Intelligent system and method for promoting neighborhood social contact based on future community

Abstract

The invention relates to the technical field of intelligent management, in particular to an intelligent system and method for promoting neighborhood social contact based on a future community, and the system comprises a data collection module which collects the information of a community activity area to obtain information data; the data processing module is in network connection with the data acquisition module to obtain the information data, classifies the information data based on a classification rule to obtain a plurality of groups of classification data sets of different types, and judges whether the data in the classification data sets are neighborhood interaction information or not; and if the data in the classification data set is neighborhood interaction information, the information recommendation module matches a corresponding information recommendation resident in a preset resident database according to the classification data set, and recommends the data in the classification data set to a user side of the corresponding information recommendation resident. According to the invention, social contact among residents in the community and mutual help among the residents are facilitated, and the activeness of the residents in the community living environment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent management, and in particular, to an intelligent system and method for promoting neighborhood social interaction based on a future community. Background Art

[0002] With the rapid development of technology, future communities have become an important part of urban modernization. Future community management refers to the intelligent and efficient management of communities based on advanced technologies such as the Internet of Things, artificial intelligence, and big data.

[0003] In related technologies, a tenant database, a server, an information module, and an owner module are adopted. The tenant database is used to store rental data in the community, and the information module is used for community management personnel to manage information. By sorting out the corresponding notice information by the community management personnel, a list of notice recipient information is obtained, and the notice information is sent to the owner module of the corresponding owner according to the list of notice recipient information, and the confirmation receipt information of the owner module is received in real time. According to the confirmation receipt information, a confirmation mark is made in the list of notice recipient information, and dynamic reminder confirmation is performed according to the confirmation mark situation in the list of notice recipient information. The owner module is used for the owners in the community to receive and manage information, receive the notice information sent by the information module, and perform corresponding information prompts. When the owner clicks to confirm receipt after browsing the information prompt, the corresponding confirmation receipt information is generated and sent to the information module. When the owner needs to rent out the house, the house rental information is managed.

[0004] In the above-mentioned disclosed technical solution, the information exchange only exists between the owners, tenants, and community management personnel. For the information exchange between the residents in the community, some corresponding information can only be obtained through face-to-face communication with each other. For people who do not like to go out or are busy with work every day, there is not enough time to communicate face-to-face with the community residents to obtain the corresponding information. Summary of the Invention

[0005] In order to facilitate social interaction between community residents, the present application provides an intelligent system and method for promoting neighborhood social interaction based on a future community.

[0006] In a first aspect, the present application provides an intelligent system for promoting neighborhood social interaction based on a future community, adopting the following technical solution: A smart system for promoting neighborhood social interaction based on future communities, including a data collection module, a data processing module, and an information recommendation module. The data collection module is used to collect information in the community activity area to obtain information data. The data processing module is network-connected to the data collection module to obtain the information data, classify the information data based on classification rules to obtain several sets of classification data sets of different types, and determine whether the data in the classification data sets is neighborhood interaction information. If the data in the classification data set is neighborhood interaction information, the information recommendation module matches the corresponding information-recommended households in the preset household database according to the classification data set, and recommends the data in the classification data set to the user terminals of the corresponding information-recommended households.

[0007] By adopting the above technical solution, the data processing module classifies and processes the information data obtained by the data collection module to generate a classification data set, and determines whether the data in the classification data set belongs to neighborhood interaction information. If so, the information recommendation module matches the corresponding information-recommended households in the preset household database according to the classification data set, and recommends the data in the classification data set to the user terminals of the corresponding information-recommended households, so as to be able to recommend corresponding data according to the actual characteristics of different households, facilitate social interaction among community residents and mutual help among residents, and improve the activity of residents in the community living environment.

[0008] In some of the embodiments, the data processing module includes a feature extraction unit, an information judgment unit, and an information division unit. The information judgment unit is network-connected to the feature extraction unit, and the information judgment unit is network-connected to the information division unit. The feature extraction unit is used to extract features from the information data and perform feature identification on the information data based on the extracted features to obtain identification data. The information judgment unit reviews the information data based on the feature identification and determines whether the information data belongs to publishable information. If the information data belongs to publishable information, the information division unit stores the information data in the corresponding classification data set according to the feature identification. If the information data does not belong to publishable information, the information division unit performs data identification on the information data to obtain suspicious information, and stores the suspicious information in the corresponding classification data set.

[0009] By adopting the above technical solution, the information data is gradually processed based on the feature extraction unit, the information judgment unit, and the information division unit to obtain a classification data set with different tags. Based on the information recommendation module, different recommendation paths are set for the classification data sets with different tags, so as to ensure the personal safety of the residents while facilitating information exchange among the community residents. The information judgment unit audits whether the information data can be published, improving the personal safety of the residents.

