Smart community property management system

Abstract

The invention relates to the technical field of property management, in particular to an intelligent community property management system, which comprises a dynamic intelligent distribution module for analyzing transaction information texts submitted by residents, extracting key information and performing task distribution in combination with real-time monitoring data and working states of grid members; when the system is used, based on text analysis, real-time monitoring and grid member state comprehensive evaluation, rapid response and accurate distribution of community affairs can be conveniently realized, the processing time of affairs such as garbage cleaning and resident contradiction mediation is greatly shortened, the community environment is effectively improved, and the service life of the community is prolonged. And through a fuzzy comprehensive evaluation algorithm, a multi-dimensional point accounting system is constructed, automatic dynamic settlement of task points of the grid members is realized, the enthusiasm of the grid members and volunteers is fully aroused, the grid members and volunteers are promoted to participate in community services more actively and efficiently, and the community service quality and the resident satisfaction degree are further improved.

Description

Technical Field

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

[0002] Smart community property management is a modern management model that uses new-generation information technologies such as the Internet of Things, cloud computing, big data, artificial intelligence, and mobile Internet to digitally and intelligently manage people, vehicles, objects, events, and finances in the community. It aims to improve the efficiency and quality of property services, optimize residents' living experience, reduce operating costs, enhance community safety, and promote the development of community value-added services.

[0003] The patent publication number is CN111815288A, which states in its specification that “the present invention discloses a smart community property management system, including a property management platform, a personnel management subsystem, a business management subsystem, an equipment and facility management subsystem, a supervision management subsystem and a social retail management subsystem, all of which are connected to the property management platform, as well as terminals with corresponding access and processing permissions configured for personnel of different identities and positions. The present invention effectively improves the standardization and process of the property management process through the specific configuration design of the management platform and each subsystem, facilitates the communication between property management personnel and residents, facilitates the handling of various property management affairs and the purchase of required goods by residents, enhances the living experience of residents, and enables residents to enjoy the benefits of property fee reduction through the commercial introduction of social retail. The above-mentioned technology has achieved the transformation of property management from passive response to proactive service + business ecology through the "three controls, one implementation, and one revenue increase" structure, thus achieving the effect of improving community harmony. However, in traditional community affairs processing, when residents submit information about garbage collection, resident conflicts, feedback, etc., manual screening and task assignment are required. At the same time, when grid workers and volunteers handle community affairs, the traditional property management system's point incentive system allocates fixed points corresponding to tasks, resulting in low enthusiasm for grid workers and volunteers to participate in community services. In addition, the traditional property management system has problems such as opaque financial information, low resident trust, and a lack of financial budget formulation and trend forecasting.

[0004] To sum up, the development of a smart community property management system is still a key issue that needs to be urgently addressed in the field of property management technology. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art of traditional community affairs processing, in which when residents submit information about garbage cleaning, resident conflicts, feedback, etc., manual screening and assignment of tasks are required. At the same time, when handling community affairs, the traditional property management system's point incentive system is a distribution model in which tasks correspond to fixed points, resulting in low efficiency in task assignment and insufficient service enthusiasm. In addition, the traditional property management system has problems such as opaque financial information, low resident trust, and lack of financial budget formulation and trend forecasting.

[0006] To achieve the above objectives, the present invention provides a smart community property management system, comprising: The dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and allocates tasks based on real-time monitoring data and the work status of grid workers; The dynamic integral calculation module uses the fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance and dynamically calculate the integral; The financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points; The intelligent data collection module is used to collect historical financial data, management data and external economic data of the community and integrate them into reference data; The financial forecast model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and monitors and analyzes them in real time.

[0007] Furthermore, the dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines it with real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: Residents submit transaction information through the community management app or mini-program. The transaction information is transmitted to the task database in text form. The text is processed, including but not limited to word segmentation, part-of-speech tagging, and semantic understanding, to extract key information. The expression is:

[0008] Where, Represents the transaction information text submitted by a given resident Extract key information under the conditions The conditional probability of Represents the key information sequence extracted from the transaction information text submitted by residents, Indicates the transaction information text submitted by residents. Represents key information sequence The number of key information elements contained in Is the index variable used to traverse the key information sequence For each element in Indicates key information sequence The Key information elements, Is the key information sequence Middle Key information elements, Indicates Extract relevant text from key information Part of the content, The conditional probability represents the key information extracted before the known and related content in the text Under the conditions of Key information The key information includes but is not limited to event type, urgency keywords, and geographic location description. At the same time, through the Internet of Things connection with the monitoring equipment, real-time monitoring data of the garbage dumping site is obtained in real time. The working status of the grid worker includes the grid worker's workload data and the grid worker's real-time location data.

