Method and device for determining information points in geographic information system and storage medium

Abstract

The present disclosure provides a method and device for determining information points in a geographic information system and a storage medium, and relates to the field of location services. By determining the weight of each word in the collected information point, the similarity of two information points is determined accordingly, and it is determined whether to merge the two information points into one information point in the geographic information system, thereby solving the problem of information point redundancy in the geographic information system and improving the service quality of the geographic information system.

Description

Technical Field

[0001] The present disclosure relates to the field of location services, and in particular to a method and device for determining information points in a geographic information system and a storage medium. Background Art

[0002] In a geographic information system, a point of information (POI) can be, for example, a house, a shop, a mailbox, a bus stop, etc. The accuracy of the information point affects the service quality of the entire geographic information system to a certain extent. In some related technologies, each information point is determined based on latitude and longitude information.

[0003] The inventors found that determining each information point based on longitude and latitude information will cause the problem of redundant information points. For example, when determining information points based on longitude and latitude, the gate of a community and the building in the community will be determined as two information points, but according to the actual situation, the two should be determined as one information point. Summary of the invention

[0004] In order to solve the problem of redundant information points in a geographic information system, the disclosed embodiment proposes a scheme for determining information points in a geographic information system. The weight of each word in the collected information point is determined, and the similarity between two information points is determined accordingly. Based on this, it is determined whether to merge the two information points into one information point in the geographic information system, thereby solving the problem of redundant information points in the geographic information system and improving the service quality of the geographic information system.

[0005] The present disclosure provides a method for determining an information point in a geographic information system, including:

[0006] Determine the weight of each word in the collected information point in the information point;

[0007] For a first information point and a second information point in the information points, determining the similarity between the first information point and the second information point according to the weight of each word in the first information point in the first information point and the weight of each word in the second information point in the second information point;

[0008] determining whether to merge the first information point and the second information point according to the similarity between the first information point and the second information point;

[0009] If merging is required, a third information point obtained by merging the first information point and the second information point is used as an information point in the geographic information system; if merging is not required, the first information point and the second information point are respectively used as information points in the geographic information system.

[0010] In some embodiments, determining the weight of each word in an information point in the information point includes:

[0011] Determine the importance weight of each word in the information point;

[0012] For any first word in an information point, determining a correlation weight between the first word and each other word in the information point;

[0013] The weight of the first word in the information point is determined according to the importance weight of each word in the information point and the correlation weight between the first word and each other word in the information point.

[0014] In some embodiments, determining the importance weight of each word in the information point includes:

[0015] Determine the information entropy of each word in the information point;

[0016] Determine the frequency of use of each word in the information point;

[0017] According to the information entropy and usage frequency of each word in the information point, the importance weight of each word in the information point is determined.

[0018] In some embodiments, determining the information entropy of each word in the information point includes: determining the information entropy of each word in the information point according to the proportion of each word in the information point in each address document.

[0019] In some embodiments, determining the usage frequency of each word in an information point includes: for each word in the information point, determining the usage frequency of each word in the information point based on the number of address documents containing the word, the total number of address documents, and the number of times the word appears.

[0020] In some embodiments, determining the importance weight of each word in an information point includes: for each word in the information point, taking a preset value as a base and the information entropy of the word as an exponent, determining an index value, and determining the importance weight of the word based on the ratio information of the frequency of use of the word and the index value.

[0021] In some embodiments, for any first word in an information point, determining a correlation weight between the first word and each other word in the information point includes:

[0022] For any other word, the correlation weight between the first word and the other word is determined according to the proportion of the first word in each address document, the proportion of the other word in each address document, and the proportion of the first word and the other word in each address document.

[0023] In some embodiments, address information with the same address code having a preset number of digits constitutes an address document.

[0024] In some embodiments, determining the weight of the first word in the information point includes:

[0025] Using the correlation weights between the first word and each other word in the information point, weighted summation is performed on the importance weights of the corresponding other words;

[0026] The accumulated value of the importance weight of the first word and the weighted sum result is determined as the weight of the first word in the information point.

