Method for determining workdays based on big data analysis and related products

Abstract

The embodiment of the invention discloses a workday determination method based on big data analysis and a related product. The workday determination method based on big data analysis comprises the steps of obtaining vehicle information data and user data of a first user; performing workday modeling analysis according to the vehicle information data and the user data to obtain a first analysis result; and analyzing the first analysis result to obtain a target analysis result of the first user workday. According to the embodiment of the invention, workday modeling analysis is carried out through the vehicle information data of the first user and the user data, and the recognition accuracy of the workday attribute tag is improved.

Description

Technical Field

[0001] This 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 both collections of attribute tags in the Internet of Everything. To meet people's needs for improving the quality of life, it is necessary to have a full understanding and knowledge of people's attributes. Among them, working days, as important attribute tags of people, have a relatively regular commuting cycle, which is a prerequisite for route recommendation, commuting entertainment recommendation, etc. On non-working days, it is a prerequisite for shopping recommendation, travel recommendation, etc.

[0003] However, the data in the existing methods for determining users' working days is scarce, resulting in low accuracy in determining users' working days. Therefore, there is an urgent need for a solution to improve the accuracy of determining users' working days. Summary of the Invention

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

[0005] In a first aspect, the embodiments of this application provide a method for determining working days based on big data analysis. The method for determining working days based on big data analysis includes:

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

[0007] Perform working day modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0008] Analyze the first analysis result to obtain a target analysis result of the working days of the first user.

[0009] Optionally, the performing working day modeling analysis based on the vehicle information data and the user data to obtain a first analysis result includes:

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

[0011] Perform 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.

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

[0013] Perform pre-extraction and cleaning on the vehicle information data and the user data in sequence to obtain second data;

[0014] Normalize the second data to obtain the first data.

[0015] Optionally, performing a 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:

[0016] Performing a working day modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result;

[0017] Performing a working day modeling analysis on the first data through the density clustering algorithm to obtain the first analysis result.

[0018] Optionally, performing a 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:

[0019] Performing a working day modeling analysis on the first data through the density clustering algorithm to obtain a second analysis result;

[0020] Performing a working day modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result.

[0021] Optionally, performing a 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:

[0022] Performing a working day modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result;

[0023] Performing a working day modeling analysis on the first data through the density clustering algorithm to obtain a third analysis result;

[0024] Integrating the second analysis result and the third analysis result to obtain the first analysis result.

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

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

[0027] Obtaining user data related to the first user, where the user data is the 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 a working day based on big data analysis. The device for determining a working day 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 working day modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0031] A second analysis unit, configured to analyze the first analysis result to obtain a target analysis result of the working day of the first user.

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

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

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the above computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the above 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 embodiments of the present application. The computer program product may be a software installation package.

[0035] It can be seen that in the embodiments of the present application, vehicle information data and user data of a first user are acquired; 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. By performing working day modeling analysis through the vehicle information data and user data of the first user, the present application embodiment improves the recognition accuracy of working day attribute tags. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

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

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

[0040] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0041] Figure 5 A block diagram of the functional units of a device for determining working days based on big data analysis provided by an embodiment of the present application. Detailed implementation manners

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present application belong to the scope protected by the present application.

[0043] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" 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 may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0044] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0045] The following details the embodiments of the present application.

[0046] Please refer to Figure 1 , Figure 1It is a schematic diagram of determining working days based on big data analysis provided by an embodiment of this application. By collecting vehicle information data and user data, analyzing the vehicle information data and user data based on big data, the commuting time, commuting scenarios, and working days of the user are obtained. Further analyzing the commuting time, commuting scenarios, and working days of the user to obtain the working time label and working day label of the user.

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

[0048] S201. An electronic device acquires vehicle information data and user data of a first user;

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

[0050] Among them, it needs to be further explained that the data acquisition duration for the electronic device to acquire vehicle information data and user data of the first user can be one week, two weeks, three weeks, one month, two months, three months, half a year, one year, two years. The data acquisition duration can be any duration, and no excessive restrictions are imposed here.

[0051] Among them, the first vehicle can be a vehicle under the name of the first user, or a vehicle bound by the first user in a vehicle-related application APP. The vehicle-related application APP can be a vehicle navigation-related APP or a positioning-related APP. The number of the first vehicles can be 1, 2, 3, 4, 5, 6, 7, etc., and no excessive restrictions are imposed here.

