Address correction method and device, equipment and storage medium

Abstract

The invention provides an address correction method and device, equipment and a storage medium, and relates to the technical field of communication networks. In some embodiments of the disclosure, a mobile communication identifier, a complaint time period and a complaint user address are acquired; according to the mobile communication identifier, determining position information of a target base station where the user resides in the complaint time period; clustering the target base stations according to the position information of the target base stations to obtain position clustering labels of the target base stations; combining the base station geographical grid coverage ranges of the target base stations with the same position clustering label to obtain a complaint address coverage range; the address of the complaint user is automatically corrected according to the address of the interest point in the coverage range of the complaint address, the address of the complaint user is automatically corrected, the correction efficiency of the address of the complaint user is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of communication network technology, and in particular to an address correction method, device, equipment and storage medium. Background Art

[0002] In the process of handling user complaints, the user address is very important and plays a vital role in the handling of user complaints. The user address can help analysts accurately locate the user's geographical location, so as to better analyze the network conditions at the user's location and accurately locate the problem. At the same time, the user address is converted into longitude and latitude through the GIS interface, which is an important basic information for automatically associating network data. However, the user complaint addresses registered in the customer service system are generally obtained through the user's oral statement and manual recording by customer service personnel. Due to human factors such as language differences, recording errors, and misunderstandings, the customer service records of user addresses often contain inaccurate factors such as homophones in the Chinese description address, which greatly affects the accuracy and efficiency of complaint handling analysis.

[0003] At present, the addresses of complaining users mainly rely on manual inspection, which has low efficiency and high labor costs. Summary of the invention

[0004] The present disclosure provides an interface display control method to at least solve the problem that the existing complaint user address relies on manual inspection, which has low efficiency and high labor cost.

[0005] The technical solution of the present disclosure is as follows:

[0006] The present disclosure provides an address correction method, including:

[0007] Obtain mobile communication identification, complaint time period and complaining user address;

[0008] Determine, according to the mobile communication identifier, the location information of the target base station where the user resides during the complaint time period;

[0009] Clustering the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations;

[0010] Merging the base station geographic grid coverage of the target base stations with the same location clustering label to obtain the complaint address coverage;

[0011] The complaining user address is modified according to the address of the point of interest within the coverage of the complaint address.

[0012] Optionally, clustering the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations includes:

[0013] Mark all longitude and latitude points as unvisited, and select a neighborhood radius ε and a minimum number of neighborhood points, wherein each longitude and latitude point corresponds to a target base station;

[0014] Randomly select an unvisited latitude and longitude point P, and mark the latitude and longitude point P as visited;

[0015] When the number of points in the neighborhood of the latitude and longitude point P is greater than or equal to the number of neighborhood points, a new cluster is expanded with the latitude and longitude point P as the center as the current cluster;

[0016] Add the latitude and longitude point P to the current cluster;

[0017] Find all unvisited points within the neighborhood radius ε of the latitude and longitude point P, add all unvisited points to the current cluster, and mark all unvisited points as visited;

[0018] For each newly added point, if the number of points in the neighborhood of the newly added point is greater than or equal to the number of neighborhood points, it is added to the current cluster;

[0019] When the number of points in the neighborhood of the longitude and latitude point P is less than the number of neighborhood points, the longitude and latitude point P is marked as a noise point until all the longitude and latitude points are visited to obtain the location clustering label of the target base station.

[0020] Optionally, before correcting the complaining user address according to the address of the point of interest within the coverage range of the complaining address, the method further includes:

[0021] The outermost grid around the complaint address coverage area is expanded outward to a set length to obtain a square area as the user complaint geographic frame;

[0022] The address of the point of interest within the user complaint geographic frame is obtained.

[0023] Optionally, the modifying the complaining user address according to the address of the point of interest within the coverage of the complaint address includes:

[0024] Perform word segmentation processing on each of the points of interest addresses and the complaining user addresses respectively to obtain a first word segmentation of each of the points of interest addresses and a second word segmentation of the complaining user addresses;

[0025] Calculating the word segmentation similarity between each of the second word segments and each of the first word segments of each of the addresses of the points of interest;

[0026] Selecting a target point of interest address from the point of interest addresses according to the word segmentation similarity;

[0027] The target point of interest address replaces the complaining user address.

