Community integrated service system

Abstract

The invention discloses a community integrated service system, and the system comprises a community demand dynamic prediction module which is used for predicting various demands of users in a community according to impact factors; the service matching module is used for recommending corresponding services to the users in the community according to the influence factors; the community vitality measuring module is used for measuring the overall health condition of the community according to the influence factors; and the auxiliary decision-making module is used for providing corresponding decision-making services according to the influence factors.

Description

Technical Field

[0001] The present invention relates to the technical field of smart communities, and in particular to a community comprehensive service system. Background Art

[0002] The community is the smallest cell and unit of the city and a microcosm of the city. When staff need to serve the needs of community members, they usually need to conduct a blanket survey and interview, and then compile statistics and summarize the results of the interview. This method is inefficient.

[0003] Based on the above problems, smart community functions have begun to be adopted in communities, integrating services, governance, security, etc. with the help of technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence. However, the current smart community functions are relatively basic, and most of them are limited to data collection and have relatively single functions. Summary of the invention

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention discloses a community comprehensive service system, including: The community demand dynamic prediction module is used to predict various demands of users in the community based on influencing factors; The service matching module is used to recommend corresponding services to users in the community based on the impact factors; Community Vitality Measurement Module, which measures the overall health of a community based on impact factors; The auxiliary decision-making module is used to provide corresponding decision-making services based on various influencing factors.

[0005] Furthermore, the prediction of various needs of users in the community according to the influencing factors includes: NP(s,t)=∑ i=1 n {Wi×Fi(s,t)}×e^(λ×T(s)); Where: NP(s,t) is the predicted demand for service s at time t, Fi(s,t) is the contribution of factor i to service s at time t, Wi is the weight of each factor, n is the total number of factors considered, λ is the time decay coefficient, and T(s) is the time since the last demand peak.

[0006] Furthermore, the recommending corresponding services to each user in the community according to the impact factor includes: M(u,s)=[∏ i=1 k Ci(u,s)]^(1 / k)×(1+tanh(∑ j=1 m Ej(u,s))) Where: M(u,s) is the matching degree between user u and service s, Ci(u,s) is the score of the i-th core matching dimension, k is the number of core matching dimensions, Ej(u,s) is the contribution of the j-th enhancement factor, m is the number of enhancement factors, and tanh is the hyperbolic tangent function.

[0007] Furthermore, the overall health status of the community measured according to the impact factors includes: CRI=log(1+∑ i=1 p (Vi×Ri))×(1-e^(-∑ j=1 q Aj)) Where: CRI is the Community Resilience Index, Vi is the score of the ith vulnerability factor, Ri is the corresponding resilience score, p is the number of vulnerability-resilience pairs considered, Aj is the impact of the jth adaptation measure, and q is the number of adaptation measures.

[0008] Furthermore, providing corresponding decision-making services according to various influencing factors includes: OD=argmax{∑ i=1 r [Si(d)×log(1+Ii(d))]-β×H(d)} Where: OD is the optimal decision, Si(d) is the score of decision d on the i-th objective, Ii(d) is the importance of the i-th category of information about decision d, r is the number of objectives considered, β is the balance parameter, and H(d) is the entropy of decision d, indicating uncertainty.

[0009] Furthermore, the influencing factors in Fi(s, t) include: demographic changes, seasonal factors, economic indicators, social events, health conditions, technology adoption rates and environmental factors.

[0010] Furthermore, the enhancement factors in Ej(u,s) include the strength of social network recommendation, the impact of current promotional activities, the user's credit score, the reputation of the service provider, and the degree of urgency.

[0011] Furthermore, the vulnerability factors include natural disaster risks, aging of infrastructure, potential risks of community conflicts, economic dependence, and imbalance in population structure.

[0012] Furthermore, the importance of the information about the decision d includes: data reliability, prediction accuracy, and multi-dimensional analysis depth.

[0013] By adopting the above technical solutions, this community comprehensive service system can significantly improve the quality of community services, enhance the overall resilience of the community, and provide scientific and comprehensive decision-making support for community managers. The system adopts advanced modular design, has excellent scalability and adaptability, and can be flexibly customized and continuously optimized according to the actual needs of different communities. In addition, the system also integrates artificial intelligence and big data analysis technology, which can continuously learn and evolve to adapt to the dynamic needs of community development, and provide strong technical support for building a smart, harmonious and sustainable modern community. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0015] Figure 1 This is the system block diagram of this system. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] Reference Figure 1 The embodiment of the present invention provides a community comprehensive service system, which is characterized by comprising: The community demand dynamic prediction module is used to predict the various demands of users in the community based on influencing factors.