[0052] Among them, the vehicle information data includes vehicle sensor information data and data of the vehicle itself. The vehicle sensor information data includes at least one of the following: vehicle fuel consumption, vehicle location, vehicle interior temperature, driving time point, driving duration. The data of the vehicle itself includes: the vehicle 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 can be the location information of the company of the first user, the location information of home, the vehicle driving time information, and the vehicle driving trajectory information in the user behavior data of the first user's navigation APP.

[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 in-vehicle device of the first vehicle; the electronic device obtains the location information of the first user's company, the location information of the home, the vehicle driving time information, and the vehicle driving trajectory information from the user behavior data in the first user's navigation APP.

[0055] S202. The electronic device performs a working day modeling analysis based on the vehicle information data and the user data to obtain a first analysis result;

[0056] In specific implementation, the electronic device performs a working day 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 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.

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

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

[0059] It should be further explained that the electronic device preprocesses the vehicle information data and the user data to obtain first data, including: the electronic device sequentially extracts and cleans the vehicle information data and the user data to obtain second data; the electronic device performs a normalization process on the second data to obtain the first data.

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

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

[0063] Randomly select M data points in the first data as initial data, and according to the cosine distance algorithm, calculate the distance between each data point in the first data and the M initial data. If the distance between the data point in the first data and the M initial data is less than the 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 obtain the first data point sets corresponding to the M initial data respectively; integrate the M first data point sets to obtain the second analysis result.

[0064] 2. The electronic device performs weekday modeling analysis on the first data through density clustering algorithm to obtain a third analysis result. The specific steps can be:

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

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

[0067] Step 3. The electronic device calculates the distance between each data in the first data except the initial data and each data point among the k data points. Whichever data point among the k data points is closer to each data in the first data except the initial data, the data follows that data point, and k grouped data are obtained;

[0068] Step 4. The electronic device selects new centroids from each of the k grouped data through the 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 change of the new centroid is small and tends to be stable, or in other words, it converges. That is, the clustering performed has achieved the desired result, and the algorithm terminates to obtain the third analysis result.

[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] Among them, for the electronic device in step 3 to calculate the distance between each data in the first data except the initial data and each data point among the k data points, the algorithm can be the cosine similarity algorithm or the Euclidean distance algorithm.

[0072] Among them, it needs to be further explained that the electronic device performs weekday 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 weekday modeling analysis on the first data through the cosine distance algorithm to obtain the second analysis result; the electronic device performs weekday 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 weekday modeling analysis on the first data through the cosine distance algorithm to obtain the second analysis result, including: randomly selecting M data points in the first data as the initial data, according to the cosine distance algorithm, calculating the distance between each data point in the first data and the M initial data. If the distance between the data point in the first data and the M initial data is less than the 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; integrating the M first data point sets to obtain the second analysis result.

[0074] It should be explained that the electronic device performs weekday modeling analysis on the second analysis result through the density clustering algorithm to obtain the first analysis result, including:

[0075] Step 1. First, the electronic device inputs the value of k. 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 the initial data, that is, the initial centroid (Centroid);

[0077] Step 3: The electronic device calculates the distance between each data in the data corresponding to the second analysis result except the initial data and each of the k data points. For each data in the data corresponding to the second analysis result except the initial data, if it is closer to a certain data point among the k data points, it is assigned to that data point, and k grouped data are obtained;

[0078] Step 4: The electronic device selects new centroids for each of the k grouped data through the 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 performed has reached the expected result, and the algorithm terminates to obtain the first analysis result.

[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] Among them, for the algorithm of the electronic device in step 3 to calculate the distance between each data in the data corresponding to the second analysis result except the initial data and each of the k data points, it can be the cosine similarity algorithm or the Euclidean distance algorithm.

[0082] Among them, it should be explained that the electronic device performs weekday 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 weekday modeling analysis on the first data through the density clustering algorithm to obtain the second analysis result; the electronic device performs weekday modeling analysis on the second analysis result through the cosine distance algorithm to obtain the first analysis result.