[0028] Optionally, the calculating the word segmentation similarity between each of the second word segments and each of the first word segments of each of the addresses of the points of interest includes:

[0029] According to the edit distance algorithm, the similarity between the pinyin of each second participle and the pinyin of each first participle of each point of interest address is calculated as the participle similarity.

[0030] Optionally, selecting a target point of interest address from the point of interest addresses according to the word segmentation similarity includes:

[0031] Calculate the sum of information entropy of the complaining user address and each of the POI addresses based on the word segmentation similarity;

[0032] A target point of interest address with the smallest total information entropy is selected from the point of interest addresses.

[0033] The present disclosure also provides an address correction device, including:

[0034] An acquisition module, used to acquire a mobile communication identification, a complaint time period and an address of a complaining user;

[0035] A determination module, configured to determine, according to the mobile communication identifier, location information of a target base station where the user resides during the complaint time period;

[0036] A clustering module, used to cluster the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations;

[0037] A merging module, used for merging the base station geographic grid coverage of the target base stations with the same location clustering label to obtain the complaint address coverage;

[0038] A correction module is used to correct the complaining user address according to the address of the point of interest within the coverage range of the complaining address.

[0039] The present disclosure also provides an electronic device, including:

[0040] processor;

[0041] a memory for storing instructions executable by the processor;

[0042] The processor is configured to execute the instructions to implement each step in the above method.

[0043] The embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step in the above method is implemented.

[0044] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements each step of the above method when executed by a processor.

[0045] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0046] In some embodiments of the present disclosure, a mobile communication identifier, a complaint time period, and a complaining user address are obtained; based on the mobile communication identifier, the location information of the target base station where the user resides during the complaint time period is determined; based on the location information of the target base stations, the target base stations are clustered to obtain location clustering labels of the target base stations; the base station geographic grid coverage ranges of the target base stations with the same location clustering labels are merged to obtain the complaint address coverage range; the complaining user address is automatically corrected based on the address of the point of interest within the complaint address coverage range, and the complaining user address is automatically corrected to improve the correction efficiency of the complaint user address and reduce labor costs.

[0047] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0049] Figure 1 A flowchart of an address correction method provided for an exemplary embodiment of the present disclosure;

[0050] Figure 2 A schematic diagram of the structure of an address correction device provided by an exemplary embodiment of the present disclosure;

[0051] Figure 3 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0054] It should be noted that the user information involved in this disclosure includes but is not limited to: user device information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in this disclosure comply with the relevant laws and regulations and do not violate public order and good morals.

[0055] In response to the above technical problems, in some embodiments of the present disclosure, a mobile communication identification, a complaint time period and a complaining user address are obtained; based on the mobile communication identification, the location information of the target base station where the user resides during the complaint time period is determined; based on the location information of the target base stations, the target base stations are clustered to obtain location clustering labels of the target base stations; the base station geographic grid coverage ranges of the target base stations with the same location clustering labels are merged to obtain the complaint address coverage range; the complaining user address is automatically corrected according to the address of the point of interest within the complaint address coverage range, and the complaining user address is automatically corrected to improve the correction efficiency of the complaint user address and reduce labor costs.

[0056] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0057] Figure 1 A flowchart of an address correction method provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method includes:

[0058] S101: Obtaining a mobile communication identification, a complaint time period, and an address of a complaining user;

[0059] S102: Determine the location information of the target base station where the user resides during the complaint period according to the mobile communication identifier;

[0060] S103: Clustering the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations;

[0061] S104: merging base station geographic grid coverages of target base stations with the same location clustering label to obtain complaint address coverage;

[0062] S105: Correct the complaining user's address according to the POI address within the coverage of the complaint address.

[0063] In this embodiment, the execution subject of the above method may be a terminal device or a server.

