CN111127035A - Confidence detection method and system based on track data - Google Patents
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Abstract
The application discloses a confidence detection method and a system based on track data, which comprises the following steps: acquiring a track factor and a correlation factor according to user data, and determining a feature vector; training a prediction model according to the feature vector and the label, and determining the identity category of the user by using the trained model; acquiring audit data of a user, and determining an operation factor according to vehicle track data; training a confidence coefficient model according to the identity category and the operation factor; and determining the confidence of the user according to the trained confidence model. Determining a feature vector according to user data, training a prediction model according to the feature vector and a label, and determining the identity category of a user by using the trained model; determining an operation factor according to the audit data of the user and the vehicle track data; the confidence coefficient model is trained through the identity category and the operation factor, the confidence coefficient of the user is determined, and the identity of the user can be judged and the confidence coefficient of the operation condition can be detected by using other data.
Description
Technical Field
The present application relates to the field of confidence detection, and in particular, to a method and a system for confidence detection based on trajectory data.
Background
In the prior art, a confidence monitoring mode based on vehicle operation cost exists, but most of the confidence monitoring modes need to rely on a large amount of determined data provided by a user to carry out identity judgment and operation analysis. And under the condition that the provided data is insufficient, calculation can not be performed according to other data, and identity judgment and operation analysis can not be performed on the user.
In view of the foregoing, it is desirable to provide a method and system for determining the identity of a user and performing confidence detection on an operation condition by using other data in the case that determined data is insufficient.
Disclosure of Invention
In order to solve the above problems, the present application provides a confidence detection method and system based on trajectory data.
In one aspect, the present application provides a confidence detection method based on trajectory data, including:
acquiring a track factor and a correlation factor according to user data, and determining a feature vector;
training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
acquiring audit data of a user, and determining an operation factor according to vehicle track data;
training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model;
and determining the confidence of the user according to the trained confidence model.
Preferably, the obtaining a trajectory factor and a correlation factor according to the user data and determining the feature vector includes:
determining a trajectory factor from user data
Determining a correlation factor according to the user data;
and extracting the track factors and the correlation factors of the confirmed identity users as feature vectors.
Preferably, the determining a trajectory factor according to the user data includes:
determining the number of vehicles matched by the user according to the user data;
determining a user stop point according to the mobile phone data in the user data and a clustering algorithm;
obtaining the type of the staying point, counting the frequency, and obtaining the type and the staying times of the staying point;
counting the number of the stay points in a period of time, taking the number as the dispersion of the position of the stay points, and determining the number of the stay points;
counting the long-distance moving frequency and the mobile phone position span of the user according to the mobile phone data;
the track factor is composed of the number of vehicles matched by the user, the type of the stop points, the stop times, the number of the stop points, the long-distance moving frequency and the position span of the mobile phone.
Preferably, the determining a correlation factor according to the user data includes:
counting the use frequency of key functions in the mobile phone of the user;
determining the number of the authentication vehicles and the number of the concerned vehicles of the user according to the user data;
the key function usage frequency, the number of certified vehicles, and the number of vehicles of interest form a correlation factor.
Preferably, the key functions include: refueling, vehicle checking, goods finding, insurance and ETC.
Preferably, the determining the identity category of the user by using the trained model comprises:
and inputting user data to the trained prediction model to obtain the identity category of the user.
Preferably, the obtaining of the audit data of the user and the determining of the operation factor according to the vehicle trajectory data includes:
acquiring vehicle data of the user according to the audit data of the user;
determining the number of the authenticated vehicles and the operating mileage of each vehicle according to the vehicle data of the user;
determining the total operating mileage and the average operating mileage of all vehicles according to the number of the authenticated vehicles and the operating mileage of each vehicle;
the number of the certification vehicles, the total operating mileage and the average operating mileage constitute an operating factor.
Preferably, the training a confidence model according to the identity category and the operation factor to obtain a trained confidence model further includes:
and training a confidence coefficient model according to the identity category, the operation factors and the external data to obtain the trained confidence coefficient model.
Preferably, the determining the number of the matched vehicles of the user according to the user data comprises:
determining the number of matched vehicles of the user by using the existing data and/or determining the number of matched vehicles of the user according to the user data and the vehicle data.
In a second aspect, the present application provides a confidence detection system based on trajectory data, including:
the identity judgment module is used for acquiring the track factor and the related factor according to the user data and determining the characteristic vector; training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
the operation analysis module is used for acquiring audit data of a user and determining an operation factor according to the vehicle track data;
the comprehensive detection module is used for training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model; and determining the confidence of the user according to the trained confidence model.