[0083] Further, it should be explained that the steps for the electronic device to perform weekday modeling analysis on the first data through the density clustering algorithm to obtain the second analysis result can be:

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

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

[0086] Step 3: The electronic device calculates the distance between each data in the first data except the initial data and each of the k data points. For each data in the first data except the initial data, if it is closer to a certain data point among the k data points, it is assigned to that data point, and k grouped data are obtained;

[0087] Step 4: The electronic device selects new centroids from each of the k grouped data through the 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 change of the new centroid is small and tends to be stable, or in other words, converges, that is, the clustering performed has reached 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] Among them, in step 3, the algorithm for the electronic device to calculate the distance between each data in the first data except the initial data and each data point among the k data points can be the cosine similarity algorithm or the Euclidean distance algorithm.

[0091] It should be further explained that the electronic device performs weekday modeling analysis on the second analysis result through the cosine distance algorithm. The first analysis result includes: randomly selecting M data points from the data corresponding to the second analysis result as the initial data, and according to the cosine distance algorithm, calculating the distance between each data point in the data corresponding to the second analysis result and the M initial data. If the distance between the data points in the data corresponding to the second analysis result and the M initial data is less than the first threshold, then the data points in the data corresponding to the second analysis result are data points in one of the first data point sets 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 should be explained that the electronic device performs weekday 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 weekday 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 respectively correspond to the vehicle information data and user data of the first day of the first user, n is the total number of vehicle information data and user data on the first day of the first user, and i is the serial number of the vehicle information data and user data on the first day of the first user.

[0094] It should be noted that the electronic device performs weekday 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, 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 the 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; integrating the M first data point sets to obtain the first analysis result.

[0095] Among them, it should be further noted that the electronic device performs weekday 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 weekday modeling analysis on the first data through the density clustering algorithm to obtain the first analysis result.

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

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

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

[0099] Step 3: The electronic device calculates the distance between each data in the first data except the initial data and each data point among the k data points. Whichever data point among the k data points the data in the first data except the initial data is closer to, it is assigned to that data point, and k grouped data are obtained;

[0100] Step 4: The electronic device selects new centroids for each grouped data among the k grouped data through the K-means algorithm;

[0101] Step 5: If the distance between the new centroid and the initial centroid in each grouped data among the k grouped data is less than the first threshold, it means that the position change of the new centroid is small and tends to be stable, or in other words, converges, that is, the clustering performed has reached the expected result, and the algorithm terminates to obtain the first analysis result.

[0102] Step 6: If the distance between the new centroid and the initial centroid in each grouped data among the k grouped data is greater than the first threshold, it is necessary to iterate steps 3-5.

[0103] Among them, for the electronic device to calculate the distance between each data in the first data except the initial data and each data point among the k data points in step 3, the algorithm can be the cosine similarity algorithm or the Euclidean distance algorithm.

[0104] S203. The electronic device analyzes the first analysis result to obtain the 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 the target analysis result of the first user's working day, including: the electronic device analyzes the first analysis result to obtain the commuting scenario, commuting time, and working day of the first user; further analyzes based on the commuting scenario, commuting time, and working day of the first user to obtain the target analysis result of the first user, that is, the working time of the first user. The above working time includes: the working days of the first user per month, 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 further includes: the electronic device performs intelligent recommendations on the first terminal of the first user according to the target analysis result of the first user's working day. The intelligent recommendations include any one of the following: route recommendations, entertainment recommendations, shopping recommendations, and travel recommendations.

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

[0108] It can be seen that the embodiments of the present application obtain the vehicle information data and user data of the first user; perform working day modeling analysis based on the vehicle information data and user data to obtain the first analysis result; analyze the first analysis result to obtain the target analysis result of the first user's working day. The embodiments of the present application perform working day modeling analysis through the vehicle information data and user data of the first user, improving the recognition accuracy of the working day attribute labels.

[0109] The following details the embodiments of the present application through a specific example.

[0110] Consistent with the above Figure 2 shown embodiment, please refer to Figure 3 , Figure 3 is a schematic flowchart of a method for determining a working day based on big data analysis provided by the embodiments of the present application. The method for determining a working day based on big data analysis includes:

[0111] S301. The electronic device obtains vehicle information data from an in-vehicle device of a first vehicle, and the user corresponding to the first vehicle is the first user;

[0112] S302. The electronic device obtains the user data of the first user, and the user data is the 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 weekday modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain a first analysis result;

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

[0117] It can be seen that in the embodiment of the present application, the electronic device obtains the vehicle information data of the in-vehicle device of the first vehicle, and the user corresponding to the first vehicle is the first user; obtains the user data of the first user, and the user data is the behavior data of the first user in the application program; pre-extracts and cleans the vehicle information data and the user data in sequence to obtain second data; performs standardization processing on the second data to obtain first data; performs weekday modeling analysis on the first data through the cosine distance algorithm and / or the density clustering algorithm to obtain a first analysis result; analyzes the first analysis result to obtain the target analysis result of the first user's weekday. In the embodiment of the present application, the weekday modeling analysis is performed on the vehicle information data and the user data of the first user through the cosine distance algorithm and / or the density clustering algorithm, so as to improve the recognition accuracy of the weekday attribute label.

