as candidates, and the best matching road segment is found from the candidate road segment set, which specifically includes the following contents: set up is the angular distance between the trajectory point and the road segment, set is the vertical distance between the trajectory point and the road segment; It can be expressed as: In the above formula, is the angle influence coefficient threshold, is the angle between the trajectory point direction and the road section direction; for and , first normalize the two, and the normalized vertical distance can be expressed as: In the above formula, is the maximum value between the trajectory point and the road segment, is the minimum value between the trajectory point and the road segment; The normalized angular distance can be expressed as: For each road segment, its comprehensive score The calculation formula is:

[0047] In the above formula, is the weight of the directional factor, is the weight of the distance factor, and + =1,0≤ ≤1,0≤ ≤1; By comparing the comprehensive scores of adjacent sections of the trajectory point, the optimal matching section is obtained.

[0048] As an example, in step 1, three candidate road sections are selected through road network topology and geographic frame selection; Section A: vertical distance 10 meters, direction angle 30°; Section B: vertical distance 20 meters, direction angle 90°; Section C: vertical distance 5 meters, direction angle 15°.

[0049] Step 2, calculating the vertical distance and the angle distance; Perpendicular distance : Section A: 10 meters; Section B: 20 ​​meters; Section C: 5 meters; The vertical distance range of all candidate road segments: = 5 meters, =20 meters.

[0050] Angular distance :set up =0.5, then section A: (A) = 0.5 × 30° = 15°; Section B: The angle exceeds the threshold ( ≈75°), (B) = ∞; Section C: (C)=0.5×15°=7.5°.

[0051] Step three, perform normalization processing; Normalization of perpendicular distance: =0.333; 1.0; 0.0; Angle distance normalization: =0.083; ∞) (set to 1); =0.042.

[0052] Step 4: Calculate the comprehensive score; Assumption Weight =0.6 (direction), =0.4(distance); 0.6×(1-0.083)+0.4×(1-0.333)≈0.817; =0.6×(1-1)+0.4×(1-1)=0.0(eliminated); =0.6×(1-0.042)+0.4×(1-0.0)≈0.975.

[0053] Step 5: Select the best matching section Section C was selected with the highest score Score=0.975 because it has the shortest vertical distance and the smallest direction deviation.

[0054] It should be noted that if the weight is adjusted to =0.4, =0.6, then segment C still has the highest score (0.983), but segment A’s score increases to (0.767), which reflects the influence of the distance weight.

[0055] If the direction angle of section B is 70° (not exceeding the threshold), its score needs to be recalculated: =0.5×70°=35°, ≈0.194; =0.6×(1-0.194)+0.4×(1-1.0)≈0.484+0.0=0.484 At this time, section C is still the best, but we can see the selection tendency of the best section: Section C scored the highest because it had the shortest vertical distance (5 meters) and the smallest direction deviation (15°); Section B was eliminated due to its large direction deviation; Section A scored in the middle because its distance and direction balance were second.

[0056] In some embodiments of the present invention, when establishing a road section-level energy consumption model, a weighted truncated mean algorithm is used to remove extreme values ​​in the energy consumption data, and an interval index storage structure is used to store the energy consumption data according to vehicle weight interval classification, specifically including: For a certain vehicle weight interval on a road section, there are n observed energy consumption values ; First, sort these energy consumption values, set k = 0.05n, remove the highest k values ​​and the lowest k values, and then calculate the weighted average of the remaining data as the road section energy consumption of the vehicle weight range. The formula is expressed as: In the above formula, is the weight of each energy consumption value, and p is the index in the sorted energy consumption value sequence.

[0057] Above The energy consumption of a road section in a specific vehicle weight range is calculated by weighted average of the processed energy consumption values. It comprehensively considers the energy consumption of the road section under various driving conditions within a specific vehicle weight range and is a key indicator for measuring the energy consumption level of the road section under this specific condition.

[0058] In summary, in the process of building a global energy consumption map, the weighted truncated mean algorithm (removing 5% extreme values) is used to effectively filter abnormal data (such as sudden energy consumption increases caused by rapid acceleration and sensor failure) to ensure that the training data input to the model has a high signal-to-noise ratio. The interval index storage structure is used to store energy consumption data according to vehicle weight intervals, which facilitates subsequent rapid query and analysis of road section energy consumption under different vehicle weight intervals. Different vehicle weights have a significant impact on the energy consumption of new energy heavy trucks. This classification storage method combined with actual conditions provides strong support for the establishment of an accurate road section-level energy consumption model. In a specific vehicle weight interval of a certain road section, the energy consumption value calculated by removing extreme values ​​can more realistically reflect the energy consumption level of the road section under this vehicle weight condition, providing a reliable data basis for the model.

