CN111339409A - Map display method and system - Google Patents
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Abstract
The invention discloses a map display method, which comprises the following steps: acquiring a map data display instruction, wherein the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information; acquiring longitude and latitude of a destination based on the destination information; obtaining the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information; screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range; and performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information. The invention also discloses a map display system. The invention has the beneficial effects that: the required information can be visually displayed on the map for the user to check.
Description
Technical Field
The embodiment of the invention relates to the field of map data processing, in particular to a map display method and a map display system.
Technical Field
The map is convenient software, and can quickly find a route to a destination in an unfamiliar place, but the existing map only inputs a place to be inquired, displays a traffic route from a current position to an inquired position, and provides an alternative route, namely only the traffic route.
If the food and the like near the destination are inquired through the map, the food and the like cannot be visually seen, and the inquiry can be carried out only near the destination, so that the time and the labor are consumed.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a map display method and system, which can intuitively display required information on a map for a user to view.
In order to achieve the above object, an embodiment of the present invention provides a map displaying method, including:
acquiring a map data display instruction, wherein the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information;
acquiring longitude and latitude of a destination based on the destination information;
obtaining the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information;
screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range;
performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information;
and sending the drawing data to a front end so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
Further, the performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information includes:
analyzing a resident address and a GPS portrait in the crowd attribute information to obtain a geographical position portrait, wherein the geographical position portrait comprises a plurality of position labels;
calculating a similarity coefficient between the position label and the destination information according to the Manhattan distance;
judging whether the similarity coefficient is larger than a preset threshold value or not;
if the similarity coefficient is larger than a preset threshold value, using the position label with the similarity coefficient larger than the preset threshold value as the drawing data of the crowd attribute information; and conversely, deleting the position labels of which the similarity coefficient is not greater than the preset threshold value.
Further, the performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information includes:
acquiring complete user information in the crowd attribute information;
clustering the complete user information to obtain a user portrait label of the complete user information;
inputting incomplete user information in the crowd attribute information and the user portrait label into a label diffusion model so as to perfect the incomplete user information and obtain drawing data corresponding to the crowd attribute information.
Further, the performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information includes:
clustering to obtain an interest label system of the crowd attribute information according to the crowd attribute information, wherein the interest label system comprises a plurality of label words;
calculating the interest value of each label word and the target product information;
and attenuating the interest value to obtain the drawing data of the target product information.
Further, the formula for calculating the interest value of each tag word and the target product information is as follows:
scorej+1＝α×scorej+C×weight；
if the label word appears in the target product information, C is 1, otherwise, C is 0; weight represents the weight value of the tag word, scorej+1And scorejRepresenting interest in tag wordsA value; j is an integer.
Further, the attenuating the interest value includes:
performing time attenuation and time attenuation on the interest value;
the calculation formula of the number attenuation is as follows:
scorei+1＝α×scorei+C×weight(0<α<1) wherein, scorei+1And scoreiA first interest value representing interest attenuated according to the number of times, α representing an attenuation factor, weight representing a weight value of a tag word, and i representing the number of times;
the calculation formula of the time attenuation is as follows:
scoreday+1＝scoreday×β(0<β<1) wherein, scoreday+1And scoredayA second interest value representing the attenuation of interest as a function of time, day representing the period, β representing an attenuation factor;
the interest value includes the first interest value and the second interest value.
In order to achieve the above object, an embodiment of the present invention further provides a map display system, including:
the map data display device comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is used for acquiring a map data display instruction, and the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information;
the second acquisition module is used for acquiring the longitude and latitude of the destination based on the destination information;
the third acquisition module is used for acquiring the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information;
the screening module is used for screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range;
the cluster analysis module is used for carrying out cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information;
and the drawing module is used for sending the drawing data to a front end so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
Further, the cluster analysis module is further configured to:
clustering to obtain an interest label system of the crowd attribute information according to the crowd attribute information, wherein the interest label system comprises a plurality of label words;
calculating the interest value of each label word and the target product information;
and attenuating the interest value to obtain the drawing data of the target product information.
In order to achieve the above object, an embodiment of the present invention further provides a computer device, where the computer device includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the computer program, when executed by the processor, implements the steps of the map displaying method described above.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor, so as to cause the at least one processor to execute the steps of the map display method described above.
