Joint user rating method and system based on intersection of multiple property data of office building

Abstract

According to the invention, big data analysis, artificial intelligence and Internet of Things technologies are utilized to carry out cross analysis on multiple pieces of property data of office buildings, including but not limited to data in the aspects of environmental comfort, facility and equipment operation conditions, safety management level, service quality and the like. Through the comprehensive analysis of the data, the system can more accurately evaluate the satisfaction and demand of the user for the property, provides decision support and improvement direction for property managers, and can carry out personalized rating and recommendation according to the actual use condition and feedback information of the user, thereby improving the user experience and satisfaction. By combining the user rating method and system, the property management of the office building can be more scientific and intelligent, the management efficiency and the user satisfaction degree are improved, and powerful support is provided for the sustainable development of the urban office building.

Description

Technical Field

[0002] This invention relates to the field of office building property management, and in particular to a joint user rating method and system based on the cross-data of multiple properties in office buildings. Background Technology

[0004] With the acceleration of urbanization, office building property management is becoming increasingly complex. Traditional property management methods often rely on manual experience and a single data source, making it difficult to comprehensively and accurately assess user satisfaction and needs. Therefore, a joint user rating method and system based on cross-data from multiple office buildings has emerged.

[0005] This system utilizes big data analytics, artificial intelligence, and the Internet of Things to cross-analyze multiple property data points within office buildings, including but not limited to data on environmental comfort, facility and equipment operation, security management level, and service quality. Through comprehensive analysis of this data, the system can more accurately assess user satisfaction and needs, providing property managers with decision support and improvement directions.

[0006] Meanwhile, the system can also provide personalized ratings and recommendations based on users' actual usage and feedback, improving user experience and satisfaction. By combining user rating methods and systems, property management of office buildings can become more scientific and intelligent, improving management efficiency and user satisfaction, and providing strong support for the sustainable development of urban office buildings. Summary of the Invention

[0008] The purpose of this invention is to provide a joint user rating system and method based on cross-data from multiple properties in office buildings. This system can solve the problem that traditional property management methods often rely on manual experience and a single data source, making it difficult to comprehensively and accurately assess user satisfaction and needs regarding the property.

[0009] The specific technical solution adopted by this invention is as follows: A joint user rating method based on cross-data analysis of multiple properties in office buildings includes: Each property management company's server runs a local user rating algorithm based on all the user data stored in its office buildings to calculate user characteristic values; The property management company's server will broadcast the calculated user characteristic values ​​to other property management company servers through the central server; Other property management company servers determine the real-time weight of user characteristics corresponding to each property management company server by cross-referencing the user characteristic values ​​obtained from the data. After the connection is established, each property management company's server negotiates and shares the real-time weights of user characteristics corresponding to each property management company's server. Based on the real-time weights of user characteristics calculated by each property management company's server through negotiation and sharing, each property management company rates the users of each office building, thus generating user ratings.

[0010] In a preferred embodiment, it further includes: After the connection is established, each property management company's server shares rating strategy information.

[0011] In a preferred embodiment, it further includes: The central server calculates the contribution-revenue ratio of each property management company's server, which is the ratio of the number of times the service was provided to the cumulative number of times the service was used; and The central server determines differentiated service users based on the contribution and revenue ratio of each property management company's server.

[0012] In a preferred embodiment, the central server determines differentiated service users based on the contribution revenue ratio of each property management company's server, including: When the contribution-to-revenue ratio of the property management company's server exceeds a set value, it provides high-value services to users; or When the revenue contribution ratio of the property management company's server is lower than the set value, it will provide users with ordinary value services.

[0013] In a preferred embodiment, it further includes: The property management company's server creates user profiles based on shared user characteristic values.

[0014] In a preferred embodiment, it further includes: The central server builds a user profile based on the user characteristics broadcast.

[0015] In a preferred embodiment, the user data includes: user profile, user consumption, user's company affiliation, and user's job information.

[0016] In a preferred embodiment, when sharing user feature values, only the user feature values ​​of public users are shared.

[0017] A property management system based on the cross-data of multiple properties in office buildings includes: a central server, used to broadcast the user feature values ​​calculated by the servers of each property company to all property company servers; Each property management company's server is used to run a local user rating algorithm based on its stored user data to calculate user characteristic values; the calculated user characteristic values ​​are then broadcast to other property management company servers through the central server; based on the obtained user characteristic values, it decides whether to connect with the property management company server that broadcast the user characteristic values; after establishing a connection, the user characteristic values ​​are shared through negotiation with the other property management company server.