[0059] like Figure 4 As shown, in some embodiments of the present invention, when constructing a global energy consumption map, the road segments in the geographical road network are layered, including low-level road segments and high-level road segments, wherein the relationship between the high-level road segments and the low-level road segments is a one-to-many relationship, for example, the high-level road segments The corresponding next level section is , , ; High-rise section The corresponding energy consumption can be expressed as: In the above formula, is the average energy consumption per kilometer of the ith road section in the low-level road section sequence, is the weighted value of the road segment attribute, is the length of the i-th road segment in the low-level road segment sequence, is the number of high-level sections corresponding to low-level sections, This hierarchical energy consumption calculation method can more comprehensively and accurately reflect the energy consumption of different road sections, provide richer information for the global energy consumption map, and help users more intuitively understand the energy consumption distribution of the entire road network.

[0060] To further refine the above scheme, 0.6× +0.4× in, is the slope (%), is the curvature (1 / km); this clarifies the specific weight distribution of slope and curvature.

[0061] In addition, a congestion index can be introduced (0-1), dynamically adjust weight : 0.5× +0.3× +0.2× The above-mentioned road segment stratification provides input features for the road segment-level model. High-level road segments (such as 5-kilometer trunk roads) optimize the efficiency of global path planning by weighted aggregation of low-level data. For establishing a road segment-level energy consumption model, it is necessary not only to pay attention to the energy consumption data of a single road segment, but also to consider the hierarchical relationship and comprehensive impact between road segments. This hierarchical calculation of energy consumption can more comprehensively and accurately reflect the energy consumption of different road segments, provide richer information for the road segment-level energy consumption model, and make it more consistent with the actual road network energy consumption distribution.

[0062] As an example, when training a road-level energy consumption model, in addition to removing extreme values ​​and storing data by vehicle weight range as mentioned above, it is also necessary to clean and preprocess the data, check the integrity of the data, fill in missing values, and use interpolation or estimation based on similar data. Then, the appropriate model algorithm should be selected based on the data characteristics and problem requirements. In addition to the weighted truncated mean algorithm for preliminary data processing, machine learning algorithms such as linear regression, decision trees, neural networks, etc. can also be used, and finally the model can be trained using the prepared data.

[0063] As an example, when integrating the model output results into a global energy consumption map, it is necessary to pay attention to the fact that since the road sections in the geographical road network are divided into low-level sections and high-level sections, when integrating the model output results, it is necessary to ensure that the energy consumption data of sections at different levels accurately correspond; and because vehicle driving data and road condition information are dynamically changing, when integrating the model output results to construct a global energy consumption map, the frequency of data updates must be considered. Regularly collect new vehicle driving data, retrain the section-level energy consumption model, and update the global energy consumption map.

[0064] Further refining the above scheme and finally presenting the model output results to users in a visual way is an important part of building a global energy consumption map. At this time, you should choose a suitable map drawing tool, such as Amap API, Baidu Map API, etc., to intuitively display the energy consumption information of the road section on the map. Different colors can be used to represent different energy consumption levels, green for low-energy sections, and red for high-energy sections. Provide user interaction functions, users can query the energy consumption of different routes according to their own needs, such as the current vehicle weight, driving destination, etc. The map can plan low-energy routes in real time according to user input and display them on the map.

[0065] As an example, given the limited number of new energy heavy-duty trucks and barriers to data sharing between manufacturers, vehicle driving data often cannot cover all sections of roads accessible to heavy trucks (road width, height and weight limits, and slopes will affect whether heavy trucks can pass). If these uncovered roads are not considered for route planning, it will be difficult to ensure that the planned energy-efficient route is optimal. To solve this problem, we propose a concept: using the energy consumption attributes of sections covered by existing driving data to estimate the energy consumption characteristics of uncovered sections accessible to heavy trucks. This method not only makes up for the problem of insufficient data, but also improves the accuracy of route planning and the effect of energy optimization, thereby more effectively supporting the development of green transportation and sustainable transportation, including the following: Mining features closely related to energy consumption from existing data, in addition to road type, slope, curvature and road width, including analysis of traffic environment features and geographic space and environmental factors. Traffic environment features include the traffic flow change pattern of the road section and the distribution of surrounding traffic facilities; geographic space and environmental factors, such as sections in the same mountain area may have similar slope and curvature characteristics; sections in the same urban area may have similar traffic flow patterns and road infrastructure characteristics, ensuring that multiple factors that may affect energy consumption are covered; Then the data obtained above is standardized and preprocessed, and the feature correlation coefficient is established. In view of the complexity and nonlinear characteristics of the energy consumption prediction problem, a variety of machine learning models are used and their performance is compared, such as random forest algorithm, neural network model (such as multi-layer perceptron), etc. The selected model is trained using the existing driving data (including various characteristics of the driving data section and the corresponding energy consumption data). The data is divided into training set, validation set and test set, for example, according to the ratio of 70%, 20%, and 10%. During the training process, the parameters of the model are learned using the training set data so that the model can predict the energy consumption value based on the input features, and finally the estimated energy consumption data of the new energy heavy truck on the uncovered section is obtained.