According to the map display method and the map display system, the map data corresponding to the destination information, the radius range information, the target product information and the crowd attribute information are obtained through the map data display instruction, the map data are analyzed, the analyzed map data are sent to the front end, the front end draws a plurality of data graphs according to the map data, and the data graphs are displayed for a user to check.
Drawings
Fig. 1 is a flowchart of a first embodiment of a map displaying method according to the present invention.
Fig. 2 is a flowchart illustrating a first embodiment of step S108 in fig. 1 according to the first embodiment of the present invention.
Fig. 3 is a flowchart illustrating a second embodiment of step S108 in fig. 1 according to the first embodiment of the present invention.
Fig. 4 is a flowchart illustrating a third embodiment of step S108 in fig. 1 according to the first embodiment of the present invention.
Fig. 5 is a schematic diagram of program modules of a second embodiment of the map display system.
Fig. 6 is a schematic diagram of a hardware structure of a third embodiment of the computer apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a flowchart illustrating steps of a map displaying method according to a first embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is made by taking a server as an execution subject. The details are as follows.
Step S100, a map data display instruction is obtained, wherein the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information.
Specifically, the map data display instruction is used for displaying information which is inquired by a user at a destination according to the requirement. The destination information may be a geographic attribute including: POI (points of interest in cities) distribution maps, people stream distribution in different time periods (weekdays), people stream distribution in different time periods (including holidays), traffic facilities, sign buildings, and the like; the radius range information includes: the longitude and latitude of the destination is taken as the center, and a preset radius range is provided for the user to select; the target product information includes: interest degree, demand and the like of people around the destination on the target product; the crowd attribute information includes: gender, age, school calendar, wealth value distribution, consumption value, product demand, etc. of the population around the destination.
For example, query information input by a user may be received through the input list, and then the map data display instruction is obtained. In the input list, the searched product information and the crowd attribute information can be selected, and the radius range information of the destination information to be inquired can be preset so as to further narrow the inquiry range.
For example: the list of destination information includes: marking the information of the building name, marking the longitude and latitude coordinate points and the foreign key fields of the building based on the Baidu map API, and the like;
the list of demographic attribute information includes: gender, age, and foreign body health.
And step S102, acquiring the longitude and latitude of the destination based on the destination information.
Specifically, a map is called according to destination information, and the longitude and latitude of a destination are determined. If the user gives corresponding fuzzy destination information which comprises a POI list or a POI marked in a map, the user can enter POI details to click, then inquire the POI ranking displayed in the map, and display the POI name when moving in a mouse; the POI list and the POI marked in the map can be clicked, and the detailed data of the POI is expanded when the POI is clicked; and if the recommended place of the map in the point is the point, changing the map point of the recommended place into a destination, and acquiring corresponding longitude and latitude.
And step S104, obtaining the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information.
Specifically, the radius range information is set in advance, and represents that the radius length of the radius is extended outwards by taking the longitude and latitude of the destination as the center, and the radius range information can be calculated from the longitude and latitude.
And S106, screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range.
Specifically, the SQL sentence is called to obtain the longitude and latitude of the destination information from the database, the longitude and latitude and the radius range information (such as 10km) are taken as the entries and are also taken as the entries if the user selects more conditions for inputting into the list. Calling and starting a Springboot-based framework through an http request to connect a database; then, the logic processing is carried out on the corresponding processing layer through the server routing request to obtain a corresponding routing address; and querying corresponding target product information and crowd attribute information in a database by using the SQL statement according to the routing address.
Step S108, carrying out cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information.
Specifically, the target product information and the crowd attribute information are subjected to clustering analysis to obtain drawing data capable of drawing graphs.
Illustratively, referring to fig. 2, an embodiment of step S108 includes:
step S108A1, analyzing the resident address and the GPS portrait in the crowd attribute information to obtain a geographical position portrait, wherein the geographical position portrait comprises a plurality of position labels.
