CN117093829B - Inferior oil filling point identification method and system based on vehicle-mounted diagnostic data - Google Patents
Inferior oil filling point identification method and system based on vehicle-mounted diagnostic data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 41
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- 239000007788 liquid Substances 0.000 claims abstract description 27
- 238000003745 diagnosis Methods 0.000 claims abstract description 19
- 238000007781 pre-processing Methods 0.000 claims abstract description 16
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 125
- 239000000446 fuel Substances 0.000 claims description 77
- 239000000945 filler Substances 0.000 claims description 63
- 238000004364 calculation method Methods 0.000 claims description 18
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Abstract
The embodiment of the specification provides a method and a system for identifying an inferior oil filling point based on vehicle-mounted diagnostic data, wherein the method comprises the steps of preprocessing the acquired vehicle-mounted diagnostic data of a plurality of vehicles; determining the position information of the oil filling point of the vehicle based on the liquid level change and the position change data of the oil tank of the vehicle; determining the longitude and latitude information of central points of a plurality of oiling points based on the position information of the oiling points of all the clustered vehicles; determining the position information of the suspected inferior oil filling point based on the longitude and latitude information of the central points of the plurality of filling points; acquiring first emission information and second emission information of each vehicle based on the suspected inferior oil product oiling point position information and vehicle-mounted diagnosis data of a plurality of vehicles; determining a first difference value and a second difference value based on the first emission amount information and the second emission amount information; and determining the probability that each oiling point is an inferior oil oiling point based on the first difference value, the second difference value and the suspected inferior oil oiling point information of each oiling point.
Description
Technical Field
The specification relates to the field of environmental protection supervision, in particular to a method and a system for identifying an inferior oil filling point based on vehicle-mounted diagnostic data.
Background
The holding quantity of the current heavy diesel vehicle accounts for 4% of the holding quantity of the vehicle, but the emission quantity of nitrogen oxides reaches 85% of the total emission quantity of the vehicle, and the heavy diesel vehicle is a main source of nitrogen oxide emission. In addition to the running of heavy diesel vehicles, fuel quality is also an important contributor to nitrogen oxide emissions. However, because the oiling behavior of the vehicle is uncontrollable, the refined management and control of the oil product is difficult to realize.
CN113404577a proposes a method for identifying fuel filling points based on internet of vehicles technology, by acquiring the filling behavior of a single vehicle, calculating the difference of the discharge ratio of nitrogen oxides of the vehicle before and after filling, and determining the poor fuel filling point. But does not take into account the different results of different vehicles and does not compare with other fuel filler point data and national standard data, there are still other undetected fuel filler points of inferior quality.
In order to solve the problems, a method and a system for identifying the oil filling point of the inferior oil based on vehicle-mounted diagnostic data are provided.
Disclosure of Invention
One or more embodiments of the present disclosure provide a method for identifying a fuel filler point of an inferior oil based on-board diagnostic data. The method for identifying the oil filling point of the inferior oil based on the vehicle-mounted diagnostic data comprises the following steps: s1: acquiring vehicle-mounted diagnosis data of a plurality of vehicles, and preprocessing to obtain an initial database; the vehicle-mounted diagnosis data comprise vehicle numbers, vehicle real-time information, vehicle abnormality information and time information for collecting data; the vehicle real-time information includes: vehicle longitude and latitude information, oil tank liquid level information and total emission amount information of nitrogen oxides; s2: determining the position information of the oil filling point of the vehicle based on the liquid level change and the position change data of the oil tank of the vehicle in the initial database; s3: clustering the position information of the oiling points of all vehicles, and determining the longitude and latitude information of the central points of a plurality of oiling points based on the position information of the oiling points; s4: comparing the longitude and latitude information of the central points of the plurality of oiling points with the distance between the conventional oiling/parking point positions, and determining the position information of the oiling points with the distance larger than a threshold value as the position information of the oiling points of the suspected inferior oil products; s5: acquiring first emission information and second emission information of each vehicle based on the suspected inferior oil filling point position information and vehicle-mounted diagnosis data of the vehicles; the first emission amount information is an average value of the emission amount of nitrogen oxides of a certain vehicle in a period of time when the vehicle normally runs after the vehicle is refueled at a target refueling point, and the second emission amount information is an average value of the emission amount of nitrogen oxides of the certain vehicle in a period of time when the vehicle normally runs after the vehicle is refueled at other refueling points; s6: determining a first difference value against a standard emission amount and a second difference value against a second emission amount information based on the first emission amount information; the first difference value is the ratio of the number of vehicles with the nitrogen oxide emission of the vehicle after the refueling at the refueling point being higher than the national standard, and the second difference value is the average ratio of the nitrogen oxide emission of all vehicles after the refueling at the refueling point being higher than other refueling points; s7: and determining the probability that each oiling point is an inferior oil oiling point based on the first difference value, the second difference value and the suspected inferior oil oiling point information of each oiling point.
