CN111190988A - Address resolution method, device, equipment and computer readable storage medium - Google Patents
Address resolution method, device, equipment and computer readable storage medium Download PDFInfo
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- CN111190988A CN111190988A CN201911424320.9A CN201911424320A CN111190988A CN 111190988 A CN111190988 A CN 111190988A CN 201911424320 A CN201911424320 A CN 201911424320A CN 111190988 A CN111190988 A CN 111190988A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The present disclosure provides an address resolution method, apparatus, device and computer-readable storage medium, including obtaining a candidate point set corresponding to a target order, the candidate point set including at least one candidate location point; obtaining resident point information which is determined in advance based on user information according to the information of the target order; and determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence. The method, the device, the equipment and the computer readable storage medium provided by the disclosure can determine the target position in a plurality of candidate position points which are possibly valid addresses by combining with the predetermined user residence point information, and the residence point information is obtained based on the user information and can embody the range of the activity area of the user, so that the scheme disclosed by the disclosure can be used for analyzing the address of the target order by combining with the user information, thereby analyzing a more accurate target address on the premise of not depending on an address library.
Description
Technical Field
The present disclosure relates to address recognition technologies, and in particular, to an address resolution method, apparatus, device, and computer-readable storage medium.
Background
In recent years, the logistics industry is rapidly developed and the coverage area is increased. The whole logistics industry is also developing towards high efficiency, convenience, automation, intellectualization and refinement, wherein rapid sorting and accurate distribution are important links for reducing cost and improving user experience in a supply link.
The existing logistics sorting process comprises the steps of scanning goods through a sorter, calling an address resolution interface to obtain a goods appropriate delivery address, sorting, and putting the goods into a to-be-processed area for manual processing if an accurate address does not exist. Wherein the address resolution interface mainly originates from a map provider or an internet map service provider.
In the address resolution scheme in the prior art, a user candidate address set needs to be resolved by using an NLP (Natural Language Processing) algorithm, then the addresses are subjected to word segmentation and classification, and confidence evaluation is performed according to calculation rules such as hierarchical address weight and an address library model, so as to determine a final delivery address. This approach depends on how fine the data contained in the address base is, and maintaining a fine address base is costly.
Disclosure of Invention
The present disclosure provides an address resolution method, apparatus, device and computer readable storage medium, to solve the problem in the prior art that an address resolution scheme has strong dependency on an address base.
A first aspect of the present disclosure is to provide an address resolution method, including:
acquiring a candidate point set corresponding to a target order, wherein the candidate point set comprises at least one candidate position point;
obtaining resident point information which is determined in advance based on user information according to the information of the target order;
and determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence.
Another aspect of the present disclosure is to provide an address resolution apparatus, including:
the candidate point acquisition module is used for acquiring a candidate point set corresponding to the target order, wherein the candidate point set comprises at least one candidate position point;
the resident point acquisition module is used for acquiring resident point information which is determined in advance based on user information according to the information of the target order;
and the target position determining module is used for determining the confidence of each candidate position point according to the resident point information and determining the target position in the candidate position points according to the confidence.
Yet another aspect of the present disclosure is to provide an address resolution apparatus, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the address resolution method as described in the first aspect above.
Yet another aspect of the present disclosure is to provide a computer-readable storage medium having stored thereon a computer program to be executed by a processor to implement the address resolution method as described in the first aspect above.
The technical effects of the address resolution method, the device, the equipment and the computer readable storage medium provided by the disclosure are as follows:
the address resolution method, the address resolution device, the address resolution equipment and the computer-readable storage medium comprise the steps of obtaining a candidate point set corresponding to a target order, wherein the candidate point set comprises at least one candidate position point; obtaining resident point information which is determined in advance based on user information according to the information of the target order; and determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence. The method, the device, the equipment and the computer readable storage medium provided by the disclosure can determine the target position in a plurality of candidate position points which are possibly valid addresses by combining with the predetermined user residence point information, and the residence point information is obtained based on the user information and can embody the range of the activity area of the user.