[0010] In some of the embodiments, the information recommendation module includes a recommendation judgment unit and a recommendation matching unit. The recommendation matching unit is network-connected to the recommendation judgment unit. The recommendation judgment unit sequentially obtains the identification data in the classification data set and gradually audits the identification data to obtain a matching identification. The recommendation matching unit receives the matching identification and performs one-by-one matching in the preset household database based on the matching identification to obtain the corresponding information-recommended households.

[0011] By adopting the above technical solution, the recommendation judgment unit audits the identification data in the classification data set one by one, and obtains the matching identification according to the identification data, facilitating the recommendation matching unit to generate information-recommended households based on the matching identification, improving the matching degree between the data in the classification data set and the recommended households, and facilitating social interaction among community residents.

[0012] In some of the embodiments, an operation management module is further included. The operation management module is used to obtain the classification data set generated by the data processing module, and obtain the corresponding identification data of the classification data set, generate different data reminders according to the identification data, and display the data reminders on the corresponding user terminals.

[0013] By adopting the above technical solution, the operation management module generates corresponding data reminders for the identification data in the classification data set and displays the data reminders on the management side, so as to improve the ability of the management personnel to audit the data reminders in a timely manner, facilitating the neighbors to obtain the corresponding data recommendations in a timely manner, and improving the efficiency of neighborhood interaction.

[0014] In some of the embodiments, the operation management module is further used to regularly obtain household information, update the preset household database based on the household information, and the information recommendation module matches the corresponding matching household information in the updated preset household database according to the classification data set.

[0015] By adopting the above technical solution, the household information can be obtained in a timely manner based on the operation management module, and the preset household database can be updated in a timely manner according to the household information, which can improve the accuracy of data recommendation by the information recommendation module, thereby improving the communication efficiency among households in the neighborhood and enhancing the adhesion between each other, and then a harmonious and friendly future community can be built.

[0016] In some of the embodiments, an operation monitoring module is further included. The operation monitoring module is used to perform real-time monitoring on the information recommendation module, obtain the feedback data of the information recommendation generated by the information recommendation module, and update the information recommended to households according to the feedback data.

[0017] By adopting the above technical solution, based on the real-time monitoring of the information recommendation module by the operation monitoring module, after the information recommendation module generates an information recommendation, the corresponding feedback data can be obtained in a timely manner, and the information recommendation can be updated in reverse according to the feedback data, improving the accuracy of information recommendation by the information recommendation module, and then facilitating the information interaction among neighbors and increasing the intensity of communication between neighbors.

[0018] Second, the present application provides an intelligent method for promoting neighborhood social interaction based on a future community, adopting the following technical solution: An intelligent method for promoting neighborhood social interaction based on a future community, which is executed based on an intelligent system for promoting neighborhood social interaction based on a future community described in the first aspect, includes the following steps: Regularly monitor the information in the community activity area to obtain information data; Classify the information data based on classification rules to obtain several sets of classification data sets of different types, and determine whether the data in the classification data sets is neighborhood interaction information; If the data in the classification data sets is neighborhood interaction information, then match the corresponding information-recommended households in the preset household database according to the classification data sets, and recommend the data in the classification data sets to the user terminals of the corresponding information-recommended households.

[0019] In some of the embodiments, the classification data sets include several sets of classification data and corresponding identification data. After the data in the classification data sets is neighborhood interaction information, the following steps are further included: Successively obtain the classification data and identification data in the classification data sets, and determine whether the classification data is a chatting topic according to the identification data; If the classification data is a chatting topic, then generate valid household information according to the preset household database, and recommend the classification data corresponding to the chatting topic to the valid households; If the classified data is not a chat topic, determine whether the classified data is a topic for peers; If the classified data is a topic for peers, filter according to the classified data set to obtain matching household information, and recommend the classified data corresponding to the topic for peers to the matching households; If the classified data is not a topic for peers, filter the preset household database according to the identification data to obtain estimated household information, and send the classified data corresponding to the identification data to the estimated households.

[0020] By adopting the above technical solution, based on the specific judgment of the content of the classified data, if the classified data is a chat topic, generate valid household information based on the preset household database, and recommend the classified data corresponding to the chat topic to the valid households; if the classified data is a topic for peers, filter according to the classified data set to obtain matching household information, and recommend the classified data corresponding to the topic for peers to the matching households; if the classified data is not a topic for peers, filter the preset household database according to the identification data to obtain estimated household information, and send the classified data corresponding to the identification data to the estimated households, thereby being able to perform different user recommendations for different classified data, improving the accuracy of classified data recommendation, and further being able to strengthen the interaction between neighbors and facilitate information exchange between neighbors.

[0021] In some of the embodiments, after classifying the information data based on the classification rules to obtain several sets of classified data of different types, the following steps are further included: Based on the data identifiers corresponding to the classified data set, and based on the data identifiers, determine whether there is any data that cannot be published in the classified data set; If there is any data that cannot be published in the classified data set, generate an artificial review signal, and based on the artificial review signal, send the data that cannot be published corresponding to the classified data set to the management user terminal.

