CN113420104A - Method and device for determining total sampling rate of interest points, electronic equipment and storage medium - Google Patents

Method and device for determining total sampling rate of interest points, electronic equipment and storage medium Download PDF

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CN113420104A
CN113420104A CN202110726388.3A CN202110726388A CN113420104A CN 113420104 A CN113420104 A CN 113420104A CN 202110726388 A CN202110726388 A CN 202110726388A CN 113420104 A CN113420104 A CN 113420104A
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CN113420104B (en
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赵光辉
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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Abstract

The disclosure provides a method and a device for determining the sampling total rate of interest points, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring pre-stored first wireless fidelity (WIFI) information of a point of interest (POI) in an area to be evaluated; acquiring WIFI information of POI (point of interest) acquired by crowdsourcing users in an area to be evaluated; determining second WIFI information matched with the first WIFI information in the acquired WIFI information; and determining the sampling rate of crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information. In the technical scheme, the adoption rate of the crowdsourcing users in the area to be evaluated is determined based on the pre-stored WIFI information of the area to be evaluated and the WIFI information collected by the crowdsourcing users, and the adoption rate determining mode does not need to verify the POI information, so that the completeness of the crowdsourcing users in the area to be evaluated for POI collection can be relatively easily measured.

Description

Method and device for determining total sampling rate of interest points, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to the field of data processing.
Background
Crowd-sourced collection is an important source of current map Point of Interest (POI) data updates, and collection work is performed by dividing real geographic space into areas of about 1km × 1km, and then delivering each area in the form of tasks to be allocated to designated crowd-sourced users. Because crowdsourcing users in reality are users from the vast Internet, the operation capacity and the operation will are difficult to control, and after the delivered area is collected by the users, a means is needed to measure the integrity degree of POI collection to determine whether the area collection reaches the standard or not and determine what degree reward and punishment are carried out on the users.
In the prior art, a certain amount of POI information subjected to source verification such as express delivery, real-time collection and the like is needed to measure the integrity of POI collection, however, the POI information subjected to verification is not easy to obtain, so that the difficulty is increased for measuring the integrity of POI collection.
Disclosure of Invention
The disclosure provides a method and a device for determining a total sampling rate of an interest point, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a method for determining a total sampling rate of a point of interest, including:
acquiring prestored first Wireless Fidelity (WIFI) information of a point of interest (POI) in an area to be evaluated;
acquiring WIFI information of POI (point of interest) acquired by crowdsourcing users in an area to be evaluated;
determining second WIFI information matched with the first WIFI information in the acquired WIFI information;
and determining the sampling rate of crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information.
According to another aspect of the present disclosure, there is provided a point of interest sampling rate determination apparatus, including:
the first acquisition module is used for acquiring prestored first wireless fidelity WIFI information of a point of interest (POI) in the area to be evaluated;
the second acquisition module is used for acquiring the WIFI information of the POI acquired by crowdsourcing users in the area to be evaluated;
the first determining module is used for determining second WIFI information matched with the first WIFI information in the acquired WIFI information;
and the second determining module is used for determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to any one of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method in any of the embodiments of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.
The technical scheme of the method and the device solves the problem of how to measure the completeness of the POI acquisition of the crowdsourcing users. In the technical scheme, the adoption rate of the crowdsourcing users in the area to be evaluated is determined based on the pre-stored WIFI information of the area to be evaluated and the WIFI information collected by the crowdsourcing users, and the adoption rate determining mode does not need to verify the POI information, so that the completeness of the crowdsourcing users in the area to be evaluated for POI collection can be relatively easily measured.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of a method for determining a total sampling rate of interest points according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of a process for determining a sign similarity of a sign image according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a method for determining a total sampling rate of interest points according to an embodiment of the disclosure;
FIG. 4 is a diagram of a device for determining a total sampling rate of interest points according to an embodiment of the disclosure;
FIG. 5 is a schematic diagram of a third determination module in an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a point of interest total rate determination method according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The technical scheme disclosed by the invention can be applied to the calculation of the POI total rate under the condition that the area to be evaluated is outdoor, and also can be applied to the calculation of the POI total rate under the condition that the area to be evaluated is indoor (for example, inside a market), the data required by the calculation is easy to obtain, and the difficulty is reduced for measuring the integrity degree of POI acquisition.