Drawings
FIG. 1 is a flow chart illustrating a method of address resolution in accordance with an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of candidate location points in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method of address resolution in accordance with another exemplary embodiment of the invention;
FIG. 4 is a diagram illustrating the results of order grouping in accordance with an exemplary embodiment of the present invention;
fig. 5 is a block diagram illustrating an address resolution apparatus according to an exemplary embodiment of the present invention;
fig. 6 is a block diagram illustrating an address resolution apparatus according to another exemplary embodiment of the present invention;
fig. 7 is a block diagram illustrating an address resolution apparatus according to an exemplary embodiment of the present invention.
Detailed Description
When the goods are sorted by the sorter in the prior art, the addresses of the good deliveries need to be identified, so that the goods are sorted according to the addresses of the good deliveries, for example, the goods with the addresses of the good deliveries belonging to the same area are sorted together. When the sorter identifies the consignment address, the consignment can be scanned and the address resolution interface is called for identification.
The core technical scheme of the address resolution interface is that a user candidate address set is resolved through an NLP algorithm, and a Chinese address resolved by the NLP algorithm is composed of address elements such as a multi-level place name, a house name, a unit name and a house number plate. After address word segmentation and classification, confidence evaluation is carried out according to calculation rules such as classification address weight and the like, an address library model and the like.
In the technical scheme, the address is analyzed only based on the NLP algorithm, the analysis result is a series of point sets, and finally, a courier needs to manually confirm the specific address. The accuracy of address resolution depends largely on the fineness of the data contained in the address library. And maintaining a highly accurate address base is very costly. In addition, the existing algorithm is too single to support multiple complex situations, and the accuracy rate needs to be improved.
In order to solve the technical problem, in the scheme provided by the application, the stay point information of the user is determined in advance according to the shopping information and the position information of the user, and the target position is screened out from the candidate position points of the user according to the stay point information. Because the user behavior has the time-space characteristic, the residence point information of the user can be determined by combining the user behavior, and the activity area of the user has certain relation with the delivery address of the user, so that the target position can be determined by combining the residence point information of the user.
Fig. 1 is a flowchart illustrating an address resolution method according to an exemplary embodiment of the present invention.
As shown in fig. 1, the address resolution method provided in this embodiment includes:
The method provided by the embodiment may be executed by an electronic device with computing capability, for example, a server. The server may be a single server, or may be a cluster server or a distributed server.
Specifically, the server may determine the candidate point set according to the information of the target order. For example, the server may obtain the shipping address information in the target order, and then call a map interface, and determine a candidate point set corresponding to the shipping address information through the map service. For example, the receiving address information may be input into a map service interface, and a plurality of specific location points may be returned through the map service interface to form a candidate point set. Further, the candidate point providing device may determine a candidate point set according to the target order, and then send the candidate point set to the server for executing the method provided in this embodiment. The candidate point providing equipment can read the information of the target order, further determine a receiving address of the target order, and then obtain a plurality of specific position points corresponding to the receiving address based on the map service interface to form a candidate point set.
In practical application, at least one candidate position point may be included in the candidate point set. The candidate location point may include specific location information, which may be, for example, latitude and longitude information. These candidate location points are likely due addresses determined based on the shipping address of the target order.
For example, the destination order receiving address is the XX park, and the determined candidate point set may include n candidate location points, and the locations corresponding to these candidate location points are related to the XX park. For example, one candidate location point may be the XX campus east gate and another candidate location point may be the XX campus west gate.
Fig. 2 is a schematic diagram of a candidate location point according to an exemplary embodiment of the present invention.
As shown in fig. 2, 21 is an area range where the XX park is located, and the determined candidate location points may be specific location points such as an east gate of the XX park 22, a west gate of the XX park 23, and a parking lot of the XX park 24.
And 102, acquiring resident point information which is determined in advance based on user information according to the information of the target order.
In the method provided in this embodiment, the residence point information is determined in advance based on the user information, and specifically, the residence point information of the user may be determined based on the order information and the location information of the user. The dwell point information may be predetermined by the device performing the method or may be determined by other devices.
In particular, the dwell points are used to characterize the active area of the user. For example, if the user is active for a period of time longer than a preset period of time in an area having a range less than a preset range, the area may be considered as a dwell point. For example, a location point is used as a center of a circle, the moving range of the user does not exceed the area range of the center of the circle by 50 meters, and the moving time in the area reaches 30 minutes, then the location point or the area range can be determined as the staying point.