Specifically, the geographic position portrait is obtained by analyzing according to the activity range of the target user. Generally divided into two parts: one part is a permanent site representation; one part is a GPS representation. The difference between the two types of images is large, the standing image is easy to construct, the label is stable, and the GPS image needs to be updated in real time. The ordinary station includes three levels of state, province and city, and is generally refined to the city granularity. And mining the permanent site based on the IP address information of the user, analyzing the IP address of the user, corresponding to a corresponding city, and counting the city where the user IP appears to obtain a resident city label. The resident city labels of the users can be used for counting the user distribution of each region, and can identify the poor people, the tourism people and the like according to the travel tracks of the users among the cities. GPS data is generally collected from a mobile phone terminal, and a user can acquire the GPS information of the user by using an one-account App authorization App.
Specifically, a resident address and a GPS portrait are analyzed by adopting an agglomeration hierarchical clustering algorithm, an address label of each resident address and the address label to be clustered in the resident address and the GPS portrait are taken as an atomic cluster, and then a plurality of atomic clusters are combined into larger and larger clusters until all objects are in one cluster or a certain terminal condition is met. The embodiment of the invention adopts a minimum distance coacervation hierarchical clustering algorithm flow:
(1) regarding each address label as a class, and calculating the minimum distance between every two address labels;
(2) combining the two classes with the minimum distance into a new class;
(3) recalculating the distances between the new class and all classes;
(4) and (3) repeating the steps (2) and (3) until all classes are finally combined into one class.
Step S108a2, calculating a similarity coefficient between the location tag and the destination information according to the manhattan distance.
Specifically, the position label and the destination information are converted into corresponding vectors, the distance between the vector of the position label and the vector of the destination information is calculated according to a Manhattan distance measurement calculation formula, and the reciprocal of the minimum Manhattan distance is taken as a similarity coefficient.
Step S108a3, determining whether the similarity coefficient is greater than a preset threshold.
Specifically, the smaller the manhattan distance is, the larger the reciprocal is, that is, the larger the similarity coefficient is, and the drawing data is screened by judging whether the similarity coefficient is larger than a preset threshold.
Step S108A4, if the similarity coefficient is larger than a preset threshold, using the position label with the similarity coefficient larger than the preset threshold as the drawing data of the crowd attribute information; and conversely, deleting the position labels of which the similarity coefficient is not greater than the preset threshold value.
Specifically, the smaller the manhattan distance is, the larger the reciprocal is, that is, the larger the similarity coefficient is, and the position label with the similarity coefficient larger than the preset threshold is selected as the drawing data of the destination information.
Illustratively, referring to fig. 3, an embodiment of step S108 includes:
and step S108B1, acquiring complete user information in the crowd attribute information.
Specifically, the user portrait tab is obtained by information filled in by the user: such as QQ, facebook, etc., including user information such as age, gender, income, etc.
And step S108B2, clustering the complete user information to obtain a user portrait label of the complete user information.
In particular, the acquired demographic property information is processed to make the displayed image more accurate, including creating a user portrait label.
Step S108B3, inputting incomplete user information in the crowd attribute information and the user portrait label into a label diffusion model so as to perfect the incomplete user information and obtain drawing data corresponding to the crowd attribute information.
Specifically, if some users fill in incomplete user information and some social software do not need to fill in user information (such as an input method, a group purchase APP, a video website, and the like), complete user information cannot be obtained, and a user portrait label established based on the user information is not accurate enough. Therefore, user information needs to be supplemented and perfected: and taking the user with the complete user information as sample data, and inputting the sample data into the label diffusion model for training to obtain the prediction of the incomplete user information.
Taking a gender training label diffusion model of user portrait labels in a video website as an example:
user data acquired by the video website is counted, and assuming that about 30% of users fill in user information during registration, the user information of the 30% of users is used as a training set. However, the feature of watching the movie is a sparse feature, and the accuracy of the user information filled by the registered user is not high, so that a part with high accuracy (such as complete user information) can be extracted from a 30% sample set for training to serve as training data. Acquiring a film list respectively watched by males and females in training data; calculating the interest degree of each film for the male and the female; and taking the film as the input of the prediction model, the corresponding gender as the output of the prediction model, and the interest degree as a judgment standard, and training the prediction model to construct a label diffusion model. The label diffusion model can achieve the purpose of predicting the gender of the user by using the watched film list. The prediction model can be MLlib, LR, FM, linear SVM, GBDT and the like, and the label diffusion model is obtained after training. And furthermore, auxiliary prediction can be carried out through the time of watching the film, the browser, the watching time length and the like of the user.