[0022] By adopting the above technical solution, for data that cannot be published, manual review by community personnel is required to ensure the personal and property safety of community residents, improve the security guarantee of future communities, prevent the release of non-compliant data, and provide a safe and healthy information interaction platform for residents.

[0023] In some of the embodiments, after matching and recommending corresponding information to households in the preset household database according to the classified data set, the following steps are further included: Obtain valid household information according to the preset household database, obtain habit characteristics based on the valid household information according to behavior habits, and perform user identification on the households based on the habit characteristics; Update the display of the valid household user terminals based on the user identification and the matching set of classified data.

[0024] By adopting the above technical solution, the behavior habits of different households are analyzed to provide them with relatively high-matching information data, thereby improving the efficiency of information interaction between neighbors.

[0025] In summary, the present application includes at least one of the following beneficial technical effects: Based on the data processing module, the information data obtained by the data acquisition module is classified to generate a set of classified data. By determining whether the data in the set of classified data belongs to neighborhood interaction information, if so, the information recommendation module matches the corresponding information-recommended households in the preset household database based on the set of classified data, and recommends the data in the set of classified data to the user terminals of the corresponding information-recommended households. Thus, corresponding data can be recommended according to the actual characteristics of different households, which is convenient for social interaction between community residents and for mutual assistance between residents, and improves the activity of residents in the community living environment; Based on the feature extraction unit, the information judgment unit, and the information division unit, the information data is gradually processed to obtain a set of classified data with different tags. Based on the information recommendation module, different recommendation paths are carried out for the set of classified data with different tags. Thus, when facilitating information exchange between community residents, the personal safety of each other's residents can be ensured. The information judgment unit audits whether the information data can be published, improving the personal safety of residents; Based on the specific judgment of the content of the classified data, if the classified data is a chatting topic, the valid household information is generated according to the preset household database, and the classified data corresponding to the chatting topic is recommended to the valid households; if the classified data is a peer topic, screening is carried out according to the set of classified data to obtain the matching household information, and the classified data corresponding to the peer topic is recommended to the matching households; if the classified data is not a peer topic, screening is carried out on the preset household database according to the identification data to obtain the estimated household information, and the classified data corresponding to the identification data is sent to the estimated households. Thus, different user recommendations can be carried out for different classified data, improving the accuracy of classified data recommendation, and further strengthening the mutual interaction between neighbors and facilitating information interaction between neighbors. Description of the Drawings

[0026] Figure 1 is a schematic structural diagram of an intelligent system for promoting neighborhood social interaction based on a future community provided by an embodiment of the present application; Figure 2 is a block diagram of an intelligent method for promoting neighborhood social interaction based on a future community provided by an embodiment of the present application; Figure 3It is a flowchart of the method provided by the embodiments of the present application after the data in the classified data set is neighborhood interaction information.

[0027] Description of the reference numerals: 10, data acquisition module; 20, data processing module; 21, feature extraction unit; 22, information judgment unit; 23, information division unit; 30, information recommendation module; 31, recommendation judgment unit; 32, recommendation matching unit; 40, operation management module; 50, operation monitoring module. Detailed implementation manners

[0028] To understand the purpose, technical solution and advantages of the present application more clearly, the present application will be described and illustrated below with reference to the drawings and embodiments. However, those of ordinary skill in the art should understand that the present application can be implemented without these details. In some cases, to avoid unnecessary descriptions from making aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be elaborated too much. For those of ordinary skill in the art, it is obvious that various changes can be made to the disclosed embodiments of the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the illustrated embodiments, but conforms to the broadest scope consistent with the scope claimed in the present application.

[0029] As Figure 1 shown, the embodiments of the present application disclose a smart system for promoting neighborhood social interaction based on a future community, which is applied to a community intelligent service platform. Through the data acquisition module 10, the data processing module 20 and the information recommendation module 30, the information in the community acquisition area is collected to obtain information data, and the information data is classified in different ways, so as to ensure that the information data of different classifications is displayed on the user terminals of different households, thereby providing a corresponding communication platform for community residents and improving the richness of the community service content in the future community.

[0030] Specifically, the smart system for promoting neighborhood social interaction based on a future community includes a data acquisition module 10, a data processing module 20 and an information recommendation module 30. The data acquisition module 10 is used to obtain the information in the community activity area to obtain information data. The data processing module 20 is network-connected to the data acquisition module 10 to obtain information data, classifies the information data based on classification rules to obtain several sets of classified data sets of different types, and judges whether the data in the classified data set is neighborhood interaction information. If the data in the classified data set is neighborhood interaction information, the information recommendation module 30 matches the corresponding information recommended households in the preset household database according to the classified data set, and recommends the data of the classified data set to the user terminals of the corresponding information recommended households.

[0031] Among them, the information data is the data existing in the community activity area. The specific acquisition method of the information data can be the data obtained through real-time monitoring by the monitoring devices in the community, or the data directly input by the community residents into the data collection module 10 through the corresponding user terminal. The monitoring devices can be the data obtained through real-time monitoring by the cameras installed inside the community. The data can be video data or text data. When processing the video data subsequently, it is necessary to collect the corresponding information of the video data and convert the collected information into text descriptions, or directly process the video data and publish the video data to the user terminal.