The application has the advantages that: determining a feature vector through user data, training a prediction model according to the feature vector and a label, and determining the identity category of a user by using the trained model; determining an operation factor according to the audit data of the user and the vehicle track data; the confidence coefficient model is trained through the identity category and the operation factor, the confidence coefficient of the user is determined, and the identity of the user can be judged and the confidence coefficient of the operation condition can be detected by using other data.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to denote like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram illustrating the steps of a confidence detection method based on trajectory data according to the present application;
FIG. 2 is a schematic diagram of a confidence detection system based on trajectory data provided herein;
fig. 3 is a schematic flowchart of a confidence detection system based on trajectory data according to the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
According to an embodiment of the present application, a confidence detection method based on trajectory data is provided, as shown in fig. 1, including:
s101, acquiring a track factor and a correlation factor according to user data, and determining a feature vector;
s102, training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
s103, acquiring audit data of a user, and determining an operation factor according to vehicle track data;
s104, training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model;
and S105, determining the confidence of the user according to the trained confidence model.
According to user data, acquiring a track factor and a correlation factor, and determining a feature vector, wherein the method comprises the following steps:
determining a trajectory factor from user data
Determining a correlation factor according to the user data;
and extracting the track factors and the correlation factors of the users with confirmed identities as feature vectors.
Determining a trajectory factor from the user data, comprising:
determining the number of vehicles matched by the user according to the user data;
determining a user stop point according to mobile phone data in the user data and a clustering algorithm;
obtaining the type of the stop point, counting the frequency, and obtaining the type and the stop times of the stop point;
counting the number of the stop points in a period of time, taking the number as the dispersion of the positions of the stop points, and determining the number of the stop points;
counting the long-distance moving frequency and the mobile phone position span of a user according to the mobile phone data;
the number of vehicles matched by the user, the types of the stop points, the stop times, the number of the stop points, the long-distance moving frequency and the position span of the mobile phone form a track factor.
Determining a correlation factor from the user data, comprising:
counting the use frequency of key functions in the mobile phone of the user;
determining the number of the authentication vehicles and the number of the concerned vehicles of the user according to the user data;
the frequency of key function usage, the number of authorized vehicles and the number of vehicles of interest form a correlation factor.
Key functions, including: refueling, vehicle checking, goods finding, insurance and ETC.
Determining the identity category of the user using the trained model, comprising:
and inputting user data to the trained prediction model to obtain the identity category of the user.
Obtaining audit data of a user, and determining an operation factor according to vehicle track data, wherein the operation factor comprises the following steps:
acquiring vehicle data of the user according to the audit data of the user;
determining the number of the authenticated vehicles and the operating mileage of each vehicle according to the vehicle data of the user;
determining the total operating mileage and the average operating mileage of all vehicles according to the number of the vehicles to be authenticated and the operating mileage of each vehicle;
the number of vehicles to be authenticated, the total operating mileage, and the average operating mileage constitute an operating factor.
Training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model, and further comprising:
and training the confidence coefficient model according to the identity category, the operation factor and the external data to obtain the trained confidence coefficient model.
A prediction model and a confidence model, each comprising: and (5) classifying the models.
Determining the number of vehicles matched by the user according to the user data comprises:
and determining the number of the matched vehicles of the users by using the existing data and/or determining the number of the matched vehicles of the users according to the user data and the vehicle data.
Not all users have an audit, so not all users have audit data.
External data includes other retrievable data such as credit ratings of banking systems, consumption records of payment software and consumption platforms, etc.
The confidence model includes: overall confidence determination and classification confidence determination. During training, selection can be performed according to needs. Because the data obtained by the operation factors is only used for the car owners, when the users for determining the confidence level include non-car owners or car owners who do not drive the car, the overall confidence level determination cannot calculate the confidence level of the users more accurately.
The dwell Point may represent a Point of Interest (POI) of the user. In the geographic information system, one POI may be one house, one shop, one mailbox, one bus station, and the like.
The tag (user identity tag) is obtained by the user data of the confirmed identity.
The user data includes: user mobile phone data and vehicle track data of the user.