[0018] The prediction of various needs of users in the community based on the influencing factors includes: NP(s,t)=∑ i=1 n {Wi×Fi(s,t)}×e^(λ×T(s)) NP(s,t) represents the predicted demand for service s at time t, Fi(s,t) represents the specific contribution of influencing factor i to service s at time t, Wi is the dynamic weight of each influencing factor, n is the total number of influencing factors considered by the system, λ is the time decay coefficient, and T(s) is the time interval since the last demand peak.

[0019] In actual applications, the system will comprehensively consider the following key influencing factors: demographic changes: including population age structure, family composition, education level, etc., seasonal factors: considering cyclical influences such as weather changes, holidays, and semester arrangements, economic indicators: covering macroeconomic factors such as residents' income level, consumption capacity, and employment rate, social events: including large-scale events, policy changes, emergencies, etc., health status: considering epidemic conditions, chronic disease incidence, distribution of medical resources, etc., technology adoption rate: evaluating the popularity of smart devices, Internet usage rate, acceptance of new technologies, etc., environmental factors: including air quality, greening rate, noise level and other ecological environmental indicators.

[0020] The system uses advanced machine learning algorithms, such as deep neural networks and gradient boosting trees, to dynamically adjust the weights of each factor and continuously optimize the prediction model to ensure the high accuracy and real-time nature of the prediction results.

[0021] The service matching module is used to recommend corresponding services to each user in the community based on the influencing factors.

[0022] The recommendation of corresponding services to each user in the community according to the impact factor includes: M(u,s)=[∏ i=1 k Ci(u,s)]^(1 / k)×(1+tanh(∑ j=1 m Ej(u,s))) Where: M(u,s) is the matching degree between user u and service s, Ci(u,s) is the score of the i-th core matching dimension, k is the number of core matching dimensions, Ej(u,s) is the contribution of the j-th enhancement factor, m is the number of enhancement factors, and tanh is the hyperbolic tangent function.

[0023] The core matching dimensions include but are not limited to: the degree of fit between user needs and service types, the degree of fit between user geographic location and service coverage, the degree of fit between user budget and service price, and the coordination between user schedule and service availability.

[0024] Enhancement factors include: Social network recommendation strength: based on service evaluation and usage in the user's social circle, current promotional activity impact: considering the promotion strength of specific services and the user's sensitivity to promotions, user credit score: reflecting the user's credit status and service usage habits, service provider reputation: based on historical evaluation and service quality statistics, urgency: considering the urgency of the user's current needs, the system continuously collects user feedback and behavior data, combines deep learning technology, and continuously optimizes the matching algorithm to improve the accuracy and personalization of recommendations, thereby significantly improving user satisfaction.

[0025] Community Vitality Measurement Module, which measures the overall health of a community based on impact factors; The factors used to measure the overall health of a community include: CRI=log(1+∑ i=1 p (Vi×Ri))×(1-e^(-∑ j=1 q Aj)) Where: CRI is the Community Resilience Index, Vi is the score of the ith vulnerability factor, Ri is the corresponding resilience score, p is the number of vulnerability-resilience pairs considered, Aj is the impact of the jth adaptation measure, and q is the number of adaptation measures.

[0026] The system focuses on the following vulnerability factors: natural disaster risks: assessing the potential threats of natural disasters such as earthquakes, floods, and typhoons; the degree of infrastructure aging: including the conditions of public facilities such as buildings, roads, and pipelines; potential risks of community conflicts: assessing social factors such as neighborhood relations and cultural conflicts; economic dependence: analyzing the degree of diversification and risk resistance of the community's economic structure; and the degree of imbalance in the population structure: considering population characteristics such as age structure, gender ratio, and occupational distribution.

[0027] The system will regularly update the CRI to provide community managers with intuitive and quantitative decision-making basis, and automatically generate improvement suggestions and early warning information based on the index change trend.

[0028] The auxiliary decision-making module is used to provide corresponding decision-making services based on various influencing factors.

[0029] Providing corresponding decision-making services according to various influencing factors includes: OD=argmax{∑ i=1 r [Si(d)×log(1+Ii(d))]-β×H(d)} Where: OD is the optimal decision, Si(d) is the score of decision d on the i-th objective, Ii(d) is the importance of the i-th category of information about decision d, r is the number of objectives considered, β is the balance parameter, and H(d) is the entropy of decision d, indicating uncertainty.