[0064] Among them, the terminal device includes but is not limited to a mobile station (MS), a mobile terminal, a mobile phone, a handset and a portable equipment, etc. The terminal device can communicate with one or more core networks via a radio access network (RAN). For example, the terminal device can be a mobile phone (or a "cellular" phone), a computer with a wireless communication function, etc. The terminal device can also be a computer with a wireless transceiver function, a virtual reality (VR) terminal device, an AR terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc., and the operating system installed on the terminal device includes but is not limited to: IOS, Android, windows, linux, Mac OS and other operating systems. In different networks, the terminal may be called different names, such as user equipment, mobile station, subscriber unit, station, cellular phone, personal digital assistant, wireless modem, wireless communication device, handheld device, laptop computer, cordless phone, wireless local loop station, television, etc. For the convenience of description, it is referred to as terminal equipment in this embodiment.

[0065] In this embodiment, the implementation form of the server is not limited. For example, the server can be a conventional server, a cloud server, a cloud host, a virtual center, or other server devices. The server mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general computer architecture type.

[0066] It should be noted that the mobile communication identifier may be a telephone number.

[0067] In this embodiment, the present disclosure obtains the complaint number and complaint time period, associates the list of base stations where the user resides during the complaint time period in the signaling control plane interface of the communication network, and obtains the corresponding base station longitude and latitude information, poor quality base station labels, and resident base station labels from the communication system. The DBSCAN clustering algorithm is used to cluster the longitude and latitude of the resident poor quality base stations to form multiple geographic coverage surfaces, and the base station geographic grid coverage range is obtained based on the MDT data. The same clustering label is merged into a geographic surface to obtain 1 to N complaint address coverage ranges, and based on the complaint address coverage range, the POI address within the coverage range is associated as a reference address set, and the user complaint is used as the address to be calibrated. By constructing a complaint address calibration model based on address information entropy, the calibrated address is output to achieve automatic evaluation and calibration of the complaint address.

[0068] In some embodiments of the present disclosure, a mobile communication identifier, a complaint time period, and an address of a complaining user are obtained; based on the mobile communication identifier, the location information of the target base station where the user resides during the complaint time period is determined. One achievable method is to obtain the complaint number and complaint time based on a customer service system, and associate a list of base stations where the user resides during the complaint time period in the signaling control plane interface between the base station and the core network in the communication network. It should be noted that the location information of the target base station can be the longitude and latitude of the base station. In addition, poor quality base station labels and resident base station labels can also be obtained from the communication system.

[0069] Among them, based on the obtained base station list, the base station latitude and longitude, poor quality base station label, and resident base station label are obtained from the communication system association according to the base station ID. Example data is shown in Table 1 below:

[0070]

[0071] Table 1

[0072] In some embodiments of the present disclosure, the target base stations are clustered according to the location information of the target base stations to obtain location clustering labels of the target base stations. One feasible method is to mark all longitude and latitude points as unvisited, and select a neighborhood radius ε and a minimum number of neighborhood points, where each longitude and latitude point corresponds to a target base station; randomly select an unvisited longitude and latitude point P, and mark the longitude and latitude point P as visited; when the number of points in the neighborhood of longitude and latitude point P is greater than or equal to the number of neighborhood points, expand a new cluster with longitude and latitude point P as the center as the current cluster; add longitude and latitude point P to the current cluster; find all unvisited points within the neighborhood radius ε of longitude and latitude point P, add all unvisited points to the current cluster, and mark all unvisited points as visited; for each newly added point, if the number of points in the neighborhood of the newly added point is greater than or equal to the number of neighborhood points, add it to the current cluster; when the number of points in the neighborhood of longitude and latitude point P is less than the number of neighborhood points, mark longitude and latitude point P as a noise point, until all longitude and latitude points are visited, and the location clustering label of the target base station is obtained.

[0073] The DBSCAN clustering algorithm is used to cluster the longitude and latitude of the permanent base stations and the base stations with poor quality, and obtain the location clustering label of each base station. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. The algorithm steps are as follows:

[0074] Initialization: Mark all longitude and latitude points as unvisited, and select a neighborhood radius ε and a minimum number of neighborhood points MinPts.

