Vehicle condition determination methods, devices, electronic equipment and media

Abstract

This application discloses a method, apparatus, electronic device, and medium for determining vehicle status. The method includes: querying whether the registered vehicle type matches a preset type based on the vehicle's identification information; if not, determining the vehicle's associated information; wherein the associated information includes at least one of driving information, passenger relationship information associated with the vehicle, and passenger dispersion information; and determining the vehicle's operational status based on the associated information. This solution addresses the problem of bias and misjudgment in determining vehicle status solely based on passenger quantity. By analyzing driving information, passenger relationships, and passenger dispersion, a deeper, more comprehensive judgment is made, improving the accuracy of vehicle status determination.

Description

Technical Field

[0001] This application relates to the field of vehicle operation status detection technology, and in particular to a method, device, electronic device and medium for determining vehicle status. Background Technology

[0002] In the transportation sector, not only do legitimate taxis transport passengers, but non-taxi vehicles also operate for profit. However, these non-taxi vehicles are typically private cars with no special markings, making it difficult to determine whether they are transporting passengers for profit or simply picking up and dropping off family members.

[0003] Currently, the methods used to detect and punish non-taxi vehicles that profit from transporting passengers rely solely on the number of passengers, which is a rather one-sided and simplistic approach that can easily lead to misjudgments. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and medium for determining vehicle status, so as to improve the accuracy of judging vehicle operating status.

[0005] In one embodiment, this application provides a method for determining vehicle conditions, the method comprising:

[0006] Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle matches the preset type;

[0007] If the conditions are not met, the vehicle's association information is determined; wherein, the association information includes at least one of the following: driving information, the personnel relationship information of the vehicle's associated passengers, and the dispersion information of the vehicle's associated passengers;

[0008] The vehicle's operational status is determined based on its associated information.

[0009] In another embodiment, this application also provides a vehicle condition determination device, the device comprising:

[0010] The query module is used to query whether the registered vehicle type of the vehicle matches a preset type based on the vehicle's identification information;

[0011] The association information determination module is used to determine the association information of the vehicle if the information does not match; wherein the association information includes at least one of driving information, personnel relationship information of the vehicle-associated passengers, and dispersion information of the vehicle-associated passengers.

[0012] The operation status determination module is used to determine the operation status of the vehicle based on the associated information of the vehicle.

[0013] In yet another embodiment, this application also provides an electronic device, including: one or more processors;

[0014] Memory, used to store one or more programs;

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle condition determination method according to any one of the embodiments of this application.

[0016] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the vehicle condition determination method as described in any one of the embodiments of this application.

[0017] The technical solution in this application embodiment queries whether the registered vehicle type of the vehicle conforms to a preset type based on the vehicle's identification information; if it does not conform, the associated information of the vehicle is determined; wherein, the associated information includes at least one of driving information, personnel relationship information of the vehicle-associated passengers, and dispersion information of the vehicle-associated passengers; based on the vehicle's associated information, the operating status of the vehicle is determined. By analyzing driving information, passenger personnel information, and passenger dispersion, the associated information of the vehicle is analyzed at a deeper level, thereby improving the accuracy of the assessment of the vehicle's operating status. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a vehicle condition determination method provided in one embodiment of this application;

[0019] Figure 2 A flowchart of a vehicle condition determination method provided in another embodiment of this application;

[0020] Figure 3 A flowchart illustrating a method for determining vehicle conditions provided in yet another embodiment of this application;

[0021] Figure 4 This is a schematic diagram of the structure of a vehicle condition determination device provided in one embodiment of this application;

[0022] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application.

[0023] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure. Detailed Implementation

[0024] Figure 1 This is a flowchart illustrating a vehicle condition determination method according to one embodiment of this application. The vehicle condition determination method provided in this embodiment is applicable to situations where the operational status of a vehicle is being assessed. Typically, this embodiment is applicable to assessing the operational status of non-taxi vehicles. Specifically, this method can be executed by a vehicle condition determination device, which can be implemented in software and / or hardware, and can be integrated into an electronic device capable of implementing the vehicle condition determination method. See also... Figure 1 The method in this application embodiment specifically includes:

[0025] S110. Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle conforms to a preset type.

[0026] The vehicle identification information is information that identifies the vehicle, such as the license plate number. The registered vehicle type can be the type of vehicle registered in the vehicle management system. The vehicle type reflects the vehicle's purpose, such as taxi, private car, or transport vehicle. The default type is the type of vehicle that is operating normally, that is, a vehicle legally used for profit-making operations, such as taxi, transport vehicle, or bus.