Exemplarily, referring to fig. 4, the third embodiment of step S108 includes:
and S108C1, clustering according to the crowd attribute information to obtain an interest label system of the crowd attribute information, wherein the interest label system comprises a plurality of label words.
Specifically, the interest tag system can extract, tag and count core information from massive behavior data of the user. The method comprises the steps of acquiring historical browsing articles of a user, and extracting keywords in the historical browsing articles, particularly proper nouns (names of people and organizations), wherein the words also represent interests of the user. Clustering the keywords, and taking a class of keywords as a label or splitting articles under a classification. Such as topic tags that are between keywords and categories, such as "hot pot," the construction of topic tags can be done using text topic clustering. Namely, the modeling of the contents of the three-layer label system of 'classification-subject-keyword' of the historical browsing articles from coarse to fine is completed. When the keywords of the user contain the gourmet, the user can be directly recommended; for subjects of smaller and more people (such as mutton chafing dish like chafing dish), if pushing is not carried out on the current day, recommendation can be carried out according to the classification labels. The interest label system comprises categories, topics and keywords of the target product, and clustering analysis is performed on the topics of each category and the topics are associated with the keywords.
And step S104C2, calculating the interest value of each label word and the target product information.
Illustratively, the formula for calculating the interest value of the tag system is:
scorej+1＝α×scorej+C×weight；
if the label word appears in the target product information, C is 1, otherwise, C is 0; weight represents the weight value of the tag word, scorej+1And scorejAn interest value representing a tag word; j is an integer.
Specifically, the interest value may indicate that if the user clicks on the target product, all tags of the target product that the user has added one to the user interest.
And step S108C3, attenuating the interest value to obtain the drawing data of the target product information.
Illustratively, the attenuating the interest value includes:
performing time attenuation and time attenuation on the interest value;
the calculation formula of the number attenuation is as follows:
scorei+1＝α×scorei+C×weight(0<α<1) wherein, scorei+1And scoreiA first interest value representing interest attenuation according to times, α representing an attenuation factor, weight representing a weight value of a tag word, i representing the times, each time the last score is attenuated, the final score converges to a stable value, and when α is 0.9, the score approaches to 10 infinitely;
the calculation formula of the time attenuation is as follows:
scoreday+1＝scoreday×β(0<β<1) wherein, scoreday+1And scoredayA second interest value representing the attenuation of interest as a function of time, day representing the period, β representing an attenuation factor;
the interest value includes the first interest value and the second interest value.
In particular, interest value decay may ensure that earlier interests become very weak after a period of time, while recent interests are weighted more heavily. Interest may also be attenuated on a weekly, monthly, or hourly level, depending on factors such as the speed at which user interest changes, user activity, and the like.
Step S110, sending the analyzed drawing data to a front end, so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
Specifically, drawing the drawing data into a data graph by calling echarts through the front end; the data pattern drawn is not limited to: a histogram, a bubble plot, a circle plot, etc. Drawing a data graph by using echarts, and transmitting the data graph to a front end, wherein the data graph comprises: displaying the information of the geographic attribute and the crowd attribute; the graph can visually see the data of the query information after big data statistical analysis, inform the user of the auxiliary information of the queried information, is convenient and efficient, and improves the query speed and the accuracy of the result.
Illustratively, when the drawing data is sent to the front end, the drawing data is marked on a map so that the format of the drawing data conforms to the format received by the front end. The data is specially processed, namely if the Baidu map data is transmitted, the Gade map data is stored in the database, the metadata in the database is converted into the data of the Baidu map, and the conversion steps comprise formula conversion, bottom layer storage of a plurality of data and the like. However, when the high-resolution map is directly converted into the hundred-degree map data, data omission may occur, and special processing is required, for example, longitude and latitude offset calculation is performed, so that more accurate data is obtained.
Illustratively, left and right linkage display data graphs can be realized, and when the longitude and latitude of a destination are acquired, the map adjusting API displays the map in a highlighted mode and draws a thermal range circle. And when the coordinate point of the map is clicked, highlighting is carried out according to the Index and the input list, so that visual linkage is realized.