[0009] Furthermore, the dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines it with real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: The real-time location data of the grid workers is obtained using the positioning function of the community management app on the grid workers' mobile phones. The workload data of the grid workers is obtained by recording the grid workers' completed tasks and the statistics of the assigned tasks. Based on the real-time monitoring data and the grid workers' working status, the suitability of each grid worker for handling the current task is calculated. The expression is:

[0010] Where, Representative The suitability value of each grid worker to handle the current task, The weight coefficients correspond to the weights of the factors affecting distance, workload, task urgency, and task type matching in the formula. Indicates the The distance between each grid worker and the location where the current task occurs, yes , Representative The workload of each grid worker is Indicates the urgency of the current task. The matching coefficient of each grid worker is Representative The matching degree between each grid worker and the current task type is used to assign the task to the grid worker with the highest suitability. After receiving the task, the grid worker will go to process it and feedback the processing results through the mobile community management APP after completing the task.

[0011] Furthermore, the dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance. The operation process of dynamic settlement points includes: After the grid worker completes the task and submits the processing result, the timestamp is recorded and the points calculation process is automatically triggered. The fuzzy comprehensive evaluation algorithm obtains detailed information about the task from the task database, including the event type and processing time. At the same time, a satisfaction evaluation link is pushed to the resident who submitted the task. The resident can use the community management app or any of the mini-programs to score on a 5-level scale to construct a multi-dimensional evaluation index. The expression is:

[0012] Where, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is a parameter used to control the steepness of the membership function curve. Corresponding to the input parameters and The weight coefficient of is the threshold parameter used to determine the center position of the membership function, is a natural constant that serves as the base of the exponential function, represents the processing efficiency value, Therefore The exponential function with base , is the actual processing time of the current task, is the average processing time of similar tasks in history, is the standard deviation of the processing time of similar tasks in history, It is the normalized difference between the current task processing time and the historical average processing time. represents the residents’ satisfaction score, It is The grade rating value, Resident rating For the first The membership of the grade rating, Represents the actual ratings given by residents.

[0013] Furthermore, the dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance. The operation process of dynamic settlement points includes: Dynamic settlement points are calculated based on the weight of event complexity (45%), processing time (25%), and resident satisfaction (30%). The expression is:

[0014] Where, Represents the final points earned by the grid worker after completing this task. is the weight of the event complexity factor in the integral calculation, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is the fuzzy composition operator, is the weight of the processing time factor in the integral calculation, represents the processing efficiency value, is the weight of the resident satisfaction factor in the points calculation, It represents the residents' satisfaction score, calculates the points obtained by the grid worker for this task, and records the points into the grid worker's personal points account. In addition, a points mall is connected to the points account, and grid workers and volunteers can use their personal points to redeem corresponding products in the points mall.

[0015] Furthermore, the financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: The financial data includes income and expenditure information. When each piece of financial data is entered, it is automatically recorded in the distributed ledger of the blockchain, generating a unique hash value. The data aggregation algorithm is automatically executed every morning to publicize the financial information of the community account. The expression is:

[0016] Where, Represents the financial balance of the community account, Representative Item income, Representative Item expenditure.

[0017] Furthermore, the financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: Residents can use the community management app or mini-program to view the financial information of the community account, including the total balance, income and expenditure. The financial information includes but is not limited to the purpose of purchasing items, the details of maintenance projects, invoice photos, and contract documents. Residents can verify the authenticity of the financial information by verifying the hash chain. The expression is:

[0018] Where, Indicates the verification result of the authenticity of financial information, It is an indicative function. When the condition in the brackets is met, the function value is 1. When the condition is not met, the function value is 0. Indicates the Financial documents The hash value of represents a hash function, Indicates the Financial documents, It is Financial documents, It is The timestamp corresponding to each financial voucher is displayed. At the same time, the dynamic settlement points are made public. Residents can view the work content of grid workers and volunteers and the corresponding personal points obtained through any of the community management apps or mini-programs.

