Working condition simulation method, device and equipment of special vehicle and storage medium

Abstract

The invention discloses a working condition simulation method, device and equipment for a special vehicle and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring historical operation data of the special vehicle operating under a to-be-simulated working condition, and dividing a stroke corresponding to the historical operation data into a plurality of short strokes based on a fuel injection quantity threshold value or a torque threshold value; performing clustering analysis according to the feature information of the short journey to obtain a clustering result; determining the number of each type of short strokes according to the time information of the simulated working condition and the duration ratio of each type of short strokes, and screening a corresponding number of target short strokes in each type of short strokes; and constructing a simulation working condition corresponding to the to-be-simulated working condition by splicing the target short strokes corresponding to each type of short strokes. According to the technical scheme, the strokes corresponding to the historical operation data are divided into the short strokes, the corresponding target short strokes are screened from each type of short strokes, the simulated working conditions corresponding to the to-be-simulated working conditions are obtained by splicing the target short strokes corresponding to each type of short strokes, and simulation of the working conditions of the special vehicle is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of vehicle technology, and in particular to a method, device, equipment, and storage medium for simulating the operating condition of a special vehicle. Background Art

[0002] Special vehicles often operate in off-road environments. To perform performance testing or fault simulation on these vehicles, it is necessary to replicate the specific operating environments of these vehicles on a test bench using simulated operating conditions. Therefore, simulating the operating conditions of special vehicles has become a pressing technical challenge.

[0003] In the prior art, the operating condition of a vehicle is usually simulated based on historical operating data of the vehicle. The historical operating data includes the vehicle speed. Therefore, the operating condition simulation needs to be performed based on the vehicle speed.

[0004] However, special vehicles are usually not equipped with speed sensors, which makes it difficult to simulate the working conditions of special vehicles using existing technologies. Summary of the Invention

[0005] The present invention provides a method, device, equipment and storage medium for simulating the working condition of a special vehicle, so as to realize the working condition simulation of a special vehicle traveling in an off-road environment.

[0006] In a first aspect, an embodiment of the present invention provides a method for simulating the operating condition of a special vehicle, comprising:

[0007] Acquiring historical operating data of the special vehicle operating under the working condition to be simulated, and dividing a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque;

[0008] Performing cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips;

[0009] Determine the number of short trips in each category based on the time information of the simulated working conditions and the duration ratio of each short trip, and select a corresponding number of target short trips in each category;

[0010] By splicing the target short strokes corresponding to each type of short stroke, a simulated working condition corresponding to the working condition to be simulated is constructed.

[0011] The technical solution of an embodiment of the present invention provides a method for simulating the working condition of a special vehicle, including: obtaining historical operating data of a special vehicle operating in a working condition to be simulated, dividing the stroke corresponding to the historical operating data into multiple short strokes based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque; performing cluster analysis based on characteristic information of the short strokes to obtain clustering results, wherein the clustering results include multiple categories of short strokes; determining the number of each category of short strokes based on time information of the simulated working condition and the duration proportion of each category of short strokes, and screening a corresponding number of target short strokes in each category of short strokes; constructing a simulated working condition corresponding to the working condition to be simulated by splicing the target short strokes corresponding to each category of short strokes. The above technical solution can first divide the strokes corresponding to the historical operating data according to the injection amount threshold or the torque threshold, so as to divide the strokes corresponding to the historical operating data into multiple short strokes, solving the problem that the short stroke division cannot be performed due to the inability to obtain the speed signal of the special vehicle. Secondly, cluster analysis can be performed based on the characteristic information of the short strokes to divide the short strokes into multiple categories of short strokes, so that short strokes with similar characteristic information can be divided into one category to achieve the classification of short strokes. Then, the number of short strokes in each category can be determined according to the time information of the simulated working conditions and the proportion of the duration of each category of short strokes, and the corresponding target short strokes can be screened in each category of short strokes according to the number of each category of short strokes, so as to achieve the screening of the target short strokes required to construct the simulated working conditions. Further, the target short strokes corresponding to each category of short strokes can be spliced ​​to obtain the simulated working conditions corresponding to the working conditions to be simulated, thereby achieving the simulation of the working conditions of special vehicles.

[0012] Furthermore, the historical operating data is composed of operating data corresponding to multiple time points. Accordingly, the trip corresponding to the historical operating data is divided into multiple short trips based on the fuel injection amount threshold or the torque threshold, including:

[0013] determining the state at each of the time points based on the fuel injection amount and the fuel injection amount threshold at each of the time points, or based on the torque and the torque threshold at each of the time points;

[0014] A critical point is determined according to the state of each of the time points, and the historical operation data is divided according to the time difference between the critical points to obtain a plurality of short trips corresponding to the historical operation data.

[0015] Furthermore, before performing cluster analysis based on the characteristic information of the short trip, the method further includes:

[0016] Determining the characteristic information of each short trip, wherein the characteristic information includes duration, average engine speed, average fuel injection amount, and maximum fuel injection amount;

[0017] Normalization is performed on the feature information of each short stroke to obtain a normalized feature.

[0018] Furthermore, cluster analysis is performed based on the characteristic information of the short trip to obtain clustering results, including:

[0019] Performing cluster analysis based on the normalized features of each of the short trips to determine the number of clusters;

[0020] The short trips are divided into multiple categories of short trips according to the number of clusters.

[0021] Furthermore, the number of each type of short trips is determined based on the time information of the simulated working condition and the duration ratio of each type of short trips, including:

[0022] Determine the duration proportion of each type of short trip based on the total duration of all short trips in the clustering results and the total duration of each type of short trip;

[0023] Determining the required duration of each type of short trip according to the time information of the simulated working condition and the duration ratio of each type of short trip;

[0024] The number of each type of short trip is determined based on the required duration of each type of short trip and the average duration of each type of short trip.