The execution subject of the present disclosure may be any electronic device, for example, a server, and specifically, may be a server corresponding to an application related to POI acquisition. The method for determining the total sampling rate of the interest points in the embodiment of the present disclosure will be described in detail below.
Fig. 1 is a schematic diagram of a method for determining a total sampling rate of an interest point in an embodiment of the disclosure. As shown in fig. 1, the method for determining the total sampling rate of the interest point may include:
step S101, first wireless fidelity (WIFI) information of a point of interest (POI) in a pre-stored area to be evaluated is acquired;
when the POI of a certain area to be evaluated is measured, the WIFI information, namely the first WIFI information, of the POI in the area to be evaluated is scanned in advance through equipment such as a mobile terminal and stored in a preset storage space. The WIFI information of the POI can be the unique identification of the WIFI equipment, and also can be other related WIFI information, and the method is not limited in the application. Each POI may correspond to WIFI information of at least one WIFI device.
Alternatively, the area to be assessed may be an indoor area, for example, the inside of a mall. The shopping mall comprises a plurality of shops, each shop can serve as a POI, at least one WIFI device can be installed in each shop, and therefore a set formed by a plurality of WIFI information is obtained, wherein each WIFI information can be WIFI information corresponding to one WIFI device.
Optionally, for a certain POI, the WIFI information is shown in table 1:
Figure BDA0003138838220000041
TABLE 1
The mac _ addr field represents the equipment identification of the WIFI equipment; the WIFI _ name field represents a WIFI name; the point field indicates the WIFI location.
When the total sampling rate of the area to be evaluated is calculated, the server reads the WIFI information of each POI of the area to be evaluated from the preset storage space.
Step S102, acquiring WIFI information of POI (point of interest) acquired by crowdsourcing users in an area to be evaluated;
and the server receives the WIFI information of the POI collected in the area to be evaluated, which is sent by the crowdsourcing user through the user terminal. The method comprises the steps that a POI acquisition related application program is installed on a user terminal, WIFI information in an area to be evaluated is acquired, then the WIFI information is uploaded to a server corresponding to the application program through the application program, and the server receives the WIFI information acquired by crowdsourcing users. The WIFI information of the POI collected by the crowdsourcing user in the area to be evaluated may be a set formed by a plurality of WIFI information.
Step S103, second WIFI information matched with the first WIFI information is determined in the collected WIFI information;
the first WIFI information may be a set formed by a plurality of stored WIFI information, the collected WIFI information may be a set formed by a plurality of WIFI information collected by crowdsourcing users, and the second WIFI information may be an intersection of the two sets.
The second WIFI information matched with the first WIFI information may include, but is not limited to, the same or similar WIFI names, the same or similar WIFI locations, and the like, wherein whether the WIFI names are similar or not may be determined by a preconfigured name similarity threshold, and whether the WIFI locations are similar may be determined by a preconfigured distance threshold.
Optionally, two WIFI names in the set formed by the first WIFI information are respectively the same as two WIFI names in the set formed by the WIFI information collected by the crowdsourcing user, and then the second WIFI information is the set formed by the two same WIFI names.
And S104, determining the sampling rate of the crowdsourced users in the area to be evaluated based on the first WIFI information and the second WIFI information.
The POI adoption rate of the crowdsourcing users in the area to be evaluated can be determined through the first WIFI information composition set and the second WIFI information composition set, and the POI adoption rate of the crowdsourcing users in the area to be evaluated can be measured by the numerical value of the adoption rate.
According to the method for determining the total rate of the POI, the total rate of the crowd-sourced users in the area to be evaluated is determined based on the pre-stored WIFI information of the area to be evaluated and the WIFI information collected by the crowd-sourced users.
The specific implementation mode for determining the total sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information is shown in the following embodiment:
in one embodiment, determining the percentage of crowd-sourced users in the area to be evaluated based on the first WIFI information and the second WIFI information includes:
determining a first cardinality of a set of first WIFI information and a second cardinality of a set of second WIFI information;
and calculating the proportion of the second base number in the first base number to obtain the total sampling rate of the crowdsourcing user for the area to be evaluated.
