Vehicle aggregation detection method

Abstract

The embodiment of the invention provides a vehicle aggregation detection method, which comprises the following steps: firstly, acquiring a login state of a to-be-detected vehicle at a cloud end, continuously monitoring positioning data of the to-be-detected vehicle, formatting the positioning data according to preset data precision to generate positioning snapshot data of the to-be-detected vehicle under the data precision, and then according to the login state, acquiring the positioning snapshot data of the to-be-detected vehicle according to the positioning snapshot data. And judging whether the identification information of the to-be-detected vehicle and / or the positioning snapshot data at a certain moment exist in a pre-constructed aggregation list or not, obtaining a judgment result, and determining an aggregation detection result for the to-be-detected vehicle according to the judgment result. According to the invention, support is provided for travel planning, whether a specific vehicle has a gathering or centralization risk can be identified, system-level full-data monitoring is realized, the easy degree of vehicle parking can be identified in advance, and a congested gathering parking area can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of aggregation detection, and in particular to a method for detecting the aggregation of a vehicle. Background Art

[0002] Vehicle gathering detection technology aims to monitor and manage the gathering of vehicles in a specific area in real time to prevent and respond to traffic congestion, safety incidents or other emergencies. However, current vehicle gathering detection methods have some significant defects, which limit their effectiveness and flexibility in practical applications.

[0003] First, the current vehicle gathering detection system usually needs to actively receive gathering identification request instructions. This passive working mode makes it impossible for the system to issue a warning in time when a vehicle gathering event occurs, thereby delaying the time window for emergency response. Secondly, the existing vehicle gathering detection method usually requires a clear list of target vehicles, which limits the monitoring scope and flexibility of the system. Since the target vehicle list needs to be set in advance, the system cannot comprehensively monitor and manage all vehicles. In addition, the lack of overall management is also a major defect of the current vehicle gathering detection method. Since the system cannot uniformly manage all vehicles, the monitoring targets are scattered, making it difficult to form an effective overall monitoring network.

[0004] Current vehicle gathering detection methods have obvious defects in active warning, monitoring target range and overall management, which limits their effectiveness and flexibility in practical applications. Summary of the invention

[0005] The embodiment of the present invention provides a method for detecting the gathering of vehicles, so as to solve the problem that the current vehicle gathering detection system needs to actively receive gathering identification request instructions and clarify the list of vehicles to be detected.

[0006] The embodiment of the present invention discloses a method for detecting the gathering of vehicles, which is applied to a vehicle system, wherein the vehicle system is connected to a cloud for communication, and the method includes:

[0007] Acquire the login status of the vehicle to be detected in the cloud, wherein the vehicle to be detected has identification information;

[0008] Monitoring the positioning data of the vehicle to be detected, and formatting the positioning data according to a preset data accuracy, to generate positioning snapshot data of the vehicle to be detected at the data accuracy;

[0009] According to the login status, determining whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list, and obtaining a determination result;

[0010] The aggregation detection result for the vehicle to be detected is determined according to the judgment result.

[0011] Optionally, the aggregation list is constructed as follows:

[0012] Collecting first positioning data and first identification information of a plurality of vehicles to be constructed, wherein the first positioning data is used to represent the latitude and longitude data of the vehicles to be constructed;

[0013] Generate first positioning snapshot data of the vehicle to be constructed at a preset data accuracy;

[0014] Dividing the first positioning snapshot data according to the data accuracy to obtain a plurality of clustering areas;

[0015] Filter out the maximum longitude and latitude values ​​in each of the clustered areas from the longitude and latitude data;

[0016] Constructing a clustering matrix corresponding to each clustering area according to the latitude and longitude maximum values, wherein the clustering matrix includes the first identification information;

[0017] The clustering matrices are aggregated to obtain a clustering list.

[0018] Optionally, the aggregating the clustering matrix to obtain a clustering list includes:

[0019] Loop through the latitude and longitude values ​​corresponding to each clustering matrix to determine whether there are overlapping latitude and longitude values;

[0020] In the case where there are overlapping latitude and longitude extremes, merging the clustering matrices corresponding to the latitude and longitude extremes to obtain a target clustering matrix;

[0021] In the case where there are no overlapping latitude and longitude extremes, determining whether there is a second clustering matrix that is an adjacent matrix to the clustering matrix;

[0022] In the case where the second clustering matrix exists, merging the clustering matrix with the second clustering matrix to obtain a target clustering matrix;

[0023] The target clustering matrix is ​​aggregated to obtain a clustering list.

[0024] Optionally, the login status of the vehicle to be detected in the cloud includes an online status and an offline status, and judging whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list according to the login status includes:

[0025] Regularly uploading the positioning snapshot data of the vehicle to be detected to the cloud;

[0026] When the login state is the online state, obtaining a first judgment result according to the identification information and the positioning snapshot data, wherein the first judgment result is used to indicate whether the cluster list includes the identification information and the positioning snapshot data;

[0027] When the login state is the offline state, a second judgment result is obtained according to the positioning snapshot data, and the second judgment result is used to indicate whether the cluster list includes the positioning snapshot data.