One or more embodiments of the present disclosure provide a system for identifying an inferior oil refueling point based on vehicle-mounted diagnostic data, where the system includes a preprocessing module, a first determining module, a center point determining module, a second determining module, an obtaining module, a difference value determining module, and a third determining module: the preprocessing module is used for acquiring vehicle-mounted diagnosis data of a plurality of vehicles and preprocessing the vehicle-mounted diagnosis data; the first determining module is used for determining the position information of the oil filling point of the vehicle based on the liquid level change and the position change data of the oil tank of the vehicle; the central point determining module is used for clustering the oiling point position information of all vehicles and determining the longitude and latitude information of central points of a plurality of oiling points based on the oiling point position information; the second determining module is used for comparing the longitude and latitude information of the central points of the plurality of oiling points with the distance between the positions of the conventional oiling/parking points, and determining the position information of the oiling points with the distance larger than a threshold value as the position information of the oiling points of the suspected inferior oil product; the acquiring module is used for acquiring first emission information and second emission information of each vehicle based on the suspected inferior oil product oiling point position information and the vehicle-mounted diagnosis data of the vehicles; the difference value determining module is used for determining a first difference value according to the first emission amount information and the standard emission amount and determining a second difference value according to the second emission amount information; the third determining module is configured to determine a probability that each oiling point is an inferior oil oiling point based on the first difference value, the second difference value, and the suspected inferior oil oiling point information of each oiling point.
One or more embodiments of the present disclosure provide an inferior oil filler point identification device based on-board diagnostic data, including a processor for performing any of the methods described above.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a method of any one of the above.
In some embodiments of the present description, the processor determines the fuel filler point locations by clustering and determines the probability that each fuel filler point is a poor quality fuel filler point based on-board diagnostic data for a plurality of vehicles. By means of the method, more accurate and comprehensive oil filling point position information can be obtained, the probability that the oil filling point is an inferior oil filling point is comprehensively determined by comparing the distance between the oil filling point and a conventional gas station/parking lot and comparing the vehicle nitrogen oxide emission after the oil filling point is filled with oil and the national standard nitrogen oxide emission and the nitrogen oxide emission after the oil filling point is filled with oil, and the judging result is more accurate and more in line with the actual situation.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic block diagram of a system for identifying a fuel filler point for an inferior oil based on-board diagnostic data according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart of a method for identifying a poor quality oil filler point based on onboard diagnostic data according to some embodiments of the present disclosure.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
FIG. 1 is a block diagram of an inferior oil filler point identification system based on onboard diagnostic data according to some embodiments of the present disclosure.
In some embodiments, the low-quality oil filler point identification system 100 based on onboard diagnostic data may include a preprocessing module 110, a first determination module 120, a center point determination module 130, a second determination module 140, an acquisition module 150, a variance value determination module 160, and a third determination module 170.
The preprocessing module 110 may be configured to acquire vehicle-mounted diagnostic data of a plurality of vehicles and perform preprocessing. For more details on the preprocessing see fig. 2 and its associated description.
The first determining module 120 may be configured to determine the fuel filler point location information for the vehicle based on the vehicle fuel tank level change and the location change data. For more details regarding the fuel filler point location information, see FIG. 2 and its associated description.
The center point determining module 130 may be configured to cluster the fuel filler point position information of all vehicles, and determine the longitude and latitude information of the center point of the plurality of fuel filler points based on the fuel filler point position information. For more details regarding the latitude and longitude information of the center point of the fuel filler point, see FIG. 2 and its associated description.
The second determining module 140 may be configured to compare the longitude and latitude information of the central points of the plurality of fuel adding points with the distance between the conventional fuel adding/stopping point positions, and determine the position information of the fuel adding point with the distance greater than the threshold value as the position information of the fuel adding point of the suspected inferior oil product. For more details regarding suspected inferior oil filler point location information, see FIG. 2 and its associated description.
The obtaining module 150 may be configured to obtain the first emission information and the second emission information of each vehicle based on the suspected inferior oil filling point position information and the vehicle-mounted diagnostic data of the plurality of vehicles. For more details on the first and second emission information, see fig. 2 and its associated description.
The difference value determining module 160 may be configured to determine a first difference value against a standard emission and a second difference value against a second emission based on the first emission information. For more details on the first and second variance values see fig. 2 and its related description.
The third determining module 170 may be configured to determine a probability that each fuel adding point is a fuel adding point of poor quality based on the first difference value, the second difference value, and the information of the fuel adding point of suspected poor quality. For more details on determining the probability that each fuel filler point is an inferior fuel filler point, see FIG. 2 and its associated description.
