Design and Implementation of Home Care Management Information System in Smart Community Based on Big Data

Ⅰ. Introduction

With the improvement of information management level of smart community home care, people pay attention to the optimization design of smart community home care information management system under the mobile Internet environment. It is necessary to build a smart community home care management information system, combine information management with adaptive scheduling methods, optimize the system design of smart community home care management, adopt big data analysis technology, establish a smart community home care management platform, and realize smart community home care management through cloud computing resource scheduling[1]. It is of great significance to study the optimal design of the management information system of home care for the aged in smart communities in promoting the informatization construction of community care for the aged [2].

The design of the intelligent community home care management information system is based on the comprehensive resource scheduling and feature fusion of the intelligent community home care management information, and a comprehensive scheduling model of the intelligent community home care management system is constructed. Combined with sample analysis and information fusion clustering technology, through adaptive resource scheduling and Internet of Things control, the design of an intelligent community home care management information system is realized[3]. In the traditional method, the smart community home-based care management mainly establishes the smart community home-based care management model under the cloud computing environment through the sample statistical analysis method, the resource scheduling method and the fuzzy PID control method, and then realizes the smart community home-based care information system through fuzzy comprehensive information decision-making. Design [4]. However, there are problems such as poor adaptability and poor output stability in the traditional smart community home care information system design method. In view of the above problems, this paper proposes a design scheme of a smart community home care management information system based on big data fusion and embedded control. First, through the methods of IoT perception information perception and topology information collection, multi-threaded observation and statistical information sampling of home care management in smart communities are realized. Then, by adopting technologies such as the Internet of Things, online perception, and data fusion clustering, information fusion and resource scheduling in the process of home-based care management in smart communities are realized. The multi-threaded observation and statistical information features of home care management information in smart communities are extracted, and big data clustering and embedded intelligent control are used to realize home care management and information scheduling in smart communities. Finally, the simulation test analysis shows that this method has superior performance in improving the management ability of home-based elderly care in smart communities [5].

Therefore, compared with previous studies, this paper has new features in the design of the smart community home care management information system, whether it is in the adaptability of the smart community home care information system design method, the output stability, or the logic of the system design. In all aspects, it is much better than the traditional smart community home care information system design method.

Ⅱ. General framework and big data analysis of smart home care management information system in community

2-1 Overall framework of home care management information system in smart communities

In order to optimize the design of the management information system for home-based aged care in smart communities, the big data analysis model of home-based aged care management in smart communities is first constructed. Based on the probabilistic model analysis method, regular parameters and probabilistic model analysis methods are adopted to control the topology of Internet of Things nodes, and the underlying database is constructed as the data processing center module of home-based aged care management in smart communities[6]. The overall structure model of home-based aged care management in smart communities is shown in Figure 1.

Fig. 1.

Overall structure model of home-based aged care management in smart communities

In the general structure model of smart community home care management shown in Figure 1, the operation and maintenance parameter fusion model of smart community home care management is constructed based on Bayesian estimation of probability model. In the multimedia scene, in the adaptive scheduling of smart community home care management information under intelligent operation and maintenance[7], the smart community home care management method based on big data fusion and embedded control, The merged set of home care management information of smart communities in each dimension is A₁⋃A₂⋃,⋯,⋃A_k = A.

According to the information sharing level of home care management information of smart communities under intelligent operation and maintenance, under the constraint of high-dimensional auxiliary information, the linear regression analysis model of home care management of smart communities is obtained:

$\[ \frac{1}{m}\sum _{i=0}^p{a}_i{m}^i\sum _{k=-q/2}^{q/2}{b}_k{c}_k^i=a_{1 } \]$

(1)

Based on the basic model evaluation index analysis, the association rules scheduling set of home care management in smart communities is obtained:

$$

(2)

Set R(t) as the real probability space (Ω, F, f(x), P), the characteristic quantity of association rules is established, and the fusion model of the information distribution structure of home care for the elderly in smart communities is constructed according to the experience and the convergence of the model. On the specific division of the rule space, the test statistical analysis model of each data point in the home care management information data set X of smart communities is obtained as follows:

$\[ \left\{\begin{array}{c}f:R^n\times R^n\times S\hfill \\ g:R^n\times R^n\times S\to R^{n\times d}\hfill \\ h:R^n\times R^n\times S\hfill \end{array}\right. \]$

(3)

Select k data points from the smart community home care management data set X as the relevant constraint parameter set, and the random distribution characteristic quantity of the smart community home care management data set satisfies:

$\[ \begin{array}{c}h:R^n\times R^n\times S\hfill \\ \delta \left(t\right):\left[0,T\right]\to R\hfill \\ v\left(dt,du\right)=v\left(dt,du\right)-\pi \left(du\right)dt\hfill \end{array} \]$

(4)

In order to avoid falling into the local optimal solution, the fuzzy comprehensive scheduling method is adopted to build a big data analysis model of home care management in smart communities, so as to improve the ability of data reliability fusion analysis[8].