Illustratively, the user analyzes according to the plurality of data graphs to judge whether the destination is a destination address, if so, a corresponding traffic route is generated, and if not, query information is further input for replanning.
Example two
Referring to fig. 5, a schematic diagram of program modules of a second embodiment of the map display system is shown. In the present embodiment, the map displaying system 20 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the map displaying method. The program modules referred to in the embodiments of the present invention refer to a series of computer program instruction segments capable of performing specific functions, and are more suitable than the program itself for describing the execution process of the map presentation system 20 in the storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:
the first obtaining module 200 is configured to obtain a map data display instruction, where the map data display instruction includes displayed destination information, radius range information, target product information, and crowd attribute information.
Specifically, the map data display instruction is used for displaying information which is inquired by a user at a destination according to the requirement. The destination information may be a geographic attribute including: POI (points of interest in cities) distribution maps, people stream distribution in different time periods (weekdays), people stream distribution in different time periods (including holidays), traffic facilities, sign buildings, and the like; the radius range information includes: the longitude and latitude of the destination is taken as the center, and a preset radius range is provided for the user to select; the target product information includes: interest degree, demand and the like of people around the destination on the target product; the crowd attribute information includes: gender, age, school calendar, wealth value distribution, consumption value, product demand, etc. of the population around the destination.
For example, query information input by a user may be received through the input list, and then the map data display instruction is obtained. In the input list, the searched product information and the crowd attribute information can be selected, and the radius range information of the destination information to be inquired can be preset so as to further narrow the inquiry range.
For example: the list of destination information includes: marking the information of the building name, marking the longitude and latitude coordinate points and the foreign key fields of the building based on the Baidu map API, and the like;
the list of demographic attribute information includes: gender, age, and foreign body health.
And a second obtaining module 202, configured to obtain the longitude and latitude of the destination based on the destination information.
Specifically, a map is called according to destination information, and the longitude and latitude of a destination are determined. If the user gives corresponding fuzzy destination information which comprises a POI list or a POI marked in a map, the user can enter POI details to click, then inquire the POI ranking displayed in the map, and display the POI name when moving in a mouse; the POI list and the POI marked in the map can be clicked, and the detailed data of the POI is expanded when the POI is clicked; and if the recommended place of the map in the point is the point, changing the map point of the recommended place into a destination, and acquiring corresponding longitude and latitude.
The third obtaining module 204 is configured to obtain the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information.
Specifically, the radius range information is set in advance, and represents that the radius length of the radius is extended outwards by taking the longitude and latitude of the destination as the center, and the radius range information can be calculated from the longitude and latitude.
A screening module 206, configured to screen the target product information and the crowd attribute information within a latitude and longitude range of the radius range.
Specifically, the SQL sentence is called to obtain the longitude and latitude of the destination information from the database, the longitude and latitude and the radius range information (such as 10km) are taken as the entries and are also taken as the entries if the user selects more conditions for inputting into the list. Calling and starting a Springboot-based framework through an http request to connect a database; then, the logic processing is carried out on the corresponding processing layer through the server routing request to obtain a corresponding routing address; and querying corresponding target product information and crowd attribute information in a database by using the SQL statement according to the routing address.
And the cluster analysis module 208 is configured to perform cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information.
Specifically, the target product information and the crowd attribute information are subjected to clustering analysis to obtain drawing data capable of drawing graphs.
Illustratively, the cluster analysis module 208 is further configured to:
and analyzing the resident address of the crowd attribute information and the GPS portrait to obtain a geographical position portrait, wherein the geographical position portrait comprises a plurality of position labels.
Specifically, the geographic position portrait is obtained by analyzing according to the activity range of the target user. Generally divided into two parts: one part is a permanent site representation; one part is a GPS representation. The difference between the two types of images is large, the standing image is easy to construct, the label is stable, and the GPS image needs to be updated in real time. The ordinary station includes three levels of state, province and city, and is generally refined to the city granularity. And mining the permanent site based on the IP address information of the user, analyzing the IP address of the user, corresponding to a corresponding city, and counting the city where the user IP appears to obtain a resident city label. The resident city labels of the users can be used for counting the user distribution of each region, and can identify the poor people, the tourism people and the like according to the travel tracks of the users among the cities. GPS data is generally collected from a mobile phone terminal, and a user can acquire the GPS information of the user by using an one-account App authorization App.
