Method for determining working days based on big data analysis and related products

Abstract

The embodiment of the present application discloses a method for determining working days based on big data analysis and related products, the method for determining working days based on big data analysis includes: obtaining vehicle information data and user data of a first user; performing working day modeling analysis based on the vehicle information data and user data to obtain a first analysis result; and analyzing the first analysis result to obtain a target analysis result of the working day of the first user. The embodiment of the present application performs working day modeling analysis through the vehicle information data and user data of the first user to improve the recognition accuracy of working day attribute labels.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for determining working days based on big data analysis and related products. Background Art

[0002] With the rapid development of communication technology, people and things are a collection of attribute labels in the Internet of Things. In order to meet people's demand for improving the quality of life, we need to have a full understanding of people's attributes. Among them, weekdays are an important attribute label for people. Weekdays have a relatively regular commuting cycle and are the premise for route recommendations, commuting entertainment recommendations, etc. On non-working days, they are the premise for shopping recommendations, travel recommendations, etc.

[0003] However, the existing method for determining the working day of a user has less data, resulting in low accuracy in determining the working day of the user. Therefore, a solution to improve the accuracy of determining the working day of the user is urgently needed. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a method for determining working days based on big data analysis and related products, which can improve the accuracy of identifying working day attribute labels.

[0005] In a first aspect, an embodiment of the present application provides a method for determining working days based on big data analysis, and the method for determining working days based on big data analysis includes:

[0006] Acquiring vehicle information data and user data of the first user;

[0007] Performing a workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0008] The first analysis result is analyzed to obtain a target analysis result of the first user's working day.

[0009] Optionally, performing workday modeling analysis according to the vehicle information data and the user data to obtain a first analysis result includes:

[0010] Preprocessing the vehicle information data and the user data to obtain first data;

[0011] The first data is subjected to working day modeling analysis by using a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result.

[0012] Optionally, the preprocessing the vehicle information data and the user data to obtain the first data includes:

[0013] Pre-extracting and cleaning the vehicle information data and the user data in sequence to obtain second data;

[0014] The second data is standardized to obtain the first data.

[0015] Optionally, performing workday modeling analysis on the first data by using a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result includes:

[0016] Performing workday modeling analysis on the first data by using the cosine distance algorithm to obtain a second analysis result;

[0017] The first data is subjected to working day modeling analysis by using the density clustering algorithm to obtain the first analysis result.

[0018] Optionally, performing workday modeling analysis on the first data by using a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result includes:

[0019] Performing workday modeling analysis on the first data by using the density clustering algorithm to obtain a second analysis result;

[0020] The first data is subjected to working day modeling analysis using the cosine distance algorithm to obtain the first analysis result.

[0021] Optionally, performing workday modeling analysis on the first data by using a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result includes:

[0022] Performing workday modeling analysis on the first data by using the cosine distance algorithm to obtain a second analysis result;

[0023] Performing workday modeling analysis on the first data by using the density clustering algorithm to obtain a third analysis result;

[0024] The second analysis result and the third analysis result are integrated to obtain the first analysis result.

[0025] Optionally, obtaining the vehicle information data and user data of the first user includes:

[0026] Acquire vehicle information data from an onboard device of a first vehicle, where a user corresponding to the first vehicle is the first user;

[0027] User data related to the first user is obtained, where the user data is behavior data of the first user in the application.

[0028] In a second aspect, an embodiment of the present application provides a device for determining working days based on big data analysis, and the device for determining working days based on big data analysis includes:

[0029] an acquisition unit, configured to acquire vehicle information data of a first user and user data of the first user;

[0030] A first analysis unit, configured to perform a workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0031] The second analysis unit is used to analyze the first analysis result to obtain a target analysis result of the first user's working day.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect of the embodiment of the present application.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0035] It can be seen that in the embodiment of the present application, the vehicle information data and user data of the first user are obtained; a working day modeling analysis is performed based on the vehicle information data and the user data to obtain a first analysis result; and the first analysis result is analyzed to obtain a target analysis result of the working day of the first user. The embodiment of the present application performs working day modeling analysis through the vehicle information data and the user data of the first user to improve the recognition accuracy of working day attribute labels. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1A schematic diagram of determining working days based on big data analysis provided in an embodiment of the present application;