The first cardinality represents the number of elements in a set formed by the first WIFI information; the second cardinality represents the number of elements in the set formed by the second WIFI information; by calculating the proportion of the second cardinal number to the first cardinal number, it can be seen how much WIFI information is collected by the crowdsourcing users in the prestored WIFI information in the area to be evaluated, so that the total collection rate of the crowdsourcing users for the area to be evaluated is obtained.
In the embodiment of the disclosure, the proportion of the number of the WIFI information collected by the crowdsourcing users in the area to be evaluated to the number of the pre-stored WIFI information is calculated, and the crowdsourcing users are used as the sampling rate of the crowdsourcing users for the area to be evaluated, so that the calculation is simple, the data required by the calculation is easy to obtain, and the practicability is high.
In addition, in the technical scheme of the present disclosure, besides calculating the total rate through the WIFI information, POI information may be obtained to serve as a data basis for subsequently calculating the total rate, which is specifically seen in the following embodiments:
in one embodiment, the method further comprises:
determining first POI information matched with the first WIFI information in prestored POI information of an area to be evaluated;
the method comprises the steps of obtaining POI information collected by crowdsourcing users in an area to be evaluated;
determining POI information matched with prestored POI information in the collected POI information;
and determining second POI information matched with the first POI information from the POI information matched with the prestored POI information.
The POI information may be attribute information of the POI, and in the case where the POI is a store, the POI information may include, but is not limited to, information such as a store name, a store location, a store classification, and a visiting heat of the store.
Each POI information in the pre-stored POI information of the area to be evaluated is matched with the pre-stored WIFI information, matching rules of the WIFI information and the POI information can be pre-configured according to specific needs, the POI information matched with the pre-stored WIFI information is used as the verified POI information, and data are easier to acquire.
And determining POI information matched with the prestored POI information from the POI information collected by the crowdsourcing user, and determining second POI information matched with the first POI information from the POI information matched with the prestored POI information so as to obtain verified POI information collected by the user.
In the embodiment of the disclosure, the pre-stored POI information is verified through the WIFI information, the verified POI information is easier to acquire, and then the verified POI information is screened out from the POI information acquired by crowdsourcing users, so that the verified POI information acquired by the users is acquired and is used as a basis for subsequent total rate calculation.
In practical application, except for calculating the total rate through the WIFI information, the POI information can be taken into consideration, and the total rate is calculated through the WIFI information and the POI information, which is specifically seen in the following embodiment:
in one embodiment, determining the percentage of crowd-sourced users in the area to be evaluated based on the first WIFI information and the second WIFI information includes:
and determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information.
The first WIFI information is pre-stored WIFI information, and the second WIFI information is WIFI information which is acquired by crowdsourcing users and is matched with the pre-stored WIFI information; the first POI information is POI information verified through WIFI information in the prestored POI information, and the second POI information is verified POI information collected by crowdsourcing users. Through the first WIFI information, the second WIFI information, the first POI information and the second POI information, the sampling rate of crowdsourcing users in the area to be evaluated can be calculated.
In the embodiment of the disclosure, when the total sampling rate is calculated, besides the WIFI information, the POI information is also added, so that the result of the total sampling rate obtained by calculation is more objective.
The specific implementation mode for determining the total sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information is shown in the following embodiment:
in one embodiment, determining the percentage of crowd-sourced users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information, and the second POI information includes:
determining a first cardinality of a set of first WIFI information and a second cardinality of a set of second WIFI information;
determining a third cardinality of the set of first POI information and a fourth cardinality of the set of second POI information;
and obtaining the total sampling rate of the crowdsourcing user for the area to be evaluated according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number.
The set formed by the first WIFI information comprises a plurality of WIFI information, and the number of elements in the set is a first cardinal number; the set formed by the second WIFI information comprises a plurality of WIFI information, and the number of elements in the set is a second cardinal number; the set formed by the first POI information comprises a plurality of POI information, and the number of elements in the set is a third base number; the set formed by the second POI information comprises a plurality of POI information, and the number of elements in the set is a fourth base number; according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number, the adoption rate of the crowdsourcing user for the area to be evaluated can be obtained.
Optionally, the ratio of the second base number to the first base number and the ratio of the fourth base number to the third base number are calculated, an average value of the two ratios is calculated, and the average value is used as the percentage of the crowdsourcing user to the area to be assessed.