[0032] For the convenience of community management, the corresponding user terminals are set for the residents in the community based on the houses. The residents can search for community services and community communication through their own user terminals, so as to deepen the understanding among the residents in the community and improve the solution efficiency of community problems and some life interaction problems.

[0033] It should be noted here that the user terminal can be presented in the form of a small program or a community APP. Each household has its own unique account information and logs in through the account interface to enter the corresponding user terminal.

[0034] In addition, a corresponding data collection unit is set on each user terminal. Community residents can click on the data collection unit to input some information they want to know and some information they want to publish. For the information input by the residents in the data collection unit. The information data input by the community residents through the data collection unit, the information received by the data collection module 10 through the data collection unit, and preliminary data processing is performed on it to generate information data. For the information that the community residents want to know and publish, it is necessary to mark the publisher of the information to facilitate subsequent reply recommendations for the community residents.

[0035] The classification rules are mainly the rules for classifying information data. In this embodiment, specifically, different classification divisions can be made according to the specific content of the information data. By setting feature words for different categories of information, the corresponding classification rules are formed. The classification rules include classifying data sets such as neighborhood interaction information, community release information, and community help information.

[0036] Specifically, for neighborhood interaction information, there are invitation actions and destinations, demand actions and demand results, etc. Exemplarily, "together" and "playing badminton", etc. In the information database belonging to neighborhood interaction information, there are multiple groups of characteristic words with the possibility of interaction. When the data processing module 20 classifies information data, according to the classification rules of neighborhood interaction information, it can reasonably generate corresponding classification data sets. Here, the invitation action and the destination appear in pairs, and the demand action and the demand result also appear in pairs. For those that do not appear in pairs, they can be manually reviewed by community service personnel, and this will not be elaborated here.

[0037] For community release information, there should be corresponding warning characteristic words, warning locations, and warning statuses, etc. Exemplarily, "attention", "the steps on the first floor of Building 2", and "prevent falling", etc. The warning characteristic words include attention, warning, and alert, etc., not limited to this.

[0038] It should be noted here that for the data actively uploaded by community residents in the data collection module 10, there may also be some warning information. In order to remind other residents, when the data collection module 10 needs to perform preliminary data processing on it, the information data can be correspondingly reviewed. When there is some warning information, after preliminary review, this warning information can be released as neighborhood interaction information or as community release information.

[0039] For community assistance information, there should be demand characteristics and demand purposes, etc. Exemplarily, "need" and "repair the window". For the information released by residents, there are some corresponding demand information. When the demand information is classified by the data processing module 20, it can be correspondingly released. For those that need community assistance, when the data processing module 20 conducts a review, it can directly release the classification data set obtained from the review to the management user terminal. For those that need to conduct demand interaction among neighbors, when the information data is divided, it can be divided into neighborhood interaction information, so as to facilitate the subsequent information recommendation module 30 to match the corresponding information to recommend to residents based on the classification data set in the preset resident database, and release the corresponding classification data set on the user terminal of the residents who may receive the information recommendation.

[0040] Refer to Figure 1 In one of the embodiments, the data processing module 20 includes a feature extraction unit 21, an information judgment unit 22, and an information division unit 23. The information judgment unit 22 is network-connected to the feature extraction unit 21, and the information judgment unit 22 is network-connected to the information division unit 23.

[0041] The feature extraction unit 21 is used to extract features from information data, and based on the extracted features, perform feature identification on the information data to obtain identification data. The information judgment unit 22 reviews the information data based on the identification data and determines whether the information data belongs to publishable information. If the information data belongs to publishable information, the information division unit 23 stores the information data corresponding to the identification data in the corresponding classification data set. If the information data does not belong to publishable information, the information division unit 23 marks the information data as suspicious information and stores the suspicious information in the corresponding classification data set.

[0042] Among them, the feature extraction unit 21 extracts features from information data. Specifically, corresponding feature extraction is performed on the information data. Here, the information data includes text data, numerical data, image data, and audio data. Different feature extraction methods are used for different data. For the feature extraction method of text data, first, the text is preprocessed, and then a word segmentation tool is used to perform word segmentation on the text data. The word segmentation is adjusted through the context to obtain accurate feature identification, and the information data is identified according to the feature identification to obtain identification data.

[0043] It should be noted here that for numerical data, corresponding feature extraction is performed according to the characteristics that the numerical values should have. For example, statistical features, time-domain features, frequency-domain features, etc. For the processing of image data, traditional methods or deep learning methods can be used for feature extraction. For example, scale-invariant feature transform, histogram of oriented gradients, local binary pattern, convolutional neural network feature extraction, and feature extraction of pre-trained models, etc. For the feature extraction of audio data, corresponding feature extraction is performed on time-domain features, frequency-domain features, spectral features, and acoustic features, etc.