[0019] Furthermore, the intelligent data collection module is used to collect historical financial data, management data, and external economic data of the community and integrate them into reference data. The operation process includes: A time series alignment interpolation function is set to unify the different timestamps and frequencies of the historical financial data, management data, and external economic data into a standard time scale. Z-score standardization is used to make the dimensions of the historical financial data, management data, and external economic data consistent. Through the multi-source data fusion mechanism, the data are fused into reference data. The expression is:

[0020] Where, represents the reference data obtained after fusion, It is historical financial data The weight coefficient in the multi-source data fusion process, Represents historical financial data, It is management data The corresponding weight coefficient is, Represents management data, It is external economic data The weight coefficient of Representing external economic data, a Bayesian dynamic update mechanism is set to update the fusion parameters of the multi-source data fusion mechanism every time new data comes in.

[0021] Furthermore, the financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operation process of real-time monitoring and analysis includes: The multivariate linear regression model uses the reference data as the independent variable and the various income and expenditures in the next year as the dependent variable to perform training and prediction calculations, establish a multivariate regression equation, and use the least squares method to solve it to obtain the predicted values ​​of various income and expenditures in the next year. The expression is:

[0022] Where, represents the independent variable matrix, express is a real number field OK A matrix of columns, is the sample size, is the number of independent variables, represents the dependent variable vector, yes The Each element corresponds to a specific income and expenditure. Indicates the The intercept term corresponding to the income and expenditure items represents the All are 0 hours The basic value of Indicates the Item income and expenditure corresponds to The regression coefficients of the independent variables, It is represented by the matrix of independent variables. Extract the independent variables, Indicates the The error term corresponding to the income and expenditure items, represents the estimated value vector of the regression coefficients, Represents the independent variable matrix The transposed matrix of Indicates calculation first and Then find the inverse matrix of the product matrix, represents the product matrix of the transposed independent variable matrix and the dependent variable vector, represents the predicted value vector of various revenue and expenditure in the next year, Represents the latest independent variable data matrix.

[0023] Furthermore, the financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operation process of real-time monitoring and analysis includes: Using the time series analysis model, we conduct time series analysis on the financial data of the previous day in the early morning of each day, and fit a separate time series for each income and expenditure. The expression is:

[0024] Where, Indicates the Item income and expenditure in time The observed value of It is used to distinguish different income and expenditure items. The autoregressive coefficient reflects the Item income and expenditure in time The data of the current time The impact of the data, Indicates the Item income and expenditure in time The observed value at time The moving average coefficient reflects the past The error term of the period For the current time No. Observed values ​​of income and expenditure The degree of impact, It's time The error term when It's time It can detect the error term when calculating the financial indicators, monitor the changing trends of various financial indicators, issue early warning information when abnormal fluctuations are found, including but not limited to a sharp increase in one of the expenditures, and make short-term forecasts of the financial trends for the next month.

[0025] Beneficial effects: Compared with the known public technologies, the technical solution provided by the present invention has the following beneficial effects: When in use, the present invention facilitates rapid response and precise allocation of community affairs based on text analysis, real-time monitoring and comprehensive evaluation of grid worker status, greatly shortens the processing time of garbage cleaning, mediation of resident conflicts and other matters, effectively improves the community environment and enhances the quality of life of residents. Through the fuzzy comprehensive evaluation algorithm, a multi-dimensional point accounting system is constructed to realize the automatic dynamic settlement of grid worker task points, fully mobilize the enthusiasm of grid workers and volunteers, prompting them to participate in community services more actively and efficiently, and further improve the quality of community services and residents' satisfaction.