[0030] The dimensions for evaluating the importance of decision-making information include: data reliability: evaluating the quality and credibility of the data source, prediction accuracy: evaluating the accuracy based on historical prediction results, and multi-dimensional analysis depth: considering the comprehensiveness and depth of the analysis.

[0031] The system will comprehensively consider multiple key goals during the decision-making process, such as: improving overall residents' satisfaction, optimizing the efficiency of community resource allocation, enhancing community cohesion and cultural identity, improving community safety and emergency response capabilities, and promoting sustainable development of the community economy.

[0032] Through the collaborative work of these four highly integrated modules, this community comprehensive service system can significantly improve the quality of community services, enhance the overall resilience of the community, and provide scientific and comprehensive decision-making support for community managers. The system adopts advanced modular design, has excellent scalability and adaptability, and can be flexibly customized and continuously optimized according to the actual needs of different communities. In addition, the system also integrates artificial intelligence and big data analysis technologies, which can continuously learn and evolve to adapt to the dynamic needs of community development, and provide strong technical support for building a smart, harmonious, and sustainable modern community.

[0033] For the convenience of description, the above devices are described in terms of functions and are divided into various units and modules. Of course, when implementing the present application, the functions of each unit and module can be implemented in the same or multiple software and / or hardware.

[0034] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0035] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0036] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A community comprehensive service system, characterized in that: include: The community demand dynamic prediction module is used to predict various demands of users in the community based on influencing factors; The service matching module is used to recommend corresponding services to users in the community based on the impact factors; Community Vitality Measurement Module, which measures the overall health of a community based on impact factors; The auxiliary decision-making module is used to provide corresponding decision-making services based on various influencing factors.

2. The system according to claim 1, characterized in that The prediction of various needs of users in the community based on the impact factors includes: NP(s,t)=∑ i=1 n {Wi×Fi(s,t)}×e^(λ×T(s)); Where: NP(s,t) is the predicted demand for service s at time t, Fi(s,t) is the contribution of factor i to service s at time t, Wi is the weight of each factor, n is the total number of factors considered, λ is the time decay coefficient, and T(s) is the time since the last demand peak.

3. The system according to claim 1, characterized in that The recommendation of corresponding services to each user in the community according to the impact factor includes: M(u,s)=[∏ i=1 k Ci(u,s)]^(1 / k)×(1+tanh(∑ j=1 m Ej(u,s))); Where: M(u,s) is the matching degree between user u and service s, Ci(u,s) is the score of the i-th core matching dimension, k is the number of core matching dimensions, Ej(u,s) is the contribution of the j-th enhancement factor, m is the number of enhancement factors, and tanh is the hyperbolic tangent function.

4. The system according to claim 1, characterized in that The factors used to measure the overall health of a community include: CRI=log(1+∑ i=1 p (Vi×Ri))×(1-e^(-∑ j=1 q Aj)); Where: CRI is the Community Resilience Index, Vi is the score of the ith vulnerability factor, Ri is the corresponding resilience score, p is the number of vulnerability-resilience pairs considered, Aj is the impact of the jth adaptation measure, and q is the number of adaptation measures.

5. The system according to claim 1, characterized in that Providing corresponding decision-making services according to various influencing factors includes: OD=argmax{∑[ i=1 r ][Si(d)×log(1+Ii(d))]-β×H(d)}; Where: OD is the optimal decision, Si(d) is the score of decision d on the i-th objective, Ii(d) is the importance of the i-th category of information about decision d, r is the number of objectives considered, β is the balance parameter, and H(d) is the entropy of decision d, indicating uncertainty.

6. The system according to claim 2, characterized in that The influencing factors in Fi(s, t) include: demographic changes, seasonal factors, economic indicators, social events, health conditions, technology adoption rates and environmental factors.

7. The system according to claim 3, characterized in that The enhancement factors in Ej(u,s) include the strength of social network recommendation, the impact of current promotional activities, user credit score, service provider reputation, and urgency.

8. The system according to claim 4, characterized in that The vulnerability factors include natural disaster risks, aging of infrastructure, potential risks of community conflicts, economic dependence, and the degree of imbalance in the population structure.

9. The system according to claim 5, characterized in that The importance of information about decision d includes: data reliability, prediction accuracy, and multi-dimensional analysis depth.

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