[0038] Figure 2 A flowchart of a method for determining working days based on big data analysis provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of a method for determining working days based on big data analysis provided in an embodiment of the present application;

[0040] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0041] Figure 5 This is a block diagram of the functional units of a device for determining working days based on big data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0044] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] The embodiments of the present application are described in detail below.

[0046] See also Figure 1 , Figure 1This is a schematic diagram of determining working days based on big data analysis provided by an embodiment of the present application. By collecting vehicle information data and user data, analyzing the vehicle information data and user data based on big data, the user's commuting time, commuting scene and working day are obtained, and the user's commuting time, commuting scene and working day are further analyzed to obtain the user's working time label and working day label.

[0047] This application provides a method for determining working days based on big data analysis, specifically: Figure 2 As shown, the method may include but is not limited to the following steps:

[0048] S201, the electronic device obtains vehicle information data and user data of a first user;

[0049] In a specific implementation, the electronic device obtains vehicle information data and user data of the first user, including: the electronic device obtains vehicle information data from an on-board device of the first vehicle, and the user corresponding to the first vehicle is the first user; the electronic device obtains user data of the first user, and the user data is the behavior data of the first user in an application.

[0050] Among them, it needs to be further explained that the time period for the electronic device to obtain the vehicle information data and user data of the first user can be one week, two weeks, three weeks, one month, two months, three months, six months, one year, or two years. The time period for obtaining data can be any time period, and there are no excessive restrictions here.

[0051] The first vehicle may be a vehicle owned by the first user, or a vehicle bound by the first user in a vehicle-related application APP. The vehicle-related application APP may be an APP related to vehicle navigation or an APP related to positioning. The number of first vehicles may be 1, 2, 3, 4, 5, 6, 7, etc., and there is no excessive restriction here.

[0052] Among them, the vehicle information data includes vehicle sensor information data and the vehicle's own data. The vehicle sensor information data includes at least one of the following: vehicle fuel consumption, vehicle location, vehicle interior temperature, driving time, and driving duration. The vehicle's own data includes: the model of the first vehicle, the corresponding user identity information, and the performance data of the first vehicle.

[0053] Among them, the user data of the first user may be the location information of the company, the location information of the home, the vehicle driving time information, and the vehicle driving trajectory information of the first user in the user behavior data in the navigation APP of the first user.

[0054] For example, when the first vehicle is the only vehicle of the first user, the electronic device obtains the vehicle fuel consumption, vehicle location, and vehicle interior temperature from the vehicle information data of the on-board device of the first vehicle; the electronic device obtains the location information of the first user's company, home location information, vehicle driving time information, and vehicle driving trajectory information from the user behavior data in the first user's navigation APP.

[0055] S202, the electronic device performs a workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0056] In a specific implementation, the electronic device performs a weekday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result, including: the electronic device preprocesses the vehicle information data and the user data to obtain first data; the electronic device performs a weekday modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result.

[0057] Among them, the density clustering algorithm includes any of the following: K-means clustering algorithm (K-means), Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Maximum Density Clustering Algorithm (MDCA).

[0058] Among them, the cosine distance algorithm is Among them, A and B correspond to the vehicle information data and user data of the first user on the first day respectively, n is the total number of vehicle information data and user data of the first user on the first day, and i is the sequence number of the vehicle information data and user data of the first user on the first day.

[0059] Among them, it needs to be further explained that the electronic device preprocesses the vehicle information data and the user data to obtain the first data, including: the electronic device extracts and cleans the vehicle information data and the user data in sequence to obtain the second data; the electronic device standardizes the second data to obtain the first data.

