A method and device for obtaining hot spot information

Abstract

The present invention discloses a method and device for obtaining hot information, and relates to the field of computer technology. A specific implementation of the method includes: preprocessing the information input by the user in a streaming computing manner to extract feature data of the information, wherein the feature data includes background features, semantic features, and clustering features of the information; dividing the information into data sets based on pre-set dimensions and the feature data, and grouping the data sets into clusters according to the feature data, and sorting the total amount of information in the clusters to obtain hot information. This implementation method quickly extracts text features and key information through streaming computing, stores text in a structured manner to make clustering extremely fast, and supports the calculation of multi-dimensional and long-term data based on the isolation of fast and slow libraries, so that massive consulting texts can be quickly analyzed, expanding the applicable scenarios for obtaining hot information.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for obtaining hotspot information. Background Art

[0002] Online customer service system is a general term for web-based instant messaging software. By analyzing various structured and unstructured data in consulting scenarios, a variety of marketing-related data can be obtained. Applying these data to pre-sales services can effectively improve the merchant's conversion rate. On the other hand, hot spot tracking is a common marketing operation tool that is widely used in social networks, e-commerce, finance, journalism and other scenarios.

[0003] In the process of realizing the present invention, the inventors found that the existing intelligent hotspot analysis system has the defects of relatively simple generated results, difficulty in achieving the purpose of analyzing public opinion, inability to be effectively reused for user consultation in the e-commerce field, complex calculations, too long time consumption, and too high clustering similarity. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for obtaining hot information, which can quickly extract text features and key information through streaming computing, store text in a structured manner to make clustering extremely fast, and support the calculation of multi-dimensional and long-term data based on the isolation of fast and slow libraries. Therefore, it is possible to quickly analyze massive consulting texts and expand the applicable scenarios for obtaining hot information.

[0005] To achieve the above object, according to a first aspect of an embodiment of the present invention, a method for obtaining hotspot information is provided, the method comprising:

[0006] Preprocessing the information input by the user by using a streaming computing method to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information;

[0007] Based on a preset clustering dimension and the feature data, the information is divided into data sets, and the data sets are grouped into clusters according to the feature data; and

[0008] The total amount of information in the cluster is sorted to obtain hot spot information.

[0009] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0010] The data set is grouped into clusters according to the clustering features.

[0011] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0012] Before preprocessing the information, the information is filtered according to the occurrence scenario of the information to reduce the amount of preprocessing data.

[0013] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0014] Extract key entities from the information to obtain the clustering features.

[0015] collecting background attributes of the information as the background features, and

[0016] Perform semantic recognition on the information to extract the semantic features.

[0017] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0018] The background features include merchant, SKU, category and brand,

[0019] The semantic features include text sentiment and text intent.

[0020] The clustering features include problem points, product parameters, operation methods and user demands, and

[0021] The clustering dimension includes at least one of the background feature and the semantic feature.

[0022] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0023] The pre-processed information within a specific time threshold is stored in a first storage device in a structured storage manner, and

[0024] The pre-processed information exceeding the specific time threshold is migrated to a second storage device in a structured storage manner.

[0025] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0026] According to a preset clustering period, the hotspot information is obtained from the first storage device and / or the second storage device.

[0027] Optionally, according to the method of the first aspect of the embodiment of the present invention, wherein:

[0028] When the clustering period is greater than the specific time threshold, the information in the second storage device that meets the clustering period is pre-clustered according to the text content, and the pre-clustered information is merged with the information in the first storage device to divide the data set.

[0029] Optionally, the method according to the first aspect of the embodiment of the present invention further includes:

[0030] Selecting specific information in each of the clusters as the hot information as representative information of the cluster, and

[0031] The hashed sequence of the dimension and the clustering feature is used as the identifier of the cluster.

[0032] According to a second aspect of an embodiment of the present invention, there is provided a hotspot information acquisition device, comprising:

[0033] A feature data acquisition module, used to pre-process the information input by the user in a streaming computing manner to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information;

[0034] an information clustering module, which divides the information into data sets based on a pre-set clustering dimension and the feature data, and groups the data sets into clusters according to the feature data, and

[0035] The hot information acquisition module sorts the total amount of information in the cluster to obtain hot information.

[0036] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0037] The data set is grouped into clusters according to the clustering features.

[0038] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0039] Before preprocessing the information, the information is filtered according to the occurrence scenario of the information to reduce the amount of preprocessing data.