[0018] A joint user rating terminal based on cross-data from multiple properties in office buildings, characterized in that it includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the aforementioned joint user rating method based on cross-data from multiple properties in office buildings.

[0019] The technical effects achieved by this invention are as follows: This invention utilizes big data analytics, artificial intelligence, and the Internet of Things (IoT) to cross-analyze multiple property data points from office buildings, including but not limited to data on environmental comfort, facility and equipment operation, security management level, and service quality. Through comprehensive analysis of this data, the system can more accurately assess user satisfaction and needs, providing property managers with decision support and improvement directions. Furthermore, it can provide personalized ratings and recommendations based on actual user usage and feedback, enhancing user experience and satisfaction. By combining user rating methods and systems, office building property management can become more scientific and intelligent, improving management efficiency and user satisfaction, and providing strong support for the sustainable development of urban office buildings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the method provided by the present invention; Figure 2 This is a system module diagram provided by the present invention. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0026] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0030] It should be understood that the sequence number of each step in this embodiment does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0031] Please see Figure 1 and Figure 2 As shown, this invention provides a joint user rating method based on cross-data analysis of multiple properties in office buildings, including: Each property management company's server runs a local user rating algorithm based on all the user data stored in its office buildings to calculate user characteristic values; The property management company's server will broadcast the calculated user characteristic values ​​to other property management company servers through the central server; Other property management company servers determine the real-time weight of user characteristics corresponding to each property management company server by cross-referencing the user characteristic values ​​obtained from the data. After the connection is established, each property management company's server negotiates and shares the real-time weights of user characteristics corresponding to each property management company's server. Based on the real-time weights of user characteristics calculated by each property management company's server through negotiation and sharing, each property management company rates the users of each office building, thus generating user ratings.

[0032] This invention utilizes big data analytics, artificial intelligence, and the Internet of Things (IoT) to cross-analyze multiple property data points from office buildings, including but not limited to data on environmental comfort, facility and equipment operation, security management level, and service quality. Through comprehensive analysis of this data, the system can more accurately assess user satisfaction and needs, providing property managers with decision support and improvement directions. Furthermore, it can provide personalized ratings and recommendations based on actual user usage and feedback, enhancing user experience and satisfaction. By combining user rating methods and systems, office building property management can become more scientific and intelligent, improving management efficiency and user satisfaction, and providing strong support for the sustainable development of urban office buildings.

[0033] The above steps describe a joint user rating method based on cross-data analysis of multiple properties in office buildings. This method determines the real-time weights of user characteristics for each property management company through data exchange and sharing between their servers, ultimately generating user ratings. The following sections will explain each step and its effects in detail:

[0034] Each property management company's server runs a local user rating algorithm based on all user data stored in its office buildings to calculate user characteristic values. This step aims to allow each property management company's server to rate the users in the office buildings it manages and obtain user characteristic values, such as user satisfaction and usage habits.

[0035] The property management company's server will broadcast the calculated user characteristic values ​​to other property management company servers through the central server; through the central server's broadcast, each property management company's server can obtain the user characteristic values ​​calculated by other property management companies.

[0036] Other property management company servers perform cross-analysis of the acquired user feature values ​​to determine the real-time weight of user features corresponding to each property management company server. In this step, each property management company server performs cross-analysis of the acquired user feature values ​​to determine the real-time weight of user features corresponding to each property management company server. This helps to comprehensively consider the data of different property management companies and improve the accuracy of the rating.

[0037] After establishing a connection, each property management company's server negotiates and shares the real-time weights of user characteristics calculated by itself for each property management company's server. In the online state, each property management company's server negotiates and shares the real-time weights of user characteristics calculated by itself for each property management company's server to ensure that each property management company's server can obtain the weight information of other property management company's servers.

[0038] Based on the real-time weights of user characteristics calculated by each property management company's server through negotiation and sharing, each property management company rates the users of each office building, thus generating user ratings.

[0039] Ultimately, based on the weight information shared by the servers of various property management companies, each company rates the users of each office building, generating user ratings. In this way, through the cross-fertilization and sharing of data from multiple property management companies, user satisfaction and needs for office buildings can be assessed more comprehensively and accurately, improving the scientific and intelligent nature of property management.