[0028] Optionally, when the login state is the online state, obtaining a first judgment result according to the identification information and the positioning snapshot data includes:

[0029] Obtaining current positioning snapshot data of the vehicle to be detected;

[0030] Determining whether the identification information and the current positioning snapshot data exist in the aggregation list;

[0031] If the current positioning snapshot data does not exist in the aggregation list, remove the identification information from the aggregation list;

[0032] In the case where the current positioning snapshot data exists in the aggregation list, obtaining the most recent positioning snapshot data of the vehicle to be detected, and determining whether the most recent positioning snapshot data exists in the aggregation list;

[0033] In a case where the most recent positioning snapshot data does not exist in the aggregation list, the identification information is removed from the aggregation list.

[0034] Optionally, when the login state is the offline state, obtaining a second judgment result according to the positioning snapshot data includes:

[0035] Acquire the last known position of the vehicle to be detected, where the last known position is the positioning snapshot data uploaded to the cloud by the vehicle to be detected for the last time;

[0036] determining whether the last known position exists in the aggregation list;

[0037] In the case where the last known location does not exist in the aggregation list, counting the number of vehicles in the cloud that are in the offline state and have the same positioning snapshot data to obtain a statistical result;

[0038] Comparing the statistical result with a preset aggregation threshold;

[0039] When the statistical result is greater than or equal to the clustering threshold, constructing a new clustering area and a new clustering matrix corresponding to the new clustering area according to the last known position;

[0040] adding the new clustering matrix to the clustering list;

[0041] In the case where the last known position exists in the aggregation list, determining whether the identification information exists in the aggregation list;

[0042] In a case where the identification information does not exist in the aggregation list, the identification information is added to the aggregation list.

[0043] Optionally, the method further comprises:

[0044] It is determined whether the gathering detection result satisfies a preset gathering warning condition, and if the gathering warning condition is met, a gathering warning for the vehicle to be detected is triggered.

[0045] The present invention also discloses a vehicle aggregation detection device, which is applied to a vehicle system, wherein the vehicle system is connected to a cloud for communication, and the device comprises:

[0046] An acquisition module, used for acquiring the login status of the vehicle to be detected in the cloud, wherein the vehicle to be detected has identification information;

[0047] A monitoring module, used for monitoring the positioning data of the vehicle to be detected, formatting the positioning data according to a preset data accuracy, and generating positioning snapshot data of the vehicle to be detected at the data accuracy;

[0048] A judgment module, used to judge whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list according to the login state, and obtain a judgment result;

[0049] A determination module is used to determine the aggregation detection result for the vehicle to be detected according to the judgment result.

[0050] Optionally, the device further comprises:

[0051] The sample data collection module is used to collect first positioning data and first identification information of a number of vehicles to be constructed, wherein the first positioning data is used to represent the latitude and longitude data of the vehicles to be constructed.

[0052] The training data generation module is used to generate first positioning snapshot data of the vehicle to be constructed at a preset data accuracy.

[0053] The clustering area division module is used to divide the first positioning snapshot data according to the data accuracy to obtain a plurality of clustering areas.

[0054] The latitude and longitude data screening module is used to screen out the maximum longitude and latitude values ​​in each of the clustered areas from the latitude and longitude data.

[0055] A clustering matrix construction module is used to construct a clustering matrix corresponding to each clustering area according to the maximum longitude and latitude values, and the clustering matrix includes the first identification information.

[0056] The clustering list generating module is used to aggregate the clustering matrix to obtain a clustering list.

[0057] Optionally, the aggregation list generation module further includes:

[0058] The traversal unit is used to loop through the latitude and longitude extremes corresponding to each clustering matrix to determine whether there are overlapping latitude and longitude extremes.

[0059] The merging unit is used to merge the clustering matrices corresponding to the latitude and longitude extremes to obtain a target clustering matrix when there are overlapping latitude and longitude extremes.

[0060] The first judgment unit is used to judge whether there is a second clustering matrix that is adjacent to the clustering matrix when there are no overlapping latitude and longitude extremes.

[0061] The second judgment unit is used to merge the clustering matrix and the second clustering matrix to obtain a target clustering matrix when the second clustering matrix exists.

[0062] A summarizing unit is used to summarize the target clustering matrix to obtain a clustering list.

[0063] Optionally, the login status of the vehicle to be detected in the cloud includes an online status and an offline status, and the judgment module includes:

[0064] The uploading submodule is used to regularly upload the positioning snapshot data of the vehicle to be detected to the cloud.

[0065] The first judgment submodule is used to obtain a first judgment result according to the identification information and the positioning snapshot data when the login state is the online state, and the first judgment result is used to indicate whether the cluster list contains the identification information and the positioning snapshot data.

[0066] The second judgment submodule is used to obtain a second judgment result according to the positioning snapshot data when the login state is the offline state, and the second judgment result is used to indicate whether the cluster list contains the positioning snapshot data.