[0027] In some embodiments, determining the similarity between the first information point and the second information point includes:

[0028] For the same words in the first information point and the second information point, accumulating the weights of each word in the first information point or the second information point to obtain a first accumulated value;

[0029] For all words in the first information point and the second information point, accumulating the weight of each word in the first information point or the second information point to obtain a second accumulated value;

[0030] The ratio information of the first accumulated value and the second accumulated value is determined as the similarity between the first information point and the second information point.

[0031] In some embodiments, the third information point is obtained by extracting and merging the common parts of the first information point and the second information point.

[0032] In some embodiments, the information point includes at least one of a delivery address, crawler address data, map data, and a manually input address.

[0033] In some embodiments, based on the longitude and latitude information of the user device and the longitude and latitude information of the information point in the geographic information system, the information point whose distance from the user device meets the preset conditions is searched with the user device as the center.

[0034] An embodiment of the present disclosure proposes a device for determining information points in a geographic information system, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a method for determining information points in a geographic information system of any embodiment based on instructions stored in the memory.

[0035] The embodiments of the present disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the steps of the method for determining an information point in a geographic information system of any embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. The present disclosure can be more clearly understood according to the following detailed description with reference to the drawings.

[0037] Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can obtain other drawings based on these drawings without creative work.

[0038] Figure 1 A schematic flow chart showing a method for determining information points in a geographic information system in some embodiments of the present disclosure.

[0039] Figure 2 A schematic diagram showing a flow chart of determining the weight of each word in an information point according to some embodiments of the present disclosure.

[0040] Figure 3 A schematic diagram showing a device for determining information points in a geographic information system in some embodiments of the present disclosure. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure.

[0042] Figure 1 A flow chart showing a method for determining information points in a geographic information system according to some embodiments of the present disclosure is provided. The method may be executed by a device for determining information points in a geographic information system, for example.

[0043] like Figure 1 As shown, the method of this embodiment includes: steps 110-140a / b, and may also selectively include steps 150 and 160.

[0044] Step 110, determining the weight of each word in the collected information point in the information point, where the weight represents a measure of the importance of the word in the information point.

[0045] The collected geographic location information is used as the collected information points. The collected information points include at least one of the delivery address of the e-commerce platform, crawler address data, third-party map data, and manually input address, but are not limited to the examples given.

[0046] The information point is segmented using a word segmentation algorithm to obtain each word of the information point. The word segmentation algorithm, for example, adopts a word segmentation algorithm based on a hidden Markov model (HMM) or other existing word segmentation algorithms. When segmenting words, it can also be combined with manually input words (such as subways, bus stations, etc.) to perform word segmentation, and the manually input words will be regarded as one word during word segmentation.

[0047] For example, if an information point (name of the information point) is collected, for example, "New Hope International Phase 1 Building 2 Unit 1", the information point after word segmentation is, for example, "New Hope / International / Phase 1 / Building 2 / Unit 1". Assume that the weights of each word in the information point are: New Hope: 12.3, International: 3.4, Phase 1: 1.02, Building 2: 0.01, Unit 1: 0.01, which means that "New Hope" is the most important in the information point, and "International" is the second. The more important the word in the information point, the greater its contribution in distinguishing the information point from other information points.

[0048] In some embodiments, Figure 2 As shown, determining the weight of each word in an information point includes: steps 111-113.

[0049] Step 111, determining the importance weight of each word in the information point, the importance weight represents a measure of the importance of the word in all address documents, for example, can be determined based on the information entropy and usage frequency of the word in all address documents.

[0050] In some embodiments, determining the importance weight of each word in the information point includes steps 111a-111c.

[0051] Step 111a, determining the information entropy of each word in the information point, which is used to represent the uncertainty of the word.