Specifically, a resident address and a GPS portrait are analyzed by adopting an agglomeration hierarchical clustering algorithm, an address label of each resident address and the address label to be clustered in the resident address and the GPS portrait are taken as an atomic cluster, and then a plurality of atomic clusters are combined into larger and larger clusters until all objects are in one cluster or a certain terminal condition is met. The embodiment of the invention adopts a minimum distance coacervation hierarchical clustering algorithm flow:
(1) regarding each address label as a class, and calculating the minimum distance between every two address labels;
(2) combining the two classes with the minimum distance into a new class;
(3) recalculating the distances between the new class and all classes;
(4) and (3) repeating the steps (2) and (3) until all classes are finally combined into one class.
And calculating a similarity coefficient between the position label and the destination information according to the Manhattan distance.
Specifically, the position label and the destination information are converted into corresponding vectors, the distance between the vector of the position label and the vector of the destination information is calculated according to a Manhattan distance measurement calculation formula, and the reciprocal of the minimum Manhattan distance is taken as a similarity coefficient.
And judging whether the similarity coefficient is larger than a preset threshold value or not.
Specifically, the smaller the manhattan distance is, the larger the reciprocal is, that is, the larger the similarity coefficient is, and the drawing data is screened by judging whether the similarity coefficient is larger than a preset threshold.
If the similarity coefficient is larger than a preset threshold value, using the position label with the similarity coefficient larger than the preset threshold value as the drawing data of the crowd attribute information; and conversely, deleting the position labels of which the similarity coefficient is not greater than the preset threshold value.
Specifically, the smaller the manhattan distance is, the larger the reciprocal is, that is, the larger the similarity coefficient is, and the position label with the similarity coefficient larger than the preset threshold is selected as the drawing data of the destination information.
Illustratively, the cluster analysis module 208 is further configured to:
and acquiring complete user information in the crowd attribute information.
Specifically, the user portrait tab is obtained by information filled in by the user: such as QQ, facebook, etc., including user information such as age, gender, income, etc.
And clustering the complete user information to obtain a user portrait label of the complete user information.
In particular, the acquired demographic property information is processed to make the displayed image more accurate, including creating a user portrait label.
Inputting incomplete user information in the crowd attribute information and the user portrait label into a label diffusion model so as to perfect the incomplete user information and obtain drawing data corresponding to the crowd attribute information.
Specifically, if some users fill in incomplete user information and some social software do not need to fill in user information (such as an input method, a group purchase APP, a video website, and the like), complete user information cannot be obtained, and a user portrait label established based on the user information is not accurate enough. Therefore, user information needs to be supplemented and perfected: and taking the user with the complete user information as sample data, and inputting the sample data into the label diffusion model for training to obtain the prediction of the incomplete user information.
Taking a gender training label diffusion model of user portrait labels in a video website as an example:
user data acquired by the video website is counted, and assuming that about 30% of users fill in user information during registration, the user information of the 30% of users is used as a training set. However, the feature of watching the movie is a sparse feature, and the accuracy of the user information filled by the registered user is not high, so that a part with high accuracy (such as complete user information) can be extracted from a 30% sample set for training to serve as training data. Acquiring a film list respectively watched by males and females in training data; calculating the interest degree of each film for the male and the female; and taking the film as the input of the prediction model, the corresponding gender as the output of the prediction model, and the interest degree as a judgment standard, and training the prediction model to construct a label diffusion model. The label diffusion model can achieve the purpose of predicting the gender of the user by using the watched film list. The prediction model can be MLlib, LR, FM, linear SVM, GBDT and the like, and the label diffusion model is obtained after training. And furthermore, auxiliary prediction can be carried out through the time of watching the film, the browser, the watching time length and the like of the user.
Illustratively, the cluster analysis module 208 is further configured to:
and clustering to obtain an interest label system of the crowd attribute information according to the crowd attribute information, wherein the interest label system comprises a plurality of label words.