[0067] Optionally, the first judgment submodule is specifically used for:

[0068] Obtaining current positioning snapshot data of the vehicle to be detected;

[0069] Determining whether the identification information and the current positioning snapshot data exist in the aggregation list;

[0070] If the current positioning snapshot data does not exist in the aggregation list, remove the identification information from the aggregation list;

[0071] In the case where the current positioning snapshot data exists in the aggregation list, obtaining the most recent positioning snapshot data of the vehicle to be detected, and determining whether the most recent positioning snapshot data exists in the aggregation list;

[0072] In a case where the most recent positioning snapshot data does not exist in the aggregation list, the identification information is removed from the aggregation list.

[0073] Optionally, the second judgment submodule is specifically used for:

[0074] Acquire the last known position of the vehicle to be detected, where the last known position is the positioning snapshot data uploaded to the cloud by the vehicle to be detected for the last time;

[0075] determining whether the last known position exists in the aggregation list;

[0076] In the case where the last known location does not exist in the aggregation list, counting the number of vehicles in the cloud that are in the offline state and have the same positioning snapshot data to obtain a statistical result;

[0077] Comparing the statistical result with a preset aggregation threshold;

[0078] When the statistical result is greater than or equal to the clustering threshold, constructing a new clustering area and a new clustering matrix corresponding to the new clustering area according to the last known position;

[0079] adding the new clustering matrix to the clustering list;

[0080] In the case where the last known position exists in the aggregation list, determining whether the identification information exists in the aggregation list;

[0081] In a case where the identification information does not exist in the aggregation list, the identification information is added to the aggregation list.

[0082] Optionally, the device further comprises:

[0083] The alarm module is used to determine whether the gathering detection result meets the preset gathering alarm condition, and trigger the gathering alarm for the vehicle to be detected if the gathering alarm condition is met.

[0084] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0085] The memory is used to store computer programs;

[0086] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0087] The embodiment of the present invention further discloses a computer-readable storage medium on which instructions are stored. When the instructions are executed by one or more processors, the processors execute the method described in the embodiment of the present invention.

[0088] The embodiments of the present invention include the following advantages:

[0089] The present invention first obtains the login status of the vehicle to be detected in the cloud, and continuously monitors the positioning data of the vehicle to be detected, and formats the positioning data according to the preset data accuracy to generate the positioning snapshot data of the vehicle to be detected under the data accuracy, and then judges whether the identification information of the vehicle to be detected itself and / or the positioning snapshot data at a certain moment exists in the pre-built aggregation list according to the login status, and obtains the judgment result, thereby determining the aggregation detection result for the vehicle to be detected according to the judgment result. The present invention provides support for travel planning, can identify whether a specific vehicle has aggregation or concentration risks, and realizes system-level full data monitoring. On the other hand, the present invention can also identify the ease of parking of vehicles in advance and avoid congested aggregation parking areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flow chart of the steps of a vehicle aggregation detection method provided in an embodiment of the present invention;

[0091] Figure 2 is a structural block diagram of a vehicle aggregation detection device provided in an embodiment of the present invention;

[0092] Figure 3 It is a block diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0093] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0094] The present invention discloses a vehicle gathering detection method, which is applied to the detection process of vehicle gathering events. On the one hand, in the prior art, the vehicle gathering detection system usually needs to actively receive the gathering identification request instruction. For example, when traffic congestion or an emergency occurs, the system cannot actively identify and report the gathering situation, resulting in the inability of relevant departments to take measures in time, increasing the severity and difficulty of handling the event. On the other hand, the existing vehicle gathering detection method usually requires a clear list of target vehicles, which limits the monitoring scope and flexibility of the system. For example, in large-scale activities or emergencies, the number and type of vehicles may change rapidly, and the preset target vehicle list cannot cover all possible situations, resulting in limited monitoring targets and inability to fully grasp the traffic conditions on the scene. In addition, the lack of overall management is also a major defect of the current vehicle gathering detection method. For example, in urban traffic management, the vehicle gathering conditions in different areas may be interrelated, but due to the lack of overall management, the system cannot fully analyze and predict the traffic conditions, thereby affecting the effect of traffic scheduling and emergency response.

[0095] Based on the above problems, the present invention provides a vehicle aggregation detection method, which is applied to a vehicle system, and the vehicle system is connected to the cloud for communication. The execution process of the method is as follows: Figure 1 As shown, the following steps may be specifically included:

[0096] Step 101, obtaining the login status of the vehicle to be detected in the cloud, and the vehicle to be detected has identification information.

[0097] In an embodiment of the present invention, the vehicle system can collect the vehicle's own location information, such as GPS (Global Positioning System) coordinate information, speed information, direction information, etc. In another embodiment, the vehicle system can also collect information about the vehicle's surrounding environment, such as road type, traffic flow, etc.

[0098] The cloud can receive data about the vehicle uploaded by the vehicle system. In the embodiment of the present invention, each vehicle to be detected is equivalent to a user and has its own corresponding user account. The owner can register the user account of the vehicle to be detected through contact information such as mobile phone number and email account. The present invention does not specifically limit the contact information used by the owner when registering the user account.

[0099] After logging into a user account for the first time on the cloud system, the user account needs to be bound to the identification information of the vehicle to be tested. The identification information can only be bound to one user account, where the identification information includes but is not limited to the vehicle's VIN (Vehicle Identification Number). VIN is a unique code consisting of 17 characters used to identify the car's manufacturer, model, engine type, year of production, production plant, serial number and other information.