[0025] Furthermore, the screening conditions for screening target short trips in each type of short trips are:

[0026] The duration of the short trip is within a duration deviation range of a corresponding type of short trip, wherein the duration deviation range of each type of short trip is determined by the average duration of each type of short trip and a deviation coefficient;

[0027] The average deviation of the short trip is smaller than a deviation threshold value, wherein the average deviation is determined by an average speed deviation, an average injection quantity deviation, and a maximum injection quantity deviation.

[0028] Furthermore, after constructing the simulated working condition corresponding to the working condition to be simulated, the method further includes:

[0029] determining an average speed deviation, an average fuel injection amount deviation, and a maximum fuel injection amount deviation of the simulated operating condition based on the average speed, the average fuel injection amount, and the maximum fuel injection amount of the simulated operating condition and the original average speed, the original average fuel injection amount, and the original maximum fuel injection amount determined from the historical operating data;

[0030] The rationality verification result of the simulated operating condition is determined based on the average speed deviation, the average fuel injection amount deviation and the maximum fuel injection amount deviation of the simulated operating condition.

[0031] In a second aspect, an embodiment of the present invention further provides a working condition simulation device for a special vehicle, comprising:

[0032] an acquisition module, configured to acquire historical operating data of the special vehicle operating under the working condition to be simulated, and divide a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque;

[0033] a clustering module, configured to perform cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips;

[0034] A determination module is used to determine the number of each type of short trips based on the time information of the simulated working condition and the duration ratio of each type of short trips, and to select a corresponding number of target short trips in each type of short trips;

[0035] The construction module is used to construct a simulation working condition corresponding to the working condition to be simulated by splicing the target short stroke corresponding to each type of short stroke.

[0036] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising:

[0037] at least one processor; and a memory communicatively coupled to the at least one processor;

[0038] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the operating condition simulation method of a special vehicle as described in any one of the first aspects.

[0039] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the method for simulating the working condition of a special vehicle as described in any one of the first aspects.

[0040] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are run on a computer, the computer executes the operating condition simulation method of a special vehicle provided in the first aspect.

[0041] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the special vehicle operating condition simulation device, or may be packaged separately from the processor of the special vehicle operating condition simulation device, and this application does not limit this.

[0042] The descriptions of the second, third, fourth and fifth aspects of this application can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second, third, fourth and fifth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0043] In this application, the name of the aforementioned special vehicle operating condition simulation device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they are within the scope of the claims of this application and their equivalents.

[0044] These and other aspects of the present application will become more readily apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A flowchart of a method for simulating the working condition of a special vehicle provided in an embodiment of the present invention;

[0047] Figure 2 A flowchart of another method for simulating the working condition of a special vehicle provided by an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of clustering results in another method for simulating operating conditions of a special vehicle provided by an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of a simulated operating condition in another operating condition simulation method for a special vehicle provided by an embodiment of the present invention;

[0050] Figure 5 A schematic structural diagram of a working condition simulation device for a special vehicle provided in an embodiment of the present invention;

[0051] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0053] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0054] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0055] Furthermore, the terms "including," "having," and any variations thereof, as used in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0056] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the process can be terminated, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the features in the embodiments of the present invention and the embodiments can be combined with each other without conflict.

[0057] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0058] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0059] Figure 1This is a flow chart of a method for simulating the working condition of a special vehicle provided in an embodiment of the present invention. This embodiment is applicable to situations where it is necessary to simulate the working condition of a special vehicle running in a specific environment. The method can be executed by a working condition simulation device for a special vehicle, such as Figure 1 As shown, the specific steps include:

[0060] Step 110: Obtain historical operating data of the special vehicle operating in the working condition to be simulated, and divide the stroke corresponding to the historical operating data into multiple short strokes based on the fuel injection amount threshold or the torque threshold.

[0061] The special vehicle may be an excavator operating in a specific off-road environment, and the historical operating data may include fuel injection volume and torque.

[0062] Excavators operating in specialized off-road environments are typically not equipped with speed sensors. Therefore, the operating data for special vehicles does not include vehicle speed or throttle signals. Different speed settings correspond to different usage scenarios for special vehicles. During operation, the engine typically maintains a constant speed, while the fuel injection rate and torque vary. In a typical complete operating condition, the fuel injection rate typically drops to the idle fuel level two to four times, and accordingly, the torque also drops to the minimum torque two to four times. Based on this, the idea behind constructing simulated operating conditions is to first divide the complete historical operating data into multiple short strokes, based on the fuel injection rate or torque.

[0063] Specifically, all operating data of the special vehicle can be obtained, specifically through CAN (Controller Area Network) communication acquisition equipment. Secondly, historical operating data of the special vehicle operating under the working condition to be simulated can be obtained from all operating data of the special vehicle. Specifically, the operating data also includes time information. Therefore, the historical operating data can be searched based on the time when the special vehicle operated under the working condition to be simulated, thereby determining the historical operating data for the working condition to be simulated. Furthermore, the trip corresponding to the historical operating data can be divided into multiple short trips based on a fuel injection threshold or a torque threshold. The historical operating data includes operating data obtained at various time points. Therefore, first, critical points can be determined. First, the critical points can be determined based on the fuel injection amount and the fuel injection threshold at each time point, or based on the torque and the torque threshold at each time point. Second, the short trips can be divided based on the time difference between each critical point. That is, if the time difference between adjacent critical points is greater than the time threshold, a short trip is generated based on the adjacent critical point. Otherwise, the next critical point is accumulated until the time difference is greater than the time threshold, thereby dividing the trip corresponding to the historical operating data into multiple short trips.

[0064] In an embodiment of the present invention, the stroke corresponding to the historical operating data is divided according to the fuel injection amount threshold or the torque threshold, so that the stroke corresponding to the historical operating data is divided into multiple short strokes, which solves the problem of being unable to perform short stroke division due to the inability to obtain the speed signal of the special vehicle.