In a second aspect, according to an embodiment of the present application, there is further provided a confidence detection system based on trajectory data, as shown in fig. 2, including:
the identity judgment module 101 is used for acquiring a track factor and a correlation factor according to user data and determining a feature vector; training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
the operation analysis module 102 is configured to obtain audit data of a user, and determine an operation factor according to vehicle trajectory data;
the comprehensive detection module 103 is used for training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model; and determining the confidence of the user according to the trained confidence model.
In the following, the embodiment of the present application is further explained, taking a shipping user as an example, as shown in fig. 3.
The identity judgment module judges the identity of the user by using a machine learning method, inputs the track data in the mobile phone data of the freight user and the track data of the freight vehicle, and finally outputs a label of the identity category of the target user.
The method comprises the following specific steps:
1. trajectory factor calculation
And calculating track factors consisting of factors such as the number of vehicles matched by the user, the types and the stay times of mobile phone stay points, the number of the stay points, the long-distance moving frequency of the mobile phone data, the position span of the mobile phone and the like by utilizing the vehicle track data and the user track data (track data in the mobile phone data of the user).
The factors have the following meanings:
the number of matched vehicles of the user is as follows: the number of vehicles with the matching degree with the target user track meeting the threshold value is generally 0 for part of owners of the vehicles which do not drive and family members which do not drive the vehicles;
POI type and stay times of the mobile phone stay point: and clustering the report points of the user mobile phone by using DBSCAN, finding the central point of the high-density area as a stop point, inquiring POI data, obtaining the POI type of the target position, and counting the frequency. The types currently defined include gas stations, logistics parks, schools, residential areas, etc. A typical scenario is that the number of the frequently-staying points of a certain family is 3, and after POI analysis, 3 points are found to be a kindergarten, a residential area and an office building respectively;
number of mobile phone stop points: and counting the number of the stop points in a period of time, wherein the number is used for representing the dispersion of the position where the user appears. For drivers, the number of the stop points is higher, and for owners and family members, the number of the stop points is lower;
the mobile phone moves for a long distance frequently: counting the number of track segments with the moving distance of more than 50 kilometers (the number of kilometers can be set) in the track sequence of the target user to represent the frequency of long-distance movement of the user. For the driver and some car owners, the value will be high;
the position span of the mobile phone is as follows: including longitude and latitude spans, to characterize the user's maximum range of motion, which is high for the driver and some car owners.
2. Correlation factor calculation
The use frequency of users of each key function point in the mobile phone software is counted, such as refueling, vehicle searching, goods finding, insurance, ETC and the like, and users with different identities have statistical preference for each function. Further, factors such as the number of vehicles authenticated by the user and the number of vehicles paid attention by the user may be used as the correlation factors of the identity determination model.
3. Constructing feature vectors
Extracting track factors, correlation factors and user identity labels of the users with confirmed identities as marking data, including but not limited to vehicle number, mobile phone stop point POI types and stop times, mobile phone stop point number, mobile phone long-distance moving frequency, mobile phone position span, refueling function use times, vehicle checking function use times, cargo finding function use times, insurance function use times, ETC function use times, user authentication vehicle number, user attention vehicle number and the like matched with the users.
4. Model training and prediction
The basic model of the prediction model is a classification model, which comprises the following steps: logistic regression, support vector machines, decision trees, and the like. Preferably, for the decision tree model, the outputting the classification comprises: owner, driver, family members, and others.
And the operation analysis module evaluates the operation condition of the authenticated vehicle by using vehicle track data aiming at the user subjected to strict identity and people-vehicle relationship examination. The input of the operation analysis module is freight vehicle track data, and the output core factors comprise total operating mileage of all vehicles, average operating mileage of the vehicles, the number of the authenticated vehicles and the like.
The factors have the following meanings:
total mileage of all vehicle operations: representing the overall operating condition of the user-authenticated vehicle;
the average mileage of the vehicle operation is as follows: representing vehicle operating efficiency;
the number of authenticated vehicles: indicating the user vehicle asset condition.
The comprehensive detection module utilizes the identity category of the user and the operation condition of the vehicle to which the user belongs, and can also use other externally introduced data (external data, such as credit rating of a bank system, consumption condition records of payment software, a consumption platform and the like, public bad record number, credit product number in use, public bankruptcy record number, first loan time and the like) to construct the confidence coefficient of the user by combining with the default record of the target user.
The confidence model of the integrated detection module generally uses logistic regression or decision trees.
This module includes two implementations:
1. overall confidence detection
And the identity category of the user is used as the characteristic of the confidence coefficient model to finally obtain a uniform model.