[0052] According to the proportion of each word in the information point in each address document, the information entropy of each word in the information point is determined, that is, the information entropy of each word in the information point in all address documents is determined. The higher the proportion of the word in each address document, the smaller the information entropy of the word, and the smaller the uncertainty of the word. Among them, the address information with the same address code of a preset number of bits constitutes an address document. For example, the same address information of K-bit GeoHash belongs to the same address document, and K is, for example, 6. GeoHash is an address encoding method that can encode two-dimensional spatial latitude and longitude data into a string.

[0053] An exemplary method for calculating the information entropy of a word is as follows:

[0054]

[0055] Among them, E c represents the information entropy of word C; p i represents the proportion of word C in the i-th address document; n represents the total number of address documents containing word C.

[0056] Step 111b, determining the usage frequency of each word in the information point.

[0057] For each word in an information point, the usage frequency of each word in the information point is determined based on the number of address documents containing the word, the total number of address documents and the number of times the word appears, that is, the usage frequency of each word in the information point in all address documents is determined.

[0058] An exemplary method for calculating the frequency of use of a word is as follows:

[0059]

[0060] Among them, TF c Indicates the frequency of use of word C, also known as TF-IDF (term frequency–inverse document frequency) weight; C c Indicates the number of times word C appears; D c represents the number of address documents containing word C; D represents the total number of address documents.

[0061] Step 111c, determining the importance weight of each word in the information point according to the information entropy and usage frequency of each word in the information point.

[0062] For each word in the information point, the index value is determined with the preset value as the base and the information entropy of the word as the exponent, and the importance weight of the word is determined according to the ratio information of the usage frequency of the word and the index value. The greater the usage frequency of the word, the smaller the information entropy of the word (that is, the smaller the uncertainty of the word), the greater the importance weight of the word, that is, the more important the word is in all address documents.

[0063] An exemplary method for calculating the importance weight of a word is as follows:

[0064]

[0065] Among them, W c represents the importance weight of word C, E c represents the information entropy of word C, TF c Indicates the usage frequency of word C.

[0066] According to the information entropy and usage frequency of each word in the information point, the importance weight of each word in the information point can be accurately determined.

[0067] Step 112, for any first word in the information point, determine the correlation weight between the first word and each other word in the information point, the correlation weight represents a measure of the degree of correlation between different words, the greater the degree of correlation between different words, the greater the corresponding correlation weight. For example, the correlation weight between different words can be determined based on the simultaneous occurrence of different words, the more the simultaneous occurrence of different words, the greater the corresponding correlation weight.

[0068] For any other word, the correlation weight between the first word and the other word is determined according to the proportion of the first word in each address document, the proportion of the other word in each address document, and the proportion of the first word and the other word in each address document.

[0069] An exemplary method for calculating the correlation weight between words is as follows:

[0070]

[0071] Among them, MI(C,D) represents the correlation weight between words C and D; p(D) i represents the proportion of word D in the i-th address document; p(C) i Indicates the proportion of word C in the i-th address document; p(C,D) i represents the proportion of words C and D in the i-th address document; n represents the total number of address documents containing words C and D; m represents the total number of address documents containing word D or word C. When C=D, MI(C,D)=1.

[0072] Step 113, determining the weight of the first word in the information point according to the importance weight of each word in the information point and the correlation weight between the first word and each other word in the information point.

[0073] The weight of the relevance between the first word and each other word in the information point is used to perform a weighted summation on the importance weights of the other words; the cumulative value of the importance weight of the first word and the weighted summation result is determined as the weight of the first word in the information point. The greater the importance weight of a word and the greater the relevance weight, the greater the weight of the word in the information point.

[0074] An exemplary method for calculating the weight of a word in an information point is as follows:

[0075]

[0076] Among them, WP crepresents the weight of word C in the information point (i.e., the name of the information point), Z represents the word set contained in the information point (i.e., the name of the information point), z represents the word in the set Z, and W c represents the importance weight of word C, W z Represents the importance weight of word z.