[0065] Step 120: Perform cluster analysis based on the characteristic information of the short trip to obtain a clustering result.

[0066] The clustering result includes multiple types of short trips.

[0067] Historical operation data also includes engine speed, and short-trip characteristic information includes duration, average engine speed, average fuel injection amount, and maximum fuel injection amount.

[0068] Specifically, after dividing the trips corresponding to the historical operating data into multiple short trips, the operating data corresponding to each short trip can be determined. For each short trip, characteristic information can be determined based on the corresponding operating data. Specifically, this short trip characteristic information includes duration, average engine speed, average fuel injection volume, and maximum fuel injection volume. Cluster analysis can then be performed based on this short trip characteristic information. Specifically, short trips with similar characteristic information can be grouped together to produce clustering results. These clustering results can be interpreted as representing multiple categories of short trips.

[0069] In the embodiment of the present invention, cluster analysis is performed based on the feature information of short trips to classify short trips into multiple categories, so that short trips with similar feature information are classified into one category, thereby achieving classification of short trips.

[0070] Step 130 : Determine the number of each type of short trips based on the time information of the simulated working condition and the duration ratio of each type of short trips, and select a corresponding number of target short trips from each type of short trips.

[0071] Specifically, first, it is necessary to determine the time information of the simulated working condition. For example, when it is necessary to simulate a 50-minute working condition to be simulated, the time information of the simulated working condition can be determined to be 50 minutes. It is also necessary to determine the duration ratio of each type of short trip, that is, the duration ratio of each type of short trip can be determined based on the total duration of all short trips in the clustering results and the total duration of each type of short trip. Secondly, the required duration of each type of short trip can be determined based on the time information of the simulated working condition and the duration ratio of each type of short trip. For each type of short trip, after determining its required duration, the number of short trips of this type can be determined based on the required duration of this type of short trip and the average duration of this type of short trip. Then, a corresponding number of target short trips can be screened in each type of short trip, that is, a corresponding number of short trips can be randomly selected in each type of short trip as target short trips.

[0072] In an embodiment of the present invention, the number of each type of short strokes is determined based on the time information of the simulated working conditions and the duration ratio of each type of short strokes, and the corresponding target short strokes are screened in each type of short strokes based on the number of each type of short strokes, thereby determining the screening of target short strokes required to construct the simulated working conditions.

[0073] Step 140 : Constructing a simulated working condition corresponding to the working condition to be simulated by splicing the target short strokes corresponding to each type of short stroke.

[0074] Specifically, first, the target short strokes corresponding to the short strokes of the same type can be spliced ​​in chronological order to obtain the spliced ​​strokes corresponding to each type of short stroke, and then the spliced ​​short strokes corresponding to each type of short stroke can be spliced ​​in chronological order to obtain the simulated working conditions corresponding to the working conditions to be simulated, thereby realizing the simulation of the working conditions to be simulated.

[0075] In the embodiment of the present invention, by splicing the target short strokes corresponding to each type of short stroke, a simulated operating condition corresponding to the operating condition to be simulated is obtained, thereby realizing the simulation of the operating condition of a special vehicle.

[0076] The working condition simulation method of a special vehicle provided by an embodiment of the present invention includes: obtaining historical operating data of the special vehicle operating in the working condition to be simulated, dividing the stroke corresponding to the historical operating data into multiple short strokes based on the injection amount threshold or the torque threshold, wherein the historical operating data includes the injection amount and the torque; performing cluster analysis based on the characteristic information of the short strokes to obtain clustering results, wherein the clustering results include multiple categories of short strokes; determining the number of each category of short strokes based on the time information of the simulated working condition and the duration proportion of each category of short strokes, and screening a corresponding number of target short strokes in each category of short strokes; constructing the simulated working condition corresponding to the working condition to be simulated by splicing the target short strokes corresponding to each category of short strokes. The above technical solution can first divide the strokes corresponding to the historical operating data according to the injection amount threshold or the torque threshold, so as to divide the strokes corresponding to the historical operating data into multiple short strokes, solving the problem that the short stroke division cannot be performed due to the inability to obtain the speed signal of the special vehicle. Secondly, cluster analysis can be performed based on the characteristic information of the short strokes to divide the short strokes into multiple categories of short strokes, so that short strokes with similar characteristic information can be divided into one category to achieve the classification of short strokes. Then, the number of short strokes in each category can be determined according to the time information of the simulated working conditions and the proportion of the duration of each category of short strokes, and the corresponding target short strokes can be screened in each category of short strokes according to the number of each category of short strokes, so as to achieve the screening of the target short strokes required to construct the simulated working conditions. Further, the target short strokes corresponding to each category of short strokes can be spliced ​​to obtain the simulated working conditions corresponding to the working conditions to be simulated, thereby achieving the simulation of the working conditions of special vehicles.

[0077] Figure 2This is a flow chart of another method for simulating the working condition of a special vehicle provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. Figure 2 As shown, in this embodiment, the method may further include:

[0078] Step 210: Obtain historical operating data of the special vehicle operating under the working condition to be simulated.

[0079] The historical operating data includes fuel injection quantity and torque.

[0080] During the operation of special vehicles, operation data can be obtained in real time and uploaded to the server. The server can store the operation data based on the database.

[0081] Specifically, first, you can search in the database based on the vehicle identification of the special vehicle to determine all the operating data of the special vehicle. Secondly, you can search in all the operating data of the special vehicle according to the time when the special vehicle operates in the working condition to be simulated to determine the historical operating data of the working condition to be simulated.

[0082] In actual applications, historical operation data is stored based on multiple road map files. After obtaining the historical operation data, the historical operation data can be cleaned, that is, abnormal point data can be removed. For example, the road map files with operating time less than the time threshold can be removed to obtain the cleaned historical operation data.