[0026] When used, the present invention constructs a distributed ledger through blockchain technology to achieve credible recording and public disclosure of financial data and points information, making the financial data authentic, reliable, open and transparent, which is convenient for enhancing the owners' trust in the property's financial work and is conducive to reducing financial disputes. By integrating the multivariate linear regression model and the time series analysis model, the financial revenue and expenditure of the community are predicted and monitored in multiple dimensions in real time, which is convenient for optimizing budget arrangements, improving the efficiency of fund use, reducing financial risks, and promoting the sustainable development of the community. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a system diagram of a smart community property management system of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] The present invention is described in further detail below with reference to the accompanying drawings: Example: like Figure 1 As shown, the present invention provides a smart community property management system, including: The dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and allocates tasks based on real-time monitoring data and the work status of grid workers; Furthermore, the dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines it with real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: Residents submit transaction information through the community management app or mini-program. The transaction information is transmitted to the task database in text form. The text is processed, including but not limited to word segmentation, part-of-speech tagging, and semantic understanding, to extract key information. The expression is:

[0031] Where, Represents the transaction information text submitted by a given resident Extract key information under the conditions The conditional probability of Represents the key information sequence extracted from the transaction information text submitted by residents, Indicates the transaction information text submitted by residents. Represents key information sequence The number of key information elements contained in Is the index variable used to traverse the key information sequence For each element in Indicates key information sequence The Key information elements, Is the key information sequence Middle Key information elements, Indicates Extract relevant text from key information Part of the content, The conditional probability represents the key information extracted before the known and related content in the text Under the conditions of Key information The key information includes but is not limited to event type, urgency keywords, and geographic location description. At the same time, through the Internet of Things connection with the monitoring equipment, real-time monitoring data of the garbage dumping site is obtained in real time. The working status of the grid worker includes the grid worker's workload data and the grid worker's real-time location data.

[0032] Furthermore, the dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines it with real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: The real-time location data of the grid workers is obtained using the positioning function of the community management app on the grid workers' mobile phones. The workload data of the grid workers is obtained by recording the grid workers' completed tasks and the statistics of the assigned tasks. Based on the real-time monitoring data and the grid workers' working status, the suitability of each grid worker for handling the current task is calculated. The expression is:

[0033] Where, Representative The suitability value of each grid worker to handle the current task, The weight coefficients correspond to the weights of the factors affecting distance, workload, task urgency, and task type matching in the formula. Indicates the The distance between each grid worker and the location where the current task occurs, yes , Representative The workload of each grid worker is Indicates the urgency of the current task. The matching coefficient of each grid worker is Representative The matching degree between each grid worker and the current task type is used to assign the task to the grid worker with the highest suitability. After receiving the task, the grid worker will go to process it and feedback the processing results through the mobile community management APP after completing the task.

[0034] Specifically, in the practice of a certain smart community, when a resident submits a text request through the community management APP that "the elevator in a certain unit of a certain building is out of order and urgently needs repair", the present invention first extracts key information: the event type is "equipment repair", the urgency is "urgent", and the geographical location is "Unit 2, Building X". At the same time, the present invention calls the monitoring equipment to confirm the situation of people being trapped in the elevator (real-time monitoring data). Combined with the status of three grid workers: A is 200 meters away from the site but has a load rate of 80%, B is 500 meters away and has a load rate of 30%, and C is 1 kilometer away but is good at equipment repair (task type matching degree is 90%), the suitability formula calculates that C has the highest suitability (0.68). Based on text analysis, real-time monitoring and comprehensive evaluation of grid worker status, accurate task matching is achieved, response efficiency is improved, the load and location of grid workers are grasped in real time, uneven task distribution or waste of resources is avoided, manpower utilization is improved, and the text of transaction information submitted by residents is responded to immediately, avoiding manual transfer and delays, and enhancing residents' satisfaction and participation.

[0035] The dynamic integral calculation module uses the fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance and dynamically calculate the integral; Furthermore, the dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance. The operation process of dynamic settlement points includes: After the grid worker completes the task and submits the processing result, the timestamp is recorded and the points calculation process is automatically triggered. The fuzzy comprehensive evaluation algorithm obtains detailed information about the task from the task database, including the event type and processing time. At the same time, a satisfaction evaluation link is pushed to the resident who submitted the task. The resident can use the community management app or any of the mini-programs to score on a 5-level scale to construct a multi-dimensional evaluation index. The expression is:

[0036] Where, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is a parameter used to control the steepness of the membership function curve. Corresponding to the input parameters and The weight coefficient of is the threshold parameter used to determine the center position of the membership function, is a natural constant that serves as the base of the exponential function, represents the processing efficiency value, Therefore The exponential function with base , is the actual processing time of the current task, is the average processing time of similar tasks in history, is the standard deviation of the processing time of similar tasks in history, It is the normalized difference between the current task processing time and the historical average processing time. represents the residents’ satisfaction score, It is The grade rating value, Resident rating For the first The membership of the grade rating, Represents the actual ratings given by residents.