[0040] Through the above steps, this method enables data sharing and cross-analysis between servers of different property management companies, thereby comprehensively considering user data from multiple companies and improving the accuracy and comprehensiveness of user ratings. Simultaneously, by negotiating and sharing real-time weights, it ensures that the weight information of each property management company's server is up-to-date during the rating process, thus improving the real-time nature and accuracy of the rating. Ultimately, this method can provide a more scientific and intelligent user rating approach for office building property management, improving management efficiency and user satisfaction.

[0041] In a preferred embodiment, it further includes: After the connection is established, each property management company's server shares rating strategy information.

[0042] In a preferred embodiment, after establishing a connection, each property management company's server shares rating strategy information. This means that the servers of each property management company, while online, share their rating strategy information with each other to better understand and learn from the rating strategies of other property management companies, and to adjust and optimize them according to actual circumstances. The purpose of this is to improve the accuracy and consistency of the ratings, ensuring that each property management company can adopt the optimal strategy during the rating process, thereby enhancing the scientific rigor and reliability of user ratings.

[0043] Specifically, after establishing an online connection, each property management company's server can share its rating strategy information with other property management company servers. This rating strategy information may include rating algorithms, weight allocation methods, rating standards, etc. Simultaneously, each property management company's server can also obtain rating strategy information from other property management companies, learning from their experiences and practices. Sharing rating strategy information can bring the following benefits: Improved rating accuracy: Property management companies can learn from other companies' rating strategy information and adjust and optimize it according to their own actual situation, thereby improving the accuracy and consistency of ratings. Accelerated strategy optimization: Sharing rating strategy information can accelerate the optimization and updating process of strategies. Property management companies can promptly understand the latest strategies of other property management companies, thereby improving management efficiency. Promoted cooperation and win-win: Sharing rating strategy information is conducive to promoting cooperation and win-win among property management companies, strengthening internal industry communication and cooperation, and bringing more innovation and progress to the entire industry. In conclusion, after establishing an online connection, each property management company's server sharing rating strategy information helps improve the accuracy and consistency of ratings, accelerates strategy optimization, promotes cooperation and win-win, and thus provides users with a more scientific and reliable method for rating, improving the level and efficiency of property management.

[0044] In a preferred embodiment, it further includes: The central server calculates the contribution-revenue ratio of each property management company's server, which is the ratio of the number of times the service was provided to the cumulative number of times the service was used; and The central server determines differentiated service users based on the contribution and revenue ratio of each property management company's server.

[0045] In this preferred embodiment, the central server calculates the contribution-revenue ratio of each property management company's server. The contribution-revenue ratio is the ratio of the number of times a server provides services to external users to the total number of times it receives services. This ratio measures the contribution of each property management company's server to the overall system, i.e., the ratio of the number of times it provides services to external users to the total number of times it receives services. This calculation method helps the central server better understand the contribution of each property management company's server, thereby identifying differentiated service users.

[0046] The central server determines differentiated service users based on the contribution-to-revenue ratio of each property management company's server. This means the central server can distinguish different users based on their contribution level and provide them with different services. Specifically, based on the contribution-to-revenue ratio, the central server can categorize users into different levels. Users with higher contribution-to-revenue ratios can receive more personalized and high-quality services, while users with lower contribution-to-revenue ratios can receive standardized services or differentiated management. The effects of this approach include: Personalized service: By determining differentiated service users based on contribution-to-revenue ratios, personalized services can be provided to high-contributing users, meeting their higher needs and expectations, and improving user satisfaction. Resource optimization: Differentiated services help property management companies allocate resources more rationally, investing more resources in users with higher external contributions, improving resource utilization efficiency. Incentive mechanism: Property management company servers with high contribution-to-revenue ratios can be given corresponding incentives to encourage them to continue providing high-quality services, thereby driving the development of the entire system. Improved management efficiency: Differentiated services help the central server manage the servers and users of each property management company more precisely, improving management efficiency and the scientific nature of decision-making. In summary, by calculating the contribution-to-revenue ratio of each property management company's server and determining differentiated service users accordingly, the central server can achieve personalized services, resource optimization, incentive mechanisms, and improved management efficiency, thereby enhancing the overall level of the property management system and user satisfaction.