[0083] Of course, it may also be necessary to delete abnormal values ​​in the historical operating data. For example, when the rotational speed or torque is an abnormal value, the operating data corresponding to the corresponding time point is deleted.

[0084] In an embodiment of the present invention, historical operating data of the special vehicle operating in the working condition to be simulated is obtained from a database based on the vehicle identification of the special vehicle and the time information of the working condition to be simulated, thereby achieving rapid acquisition of historical operating data of the special vehicle operating in the working condition to be simulated.

[0085] Step 220: Divide the trip corresponding to the historical operating data into multiple short trips based on the fuel injection amount threshold or the torque threshold.

[0086] In one embodiment, the historical operation data is composed of operation data corresponding to multiple time points. Accordingly, step 220 may specifically include:

[0087] Based on the fuel injection amount and the fuel injection amount threshold at each time point, or based on the torque and the torque threshold at each time point, the state of each time point is determined; the critical point is determined according to the state of each time point, and the historical operation data is divided according to the time difference between each critical point to obtain multiple short strokes corresponding to the historical operation data.

[0088] The fuel injection amount threshold can be understood as the idle fuel amount, and the torque threshold can be understood as the idle torque.

[0089] Specifically, the state at each time point can be determined first. Specifically, for each time point, when determining the state based on the injection amount and the injection amount threshold, if the corresponding injection amount is greater than the idle fuel amount, the corresponding state can be determined to be the first state, and if the corresponding injection amount is not greater than the idle fuel amount, the corresponding state can be determined to be the second state. When determining the state based on the torque and the torque threshold, if the corresponding torque is greater than the idle torque, the corresponding state can be determined to be the first state, and if the corresponding torque is not greater than the idle torque, the corresponding state can be determined to be the second state. In this application, the first state can be represented by 1 and the second state can be represented by 0. Then, the states corresponding to adjacent time points can be compared. If the difference between the states corresponding to adjacent time points is 1, it means that the relatively earlier time point among the adjacent time points is determined as the critical point. Furthermore, short trips can be divided according to the time difference between each critical point. That is, if the time difference between adjacent critical points is greater than the time threshold, the trip corresponding to the adjacent critical points is determined as a short trip. Otherwise, the next critical point is accumulated until the time difference is greater than the time threshold. The trips corresponding to the two critical points with a time difference greater than the time threshold are determined as a short trip, thereby dividing the trips corresponding to the historical operation data into multiple short trips.

[0090] In addition, if the maximum injection quantity of a short stroke is not greater than the maximum injection quantity threshold, the short stroke is eliminated.

[0091] In an embodiment of the present invention, the stroke corresponding to the historical operating data is divided into multiple short strokes based on the fuel injection amount threshold or the torque threshold as the critical point for short stroke division, thereby solving the problem of being unable to perform short stroke division due to the inability to obtain the speed signal of the special vehicle.

[0092] Step 230: Determine the feature information of each short stroke; and perform normalization processing on the feature information of each short stroke to obtain normalized features.

[0093] The characteristic information includes duration, average engine speed, average fuel injection amount and maximum fuel injection amount.

[0094] Specifically, for each short stroke, the characteristic information of the short stroke can be determined based on the operating data corresponding to the short stroke, that is, the duration of the short stroke can be determined according to the time information corresponding to the two critical points that divide the short stroke, the average engine speed can be determined according to the engine speed corresponding to each time point in the short stroke, the average fuel injection amount can be determined according to the fuel injection amount corresponding to each time point in the short stroke, and the maximum fuel injection amount can be determined according to the maximum fuel injection amount corresponding to each time point in the short stroke.

[0095] The duration, average engine speed, average fuel injection amount, and maximum fuel injection amount have different dimensions. In order to eliminate the impact of inconsistent dimensions, the duration, average engine speed, average fuel injection amount, and maximum fuel injection amount can be normalized to obtain normalized features.

[0096] In the embodiment of the present invention, feature extraction is performed on the short stroke based on the operation data corresponding to the short stroke to determine feature information of the short stroke, and normalization processing is performed on the feature information of the short stroke to determine the normalized feature of the short stroke.

[0097] Step 240 : performing cluster analysis based on the normalized features of each short trip to determine the number of clusters; and dividing the short trips into multiple categories of short trips according to the number of clusters.

[0098] Specifically, cluster analysis can be performed based on the normalized features of each short trip. The clustering results can be evaluated using the silhouette coefficient, CH (Calinski-Harabasz) coefficient, and DBI (Davies-Bouldin Index) coefficient to determine the number of clusters. Based on the number of clusters, short trips with similar feature information can be divided into one category to obtain multiple categories of short trips.

[0099] Figure 3 A schematic diagram of clustering results in another method for simulating the working condition of a special vehicle provided by an embodiment of the present invention, such as Figure 3 As shown, the number of clusters is 3, and the short trip is divided into three categories: Figure 3 The three categories are represented based on squares, circles, and triangles respectively.

[0100] In practical applications, various types of short strokes can be determined as acceleration short strokes, deceleration short strokes, uniform speed short strokes, etc. based on their characteristic information.

[0101] In an embodiment of the present invention, after cluster analysis is performed based on the normalized features of short trips to determine the number of clusters, the short trips are divided into multiple categories according to the number of clusters, so that short trips with similar feature information are divided into one category, thereby achieving classification of short trips.

[0102] Step 250: Determine the number of each type of short trips based on the time information of the simulated working condition and the duration ratio of each type of short trips.

[0103] In one implementation, step 250 may specifically include:

[0104] The duration ratio of each type of short trip is determined based on the total duration of all short trips in the clustering results and the total duration of each type of short trip; the required duration of each type of short trip is determined based on the time information of the simulated working conditions and the duration ratio of each type of short trip; the number of short trips of each type is determined based on the required duration of each type of short trip and the average duration of each type of short trip.