[0027] In this embodiment, during the process of determining the vehicle's operational status, the vehicle's registration type is first checked against a preset type based on its identification information. For example, the vehicle's license plate number is used to query the vehicle management system for its corresponding registration type. If the vehicle's registration type recorded in the vehicle management system matches a preset type, such as taxi, transport vehicle, or bus, then the vehicle is determined to be a normally operating vehicle, and its operational status is normal. If the vehicle's registration type does not match a preset type, such as private car or personal vehicle, then the vehicle is determined not to be a commercial vehicle, and further steps are needed to determine its operational status and whether it is operating illegally.

[0028] S120. If not, determine the vehicle's association information; wherein the association information includes at least one of driving information, the personnel relationship information of the vehicle's associated passengers, and the dispersion information of the vehicle's associated passengers.

[0029] The driving information can include the vehicle's average daily distance traveled and / or average daily driving time. Specifically, images of the vehicle can be obtained through roadside monitoring systems. Based on the latitude and longitude of the image acquisition points and the acquisition time, the vehicle's trajectory over a certain period can be determined, and the vehicle's average daily distance traveled and / or average daily driving time can be calculated based on the trajectory over that period. If the vehicle is not a commercial vehicle but a private car or other personal vehicle, its distance traveled and driving time should be much less than those of commercial vehicles that carry passengers daily. Therefore, the vehicle's operational status can be analyzed based on the driving information.

[0030] Passenger relationship information can include the relationship between the passenger and the vehicle driver and / or the relationship between the passenger and other passengers. These relationships include kinship, cohabitation, colleagueship, and travel companionship. Specifically, the monitoring system can acquire images of the passenger and the vehicle driver, and / or images of the passenger and other passengers. The identities of individuals can be determined based on these images, and relationships can be established based on their identities. Alternatively, image recognition can be performed to determine relationships based on actions observed in the images. Passenger dispersion can be assessed based on the dispersion of passenger boarding and alighting locations, boarding and alighting times, and residential locations. This dispersion can be determined based on the number of times passengers board and alight at each location, at each time, and at each residential location.

[0031] In this embodiment of the application, at least one of the following is determined: driving information, number of passengers associated with the vehicle, personnel relationship information of passengers associated with the vehicle, and dispersion information of passengers associated with the vehicle. This solves the problem that determining the vehicle's operating status solely based on the number of passengers is one-sided and prone to misjudgment. By evaluating the vehicle's operating status through in-depth analysis, the accuracy is improved.

[0032] In this embodiment, the associated information may further include the number of passengers associated with the vehicle. This number of passengers can be the number of passengers captured by the monitoring system getting on and off the vehicle. For example, image recognition can be performed on the vehicle's image; if passenger boarding or alighting is detected, the database is checked to see if there is a record of that passenger riding the vehicle. If not, the ride is recorded in the database. The number of passengers riding the vehicle over a period of time is statistically analyzed, and the daily average number of passengers is calculated to determine whether the vehicle is carrying passengers.

[0033] S130. Determine the operating status of the vehicle based on its associated information.

[0034] The operational status is categorized as normal or abnormal. Normal means the vehicle is operating normally, while abnormal means the vehicle is not operating normally and is not a legitimate commercial vehicle, but is engaged in passenger transport for profit; in this case, the vehicle's operational status is determined to be abnormal. Based on the vehicle's associated information, the operational status can be analyzed to determine its abnormality. Specifically, at least one of the following can be used to determine the confidence level of the abnormal operational status: driving information, the number of passengers associated with the vehicle, the personnel relationship information of the passengers associated with the vehicle, and the dispersion information of the passengers associated with the vehicle. The sum of the confidence levels for each piece of information determines the overall confidence level of the abnormal operational status, thus confirming whether the vehicle's operational status is abnormal.

[0035] The technical solution in this application embodiment queries whether the registered vehicle type of the vehicle conforms to a preset type based on the vehicle's identification information; if it does not conform, the associated information of the vehicle is determined; wherein, the associated information includes at least one of driving information, personnel relationship information of the vehicle-associated passengers, and dispersion information of the vehicle-associated passengers; based on the vehicle's associated information, the operating status of the vehicle is determined. By analyzing driving information, passenger personnel information, and passenger dispersion, the associated information of the vehicle is analyzed at a deeper level, thereby improving the accuracy of the assessment of the vehicle's operating status.

[0036] Figure 2 A flowchart illustrating a vehicle condition determination method according to another embodiment of this application. This embodiment is a further optimization of the above embodiments; details not described in detail in this embodiment are provided in the above embodiments. See also... Figure 2 The vehicle condition determination method provided in this application embodiment may include:

[0037] S210. Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle conforms to a preset type.

[0038] S220. If not, determine the personnel relationship information of the vehicle-associated passengers.