[0100] When performing cluster detection on vehicles, the vehicle system first obtains the vehicle's login status in the cloud system, and then executes the corresponding detection method according to different login statuses.

[0101] Step 102 , monitoring the positioning data of the vehicle to be detected, and formatting the positioning data according to a preset data accuracy, to generate positioning snapshot data of the vehicle to be detected at the data accuracy.

[0102] After the vehicle system collects the positioning data of the vehicle to be detected, it pre-processes the positioning data. First, the data accuracy of the positioning data needs to be unified. In a feasible embodiment of the present invention, a unified data accuracy is preset. For example, all the collected positioning data are formatted into a data format of one ten-thousandth, such as: 106.36961 degrees east longitude, 29.68192 degrees north latitude. The formatted positioning data is used as the positioning snapshot data of the vehicle to be detected.

[0103] Step 103: According to the login status, it is determined whether the identification information and / or the positioning snapshot data exist in the pre-built aggregation list to obtain a determination result.

[0104] In the embodiment of the present invention, it is necessary to construct an aggregation list for determining whether an aggregation event occurs among the vehicles to be detected. The specific construction method is as follows:

[0105] The first positioning data and first identification information of a plurality of vehicles to be constructed are collected, wherein the first positioning data is used to represent the latitude and longitude data of the vehicles to be constructed.

[0106] In the embodiment of the present invention, after the first positioning data of the vehicle used to construct the cluster list is collected, the first positioning data also needs to be formatted according to the preset data accuracy to generate the first positioning snapshot data of the vehicle to be constructed at the preset data accuracy. The data accuracy used in the present invention when constructing the cluster list is the same as the data accuracy used when performing cluster detection on the vehicle.

[0107] After obtaining the first positioning snapshot data, the first positioning snapshot data is divided according to the above data accuracy to obtain several clustered areas. In one example, the preset data accuracy is one ten-thousandth, then according to the GPS principle, every 0.0001 degree of longitude and latitude (i.e., one ten-thousandth accuracy) corresponds to approximately 11.13 meters on the earth's surface, that is, if two GPS coordinates are the same in the fourth decimal place (i.e., one ten-thousandth accuracy), then their distance on the earth's surface is approximately 11.13 meters, so the area of ​​each clustered partition is approximately:

[0108] 11.13 meters x 10 meters = 111 square meters

[0109] The 10 meters in the calculation formula is an auxiliary parameter value preset by relevant personnel for calculating the area of ​​the gathering area. The auxiliary parameter value can be adjusted according to different data precisions, and the present invention does not limit the specific numerical value.

[0110] The first snapshot data obtained after formatting is longitude and latitude data with a data accuracy of one ten-thousandth. The maximum longitude and latitude values ​​in each cluster area are screened out from the longitude and latitude data. The maximum longitude and latitude values ​​include the maximum longitude (longMax), the minimum longitude (longMin), the maximum latitude (latMax) and the minimum latitude (latMin).

[0111] A clustering matrix corresponding to each clustering area is constructed according to the maximum values ​​of longitude and latitude, and the clustering matrix includes first identification information.

[0112] Specifically, the embodiment of the present invention constructs a clustering matrix corresponding to each clustering area according to the maximum longitude (longMax), the minimum longitude (longMin), the maximum latitude (latMax) and the minimum latitude (latMin). The format of the clustering matrix is ​​as follows:

[0113] {(minimum longitude, minimum latitude), (maximum longitude, maximum latitude)}

[0114] That is, {(longMin,latMin),(longMax,latMax)}.

[0115] Each clustering region can generate a clustering matrix, so several regions can generate several clustering matrices, and finally the clustering matrices are summarized to obtain a clustering list.

[0116] In an embodiment of the present invention, summarizing the clustering matrix to obtain the clustering list specifically includes the following steps:

[0117] The longitude and latitude values ​​corresponding to each clustering matrix are looped through to determine whether there are overlapping longitude and latitude values. In other words, the longitude and latitude values ​​corresponding to several clustering matrices generated by several clustering areas are looped through to determine whether there are clustering areas with overlapping longitude and latitude values.

[0118] In the case of overlapping latitude and longitude extremes, the clustering matrices corresponding to the latitude and longitude extremes are merged to obtain the target clustering matrix. For example:

[0119] The clustering matrix of clustering area A is {(106.36961 degrees east longitude, 29.68192 degrees north latitude), (106.36970 degrees east longitude, 29.68201 degrees north latitude)};

[0120] The clustering area of ​​clustering area B is {(106.36965 degrees east longitude, 29.68195 degrees north latitude), (106.36974 degrees east longitude, 29.68204 degrees north latitude)};

[0121] It can be seen that there is an overlap between the aggregation areas A and B, so the two are merged into a larger area AB {(106.36961 degrees east longitude, 29.68192 degrees north latitude)), (106.36974 degrees east longitude, 29.68204 degrees north latitude)}.