Specifically, the interest tag system can extract, tag and count core information from massive behavior data of the user. The method comprises the steps of acquiring historical browsing articles of a user, and extracting keywords in the historical browsing articles, particularly proper nouns (names of people and organizations), wherein the words also represent interests of the user. Clustering the keywords, and taking a class of keywords as a label or splitting articles under a classification. Such as topic tags that are between keywords and categories, such as "hot pot," the construction of topic tags can be done using text topic clustering. Namely, the modeling of the contents of the three-layer label system of 'classification-subject-keyword' of the historical browsing articles from coarse to fine is completed. When the keywords of the user contain the gourmet, the user can be directly recommended; for subjects of smaller and more people (such as mutton chafing dish like chafing dish), if pushing is not carried out on the current day, recommendation can be carried out according to the classification labels. The interest label system comprises categories, topics and keywords of the target product, and clustering analysis is performed on the topics of each category and the topics are associated with the keywords.
And calculating the interest value of each label word and the target product information.
Illustratively, the formula for calculating the interest value of the tag system is:
scorej+1＝α×scorej+C×weight；
if the label word appears in the target product information, C is 1, otherwise, C is 0; weight represents the weight value of the tag word, scorej+1And scorejAn interest value representing a tag word; j is an integer.
Specifically, the interest value may indicate that if the user clicks on the target product, all tags of the target product that the user has added one to the user interest.
And attenuating the interest value to obtain the drawing data of the target product information.
Illustratively, the attenuating the interest value includes:
performing time attenuation and time attenuation on the interest value;
the calculation formula of the number attenuation is as follows:
scorei+1＝α×scorei+C×weight(0<α<1) wherein, scorei+1And scoreiA first interest value representing interest attenuation according to times, α representing an attenuation factor, weight representing a weight value of a tag word, i representing the times, each time the last score is attenuated, the final score converges to a stable value, and when α is 0.9, the score approaches to 10 infinitely;
the calculation formula of the time attenuation is as follows:
scoreday+1＝scoreday×β(0<β<1) wherein, scoreday+1And scoredayA second interest value representing the attenuation of interest as a function of time, day representing the period, β representing an attenuation factor;
the interest value includes the first interest value and the second interest value.
In particular, interest value decay may ensure that earlier interests become very weak after a period of time, while recent interests are weighted more heavily. Interest may also be attenuated on a weekly, monthly, or hourly level, depending on factors such as the speed at which user interest changes, user activity, and the like.
The drawing module 210 is configured to send the analyzed drawing data to a front end, so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
Specifically, drawing the drawing data into a data graph by calling echarts through the front end; the data pattern drawn is not limited to: a histogram, a bubble plot, a circle plot, etc. Drawing a data graph by using echarts, and transmitting the data graph to a front end, wherein the data graph comprises: displaying the information of the geographic attribute and the crowd attribute; the graph can visually see the data of the query information after big data statistical analysis, inform the user of the auxiliary information of the queried information, is convenient and efficient, and improves the query speed and the accuracy of the result.
EXAMPLE III
Fig. 6 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown in FIG. 6, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a map presentation system 20, which are communicatively coupled to each other via a system bus. Wherein:
in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In this embodiment, the memory 21 is generally used for storing an operating system installed on the computer device 2 and various application software, such as the program code of the map showing system 20 of the second embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the server 2 and other electronic devices. For example, the network interface 23 is used to connect the server 2 to an external terminal via a network, establish a data transmission channel and a communication connection between the server 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like. It is noted that fig. 6 only shows the computer device 2 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the map display system 20 stored in the memory 21 can be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to complete the present invention.
For example, fig. 5 is a schematic diagram illustrating program modules of a second embodiment of the map display system 20, in which the map display system 20 may be divided into a first obtaining module 200, a second obtaining module 202, a third obtaining module 204, a filtering module 206, a cluster analysis 208, and a drawing module 210. The program modules referred to herein refer to a series of computer program instruction segments that can perform specific functions, and are more suitable than programs for describing the execution process of the map presentation system 20 in the computer device 2. The specific functions of the program modules 200 and 210 have been described in detail in the second embodiment, and are not described herein again.