It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways.
It should be noted that the above description of the system and the module thereof for identifying the oil filling point of inferior oil based on the vehicle-mounted diagnostic data is only for convenience of description, and the present disclosure should not be limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the preprocessing module 110, the first determining module 120, the center point determining module 130, the second determining module 140, the obtaining module 150, the difference value determining module 160, and the third determining module 170 disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
FIG. 2 is an exemplary flow chart of a method for identifying a poor quality oil filler point based on onboard diagnostic data according to some embodiments of the present disclosure. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by a processor.
S1, acquiring vehicle-mounted diagnosis data of a plurality of vehicles by a processor, and preprocessing to obtain an initial database; the vehicle-mounted diagnosis data comprise vehicle numbers, vehicle real-time information, vehicle abnormality information and time information for collecting data; the vehicle real-time information includes: vehicle longitude and latitude information, oil tank level information and total nitrogen oxide emission amount information.
The vehicle-mounted diagnostic data are various running data of the vehicle collected by the OBD device. For example, the collected data may include a vehicle number, vehicle real-time information (longitude, latitude, tank level, total nitrogen oxide emissions), vehicle anomaly information, and time information for the collected data; the OBD device is an on-board diagnostic device, and the real-time information of the vehicle may be stored by means of a key, for example, the key of longitude (key) may be 6422, the key of latitude (key) may be 6423, the key of tank level (key) may be 6420, and the key of total emission amount of nitrogen oxides (key) may be 6441.
The vehicle anomaly information is the data which is not collected and has the key value (key) and is abnormal in the operation of the OBD equipment.
In some embodiments, the processor may determine the on-board diagnostic data based on an OBD device of the vehicle. For example, the processor may acquire a vehicle number based on the OBD device of the vehicle, collect vehicle real-time information, vehicle anomaly information, corresponding time points, etc. every 10 to 20 seconds, determine the on-board diagnostic data.
The initial database is a plurality of on-board diagnostic data for subsequent calculations. For example, the initial database may include numeric data with null values deleted.
In some embodiments, the processor may determine the initial database based on the preprocessing. For example, the processor may convert the vehicle-mounted diagnostic data of a plurality of vehicles into numerical data, and reject abnormal data therein to obtain an initial database; the abnormal data may include null data and data with large fluctuation of the liquid level for a period of time, for example, if a certain data is 10% higher than the liquid level in the previous and later time, the data is the abnormal data.
And S2, the processor determines the position information of the oiling point of the vehicle based on the liquid level change and the position change data of the vehicle oil tank in the initial database.
The vehicle tank level change data is data reflecting the increase or decrease of the vehicle fuel amount.
The vehicle position change data is data reflecting the running state of the vehicle in motion.
In some embodiments, the processor may determine vehicle tank level change and vehicle position change data based on the calculation. For example, the processor may determine vehicle tank level change data by comparing the difference in tank level data of a vehicle at adjacent times and vehicle position change data by comparing the difference in longitude and latitude of the vehicle at adjacent times.
The fuel filler point position information is position information of a point at which fuel can be added to the vehicle. For example, longitude and latitude coordinates of points such as gas stations and large parking lots.
In some embodiments, the processor may determine the fuel filler point location information based on vehicle fuel tank level changes and location change data. For example, the processor may use as the fuel filler point position information the longitude and latitude of the data of the vehicle fuel tank level change data of the adjacent time point exceeding 5% while the vehicle position change data being within 50 m.
In some embodiments, the processor may calculate a tank level difference, a time difference, and a longitude and latitude distance difference between two adjacent time points before and after the vehicle based on the vehicle tank level change and the position change data in the initial database, and use all the data satisfying the conditions at the same time as one suspected refueling behavior data set; based on the suspected refueling behavior data set, taking a plurality of pieces of data before and after possible refueling behaviors as research objects to form a research data set; calculating a liquid level range, a stationary standard deviation, a distance difference and a time difference in the research data set based on the research data set; and taking the data meeting the conditions as fueling behavior data, and taking longitude and latitude information of the fueling behavior data as fueling point position information of the vehicle.
In some embodiments, the processor may calculate the tank head of the two adjacent time point data before and after the vehicle based on the vehicle tank head change and the position change data in the initial databaseTime difference->And longitude and latitude distance difference->All data satisfying the following conditions simultaneously are taken as a suspected fueling behavior data set +.>：
；
；
；
Wherein,、/>、/>the threshold values respectively representing the liquid level difference, the time difference and the longitude and latitude distance difference data distribution of the oil tank are obtained.
Liquid level difference of oil tankIs the difference of the liquid level of the oil tank in the initial database of the front and rear two adjacent time points of the vehicle.
Time differenceIs the time difference in the initial database of two adjacent time points in front and back of the vehicle.