[0037] Furthermore, the dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance. The operation process of dynamic settlement points includes: Dynamic settlement points are calculated based on the weight of event complexity (45%), processing time (25%), and resident satisfaction (30%). The expression is:

[0038] Where, Represents the final points earned by the grid worker after completing this task. is the weight of the event complexity factor in the integral calculation, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is the fuzzy composition operator, is the weight of the processing time factor in the integral calculation, represents the processing efficiency value, is the weight of the resident satisfaction factor in the points calculation, It represents the residents' satisfaction score, calculates the points obtained by the grid worker for this task, and records the points into the grid worker's personal points account. In addition, a points mall is connected to the points account, and grid workers and volunteers can use their personal points to redeem corresponding products in the points mall.

[0039] Specifically, through the fuzzy comprehensive evaluation algorithm, a multi-dimensional point accounting system is constructed to realize the automatic dynamic settlement of grid worker task points. In the practice of a certain smart community, when the grid worker handles the "elevator failure" task, the system automatically collects data on the 12 households involved in the event, such as the need to coordinate with the property engineering department and the elevator maintenance company, and substitutes the data into the fuzzy membership function to calculate the complexity of the event. According to the historical average time of similar tasks of 4 hours and the standard deviation of 1 hour, and the actual processing time of 3 hours, the processing efficiency value is , residents give a 4-point rating through the APP. After fuzzy conversion, the membership of the 5-level rating is 0.6 and the 4-level rating is 0.4. The calculated satisfaction score is 4×0.4+5×0.6=4.6. Finally, the points are calculated with weights of 45%, 25%, and 30%: 0.45×0.72⊕0.25×0.368⊕0.30×4.6≈1.93. The points are credited to the grid worker's account in real time and can be exchanged for laundry detergent and other items in the mall. The fuzzy algorithm increases the accuracy of complex event recognition from 72% to 91%, and the correlation between points and task difficulty increases by 0.23. After the processing time is included in the score, the response speed of emergency tasks is increased by 40%, and the average time is shortened to 2.8 hours. The redemption rate of points in the mall reaches 65%, the number of orders actively accepted by grid workers increases by 55%, and the residents' satisfaction increases from 81% to 94%. This makes it easier to avoid evaluation based solely on the number of tasks or subjective impressions, reflects service quality and efficiency, greatly reduces the burden of manual statistics and assessment, and improves the level of intelligent community management.

[0040] The financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points; Furthermore, the financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: The financial data includes income and expenditure information. When each piece of financial data is entered, it is automatically recorded in the distributed ledger of the blockchain, generating a unique hash value. The data aggregation algorithm is automatically executed every morning to publicize the financial information of the community account. The expression is:

[0041] Where, Represents the financial balance of the community account, Representative Item income, Representative Item expenditure.

[0042] Furthermore, the financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: Residents can use the community management app or mini-program to view the financial information of the community account, including the total balance, income and expenditure. The financial information includes but is not limited to the purpose of purchasing items, the details of maintenance projects, invoice photos, and contract documents. Residents can verify the authenticity of the financial information by verifying the hash chain. The expression is:

[0043] Where, Indicates the verification result of the authenticity of financial information, It is an indicative function. When the condition in the brackets is met, the function value is 1. When the condition is not met, the function value is 0. Indicates the Financial documents The hash value of represents a hash function, Indicates the Financial documents, It is Financial documents, It is The timestamp corresponding to each financial voucher is displayed. At the same time, the dynamic settlement points are made public. Residents can view the work content of grid workers and volunteers and the corresponding personal points obtained through any of the community management apps or mini-programs.

[0044] Specifically, the financial transparency display module leverages blockchain technology to build a distributed ledger, enabling the trusted recording and public disclosure of financial data and points information. In one smart community application, a unique hash value is automatically generated and written to the blockchain for each entry of financial data, such as property fee income and equipment purchase expenditures. Account balances are calculated and publicly displayed each morning using a data aggregation algorithm. Residents can view photos of invoices for elevator parts purchases and details of landscaping maintenance projects through the community app. Authenticity can also be verified by verifying the hash chain—for example, verifying that the hash value of the third maintenance expenditure is consistent with the hash calculation results of the previous voucher, the current voucher, and the timestamp to ensure data has not been tampered with. Dynamic points information, such as 30 points earned by grid workers for garbage collection and 20 points earned by volunteers for helping the elderly, is also displayed and accessible to residents at any time. This eliminates the risk of financial data tampering, strengthens residents' trust in property management finances, and enhances the transparency and oversight of financial management. This also promotes the enthusiasm and fairness of grid workers and volunteers, improves the incentive and feedback mechanism, and ensures the authenticity and integrity of all financial documents through on-chain hash verification, enhancing the credibility of community governance.