[0047] In a preferred embodiment, the central server determines differentiated service users based on the contribution revenue ratio of each property management company's server, including: When the contribution-to-revenue ratio of the property management company's server exceeds a set value, it provides high-value services to users; or When the revenue contribution ratio of the property management company's server is lower than the set value, it will provide users with ordinary value services.

[0048] In this preferred embodiment, the central server determines differentiated service users based on the contribution-to-revenue ratio of each property management company's server. Specifically, when a property management company's server's contribution-to-revenue ratio is higher than a set value, high-value services are provided to the user; when the contribution-to-revenue ratio is lower than the set value, ordinary-value services are provided to the user. The effects of this approach include: providing high-value services: For property management company servers with high contribution-to-revenue ratios, the central server can provide higher-value services to the users it serves based on their contribution level, such as faster response times, more comprehensive service content, and more personalized customization. This can improve user satisfaction and enhance user trust and loyalty to the property management company. Optimizing resource allocation: Through differentiated services, the central server can better allocate resources, investing more resources in users with higher external contributions, improving resource utilization efficiency, while avoiding investing too many resources in users with lower external contributions, thereby improving overall efficiency. Incentivizing high-contribution property management companies: Providing high-value services to property management company servers with high contribution-to-revenue ratios can be seen as an incentive, encouraging these property management companies to continue providing high-quality services, thereby promoting the development of the entire system. Improving Management Efficiency: Differentiated services help the central server manage the servers and users of various property management companies more precisely, improving management efficiency and the scientific basis of decision-making. In general, by determining users for differentiated services based on contribution-benefit ratios, personalized services, resource optimization, incentive mechanisms, and improved management efficiency can be achieved, contributing to an overall improvement in the property management system and user satisfaction.

[0049] In a preferred embodiment, it further includes: The property management company's server creates user profiles based on shared user characteristic values.

[0050] In this preferred embodiment, the property management company's server creates user profiles based on shared user characteristic values. This means the server collects relevant user data, such as user behavior habits, preferences, spending power, and geographical location, and then classifies and analyzes these data to form user profiles. The benefits of this approach include: Personalized services: Through user profiles, the property management company can better understand user needs and preferences, thereby providing more personalized services. For example, to address the different needs of different user groups, the property management company can launch customized service plans to meet the specific needs of different users and improve user satisfaction. Targeted marketing: Through user profiles, the property management company can conduct more targeted marketing and promotional activities. Based on the information in the user profiles, it can accurately select target user groups, formulate targeted marketing strategies, improve marketing effectiveness, and reduce marketing costs. Improved service quality: Through user profiles, the property management company can better understand user behavior habits and needs, thereby optimizing service processes and service content, improving service quality, and enhancing user experience. Data-driven decision-making: User profiles provide rich user data, which the property management company can use for data analysis and mining to make more scientific and accurate decisions, improving management efficiency and the scientific nature of decision-making. In summary, by creating user profiles, property management companies can better understand their users, provide personalized services, conduct targeted marketing, improve service quality, and make data-driven decisions, thereby enhancing overall operational efficiency and user satisfaction.

[0051] In a preferred embodiment, it further includes: The central server builds a user profile based on the user characteristics broadcast.

[0052] In this preferred embodiment, the central server builds user profiles based on broadcast user characteristic values. This means the central server collects user characteristic values ​​broadcast from various property management company servers, then classifies and analyzes users based on these characteristics to form user profiles. The benefits of this approach include: Precise user group positioning: Through user profiles, the central server can more accurately locate different user groups, understand their common characteristics and needs, and thus provide more precise services to different user groups. Personalized recommendations: Based on user profile analysis, the central server can recommend service content that matches the characteristics and needs of different user groups, thereby improving user experience and satisfaction. Customized services: Through user profiles, the central server can provide customized service solutions based on the characteristics and needs of different user groups, meeting the specific needs of different user groups and improving user satisfaction. Data-driven decision-making: User profiles provide rich user data, which the central server can use for data analysis and mining to make more scientific and accurate decisions, improving management efficiency and the scientific nature of decision-making. In summary, through user profiles, the central server can better understand different user groups, provide personalized recommendations, customized services, and data-driven decision-making, thereby improving overall operational efficiency and user satisfaction.

[0053] In a preferred embodiment, the user data includes: user profile, user consumption, user's company affiliation, and user's job information.