Difference in longitude and latitude distanceIs the difference value of longitude and latitude distances in an initial database of two adjacent time points in front and back of the vehicle.
In some embodiments, the processor may determine the longitude and latitude distance difference based on a calculation. For example, the processor may determine the longitude and latitude distance difference based on equation (8)：
（8）；
Wherein,for the earth radius>Latitude information indicating the later point in time, +.>Latitude information indicating the previous time point, +.>Longitude information representing a later point in time, +.>Longitude information indicating a point in time.
The suspected fueling behavior data set is a data set in the initial database where fueling behavior is likely to be performed by the vehicle. For example, the suspected fueling behavior data set may include time data, latitude and longitude data, etc. that the vehicle is likely to be fueling behavior.
In some embodiments, the processor may determine the suspected fueling behavior data set based on a preset threshold. For example, certain data simultaneously satisfies、/>、/>The data may be used as one of a set of suspected fueling behavior data; wherein (1)>、/>、/>Can take->、/>、/>As a threshold for data distribution.
In some embodiments, the processor may determine the research data set based on the suspected fueling behavior data set.
The research dataset is a collection of fueling behavior data for researching whether each of the suspected fueling behavior data set is fueling behavior data.
In some embodiments, the processor may determine the study dataset based on a temporal order. For example, for a suspected fueling behavior dataThe processor can take its front and back +.>The strip data is taken as a study object to form a study data setThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>10 strips, 20 strips, etc. can be taken.
In some embodiments, the processor may determine, based on the calculation, that the liquid level in the study data set is poorSmooth standard deviation->Distance difference->And time difference->、/>。
Extremely poor liquid levelIs the difference between the maximum and minimum values of the level data in the study dataset.
Smooth standard deviationIs fluctuation degree data of liquid level data in the research data set.
In some embodiments, the stationary standard deviation may be determined by substituting tank level data in the study dataset into a standard deviation calculation formula.
Distance differenceIs the difference in distance between adjacent data in the study dataset.
In some embodiments, the distance difference may be determined based on equation (8).
Time difference、/>To study the time differences between adjacent data in a dataset.
In some embodiments, the processor may determine the level range, the stationary standard deviation, the distance difference, and the time difference based on a difference equation. For example, the processor may determine the liquid level range, the stationary standard deviation, the distance difference, and the time difference based on a range calculation, a standard deviation calculation, and the like.
In some embodiments, the processor may use the data satisfying the condition as fueling behavior data and longitude and latitude information of the fueling behavior data as fueling point position information of the vehicle.
The fueling behavior data is data of the initial database for fueling the vehicle. For example, fueling behavior data may include information about the time the vehicle was fueling, latitude and longitude, and the like.
In some embodiments, the processor may determine fueling behavior data for the vehicle based on preset conditions. For example, the preset condition may be:
(1) The oil quantity changes obviously before and after oiling and the liquid level is stable after oiling, namely、And->Is a function of the data of (a),
(2) The liquid level before oiling is stable, the OBD equipment is closed during oiling, and the longitude and latitude positions before and after oiling are unchanged, namelyAnd->Is a function of the data of (a),
(3) The liquid level before oiling is stable, the OBD equipment works during oiling, the production time is continuous, the position is unchanged, and the liquid level steadily rises, namelyAnd->Is a function of the data of (a),
wherein,threshold value representing the suspected fueling behaviour and standard deviation of the previous period of data, +.>Threshold value representing the standard deviation of the suspected fueling behaviour and the data for a period of time thereafter +.>Indicating that the liquid level in the study dataset was very poor,indicating fueling behaviour +.>Is->The steady standard deviation of the bar level data,indicating fueling behaviour +.>Before->The steady standard deviation of the bar level data,indicating fueling behaviour +.>Before->Smooth standard deviation of bar displacement data +.>Indicating the difference in distance between the fueling behaviour and the previous data +.>Indicating the time difference between the fueling event and the previous data,representing the time difference between two adjacent data.
In some embodiments of the present invention, in some embodiments,the value of (2) may be 0.05, (-)>The value of (c) may be 0.05,，/>，/>representing time data between any two adjacent data, < +. >Indicating the liquid level difference between any two adjacent data, < > and->Indicating fueling behaviour +.>Before->Time data set of stripe data.
In some embodiments, the processor may determine the fuel filler point location information for the vehicle based on the fuel filler behavior data for the vehicle. For example, the processor may aggregate all longitude and latitude information in the fueling behavior data of a vehicle as all fueling point location information for the vehicle.
In some embodiments of the present description, the processor determines the fuel filler point location information of the vehicle by processing data in the initial database. The vehicle refueling behavior determined by the mode is more in accordance with logic, and the obtained vehicle refueling point position information is more comprehensive and accurate and is in accordance with actual conditions.