2-2 Big Data analysis of smart home care management in community

Using sensing information perception and Internet of Things topology information collection methods to realize multi-thread observation and statistical information sampling of smart community home care management, combining with big data fusion scheduling model to realize information management and multi-thread observation and statistical information fusion of smart community home care management, and adopting ZigBee networking protocol[9], a data set average statistical analysis model of smart community home care management information management is constructed, which is expressed as:

(5)

(6)

Calculate the European clustering of data points P and X, and adopt the fuzzy centroid clustering analysis method to obtain the linear combination feature distribution model of home care management in smart communities, which is expressed as:

$\[ \left\{\begin{array}{c} X\left(t\right)=\xi \left(t\right)\hfill \\ X\left(k+1\right)=X\left(K\mathrm{\Delta }\right)+f\left(X\left(K\mathrm{\Delta }\right)\right)\hfill \\ +g\left(X\left(K\mathrm{\Delta }\right)+f\left(X\left(K\mathrm{\Delta }\right)\right)\right)\hfill \\ +\iint \left(h\left(x_1,x_2,i\right)-g\left(y_1,y_2,i\right)\right)xdu\hfill \end{array}\right. \]$

(7)

Wherein, ΔB = B(t_n-1)+B(t₁); I[u] the initial centroid of K-means is determined according to the data dimensions assigned to each processor of the data points, and the multi-thread observation and statistical information clustering output of the management information of home care for the aged in smart communities are obtained as follows:

$\[ \begin{array}{c}Z\left(t\right)=\sum I_{xy}\left(t\right)X\left(k\mathrm{\Delta }\right)\hfill \\ =\sum I_{xy}\left(t\right)X\left(\left(k\mathrm{\Delta }\right)\left[\sigma \left(k\right)\right]\right)\hfill \end{array} \]$

(8)

$\[ R\left(t\right)=\sum \sum I_{xy}\left(t\right)X\left(k\Delta \right)r_t \]$

(9)

Combining feature clustering and fusion technology, the processed home care information is scheduled by association rules, and the big data processing structure model of home care management in smart communities is obtained as shown in Figure 2.

Fig. 2.

Big data processing structure model of home care management in smart community

Ⅲ. Optimal design of smart home care management in community

Ⅳ. System software development and design

In the LOG-CONTROL-BLOCK model, a systematic data loading and recording model is established. Through visual guidance technology and image analysis and processing technology, the standardized IEC 61850 standard control protocol is adopted to realize the management control and serial data processing of smart community home care, and the intelligent visual guidance model is constructed. In the ACSI independent terminal of smart community home care management, combined with the single chip microcomputer design, the intelligent community home care management is realized, and the IEC 61850-9-1/2 protocol is adopted to realize the bottom communication of the network. Based on the above analysis, the network structure and functional module structure of the system are shown in Figures 3.

Fig. 3.

data processing flow distribution structure of the system

In the integration layer, data feature analysis and network control are realized through AD module and I/O data output module, physical data integration is realized in the data layer, and information detection and scheduling of home care management in smart communities are realized by cloud integration. In summary, the software and hardware integration diagram of the system is shown in Figure 4.

Fig. 4.

System integration design

V. Simulation analysis

In order to verify the application performance of this method in the smart community home care management, an experimental test analysis was carried out. Based on Matlab/Simulink simulation software, a data processing platform of smart community home care management was built. The number of samples of smart community home care management information was 2000, the correlation distribution set was 120, the iteration step of data fusion was 100, and the statistical period of smart community home care management information was 1.38. According to the above parameters, the smart community home care management was carried out, and the smart community home care management was obtained.

The analysis of Figure 5 shows that this method can effectively realize the home care management for the aged in smart communities, thereby realizing multi-thread observation and statistical information integration of the management information of home care for the aged in smart communities, and testing the real-time performance of end-to-end monitoring data transmission of the system. Test the output of the information system, as shown in Figure 6.

Fig. 5.

Output spectrum distribution of home care management in smart communities

Fig. 6.

Information Management Fusion Results

According to the analysis of Figure 6, the information fusion performance of this method for home care management in smart communities is better. Taking the time delay as the test identification, the reliability of the system is tested. The shorter the time delay, the better the data transmission effect. Table 1 shows the end-to-end data communication delay comparison of the three methods in real-time communication.

Table 1.

End-to-end delay comparison

From the analysis of Table 1, it is known that the data fusion level of the smart community home care management and multi-thread observation and statistical information integration by this method is good, and the time delay is small. The recall rate of the smart community home care management is tested, and the comparison results are shown in Figure 7.

Fig. 7.

Comparison of recall performance of smart community home care management

According to the analysis of Figure 7, the output reliability of smart community home care management by this method is good, the data fusion clustering ability is strong, and the recall rate is high.

Ⅵ. Conclusion

In this paper, using big data analysis technology, a smart community home care management platform is established, and the smart community home care management is realized through cloud computing resource scheduling. This paper puts forward a design scheme of home-based aged care management information system in smart community based on big data fusion and embedded control. The underlying database is constructed as the data processing center module of home care for the elderly in smart communities, and the information management and multi-thread observation and statistical information fusion of home care for the elderly in smart communities are realized by combining the big data fusion scheduling model. The technologies of Internet of Things, online perception and data fusion clustering are adopted to construct the spatial clustering model, and the comprehensive management scheduling of home care management information in smart communities is carried out. The research shows that this method has good output reliability, strong data fusion and clustering ability, high recall rate, and good application value.

Acknowledgments

This work was supported by WST2021020.

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