[0066] It should be noted that a small amount of actual driving data from uncovered road sections can also be collected to fine-tune the above model and further optimize the energy consumption prediction results.

[0067] In addition, the technical solution of the present invention can be further refined. For example, during the driving of the vehicle, the system will record the actual energy consumption value in real time and upload this data to the cloud. After the cloud platform receives the new driving data from different vehicles, it will regularly update and optimize the energy consumption value of each road section. This process not only includes the cleaning of new trajectory data, but also involves the integration and matching with the existing road network information to ensure the accuracy and consistency of the data. Please refer to the previous text for details.

[0068] As for the energy consumption update method of a certain vehicle weight range on a specific road section, the energy consumption model of the road section is adjusted and optimized by continuously adding the latest driving data.

[0069] Specifically, It indicates the latest energy consumption value of the corresponding vehicle weight range of the updated road section; Refers to the energy consumption value within the same vehicle weight range calculated based on the newly added new vehicle data; It represents the energy consumption value of the corresponding vehicle weight range previously recorded on the road section. λ is the threshold value, which can be changed according to the road conditions and data sample conditions.

[0070] By comparing and integrating and , we can get a more precise , improves accuracy through self-update. As more driving data accumulates, the energy consumption model can gradually exclude outliers, making the prediction results closer to the actual situation. External factors such as adaptation changes, traffic conditions, and road conditions may change over time. The self-update mechanism can help the model reflect these changes in a timely manner and maintain its timeliness.

[0071] Corresponding to the above embodiment, the present invention further provides an electronic device.

[0072] like Figure 5 The figure shows a schematic diagram of the structure of an electronic device in the present invention, where the electronic device 200 includes: a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in actual applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present invention.

[0073] The processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor 201 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0074] The bus 202 may include a path to transmit information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0075] The memory 203 is used to store a computer program corresponding to the method for constructing a new energy heavy truck energy consumption map according to the above embodiment of the present invention, and the computer program is controlled and executed by the processor 201. The processor 201 is used to execute the computer program stored in the memory 203 to implement the contents shown in the above method embodiment.

[0076] The electronic device 200 includes, but is not limited to, mobile terminals such as laptop computers, PADs (tablet computers), and fixed terminals such as desktop computers. Figure 5 The electronic device 200 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0077] The electronic device 200 of the embodiment of the present invention deeply combines geographic information with vehicle energy consumption data, and constructs a global energy consumption map based on the energy consumption of different road sections. It can intuitively compare the energy consumption differences of different routes and accurately select the driving route with the lowest energy consumption. Accurate low-energy consumption route planning can not only reduce the energy consumption of the vehicle itself, but also guide the reasonable distribution of vehicles in the road network. When many drivers can choose appropriate routes based on the energy consumption map, it can effectively avoid excessive concentration of vehicles in certain high-energy consumption or congested sections, so that road resources can be used more reasonably, traffic congestion can be alleviated, and the utilization efficiency of the entire road network can be improved; Taking into account the significant changes in the weight of new energy heavy-duty trucks, clustering analysis of driving data by vehicle weight can more accurately reflect the energy consumption characteristics of the vehicle under different vehicle weights. The energy consumption model and energy consumption map established based on this can provide drivers with route planning suggestions that are more in line with the actual operation of the vehicle. Drivers can choose routes with lower energy consumption based on the current weight of the vehicle, reduce energy consumption, and thus increase the vehicle's range. For new energy heavy-duty trucks that are highly dependent on energy replenishment facilities, this can reduce dependence on energy replenishment facilities and reduce operational risks caused by inconvenience in energy replenishment.

[0078] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, regenerate energy, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0079] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0080] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0081] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0082] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for constructing an energy consumption map of a new energy heavy truck, characterized in that: The method comprises: Clean invalid data in vehicle driving data to obtain valid data; Clustering the above valid data according to the vehicle weight to form a data structure of {vehicle weight interval:{driving data segment set}}, wherein each vehicle weight interval corresponds to a set of clustered driving data segments; According to the above-obtained data structure, the data is associated with the geographical road network, the vehicle energy consumption information is matched with the road section where the vehicle actually travels, and a "road section-energy consumption" mapping relationship is established; Based on the above matched data, a section-level energy consumption model is established and trained, and the output results of the model are integrated into a global energy consumption map.