Optionally, different weights may be set for the two proportions, and the percentage of the crowd-sourced user to the area to be evaluated is obtained through weighting calculation.
In a specific embodiment, the first WIFIThe set of information is waAnd a set formed by WIFI information acquired by crowdsourcing users in the area to be evaluated is wbAnd the set formed by the second WIFI information is wa n wbThe set of the first POI information is paThe set formed by POI information matched with prestored POI information in POI information collected by crowdsourcing users is pbThe set of the second POI information is pa∩pbThen, the sampling rate is calculated according to the following formula (1):
Figure BDA0003138838220000081
wherein, | WaI denotes the first base, | Wa∩WbI denotes the second base, | PaI denotes the third base, | Pa∩Pb| represents the fourth base.
According to the embodiment of the disclosure, the total rate of the crowdsourcing users for the area to be evaluated is obtained according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number, the calculation is simple, and the total rate result obtained through calculation is more objective.
In one embodiment, determining first POI information matched with first WIFI information in pre-stored POI information of an area to be evaluated includes:
under the condition that the first WIFI information comprises a WIFI name and a WIFI position and the prestored POI information of the area to be evaluated comprises a POI name and a POI position, determining the POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
and determining a POI name matched with the WIFI name as first POI information from the POI information of the POI position in the preset range.
The method comprises the steps that when first WIFI information comprises a WIFI name and a WIFI position, and the prestored POI information of an area to be evaluated comprises a POI name and a POI position, for each WIFI information, the WIFI position is used as a circle center, a preset range is determined by taking a preset distance as a radius, the POI information of the POI position in the preset range is determined, multiple POI information can be obtained, for the POI information of the POI position in the preset range, the WIFI name and the POI name are matched, the matching can comprise a Chinese name, a Chinese pinyin name, an English name and the like, public substrings are obtained through a Longest Common Subsequence (LCS) algorithm, and whether the matching is carried out or not is judged by setting a certain threshold value.
In the embodiment of the disclosure, the first POI information matched with the first WIFI information is determined in the preset range in a name matching mode, the calculation is simple, and the application is convenient.
Except that the first POI information matched with the first WIFI information can be determined in other modes by name matching, the method is concretely seen in the following embodiment:
in one embodiment, determining first POI information matched with first WIFI information in pre-stored POI information of an area to be evaluated includes:
under the condition that the first WIFI information comprises a WIFI position and the prestored POI information of the area to be evaluated comprises a POI position, determining the POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
extracting WIFI characteristics of the first WIFI information;
extracting POI characteristics of POI information of the POI position in a preset range;
extracting relevant interaction characteristics of the first WIFI information and POI information of which the POI positions are in a preset range;
based on the WIFI characteristics, the POI characteristics and the related interactive characteristics, first POI information matched with the first WIFI information is determined from POI information of the POI position in a preset range.
The method comprises the steps that when first WIFI information comprises a WIFI name and a WIFI position, and prestored POI information of an area to be evaluated comprises a POI name and a POI position, for each WIFI information, a preset range is determined by taking the WIFI position as a circle center and a preset distance as a radius, and POI information of the POI position in the preset range is determined, wherein the POI information can be a plurality of POI information.
Extracting WIFI characteristics from the first WIFI information, wherein the WIFI characteristics can include but are not limited to whether a WIFI name is Chinese or not, whether numbers are contained or not and the like. The POI features may include, but are not limited to, POI category (e.g., gourmet, banking, shopping) features, POI visit heat features, and the like. The relevant interaction features may include, but are not limited to, euclidean distances between WIFI and POI location coordinates, similarities between WIFI and POI names, and the like.
The WIFI features, the POI features, and the related interaction features are input into a machine learning algorithm to determine whether matching is performed, for example, algorithms such as a Gradient Boosting Decision Tree (GBDT). The GBDT outputs a score, and whether the WIFI information is matched with the POI information can be obtained by limiting a threshold value.
In the embodiment of the disclosure, the first POI information matched with the first WIFI information is determined through the WIFI characteristics, the POI characteristics and the multiple characteristics of the related interaction characteristics, and the accuracy rate of the calculation result is high.