[0077] For example, to calculate the weight of each word in the information point "New Hope / International / Phase 1 / Building 2 / Unit 1", first, the importance weight W of all words is calculated based on formulas (1)-(3): x , the calculation results are, for example, New Hope: 12.3, International: 3.4, Phase I: 1.02, Building 2: 0.01, Unit 1: 0.01. Then, based on formula (4), the relevance weights MI(C,D) of all words are calculated. The calculation results are, for example, (New Hope, International): 0.01, (New Hope, Phase I): 0.01, (New Hope, Building 2): 0.01, (International, Phase I): 0.02, etc. Then, based on formula (5), the weight WP of each word in "New Hope / International / Phase I / Building 2 / Unit 1" is calculated. x The calculation results are, for example, New Hope: 25.2, International: 6.8, Phase I: 2.06, Building 2: 0.04, Unit 1: 0.04.

[0078] According to the importance weight of a word and the relevance weight with other words, the weight of the word in the information point can be accurately determined.

[0079] Step 120, for a first information point and a second information point in the information points, determine the similarity between the first information point and the second information point according to the weight of each word in the first information point and the weight of each word in the second information point.

[0080] For the same words in a first information point and a second information point, the weights of the respective words in the first information point or the second information point are accumulated to obtain a first accumulated value; for all the words in the first information point and the second information point, the weights of the respective words in the first information point or the second information point are accumulated to obtain a second accumulated value; and the ratio information of the first accumulated value and the second accumulated value is determined as the similarity between the first information point and the second information point.

[0081] An exemplary method for calculating the similarity between two information points is as follows:

[0082]

[0083] Among them, 5iM(A,B) represents the similarity between information point names A and B; X represents all the same words in information point names A and B, and x represents a word in X; Y represents all the words that appear in information point names A and B, and y represents a word in Y; WP Ax Represents the weight WP of word x in information point name A x ; WP Bx Represents the weight WP of word x in information point name B x ; WP Ay Represents the weight WP of word y in information point name A x ; WP By Represents the weight WP of word y in information point name B x ; Max means taking the maximum value.

[0084] For example, based on formulas (1) to (5), the weight of the words in the information point is calculated, for example:

[0085] Information point A: "New Hope / International / Phase 1 / Building 2 / Unit 1"=>

[0086] New Hope: 25.2, International: 6.8, Phase I: 2.06, Building 2: 0.04, Unit 1: 0.04

[0087] Information point B: "New Hope / International / Phase II"=>

[0088] New Hope: 24.5, International: 5.8, Phase II: 1.06

[0089] The similarity of the above two information points is as follows:

[0090]

[0091] Among them, X∈{New Hope, International}, Y∈{New Hope, International, Phase I, Phase II, Building 2, Unit 1}.

[0092] Step 130: Determine whether to merge the first information point and the second information point according to the similarity between the first information point and the second information point.

[0093] Determine whether the similarity between the first information point and the second information point exceeds a preset similarity. If so, it is considered that the first information point and the second information point are similar and need to be merged. If not, it is considered that the first information point and the second information point are not similar and do not need to be merged.

[0094] For example, as mentioned above, the similarity between information point A and information point B is 90%, and the preset similarity is 80%. It is considered that information point A and information point B are similar, and information point A and information point B need to be merged.

[0095] Step 140a: if a merger is required, a third information point obtained by merging the first information point and the second information point is used as an information point in the geographic information system.

[0096] In some embodiments, the third information point is obtained by extracting and merging the common parts of the first information point and the second information point.

[0097] For example, as mentioned above, information point A: "New Hope / International / Phase 1 / Building 2 / Unit 1" and information point B: "New Hope / International / Phase 2" need to be merged. The common part of the two is "New Hope / International". Therefore, the third information point obtained after the merger is "New Hope / International".