[0075] Randomly select an unvisited latitude and longitude point P and mark it as visited.

[0076] If the number of points in the neighborhood of point P is greater than or equal to MinPts, a new cluster is expanded with P as the center.

[0077] Add point P to the current cluster.

[0078] Find all unvisited points in the ε-neighborhood of P and add them to the current cluster, marking them as visited.

[0079] For each newly added point, if the number of points in its neighborhood is greater than or equal to MinPts, it is added to the current cluster.

[0080] If the number of points in the neighborhood of point P is less than MinPts, P is marked as a noise point.

[0081] Repeat the above steps until all data points have been visited.

[0082] According to the above DBSCAN clustering algorithm, the neighborhood radius ε is set to 1000 meters, the number of neighborhood points MinPts is set to 3, and clustering operations are performed on all base station longitude and latitude points, and location cluster labels are marked for each base station. The data example is shown in Table 2 below:

[0083] Base station ID Base station longitude Base station latitude Latitude and longitude cluster labels 189921 114.1667999 22.75324488 10001 322112 114.1667667 22.69061258 10001 231112 114.1667617 22.72335698 10001 ... ... ... ... 433121 114.1266598 22.74231704 10004

[0084] Table 2

[0085] In some embodiments of the present disclosure, the base station geographic grid coverage of the target base stations with the same location clustering label is merged to obtain the complaint address coverage. The base station geographic grid coverage is obtained based on MDT (Minimization Drive Test) data, and the same clustering label is merged into a geographic surface information to obtain the complaint address coverage of 1 to N clusters, as shown in Table 3 below.

[0086]

[0087] Table 3

[0088] In some embodiments of the present disclosure, the outermost grids in the four directions of the complaint address coverage are expanded outward by a set length to obtain a square area as the user complaint geographic frame; the addresses of points of interest in the user complaint geographic frame are obtained. For example, the outermost grids in the southeast, northwest and northeast of the complaint address coverage are expanded outward by 100 meters to generate a square surface to form the expanded user complaint geographic frame; based on the expanded complaint geographic frame, all POI (Points of Interest) address data in the user complaint geographic frame are obtained.

[0089] The obtained POI address examples are shown in Table 4 below:

[0090]

[0091] Table 4

[0092] In some embodiments of the present disclosure, the complaining user address is modified according to the POI addresses within the coverage of the complaining address. One achievable method is to perform word segmentation processing on each POI address and the complaining user address respectively, to obtain the first word segmentation of each POI address and the second word segmentation of the complaining user address; to calculate the word segmentation similarity between each second word segmentation and each first word segmentation of each POI address; to select the target POI address from the POI addresses according to the word segmentation similarity; and to replace the complaining user address with the target POI address.

[0093] Among them, based on the customer service system, the address of the complaining user is obtained as the address to be calibrated, as shown in Table 5 below.

[0094]

[0095] Table 5

[0096] Among them, the obtained point of interest address is used as the reference address for correction. The address to be calibrated and each reference address string are cut into phrases, and the edit distance algorithm is used to calculate the similarity of the word segmentation pinyin of the address to be calibrated and each reference address respectively. Then, the address information entropy algorithm is used to calculate the sum of the information entropy of the address to be calibrated and each reference address. The sum of the information entropy is used to represent the degree of difference between the two strings. The smaller the entropy value, the more similar the strings of the two addresses are. The reference address with the smallest information entropy is selected as the calibration address output.

[0097] For example, the address to be calibrated and the reference address are segmented separately to obtain the segmentation results. The data example is as follows:

[0098] Address to be calibrated: 10th Floor, Jiantao Plaza, Huacheng Avenue, Tianhe District, Guangzhou City, Guangdong Province.

[0099] Reference address 1: Atrium on the 2nd floor of Times Square, 28-32 Tianhe North Road, Tianhe District, Guangzhou City, Guangdong Province.