[0118] Please refer to Figure 4 , Figure 4 is a schematic 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. Among them, 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. The one or more programs 421 include steps for performing the following:

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

[0120] Perform weekday modeling analysis according to the vehicle information data and the user data to obtain a first analysis result;

[0121] Analyze the first analysis result to obtain the target analysis result of the first user's weekday.

[0122] In a possible example, in the aspect of performing weekday 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 steps for: preprocessing the vehicle information data and the user data to obtain first data; performing weekday modeling analysis on the first data through a cosine distance algorithm and / or a density clustering algorithm to obtain the first analysis result.

[0123] In a possible example, in the aspect of preprocessing the vehicle information data and the user data to obtain first data, the one or more programs 421 include steps for: sequentially pre-extracting and cleaning the vehicle information data and the user data to obtain second data; performing normalization processing on the second data to obtain the first data.

[0124] In a possible example, in the aspect of performing weekday 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 steps for: performing weekday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; performing weekday modeling analysis on the first data through the density clustering algorithm to obtain the first analysis result.

[0125] In a possible example, in the aspect of performing weekday 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 steps for: performing weekday modeling analysis on the first data through the density clustering algorithm to obtain a second analysis result; performing weekday modeling analysis on the first data through the cosine distance algorithm to obtain the first analysis result.

[0126] In a possible example, in the aspect of performing weekday 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 steps for: performing weekday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; performing weekday modeling analysis on the first data through the density clustering algorithm to obtain a third analysis result; integrating the second analysis result and the third analysis result to obtain the first analysis result.

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

[0128] It can be seen that the embodiments of the present application obtain the vehicle information data and user data of the first user; perform working day modeling analysis based on the vehicle information data and user data to obtain a first analysis result; and analyze the first analysis result to obtain a target analysis result for the working day of the first user. The embodiments of the present application perform working day modeling analysis through the vehicle information data and user data of the first user, improving the recognition accuracy of working day attribute tags.

[0129] The above mainly introduces the solutions of the embodiments of the present application from the perspective of the execution process on the method side. It can be understood that in order for an electronic device to implement the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain 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 technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0130] The embodiments of the present application can perform functional unit division on the electronic device according to the above method examples. For example, each functional unit can be divided corresponding 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 a software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, merely a logical function division, and there may be other division methods in actual implementation.

[0131] Figure 5 It is a functional unit composition block diagram of a working day determination device 500 based on big data analysis involved in the embodiments of the present application. The working day determination device 500 based on big data analysis includes:

[0132] An acquisition unit 501, configured to acquire the vehicle information data of the first user and the user data of the first user;

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

[0134] A second analysis unit 503, configured to analyze the first analysis result to obtain a target analysis result of the first user's working days.

[0135] Wherein, the working day determining device 500 based on big data analysis may further include a storage unit 504, configured to store program codes and data of the electronic device. The storage unit 504 may be a memory.

[0136] In a possible example, in terms of performing working day modeling analysis on the vehicle information data and the user data to obtain a first analysis result, the first analysis unit 502 is specifically configured to: preprocess the vehicle information data and the user data to obtain first data; perform 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.

[0137] In a possible example, in terms of preprocessing the vehicle information data and the user data to obtain first data, the first analysis unit 502 is specifically configured to: sequentially perform pre-extraction and cleaning on the vehicle information data and the user data to obtain second data; perform normalization processing on the second data to obtain the first data.

[0138] In a 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 configured 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 a 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 configured to: perform working day modeling analysis on the first data through the density clustering algorithm to obtain a 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 a possible example, in the aspect of performing weekday 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 configured to: perform weekday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; perform weekday modeling analysis on the first data through the 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 a possible example, in the aspect of obtaining the vehicle information data and user data of the first user, the obtaining unit 501 is specifically configured to: obtain the vehicle information data from the in-vehicle device of the first vehicle, where the user corresponding to the first vehicle is the first user; and obtain the user data of the first user, where the user data is the behavior data of the first user in the application program.