[0039] For example, if the registered vehicle type matches a preset type, i.e., a private car, personal vehicle, etc., and not a commercial vehicle such as a taxi or bus, then the relationship information of the passengers associated with the vehicle is further determined. This relationship information can be the relationship between the passenger and the driver, and / or the relationship between the passenger and other passengers. Relationships reflect whether there is a certain relationship between the passenger and the driver, or between passengers themselves, such as kinship, colleagueship, travel companionship, or cohabitation. If there is a relationship between the passenger and the driver, or between the passenger and other passengers, and they are not strangers, then it can be determined that the vehicle is for normal passenger transport and is not for commercial profit. For example, if the passenger and the driver are colleagues, then the vehicle is a company vehicle, and the passenger transport is for normal company commuting or work trips, not for commercial profit. Similarly, if the passenger and the driver are relatives, then the vehicle is a family vehicle, and the passenger transport is for normal family travel, not for commercial profit. The same logic applies to travel companionship and cohabitation; both reflect that the passenger and driver know each other, and the passenger transport is a normal, non-profit travel activity.

[0040] In this embodiment of the application, determining the personnel relationship information of the vehicle-associated passengers includes: traversing the passengers associated with the vehicle, and determining at least one of the following relationships between the passenger and the target person: kinship, cohabitation, and colleague relationship; and / or, traversing the passengers associated with the vehicle, and determining at least one of the following relationships between the passenger and the target person: trajectory overlap, co-occurrence information, intimacy, and ride-sharing information; wherein, the target person includes the driver of the vehicle and / or other passengers associated with the vehicle.

[0041] For example, images of passengers and target individuals acquired by the monitoring system can be identified to determine their identities. Based on these identities, the registration information of the passenger and target individual by the personnel management department can be determined to ascertain whether the passenger and target individual have at least one of the following relationships: kinship, cohabitation, or colleagueship. If such a relationship exists, it is determined that the passenger and target individual have a certain relationship and are not strangers. If not, the behavioral connection between the passenger and target individual can be further determined, or, if such a relationship exists, the behavioral connection can be further determined. Specifically, determining the behavioral connection between the passenger and target individual can be done by analyzing the passenger's and target individual's images to determine at least one of the following: trajectory overlap, co-occurrence information, intimacy, and co-transportation information, thereby determining whether a behavioral connection exists between the passenger and target individual. If it is determined that the passenger and target individual have a behavioral connection and are not strangers, it is determined that the vehicle is carrying familiar passengers and not transporting unfamiliar passengers for profit.

[0042] In this embodiment, determining the relationship information of the vehicle-associated passengers in the associated information is not limited to the above-described scheme. It can also be based on at least one of the following: facial image similarity between the passenger and the target person; trajectory information and the time period during which they are both at the same location; or the duration of the time period during which they are both at the same location. This allows for determining whether the passenger and the target person have at least one of the following relationships: kinship, cohabitation, or colleagueship. For example, if the facial similarity between the passenger and the target person is greater than a pre-set kinship similarity threshold, then the passenger and the target person are determined to be kinship. Or, if the facial similarity between the passenger and the target person is greater than a pre-set kinship similarity threshold, and the passenger and the target person are generally together at night for a duration greater than a pre-set sleep duration threshold (e.g., from 6:00 PM to 7:00 AM the next day), then the passenger and the target person are determined to be kinship. If the passenger and the target person only meet the requirement of being together at night for a duration greater than a pre-set sleep duration threshold, then the passenger and the target person are determined to be cohabiting. If the passenger and the target person are usually in the same location during the day and the duration exceeds the preset working time threshold, such as 9:00 AM to 6:00 PM, then it is determined that the passenger and the target person have a colleague relationship.

[0043] In this embodiment of the application, determining the co-occurrence information of the passenger and the target person includes: determining the co-occurrence information of the passenger and the target person based on the number of co-occurring images in the same image within a first time interval, and / or the ratio of the number of co-occurring images to the total number of images collected within a second time interval; wherein, the number of co-occurring images is determined based on the images of the passenger and the target person collected within the first time interval, the total number of images is the sum of the number of images of the passenger and the number of images of the target person, and the first time interval is truly included in the second time interval.