And S3, clustering the oiling point position information of all vehicles by the processor, and determining the longitude and latitude information of central points of a plurality of oiling points based on the oiling point position information.
The longitude and latitude information of the central point of the oil filling point is the longitude and latitude information of the central point of the oil filling point in a plurality of certain ranges. For example, latitude and longitude information of the center position of all the fuel filling points within 100 m.
In some embodiments, the processor may determine the center point latitude and longitude information for the fuel filler point based on a clustered manner. For example, the processor may perform noisy density-based clustering (DBSCAN clustering) or on location information of a plurality of fuel filler points Mean clustering (K means clustering) determines the central point latitude and longitude information of the fuel filler points.
In some embodiments, the processor may use a density-based clustering algorithm with noise to cluster latitude and longitude points, set a minimum number of samples of the clusters, a distance threshold value and a calculation distance formula, reject isolated points, add a label corresponding to each cluster, and obtain the cluster number K of the clustering result; and taking the number K of clusters as the number of clustering center points of a K-means clustering algorithm, calculating the distance between the center points and the rest points, and when the distance is smaller than a preset threshold value, regarding the distance as the same oiling point, and obtaining the longitude and latitude information of the center points of the K oiling points through K-means clustering calculation.
In some embodiments, the processor may cluster the latitude and longitude points using a noisy density-based clustering (DBSCAN clustering) algorithm to determine the number of clusters of the clustered results.
The clustering result is the number of clusters of all fuel filler points determined as one cluster for all fuel filler points within a certain range.
In some embodiments, the processor may determine the number of clusters of the clustering result based on a DBSCAN clustering algorithm. For example, the processor may reject isolated points based on a preset minimum number of samples of the clusters, a distance threshold and a calculated distance formula, add a label corresponding to each cluster, and obtain the number of clusters of the clustering result 。
The processor may be based onMean clustering (K_means) algorithm to determine +.>And the longitude and latitude information of the central point of each oiling point.
In some embodiments, the processor may count the number of clustersAs the number of clustering center points of the K_means algorithm, calculating the distance between the center points and the remaining points, and when the distance is smaller than a preset threshold value, regarding the distance as the same oiling point, and determining the ∈through the K_means calculation>Longitude and latitude information of central points of the oil filling points; wherein, the threshold value can be 100m, and the threshold value can be preset by an expert.
In some embodiments of the present description, the processor determines the center point latitude and longitude information for a plurality of fuel filler points by clustering the location information for all of the fuel filler points. By the method, the judgment error of the oil filling point position can be reduced, more accurate longitude and latitude information of the central point is obtained, and subsequent calculation of suspected inferior oil filling point position information is facilitated.
And S4, comparing the longitude and latitude information of the central points of the plurality of oiling points with the distance between the conventional oiling/parking point positions by the processor, and determining the position information of the oiling point with the distance larger than a threshold value as the position information of the oiling point of the suspected inferior oil product.
The suspected inferior oil refueling point location information is unconventional refueling point location information. For example, fuel site location information other than conventional fueling/parking spots, etc.
In some embodiments, the processor may compare the center point latitude and longitude information of the fuel filler point to the distance of the conventional fuel filler/parking point location, and take the location information of the fuel filler point at a distance greater than 100m as the location information of the suspected inferior fuel filler point.
And S5, the processor acquires first emission information and second emission information of each vehicle based on the suspected inferior oil filling point position information and vehicle-mounted diagnosis data of a plurality of vehicles.
The first emission information is an average value of emission amounts of nitrogen oxides of a certain vehicle for a period of time after the vehicle is refueled at the target refuel point. For example, the average value of the nitrogen oxide discharge amount of the vehicle running for one hour at a speed of 20-80km/h after refueling at the target refueling point, and the like.
In some embodiments, the processor may determine the first emissions information based on the calculation. For example, the processor may calculate data for a period of time after the vehicle is refueled at the target fueling point based on equation (1) to determine the first emissions：
（1）；
Wherein,time difference representing data of two adjacent time points of the vehicle, < >>Longitude and latitude distance difference representing two adjacent time point data, +.>Indicate->Group difference data,/->Representing the total amount of difference data, +. >Indicating the amount of nitrogen oxide emissions.
The second emission information is an average of emission of nitrogen oxides of the vehicle for a period of time after the vehicle is refueled at other refueled points.
In some embodiments, the processor may determine the second emission information based on the calculation. For example, the processor may calculate data for a period of time after the vehicle is refueled at other refueled points based on equation (1) to determine the second emissions information.
In some embodiments, the processor regards the vehicle containing the oiling point in the oiling behavior data of all vehicles as the target vehicle based on the suspected inferior oil oiling point position information; first emission information and second emission information of the target vehicle are acquired based on fueling behavior data of the target vehicle.