[0122] In the case where there are no overlapping latitude and longitude extremes, determining whether there is a second clustering matrix that is an adjacent matrix to the clustering matrix;

[0123] The clustering matrix of cluster area C is {(106.36961 degrees east longitude, 29.68192 degrees north latitude), (106.36970 degrees east longitude, 29.68201 degrees north latitude)};

[0124] The clustering area of ​​clustering area D is {(106.36971 degrees east longitude, 29.68202 degrees north latitude), (106.36980 degrees east longitude, 29.68211 degrees north latitude)};

[0125] It can be seen that there is no overlapping part between the cluster area C and the cluster area D, so on the basis of the data accuracy set above, it is judged whether the two are adjacent matrices. In one example, it is judged whether the maximum longitude and latitude values ​​of the two are continuous at a data accuracy of one ten-thousandth.

[0126] It can be seen that (106.36970 degrees east longitude, 29.68201 degrees north latitude) in the clustering matrix of clustering area C and (106.36971 degrees east longitude, 29.68202 degrees north latitude) in the clustering matrix of clustering area D are continuous at an accuracy of one ten-thousandth, so clustering area C and clustering area D are two non-overlapping but adjacent clustering matrices, that is, clustering matrix C and clustering matrix D are each other's second clustering matrices. Therefore, in the case of the existence of a second clustering matrix, the clustering matrix and the second clustering matrix are merged to obtain the target clustering matrix.

[0127] After performing the above processing on several clustering matrices, independent, non-overlapping and non-adjacent target clustering matrices are obtained. The target clustering matrices are summarized to obtain a clustering list, which also includes vehicle identification information.

[0128] In one embodiment of the present invention, the login status of the vehicle to be detected in the cloud includes an online state and an offline state. When the vehicle is in motion, the vehicle system maintains a communication connection with the cloud, continuously collects the positioning data of the vehicle, and uploads the positioning snapshot data obtained after preprocessing to the cloud. At this time, the login status of the vehicle in the cloud is an online state. When the vehicle is in a parked state, the vehicle system is disconnected from the cloud, and thus cannot continue to upload the positioning snapshot data of the vehicle to the cloud. At this time, the login status of the vehicle in the cloud is an offline state. For two different login states, the detection method may include:

[0129] Regularly upload the positioning snapshot data of the vehicle to be detected to the cloud.

[0130] When the login state is an online state, a first judgment result is obtained according to the identification information and the positioning snapshot data, and the first judgment result is used to indicate whether the cluster list contains the identification information and the positioning snapshot data.

[0131] When the login state is an offline state, a second judgment result is obtained according to the positioning snapshot data, and the second judgment result is used to indicate whether the clustered list includes the positioning snapshot data.

[0132] In a specific embodiment of the present invention, when the login state is an online state, obtaining a first judgment result according to the identification information and the positioning snapshot data specifically includes the following steps:

[0133] Get the current positioning snapshot data of the vehicle to be detected.

[0134] Determine whether the identification information of the vehicle to be detected and the current positioning snapshot data exist in the aggregation list.

[0135] When the current positioning snapshot data does not exist in the aggregation list, it means that the vehicle has left the aggregation area included in the aggregation list, so the identification information of the vehicle is removed from the aggregation list.

[0136] When the current positioning snapshot data exists in the aggregation list, the positioning snapshot data reported by the vehicle to the cloud is continuously detected to obtain the most recent positioning snapshot data of the vehicle to be detected, and continue to determine whether the most recent positioning snapshot data exists in the aggregation list. When the most recent positioning snapshot data does not exist in the aggregation list, it means that the vehicle has just left the aggregation area included in the aggregation list, and therefore the identification information of the vehicle is removed from the aggregation list.

[0137] In a specific embodiment of the present invention, when the login state is offline, obtaining a second judgment result according to the positioning snapshot data specifically includes the following steps:

[0138] Obtain the data uploaded to the cloud by the vehicle system for the last time before the communication connection with the cloud is disconnected, that is, the last known position of the vehicle to be detected. The last known position is the positioning snapshot data uploaded to the cloud by the vehicle to be detected for the last time, and determine whether the last known position exists in the aggregation list.

[0139] When the last known location does not exist in the aggregation list, it means that the vehicle is not in the aggregation area included in the aggregation list, but is parked in a new area. Therefore, it is necessary to count the number of vehicles that are offline in the cloud and have the same positioning snapshot data to obtain the statistical results.

[0140] First, you need to preset a clustering threshold for vehicles that are offline and have the same positioning snapshot data, and then compare the statistical results with the preset clustering threshold.

[0141] When the statistical result is greater than or equal to the aggregation threshold, it means that the area where these offline vehicles are parked meets the conditions to be used as a new aggregation area. Therefore, a new aggregation area and a new aggregation matrix corresponding to the new aggregation area are constructed based on the last known position of the vehicle to be detected. The specific construction method refers to the aforementioned method of constructing the aggregation area and the aggregation matrix corresponding to the aggregation area, and the new aggregation matrix is ​​added to the previously constructed aggregation list.

[0142] When the last known position exists in the aggregation list, it means that the vehicle is parked in the aggregation area included in the aggregation list. Then, it is determined whether the identification information corresponding to the vehicle exists in the aggregation list.