[0044] For example, a monitoring system acquires images of passengers and target persons collected within a first time interval. It detects whether passengers and target persons appear in the same image and counts the number of co-occurring images where both appear in the same image. If a passenger image detects that both passengers and target persons appear in the same image, the co-occurrence count is incremented by one; similarly, if a target person image detects that both appear in the same image, the co-occurrence count is incremented by one. Based on the number of co-occurrence images, the co-occurrence information of passengers and target persons is determined. For example, the number of co-occurrence images can be used as the co-occurrence information, or the number of co-occurrence images can be multiplied by a preset coefficient. Alternatively, the total number of passenger and target person images acquired in a second time interval can be obtained. The co-occurrence information is determined based on the ratio of the number of co-occurrence images to the total number of images. For example, the ratio of the number of co-occurrence images to the total number of images can be used as the co-occurrence information, or the ratio of the number of co-occurrence images to the total number of images can be multiplied by a preset coefficient. Alternatively, co-occurrence information can be determined based on the number of co-occurring images and the ratio of the number of co-occurring images to the total number of images. For example, the weighted sum of the number of co-occurring images and the ratio can be used as the co-occurrence information. If the total number of images is the total number of passenger images and target person images collected within the first time interval, it is possible that passengers and target persons appear simultaneously in all images, meaning the number of co-occurring images is equal to the total number of images, and the ratio of the number of co-occurring images to the total number of images is equal to 1. Therefore, the time interval is expanded based on the first time interval to obtain a second time interval, so that the total number of images acquired in the second time interval is greater than the number of co-occurring images, thus accurately determining the co-occurrence information.

[0045] In this embodiment, determining the co-occurrence information of the passenger and the target person is not limited to the above-described scheme. It can also involve determining the locations of the passenger and the target person at different times. If the passenger and the target person are in the same location at the same time, then co-occurrence is determined. Furthermore, the number of times the passenger and the target person co-occur and their locations within a certain time period can be statistically analyzed.

[0046] In this embodiment of the application, determining the co-travel information of the passenger and the target person includes: acquiring images of the passenger and the target person within a third time interval; determining the co-travel information of the passenger and the target person based on the following conditions: the images of the passenger and the target person are at the same location, the time difference between the images is less than a preset time difference, and the means of transportation ridden by the passenger and the target person in the images are the same.

[0047] For example, if a passenger and a target person travel on the same means of transportation, it can be determined that there is a behavioral connection between them. The system detects images of the passenger and the target person collected within a third time interval. If the image acquisition points of the passenger's and the target person's images are consistent, the time difference between image acquisition is less than a preset time difference, and the means of transportation used by the passenger and the target person are the same (e.g., the same license plate number), then it is determined that the passenger and the target person traveled on the same means of transportation, thus confirming the shared travel information. The preset time difference can be determined based on actual conditions, such as 30 seconds. A time difference less than the preset time difference means that the time difference between the passenger's and the target person's image acquisition is less than 30 seconds, reflecting that the passenger and the target person traveled on the same means of transportation almost simultaneously.

[0048] For example, the overlap between the passenger's and the target person's trajectories can also be determined. Specifically, images of the passenger and the target person are acquired through a monitoring system. Based on the location and time of image acquisition, the trajectories of the passenger and the target person are determined, and the overlap between their trajectories is then determined. The intimacy between the passenger and the target person can also be determined. Specifically, in images where both the passenger and the target person appear simultaneously, their behaviors are identified, such as actions like walking side-by-side, holding hands, hugging, or handing over items. When these actions are identified, a high degree of intimacy between the passenger and the target person is determined.

[0049] In this embodiment of the application, determining the overlap of the trajectory between the passenger and the target person is not limited to the above-described scheme. It can also be achieved by acquiring images of the passenger and the target person through a monitoring system, detecting whether the passenger and the target person appear in the same image, and determining the location corresponding to the co-occurring image when the passenger and the target person appear in the same image, that is, in a co-occurring image. Based on the location of at least two co-occurring images and the time of each co-occurring image, the overlap of the trajectory between the passenger and the target person is determined.

[0050] S230. Determine the operating status of the vehicle based on the vehicle's association information; wherein, the association information includes the personnel relationship information of the vehicle's associated passengers.

[0051] In this embodiment of the application, determining the operating status of the vehicle based on the vehicle's association information includes: if the vehicle's association information satisfies at least one of the following, then the operating status of the vehicle is determined to be abnormal: the vehicle's average daily driving distance is greater than a preset distance, and / or the average daily driving time is greater than a preset time; the passenger's relationship does not include any one of kinship, cohabitation, and colleague relationships, and / or, based on at least one of the passenger's trajectory overlap, co-occurrence information, intimacy, and ride-sharing information, it is determined that the passenger and the target person have no actual relationship; the passenger's dispersion degree is greater than a preset dispersion degree.