In one embodiment, determining, from the collected POI information, POI information that matches pre-stored POI information includes:
respectively extracting feature information of the first shop signboard image and feature information of the second shop signboard image under the condition that the prestored POI information comprises the first shop signboard image and the collected POI information comprises the second shop signboard image;
determining signboard similarity of the first shop signboard image and the second shop signboard image based on the characteristic information of the first shop signboard image and the characteristic information of the second shop signboard image;
based on the signboard similarity, a shop signboard image matching the first shop signboard image is determined from the second shop signboard images.
When the pre-stored POI information comprises shop signboard images, and the POI information collected by crowdsourcing users also comprises the shop signboard images, respectively extracting the characteristic information of the shop signboard images, calculating the signboard similarity according to the characteristic information, and determining the POI information matched with the pre-stored POI information from the collected POI information in a mode of calculating the signboard similarity.
In the embodiment of the disclosure, the POI information matched with the prestored POI information is determined in the collected POI information in a mode of calculating the signboard similarity, the calculation mode is simple, and the calculation result accuracy is high.
In one embodiment, the feature information includes at least one of a text feature or an image feature.
The characteristic information of the shop signboard image may be a text characteristic, an image characteristic, a text characteristic and an image characteristic.
In the embodiment of the disclosure, the similarity of the shop signboard images can be calculated through various characteristic information, and various different requirements can be met.
In one embodiment, determining a signboard similarity of a first shop signboard image and a second shop signboard image based on feature information of the first shop signboard image and feature information of the second shop signboard image includes:
determining the text similarity of the first shop signboard image and the second shop signboard image based on the text feature of the first shop signboard image and the text feature of the second shop signboard image under the condition that the feature information is the text feature and the image feature;
determining image similarity of the first store signboard image and the second store signboard image based on the image features of the first store signboard image and the image features of the second store signboard image;
signboard similarity of the first shop signboard image and the second shop signboard image is determined based on the text similarity and the image similarity.
The feature information may be a text feature and an image feature, among others. Text Recognition can be performed in an Optical Character Recognition (OCR) manner, text features are extracted, and text similarity is obtained through text matching. The image similarity may be calculated by extracting image features of the signboard images.
Optionally, corresponding weights may be set for the text similarity and the image similarity, respectively, and the signboard similarity of the first shop signboard image and the second shop signboard image may be determined in a weighting calculation manner.
In the embodiment of the disclosure, the signboard similarity of the first shop signboard image and the second shop signboard image is determined through the text similarity and the image similarity, and the obtained similarity calculation result is more accurate.
In a specific embodiment, a specific process of determining the signboard similarity of the shop signboard image is shown in fig. 2, the server receives a shop photo image (as shown in the figure, "capture image") captured by a crowdsourcing user through a user terminal, performs signboard image detection (as shown in the figure, "signboard detection"), matches the detected shop signboard image with the shop signboard image in the pre-stored POI information, and a specific matching process is shown in the figure, "signboard POI automatic association", performs text recognition by means of OCR recognition, and performs text matching to obtain the text similarity between the shop signboard image captured by the user and the shop signboard image in the pre-stored POI information; the method comprises the steps of extracting image features of shop signboard images collected by a user and shop signboard images in a pre-stored POI respectively, calculating image similarity, determining the similarity of the shop signboard images through text similarity and image similarity, and determining the shop signboard images matched with the pre-stored POI in the collected shop signboard images, such as the 'associated POI' shown in the figure.
Fig. 3 is a schematic diagram of a method for determining a total sampling rate of an interest point in an embodiment of the disclosure. As shown in fig. 3, the method for determining the total sampling rate of the interest point may include:
step S301, acquiring prestored first WIFI information of POI in an area to be evaluated;
step S302, acquiring WIFI information of POI (point of interest) acquired by crowdsourcing users in an area to be evaluated;
step S303, determining second WIFI information matched with the first WIFI information in the acquired WIFI information;
step S304, determining first POI information matched with the first WIFI information in the prestored POI information of the area to be evaluated;
step S305, POI information collected by crowdsourcing users in an area to be evaluated is obtained;
step S306, POI information matched with prestored POI information is determined in the collected POI information;
in step S307, second POI information matching the first POI information is determined among the POI information matching the prestored POI information.
Step S308, determining the sampling rate of crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information.
According to the adoption rate determining method provided by the embodiment of the disclosure, when the adoption rate is calculated, besides the WIFI information, the POI information is added, so that the result of the obtained adoption rate can be more objective.