[0100] Reference address 2: 10th Floor, Jiantao Plaza, Huacheng Avenue, Tianhe District, Guangzhou City, Guangdong Province.

[0101] Reference address 3: Tianhui Pavilion, No. 18, Tianhe East Road, Tianhe District, Guangzhou City, Guangdong Province.

[0102] Word segmentation results:

[0103] Address to be calibrated: 10th Floor, Jiantao Plaza, Huacheng Avenue, Tianhe District, Guangzhou City, Guangdong Province.

[0104] Reference address 1: 28-32 Times Square, 2nd Floor Atrium, Tianhe District, Guangzhou City, Guangdong Province.

[0105] Reference address 2: 10th Floor, Jiantao Plaza, Huacheng Avenue, Tianhe District, Guangzhou City, Guangdong Province.

[0106] Reference address 3: Tianhui Pavilion, No. 18, Tianhe East Road, Tianhe District, Guangzhou City, Guangdong Province.

[0107] In an exemplary embodiment of the present disclosure, for each word segment of the address to be calibrated, the edit distance algorithm is used to match the word segment of the reference address one by one, and the word segment similarity of each second word segment and each first word segment of each point of interest address is calculated. One achievable method is to calculate the similarity of the pinyin of each second word segment and the pinyin of each first word segment of each point of interest address according to the edit distance algorithm as the word segment similarity.

[0108] The specific steps of the edit distance algorithm are as follows: for each word segment of the address to be calibrated, match and compare it with the word segment of the reference address one by one. Calculate the edit distance between the two word segments, that is, the minimum number of operations required to convert the word segment of the address to be calibrated into the word segment of the reference address. Calculate the similarity based on the edit distance, and use the following formula to calculate the similarity of the pinyin of each word segment: Similarity = 1-edit distance / max(len(word segment of the address to be calibrated), len(word segment of the reference address)). Match each word segment of the address to be calibrated with the word segment of the reference address and calculate the similarity. Return the similarity results of all word segments.

[0109] For example, for the address in the above example, the word segmentation similarity is calculated as follows:

[0110] The calculation diagram of the pinyin similarity of each word of the address to be calibrated and the reference address 1 is as follows:

[0111] Guangdong Province: Similarity = 1-edit distance ("guang dong sheng", "guang dong sheng") / max(3,3) = 1-0 / 3 = 1.

[0112] Guangzhou City: Similarity = 1-edit distance ("guang zhou shi", "guang zhou shi") / max(3,3) = 1-0 / 3 = 1.

[0113] According to the above formula, calculate the similarity between the address to be calibrated and each reference address:

[0114] Among them, the word segmentation similarity between the address to be calibrated and the reference address 1 is: Guangdong Province / Guangdong Province has a similarity of 1; Guangzhou City / Guangzhou City has a similarity of 1; Tianhe District / Tianhe District has a similarity of 1; Huacheng Avenue / Tianhe North Road has a similarity of 0; Jiantao Plaza / 28-32 Times Square has a similarity of 0.5; and the 10th floor / 2nd floor atrium has a similarity of 0.

[0115] The similarity between the word segmentation result of the address to be calibrated and the word segmentation of reference address 2 is: Guangdong Province / Guangdong Province has a similarity of 1; Guangzhou City / Guangzhou City has a similarity of 1; Tianhe District / Tianhe District has a similarity of 1; Huacheng Avenue / Huacheng Avenue has a similarity of 1; Jiantao Plaza / Jiantao Plaza has a similarity of 1; and the 10th Floor / 10th Floor has a similarity of 1.

[0116] The similarity between the word segmentation result of the address to be calibrated and the word segmentation of reference address 3 is: Guangdong Province / Guangdong Province has a similarity of 1; Guangzhou City / Guangzhou City has a similarity of 1; Tianhe District / Tianhe District has a similarity of 1; Huacheng Avenue / Tianhe East Road has a similarity of 0; Jiantao Plaza / No. 18 has a similarity of 0; and the 10th Floor / Tianhui Pavilion has a similarity of 0.