[0060] The electronic device extracts and cleanses the vehicle information data and the user data in sequence to obtain second data, including but not limited to: the electronic device extracts, cleanses and converts the vehicle information data and the user data through the ETL data service to obtain the second data.

[0061] Among them, it needs to be further explained that the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result, including: the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm to obtain the second analysis result; the electronic device performs working day modeling analysis on the first data through the density clustering algorithm to obtain the third analysis result; the electronic device integrates the second analysis result and the third analysis result to obtain the first analysis result.

[0062] 1. The electronic device performs a workday modeling analysis on the first data by using a cosine distance algorithm to obtain a second analysis result, which may be:

[0063] M data points are randomly selected from the first data as initial data, and the distance between each data point in the first data and the M initial data is calculated according to the cosine distance algorithm. If the distance between a data point in the first data and the M initial data is less than a first threshold, the data point in the first data is a data point in a first data point set corresponding to the initial data, and first data point sets corresponding to the M initial data are obtained; the M first data point sets are integrated to obtain a second analysis result.

[0064] 2. The electronic device performs workday modeling analysis on the first data by using a density clustering algorithm to obtain a third analysis result. The specific steps may be:

[0065] Step 1: First, the electronic device inputs a value of k, where k is the number of groups obtained by clustering the first data into k groups through the K-means algorithm;

[0066] Step 2: The electronic device randomly selects k data points from the first data as initial data, namely, initial centroids;

[0067] Step 3, the electronic device calculates the distance between each data except the initial data in the first data and each data point in the k data points, and follows the data point to which each data except the initial data in the first data is closest, thereby obtaining k grouped data;

[0068] Step 4: The electronic device selects a new centroid from each of the k grouped data using a K-means algorithm;

[0069] Step 5: If the distance between the new centroid and the initial centroid in each of the k grouped data is less than the first threshold, it means that the position of the new centroid does not change much and tends to be stable, or converges, that is, the clustering has achieved the desired result, the algorithm terminates, and the third analysis result is obtained.

[0070] Step 6: If the distance between the new centroid and the initial centroid in each of the k grouped data is greater than the first threshold, steps 3-5 need to be iterated.

[0071] The electronic device in step 3 calculates the distance algorithm between each data in the first data except the initial data and each data point in the k data points, which may be a cosine similarity algorithm or a Euclidean distance algorithm.

[0072] Among them, it needs to be further explained that the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result, including: the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm to obtain the second analysis result; the electronic device performs working day modeling analysis on the second analysis result through the density clustering algorithm to obtain the first analysis result.

[0073] It should be explained that the electronic device performs workday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result, including: randomly selecting M data points in the first data as initial data, and calculating the distance between each data point in the first data and the M initial data according to the cosine distance algorithm; if the distance between the data point in the first data and the M initial data is less than a first threshold, then the data point in the first data is a data point in the first data point set corresponding to the initial data, and the first data point sets corresponding to the M initial data are obtained; the M first data point sets are integrated to obtain the second analysis result.

[0074] It should be explained that the electronic device performs workday modeling analysis on the second analysis result through a density clustering algorithm, and the first analysis result includes:

[0075] Step 1, first, the electronic device inputs a value of k, where k is the number of groups obtained by clustering the data corresponding to the second analysis result through the K-means algorithm, and k is a positive integer;

[0076] Step 2: The electronic device randomly selects k data points from the data corresponding to the second analysis result as initial data, namely, initial centroids;

[0077] Step 3, the electronic device calculates the distance between each data except the initial data in the data corresponding to the second analysis result and each data point in the k data points, and follows the data point to which each data except the initial data in the data corresponding to the second analysis result is closest, thereby obtaining k grouped data;

[0078] Step 4: The electronic device selects a new centroid from each of the k grouped data using a K-means algorithm;

[0079] Step 5: If the distance between the new centroid and the initial centroid in each of the k grouped data is less than the first threshold, it means that the position of the new centroid does not change much and tends to be stable, or converges, that is, the clustering has achieved the desired result, the algorithm terminates, and the first analysis result is obtained.