[0105] To simulate the desired working condition, it is necessary to determine the time information corresponding to the simulated working condition. Specifically, this means determining the duration of the simulated working condition. This allows the determination of the short strokes required to construct the simulated working condition corresponding to the time information. Since short strokes can be classified into multiple types, it is necessary to determine the various types of short strokes required to construct the simulated working condition corresponding to the time information.

[0106] Specifically, first, the duration ratio of each type of short trip required for the simulated working condition corresponding to the time information can be determined. Specifically, the duration ratio of each type of short trip can be determined based on the total duration of all short trips in the clustering results and the total duration of each type of short trip, that is, the total duration of all short trips in the clustering results and the total duration of each type of short trip can be determined as the duration ratio of each type of short trip. Secondly, the required duration of each type of short trip can be determined based on the time information of the simulated working condition and the duration ratio of each type of short trip. For example, the time information of the simulated working condition is 50 minutes, and the duration ratios of the acceleration short trip, deceleration short trip, and uniform speed short trip are 50%, 25%, and 25%, respectively. Therefore, it can be determined that the required durations of the acceleration short trip, deceleration short trip, and uniform speed short trip are 25 minutes, 12.5 minutes, and 12.5 minutes, respectively. For each type of short trip, after determining its required duration, the number of short trips of this type can be determined based on the required duration of this type of short trip and the average duration of this type of short trip. For example, when the average durations of acceleration short trips, deceleration short trips, and uniform-speed short trips are 2 minutes, 1 minute, and 3.5 minutes respectively, the number of acceleration short trips, deceleration short trips, and uniform-speed short trips can be determined to be 25 / 2=12.5 (rounded up to 13), 25 / 1=1, and 12.5 / 3.5≈3.57 (rounded up to 4).

[0107] In an embodiment of the present invention, the number of each type of short trips is determined based on the time information of the simulated working condition and the duration ratio of each type of short trip, so as to determine the number of each type of short trips required for the simulated working condition corresponding to the time information.

[0108] Step 260 : Filter a corresponding number of target short trips in each type of short trip.

[0109] Specifically, after determining the number of each type of short strokes required to constitute the simulated working condition corresponding to the time information, a corresponding number of target short strokes may be screened from each type of short strokes.

[0110] Among them, the screening conditions for screening the target short stroke in each type of short stroke are: the duration of the short stroke is within the duration deviation range of the corresponding type of short stroke, wherein the duration deviation range of each type of short stroke is determined by the average duration and deviation coefficient of each type of short stroke; the average deviation of the short stroke is less than the deviation threshold, wherein the average deviation is determined by the average speed deviation, the average fuel injection amount deviation and the maximum fuel injection amount deviation.

[0111] The target short trip must be within the duration deviation range of the corresponding short trip. Therefore, the duration deviation range of each type of short trip can be determined based on the average duration and deviation coefficient of each type of short trip. For each type of short trip, the duration deviation range can be determined as: average duration × (1 ± deviation coefficient). Secondly, the short trips belonging to each type can be screened based on the duration deviation range of each type of short trip. Short trips belonging to each type and with durations exceeding the corresponding duration deviation range can be eliminated to obtain various types of short trips that meet the duration deviation, thus achieving the initial screening of short trips.

[0112] The target short stroke also needs to meet the requirement that the average deviation determined by the average speed deviation, the average injection amount deviation and the maximum injection amount deviation is less than the deviation threshold. Therefore, after the initial screening of the short strokes, the average deviation of each short stroke in the various types of short strokes that meet the duration deviation is determined to reduce the amount of data for calculating the average deviation.

[0113] Specifically, for each short trip within each category that meets the duration deviation, the following can be determined: average speed deviation of the short trip = | average engine speed - average engine speed corresponding to the category | / average engine speed corresponding to the category; average fuel injection quantity deviation = | fuel injection quantity - average fuel injection quantity corresponding to the category | / average fuel injection quantity corresponding to the category; maximum fuel injection quantity deviation = | maximum fuel injection quantity - average maximum fuel injection quantity corresponding to the category | / average maximum fuel injection quantity corresponding to the category; and average deviation = | average speed deviation + average fuel injection quantity deviation + maximum fuel injection quantity deviation | / 3. Furthermore, based on the deviation threshold, short trips within each category that meet the duration deviation can be screened. Short trips that meet the duration deviation and have an average deviation less than the deviation threshold can be identified as target short trips for each category, achieving secondary screening of short trips.

[0114] The target short trips are selected by randomly selecting a corresponding number of short trips that meet the screening conditions in each type of short trips, which improves the screening efficiency of the target short trips.

[0115] In an embodiment of the present invention, short strokes are screened twice according to the number of each type of short strokes and the screening conditions to obtain target short strokes corresponding to each type of short stroke, thereby screening the corresponding target short strokes in each type of short stroke and determining the screening of the target short strokes required to construct the simulated working conditions.

[0116] Step 270: Construct the simulated working condition corresponding to the to-be-simulated working condition by splicing the target short strokes corresponding to each type of short stroke.

[0117] As described above, first, the target short strokes corresponding to the short strokes of the same type can be spliced in chronological order to obtain the spliced strokes corresponding to each type of short stroke, and then the spliced short strokes corresponding to each type of short stroke can be spliced in chronological order to obtain the simulated working condition corresponding to the to-be-simulated working condition, so as to simulate the to-be-simulated working condition.

[0118] Figure 4 It is a schematic diagram of the simulated working condition in another method for simulating the working condition of a special vehicle provided by an embodiment of the present invention. As Figure 4 shown, the horizontal axis represents time, and the vertical axis represents the values of engine speed, fuel injection quantity, and torque.

[0119] In an embodiment of the present invention, the simulated working condition corresponding to the to-be-simulated working condition is obtained by splicing the target short strokes corresponding to each type of short stroke, so as to simulate the working condition of the special vehicle.