In some embodiments, the processor may determine that the vehicle including the fueling point in the fueling behavior data of all vehicles is the target vehicle based on the suspected inferior fuel fueling point location information.
The target vehicle is a vehicle which is filled with oil at a certain suspected inferior oil filling point.
In some embodiments, the processor may determine the target vehicle based on suspected inferior fuel filler point location information. For example, the processor may compare the suspected inferior oil filler point location information with the filler point location information of all vehicles, and take the vehicle with a distance less than a threshold as the target vehicle; wherein the threshold may be a distance of no more than 100m.
In some embodiments, the processor may obtain the first emissions information and the second emissions information of the target vehicle based on fueling behavior data of the target vehicle.
In some embodiments, the processor may calculate an average of nitrogen oxide emissions of the vehicle for a period of time after the target fueling point is fueling as the first emissions information of the target vehicle based on fueling behavior data of the target vehicle and all data of the target vehicle in the initial database; and calculating the average value of the nitrogen oxide discharge amount of the vehicle after the vehicle is refueled at other refueling points and normally running for a period of time, and taking the average value as second discharge amount information.
In some embodiments of the present disclosure, the processor obtains first emissions information and second emissions information for the target vehicle based on suspected inferior fuel filler point location information. By the method, more comprehensive sample data can be obtained, the second emission information is added as the reference quantity, and the final judgment result can be more in line with the actual situation.
And S6, the processor determines a first difference value according to the first emission amount information and the standard emission amount and determines a second difference value according to the second emission amount information.
The standard emissions may be determined based on national standards. For example, the concentration of nitrogen oxides (NOx) in heavy duty diesel vehicles must not exceed 0.4g/kWh, etc.
The first difference value is the vehicle number of times that the emission of nitrogen oxides of the vehicle after refueling at the refueling point is higher than the national standard.
In some embodiments, the processor may obtain the first difference value by calculation. For example, the processor may determine the first difference value based on formulas (2), (3)：
（2）；
（3）；
Wherein,indicating the oil filling point->Mean value of the nitrogen oxide emissions of all vehicles in the middle, if the vehicle is driving normally after refueling, +.>Representing taking the minimum value thereof, +.>Indicating national regulatory vehicle emissions standards for nitrogen oxides.
The second difference is the average ratio of the emissions of nitrogen oxides after fueling of all vehicles at that fueling point to that of other fueling points.
In some embodiments, the processor may obtain the second difference value by calculation. For example, the processor may determine the second difference value based on equations (4), (5)：
（4）；
（5）；
Wherein,indicating vehicle->Second emission amount information of->Indicating vehicle->At the fuel filling point->Average nitrogen oxide emissions after refueling, +.>Indicating that a vehicle is +.>The nitrogen oxide discharge amount after oiling of the oiling point exceeds the proportion of other oiling points, and the ratio is +. >Indicating the total number of other oil filling points, +.>Indicate->Other conventional oil filling points, ">Indicating the target suspected inferior oil filling point of the current diagnosis.
And S7, the processor determines the probability that each oiling point is an inferior oil oiling point based on the first difference value, the second difference value and the suspected inferior oil oiling point information of each oiling point.
The probability that the fuel filler point is an inferior fuel filler point is data for judging whether the fuel filler point is an inferior fuel filler point. For example, the closer the probability that the fuel filler is an inferior fuel filler is to 1, the greater the probability that the fuel filler is an inferior fuel filler.
In some embodiments, the processor may calculate a probability that the fuel filler point is an inferior fuel filler point. For example, the processor may determine the probability that the fuel filler point is an inferior fuel filler point based on equations (6), (7):
（6）；
（7）；
wherein,representation->Probability of the oil filling point being an inferior oil filling point, +.>Is the standard weight of nitrogen oxide emission>Weight for fuel emissions contrast for other fuel points, +.>For the nature of the fuel filler point +.>Representing a first difference value, ">Representing a second difference value, ">Indicating that if the oil filling point is not a heavy vehicle parking lot, the oil filling point is 1, otherwise, the oil filling point is 0; wherein (1) >，/>And->The value of (2) may take the form of 0.4, (-)>The value of (2) may take 0.2.
In some embodiments of the present disclosure, the processor determines the location of all of the fuel filler points by processing on-board diagnostic data for a plurality of vehicles and determines the probability that each fuel filler point is a poor quality fuel filler point. By the method, the position information of all the oiling points can be more comprehensively and accurately determined, and the judging result of the probability of the oiling points of the inferior oil products is more accurate based on the comparison of various reference data.
It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.
In some embodiments, an apparatus for identifying a point of a poor quality oil filler based on-board diagnostic data includes a processor configured to perform a method for identifying a point of a poor quality oil filler based on-board diagnostic data.