The location information of the user can be obtained from the location data reported by the user terminal, for example, after the user starts an application program, the application program can report specific location data to a background.
Further, each dwell point may have corresponding information, such as location information. In one approach, the location information of the dwell point P1 may be (X)1-X2、Y1-Y2) Where X may be used to represent an abscissa or a dimension on the map and Y is used to represent an ordinate or a longitude on the map, the extent of the area in which the point resides may be indicated in this manner. In yet another embodiment, the location information of the resident point P2 may be (X)3、Y3) And the radius is R, the area range of the residing point P2 is X3、Y3The radius is the range of the region of R as the center of the circle.
The resident point can also have order information when actually applied. Such as the corresponding order number. The number of orders corresponding to each residence point can be determined according to the order information of the user, and specifically, the orders of the user can be grouped according to the residence point and the receiving address in the order information, for example, if the receiving address of the order 1 is within the range of the residence point P1, the order 1 is grouped into the order group corresponding to P1. In this way, the order quantity corresponding to each residence point, that is, the order quantity of each residence point to which the delivery address belongs when the user purchases goods, can be determined.
Wherein, the residence point can also have type information, for example, the type of the residence point is school, home, company, etc. It can be determined from the map data that a residence point is in a residential area, or in a school area, or belongs to a company area.
Specifically, after the server obtains the candidate point set corresponding to the target order, the server may read information in the target order, so as to determine user information. For example, a user identification, which may be a unique identification of the user, such as an account number of the user.
Further, the server may obtain the residence point information corresponding to the user according to the user identifier. Due to the wide range of activities of people, the information of a plurality of residence points can be acquired. Such as a user having long activity at both home and business, it is possible to obtain both points of residence information.
In actual application, a receiving address corresponding to the target order can be obtained, and the information of the alternative residence points is screened from the residence point information of the user. For example, the residence points whose distance from the shipping address does not exceed the distance threshold may be determined as alternative residence points, and the information of these alternative residence points may be acquired.
And 103, determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence.
In practical applications, the dwell point refers to an area where the user frequently moves, and is determined based on the information of the user. And the user information has certain relevance with the receiving address when the user information is shopping, so that the confidence of the candidate position points can be determined according to the information of each residence point, and the target position is determined in the candidate position points according to the confidence.
If the resident point information includes the position information, the distance confidence of each candidate position point can be determined according to the position information of the resident point. I.e. the likelihood that the candidate location point is a valid address is determined by the location relationship with the resident point. For example, the closest resident point to each candidate location point may be determined, and the distance between the corresponding points may be calculated, and the candidate location point with the smallest distance is considered to be the most likely due address.
Specifically, if the residence point information includes order information, the shopping confidence of each candidate location point may be determined according to the order information of the residence point. The residence point closest to each candidate position point can be determined, and then the shopping confidence of the candidate position point is determined according to the order information of the closest residence point. For example, if the nearest dwell point of candidate location point a is P1, the shopping confidence of a may be determined from the order information of P1. For example, if the number of orders of the nearest location point is larger, the more likely the corresponding candidate location point is a valid address.
Further, if the resident point information includes type information, the shopping confidence of each candidate location point may be determined according to the type information. Residence points closest to the candidate position points can be determined, and then the shopping confidence of the corresponding candidate position points is determined by combining the types of the closest residence points. For example, if the nearest location point of a candidate location point is P2, the type thereof is a residential area, and the type of the product in the target order is used in a home, the probability that the candidate location point is a valid address may be considered to be high.
The method provided by this embodiment may determine one or more confidence levels according to the residence point information, and determine the target location in the candidate location point in combination with the confidence levels, and may determine the determined target location as the successful delivery address.
In actual application, if a confidence is determined, the candidate position point with the highest confidence may be directly determined as the target position. If a plurality of confidence levels are determined, the sum or the weighted sum of the confidence levels can be determined, so that the final confidence level is obtained, and then the target position is determined in the candidate position points according to the final confidence level.
The method provided by the present embodiment is used for address resolution, and the method is executed by a device provided with the method provided by the present embodiment, and the device is generally implemented in a hardware and/or software manner.