[0044] To determine whether the information data belongs to publishable information, specifically, the feature identification in the information data is matched in the illegal information database. If a corresponding matching information is found in the illegal information database, it is determined that the information data does not belong to publishable information. If no corresponding matching information is found in the illegal information database, it is determined that the information data belongs to publishable information. For information data that does not belong to publishable information, the information division unit 23 needs to mark the information data as suspicious and store the marked suspicious information in the classification data set. This classification data set includes several groups of suspicious information with suspicious marks.

[0045] Refer to Figure 1, in one of the embodiments, the information recommendation module 30 includes a recommendation judgment unit 31 and a recommendation matching unit 32. The recommendation matching unit 32 is network-connected to the recommendation judgment unit 31. The recommendation judgment unit 31 sequentially obtains the identification data in the classification data set and gradually reviews the identification data to obtain matching identifications. The recommendation matching unit 32 receives the matching identifications and performs one-by-one matching in the preset household database based on the matching identifications to obtain the corresponding information-recommended households.

[0046] Among them, the matching identification represents an identification that can be matched with the identification data. Specifically, it is the corresponding matchable feature generated based on the identification data. Here, the matching identification is an identification that may satisfy the identification data based on the identification data. The community households with existing matching identifications may be able to complete the identification data. Therefore, this matching identification is based on the personal characteristics of the community households. For example, if the identification data is playing badminton, the matching identification can be set as sports. Only those who do sports may play badminton.

[0047] It should be noted here that since several groups of information data are stored in the classification data set, and each information data includes at least one item of identification data, it is necessary to gradually review the identification data of the information data in the classification data set, and generate corresponding matching identifications for each identification data, so as to be able to comprehensively promote the information data and accurately promote it, so that the households in need can see it, and it will not affect the households not interested, and can increase the possibility of interaction among neighbors.

[0048] The preset household database stores the characteristics corresponding to all households. For example, the characteristic identifications of household A are sports, food, and tourism, etc. By performing one-by-one matching in the preset household database through the matching identifications, the corresponding information-recommended households are obtained.

[0049] Refer to Figure 1 , in one of the embodiments, it further includes an operation and management module 40. The operation and management module 40 is used to obtain the classification data set generated by the data processing module 20, obtain the identification data corresponding to the classification data set, generate different data reminders according to the identification data, and display the data reminders on the corresponding user terminals.

[0050] Among them, the data reminder represents the data that needs to be viewed. For the data in different classified data sets, it is necessary to review it. For the identification data such as property maintenance, property inspection, and property audit, etc., these data reminders need to be displayed on the user side of management. For the identification data such as playing badminton and scenic spot tourism, etc., the corresponding information can be matched through these identification data to recommend to the residents, and corresponding data reminders are generated based on these identification data, and these data reminders are sent to the user side of the corresponding information recommended to the residents, so as to facilitate the corresponding residents to view and promote the information interaction between neighbors.

[0051] It should be noted here that the operation management module 40 includes two entrances: the small program and the Web side. The small program mainly focuses on handling some daily problems and audits to facilitate users to perform quick operations, while the Web side mainly operates and manages the background data, and can generate multiple small modules in each small program. Based on the WeChat small program and the Web platform, application sides can be provided for community neighborhood committees, residential property owners, property management companies, merchants, and residents, which is convenient for efficient management of the community and can improve the information interaction among community residents.

[0052] For example, for the user side of the community manager, the small program includes dynamic review, topic review, order sharing review, community activities, community review, second-hand review, resident review, supervision reminder, and warranty reminder, etc. For the user side of the residents, the small program includes articles by experts, expert activities, neighborhood mutual assistance, venue reservation, second-hand trading, and warranty entrance, etc.

[0053] Refer to Figure 1 , in one of the embodiments, the operation management module 40 is further configured to regularly obtain resident information, update the preset resident database based on the resident information, and the information recommendation module 30 matches the corresponding matching resident information in the updated preset resident database according to the classified data set.

[0054] Among them, the resident information represents the information obtained by auditing the residents of each household. The specific acquisition method can be through information registration, property personnel visiting the door, and phone confirmation, etc., to timely confirm the living conditions of the people in this community. In the user side of the community manager, it also includes resident management, and the resident management mainly manages according to the specific situations of the residents and the household members. The scope of community management includes personal information of residents, household situations, relationship with the household head, and whether they need to move out of the current resident's location.

[0055] Exemplarily, specific management is carried out for the household members of Room 101, Unit 1, Building 15, including Household A and Household B. The situation of Household A is that the household is there but the people are not. The household is the householder. And for Household B, the situation is that the household is there but the people are not, and the relationship with the householder is spouse, etc. For the case where the household is there but the people are not, it means that the current household family does not live in this community. For the convenience of management, only the household can be retained for the household members of this household, and the rest of the people can be moved out of the household management. When the household members move in for residence, corresponding addition can be carried out. For other tenants, corresponding price adjustment can also be made, and the relationship with the householder can be set as tenant.