[0054] In a preferred embodiment, when sharing user characteristic values, only the user characteristic values ​​of public users are shared. This means that during data sharing, only user characteristic values ​​that meet certain conditions are shared, while private user characteristic values ​​are not shared. The effects of this approach include: Protecting user privacy: By sharing only the user characteristic values ​​of public users, user privacy can be better protected. Private user characteristic values ​​are not shared, thus reducing user information leakage and privacy risks. Improving data security: Sharing only the user characteristic values ​​of public users can reduce the risk of data leakage and improve data security. Private user characteristic values ​​are not shared, reducing the possibility of data misuse or leakage. Reducing data transmission costs: Sharing only the user characteristic values ​​of public users can reduce unnecessary data transmission costs. Only user characteristic values ​​that meet certain conditions are shared, reducing data transmission redundancy and costs. Improving data utilization efficiency: By sharing only the user characteristic values ​​of public users, data can be shared more accurately, improving data utilization efficiency. Only the user characteristic values ​​of public users are shared, better meeting the needs of data utilization. In summary, sharing only the user characteristics of public users can protect user privacy, improve data security, reduce data transmission costs, and increase data utilization efficiency. This approach helps to balance data utilization and user privacy during data sharing, thereby improving both the efficiency and security of data sharing.

[0055] This invention further provides a property management system based on cross-data from multiple properties in an office building, comprising: a central server, used to broadcast user characteristic values ​​calculated by each property company's server to all property company servers; each property company server, used to run a local user rating algorithm based on its stored user data to calculate user characteristic values; broadcasting the calculated user characteristic values ​​to other property company servers through the central server; determining whether to connect with the property company server broadcasting the user characteristic values ​​based on the acquired user characteristic values; and sharing user characteristic values ​​through negotiation with the property company server on the other end after establishing a connection.

[0056] This invention further provides a joint user rating terminal based on cross-data from multiple properties in office buildings, comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the aforementioned joint user rating method based on cross-data from multiple properties in office buildings.

Claims

1. A joint user rating method based on cross-data analysis of multiple properties in office buildings, characterized in that, include: Each property management company's server runs a local user rating algorithm based on all the user data stored in its office buildings to calculate user characteristic values; The property management company's server will broadcast the calculated user characteristic values ​​to other property management company servers through the central server; Other property management company servers determine the real-time weight of user characteristics corresponding to each property management company server by cross-referencing the user characteristic values ​​obtained from the data. After the connection is established, each property management company's server negotiates and shares the real-time weights of user characteristics corresponding to each property management company's server. Based on the real-time weights of user characteristics calculated by each property management company's server through negotiation and sharing, each property management company rates the users of each office building, thus generating user ratings.

2. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, Also includes: After the connection is established, each property management company's server shares rating strategy information.

3. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, Also includes: The central server calculates the contribution-to-revenue ratio of each property management company's server, which is the ratio of the number of external services provided to the cumulative number of services enjoyed. as well as The central server determines differentiated service users based on the contribution and revenue ratio of each property management company's server.

4. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 3, characterized in that, The central server determines differentiated service users based on the contribution and revenue ratio of each property management company's server, including: When the contribution-to-revenue ratio of the property management company's server exceeds a set value, it provides high-value services to users; or When the revenue contribution ratio of the property management company's server is lower than the set value, it will provide users with ordinary value services.

5. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, Also includes: The property management company's server creates user profiles based on shared user characteristic values.

6. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, Also includes: The central server builds a user profile based on the user characteristics broadcast.

7. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, User data includes: user profiles, user spending, user's company affiliation, and user job information.

8. The joint user rating method based on cross-data analysis of multiple properties in office buildings according to claim 1, characterized in that, When sharing user characteristic values, only the user characteristic values ​​of public users are shared.

9. A property management system based on the cross-data integration of multiple properties in office buildings, characterized in that, include: The central server is used to broadcast the user characteristic values ​​calculated by the servers of each property management company to all property management company servers. Each property management company's server is used to run a local user rating algorithm based on its stored user data to calculate user characteristic values; the calculated user characteristic values ​​are then broadcast to other property management company servers through the central server; based on the obtained user characteristic values, it decides whether to connect with the property management company server that broadcast the user characteristic values; after establishing a connection, the user characteristic values ​​are shared through negotiation with the other property management company server.

10. A joint user rating terminal based on cross-data analysis of multiple properties in office buildings, characterized in that: include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the joint user rating method based on cross-data of multiple properties in office buildings as described in any one of claims 1 to 8.

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