[0120] Step 280: Determine the average speed deviation, average fuel injection quantity deviation, and maximum fuel injection quantity deviation of the simulated working condition according to the average speed, average fuel injection quantity, and maximum fuel injection quantity of the simulated working condition, and the original average speed, original average fuel injection quantity, and original maximum fuel injection quantity determined from the historical operation data; determine the rationality verification result of the simulated working condition according to the average speed deviation, average fuel injection quantity deviation, and maximum fuel injection quantity deviation of the simulated working condition.

[0121] Specifically, first, the average speed, average fuel injection quantity, and maximum fuel injection quantity of the simulated working condition can be determined, that is, the average speed, average fuel injection quantity, and maximum fuel injection quantity of the simulated working condition can be determined according to the engine speed, fuel injection quantity, and maximum fuel injection quantity of each short stroke constituting the simulated working condition. Secondly, the average speed deviation of the simulated working condition = |engine average speed - original average speed determined from historical operation data| / original average speed determined from historical operation data, the average fuel injection quantity deviation = |fuel injection quantity - original average fuel injection quantity| / original average fuel injection quantity, and the maximum fuel injection quantity deviation = |maximum fuel injection quantity - original maximum fuel injection quantity| / original maximum fuel injection quantity. <0​​​​​

[0124] If the simulated operating condition satisfies the conditions that the average speed deviation is not greater than the average speed deviation threshold, the average fuel injection quantity deviation is not greater than the average fuel injection quantity deviation threshold, and the maximum fuel injection quantity deviation is not greater than the maximum fuel injection quantity deviation threshold, then the rationality verification result of the simulated operating condition is determined to be a reasonable construction; otherwise, the rationality verification result of the simulated operating condition is determined to be an unreasonable construction.

[0125] In another embodiment, when the preset deviation standard includes an average deviation threshold, determining the rationality verification result of the simulated operating condition based on the average speed deviation, average fuel injection amount deviation, and maximum fuel injection amount deviation of the simulated operating condition may include:

[0126] Determine the average deviation of the simulated working condition = |average speed deviation+average injection amount deviation+maximum injection amount deviation| / 3. If the average deviation is not greater than the average deviation threshold, then determine that the rationality verification result of the simulated working condition is reasonable. Otherwise, determine that the rationality verification result of the simulated working condition is unreasonable.

[0127] Of course, after determining that the rationality verification result of the simulation working condition is unreasonable, the program can be adjusted according to the above steps to re-construct the simulation working condition, and the construction result with the minimum deviation can be determined as the simulation working condition.

[0128] In an embodiment of the present invention, after determining the average speed deviation, average fuel injection amount deviation and maximum fuel injection amount deviation of the simulated working condition, the rationality verification result of the simulated working condition is determined based on the pre-set deviation standard and the average speed deviation, average fuel injection amount deviation and maximum fuel injection amount deviation of the simulated working condition, thereby realizing the rationality verification of the simulated working condition.

[0129] The working condition simulation method of a special vehicle provided by an embodiment of the present invention includes: obtaining historical operating data of a special vehicle operating in a working condition to be simulated; dividing the stroke corresponding to the historical operating data into multiple short strokes based on a fuel injection amount threshold or a torque threshold; determining the characteristic information of each short stroke; normalizing the characteristic information of each short stroke to obtain a normalized feature; performing cluster analysis based on the normalized feature of each short stroke to determine the number of clusters; dividing the short stroke into multiple categories of short strokes according to the number of clusters; determining the number of categories of short strokes according to the time information of the simulated working condition and the duration proportion of each category of short strokes. Determine the number of each type of short strokes; select a corresponding number of target short strokes in each type of short strokes; construct a simulated operating condition corresponding to the operating condition to be simulated by splicing the target short strokes corresponding to each type of short strokes; determine the average speed deviation, average fuel injection amount and maximum fuel injection amount of the simulated operating condition according to the average speed, average fuel injection amount and maximum fuel injection amount of the simulated operating condition and the original average speed, original average fuel injection amount and original maximum fuel injection amount determined by the historical operation data; determine the rationality verification result of the simulated operating condition according to the average speed deviation, average fuel injection amount deviation and maximum fuel injection amount deviation of the simulated operating condition. The above technical solution can first obtain the historical operation data of the special vehicle running in the working condition to be simulated based on the vehicle identification of the special vehicle and the time information of the working condition to be simulated, so as to realize the rapid acquisition of the historical operation data of the special vehicle running in the working condition to be simulated, and then divide the stroke corresponding to the historical operation data according to the injection amount threshold or the torque threshold, so as to realize the division of the stroke corresponding to the historical operation data into multiple short strokes, thereby solving the problem that the short stroke cannot be divided due to the inability to obtain the speed signal of the special vehicle. Secondly, cluster analysis can be performed based on the normalized characteristics of the short strokes to divide the short strokes into multiple categories, so as to classify the short strokes with similar feature information. The trips are divided into a category to realize the classification of short trips. Then, the number of each type of short trips can be determined according to the time information of the simulated working condition and the proportion of the duration of each type of short trips, so as to determine the number of each type of short trips required for the simulated working condition corresponding to the time information. The short trips can also be screened twice according to the number of each type of short trips and the screening conditions to obtain the target short trips corresponding to each type of short trips, so as to screen the corresponding target short trips in each type of short trips, and determine the screening of the target short trips required to construct the simulated working condition. Further, the target short trips corresponding to each type of short trips can be spliced ​​to obtain the simulated working condition corresponding to the working condition to be simulated, so as to realize the simulation of the working condition of special vehicles.

[0130] In addition, the rationality verification result of the simulated working condition can be determined based on the pre-set deviation standard and the average speed deviation, average injection amount deviation and maximum injection amount deviation of the simulated working condition, thereby realizing the rationality verification of the simulated working condition.