[0143] When the identification information does not exist in the aggregation list, it means that although the vehicle itself has been parked in the aggregation area included in the aggregation list, the vehicle cannot be found in the identification information contained in the aggregation list. Therefore, the identification information needs to be added to the aggregation list, so that relevant personnel can find the vehicle when locking the parked vehicle in the aggregation area in the aggregation list. Or, when relevant personnel search for the parking area of ​​the vehicle through the identification information, since the identification information of the vehicle has been added to the aggregation list, the area where the vehicle is parked at this time can be clearly presented.

[0144] Step 104: Determine the aggregation detection result for the vehicle to be detected according to the judgment result.

[0145] After obtaining the above series of judgment results, the embodiment of the present invention can determine whether the parking area of ​​the vehicle to be detected at this time is a gathering area, thereby realizing the gathering detection of vehicles.

[0146] The method also includes:

[0147] Determining whether the gathering detection result meets the preset gathering warning condition, and triggering the gathering warning for the vehicle to be detected when the gathering warning condition is met, may specifically include the following steps:

[0148] The embodiment of the present invention may preset a gathering alarm condition, such as a threshold value of the number of gathering vehicles, a threshold value of the gathering duration, etc. After obtaining the gathering detection result of the vehicle to be detected, it is determined whether the gathering detection result meets the preset gathering alarm condition.

[0149] In one example, if the preset aggregation alarm condition is a threshold value for the number of aggregated vehicles, then it is checked whether the number of vehicles in the aggregation list with the same position as the vehicle to be detected reaches or exceeds the preset threshold value for the number of aggregated vehicles. The embodiment of the present invention can query other vehicles in the aggregation list with the same position as the vehicle to be detected through positioning snapshot data.

[0150] In one example, if the preset aggregation alarm condition is an aggregation duration threshold, then it is checked whether the vehicles in the aggregation list that are at the same position as the vehicle to be detected have stayed at the same position for a period of time that reaches or exceeds the preset aggregation duration threshold. The embodiment of the present invention can query other vehicles in the aggregation list that have stayed at the same position as the vehicle to be detected through positioning snapshot data.

[0151] In addition, the gathering alarm condition may also be set to: triggering a gathering vehicle quantity threshold and a gathering alarm time threshold at the same time, and the present invention does not specifically limit this.

[0152] When the gathering detection result meets the preset gathering alarm condition, the gathering alarm for the vehicle to be detected is triggered. The embodiment of the present invention can send the alarm information to the car owner, and the content of the alarm information can include the number of gathered vehicles, the location of the gathering, the duration of the gathering, etc. For example, a text message is sent to the mobile terminal of the car owner, or an artificial voice phone is called to the car owner, or an email is sent to the mailbox of the car owner, and the present invention does not specifically limit this.

[0153] In another embodiment of the present invention, the car owner can independently set the sending method of the alarm content and the sending format corresponding to each sending method. For example, a variety of text message sending formats can be provided for the car owner to choose from, and a variety of artificial voice tones can also be provided for the car owner to choose from.

[0154] In one embodiment of the present invention, if the alarm condition is preset as: triggering the gathering vehicle quantity threshold and the gathering alarm time threshold at the same time, the specific steps are as follows:

[0155] Obtain the gathering detection results of the vehicles to be detected, including identification information and positioning snapshot data;

[0156] Acquire, from the aggregation list according to the positioning snapshot data, the number of aggregated vehicles located in the same aggregation area as the vehicle to be detected and the aggregation duration of the aggregated vehicles;

[0157] Comparing the aggregation quantity with a preset aggregation quantity threshold;

[0158] comparing the aggregation duration with a preset aggregation duration threshold;

[0159] If the number of gatherings is not less than the preset gathering number threshold and the gathering duration is not less than the preset gathering duration threshold, the gathering alarm for the vehicle to be detected is triggered and an alarm message is sent to the vehicle owner. The content of the alarm message includes at least information such as the number of gathered vehicles, the location of the gathering, and the duration of the gathering.

[0160] The present invention first obtains the login status of the vehicle to be detected in the cloud, and continuously monitors the positioning data of the vehicle to be detected, and formats the positioning data according to the preset data accuracy to generate the positioning snapshot data of the vehicle to be detected under the data accuracy, and then judges whether the identification information of the vehicle to be detected itself and / or the positioning snapshot data at a certain moment exists in the pre-built aggregation list according to the login status, and obtains the judgment result, thereby determining the aggregation detection result for the vehicle to be detected according to the judgment result. The present invention provides support for travel planning, can identify whether a specific vehicle has aggregation or concentration risks, and realizes system-level full data monitoring. On the other hand, the present invention can also identify the ease of parking of vehicles in advance and avoid congested aggregation parking areas.

[0161] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0162] Reference Figure 2 , shows a structural block diagram of a vehicle aggregation detection device provided in an embodiment of the present invention, which may specifically include the following modules:

[0163] The acquisition module 201 is used to acquire the login status of the vehicle to be detected in the cloud, and the vehicle to be detected has identification information.

[0164] The monitoring module 202 is used to monitor the positioning data of the vehicle to be detected, and format the positioning data according to a preset data accuracy to generate positioning snapshot data of the vehicle to be detected at the data accuracy.