[0080] Step 6: If the distance between the new centroid and the initial centroid in each of the k grouped data is greater than the first threshold, steps 3-5 need to be iterated.

[0081] The electronic device in step 3 calculates the distance algorithm between each data except the initial data in the data corresponding to the second analysis result and each data point in the k data points, which may be a cosine similarity algorithm or a Euclidean distance algorithm.

[0082] Among them, it needs to be explained that the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result, including: the electronic device performs working day modeling analysis on the first data through the density clustering algorithm to obtain the second analysis result; the electronic device performs working day modeling analysis on the second analysis result through the cosine distance algorithm to obtain the first analysis result.

[0083] It is further explained that the electronic device performs workday modeling analysis on the first data by using a density clustering algorithm to obtain a second analysis result, and the specific steps may be:

[0084] Step 1: First, the electronic device inputs a value of k, where k is the number of groups obtained by clustering the first data into k groups through the K-means algorithm;

[0085] Step 2: The electronic device randomly selects k data points from the first data as initial data, namely, initial centroids;

[0086] Step 3, the electronic device calculates the distance between each data except the initial data in the first data and each data point in the k data points, and follows the data point to which each data except the initial data in the first data is closest, thereby obtaining k grouped data;

[0087] Step 4: The electronic device selects a new centroid from each of the k grouped data using a K-means algorithm;

[0088] Step 5: If the distance between the new centroid and the initial centroid in each of the k grouped data is less than the first threshold, it means that the position of the new centroid does not change much and tends to be stable, or converges, that is, the clustering has achieved the desired result, the algorithm terminates, and the second analysis result is obtained.

[0089] Step 6: If the distance between the new centroid and the initial centroid in each of the k grouped data is greater than the first threshold, steps 3-5 need to be iterated.

[0090] The electronic device in step 3 calculates the distance algorithm between each data in the first data except the initial data and each data point in the k data points, which may be a cosine similarity algorithm or a Euclidean distance algorithm.

[0091] It needs to be further explained that the electronic device performs workday modeling analysis on the second analysis result through the cosine distance algorithm, and the first analysis result includes: randomly selecting M data points as initial data in the data corresponding to the second analysis result, and calculating the distance between each data point in the data corresponding to the second analysis result and the M initial data according to the cosine distance algorithm; if the distance between the data point in the data corresponding to the second analysis result and the M initial data is less than a first threshold, then the data point in the data corresponding to the second analysis result is a data point in the first data point set corresponding to the initial data, and the first data point sets corresponding to the M initial data are obtained; the M first data point sets are integrated to obtain the first analysis result.

[0092] Among them, it needs to be explained that the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result, including: the electronic device performs working day modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result.

[0093] Among them, the cosine distance algorithm is Among them, A and B correspond to the vehicle information data and user data of the first user on the first day respectively, n is the total number of vehicle information data and user data of the first user on the first day, and i is the sequence number of the vehicle information data and user data of the first user on the first day.

[0094] It should be explained that the electronic device performs workday modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result, including: randomly selecting M data points in the first data as initial data, and calculating the distance between each data point in the first data and the M initial data according to the cosine distance algorithm; if the distance between the data point in the first data and the M initial data is less than a first threshold, then the data point in the first data is a data point in the first data point set corresponding to the initial data, and the first data point sets corresponding to the M initial data are obtained; the M first data point sets are integrated to obtain the first analysis result.

[0095] Among them, it needs to be further explained that the electronic device performs working day modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, including: the electronic device performs working day modeling analysis on the first data through a density clustering algorithm to obtain the first analysis result.