2. The method for constructing a new energy heavy truck energy consumption map according to claim 1, characterized in that: The invalid data in the cleaning vehicle driving data includes the following: For any vehicle driving trajectory point, if the following conditions are met, it is invalid driving data: ① Data that is marked as invalid; ② Abnormal position; ③ Abnormal speed; ④Abnormal current or voltage; The clearing condition of the track point is as follows: In the above formula, It is the data valid flag. is the lower left longitude of China’s geographical area, is the upper right longitude of China’s geographical area, is the lower left latitude of China’s geographical area, is the upper right latitude of China’s geographical area, is the theoretical maximum speed, is the maximum current threshold, is the minimum current threshold, is the maximum voltage threshold, is the minimum voltage threshold.

3. The method for constructing an energy consumption map of a new energy heavy truck according to claim 1, characterized in that: The clustering of the valid data according to the vehicle weight includes the following contents: Firstly, the vehicle's driving trajectory is divided into several driving data segments according to the actual vehicle weight during driving, and each driving data segment represents the continuous driving process of the vehicle under a certain driving weight; Then, for each defined driving vehicle weight interval, all driving data segments belonging to the interval are collected to form a data structure of {vehicle weight interval: {driving data segment set}}.

4. The method for constructing a new energy heavy truck energy consumption map according to claim 1, characterized in that: The matching of the vehicle energy consumption information with the road section where the vehicle actually travels includes the following contents: Obtain matching road sections and select different acquisition methods according to different situations of vehicle trajectory points; If the last track point of the vehicle successfully matches a road segment in the geographic road network, and the distance between the current track point and the matching point corresponding to the last successful match is less than the vertical distance, the matching road segment is obtained using the road network topology method; If the vehicle's last trajectory point fails to match a road section in the geographic road network, a geographic rectangular box is constructed based on the current trajectory point, and the matching road section is obtained by geographic box selection.

5. The method for constructing a new energy heavy truck energy consumption map according to claim 4, characterized in that: The method of obtaining the matching road segments by using the road network topology is expressed as follows: In the above formula, is the road network topology segment set, is a topological segment, It is a secondary topological section.

6. The method for constructing a new energy heavy truck energy consumption map according to claim 4, characterized in that: When the geographic selection method is used to obtain the matching road section, the selection area should be dynamically adjusted according to the vehicle movement state, including the following contents: First, dynamically calculate the length of the frame selection: Based on the current vehicle speed v and direction change rate at the trajectory point Adjust the selection side length L. The selection side length L is expressed as follows: In the above formula, is the base side length, and is the adjustment coefficient, is the maximum direction change angle; Then constrain the fan-shaped area in the direction of the box selection: Defines the angle range of the fan-shaped search area , only matching candidate road segments with the same direction.

7. The method for constructing an energy consumption map of a new energy heavy truck according to claim 4, characterized in that: The matching sections obtained by the road network topology method and the geographic frame selection method are selected as candidates, and the best matching section is found from the candidate section set. Includes the following: set up is the angular distance between the trajectory point and the road segment, set is the vertical distance between the trajectory point and the road segment; It can be expressed as: In the above formula, is the angle influence coefficient threshold, is the angle between the trajectory point direction and the road section direction; for and , first normalize the two, and the normalized vertical distance can be expressed as: In the above formula, is the maximum value between the trajectory point and the road segment, is the minimum value between the trajectory point and the road segment; The normalized angular distance can be expressed as: For each road segment, its comprehensive score The calculation formula is: In the above formula, is the weight of the directional factor, is the weight of the distance factor, and + =1,0≤ ≤1,0≤ ≤1; By comparing the comprehensive scores of adjacent sections of the trajectory point, the optimal matching section is obtained.

8. The method for constructing an energy consumption map of a new energy heavy truck according to claim 1, characterized in that: When establishing the road section level energy consumption model, a weighted truncated mean algorithm is used to remove extreme values ​​in the energy consumption data, and an interval index storage structure is used to store the energy consumption data according to the vehicle weight interval classification, specifically including: For a certain vehicle weight interval on a road section, there are n observed energy consumption values ; First, sort these energy consumption values, set k = 0.05n, remove the highest k values ​​and the lowest k values, and then calculate the weighted average of the remaining data as the road section energy consumption of the vehicle weight range. The formula is expressed as: In the above formula, is the weight of each energy consumption value, and p is the index in the sorted energy consumption value sequence.

9. The method for constructing an energy consumption map of a new energy heavy truck according to claim 1, characterized in that: When constructing the global energy consumption map, the road sections in the geographical road network are layered, including low-level road sections and high-level road sections, wherein the relationship between high-level road sections and low-level road sections is a one-to-many relationship, and the energy consumption corresponding to the high-level road sections can be expressed as: In the above formula, is the average energy consumption per kilometer of the ith road section in the low-level road section sequence, is the weighted value of the road segment attribute, is the length of the i-th road segment in the low-level road segment sequence, is the number of high-level sections corresponding to low-level sections, is the length of the high-rise section.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the method for constructing an energy consumption map of a new energy heavy truck as described in any one of claims 1 to 9 is implemented.

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