The address resolution method provided by the embodiment comprises the steps of obtaining a candidate point set corresponding to a target order, wherein the candidate point set comprises at least one candidate position point; obtaining resident point information which is determined in advance based on user information according to the information of the target order; and determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence. The method provided by the embodiment can determine the target position in a plurality of candidate position points which are possibly valid addresses by combining with the predetermined user residence point information, and the residence point information is obtained based on the user information and can embody the range of the activity area of the user.
Fig. 3 is a flowchart illustrating an address resolution method according to another exemplary embodiment of the present invention.
As shown in fig. 3, the address resolution method provided in this embodiment includes:
The method provided by the embodiment can determine the dwell point information in advance.
Specifically, the server may receive the location information reported by the user. For example, the user terminal starts an application program, and the application program can read the data of the positioning device installed in the terminal, further obtain the position of the terminal, and report the position information to the server. The specific reported content may include a user identifier and a specific location.
Furthermore, the range of the activity area of a user can be determined according to the position information of the user, and then the residence point is determined. For example, if the user stays near the position P1 for 30 minutes, the area near P1 can be considered as a staying point. Specifically, according to the location information, an area where the user is within a preset duration range and the activity area does not exceed a preset range is determined, and the area is determined as the residence point
In practice, an area may be defined by centering on the reported position, for example, a range of an area with a radius R centering on the reported position, and the time t1 when the user enters the area and the time t2 when the user leaves the area are determined, if the time difference between t1 and t2 is greater than or equal to a time threshold, the area may be considered as a dwell point, or the reported position may be determined as the dwell point.
And step 302, grouping the shopping orders of the users according to the residence point, and determining order information corresponding to the residence point according to a grouping result.
In the method provided in this embodiment, the residence point information may include order information.
The user's shopping orders may be obtained for grouping. Specifically, a receiving address of the shopping order can be obtained, and a residence point corresponding to the order is determined according to the receiving address. For example, if the receiving address 1 belongs to the area range of the residence point 1, the order corresponding to the receiving address may be divided into the order group corresponding to the residence point 1, and for example, if the receiving address 2 does not belong to the area range of any residence point, the residence point 1 closest to the receiving address 2 may be determined, and the order corresponding to the receiving address 2 may be divided into the order group corresponding to the residence point 1.
Fig. 4 is a diagram illustrating an order grouping result according to an exemplary embodiment of the present invention.
As shown in fig. 4, 3 points A, B, C may be included, with the squares indicating the shipping address of each order, of which orders 1, 2 may be divided into order groups for point a, orders 3, 4, 5 may be divided into order groups for point B, and order 6 may be divided into order groups for point C.
Specifically, the order information corresponding to the residence point may be determined according to the grouping result, and the order information may be, for example, an order quantity, that is, the order quantity corresponding to each residence point. For example, the order quantity for residence point a is 2, the order quantity for residence point B is 3, and the order quantity for residence point C is 1.
The execution timing of steps 302 and 303 is not limited.
Further, in the method provided in this embodiment, the dwell point information may further include location type information.
In actual application, the map data can be preset to determine the position type information of the residence point.
The location type information may be, for example, a Point of Interest (POI) of a residence Point, or a road network type.
The POI can be a house, a shop, a mailbox, a bus station and the like, and can determine the type of the corresponding interest point based on the position of the residence point by combining preset map data. Road network types may be used to characterize road information around a residence point, such as between road line1 and line2, and further such as on road line 3.
Confidence of the candidate location point may be determined in conjunction with the location of the residence point, such as higher confidence of the residence point on the residence point avoidance road for the residential area.
The resident point information of the user can be determined in advance according to the user information, and when the order receiving address of the user is analyzed, the resident point information of the user can be directly obtained and analyzed.
And 305, acquiring resident point information which is determined in advance based on the user information according to the information of the target order.
The steps 304-305 are similar to the specific principles and implementation of the steps 101-102, and are not described herein again.
And step 306, determining a distance value confidence, a shopping behavior confidence and a type confidence corresponding to each candidate position point according to the resident point information.
Specifically, the method provided in this embodiment may determine the distance value confidence corresponding to each candidate location point according to the location of the residence point in the residence point information.