2. Classification confidence detection
And respectively constructing an owner confidence coefficient model, a driver confidence coefficient model and a family confidence coefficient model according to the identity label of the user, so as to realize differentiated confidence coefficient detection.
Further explanation is made by taking classification confidence detection as an example.
The user a judges that the user a is the owner of the vehicle in the identity judgment module through user data and other related data, and then confidence detection can be carried out by utilizing an owner confidence model;
the user b judges the user b as a driver in the identity judgment module through user data and other related data, and confidence detection can be carried out by utilizing a driver confidence model;
and the user c judges the user c as a family in the identity judgment module through user data and other related data, and then confidence detection can be carried out by utilizing a family confidence model.
In the method, a characteristic vector is determined through user data, a prediction model is trained according to the characteristic vector and a label, and the identity category of a user is determined by using the trained model; determining an operation factor according to the audit data of the user and the vehicle track data; the confidence coefficient model is trained through the identity category and the operation factor, the confidence coefficient of the user is determined, and the identity of the user can be judged and the confidence coefficient of the operation condition can be detected by using other data.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A confidence detection method based on track data is characterized by comprising the following steps:
acquiring a track factor and a correlation factor according to user data, and determining a feature vector;
training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
acquiring audit data of a user, and determining an operation factor according to vehicle track data;
training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model;
and determining the confidence of the user according to the trained confidence model.
2. The method of claim 1, wherein the obtaining trajectory factors and correlation factors from the user data and determining feature vectors comprises:
determining a track factor according to user data;
determining a correlation factor according to the user data;
and extracting the track factors and the correlation factors of the confirmed identity users as feature vectors.
3. The method of claim 2, wherein determining a trajectory factor based on the user data comprises:
determining the number of vehicles matched by the user according to the user data;
determining a user stop point according to the mobile phone data in the user data and a clustering algorithm;
obtaining the type of the staying point, counting the frequency, and obtaining the type and the staying times of the staying point;
counting the number of the stay points in a period of time, taking the number as the dispersion of the position of the stay points, and determining the number of the stay points;
counting the long-distance moving frequency and the mobile phone position span of the user according to the mobile phone data;
the track factor is composed of the number of vehicles matched by the user, the type of the stop points, the stop times, the number of the stop points, the long-distance moving frequency and the position span of the mobile phone.
4. The method of claim 2, wherein determining a correlation factor based on user data comprises:
counting the use frequency of key functions in the mobile phone of the user;
determining the number of the authentication vehicles and the number of the concerned vehicles of the user according to the user data;
the key function usage frequency, the number of certified vehicles, and the number of vehicles of interest form a correlation factor.
5. The method of claim 4, wherein the critical functions comprise: refueling, vehicle checking, goods finding, insurance and ETC.
6. The method of claim 1, wherein the determining the identity class of the user using the trained model comprises:
and inputting user data to the trained prediction model to obtain the identity category of the user.
7. The method of claim 1, wherein the obtaining audit data for a user, determining an operational factor based on vehicle trajectory data, comprises:
acquiring vehicle data of the user according to the audit data of the user;
determining the number of the authenticated vehicles and the operating mileage of each vehicle according to the vehicle data of the user;
determining the total operating mileage and the average operating mileage of all vehicles according to the number of the authenticated vehicles and the operating mileage of each vehicle;
the number of the certification vehicles, the total operating mileage and the average operating mileage constitute an operating factor.
8. The method of claim 1, wherein training a confidence model based on the identity class and the operation factor to obtain a trained confidence model, further comprises:
and training a confidence coefficient model according to the identity category, the operation factors and the external data to obtain the trained confidence coefficient model.
9. The method of claim 3, wherein determining a number of user matching vehicles based on the user data comprises:
determining the number of matched vehicles of the user by using the existing data and/or determining the number of matched vehicles of the user according to the user data and the vehicle data.
10. A trajectory data based confidence detection system, comprising:
the identity judgment module is used for acquiring the track factor and the related factor according to the user data and determining the characteristic vector; training a prediction model according to the feature vector and the label to obtain a trained prediction model, and determining the identity category of the user by using the trained prediction model;
the operation analysis module is used for acquiring audit data of a user and determining an operation factor according to the vehicle track data;
the comprehensive detection module is used for training a confidence coefficient model according to the identity category and the operation factor to obtain a trained confidence coefficient model; and determining the confidence of the user according to the trained confidence model.
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