[0056] After obtaining the household information, the household management is updated in real time, and the preset household database is updated according to the updated household management, so as to facilitate the information recommendation module 30 to match the corresponding matching household information in the updated preset household database based on the classified data set.

[0057] Refer to Figure 1 , in one of the embodiments, an operation monitoring module 50 is further included. The operation monitoring module 50 is used to monitor the information recommendation module 30 in real time, obtain the feedback data of the information recommendation generated by the information recommendation module 30, and update the information recommended households according to the feedback data.

[0058] Among them, the feedback data represents the corresponding feedback obtained when the information recommendation module 30 is monitored in real time. Specifically, the feedback data can extract information from the information interaction between households, judge whether the current information data release has received responses from other households and the response results, and judge through the responses and response results to obtain effective feedback data. If there is no feedback, by analyzing the operation conditions of the households on their respective user terminals, it is judged whether the information recommended households are not interested in the recommended information data or the households have not viewed the recommended information data. If the information recommended households are not interested in the recommended information data, the matching mechanism for matching the information recommended households needs to be readjusted. If the information recommended households have not seen the recommended information data, the recommended range of the information data can be expanded, and the associated households are obtained according to the information recommended households, and the associated households are also listed as the information recommended households for the recommendation of the information data.

[0059] It should be noted here that to determine whether the information recommendation is not of interest to the household or the household has not seen the corresponding information data, it mainly depends on whether the household opens the corresponding mini-program. If the mini-program is opened and the corresponding information data is clicked, but there is no corresponding information feedback, it is determined that the current information recommendation is not of interest to the household for the recommended information data. For the household that has not opened the corresponding mini-program, it is determined that the household has not seen the corresponding information data. According to whether the information recommended to the household is not of interest or the household has not seen the corresponding information data, the information recommended users are updated accordingly to promote social interaction among neighbors.

[0060] The embodiment of the present application also discloses an intelligent method for promoting neighborhood social interaction based on a future community, which is executed based on an intelligent system for promoting neighborhood social interaction based on a future community.

[0061] As Figure 2 shown, the intelligent method for promoting neighborhood social interaction based on a future community includes the following steps: S100, regularly monitor the information in the community activity area to obtain information data.

[0062] S200, classify the information data based on classification rules to obtain several sets of classification data sets of different types, and determine whether the data in the classification data set is neighborhood interaction information.

[0063] S300, if the data in the classification data set is neighborhood interaction information, then match the corresponding information-recommended households in the preset household database according to the classification data set, and recommend the data in the classification data set to the user terminals of the corresponding information-recommended households.

[0064] Among them, the meanings and acquisition methods of the information data, classification data set, neighborhood interaction information, and information-recommended households are the same as or similar to those described in the intelligent system for promoting neighborhood social interaction based on a future community, so they will not be elaborated here.

[0065] It should be noted here that if the data in the classification data set is not neighborhood interaction information, then the information recommendation module 30 matches the corresponding property user terminal in the preset household database according to the classification data set, and recommends the data in the classification data set to the property user terminal.

[0066] Referring to Figure 1 and Figure 3 , in one of the embodiments, the classification data set includes several sets of classification data and corresponding identification data. After the data in the classification data set is neighborhood interaction information, the following steps are further included: S310, sequentially obtain the classification data and identification data in the classification data set, and determine whether the classification data is a chat topic according to the identification data.

[0067] S320. If the classified data is a chatting topic, generate valid household information based on a preset household database, and recommend the classified data corresponding to the chatting topic to the valid households.

[0068] S330. If the classified data is not a chatting topic, determine whether the classified data is a peer topic.

[0069] S340. If the classified data is a peer topic, perform screening based on the classified data set to obtain matching household information, and recommend the classified data corresponding to the peer topic to the matching households.

[0070] S350. If the classified data is not a peer topic, screen the preset household database based on the identification data to obtain estimated household information, and send the classified data corresponding to the identification data to the estimated households.

[0071] Among them, the chatting topic indicates that the classified data is a topic for chatting. For example, for some topics, the corresponding valid households indicate the households living in this community. Persons not living in the community can be excluded to reduce the scope of information promotion and improve the efficiency of information promotion.

[0072] The peer topic indicates topics that require peers, such as making an appointment to play together and doing sports together. The matching household information is specifically matched based on the peer topic. For the peer topic of playing together, some households with a hobby of playing need to be recommended. For the peer topic of doing sports together, some households with a hobby of sports need to be recommended. Specifically, how to recommend is to perform data analysis through the data processing module 20 and the information recommendation module 30, classify the information data based on the classification rules to obtain several sets of classified data of different types, and then pair the identification data corresponding to the classified data in the classified data set with the matching identification of the households in the preset household database, so as to obtain the matching household information.