[0040] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0041] Extract key entities from the information to obtain the clustering features.

[0042] collecting background attributes of the information as the background features, and

[0043] Perform semantic recognition on the information to extract the semantic features.

[0044] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0045] The background features include merchant, SKU, category and brand,

[0046] The semantic features include text sentiment and text intent.

[0047] The clustering features include problem points, product parameters, operation methods and user demands, and

[0048] The clustering dimension includes at least one of the background feature and the semantic feature.

[0049] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0050] The pre-processed information within a specific time threshold is stored in a first storage device in a structured storage manner, and

[0051] The pre-processed information exceeding the specific time threshold is migrated to a second storage device in a structured storage manner.

[0052] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0053] According to a preset clustering period, the hotspot information is obtained from the first storage device and / or the second storage device.

[0054] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0055] When the clustering period is greater than the specific time threshold, the information in the second storage device that meets the clustering period is pre-clustered according to the text content, and the pre-clustered information is merged with the information in the first storage device to divide the data set.

[0056] Optionally, according to the device described in the second aspect of the embodiment of the present invention, wherein:

[0057] Selecting specific information in each of the clusters as the hot information as representative information of the cluster, and

[0058] The hashed sequence of the dimension and the clustering feature is used as the identifier of the cluster.

[0059] According to a third aspect of an embodiment of the present invention, there is provided an electronic device for acquiring hotspot information, including:

[0060] one or more processors;

[0061] a storage device for storing one or more programs,

[0062] When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to the first aspect of the embodiment of the present invention.

[0063] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the embodiment of the present invention is implemented.

[0064] An embodiment of the above invention has the following advantages or beneficial effects: According to the technical solution of the embodiment of the present invention, it is possible to quickly extract text features and key information through streaming computing, store text in a structured manner to make clustering extremely fast, and support the calculation of multi-dimensional and long-term data based on the isolation of fast and slow libraries. Therefore, it is possible to quickly analyze massive consulting texts and expand the applicable scenarios for obtaining hot information.

[0065] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0067] Figure 1 is a schematic diagram of the main process of the hotspot information acquisition method according to an embodiment of the present invention;

[0068] Figure 2 is a schematic diagram of the main flow of a feature data acquisition step of a hotspot information acquisition method according to an embodiment of the present invention;

[0069] Figure 3 is a schematic diagram of main modules of a hotspot information acquisition device according to an embodiment of the present invention;

[0070] Figure 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0071] Figure 5 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0073] Figure 1 According to the hot spot information acquisition method of the embodiment of the present invention, Figure 1 As shown, the hotspot information acquisition method of the embodiment of the present invention includes: a feature data acquisition step S101, an information clustering step S102 and a hotspot information acquisition step S103.

[0074] Feature data acquisition step S101

[0075] In the feature data acquisition step S101, the information input by the user is preprocessed to acquire feature data of the information.

[0076] like Figure 2 As shown, the preprocessing includes a cluster feature extraction step S1011, a semantic feature recognition step S1012 and a background feature acquisition step S1013, so as to respectively obtain background features (ie, static features), semantic features (ie, dynamic features) and cluster features of the information.

[0077] Clustering features refer to key entities of information. Specifically, taking the information input by the user as text as an example, in the clustering feature extraction step S1011, the key entities of the text are extracted through keyword extraction or a pre-trained feature recognition model as clustering features. Clustering features may include user problem points, product parameters, operation methods, and user demands.

[0078] Semantic features refer to features that change with the content of information, such as emotions, intentions, needs, etc. In the semantic feature recognition step S1012, text emotions, text intentions, etc. are recognized as semantic features of the text through a semantic recognition model (such as an emotion model, an intention model, or other models). Text emotions can include anxiety, anger, and satisfaction, etc. Text intentions can include price, activities, and logistics, etc.

[0079] Background features refer to inherent features of information and do not change with changes in the content of the information. In the background feature collection step S1013, background attributes of the information, such as the scene where the conversation occurs (merchants, categories and brands, etc.), are collected as background features of the text.

[0080] Optionally, when the information input by the user is not text (such as voice), the non-text information can be converted into text in advance and then pre-processed.