Fig. 4 is a schematic diagram of a device for determining a total sampling rate of interest points according to an embodiment of the disclosure. As shown in fig. 4, the point of interest total rate determining device may include:
the first obtaining module 401 is configured to obtain first wireless fidelity WIFI information of a point of interest POI in a pre-stored to-be-evaluated area;
a second obtaining module 402, configured to obtain WIFI information of a POI, which is collected by a crowdsourcing user in an area to be evaluated;
the first determining module 403 is configured to determine, from the acquired WIFI information, second WIFI information that matches the first WIFI information;
the second determining module 404 is configured to determine, based on the first WIFI information and the second WIFI information, a percentage of crowdsourcing users in the area to be evaluated.
The device for determining the total rate of the interest points determines the total rate of crowdsourcing users in the area to be evaluated based on the pre-stored WIFI information of the area to be evaluated and the WIFI information collected by the crowdsourcing users.
In an embodiment, the second determining module 404 is specifically configured to:
determining a first cardinality of a set of first WIFI information and a second cardinality of a set of second WIFI information;
and calculating the proportion of the second base number in the first base number to obtain the total sampling rate of the crowdsourcing user for the area to be evaluated.
FIG. 5 is a schematic diagram of a third determination module in an embodiment of the present disclosure; in one embodiment, as shown in fig. 5, the apparatus for determining the total sampling rate of interest further includes a third determining module, where the third determining module includes a first determining unit 501, an obtaining unit 502, a second determining unit 503, and a third determining unit 504:
the first determination unit 501 is configured to: determining first POI information matched with the first WIFI information in prestored POI information of an area to be evaluated;
the obtaining unit 502 is configured to: the method comprises the steps of obtaining POI information collected by crowdsourcing users in an area to be evaluated;
the second determination unit 503 is configured to: determining POI information matched with prestored POI information in the collected POI information;
the third determining unit 504 is configured to: and determining second POI information matched with the first POI information from the POI information matched with the prestored POI information.
In an embodiment, the second determining module 404 is specifically configured to:
and determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information.
In an embodiment, the second determining module 404 is specifically configured to:
determining a first cardinality of a set of first WIFI information and a second cardinality of a set of second WIFI information;
determining a third cardinality of the set of first POI information and a fourth cardinality of the set of second POI information;
and obtaining the total sampling rate of the crowdsourcing user for the area to be evaluated according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number.
In one embodiment, the first determining unit 501 is configured to:
under the condition that the first WIFI information comprises a WIFI name and a WIFI position and the prestored POI information of the area to be evaluated comprises a POI name and a POI position, determining the POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
and determining a POI name matched with the WIFI name as first POI information from the POI information of the POI position in the preset range.
In one embodiment, the first determining unit 501 is configured to:
under the condition that the first WIFI information comprises a WIFI position and the prestored POI information of the area to be evaluated comprises a POI position, determining the POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
extracting WIFI characteristics of the first WIFI information;
extracting POI characteristics of POI information of the POI position in a preset range;
extracting relevant interaction characteristics of the first WIFI information and POI information of which the POI positions are in a preset range;
based on the WIFI characteristics, the POI characteristics and the related interactive characteristics, first POI information matched with the first WIFI information is determined from POI information of the POI position in a preset range.
In one embodiment, the second determining unit 503 is configured to:
respectively extracting feature information of the first shop signboard image and feature information of the second shop signboard image under the condition that the prestored POI information comprises the first shop signboard image and the collected POI information comprises the second shop signboard image;
determining signboard similarity of the first shop signboard image and the second shop signboard image based on the characteristic information of the first shop signboard image and the characteristic information of the second shop signboard image;
based on the signboard similarity, a shop signboard image matching the first shop signboard image is determined from the second shop signboard images.
In one embodiment, the feature information includes at least one of a text feature or an image feature.
In one embodiment, the second determining unit 503, when determining the signboard similarity of the first shop signboard image and the second shop signboard image based on the feature information of the first shop signboard image and the feature information of the second shop signboard image, is configured to:
determining the text similarity of the first shop signboard image and the second shop signboard image based on the text feature of the first shop signboard image and the text feature of the second shop signboard image under the condition that the feature information is the text feature and the image feature;
determining image similarity of the first store signboard image and the second store signboard image based on the image features of the first store signboard image and the image features of the second store signboard image;
signboard similarity of the first shop signboard image and the second shop signboard image is determined based on the text similarity and the image similarity.