Example four
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer-readable storage medium of the embodiment is used for storing the map display system 20, and when being executed by the processor, the map display method of the first embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A map display method, comprising:
acquiring a map data display instruction, wherein the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information;
acquiring longitude and latitude of a destination based on the destination information;
obtaining the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information;
screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range;
performing cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information;
and sending the drawing data to a front end so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
2. The map display method of claim 1, wherein the performing cluster analysis on the target product information and the crowd attribute information to obtain mapping data corresponding to the target product information and the crowd attribute information comprises:
analyzing a resident address and a GPS portrait in the crowd attribute information to obtain a geographical position portrait, wherein the geographical position portrait comprises a plurality of position labels;
calculating a similarity coefficient between the position label and the destination information according to the Manhattan distance;
judging whether the similarity coefficient is larger than a preset threshold value or not;
if the similarity coefficient is larger than a preset threshold value, using the position label with the similarity coefficient larger than the preset threshold value as the drawing data of the crowd attribute information; and conversely, deleting the position labels of which the similarity coefficient is not greater than the preset threshold value.
3. The map display method of claim 1, wherein the performing cluster analysis on the target product information and the crowd attribute information to obtain mapping data corresponding to the target product information and the crowd attribute information comprises:
acquiring complete user information in the crowd attribute information;
clustering the complete user information to obtain a user portrait label of the complete user information;
inputting incomplete user information in the crowd attribute information and the user portrait label into a label diffusion model so as to perfect the incomplete user information and obtain drawing data corresponding to the crowd attribute information.
4. The map display method of claim 1, wherein the performing cluster analysis on the target product information and the crowd attribute information to obtain mapping data corresponding to the target product information and the crowd attribute information comprises:
clustering to obtain an interest label system of the crowd attribute information according to the crowd attribute information, wherein the interest label system comprises a plurality of label words;
calculating the interest value of each label word and the target product information;
and attenuating the interest value to obtain the drawing data of the target product information.
5. The map displaying method according to claim 4, wherein the formula for calculating the interest value of each tag word and the target product information is:
scorej+1＝α×scorej+C×weight；
if the label word appears in the target product information, C is 1, otherwise, C is 0; weight represents the weight value of the tag word, scorej+1And scorejAn interest value representing a tag word; j is an integer.
6. The map presentation method of claim 4, wherein said attenuating the interest value comprises:
performing time attenuation and time attenuation on the interest value;
the calculation formula of the number attenuation is as follows:
scorei+1＝α×scorei+C×weight(0<α<1) wherein, scorei+1And scoreiA first interest value representing interest attenuated according to times, α representing attenuation factor, weight representing weight value of tag word, and i tableShowing times;
the calculation formula of the time attenuation is as follows:
scoreday+1＝scoreday×β(0<β<1) wherein, scoreday+1And scoredayA second interest value representing the attenuation of interest as a function of time, day representing the period, β representing an attenuation factor;
the interest value includes the first interest value and the second interest value.
7. A map presentation system, comprising:
the map data display device comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is used for acquiring a map data display instruction, and the map data display instruction comprises displayed destination information, radius range information, target product information and crowd attribute information;
the second acquisition module is used for acquiring the longitude and latitude of the destination based on the destination information;
the third acquisition module is used for acquiring the longitude and latitude of the radius range of the destination according to the longitude and latitude of the destination and the radius range information;
the screening module is used for screening the target product information and the crowd attribute information in the longitude and latitude range of the radius range;
the cluster analysis module is used for carrying out cluster analysis on the target product information and the crowd attribute information to obtain drawing data corresponding to the target product information and the crowd attribute information;
and the drawing module is used for sending the drawing data to a front end so that the front end draws a plurality of data graphs according to the drawing data and displays the data graphs.
8. The map presentation system of claim 7, wherein the cluster analysis module is further configured to:
clustering to obtain an interest label system of the crowd attribute information according to the crowd attribute information, wherein the interest label system comprises a plurality of label words;
calculating the interest value of each label word and the target product information;
and attenuating the interest value to obtain the drawing data of the target product information.
9. Computer device, characterized in that it comprises a memory, a processor, on which a computer program is stored that is executable on the processor, which computer program, when being executed by the processor, carries out the steps of the map presentation method according to any one of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor for causing the at least one processor to carry out the steps of the map presentation method as claimed in any one of claims 1 to 6.
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