[0081] Alternatively, if Figure 2As shown, before preprocessing the information input by the user, the information can be filtered and processed by methods such as rule matching and text classification models according to the information occurrence scenario such as business characteristics. For example, when the conversation occurs in an e-commerce scenario, only the question sentence in the information text needs to be retained. Through this step, the amount of data to be processed in the preprocessing step can be reduced.

[0082] Alternatively, if Figure 2 As shown, after the user inputs information, the information can be sent to the information gateway through the message middleware in a streaming manner to perform streaming calculations on the information.

[0083] Alternatively, if Figure 2 As shown, after preprocessing the information input by the user, the clustering features, semantic features and background features of the information can be supplemented after the text and distributed and stored in a structured manner.

[0084] Alternatively, if Figure 2 As shown, the pre-processed information within a specific time threshold can be stored in a short-term memory cache to facilitate fast access to data. Specific examples of short-term memory caches may include remote dictionary services (redis) and relational database management systems (mysql).

[0085] Alternatively, if Figure 2 As shown, the pre-processed information beyond a specific time threshold can also be migrated to long-term mass storage to achieve storage of mass data. Examples of long-term mass storage may include a distributed file system (hdfs) and a distributed document database (elasticsearch).

[0086] In the feature data acquisition step S101, the text of the information is preprocessed by streaming computing while it is being generated, and fast clustering of the text is supported by distributing fast and slow storage databases.

[0087] Information clustering step S102

[0088] In the information clustering step S102, clustering dimensions (hereinafter sometimes referred to as "dimensions") are preset. The dimensions will determine the scope and magnitude of the data selected in the database. Specifically, the dimensions may include at least one of the semantic features and background features of the information. For example, "merchant" in the background features may be selected as a dimension, or the text emotion (e.g., "angry") in the semantic features may be selected as a dimension, or a combination of background features and semantic features may be selected, for example, "merchant" + "brand" + "angry" as a dimension.

[0089] The data set is divided according to the pre-set dimension. For example, when the dimension is merchant, the data of each merchant is divided into the merchant's data set. Then the data set is grouped into clusters according to the clustering characteristics of the information.

[0090] Optionally, in the information clustering step S102, in addition to presetting the dimensions, a clustering period may also be pre-set. The clustering period will determine the database to be queried during data collection and the corresponding data partition.

[0091] For example, when the set clustering period is short, such as less than a specific time threshold, the required data set can be directly obtained from the memory cache according to the dimension.

[0092] On the other hand, when the set clustering period is longer, for example, greater than a specific time threshold, historical data can be obtained from the long-term mass storage, and then the historical data is merged with the data in the cache, and then the data set is divided.

[0093] Optionally, after acquiring the historical data, the historical data may be pre-clustered according to information content, and then the pre-clustered historical data may be merged with the data in the cache to reduce the amount of subsequent calculations.

[0094] Hotspot information acquisition step S103

[0095] In the hot information acquisition step S103, the number of information items in each cluster is summed, and then sorted according to the total amount of information in the cluster to obtain hot data. For example, the first N (N is a natural number) of the total amount of information can be used as hot information.

[0096] After preliminary testing, the hot spot information acquisition method of the present invention can complete the task of acquiring hot spot information of 70 million levels and 200,000 dimensions within 5 minutes.

[0097] Optionally, in each cluster of hot information, select specific information in the cluster, for example, the piece of information with the largest number, as the representative information of the cluster. At the same time, the hashed sequence of the dimension and clustering characteristics of the cluster is used as the identifier of the cluster to facilitate the tracking and tracing of hot information.

[0098] Figure 3 The device for implementing the hot spot information acquisition method of the present invention is shown. Figure 3 As shown, the hotspot information acquisition device 300 of the embodiment of the present invention includes:

[0099] The feature data acquisition module 301 is used to pre-process the information input by the user in a streaming computing manner to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information;

[0100] An information clustering module 302, based on a preset clustering dimension and the feature data, divides the information into data sets, and groups the data sets into clusters according to the feature data; and

[0101] The hot information acquisition module 303 is used to sort the total amount of information in the cluster to obtain hot information.

[0102] Optionally, the hot information acquisition device of the present invention further includes an information gateway module. The information gateway module can filter and process the first information according to business characteristics through rule matching and text classification models. For example, when the conversation occurs in an e-commerce scenario, only the question sentence in the information text needs to be retained. Through this step, the amount of data to be processed in the preprocessing step can be reduced.