[0165] The judgment module 203 is used to judge whether the identification information and / or the positioning snapshot data exist in the pre-built aggregation list according to the login status, and obtain a judgment result.

[0166] The determination module 204 is used to determine the aggregation detection result for the vehicle to be detected according to the judgment result.

[0167] The device also includes:

[0168] The sample data collection module is used to collect first positioning data and first identification information of a number of vehicles to be constructed, wherein the first positioning data is used to represent the latitude and longitude data of the vehicles to be constructed.

[0169] The training data generation module is used to generate the first positioning snapshot data of the vehicle to be constructed under a preset data accuracy.

[0170] The clustering area division module is used to divide the first positioning snapshot data according to data accuracy to obtain a number of clustering areas.

[0171] The latitude and longitude data screening module is used to screen out the maximum longitude and latitude values ​​in each cluster area from the longitude and latitude data.

[0172] The clustering matrix construction module is used to construct a clustering matrix corresponding to each clustering area according to the maximum longitude and latitude values, and the clustering matrix includes first identification information.

[0173] The cluster list generation module is used to summarize the cluster matrix to obtain the cluster list.

[0174] The aggregation list generation module also includes:

[0175] The traversal unit is used to loop through the latitude and longitude extremes corresponding to each clustering matrix to determine whether there are overlapping latitude and longitude extremes.

[0176] The merging unit is used to merge the clustering matrices corresponding to the latitude and longitude extremes to obtain a target clustering matrix when there are overlapping latitude and longitude extremes.

[0177] The first judgment unit is used to judge whether there is a second clustering matrix that is adjacent to the clustering matrix when there are no overlapping latitude and longitude extremes.

[0178] The second judgment unit is used to merge the clustering matrix with the second clustering matrix to obtain a target clustering matrix when the second clustering matrix exists.

[0179] The aggregation unit is used to aggregate the target aggregation matrix to obtain the aggregation list.

[0180] The login status of the vehicle to be detected in the cloud includes an online status and an offline status. The judgment module 203 includes:

[0181] The upload submodule is used to periodically upload the positioning snapshot data of the vehicle to be detected to the cloud.

[0182] The first judgment submodule is used to obtain a first judgment result according to the identification information and the positioning snapshot data when the login state is an online state, and the first judgment result is used to indicate whether the cluster list contains the identification information and the positioning snapshot data.

[0183] The second judgment submodule is used to obtain a second judgment result according to the positioning snapshot data when the login state is offline, and the second judgment result is used to indicate whether the cluster list contains the positioning snapshot data.

[0184] The first judgment submodule is specifically used for:

[0185] Get the current positioning snapshot data of the vehicle to be detected;

[0186] Determine whether the identification information and the current positioning snapshot data exist in the aggregation list;

[0187] If the current positioning snapshot data does not exist in the aggregation list, remove the identification information from the aggregation list;

[0188] If the current positioning snapshot data exists in the aggregation list, obtain the most recent positioning snapshot data of the vehicle to be detected, and determine whether the most recent positioning snapshot data exists in the aggregation list;

[0189] When the most recently located snapshot data does not exist in the aggregation list, the identification information is removed from the aggregation list.

[0190] The second judgment submodule is specifically used for:

[0191] Get the last known position of the vehicle to be detected. The last known position is the positioning snapshot data uploaded to the cloud by the vehicle to be detected for the last time.

[0192] Determine whether the last known position exists in the aggregation list;

[0193] When the last known location does not exist in the cluster list, the number of vehicles in the cloud that are offline and have the same location snapshot data is counted to obtain a statistical result;

[0194] Compare the statistical results with the preset aggregation threshold;

[0195] When the statistical result is greater than or equal to the aggregation threshold, a new aggregation area and a new aggregation matrix corresponding to the new aggregation area are constructed according to the last known position;

[0196] Add the new clustering matrix to the clustering list;

[0197] In the case where the last known position exists in the aggregation list, determining whether the identification information exists in the aggregation list;

[0198] In the case where the identification information does not exist in the aggregation list, the identification information is added to the aggregation list.

[0199] The device also includes:

[0200] The alarm module is used to determine whether the gathering detection result meets the preset gathering alarm condition, and trigger the gathering alarm for the vehicle to be detected if the gathering alarm condition is met.

[0201] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0202] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0203] The electronic equipment includes but is not limited to:

[0204] Memory 303, used for storing computer programs;

[0205] The processor 301 is used to execute the program stored in the memory 303 to implement the following steps:

[0206] Obtain the login status of the vehicle to be detected in the cloud, where the vehicle to be detected has identification information;

[0207] Monitor the positioning data of the vehicle to be detected, and format the positioning data according to the preset data accuracy to generate the positioning snapshot data of the vehicle to be detected at the data accuracy;

[0208] According to the login status, determine whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list, and obtain a determination result;

[0209] The aggregation detection result for the vehicle to be detected is determined according to the judgment result.

[0210] The bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0211] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0212] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0213] The present invention also provides a computer-readable storage medium having instructions stored thereon. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the vehicle aggregation detection method of the aforementioned embodiment.