Further, the method for determining the distance confidence may further include:
determining a nearest dwell point, which is closest to each candidate location point, among the dwell points;
determining the distance between the points according to the candidate position points and the corresponding nearest resident points;
determining a maximum distance and a minimum distance among the inter-point distances;
and determining the distance confidence of the candidate position point according to the distance between the candidate position point and the corresponding nearest resident point, the maximum distance and the minimum distance.
The closest dwell point that is closest in distance to each candidate location point may be determined among the dwell points. The received candidate point set may include a plurality of candidate location points, such as a set of candidate points Q, which is a set of candidate location points { Q1, Q2, Q3 … qn }.
In practical application, the obtained residence point information may also be a set, including each residence point and its corresponding information. For example, the dwell point may be an SP, including { SP1, SP2, SP3 … spm }.
Wherein the closest dwell point staypoint (q) closest to each candidate location point may be determined among the dwell points. For example, the closest dwell point for q1 may be sp2 and the closest dwell point for q2 may be sp 3.
Specifically, the inter-point distance dist (q, sp) may be determined according to the candidate position point and its corresponding nearest residing point. Specifically, the euclidean distance between the candidate position point q and the nearest stationary point sp corresponding to the candidate position point q may be calculated as the inter-point distance.
Further, for each set of corresponding q and sp, an inter-point distance can be determined. Among the inter-point distances, a maximum distance and a minimum distance may be determined.
In practical application, the distance confidence of the candidate position point can be determined according to the distance between the candidate position point and the corresponding nearest resident point, the maximum distance and the minimum distance. For a candidate location point q, the distance confidence of the candidate location point q may be determined in combination with the inter-point distance of the point q and its corresponding nearest dwell point, and the determined maximum and minimum distances.
The distance between the candidate position point and the corresponding nearest resident point can be normalized to belong to the distance confidence degree between 0 and 1 according to the maximum distance and the minimum distance. For example, for a candidate location point q whose inter-point distance from the corresponding nearest residing point is L, the inter-point distance L may be normalized to belong to a distance confidence between 0 and 1 according to the determined maximum distance and minimum distance, and the distance confidence may represent the probability value that each candidate location point is the target address in terms of the distance from the residing point.
Specifically, the method provided by this embodiment may determine the confidence of the shopping behavior corresponding to each candidate location point according to the order information in the residence point information.
Further, the method for determining the confidence of shopping behavior may further include:
determining the number of orders corresponding to each resident point according to the order information corresponding to each resident point;
determining the maximum order number and the minimum order number in the order number;
and determining the confidence coefficient of the shopping behavior of the candidate position point according to the order number, the maximum order number and the minimum order number of the nearest resident point corresponding to the candidate position point.
The closest dwell point that is closest in distance to each candidate location point may be determined among the dwell points. The specific determination method is similar to the above, and the latest residence point determined before can be directly read.
In one embodiment, order information corresponding to each residence point may be obtained, for example, order data that a receiving address belongs to the residence point may be determined, and the order quantity corresponding to each residence point may be determined according to the order data.
In another embodiment, the predetermined order information corresponding to each residence point may include an order quantity, and at this time, the order quantity corresponding to the residence point may be directly determined according to the read order information. For example, the order number for the residence point sp1 is 5, and the order number for the residence point sp2 is 10.
Further, the maximum order number and the minimum order number may be determined for the order number corresponding to each resident point. For example, for a user of a target order, 20 corresponding residence points are obtained, and for each residence point, an order quantity can be determined, so that 20 order quantities can be obtained. A maximum order quantity and a minimum order quantity may be determined therein.
In practical application, the confidence of the shopping behavior of the candidate position point can be determined according to the order number, the maximum order number and the minimum order number of the nearest resident point corresponding to the candidate position point. For a candidate position point q, the confidence of the shopping behavior of the candidate position point q can be determined according to the order number of the nearest resident point corresponding to the point q, and the determined maximum order number and the determined minimum order number.
The order number of the nearest resident point corresponding to the candidate position point can be normalized to belong to the confidence degree of the shopping behavior between 0 and 1 according to the maximum order number and the minimum order number. For example, for a candidate location point q, the order number of the corresponding nearest resident point is n, the inter-point distance n may be normalized to a shopping behavior confidence belonging to 0-1 according to the determined maximum order number and minimum order number, and the shopping behavior confidence may show the probability value of each candidate location point being the target address in terms of the shopping behavior of the user.