[0096] For example, when the density clustering algorithm is a K-means algorithm, the electronic device performs a workday modeling analysis on the first data by using the density clustering algorithm to obtain the first analysis result, which may be:

[0097] Step 1: First, the electronic device inputs a value of k, where k is the number of groups obtained by clustering the first data into k groups through the K-means algorithm;

[0098] Step 2: The electronic device randomly selects k data points from the first data as initial data, namely, initial centroids;

[0099] Step 3, the electronic device calculates the distance between each data except the initial data in the first data and each data point in the k data points, and follows the data point to which each data except the initial data in the first data is closest, thereby obtaining k grouped data;

[0100] Step 4: The electronic device selects a new centroid from each of the k grouped data using a K-means algorithm;

[0101] Step 5: If the distance between the new centroid and the initial centroid in each of the k grouped data is less than the first threshold, it means that the position of the new centroid does not change much and tends to be stable, or converges, that is, the clustering has achieved the desired result, the algorithm terminates, and the first analysis result is obtained.

[0102] Step 6: If the distance between the new centroid and the initial centroid in each of the k grouped data is greater than the first threshold, steps 3-5 need to be iterated.

[0103] The electronic device in step 3 calculates the distance algorithm between each data in the first data except the initial data and each data point in the k data points, which may be a cosine similarity algorithm or a Euclidean distance algorithm.

[0104] S203: The electronic device analyzes the first analysis result to obtain a target analysis result of the first user's working day.

[0105] In a specific implementation, the electronic device analyzes the first analysis result to obtain a target analysis result of the first user's working day, including: the electronic device analyzes the first analysis result to obtain the first user's commuting scenario, commuting time, and working days; further analyzes the first user's commuting scenario, commuting time, and working days to obtain the target analysis result of the first user, that is, the first user's working time, and the above working time includes: the first user's monthly working days, the working hours corresponding to each working day, and the working time period.

[0106] In a specific implementation, after the electronic device analyzes the first analysis result to obtain the target analysis result of the first user's working day, it also includes: the electronic device makes an intelligent recommendation to the first terminal of the first user based on the target analysis result of the first user's working day, and the intelligent recommendation includes any one of the following: route recommendation, entertainment recommendation, shopping recommendation and travel recommendation.

[0107] For example, the electronic device determines, based on the target analysis result of the first user's working day, that the current day is the first user's working day, and recommends to the first terminal of the first user a driving route with the shortest driving time from the first user's home to the company.

[0108] It can be seen that the embodiment of the present application obtains the vehicle information data and user data of the first user; performs workday modeling analysis based on the vehicle information data and user data to obtain a first analysis result; and analyzes the first analysis result to obtain a target analysis result of the first user's workday. The embodiment of the present application performs workday modeling analysis through the vehicle information data and user data of the first user to improve the recognition accuracy of workday attribute labels.

[0109] The following is a detailed description of the embodiments of the present application through a specific example.

[0110] With the above Figure 2 The embodiment shown is consistent with that shown in FIG. Figure 3 , Figure 3 : is a flow chart of a method for determining working days based on big data analysis provided in an embodiment of the present application, and the method for determining working days based on big data analysis includes:

[0111] S301, the electronic device obtains vehicle information data from an onboard device of a first vehicle, where a user corresponding to the first vehicle is a first user;

[0112] S302, the electronic device acquires user data of the first user, where the user data is behavior data of the first user in the application program;

[0113] S303, the electronic device pre-extracts and cleans the vehicle information data and the user data in sequence to obtain second data;

[0114] S304, the electronic device performs standardization processing on the second data to obtain first data;

[0115] S305. The electronic device performs workday modeling analysis on the first data by using a cosine distance algorithm and / or a density clustering algorithm to obtain a first analysis result.

[0116] S306: The electronic device analyzes the first analysis result to obtain a target analysis result of the first user's working day.

[0117] It can be seen that the electronic device in the embodiment of the present application obtains vehicle information data from the vehicle-mounted device of the first vehicle, and the user corresponding to the first vehicle is the first user; obtains user data of the first user, and the user data is the behavior data of the first user in the application; pre-extracts and cleans the vehicle information data and the user data in turn to obtain the second data; standardizes the second data to obtain the first data; performs workday modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result; analyzes the first analysis result to obtain the target analysis result of the first user's workday. The embodiment of the present application performs workday modeling analysis on the vehicle information data and user data of the first user through the cosine distance algorithm and / or the density clustering algorithm to improve the recognition accuracy of workday attribute labels.