[0052] The preset distance and preset time can be set according to actual conditions, such as the average daily driving distance and average daily driving time of a private car under normal use. For example, the vehicle's operating status can be judged based on at least one of the associated information. If the associated information meets at least one of the above conditions, the vehicle's operating status is determined to be abnormal, meaning the vehicle is profitable in transporting passengers. To more comprehensively judge the vehicle's operating status, all items in the associated information can also be checked. If the vehicle's associated information meets all the above conditions, the vehicle's operating status is determined to be abnormal. This multi-dimensional approach comprehensively judges the vehicle's operating status, improving the comprehensiveness and accuracy of the judgment results.

[0053] The technical solution in this application embodiment determines the relationship between the passenger and the target person, thereby deeply analyzing whether there is a connection between the passenger and the target person. Based on the connection between the passenger and the target person, it determines whether the vehicle is normally picking up and dropping off acquaintances or transporting strangers for profit, thus determining the vehicle's operating status and improving the accuracy of the operating status determination.

[0054] Figure 3 This is a flowchart illustrating a vehicle condition determination method according to another embodiment of this application. This embodiment is a further optimization of the above embodiments; details not described in detail in this embodiment are provided in the above embodiments. See also... Figure 3 The vehicle condition determination method provided in this application embodiment may include:

[0055] S310. Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle conforms to a preset type.

[0056] S320. If not, determine the discreteness information of the vehicle-associated passengers.

[0057] For example, if the vehicle is a private car in normal use, the dispersion of passengers it picks up and drops off should be low, and the passengers' pick-up and drop-off locations, times, and residences should have a certain regularity. However, if the private car is profiting from transporting passengers, the passengers' pick-up and drop-off locations, times, and residences should be more dispersed, with a higher degree of dispersion. Therefore, by determining the dispersion information of the vehicle associated with its passengers, it is possible to determine whether the vehicle is profitable in transporting passengers.

[0058] In this embodiment of the application, determining the dispersion information of the vehicle-associated passengers in the association information includes: for the vehicle, based on the collected images of passengers getting on and off the vehicle, determining the number of times passengers get on and off the vehicle corresponding to a preset indicator within a unit time period; wherein, the preset indicator includes at least one of the image collection location, a preset time period, and the passenger's place of residence; and determining the dispersion information of the vehicle-associated passengers in the vehicle association information based on the number of times passengers get on and off the vehicle corresponding to the preset indicator within a unit time period.

[0059] For example, the frequency of passenger boarding and alighting can be counted separately for the locations of boarding and alighting behavior, namely the image acquisition location, the preset time period, and the passenger's residence, to determine the dispersion of passenger boarding and alighting at the image acquisition location, time period, and residence. Specifically, if the frequency of passenger boarding and alighting behavior at a single image acquisition location, a single preset time period, or a single residence is relatively high, the dispersion is determined to be low; if the frequency of passenger boarding and alighting behavior at a single image acquisition location, a single preset time period, or a single residence is relatively low, but the frequency of boarding and alighting behavior at a single image acquisition location, a single preset time period, or a single residence is relatively high, the dispersion is determined to be high.

[0060] In this embodiment of the application, the dispersion information of the vehicle-associated passengers in the vehicle association information is determined based on the number of times passengers get on and off the vehicle corresponding to the preset indicator within a unit time period. This includes: arranging the numbers of the preset indicator according to the number of times passengers get on and off the vehicle corresponding to the preset indicator within a unit time period to obtain a number group, and determining the midpoint of the number group; determining the standard deviation based on the sum of the number of times passengers get on and off the vehicle corresponding to the preset indicator within a unit time period and the midpoint of the group; and determining the dispersion information based on the standard deviation and the mean of the numbers in the number group.

[0061] For example, as shown in Table 1, preset indicators are numbered to obtain X1, X2, X3, X4, X5, etc. The number of times passengers get on and off the bus corresponding to the preset indicators is recorded within a unit of time, such as a day, resulting in Table 1. The preset indicator numbers are arranged according to the number of passenger boarding and alighting behaviors. For example, assuming a1, a2, a3, a4, and a5 are 1, 2, 2, 1, and 3 respectively, for the preset indicator number on the date 2021 / 8 / 18, the numbering based on the number of times can obtain the number group {1, 2, 2, 3, 3, 4, 5, 5, 5}. The midpoint of the number group is 3. The sum of the number of times passengers get on and off the bus corresponding to the preset indicators within a unit of time is 9. The mean of the numbers in the number group is 3.33. Then, based on the sum of the number of times passengers get on and off the bus corresponding to the preset indicators within a unit of time and the midpoint of the group, the standard deviation is determined; based on the standard deviation and the mean of the numbers in the number group, the dispersion information is determined. For example, the dispersion information can be determined based on the ratio of the standard deviation to the mean of the numbers. The unit time period can be each day, and the preset index can be at least one of the image acquisition location, the preset time period, and the passenger's residence location. When the preset index is the preset time period, it can be a preset time period within the unit time period, such as 0:00-1:00, 1:00-2:00, 2:00-3:00, etc. in a day.