The functions of each unit, module or sub-module in each apparatus in the embodiments of the present disclosure may refer to the corresponding description in the above method embodiments, and are not described herein again.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the point of interest sampling rate determination method. For example, in some embodiments, the point of interest recall determination method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the point of interest percentage determination method described above may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured by any other suitable means (e.g. by means of firmware) to perform the point of interest percentage determination method.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (23)

1. A point of interest sampling rate determination method, the method comprising:
acquiring pre-stored first wireless fidelity (WIFI) information of a point of interest (POI) in an area to be evaluated;
acquiring WIFI information of POIs (point of interest) acquired by crowdsourcing users in the area to be evaluated;
determining second WIFI information matched with the first WIFI information in the acquired WIFI information;
and determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information.
2. The method of claim 1, wherein the determining the percentage of the crowd-sourced users in the area to be evaluated based on the first and second WIFI information comprises:
determining a first cardinality of the set of first WIFI information and a second cardinality of the set of second WIFI information;
and calculating the proportion of the second base number in the first base number to obtain the total sampling rate of the crowdsourcing user for the area to be evaluated.
3. The method of claim 1, further comprising:
determining first POI information matched with the first WIFI information in the prestored POI information of the area to be evaluated;
acquiring POI information collected by the crowdsourcing user in the area to be evaluated;
POI information matched with prestored POI information is determined in the collected POI information;
and determining second POI information matched with the first POI information in the POI information matched with the prestored POI information.
4. The method of claim 3, wherein the determining the percentage of the crowd-sourced users in the area to be evaluated based on the first WIFI information and the second WIFI information comprises:
and determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information.
5. The method of claim 4, wherein the determining a percentage of the crowd-sourced users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information, and the second POI information comprises:
determining a first cardinality of the set of first WIFI information and a second cardinality of the set of second WIFI information;
determining a third cardinality of the set of first POI information and a fourth cardinality of the set of second POI information;
and obtaining the total sampling rate of the crowdsourcing user for the area to be evaluated according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number.
6. The method of claim 3, wherein the determining, from the pre-stored POI information of the area to be evaluated, first POI information matched with the first WIFI information comprises:
under the condition that the first WIFI information comprises a WIFI name and a WIFI position and the prestored POI information of the area to be evaluated comprises a POI name and a POI position, determining POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
and determining a POI name matched with the WIFI name as first POI information from the POI information of the POI position in a preset range.
7. The method of claim 3, wherein the determining, from the pre-stored POI information of the area to be evaluated, first POI information matched with the first WIFI information comprises:
under the condition that the first WIFI information comprises a WIFI position and the prestored POI information of the area to be evaluated comprises a POI position, determining POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
extracting WIFI characteristics of the first WIFI information;
extracting POI characteristics of POI information of the POI position in a preset range;
extracting relevant interaction characteristics of the first WIFI information and POI information of the POI position in a preset range;
and determining first POI information matched with the first WIFI information from the POI information of the POI position in a preset range based on the WIFI characteristics, the POI characteristics and the related interactive characteristics.
8. The method of claim 3, wherein the determining, from the collected POI information, POI information that matches the pre-stored POI information comprises:
respectively extracting feature information of a first shop signboard image and feature information of a second shop signboard image under the condition that the prestored POI information comprises the first shop signboard image and the collected POI information comprises the second shop signboard image;
determining signboard similarity of the first shop signboard image and a second shop signboard image based on the characteristic information of the first shop signboard image and the characteristic information of the second shop signboard image;
determining a shop signboard image matching the first shop signboard image from the second shop signboard image based on the signboard similarity.
9. The method of claim 8, wherein the feature information comprises at least one of a text feature or an image feature.
10. The method of claim 8, wherein determining a sign similarity of the first and second store sign images based on the feature information of the first and second store sign images comprises:
determining a text similarity of the first shop signboard image and the second shop signboard image based on the text feature of the first shop signboard image and the text feature of the second shop signboard image when the feature information is the text feature and the image feature;
determining image similarity of the first store signboard image and the second store signboard image based on image features of the first store signboard image and image features of the second store signboard image;
determining a signboard similarity of the first shop signboard image and the second shop signboard image based on the text similarity and the image similarity.