[0118] See also Figure 4 , Figure 4 4 is a structural diagram of an electronic device 400 provided in an embodiment of the present application. As shown in the figure, the electronic device 400 includes an application processor 410, a memory 420, a communication interface 430, and one or more programs 421, wherein the one or more programs 421 are stored in the above-mentioned memory 420 and are configured to be executed by the above-mentioned application processor 410, and the one or more programs 421 include for performing the following steps:

[0119] Acquiring vehicle information data and user data of the first user;

[0120] Performing a workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0121] The first analysis result is analyzed to obtain a target analysis result of the first user's working day.

[0122] In one possible example, in terms of performing workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result, the one or more programs 421 include functions for performing the following steps: preprocessing the vehicle information data and the user data to obtain first data; performing workday modeling analysis on the first data by using a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result.

[0123] In one possible example, in terms of preprocessing the vehicle information data and the user data to obtain the first data, the one or more programs 421 include the steps of: pre-extracting and cleaning the vehicle information data and the user data in sequence to obtain the second data; and standardizing the second data to obtain the first data.

[0124] In one possible example, in terms of performing workday modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, the one or more programs 421 include functions for performing the following steps: performing workday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; performing workday modeling analysis on the first data through the density clustering algorithm to obtain the first analysis result.

[0125] In one possible example, in terms of performing workday modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, the one or more programs 421 include being used to perform the following steps: performing workday modeling analysis on the first data through the density clustering algorithm to obtain a second analysis result; performing workday modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result.

[0126] In one possible example, in terms of performing working day modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, the one or more programs 421 include the steps of: performing working day modeling analysis on the first data through a cosine distance algorithm to obtain a second analysis result; performing working day modeling analysis on the first data through a density clustering algorithm to obtain a third analysis result; and integrating the second analysis result and the third analysis result to obtain the first analysis result.

[0127] In one possible example, in terms of obtaining the vehicle information data and user data of the first user, the one or more programs 421 include commands for performing the following steps: obtaining vehicle information data from an onboard device of a first vehicle, where the user corresponding to the first vehicle is the first user; and obtaining user data related to the first user, where the user data is behavior data of the first user in an application.

[0128] It can be seen that the embodiment of the present application obtains the vehicle information data and user data of the first user; performs workday modeling analysis based on the vehicle information data and user data to obtain a first analysis result; and analyzes the first analysis result to obtain a target analysis result of the first user's workday. The embodiment of the present application performs workday modeling analysis through the vehicle information data and user data of the first user to improve the recognition accuracy of workday attribute labels.

[0129] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware 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 application.

[0130] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0131] Figure 5 : is a functional unit composition block diagram of a device 500 for determining working days based on big data analysis involved in an embodiment of the present application. The device 500 for determining working days based on big data analysis includes:

[0132] An acquisition unit 501 is used to acquire vehicle information data of a first user and user data of the first user;

[0133] A first analysis unit 502, configured to perform a workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0134] The second analysis unit 503 is used to analyze the first analysis result to obtain a target analysis result of the first user's working day.

[0135] The apparatus 500 for determining working days based on big data analysis may further include a storage unit 504 for storing program codes and data of the electronic device. The storage unit 504 may be a memory.

[0136] In one possible example, in terms of performing workday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result, the first analysis unit 502 is specifically used to: preprocess the vehicle information data and the user data to obtain first data; perform workday modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result.

[0137] In one possible example, in terms of preprocessing the vehicle information data and the user data to obtain the first data, the first analysis unit 502 is specifically used to: pre-extract and clean the vehicle information data and the user data in sequence to obtain the second data; and standardize the second data to obtain the first data.