In some embodiments, a computer readable storage medium stores computer instructions that, when read by a computer, enable the computer to perform a method of identifying a fuel filler point for an inferior oil based on-board diagnostic data.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (9)
1. The method for identifying the oil filling point of the inferior oil product based on the vehicle-mounted diagnostic data is characterized by comprising the following steps of:
S1: acquiring vehicle-mounted diagnosis data of a plurality of vehicles, and preprocessing to obtain an initial database; the vehicle-mounted diagnosis data comprise vehicle numbers, vehicle real-time information, vehicle abnormality information and time information for collecting data; the vehicle real-time information includes: vehicle longitude and latitude information, vehicle oil tank liquid level information and total nitrogen oxide emission amount information;
s2: determining the position information of the oil filling point of the vehicle based on the liquid level change and the position change data of the oil tank of the vehicle in the initial database;
s3: clustering the position information of the oiling points of all vehicles, and determining the longitude and latitude information of the central points of a plurality of oiling points based on the position information of the oiling points;
s4: comparing the longitude and latitude information of the central points of the plurality of oiling points with the distance between the conventional oiling/parking point positions, and determining the position information of the oiling points with the distance larger than a threshold value as the position information of the oiling points of the suspected inferior oil products;
s5: acquiring first emission information and second emission information of each vehicle based on the suspected inferior oil filling point position information and vehicle-mounted diagnosis data of the vehicles; the first emission amount information is an average value of the emission amount of nitrogen oxides of a certain vehicle in a period of time when the vehicle normally runs after the vehicle is refueled at a target refueling point, and the second emission amount information is an average value of the emission amount of nitrogen oxides of the certain vehicle in a period of time when the vehicle normally runs after the vehicle is refueled at other refueling points;
S6: determining a first difference value against a standard emission amount and a second difference value against a second emission amount information based on the first emission amount information; the first difference value is the ratio of the number of vehicles with the nitrogen oxide emission of the vehicle after the refueling at the refueling point being higher than the national standard, and the second difference value is the average ratio of the nitrogen oxide emission of all vehicles after the refueling at the refueling point being higher than other refueling points;
s7: determining the probability that each oiling point is an inferior oil oiling point based on the first difference value, the second difference value and the suspected inferior oil oiling point information of each oiling point:
；
；
wherein,representation->Probability of the oil filling point being an inferior oil filling point, +.>Is the standard weight of nitrogen oxide emission>Weight for fuel emissions contrast for other fuel points, +.>For the nature of the fuel filler point +.>Representing a first difference value, ">Representing a second difference value, ">Indicating that if the fueling point is not a heavy car park, it is 1, otherwise it is 0.
2. The method for identifying an inferior oil filling point based on vehicle-mounted diagnostic data according to claim 1, wherein the step S2 specifically comprises:
calculating the tank level difference of two adjacent time point data before and after the vehicle based on the vehicle tank level change and the position change data in the initial database Time difference->And longitude and latitude distance difference->All data satisfying the following conditions simultaneously are taken as a suspected fueling behavior data set +.>：
；
；
；
Wherein,、/>、/>threshold values respectively representing the liquid level difference, time difference and longitude and latitude distance difference data distribution of the oil tank;
based on the suspected fueling behavior data set, taking a potential fueling behaviorBefore and after->Strip data as subject, form study dataset +.>；
Calculating the liquid level range, the stable standard deviation, the distance difference and the time difference in the research data set based on the research data set; wherein the stable standard deviation is determined by substituting the tank level data in the research data set into a standard deviation calculation formula;
taking data meeting any one of the following conditions as fueling behavior data, and taking longitude and latitude information of the fueling behavior data as fueling point position information of the vehicle:
(1)、/>and->；
(2)And->；
(3)And->；
Wherein,threshold value representing the suspected fueling behaviour and standard deviation of the previous period of data, +.>Threshold value representing the standard deviation of the suspected fueling behaviour and the data for a period of time thereafter +.>Indicating that the liquid level in the study dataset was very poor,indicating fueling behaviour +.>Is->The steady standard deviation of the bar level data, Indicating fueling behaviour +.>Before->The steady standard deviation of the bar level data,indicating fueling behaviour +.>Before->Smooth standard deviation of bar displacement data +.>Indicating the difference in distance between the fueling behaviour and the previous data +.>Indicating the time difference between the fueling event and the previous data,representing the time difference between two adjacent data.
3. The method for identifying the oil filling point of the inferior oil based on the vehicle-mounted diagnostic data according to claim 2, wherein the longitude and latitude distance difference is characterized in thatThe method comprises the following steps:
；
wherein,for the earth radius>Latitude information indicating the later point in time, +.>Latitude information indicating the previous time point, +.>Longitude information representing a later point in time, +.>Longitude information indicating a previous point in time.