[0117] In some embodiments of the present disclosure, the target point of interest address is selected from the point of interest addresses according to the word segmentation similarity. One achievable method is to calculate the sum of information entropy of the complaining user address and each point of interest address according to the word segmentation similarity; and select the target point of interest address with the smallest sum of information entropy from the point of interest addresses.

[0118] Among them, for each pair of word segments in the matching results, the address information entropy algorithm is used to calculate their sum of information entropy. The sum of information entropy is used to represent the degree of difference between two character strings. The smaller the entropy value, the more similar the character strings of the two addresses are.

[0119] The calculation formula for the total information entropy is:

[0120] H(X)=-∑(p(x)log2p(x))

[0121] Among them, H(X): represents the sum of information entropy; p(x): represents the probability that the random variable X takes the value x, x is the word segmentation similarity; log2p(x): calculates the logarithm with base 2, that is, log2(p(x)).

[0122] For example, the sum of the information entropy of the address to be calibrated and the reference address 1 is: H(X)=-(1*log21+1*log21+1*log21+0*log20+0.5*log20.5+0*log20)=-(3+3+3+0+0.693+0)=-9.693.

[0123] The sum of information entropy of the address to be calibrated and the reference address 2: H(X)=-(1*log21+1*log21+1*log21+1*log21+1*log21+1*log21)=-(3+3+3+3+3+3)=-18.

[0124] The sum of information entropy of the address to be calibrated and the reference address 3: H(X)=-(1*log21+1*log21+1*log21+0*log20+0*log20+0*log20)=-(3+3+3+0+0+0)=-9.

[0125] According to the calculated sum of information entropy of the address to be calibrated and each reference address, the matching result corresponding to the smallest entropy value is selected as the final calibrated address. As shown in the above calculation example, the minimum information entropy of the reference address is -18, that is, the final calibrated address output is: 10th Floor, Jiantao Plaza, Huacheng Avenue, Tianhe District, Guangzhou City, Guangdong Province.

[0126] In some embodiments of the present disclosure, the address quality is evaluated before and after calibration. Optionally, the addresses before and after calibration are used to match the complaint addresses obtained from field testing. The 20 addresses before calibration are inconsistent with the measured addresses, and the 20 addresses after calibration are matched with the measured addresses, with an accuracy rate of 100%. It can be seen that the model works well. Example data is shown in Table 6 below.

[0127]

[0128]

[0129] Table 6

[0130] In the above method embodiment of the present disclosure, a mobile communication identification, a complaint time period and a complaining user address are obtained; based on the mobile communication identification, the location information of the target base station where the user resides during the complaint time period is determined; based on the location information of the target base stations, the target base stations are clustered to obtain location clustering labels of the target base stations; the base station geographic grid coverage ranges of the target base stations with the same location clustering labels are merged to obtain the complaint address coverage range; the complaining user address is automatically corrected based on the address of the point of interest within the complaint address coverage range, and the complaining user address is automatically corrected to improve the correction efficiency of the complaint user address and reduce labor costs.

[0131] Figure 2 FIG. 2 is a schematic diagram of the structure of an address correction device 20 provided by an exemplary embodiment of the present disclosure. Figure 2 As shown, the address correction device 20 includes: an acquisition module 21, a determination module 22, a clustering module 23, a merging module 24 and a correction module 25.

[0132] The acquisition module 21 is used to acquire the mobile communication identification, the complaint time period and the complaining user address;

[0133] A determination module 22, configured to determine the location information of the target base station where the user resides during the complaint period according to the mobile communication identifier;

[0134] A clustering module 23, configured to cluster the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations;

[0135] A merging module 24 is used to merge the base station geographic grid coverage of the target base stations with the same location clustering label to obtain the complaint address coverage;

[0136] The correction module 25 is used to correct the complaining user address according to the address of the point of interest within the coverage range of the complaining address.