11. A point of interest recall determination apparatus, the apparatus comprising:
the first acquisition module is used for acquiring prestored first wireless fidelity WIFI information of a point of interest (POI) in the area to be evaluated;
the second acquisition module is used for acquiring WIFI information of POIs acquired by crowdsourcing users in the area to be evaluated;
the first determining module is used for determining second WIFI information matched with the first WIFI information in the acquired WIFI information;
and the second determining module is used for determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information and the second WIFI information.
12. The apparatus of claim 11, wherein the second determining module is specifically configured to:
determining a first cardinality of the set of first WIFI information and a second cardinality of the set of second WIFI information;
and calculating the proportion of the second base number in the first base number to obtain the total sampling rate of the crowdsourcing user for the area to be evaluated.
13. The apparatus of claim 11, further comprising a third determination module comprising the first determination unit, the acquisition unit, the second determination unit, and the third determination unit:
the first determination unit is configured to: determining first POI information matched with the first WIFI information in the prestored POI information of the area to be evaluated;
the acquisition unit is configured to: acquiring POI information collected by the crowdsourcing user in the area to be evaluated;
the second determination unit is configured to: POI information matched with prestored POI information is determined in the collected POI information;
the third determination unit is configured to: and determining second POI information matched with the first POI information in the POI information matched with the prestored POI information.
14. The apparatus of claim 13, wherein the second determining module is specifically configured to:
and determining the sampling rate of the crowdsourcing users in the area to be evaluated based on the first WIFI information, the second WIFI information, the first POI information and the second POI information.
15. The apparatus of claim 14, wherein the second determining module is specifically configured to:
determining a first cardinality of the set of first WIFI information and a second cardinality of the set of second WIFI information;
determining a third cardinality of the set of first POI information and a fourth cardinality of the set of second POI information;
and obtaining the total sampling rate of the crowdsourcing user for the area to be evaluated according to the proportion of the second base number in the first base number and the proportion of the fourth base number in the third base number.
16. The apparatus of claim 13, wherein the first determining unit is to:
under the condition that the first WIFI information comprises a WIFI name and a WIFI position and the prestored POI information of the area to be evaluated comprises a POI name and a POI position, determining POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
and determining a POI name matched with the WIFI name as first POI information from the POI information of the POI position in a preset range.
17. The apparatus of claim 13, wherein the first determining unit is to:
under the condition that the first WIFI information comprises a WIFI position and the prestored POI information of the area to be evaluated comprises a POI position, determining POI information of the POI position in a preset range in the prestored POI information of the area to be evaluated, wherein the preset range is determined according to the WIFI position;
extracting WIFI characteristics of the first WIFI information;
extracting POI characteristics of POI information of the POI position in a preset range;
extracting relevant interaction characteristics of the first WIFI information and POI information of the POI position in a preset range;
and determining first POI information matched with the first WIFI information from the POI information of the POI position in a preset range based on the WIFI characteristics, the POI characteristics and the related interactive characteristics.
18. The apparatus of claim 13, wherein the second determining unit is to:
respectively extracting feature information of a first shop signboard image and feature information of a second shop signboard image under the condition that the prestored POI information comprises the first shop signboard image and the collected POI information comprises the second shop signboard image;
determining signboard similarity of the first shop signboard image and a second shop signboard image based on the characteristic information of the first shop signboard image and the characteristic information of the second shop signboard image;
determining a shop signboard image matching the first shop signboard image from the second shop signboard image based on the signboard similarity.
19. The apparatus of claim 18, wherein the feature information comprises at least one of a text feature or an image feature.
20. The apparatus according to claim 18, wherein the second determining unit, when determining the signboard similarity of the first and second shop signboard images based on the feature information of the first and second shop signboard images, is configured to:
determining a text similarity of the first shop signboard image and the second shop signboard image based on the text feature of the first shop signboard image and the text feature of the second shop signboard image when the feature information is the text feature and the image feature;
determining image similarity of the first store signboard image and the second store signboard image based on image features of the first store signboard image and image features of the second store signboard image;
determining a signboard similarity of the first shop signboard image and the second shop signboard image based on the text similarity and the image similarity.
21. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-10.
23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.
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