[0098] Assuming that the information point identification, information point name, information point type, longitude and latitude of information points A and B are input, after processing according to the scheme of the embodiment of the present disclosure, the information point identification, information point name, information point type, average longitude and latitude (i.e., the average of the longitude and latitude of information points A and B), reference longitude and latitude (optional), similar longitude and latitude (optional) and other information of the third information point are output. If the longitude and latitude of information point A are selected as the reference longitude and latitude, the longitude and latitude of information point B are the similar longitude and latitude.

[0099] Step 140b: If merging is not required, the first information point and the second information point are respectively used as information points in the geographic information system.

[0100] At this point, by determining the weight of each word in the collected information point, the similarity of two information points is determined, and then it is determined whether to merge the two information points into one information point in the geographic information system, thereby solving the problem of information point redundancy in the geographic information system and improving the service quality of the geographic information system.

[0101] Step 150, based on the longitude and latitude information of the user equipment and the longitude and latitude information of the information point in the geographic information system, searching for the information point whose distance from the user equipment meets the preset conditions with the user equipment as the center.

[0102] The preset condition is, for example, searching for information points whose distance from the user equipment is less than a preset distance, or searching for a preset number of information points whose distance from the user equipment is closest. The distance between the user equipment and the information point can be determined using an existing distance calculation algorithm.

[0103] For example, input: user data <user identifier, device number, longitude and latitude>, POI data <POI identifier, POI name, POI type, average longitude and latitude>, output: user identifier, device number, longitude and latitude, TOP20 POI identifiers (i.e., the 20 information points closest to the user's device).

[0104] Based on the streamlined and effective information points in the geographic information system, provide efficient information point services related to user devices.

[0105] Step 160, according to the requirements of the external service system, provide the information points in the geographic information system or the information points whose distance from the user device meets the preset conditions to the external service system through the external service interface, so that the external service system can provide services such as information recommendation, precision marketing, and user risk identification based on the information points.

[0106] Based on the streamlined and effective information points in the geographic information system, improve the service quality of the geographic information system, and thus improve the service quality of the external service system.

[0107] Figure 3 Schematic diagram of a device for determining information points in a geographic information system showing some embodiments of the present disclosure.

[0108] As Figure 3 shown, the device 300 for determining information points in the geographic information system includes: a memory 310 and a processor 320 coupled to the memory 310. The processor 320 is configured to execute the method for determining information points in the geographic information system in any of the foregoing embodiments based on the instructions stored in the memory 310 (specific reference Figure 1-2 to the description of the embodiments, which will not be elaborated here).

[0109] Among them, the memory 310 is, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory stores, for example, an operating system, application programs, a boot loader, and other programs. The memory 310 can store, for example, an algorithm for jointly determining information points in the geographic information system (also called an algorithm for eliminating ambiguity), a word segmentation algorithm, a distance calculation algorithm, etc.

[0110] The device 300 may also include, for example, a geographic location information input interface 330. Through this input interface 330, the collected geographic location information (such as the delivery address of an e-commerce platform, crawler address data, map data of a third party, etc.) is input into the device 300 as the information points to be processed collected.

[0111] The device 300 may further include a management interface 340, through which the manual input address input device 300 is used to collect information points to be processed. The manual input word input device 300 is used to segment the information points through the management interface 340, so that the manual input words will be regarded as one word during segmentation.

[0112] The device 300 may further include a user interface 350, through which user data (such as user identification, device number, longitude and latitude, etc.) is input into the device 300, so as to search for information points whose distances from the corresponding user device meet the preset conditions according to the user data. Thus, based on the streamlined and effective information points in the geographic information system, information point services related to the user device are provided.

[0113] The device 300 may also include an external service interface 360, through which the external service system makes a request to the device 300. According to the request of the external service system, the device 300 provides the information point in the geographic information system or the information point whose distance from the user device meets the preset conditions to the external service system through the external service interface 360, so that the external service system can provide services such as information recommendation, precision marketing, user risk identification, etc. based on the information point. Based on the streamlined and effective information points in the geographic information system, the service quality of the geographic information system is improved, and then the service quality of the external service system is improved.