[0073] For the classified data that is not a peer topic, there may be some demand topics, and it is necessary to screen the households in the preset household database according to the identification data, and use the possible households as the estimated household information. Exemplarily, for a household that posts a request to find an aunt who only cooks, after review, this information data does not belong to the chatting topic nor the peer topic. The household information that may be provided in the same community can be screened in the preset household database, and this household information can be used as the estimated household information. Here, "may be provided" means that the household meets the corresponding age range and the corresponding time period.

[0074] It should be noted here that for classification data that is neither a peer topic nor a chat topic, it is also possible that it is a topic targeted at the community property. Therefore, in order to facilitate the timely processing of classification data, the property can also be regarded as a member of the preset household database and the classification data can be processed in the same way. For classification data that requires the property to provide corresponding services, the classification data with a demand for the property can be sent to the user side of the property according to the identification data of the classification data.

[0075] In one of the embodiments, after classifying the classification data based on the classification rules to obtain several sets of classification data of different types, the following steps are further included: S210, based on the data identifier corresponding to the classification data set, and based on the data identifier, determine whether there is any data that cannot be published in the classification data set.

[0076] S220, if there is any data that cannot be published in the classification data set, generate an artificial review signal, and based on the artificial review signal, send the data that cannot be published corresponding to the classification data set to the management user side.

[0077] Among them, the data identifier is to mark the information data as suspicious. Based on the data identifier, it can be determined whether there is any data that cannot be published in the classification data set. If there is any data that cannot be published in the classification data set, an artificial review signal is generated, and based on the artificial review signal, the data that cannot be published corresponding to the classification data set is sent to the management user side.

[0078] Refer to Figure 1 , it should be noted here that the artificial review signal represents the information that requires property artificial review. The artificial review signal can be generated by the data processing module 20 when there is data that cannot be published in the classification data set, so as to remind the property that it needs to review these information data to be published and determine whether the information data to be published can be normally published. The publication here can be carried out by manual screening and then publishing. For misjudgment situations, the characteristic times with misjudgment can be manually adjusted to facilitate accurate judgment of the information data next time.

[0079] In addition, for the classification data set where there is no data that cannot be published, it can be published in the normal order. Here, the publication is to determine whether the data in the classification data set is neighborhood interaction information through the data processing module 20. If the data in the classification data set is neighborhood interaction information, the information recommendation module 30 matches the corresponding information-recommended households in the preset household database according to the classification data set, and recommends the data of the classification data set to the user sides of the corresponding information-recommended households. For data that is not neighborhood interaction information, it can be displayed on the user side of the property through the information recommendation module 30.

[0080] In one of the embodiments, matching corresponding information in the preset household database according to the classification data set to recommend households includes the following steps: S360, obtaining valid household information according to the preset household database, obtaining habit characteristics based on the valid household information according to behavior habits, and identifying households based on the habit characteristics.

[0081] S370, updating the display of the valid household client based on the household identification and the matched classification data set.

[0082] Among them, the habit characteristics include the characteristics of the household itself. For example, the household likes sports, likes quietness, etc. Analyze the behavior habits of different households to provide them with relatively high-matching information data, thereby improving the efficiency of information interaction between neighbors.

[0083] In addition, the habit characteristics also include the needs of the valid household information for the client. For example, if the household likes to click on articles by experts, expert activities, neighborhood mutual assistance, etc., mark the commonly used small modules, and then update the display of the client of this user according to the household identification and the matched classification data set, and display it in the area that is convenient for the user to click. For those that the household does not often click, they can be displayed in other areas.

[0084] It should be noted here that for the update of the client, different displays of the client can be performed according to the operations and management of the background data by the Web end, so as to perform corresponding operations according to the personal habits of the households.

[0085] The implementation principle is as follows: The data acquisition module 10 collects information in the community activity area to obtain information data. The data processing module 20 is network-connected to the data acquisition module 10 to obtain information data, classifies the information data based on classification rules to obtain several sets of different types of classification data sets, and determines whether the data in the classification data set is neighborhood interaction information. If the data in the classification data set is neighborhood interaction information, the information recommendation module 30 matches the corresponding information in the preset household database according to the classification data set to recommend households, and recommends the data in the classification data set to the clients of the corresponding information-recommended households. If the data in the classification data set is not neighborhood interaction information, the information recommendation module 30 matches the corresponding property client in the preset household database according to the classification data set, and recommends the data in the classification data set to the property client.

[0086] It should be understood that although each step in the flowchart of the accompanying drawings is shown in sequence according to the indication of the arrow, these steps do not necessarily have to be executed in the order indicated by the arrow. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders.

[0087] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Therefore, all equivalent changes made according to the structure, shape and principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A smart system for promoting neighborhood social interaction based on future communities, characterized in that, It includes a data collection module (10), a data processing module (20), and an information recommendation module (30). The data collection module (10) is used to collect information in the community activity area to obtain information data. The data processing module (20) is network-connected to the data collection module (10) to obtain the information data, classify the information data based on classification rules to obtain several sets of classification data sets of different types, and determine whether the data in the classification data sets is neighborhood interaction information. If the data in the classification data sets is neighborhood interaction information, the information recommendation module (30) matches the corresponding information-recommended households in the preset household database according to the classification data sets, and recommends the data in the classification data sets to the user terminals of the corresponding information-recommended households.