In the method provided by this embodiment, the type confidence corresponding to each candidate location point may be determined according to the location type information in the residence point information.
Further, the method for determining the type confidence may further include:
determining a position type score corresponding to each resident point according to the position type information corresponding to each resident point;
determining a maximum type score and a minimum type score in the position type scores;
and determining the type confidence of the candidate position point according to the position type score, the maximum type score and the minimum type score of the nearest resident point corresponding to the candidate position point.
The closest dwell point that is closest in distance to each candidate location point may be determined among the dwell points. The specific determination method is similar to the above, and the latest residence point determined before can be directly read.
The location type information of each resident point can be obtained, and a location type score corresponding to each resident point is determined. For example, a score for a dwell point as belonging to a location type may be determined based on the location type information for the dwell point. The corresponding scores of the position types can be preset, so that the corresponding scores can be directly obtained based on the position types to which the resident points belong.
For example, if the location type includes a POI type, a score corresponding to each POI type, for example, a score corresponding to a house and a score corresponding to an office area, may be set in advance.
For another example, if the location type information includes a road network type, road information around a residence point may be determined by combining preset map data, and it is determined that the road network information corresponding to the residence point is information of a national road, a provincial road, a county road, or the like. The corresponding road type score may be determined from the road type of the waypoint.
In particular, a corresponding location type score may be determined for each dwell point. The position type information can comprise POI types and road network types; correspondingly, the position type score comprises a POI type score and a road network type score of the residence point.
The score can be used to characterize the score of the location type of the resident point, for example, the higher the score, the more the resident point belongs to the corresponding location type
Further, the sum of the POI type score and the road network type score can be used as the final location type score of the residence point.
In practical applications, the maximum type score and the minimum type score may be determined among the determined location type scores.
For a candidate location point q, the type confidence of the candidate location point q may be determined according to the location type score of the nearest resident point corresponding to the location point q, and the determined maximum type score and minimum type score.
The position type score of the nearest resident point corresponding to the candidate position point can be normalized to belong to the type confidence degree between 0 and 1 according to the maximum type score and the minimum type score. For example, for a candidate location point q, the location type score of the corresponding nearest residing point is s, the location type score s may be normalized to belong to a type confidence between 0 and 1 according to the determined maximum type score and minimum type score, and the type confidence may show a probability value of each candidate location point being a target address in terms of the location type.
And 307, determining the confidence of each candidate position point according to the distance value confidence, the shopping behavior confidence and the type confidence.
Specifically, after the distance value confidence, the shopping behavior confidence and the type confidence of a candidate position point are obtained, the confidence of the candidate position point can be determined by combining the obtained values, and specifically, an average value of the distance value confidence, the shopping behavior confidence and the type confidence can be calculated and used as the confidence of the candidate position point.
Based on the above manner, the confidence level of each candidate position point can be obtained.
And step 308, determining the target position in the candidate position points according to the confidence.
Step 308 is similar to the method of determining the target position in step 103, and is not described again.
Fig. 5 is a block diagram illustrating an address resolution apparatus according to an exemplary embodiment of the present invention.
As shown in fig. 5, the address resolution apparatus provided in this embodiment includes:
a candidate point obtaining module 51, configured to obtain a candidate point set corresponding to the target order, where the candidate point set includes at least one candidate location point;
a resident point obtaining module 52, configured to obtain, according to the information of the target order, resident point information determined in advance based on the user information;
and a target position determining module 53, configured to determine a confidence of each candidate position point according to the dwell point information, and determine a target position in the candidate position points according to the confidence.
The address resolution device provided by the embodiment of the application comprises: the candidate point acquisition module is used for acquiring a candidate point set corresponding to the target order, wherein the candidate point set comprises at least one candidate position point; the resident point acquisition module is used for acquiring resident point information which is determined in advance based on user information according to the information of the target order; and the target position determining module is used for determining the confidence of each candidate position point according to the resident point information and determining the target position in the candidate position points according to the confidence. The device provided by the embodiment can determine the target position in a plurality of candidate position points which are possibly valid addresses by combining with the predetermined user residence point information, and the residence point information is obtained based on the user information and can embody the range of the activity area of the user.