[0131] Figure 5This is a schematic diagram of the structure of a special vehicle operating condition simulation device provided in an embodiment of the present invention. This device is suitable for situations where operating condition simulation of special vehicles operating in specific environments is required, thereby improving simulation efficiency. The device can be implemented using software and / or hardware and is generally integrated into electronic equipment, such as a computer.

[0132] like Figure 5 As shown, the device includes:

[0133] an acquisition module 510 for acquiring historical operating data of the special vehicle operating under the working condition to be simulated, and dividing a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque;

[0134] A clustering module 520 is configured to perform cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips;

[0135] a determination module 530 for determining the number of each type of short trips based on the time information of the simulated working condition and the duration ratio of each type of short trips, and selecting a corresponding number of target short trips from each type of short trips;

[0136] The construction module 540 is used to construct the simulated working condition corresponding to the working condition to be simulated by splicing the target short strokes corresponding to each type of short stroke.

[0137] The operating condition simulation device for special vehicles provided in this embodiment obtains historical operating data of the special vehicle operating in the operating condition to be simulated, and divides the stroke corresponding to the historical operating data into multiple short strokes based on the injection amount threshold or the torque threshold, wherein the historical operating data includes the injection amount and the torque; performs cluster analysis based on the characteristic information of the short strokes to obtain clustering results, wherein the clustering results include multiple categories of short strokes; determines the number of each category of short strokes based on the time information of the simulated operating condition and the proportion of the duration of each category of short strokes, and screens a corresponding number of target short strokes in each category of short strokes; constructs the simulated operating condition corresponding to the operating condition to be simulated by splicing the target short strokes corresponding to each category of short strokes. The above technical solution can first divide the strokes corresponding to the historical operating data according to the injection amount threshold or the torque threshold, so as to divide the strokes corresponding to the historical operating data into multiple short strokes, solving the problem that the short stroke division cannot be performed due to the inability to obtain the speed signal of the special vehicle. Secondly, cluster analysis can be performed based on the characteristic information of the short strokes to divide the short strokes into multiple categories of short strokes, so that short strokes with similar characteristic information can be divided into one category to achieve the classification of short strokes. Then, the number of short strokes in each category can be determined according to the time information of the simulated working conditions and the proportion of the duration of each category of short strokes, and the corresponding target short strokes can be screened in each category of short strokes according to the number of each category of short strokes, so as to achieve the screening of the target short strokes required to construct the simulated working conditions. Further, the target short strokes corresponding to each category of short strokes can be spliced ​​to obtain the simulated working conditions corresponding to the working conditions to be simulated, thereby achieving the simulation of the working conditions of special vehicles.

[0138] Based on the above embodiment, the historical operation data is composed of operation data corresponding to multiple time points. Accordingly, the acquisition module 510 is specifically configured to:

[0139] Obtain historical operating data of the special vehicle operating in the working conditions to be simulated; determine the state of each time point based on the fuel injection amount and the fuel injection amount threshold at each time point, or based on the torque and the torque threshold at each time point; determine the critical point according to the state at each time point, and divide the historical operating data according to the time difference between each critical point to obtain multiple short trips corresponding to the historical operating data.

[0140] Based on the above embodiment, the device further includes:

[0141] A normalization module is used to determine the characteristic information of each short trip before performing cluster analysis based on the characteristic information of the short trip, wherein the characteristic information includes duration, average engine speed, average fuel injection amount and maximum fuel injection amount; and normalize the characteristic information of each short trip to obtain normalized features.

[0142] Based on the above embodiment, the clustering module 520 is specifically configured to:

[0143] A cluster analysis is performed based on the normalized features of each of the short trips to determine the number of clusters; and the short trips are divided into multiple categories of short trips according to the number of clusters.

[0144] Based on the above embodiment, the determination module 530 is specifically configured to:

[0145] The duration ratio of each type of short trip is determined based on the total duration of all short trips in the clustering results and the total duration of each type of short trip; the required duration of each type of short trip is determined based on the time information of the simulated working conditions and the duration ratio of each type of short trip; the number of short trips of each type is determined based on the required duration of each type of short trip and the average duration of each type of short trip.

[0146] In one embodiment, the screening conditions for screening target short trips in each type of short trips are:

[0147] The duration of the short trip is within a duration deviation range of a corresponding type of short trip, wherein the duration deviation range of each type of short trip is determined by the average duration of each type of short trip and a deviation coefficient;

[0148] The average deviation of the short trip is smaller than a deviation threshold value, wherein the average deviation is determined by an average speed deviation, an average injection quantity deviation, and a maximum injection quantity deviation.

[0149] Based on the above embodiment, the device further includes:

[0150] A verification module is used to determine the average speed deviation, average fuel injection amount deviation and maximum fuel injection amount deviation of the simulated working condition after constructing the simulated working condition corresponding to the working condition to be simulated, based on the average speed, average fuel injection amount and maximum fuel injection amount of the simulated working condition and the original average speed, original average fuel injection amount and original maximum fuel injection amount determined by the historical operation data; and determine the rationality verification result of the simulated working condition based on the average speed deviation, average fuel injection amount deviation and maximum fuel injection amount deviation of the simulated working condition.

[0151] The operating condition simulation device for a special vehicle provided in an embodiment of the present invention can execute the operating condition simulation method for a special vehicle provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the operating condition simulation method for a special vehicle.

[0152] It is worth noting that in the embodiment of the working condition simulation device for the above-mentioned special vehicle, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0153] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. Figure 6 A block diagram of an exemplary electronic device 6 suitable for implementing embodiments of the present invention is shown. Figure 6 The electronic device 6 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.

[0154] like Figure 6 As shown, electronic device 6 is in the form of a general purpose computing electronic device. Components of electronic device 6 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and bus 18 connecting various system components (including system memory 28 and processing unit 16).

[0155] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0156] The electronic device 6 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 6, including volatile and non-volatile media, removable and non-removable media.