2. The intelligent system for promoting neighborhood social interaction based on the future community according to claim 1, characterized in that The data processing module (20) includes a feature extraction unit (21), an information judgment unit (22), and an information division unit (23). The information judgment unit (22) is network-connected to the feature extraction unit (21), and the information judgment unit (22) is network-connected to the information division unit (23). The feature extraction unit (21) is used to extract features from the information data and perform feature identification on the information data based on the extracted features to obtain identification data. The information judgment unit (22) audits the information data based on the identification data and determines whether the information data belongs to publishable information. If the information data belongs to publishable information, the information division unit (23) stores the information data corresponding to the identification data in the corresponding classification data set. If the information data does not belong to publishable information, the information division unit (23) performs data identification on the information data to obtain suspicious information, and stores the suspicious information in the corresponding classification data set.

3. The intelligent system for promoting neighborhood social interaction based on the future community according to claim 2, wherein, The information recommendation module (30) includes a recommendation judgment unit (31) and a recommendation matching unit (32). The recommendation matching unit (32) is network-connected to the recommendation judgment unit (31). The recommendation judgment unit (31) sequentially obtains the identification data in the classification data sets and performs step-by-step auditing on the identification data to obtain matching identifications. The recommendation matching unit (32) receives the matching identifications and performs one-by-one matching in the preset household database based on the matching identifications to obtain the corresponding information-recommended households.

4. The intelligent system for promoting neighborhood social interaction based on future communities according to claim 2, wherein, It further includes an operation management module (40). The operation management module (40) is used to obtain the classification data sets generated by the data processing module (20), obtain the corresponding identification data of the classification data sets, generate different data reminders according to the identification data, and display the data reminders on the corresponding user terminals.

5. The intelligent system for promoting neighborhood social interaction based on future communities according to claim 4, wherein The operation management module (40) is further used to regularly obtain household information, update the preset household database based on the household information, and the information recommendation module (30) matches the corresponding matching household information in the updated preset household database according to the classification data sets.

6. The intelligent system for promoting neighborhood social interaction based on a future community according to claim 1, characterized in that It further includes an operation monitoring module (50), and the operation monitoring module (50) is used to monitor the information recommendation module (30) in real time, obtain the feedback data of the information recommendation generated by the information recommendation module (30), and update the information recommended households according to the feedback data.

7. A smart method for promoting neighborhood social interaction based on future communities, characterized in that, Executed based on the intelligent system for promoting neighborhood social interaction in a future community according to any one of claims 1-6, including the following steps: Regularly monitor the information in the community activity area to obtain information data; Classify the information data based on classification rules to obtain several sets of classification data sets of different types, and judge whether the data in the classification data sets is neighborhood interaction information; If the data in the classification data sets is neighborhood interaction information, then match the corresponding information-recommended households in the preset household database according to the classification data sets, and recommend the data in the classification data sets to the user terminals of the corresponding information-recommended households.

8. The intelligent method for promoting neighborhood social interaction based on future communities according to claim 7, characterized in that, The classification data sets include several sets of classification data and corresponding identification data. After the data in the classification data sets is neighborhood interaction information, the following steps are further included: Successively obtain the classification data and identification data in the classification data sets, and judge whether the classification data is a chatting topic according to the identification data; If the classification data is a chatting topic, then generate valid household information based on the preset household database, and recommend the classification data corresponding to the chatting topic to the valid households; If the classification data is not a chatting topic, then judge whether the classification data is a peer topic; If the classification data is a peer topic, then screen according to the classification data sets to obtain matching household information, and recommend the classification data corresponding to the peer topic to the matching households; If the classification data is not a peer topic, then screen the preset household database according to the identification data to obtain estimated household information, and send the classification data corresponding to the identification data to the estimated households.

9. The intelligent method for promoting neighborhood social interaction based on future communities according to claim 8, wherein, After classifying the information data based on classification rules to obtain several sets of classification data sets of different types, the following steps are further included: Based on the data identifiers corresponding to the classification data sets, and judge whether there is non-publishable data in the classification data sets according to the data identifiers; If there is non-publishable data in the classification data sets, then generate an artificial review signal, and send the non-publishable data corresponding to the classification data sets to the management user terminal based on the artificial review signal.

10. The intelligent method for promoting neighborhood social interaction based on a future community according to claim 7, wherein After matching the corresponding information-recommended households in the preset household database according to the classification data sets, the following steps are further included: Obtain valid household information according to the preset household database, obtain habit characteristics based on the valid household information according to behavior habits, and identify the households based on the habit characteristics; Update the display of the valid household user terminals based on the user identification and the matching classification data sets.

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