The specific principle and implementation of the address resolution apparatus provided in this embodiment are similar to those of the embodiment shown in fig. 2, and are not described here again.
Fig. 6 is a block diagram illustrating an address resolution apparatus according to another exemplary embodiment of the present invention.
As shown in fig. 6, on the basis of the above-mentioned embodiment, the address resolution device provided by this embodiment,
optionally, the information of the residence point includes order information corresponding to the residence point and location type information corresponding to the residence point;
the apparatus further comprises a pre-processing module 54 for:
determining at least one residence point of a user according to the position information of the user;
grouping shopping orders of the users according to the residence point, and determining order information corresponding to the residence point according to a grouping result;
and determining the position type information of each resident point according to preset map data.
Optionally, the target position determining module 53 includes:
a first confidence determining unit 531, configured to determine, according to the residence point information, a distance confidence, a shopping behavior confidence, and a type confidence corresponding to each candidate location point;
a second confidence determining unit 532, configured to determine a confidence of each candidate location point according to the distance value confidence, the shopping behavior confidence, and the type confidence.
Optionally, the first confidence determining unit 531 is specifically configured to:
determining a nearest dwell point in the dwell points that is closest in distance to each of the candidate location points;
determining the distance between points according to the candidate position points and the corresponding nearest residence points;
determining a maximum distance and a minimum distance among the inter-point distances;
and determining the distance confidence of the candidate position point according to the distance between the candidate position point and the corresponding nearest resident point, the maximum distance and the minimum distance.
Optionally, the first confidence determining unit 531 is specifically configured to:
determining the number of orders corresponding to each residence point according to the order information corresponding to each residence point;
determining the maximum order number and the minimum order number in the order number;
and determining the confidence degree of the shopping behavior of the candidate position point according to the order number, the maximum order number and the minimum order number of the nearest resident point corresponding to the candidate position point.
Optionally, the first confidence determining unit 531 is specifically configured to:
determining a position type score corresponding to each residence point according to the position type information corresponding to each residence point;
determining a maximum type score and a minimum type score in the position type scores;
and determining the type confidence of the candidate position point according to the position type score, the maximum type score and the minimum type score of the nearest resident point corresponding to the candidate position point.
Optionally, the preprocessing module 54 is specifically configured to:
and determining an area of the user within a preset duration range and with an activity area not exceeding a preset range according to the position information, and determining the area as the residence point.
The first confidence determining unit 531 is specifically configured to:
and according to the maximum distance and the minimum distance, normalizing the distance between the candidate position point and the corresponding nearest resident point to be a distance confidence coefficient between 0 and 1.
The first confidence determining unit 531 is specifically configured to: and normalizing the order number of the nearest resident point corresponding to the candidate position point to be the confidence coefficient of the shopping behavior between 0 and 1 according to the maximum order number and the minimum order number.
The first confidence determining unit 531 is specifically configured to: and according to the maximum type score and the minimum type score, normalizing the position type score of the nearest resident point corresponding to the candidate position point to belong to a type confidence coefficient between 0 and 1.
The second confidence determining unit 532 is specifically configured to: and determining the distance value confidence of the candidate position point, the shopping behavior confidence and the average value of the type confidence as the confidence of the candidate position point.
The specific principle and implementation of the address resolution apparatus provided in this embodiment are similar to those of the embodiment shown in fig. 3, and are not described here again.
Fig. 7 is a block diagram illustrating an address resolution apparatus according to an exemplary embodiment of the present invention.
As shown in fig. 7, the address resolution device provided in this embodiment includes:
a memory 71;
a processor 72; and
a computer program;
wherein the computer program is stored in the memory 71 and configured to be executed by the processor 72 to implement any of the address resolution methods as described above.
The present embodiments also provide a computer-readable storage medium, having stored thereon a computer program,
the computer program is executed by a processor to implement any of the address resolution methods described above.
The present embodiment also provides a computer program comprising a program code for executing any one of the address resolution methods described above when the computer program is run by a computer.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. An address resolution method, comprising:
acquiring a candidate point set corresponding to a target order, wherein the candidate point set comprises at least one candidate position point;
obtaining resident point information which is determined in advance based on user information according to the information of the target order;
and determining the confidence of each candidate position point according to the resident point information, and determining the target position in the candidate position points according to the confidence.