[0103] Optionally, the hot spot information acquisition device of the present invention further includes a message middleware. After the user inputs information, the information can be sent to the information gateway through the message middleware in a streaming manner to perform streaming calculation on the information.

[0104] Figure 4 An exemplary system architecture 400 is shown to which the hotspot information acquisition method or hotspot information acquisition device according to the embodiment of the present invention can be applied.

[0105] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, 403, network 404 and server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0106] Users can use terminal devices 401, 402, 403 to interact with server 405 through network 404 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 401, 402, 403, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0107] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0108] The server 405 may be a server that provides various services, such as a backend management server (only an example) that provides support for shopping websites browsed by users using the terminal devices 401, 402, and 403. The backend management server may analyze and process the received data such as product information query requests, and feed back the processing results (such as target push information, product information - only an example) to the terminal device.

[0109] It should be noted that the hotspot information acquisition method provided in the embodiment of the present invention is generally executed by the server 405 , and accordingly, the hotspot information acquisition device is generally disposed in the server 405 .

[0110] It should be understood that Figure 4 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0111] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0112] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0113] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0114] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0115] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0116] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0117] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor. For example, they may be described as: a processor includes a feature data acquisition module, an information clustering module, and a hotspot information acquisition module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the feature data acquisition module may also be described as a "module for preprocessing information input by a user."

[0118] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: preprocessing the information input by the user in a streaming computing manner to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information; dividing the information into data sets based on pre-set dimensions and the feature data, and grouping the data sets into clusters according to the feature data; and sorting the total amount of information in the clusters to obtain hot information.

[0119] According to the technical solution of the embodiment of the present invention, it is possible to quickly extract text features and key information through streaming computing, store text in a structured manner to make clustering extremely fast, and support the calculation of multi-dimensional and long-term data based on the isolation of fast and slow libraries. Therefore, it is possible to quickly analyze massive consulting texts and enable innovative design of merchants' marketing capabilities.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for obtaining hotspot information, characterized in that: include: The information input by the user is preprocessed by a streaming computing method to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information; wherein the clustering features refer to key entities of the information, the semantic features refer to features that change as the content of the information changes, and the background features refer to inherent features of the information; Based on a preset clustering dimension and the characteristic data, the information is divided into data sets, and the data sets are grouped into cluster clusters according to the clustering characteristics; as well as The total amount of information in the cluster is sorted to obtain hot spot information.

2. The method according to claim 1, characterized in that Before preprocessing the information, the information is filtered according to the occurrence scenario of the information to reduce the amount of preprocessing data.

3. The method according to claim 1, characterized in that Extract key entities from the information to obtain the clustering features. collecting background attributes of the information as the background features, and Perform semantic recognition on the information to extract the semantic features.

4. The method according to claim 1, characterized in that The background features include merchant, SKU, category and brand, The semantic features include text sentiment and text intent. The clustering features include problem points, product parameters, operation methods and user demands, and The clustering dimension includes at least one of the background feature and the semantic feature.

5. The method according to claim 1, characterized in that The pre-processed information within a specific time threshold is stored in a first storage device in a structured storage manner, and The pre-processed information exceeding the specific time threshold is migrated to a second storage device in a structured storage manner.

6. The method according to claim 5, characterized in that Also includes: According to a preset clustering period, the hotspot information is obtained from the first storage device and / or the second storage device.

7. The method according to claim 6, characterized in that When the clustering period is greater than the specific time threshold, the information in the second storage device that meets the clustering period is pre-clustered according to the text content, and the pre-clustered information is merged with the information in the first storage device to divide the data set.

8. The method according to claim 1, characterized in that Also includes: Selecting specific information in each of the clusters as the hot information as representative information of the cluster, and The hashed sequence of the dimension and the clustering feature is used as the identifier of the cluster.

9. A hotspot information acquisition device, characterized in that: include: A feature data acquisition module, used to pre-process the information input by the user in a streaming computing manner to extract feature data of the information, wherein the feature data includes background features, semantic features and clustering features of the information; wherein the clustering feature refers to a key entity of the information, the semantic feature refers to a feature that changes as the content of the information changes, and the background feature refers to an inherent feature of the information; an information clustering module, which divides the information into data sets based on a pre-set clustering dimension and the characteristic data, and groups the data sets into cluster clusters according to the clustering characteristics, and The hot information acquisition module sorts the total amount of information in the cluster to obtain hot information.

10. An electronic device for obtaining hotspot information, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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