[0157] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 6 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 6 Not shown, often called a "hard drive"). Although Figure 6 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0158] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0159] The electronic device 6 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the electronic device 6, and / or any device that enables the electronic device 6 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 6 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 6 As shown, the network adapter 20 communicates with other modules of the electronic device 6 via the bus 18. Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 6, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0160] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, for example, implementing the operating condition simulation method of a special vehicle provided in an embodiment of the present invention, which includes:

[0161] Acquiring historical operating data of the special vehicle operating under the working condition to be simulated, and dividing a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque;

[0162] Performing cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips;

[0163] Determine the number of short trips in each category based on the time information of the simulated working conditions and the duration ratio of each short trip, and select a corresponding number of target short trips in each category;

[0164] By splicing the target short strokes corresponding to each type of short stroke, a simulated working condition corresponding to the working condition to be simulated is constructed.

[0165] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the operating condition simulation method for special vehicles provided in any embodiment of the present invention.

[0166] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, for example, a method for simulating the operating condition of a special vehicle provided in an embodiment of the present invention is implemented. The method includes:

[0167] Acquiring historical operating data of the special vehicle operating under the working condition to be simulated, and dividing a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque;

[0168] Performing cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips;

[0169] Determine the number of short trips in each category based on the time information of the simulated working conditions and the duration ratio of each short trip, and select a corresponding number of target short trips in each category;

[0170] By splicing the target short strokes corresponding to each type of short stroke, a simulated working condition corresponding to the working condition to be simulated is constructed.

[0171] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0172] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0173] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0174] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0175] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

[0176] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with relevant provisions of laws and regulations.

[0177] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for simulating the working condition of a special vehicle, characterized in that: include: Acquiring historical operating data of the special vehicle operating under the working condition to be simulated, and dividing a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque; Performing cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips; Determine the number of short trips in each category based on the time information of the simulated working conditions and the duration ratio of each short trip, and select a corresponding number of target short trips in each category; By splicing the target short strokes corresponding to each type of short stroke, a simulated working condition corresponding to the working condition to be simulated is constructed.

2. The method for simulating the working condition of a special vehicle according to claim 1, characterized in that: The historical operation data is composed of operation data corresponding to multiple time points. Accordingly, the trip corresponding to the historical operation data is divided into multiple short trips based on the fuel injection amount threshold or the torque threshold, including: determining the state at each of the time points based on the fuel injection amount and the fuel injection amount threshold at each of the time points, or based on the torque and the torque threshold at each of the time points; A critical point is determined according to the state of each of the time points, and the historical operation data is divided according to the time difference between the critical points to obtain a plurality of short trips corresponding to the historical operation data.

3. The method for simulating the working condition of a special vehicle according to claim 1, characterized in that: Before performing cluster analysis based on the characteristic information of the short trip, the method further includes: Determining the characteristic information of each short trip, wherein the characteristic information includes duration, average engine speed, average fuel injection amount, and maximum fuel injection amount; Normalization is performed on the feature information of each short stroke to obtain a normalized feature.

4. The method for simulating the working condition of a special vehicle according to claim 3, characterized in that: Cluster analysis is performed based on the characteristic information of the short trip to obtain clustering results, including: Performing cluster analysis based on the normalized features of each of the short trips to determine the number of clusters; The short trips are divided into multiple categories of short trips according to the number of clusters.

5. The method for simulating the working condition of a special vehicle according to claim 1, characterized in that: The number of each type of short trip is determined based on the time information of the simulated working conditions and the duration ratio of each type of short trip, including: Determine the duration proportion of each type of short trip based on the total duration of all short trips in the clustering results and the total duration of each type of short trip; Determining the required duration of each type of short trip according to the time information of the simulated working condition and the duration ratio of each type of short trip; The number of each type of short trip is determined based on the required duration of each type of short trip and the average duration of each type of short trip.

6. The method for simulating the working condition of a special vehicle according to claim 1, characterized in that: The criteria for filtering target short trips in each type of short trip are: The duration of the short trip is within a duration deviation range of a corresponding type of short trip, wherein the duration deviation range of each type of short trip is determined by the average duration of each type of short trip and a deviation coefficient; The average deviation of the short trip is smaller than a deviation threshold value, wherein the average deviation is determined by an average speed deviation, an average injection quantity deviation, and a maximum injection quantity deviation.

7. The method for simulating the working condition of a special vehicle according to claim 1, characterized in that: After constructing the simulation working condition corresponding to the working condition to be simulated, the method further includes: determining an average speed deviation, an average fuel injection amount deviation, and a maximum fuel injection amount deviation of the simulated operating condition based on the average speed, the average fuel injection amount, and the maximum fuel injection amount of the simulated operating condition and the original average speed, the original average fuel injection amount, and the original maximum fuel injection amount determined from the historical operating data; The rationality verification result of the simulated operating condition is determined based on the average speed deviation, the average fuel injection amount deviation and the maximum fuel injection amount deviation of the simulated operating condition.

8. A working condition simulation device for a special vehicle, characterized in that: include: an acquisition module, configured to acquire historical operating data of the special vehicle operating under the working condition to be simulated, and divide a trip corresponding to the historical operating data into a plurality of short trips based on a fuel injection amount threshold or a torque threshold, wherein the historical operating data includes fuel injection amount and torque; a clustering module, configured to perform cluster analysis based on the characteristic information of the short trips to obtain a clustering result, wherein the clustering result includes multiple types of short trips; A determination module is used to determine the number of each type of short trips based on the time information of the simulated working condition and the duration ratio of each type of short trips, and to select a corresponding number of target short trips in each type of short trips; The construction module is used to construct a simulation working condition corresponding to the working condition to be simulated by splicing the target short stroke corresponding to each type of short stroke.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the operating condition simulation method of a special vehicle as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the operating condition simulation method for a special vehicle as described in any one of claims 1 to 7.

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