[0062] Table 1

[0063]

[0064] In this embodiment, determining the dispersion information of the vehicle-associated passengers is not limited to the methods described above. It can also be achieved by determining the locations visited by the passengers using image information of the vehicle-associated passengers, or by determining the locations visited by the passengers using their positioning devices. The dispersion of these locations is then used to determine the dispersion of the vehicle-associated passengers. For example, if the locations visited by the passengers are the same or close to each other, the dispersion of the vehicle-associated passengers is considered low. If the locations visited by the passengers are far apart and scattered, the dispersion of the vehicle-associated passengers is considered high. Furthermore, the dispersion can be quantified based on the distances between the locations visited by the passengers. For example, the distance between each location visited by the passengers and every other location can be determined, and the distances between each location and every other location can be summed to reflect the dispersion of the vehicle-associated passengers.

[0065] S330. Determine the operating status of the vehicle based on the vehicle's association information; wherein, the association information includes the dispersion information of the passengers associated with the vehicle.

[0066] The technical solution in this application embodiment determines whether passenger boarding and alighting behavior is regular or irregular by associating the discrete correlation information between the vehicle and passengers. This allows for a more accurate assessment of the vehicle's operational status by determining whether the vehicle is used to regularly pick up and drop off people with certain relationships or to transport unrelated people for profit.

[0067] Figure 4 This is a schematic diagram of a vehicle condition determination device according to one embodiment of this application. This device is applicable to situations where the operational status of a vehicle is being assessed. Typically, embodiments of this application are applicable to assessing the operational status of non-taxi vehicles. The device can be implemented in software and / or hardware and can be integrated into an electronic device. See also... Figure 3 The device specifically includes:

[0068] The query module 410 is used to query whether the registered vehicle type of the vehicle conforms to a preset type based on the vehicle's identification information.

[0069] The association information determination module 420 is used to determine the association information of the vehicle if the information does not match; wherein the association information includes at least one of driving information, personnel relationship information of the vehicle-associated passengers, and dispersion information of the vehicle-associated passengers.

[0070] The operation status determination module 430 is used to determine the operation status of the vehicle based on the associated information of the vehicle.

[0071] In this embodiment of the application, the association information determination module 420 includes:

[0072] The first traversal unit is used to traverse the passengers associated with the vehicle, and determine at least one of the following relationships based on the passenger and target person registration information: kinship, cohabitation, and colleagueship; and / or,

[0073] The second traversal unit is used to traverse the passengers associated with the vehicle, and determine at least one of the following based on the images of the passengers and the images of the target person: trajectory overlap, co-occurrence information, intimacy, and co-riding information;

[0074] The target personnel include the driver of the vehicle and / or other passengers associated with the vehicle.

[0075] In this embodiment of the application, the second traversal unit is specifically used for:

[0076] Based on the images of the passenger and the target person collected within the first time interval, determine the number of co-occurring images in which the passenger and the target person appear together in the same image;

[0077] The co-occurrence information of the passenger and the target person is determined based on the ratio of the number of co-occurring images to the total number of images collected within the second time interval; wherein the total number of images is the sum of the number of images of the passenger and the number of images of the target person, and the first time interval is contained within the second time interval.

[0078] In this embodiment of the application, the second traversal unit is specifically used for:

[0079] Acquire images of the passenger and the target person within a third time interval;

[0080] Based on the images of the passenger and the target person that meet the following conditions, determine the passenger and target person's travel information together:

[0081] The image acquisition points are consistent, the time difference between image acquisition is less than the preset time difference, and the vehicles used by the passengers and the target person in the image are consistent.

[0082] In this embodiment of the application, the association information determination module 420 includes:

[0083] The frequency counting unit is used to determine, for the vehicle, the number of times passengers get on and off the vehicle within a unit time period corresponding to a preset indicator, based on the collected images of passengers getting on and off the vehicle; wherein, the preset indicator includes at least one of the following: the image collection location, the preset time period, and the passenger's place of residence.

[0084] The discreteness information determination unit is used to determine the discreteness information of the associated passengers of the vehicle in the vehicle's association information based on the number of times passengers get on and off the vehicle corresponding to a preset indicator within a unit time period.

[0085] In this embodiment of the application, the discreteness information determination unit is specifically used for:

[0086] Based on the number of times passengers get on and off the bus corresponding to the preset indicators within a unit time period, the preset indicator numbers are arranged to obtain number groups, and the group midpoint of the number group is determined.

[0087] The standard deviation is determined based on the sum of the number of times passengers board and alight within a unit time period corresponding to the preset indicators, and the midpoint of the group.