2. The method of claim 1, wherein the residence point information includes order information corresponding to a residence point, location type information corresponding to the residence point;
the method further comprises the following steps:
determining at least one residence point of a user according to the position information of the user;
grouping shopping orders of the users according to the residence point, and determining order information corresponding to the residence point according to a grouping result;
and determining the position type information of each resident point according to preset map data.
3. The method of claim 2, wherein determining a confidence level for each of the candidate location points based on the dwell point information comprises:
determining a distance confidence coefficient, a shopping behavior confidence coefficient and a type confidence coefficient corresponding to each candidate position point according to the information of the residence point;
and determining the confidence level of each candidate position point according to the distance value confidence level, the shopping behavior confidence level and the type confidence level.
4. The method of claim 3, wherein determining a distance confidence for each candidate location point from the dwell point information comprises:
determining a nearest dwell point in the dwell points that is closest in distance to each of the candidate location points;
determining the distance between points according to the candidate position points and the corresponding nearest residence points;
determining a maximum distance and a minimum distance among the inter-point distances;
and determining the distance confidence of the candidate position point according to the distance between the candidate position point and the corresponding nearest resident point, the maximum distance and the minimum distance.
5. The method of claim 3, wherein determining a shopping behavior confidence level for each candidate location point based on the dwell point information comprises:
determining the number of orders corresponding to each residence point according to the order information corresponding to each residence point;
determining the maximum order number and the minimum order number in the order number;
and determining the confidence degree of the shopping behavior of the candidate position point according to the order number, the maximum order number and the minimum order number of the nearest resident point corresponding to the candidate position point.
6. The method of claim 3, wherein determining a type confidence for each candidate location point from the dwell point information comprises:
determining a position type score corresponding to each residence point according to the position type information corresponding to each residence point;
determining a maximum type score and a minimum type score in the position type scores;
and determining the type confidence of the candidate position point according to the position type score, the maximum type score and the minimum type score of the nearest resident point corresponding to the candidate position point.
7. The method of claim 2, wherein determining at least one dwell point for a user based on location information of the user comprises:
and determining an area of the user within a preset duration range and with an activity area not exceeding a preset range according to the position information, and determining the area as the residence point.
8. The method of claim 4, wherein determining the distance value confidence of the candidate location point according to the inter-point distance, the maximum distance, and the minimum distance between the candidate location point and the corresponding nearest dwell point comprises:
and according to the maximum distance and the minimum distance, normalizing the distance between the candidate position point and the corresponding nearest resident point to be a distance confidence coefficient between 0 and 1.
9. The method of claim 5, wherein determining the shopping behavior confidence level for the candidate location point based on the number of orders for the nearest dwell point corresponding to the candidate location point, the maximum amount of orders, the minimum amount of orders comprises:
and normalizing the order number of the nearest resident point corresponding to the candidate position point to be the confidence coefficient of the shopping behavior between 0 and 1 according to the maximum order number and the minimum order number.
10. The method of claim 6, wherein determining the type confidence of the candidate location point according to the location type score, the maximum type score, and the minimum type score of the nearest dwell point corresponding to the candidate location point comprises:
and according to the maximum type score and the minimum type score, normalizing the position type score of the nearest resident point corresponding to the candidate position point to belong to a type confidence coefficient between 0 and 1.
11. The method of claim 3, wherein said determining a confidence level for each of said candidate location points based on said distance value confidence level, said shopping behavior confidence level, and said type confidence level comprises:
and determining the distance value confidence of the candidate position point, the shopping behavior confidence and the average value of the type confidence as the confidence of the candidate position point.
12. An address resolution device, comprising:
the candidate point acquisition module is used for acquiring a candidate point set corresponding to the target order, wherein the candidate point set comprises at least one candidate position point;
the resident point acquisition module is used for acquiring resident point information which is determined in advance based on user information according to the information of the target order;
and the target position determining module is used for determining the confidence of each candidate position point according to the resident point information and determining the target position in the candidate position points according to the confidence.
13. An address resolution device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any of claims 1-11.
14. A computer-readable storage medium, having stored thereon a computer program,
the computer program is executed by a processor to implement the method according to any of claims 1-11.
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