[0114] In some embodiments, the apparatus 300 may be, for example, a geographic information system or a part thereof.

[0115] The present disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the steps of the method for determining information points in a geographic information system in any of the aforementioned embodiments.

[0116] 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 non-transient computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code.

[0117] 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.

[0118] 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.

[0119] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for determining information points in a geographic information system, characterized in that: include: Determine the weight of each word in the collected information point in the information point, wherein the weight represents a measure of the importance of each word in the information point; For a first information point and a second information point in the information points, determining the similarity between the first information point and the second information point according to the weight of each word in the first information point in the first information point and the weight of each word in the second information point in the second information point, comprising: for the same words in the first information point and the second information point, accumulating the weights of each word in the first information point or the second information point to obtain a first accumulated value; For all words in a first information point and a second information point, weights of the words in the first information point or the second information point are accumulated to obtain a second accumulated value; and ratio information of the first accumulated value and the second accumulated value is determined as the similarity between the first information point and the second information point; determining whether to merge the first information point and the second information point according to the similarity between the first information point and the second information point; If merging is required, a third information point obtained by merging the first information point and the second information point is used as an information point in the geographic information system; if merging is not required, the first information point and the second information point are respectively used as information points in the geographic information system.

2. The method according to claim 1, characterized in that Determining the weight of each word in an information point in the information point includes: Determine the importance weight of each word in the information point; For any first word in an information point, determining a correlation weight between the first word and each other word in the information point; The weight of the first word in the information point is determined according to the importance weight of each word in the information point and the correlation weight between the first word and each other word in the information point.

3. The method according to claim 2, characterized in that Determining the importance weight of each word in an information point includes: Determine the information entropy of each word in the information point; Determine the frequency of use of each word in the information point; According to the information entropy and usage frequency of each word in the information point, the importance weight of each word in the information point is determined.

4. The method according to claim 3, characterized in that Determining the information entropy of each word in an information point includes: The information entropy of each word in the information point is determined according to the proportion of each word in the information point in each address document.

5. The method according to claim 3, characterized in that: Determining the frequency of use of each word in an information point includes: For each word in the information point, the usage frequency of each word in the information point is determined according to the number of address documents containing the word, the total number of address documents and the number of times the word appears.

6. The method according to claim 3, characterized in that Determining the importance weight of each word in an information point includes: For each word in the information point, a preset value is used as a base, and the information entropy of the word is used as an exponent to determine an index value. The importance weight of the word is determined according to the ratio information between the usage frequency of the word and the index value.

7. The method according to claim 2, characterized in that For any first word in an information point, determining the correlation weight between the first word and each other word in the information point includes: For any other word, the correlation weight between the first word and the other word is determined according to the proportion of the first word in each address document, the proportion of the other word in each address document, and the proportion of the first word and the other word in each address document.

8. The method according to claim 4, 5 or 7, characterized in that: Address information with the same address code of a preset number of digits constitutes an address document.

9. The method according to claim 2, characterized in that: Determining the weight of the first word in the information point includes: Using the correlation weights between the first word and each other word in the information point, weighted summation is performed on the importance weights of the corresponding other words; The accumulated value of the importance weight of the first word and the weighted sum result is determined as the weight of the first word in the information point.

10. The method according to claim 1, characterized in that The third information point is obtained by extracting and merging the common parts of the first information point and the second information point.

11. The method according to claim 1, characterized in that: The information point includes at least one of a delivery address, crawler address data, map data, and a manually input address.

12. The method according to claim 1, characterized in that Also includes: According to the latitude and longitude information of the user equipment and the latitude and longitude information of the information point in the geographic information system, the information point whose distance from the user equipment meets the preset condition is searched with the user equipment as the center.

13. A device for determining information points in a geographic information system, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the method of any one of claims 1-12 based on instructions stored in the memory.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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