[0088] The dispersion information is determined based on the standard deviation and the mean of the numbers in the numbering group.

[0089] In this embodiment of the application, the operation status determination module 430 is specifically used for:

[0090] The vehicle's operating status is determined to be abnormal if the associated information of the vehicle meets at least one of the following conditions:

[0091] The vehicle's average daily driving distance is greater than a preset distance, and / or its average daily driving time is greater than a preset time;

[0092] The number of passengers associated with the vehicle is greater than the preset number;

[0093] The passenger's relationship does not include any of the following: kinship, cohabitation, and colleague relationships, and / or, the passenger and the target person are determined to have no actual relationship based on at least one of the following: trajectory overlap, co-occurrence information, intimacy, and co-ride information.

[0094] The dispersion of the passengers is greater than the preset dispersion.

[0095] The vehicle condition determination device provided in the application embodiments can execute the vehicle condition determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0096] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Figure 5 A block diagram is shown that is suitable for implementing an exemplary electronic device 512 according to embodiments of this application. Figure 5 The electronic device 512 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 5 As shown, the electronic device 512 may include: one or more processors 516; and a memory 528 for storing one or more programs, which, when executed by the one or more processors 516, cause the one or more processors 516 to implement the vehicle condition determination method provided in this application embodiment, including:

[0098] Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle matches the preset type;

[0099] If the conditions are not met, the vehicle's association information is determined; wherein, the association information includes at least one of the following: driving information, the personnel relationship information of the vehicle's associated passengers, and the dispersion information of the vehicle's associated passengers;

[0100] The vehicle's operational status is determined based on its associated information.

[0101] The components of the electronic device 512 may include, but are not limited to: one or more processors 516, memory 528, and bus 518 connecting different device components (including memory 528 and processor 516).

[0102] Bus 518 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Processor ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0103] Electronic device 512 typically includes a variety of computer-readable storage media. These storage media can be any available storage media that can be accessed by electronic device 512, including volatile and non-volatile storage media, and removable and non-removable storage media.

[0104] Memory 528 may include computer device readable storage media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. Electronic device 512 may further include other removable / non-removable, volatile / non-volatile computer device storage media. By way of example only, storage system 534 may be used to read and write non-removable, non-volatile magnetic storage media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile 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 storage medium) may be provided. In these cases, each drive may be connected to bus 518 via one or more data storage medium interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0105] A program / utility 540 having a set (at least one) of program modules 542 may be stored, for example, in memory 528. Such program modules 542 include, but are not limited to, operating devices, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 542 typically perform the functions and / or methods described in the embodiments of this application.

[0106] Electronic device 512 can also communicate with one or more external devices 514 and / or display 524, and with one or more devices that enable a user to interact with the electronic device 512, and / or with any device (e.g., network card, modem, etc.) that enables the electronic device 512 to communicate with one or more other computing devices. This communication can be performed via input / output (I / O) interface 522. Furthermore, electronic device 512 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 520. Figure 5 As shown, network adapter 520 communicates with other modules of electronic device 512 via bus 518. It should be understood that, although... Figure 5 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices.

[0107] One or more processors 516 execute various functional applications and data processing by running at least one of the other programs among a plurality of programs stored in memory 528, such as implementing a vehicle condition determination method provided in the embodiments of this application.

[0108] One embodiment of this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a vehicle condition determination method, including:

[0109] Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle matches the preset type;

[0110] If the conditions are not met, the vehicle's association information is determined; wherein, the association information includes at least one of the following: driving information, the personnel relationship information of the vehicle's associated passengers, and the dispersion information of the vehicle's associated passengers;

[0111] The vehicle's operational status is determined based on its associated information.

[0112] The computer storage medium in this application embodiment can be any combination of one or more computer-readable storage media. The computer-readable storage medium can be a computer-readable signal storage medium or a computer-readable storage medium in general. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application embodiment, the computer-readable storage medium can be any tangible storage medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or device.

[0113] Computer-readable signal storage media may include data signals propagated in baseband or as part of a carrier wave, carrying 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. Computer-readable signal storage media may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution device, apparatus, or apparatus.