[0137] Optionally, when clustering the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations, the clustering module 23 is used to:

[0138] Mark all longitude and latitude points as unvisited, and select a neighborhood radius ε and a minimum number of neighborhood points, where each longitude and latitude point corresponds to a target base station;

[0139] Randomly select an unvisited latitude and longitude point P and mark it as visited;

[0140] When the number of points in the neighborhood of the longitude and latitude point P is greater than or equal to the number of neighboring points, a new cluster is expanded with the longitude and latitude point P as the center as the current cluster;

[0141] Add the latitude and longitude point P to the current cluster;

[0142] Find all unvisited points within the neighborhood radius ε of the latitude and longitude point P, add all unvisited points to the current cluster, and mark all unvisited points as visited;

[0143] For each newly added point, if the number of points in the neighborhood of the newly added point is greater than or equal to the number of neighboring points, it will be added to the current cluster;

[0144] When the number of points in the neighborhood of the longitude and latitude point P is less than the number of neighborhood points, the longitude and latitude point P is marked as a noise point until all longitude and latitude points are visited to obtain the location cluster label of the target base station.

[0145] Optionally, before correcting the complaining user address according to the POI address within the coverage of the complaining address, the correction module 25 may also be used to:

[0146] The outermost grid around the complaint address coverage area is expanded outward to a set length to obtain a square area as the user complaint geographic frame;

[0147] Get the address of the point of interest within the user's complaint geographic frame.

[0148] Optionally, when the correction module 25 corrects the complaining user address according to the address of the point of interest within the coverage range of the complaining address, it is used to:

[0149] Perform word segmentation on each point of interest address and complaint user address respectively to obtain a first word segmentation on each point of interest address and a second word segmentation on the complaint user address;

[0150] Calculate the word segmentation similarity between each second word segmentation and each first word segmentation of each point of interest address;

[0151] According to the segmentation similarity, the target point of interest address is selected from the points of interest addresses;

[0152] Replace the complaining user's address with the target POI address.

[0153] Optionally, when calculating the segmentation similarity between each second segmentation and each first segmentation of each point of interest address, the correction module 25 is used to:

[0154] According to the edit distance algorithm, the similarity between the pinyin of each second segmented word and the pinyin of each first segmented word of each point of interest address is calculated as the segmented word similarity.

[0155] Optionally, when selecting a target point of interest address from the point of interest addresses according to the segmentation similarity, the correction module 25 is used to:

[0156] According to the similarity of the word segments, the sum of the information entropy between the complaining user address and the address of each point of interest is calculated;

[0157] Select the target point of interest address with the smallest total information entropy from the point of interest addresses.

[0158] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0159] Figure 3 FIG. 1 is a schematic diagram of a structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 3 As shown, the electronic device includes: a memory 31 and a processor 32. In addition, the electronic device also includes a power supply component 33 and a communication component 34.

[0160] The memory 31 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application program or method operating on the electronic device.

[0161] The memory 31 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0162] The communication component 34 is used for data transmission with other devices.

[0163] The processor 32 can execute computer instructions stored in the memory 31 to: obtain a mobile communication identifier, a complaint time period, and a complaining user address; determine, based on the mobile communication identifier, the location information of the target base station where the user resides during the complaint time period; cluster the target base stations based on the location information of the target base stations to obtain location clustering labels of the target base stations; merge the base station geographic grid coverage of the target base stations with the same location clustering labels to obtain the complaint address coverage; and correct the complaining user address based on the address of the point of interest within the complaint address coverage.

[0164] Accordingly, the present disclosure also provides a computer-readable storage medium storing a computer program. When the computer-readable storage medium stores the computer program and the computer program is executed by one or more processors, the one or more processors execute Figure 1 Each step in the method embodiment.

[0165] Accordingly, the present disclosure also provides a computer program product, the computer program product includes a computer program / instruction, the computer program / instruction is executed by a processor Figure 1 Each step in the method embodiment.

[0166] Above Figure 3 The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0167] Above Figure 3 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0168] The electronic device also includes a display screen and an audio component.