[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; weekday modeling analysis is performed based on the vehicle information data and user data to obtain a first analysis result; and the first analysis result is analyzed to obtain the target analysis result of the weekday of the first user. In the embodiment of the present application, weekday modeling analysis is performed through the vehicle information data and user data of the first user, thereby improving the recognition accuracy of the weekday attribute tags.

[0143] The embodiment of the present application further provides a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps of any of the methods recorded in the above method embodiments, and the above computer includes an electronic device.

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

[0145] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be in other sequences or performed simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application. In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical or other forms.

[0147] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0149] If the above 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, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), external hard drives, magnetic disks, or optical discs.

[0150] Those of ordinary skill 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 relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memories (abbreviation: ROM, English: Read-Only Memory), random access memories (abbreviation: RAM, English: Random Access Memory), magnetic disks, or optical discs, etc.

[0151] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for determining working days based on big data analysis, characterized in that, The method includes: Obtaining vehicle information data and user data of a first user; Preprocessing the vehicle information data and the user data to obtain first data; Performing weekday modeling analysis on the first data through a cosine distance algorithm and a density clustering algorithm respectively to obtain a first analysis result; Analyzing the first analysis result to obtain the commuting scenario, commuting time, and weekdays of the first user; Further analyzing based on the commuting scenario, commuting time, and weekdays of the first user to obtain a target analysis result of the first user; Performing intelligent recommendation on the first terminal of the first user according to the target analysis result of the first user's weekdays.

2. The method according to claim 1, characterized in that, The obtaining of the vehicle information data and user data of the first user includes: Obtaining vehicle information data from the in-vehicle device of a first vehicle, where the user corresponding to the first vehicle is the first user; Obtaining the user data of the first user, where the user data is the behavior data of the first user in the application.

3. The method according to claim 2, characterized in that, The preprocessing of the vehicle information data and the user data to obtain first data includes: Performing pre-extraction and cleaning on the vehicle information data and the user data in sequence to obtain second data; Performing normalization processing on the second data to obtain the first data.

4. The method according to claim 2, wherein The performing of weekday modeling analysis on the first data through a cosine distance algorithm and a density clustering algorithm to obtain a first analysis result includes: Performing weekday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; Performing weekday modeling analysis on the second analysis result through the density clustering algorithm to obtain the first analysis result.

5. The method according to claim 2, wherein The performing of weekday modeling analysis on the first data through a cosine distance algorithm and a density clustering algorithm to obtain the first analysis result includes: Performing weekday modeling analysis on the first data through the density clustering algorithm to obtain a second analysis result; Performing weekday modeling analysis on the second analysis result through the cosine distance algorithm to obtain the first analysis result.

6. The method according to claim 2, wherein The performing of weekday modeling analysis on the first data through a cosine distance algorithm and a density clustering algorithm to obtain the first analysis result includes: Performing weekday modeling analysis on the first data through the cosine distance algorithm to obtain a second analysis result; Performing weekday modeling analysis on the first data through the density clustering algorithm to obtain a third analysis result; Integrating the second analysis result and the third analysis result to obtain the first analysis result.

7. The method according to claim 1, characterized in that, The further analyzing based on the commuting scenario, commuting time, and weekdays of the first user to obtain a target analysis result of the first user includes: The target analysis result is the working time of the first user, and the working time includes: the weekdays of each month of the first user, the working duration corresponding to each weekday, and the working time period.

8. The method according to claim 1, wherein The performing of intelligent recommendation on the first terminal of the first user according to the target analysis result of the first user's weekdays, where the intelligent recommendation includes: route recommendation, entertainment recommendation, shopping recommendation, and travel recommendation.

9. An apparatus for determining working days based on big data analysis, characterized in that, The device includes: An acquisition unit, configured to acquire vehicle information data of a first user and user data of the first user; A first analysis unit, configured to perform weekday modeling analysis based on the vehicle information data and the user data to obtain a first analysis result; A second analysis unit, configured to analyze the first analysis result to obtain a target analysis result for the weekday of the first user.

10. A computer-readable storage medium, characterized in that, It stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the method according to any one of claims 1-8.

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