[0138] In one possible example, in terms of performing working day modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, the first analysis unit 502 is specifically used to: perform working day modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; perform working day modeling analysis on the first data through the density clustering algorithm to obtain the first analysis result.

[0139] In one possible example, in terms of performing working day modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain the first analysis result, the first analysis unit 502 is specifically used to: perform working day modeling analysis on the first data through the density clustering algorithm to obtain the second analysis result; perform working day modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result.

[0140] In one possible example, in terms of performing working day modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result, the first analysis unit 502 is specifically used to: perform working day modeling analysis on the first data through a cosine distance algorithm to obtain a second analysis result; perform working day modeling analysis on the first data through a density clustering algorithm to obtain a third analysis result; and integrate the second analysis result and the third analysis result to obtain the first analysis result.

[0141] In one possible example, in terms of obtaining the vehicle information data and user data of the first user, the acquisition unit 501 is specifically used to: obtain vehicle information data from an on-board device of the first vehicle, where the user corresponding to the first vehicle is the first user; and obtain user data related to the first user, where the user data is the behavior data of the first user in an application.

[0142] It can be seen that in the embodiment of the present application, the vehicle information data and user data of the first user are obtained; a working day modeling analysis is performed based on the vehicle information data and the user data to obtain a first analysis result; and the first analysis result is analyzed to obtain a target analysis result of the working day of the first user. The embodiment of the present application performs working day modeling analysis through the vehicle information data and the user data of the first user to improve the recognition accuracy of working day attribute labels.

[0143] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0144] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0145] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, 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 and modules involved are not necessarily required by the present application. In the above embodiments, the description of each embodiment has its own emphasis, and for the parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0146] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which 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, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0147] The units described above 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.

[0148] In addition, each functional unit in each embodiment of the present application 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0149] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0150] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0151] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining working days based on big data analysis, characterized in that: The method comprises: Acquiring vehicle information data and user data of the first user; Preprocessing the vehicle information data and the user data to obtain first data; Performing workday modeling analysis on the first data by using a cosine distance algorithm and a density clustering algorithm to obtain a first analysis result; Analyze the first analysis result to obtain a target analysis result of the first user's working day; The first data is subjected to workday modeling analysis by using a cosine distance algorithm and a density clustering algorithm to obtain a first analysis result, including: Performing workday modeling analysis on the first data using the cosine distance algorithm to obtain a second analysis result; Performing workday modeling analysis on the first data by using the density clustering algorithm to obtain a third analysis result; The second analysis result and the third analysis result are integrated to obtain the first analysis result.

2. The method according to claim 1, characterized in that The preprocessing of the vehicle information data and the user data to obtain the first data includes: Pre-extracting and cleaning the vehicle information data and the user data in sequence to obtain second data; The second data is standardized to obtain the first data.

3. The method according to claim 1, characterized in that The obtaining of the vehicle information data and user data of the first user includes: Acquire vehicle information data from an onboard device of a first vehicle, where a user corresponding to the first vehicle is the first user; The user data of the first user is obtained, where the user data is behavior data of the first user in the application.

4. A device for determining working days based on big data analysis, characterized in that: The device comprises: an acquisition unit, configured to acquire vehicle information data of a first user and user data of the first user; A first analysis unit is used to pre-process the vehicle information data and the user data to obtain first data; and to perform workday modeling analysis on the first data by using a cosine distance algorithm and a density clustering algorithm to obtain a first analysis result; A second analysis unit, configured to analyze the first analysis result to obtain a target analysis result of the first user's working day; The first data is subjected to workday modeling analysis by using a cosine distance algorithm and a density clustering algorithm to obtain a first analysis result, including: Performing workday modeling analysis on the first data using the cosine distance algorithm to obtain a second analysis result; Performing workday modeling analysis on the first data by using the density clustering algorithm to obtain a third analysis result; The second analysis result and the third analysis result are integrated to obtain the first analysis result.

5. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: It stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 3.

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