4. The method for identifying an inferior oil filling point based on vehicle-mounted diagnostic data according to claim 1, wherein the step S3 specifically comprises:
clustering longitude and latitude points by using a clustering algorithm with noise and based on density, setting the minimum number of samples, a distance threshold and a calculation distance formula of the clustering, removing isolated points, adding labels corresponding to each cluster, and obtaining the cluster number of clustering results；
The number of clusters is counted As->Calculating the distance between the central points and the rest points according to the number of clustering central points of the mean value clustering algorithm, and regarding the same oiling point when the distance is smaller than a preset threshold value, wherein the distance is equal to or greater than the threshold value>Mean clustering calculation to get->And the longitude and latitude information of the central point of each oiling point.
5. The method for identifying an inferior oil fueling point based on vehicle-mounted diagnostic data according to claim 2, wherein said S5 specifically comprises said normal running speed being 20-80km/h, and said period of time being one hour after fueling:
based on the position information of the oiling point of the suspected inferior oil product, taking the vehicle containing the oiling point in the oiling behavior data of all vehicles as a target vehicle;
acquiring all oiling points and oiling time information of the target vehicle based on vehicle-mounted diagnostic data of the target vehicle;
and acquiring the first emission amount information and the second emission amount information of the target vehicle based on the oiling point and oiling time information and vehicle-mounted diagnosis data of the target vehicle.
6. The method for identifying an inferior oil filling point based on vehicle-mounted diagnostic data according to claim 1, wherein the first emission amount information in S6 is:
；
Wherein,time difference representing data of two adjacent time points of the vehicle, < >>Longitude and latitude distance difference representing two adjacent time point data, +.>Representing first discharge amount information,/for>Indicate->Group difference data,/->Representing the total amount of difference data, +.>Indicating nitrogen oxide emissions;
the first difference valueThe method comprises the following steps:
；
；
wherein,indicating the oil filling point->The average value of the nitrogen oxide discharge amounts of all vehicles during normal running after refueling,representing taking the minimum value thereof, +.>Indicating national vehicle nitrogen oxide emissions standards;
the second difference valueThe method comprises the following steps:
；
；
wherein,indicating vehicle->Second emission amount information of->Indicating vehicle->At the fuel filling point->Average nitrogen oxide emissions after refueling, +.>Indicating that a vehicle is +.>The nitrogen oxide discharge amount after oiling of the oiling point exceeds the proportion of other oiling points, and the ratio is +.>Indicating the total number of other oil filling points, +.>Indicate->Other conventional oil filling points, ">Indicating the target suspected inferior oil filling point of the current diagnosis.
7. Inferior oil filling point identification system based on-vehicle diagnostic data, characterized in that, the system includes preprocessing module, first determination module, central point determination module, second determination module, acquisition module, difference value determination module and third determination module:
The preprocessing module is used for acquiring vehicle-mounted diagnosis data of a plurality of vehicles and preprocessing the vehicle-mounted diagnosis data;
the first determining module is used for determining the position information of the oil filling point of the vehicle based on the liquid level change and the position change data of the oil tank of the vehicle;
the central point determining module is used for clustering the oiling point position information of all vehicles and determining the longitude and latitude information of central points of a plurality of oiling points based on the oiling point position information;
the second determining module is used for comparing the longitude and latitude information of the central points of the plurality of oiling points with the distance between the positions of the conventional oiling/parking points, and determining the position information of the oiling points with the distance larger than a threshold value as the position information of the oiling points of the suspected inferior oil product;
the acquiring module is used for acquiring first emission information and second emission information of each vehicle based on the suspected inferior oil product oiling point position information and the vehicle-mounted diagnosis data of the vehicles;
the difference value determining module is used for determining a first difference value according to the first emission amount information and the standard emission amount and determining a second difference value according to the second emission amount information;
the third determining module is configured to determine, based on the first difference value, the second difference value, and the suspected inferior oil filling point information of each filling point, a probability that each filling point is an inferior oil filling point:
；
；
Wherein,representation->Probability of the oil filling point being an inferior oil filling point, +.>Is the standard weight of nitrogen oxide emission>Weight for fuel emissions contrast for other fuel points, +.>For the nature of the fuel filler point +.>Representing a first difference value, ">Representing a second difference value, ">Indicating that if the fueling point is not a heavy car park, it is 1, otherwise it is 0.
8. The device for identifying the oil filling point of the inferior oil based on the vehicle-mounted diagnostic data is characterized by comprising a processor, wherein the processor is used for executing the method for identifying the oil filling point of the inferior oil based on the vehicle-mounted diagnostic data according to any one of claims 1-6.
9. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the method for identifying the fuel filling point of the inferior oil based on the vehicle-mounted diagnostic data according to any one of claims 1 to 6.
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