[0214] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0215] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a disk, an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0216] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0217] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0218] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0219] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0220] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0221] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0222] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical disks.

[0223] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting a gathering of vehicles, characterized in that: Applied to a vehicle system, the vehicle system is connected to the cloud for communication, and the method includes: Acquire the login status of the vehicle to be detected in the cloud, wherein the vehicle to be detected has identification information; Monitoring the positioning data of the vehicle to be detected, and formatting the positioning data according to a preset data accuracy, to generate positioning snapshot data of the vehicle to be detected at the data accuracy; According to the login status, determining whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list, and obtaining a determination result; The aggregation detection result for the vehicle to be detected is determined according to the judgment result.

2. The method according to claim 1, characterized in that: The aggregation list is constructed as follows: Collecting first positioning data and first identification information of a plurality of vehicles to be constructed, wherein the first positioning data is used to represent the latitude and longitude data of the vehicles to be constructed; Generate first positioning snapshot data of the vehicle to be constructed at a preset data accuracy; Dividing the first positioning snapshot data according to the data accuracy to obtain a plurality of clustering areas; Filter out the maximum longitude and latitude values ​​in each of the clustered areas from the longitude and latitude data; Constructing a clustering matrix corresponding to each clustering area according to the latitude and longitude maximum values, wherein the clustering matrix includes the first identification information; The clustering matrices are aggregated to obtain a clustering list.

3. The method according to claim 2, characterized in that The step of aggregating the clustering matrix to obtain a clustering list includes: Loop through the latitude and longitude values ​​corresponding to each clustering matrix to determine whether there are overlapping latitude and longitude values; In the case where there are overlapping latitude and longitude extremes, merging the clustering matrices corresponding to the latitude and longitude extremes to obtain a target clustering matrix; In the case where there are no overlapping latitude and longitude extremes, determining whether there is a second clustering matrix that is an adjacent matrix to the clustering matrix; In the case where the second clustering matrix exists, merging the clustering matrix with the second clustering matrix to obtain a target clustering matrix; The target clustering matrix is ​​aggregated to obtain a clustering list.

4. The method according to claim 1, characterized in that The login status of the vehicle to be detected in the cloud includes an online status and an offline status, and judging whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list according to the login status includes: Regularly uploading the positioning snapshot data of the vehicle to be detected to the cloud; When the login state is the online state, obtaining a first judgment result according to the identification information and the positioning snapshot data, wherein the first judgment result is used to indicate whether the cluster list includes the identification information and the positioning snapshot data; When the login state is the offline state, a second judgment result is obtained according to the positioning snapshot data, and the second judgment result is used to indicate whether the cluster list includes the positioning snapshot data.

5. The method according to claim 4, characterized in that When the login state is the online state, obtaining a first judgment result according to the identification information and the positioning snapshot data includes: Obtaining current positioning snapshot data of the vehicle to be detected; Determining whether the identification information and the current positioning snapshot data exist in the aggregation list; If the current positioning snapshot data does not exist in the aggregation list, remove the identification information from the aggregation list; In the case where the current positioning snapshot data exists in the aggregation list, obtaining the most recent positioning snapshot data of the vehicle to be detected, and determining whether the most recent positioning snapshot data exists in the aggregation list; In a case where the most recent positioning snapshot data does not exist in the aggregation list, the identification information is removed from the aggregation list.

6. The method according to claim 4, characterized in that When the login state is the offline state, obtaining a second judgment result according to the positioning snapshot data includes: Acquire the last known position of the vehicle to be detected, where the last known position is the positioning snapshot data uploaded to the cloud by the vehicle to be detected for the last time; determining whether the last known position exists in the aggregation list; In the case where the last known location does not exist in the aggregation list, counting the number of vehicles in the cloud that are in the offline state and have the same positioning snapshot data to obtain a statistical result; Comparing the statistical result with a preset aggregation threshold; When the statistical result is greater than or equal to the clustering threshold, constructing a new clustering area and a new clustering matrix corresponding to the new clustering area according to the last known position; adding the new clustering matrix to the clustering list; In the case where the last known position exists in the aggregation list, determining whether the identification information exists in the aggregation list; In a case where the identification information does not exist in the aggregation list, the identification information is added to the aggregation list.

7. The method according to claim 1, characterized in that The method further comprises: It is determined whether the gathering detection result satisfies a preset gathering warning condition, and if the gathering warning condition is met, a gathering warning for the vehicle to be detected is triggered.

8. A vehicle gathering detection device, characterized in that: Applied to a vehicle system, the vehicle system is connected to the cloud for communication, and the device includes: An acquisition module, used for acquiring the login status of the vehicle to be detected in the cloud, wherein the vehicle to be detected has identification information; A monitoring module, used for monitoring the positioning data of the vehicle to be detected, formatting the positioning data according to a preset data accuracy, and generating positioning snapshot data of the vehicle to be detected at the data accuracy; A judgment module, used to judge whether the identification information and / or the positioning snapshot data exists in a pre-built aggregation list according to the login state, and obtain a judgment result; A determination module is used to determine the aggregation detection result for the vehicle to be detected according to the judgment result.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to execute the method according to any one of claims 1 to 7.

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