[0114] Program code contained on a computer-readable storage medium may be transmitted using any suitable storage medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0115] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or device. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining vehicle status, characterized in that, The method includes: Based on the vehicle's identification information, query whether the registered vehicle type of the vehicle matches the preset type; If the conditions are not met, the vehicle's association information is determined; wherein, the association information includes at least one of the following: driving information, the personnel relationship information of the vehicle's associated passengers, and the dispersion information of the vehicle's associated passengers; Based on the vehicle's associated information, determine the vehicle's operational status; Determining the discreteness information of the vehicle-associated passengers in the associated information includes: For the vehicle, based on the images of passengers getting on and off the vehicle, the number of times passengers get on and off the vehicle corresponding to a preset indicator within a unit time period is determined; wherein, the preset indicator includes at least one of the following: the image acquisition location, the preset time period, and the passenger's place of residence. Based on the number of times passengers get on and off the vehicle corresponding to a preset indicator within a unit time period, the dispersion information of the associated passengers of the vehicle in the vehicle's association information is determined. Based on the number of passenger boarding and alighting behaviors corresponding to preset indicators within a unit time period, the dispersion information of the vehicle-associated passengers in the vehicle's association information is determined, including: Based on the number of times passengers get on and off the bus corresponding to the preset indicators within a unit time period, the preset indicator numbers are arranged to obtain number groups, and the group midpoint of the number group is determined. The standard deviation is determined based on the sum of the number of times passengers board and alight within a unit time period corresponding to the preset indicators, and the midpoint of the group. The dispersion information is determined based on the standard deviation and the mean of the numbers in the numbering group.

2. The method according to claim 1, characterized in that, Determining the personnel relationship information of the vehicle-associated passengers in the associated information includes: Iterate through the passengers associated with the vehicle, and based on the passenger and target person registration information, determine at least one of the following relationships between the passenger and target person: kinship, cohabitation, and colleagueship; and / or, Traverse the passengers associated with the vehicle, and determine at least one of the following based on the passenger's image and the target person's image: trajectory overlap, co-occurrence information, intimacy, and co-ride information; The target personnel include the driver of the vehicle and / or other passengers associated with the vehicle.

3. The method according to claim 2, characterized in that, Determining the co-occurrence information of the passenger and the target person includes: The co-occurrence information of the passenger and the target person is determined based on the number of co-occurring images in the same image within the first time interval, and / or the ratio of the number of co-occurring images to the total number of images collected within the second time interval. The number of co-occurring images is determined based on the images of the passenger and the target person collected within the first time interval. The total number of images is the sum of the number of images of the passenger and the number of images of the target person. The first time interval is contained within the second time interval.

4. The method according to claim 2, characterized in that, Determining the passenger's information regarding traveling on the same vehicle as the target person includes: Acquire images of the passenger and the target person within a third time interval; Based on the images of the passenger and the target person that meet the following conditions, determine the passenger and target person's travel information together: The image acquisition points are consistent, the time difference between image acquisition is less than the preset time difference, and the vehicles used by the passengers and the target person in the image are consistent.

5. The method according to claim 2, characterized in that, Based on the vehicle's associated information, the operational status of the vehicle is determined, including: The vehicle's operating status is determined to be abnormal if the associated information of the vehicle meets at least one of the following conditions: The vehicle's average daily driving distance is greater than a preset distance, and / or its average daily driving time is greater than a preset time; The passenger's relationship does not include any of the following: kinship, cohabitation, and colleague relationships, and / or, the passenger and the target person are determined to have no actual relationship based on at least one of the following: trajectory overlap, co-occurrence information, intimacy, and co-ride information. The dispersion of the passengers is greater than the preset dispersion.

6. A vehicle condition determination device, characterized in that, The device includes: The query module is used to query whether the registered vehicle type of the vehicle matches a preset type based on the vehicle's identification information; The association information determination module is used to determine the association information of the vehicle if the information does not match; wherein the association information includes at least one of driving information, personnel relationship information of the vehicle-associated passengers, and dispersion information of the vehicle-associated passengers. The operation status determination module is used to determine the operation status of the vehicle based on the associated information of the vehicle; The associated information determination module includes: The frequency counting unit is used to determine, for the vehicle, the number of times passengers get on and off the vehicle within a unit time period corresponding to a preset indicator, based on the collected images of passengers getting on and off the vehicle; wherein, the preset indicator includes at least one of the following: the image collection location, the preset time period, and the passenger's place of residence. The discreteness information determination unit is used to determine the discreteness information of the vehicle-associated passengers in the vehicle's association information based on the number of times passengers get on and off the vehicle corresponding to a preset indicator within a unit time period. The discreteness information determination unit is specifically used for: Based on the number of times passengers get on and off the bus corresponding to the preset indicators within a unit time period, the preset indicator numbers are arranged to obtain number groups, and the group midpoint of the number group is determined. The standard deviation is determined based on the sum of the number of times passengers board and alight within a unit time period corresponding to the preset indicators, and the midpoint of the group. The dispersion information is determined based on the standard deviation and the mean of the numbers in the numbering group.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle condition determination method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the vehicle condition determination method as described in any one of claims 1-5.

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