[0169] The display screen includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0170] The audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0171] In the above-mentioned apparatus, device, storage medium and computer program product embodiments of the present disclosure, a mobile communication identification, a complaint time period and a complaint user address are obtained; based on the mobile communication identification, the location information of the target base station where the user resides during the complaint time period is determined; based on the location information of the target base stations, the target base stations are clustered to obtain location clustering labels of the target base stations; the base station geographic grid coverage ranges of the target base stations with the same location clustering labels are merged to obtain the complaint address coverage range; the complaint user address is automatically corrected according to the address of the point of interest within the complaint address coverage range, and the complaint user address is automatically corrected to improve the correction efficiency of the complaint user address and reduce labor costs.

[0172] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0176] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0177] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0178] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0179] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0180] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An address correction method, characterized in that: include: Obtain mobile communication identification, complaint time period and complaining user address; Determine, according to the mobile communication identifier, the location information of the target base station where the user resides during the complaint time period; Clustering the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations; Merging the base station geographic grid coverage of the target base stations with the same location clustering label to obtain the complaint address coverage; The complaining user address is modified according to the address of the point of interest within the coverage of the complaint address.

2. The method according to claim 1, characterized in that The clustering of the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations includes: Mark all longitude and latitude points as unvisited, and select a neighborhood radius ε and a minimum number of neighborhood points, wherein each longitude and latitude point corresponds to a target base station; Randomly select an unvisited latitude and longitude point P, and mark the latitude and longitude point P as visited; When the number of points in the neighborhood of the latitude and longitude point P is greater than or equal to the number of neighborhood points, a new cluster is expanded with the latitude and longitude point P as the center as the current cluster; Add the latitude and longitude point P to the current cluster; Find all unvisited points within the neighborhood radius ε of the latitude and longitude point P, add all unvisited points to the current cluster, and mark all unvisited points as visited; For each newly added point, if the number of points in the neighborhood of the newly added point is greater than or equal to the number of neighborhood points, it is added to the current cluster; When the number of points in the neighborhood of the longitude and latitude point P is less than the number of neighborhood points, the longitude and latitude point P is marked as a noise point until all the longitude and latitude points are visited to obtain the location clustering label of the target base station.

3. The method according to claim 1, characterized in that Before correcting the complaining user address according to the address of the point of interest within the coverage range of the complaining address, the method further includes: The outermost grid around the complaint address coverage area is expanded outward to a set length to obtain a square area as the user complaint geographic frame; The address of the point of interest within the geographic frame of the user complaint is obtained.

4. The method according to claim 1, characterized in that: The modifying of the complaining user address according to the address of the point of interest within the coverage of the complaining address includes: Perform word segmentation processing on each of the points of interest addresses and the complaining user addresses respectively to obtain a first word segmentation of each of the points of interest addresses and a second word segmentation of the complaining user addresses; Calculating the word segmentation similarity between each of the second word segments and each of the first word segments of each of the addresses of the points of interest; Selecting a target point of interest address from the point of interest addresses according to the word segmentation similarity; The target point of interest address replaces the complaining user address.

5. The method according to claim 4, characterized in that The calculating the word segmentation similarity between each of the second word segments and each of the first word segments of each of the interest point addresses includes: According to the edit distance algorithm, the similarity between the pinyin of each second participle and the pinyin of each first participle of each point of interest address is calculated as the participle similarity.

6. The method according to claim 4, characterized in that The step of selecting a target point of interest address from the point of interest addresses according to the word segmentation similarity includes: Calculate the sum of information entropy of the complaining user address and each of the POI addresses based on the word segmentation similarity; A target point of interest address with the smallest total information entropy is selected from the point of interest addresses.

7. An address correction device, characterized in that: include: An acquisition module, used to acquire a mobile communication identification, a complaint time period and an address of a complaining user; A determination module, configured to determine, according to the mobile communication identifier, location information of a target base station where the user resides during the complaint time period; A clustering module, used to cluster the target base stations according to the location information of the target base stations to obtain location clustering labels of the target base stations; A merging module, used for merging the base station geographic grid coverage of the target base stations with the same location clustering label to obtain the complaint address coverage; A correction module is used to correct the complaining user address according to the address of the point of interest within the coverage range of the complaining address.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement each step in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

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