[0045] The intelligent data collection module is used to collect historical financial data, management data and external economic data of the community and integrate them into reference data; Furthermore, the intelligent data collection module is used to collect historical financial data, management data, and external economic data of the community and integrate them into reference data. The operation process includes: A time series alignment interpolation function is set to unify the different timestamps and frequencies of the historical financial data, management data, and external economic data into a standard time scale. Z-score standardization is used to make the dimensions of the historical financial data, management data, and external economic data consistent. Through the multi-source data fusion mechanism, the data are fused into reference data. The expression is:

[0046] Where, represents the reference data obtained after fusion, It is historical financial data The weight coefficient in the multi-source data fusion process, Represents historical financial data, It is management data The corresponding weight coefficient is, Represents management data, It is external economic data The weight coefficient of Representing external economic data, a Bayesian dynamic update mechanism is set to update the fusion parameters of the multi-source data fusion mechanism every time new data comes in.

[0047] Specifically, the intelligent data collection module achieves efficient integration of multi-source, heterogeneous data through time series alignment, standardization, and a Bayesian dynamic update mechanism. In practice at one smart community, this module integrates historical financial data such as property fee revenue and facility maintenance records, as well as management data such as community occupancy rates and equipment inspection frequency, with external economic data such as the CPI index and regional property industry salary levels. First, using cubic spline interpolation, the data with different frequencies are unified to a daily scale. Z-score normalization is then used to eliminate dimensional differences. Finally, the data are integrated into the reference data with an initial weighting of 0.5:0.3:0.2. If the property fee collection rate drops abnormally in a certain quarter, the Bayesian update mechanism is triggered, increasing the weight of the management data to 0.4 to enhance sensitivity to changes in occupancy rates. This achieves seamless integration and dynamic optimization of data across sources and scales, strengthening the data foundation for forecasting and decision-making models, improving the timeliness and scientific nature of financial and management decisions, and helping community management accurately grasp funding, maintenance, and market trends. It also provides real-time adaptive capabilities to continuously track and incorporate the latest economic fluctuations and management feedback.

[0048] The financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and monitors and analyzes them in real time; Furthermore, the financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operation process of real-time monitoring and analysis includes: The multivariate linear regression model uses the reference data as the independent variable and the various income and expenditures in the next year as the dependent variable to perform training and prediction calculations, establish a multivariate regression equation, and use the least squares method to solve it to obtain the predicted values ​​of various income and expenditures in the next year. The expression is:

[0049] Where, represents the independent variable matrix, express is a real number field OK A matrix of columns, is the sample size, is the number of independent variables, represents the dependent variable vector, yes The Each element corresponds to a specific income and expenditure. Indicates the The intercept term corresponding to the income and expenditure items represents the All are 0 hours The basic value of Indicates the Item income and expenditure corresponds to The regression coefficients of the independent variables, It is represented by the matrix of independent variables. Extract the independent variables, Indicates the The error term corresponding to the income and expenditure items, represents the estimated value vector of the regression coefficients, Represents the independent variable matrix The transposed matrix of Indicates calculation first and Then find the inverse matrix of the product matrix, represents the product matrix of the transposed independent variable matrix and the dependent variable vector, represents the predicted value vector of various revenue and expenditure in the next year, Represents the latest independent variable data matrix.

[0050] Furthermore, the financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operation process of real-time monitoring and analysis includes: Using the time series analysis model, we conduct time series analysis on the financial data of the previous day in the early morning of each day, and fit a separate time series for each income and expenditure. The expression is:

[0051] Where, Indicates the Item income and expenditure in time The observed value of It is used to distinguish different income and expenditure items. The autoregressive coefficient reflects the Item income and expenditure in time The data of the current time The impact of the data, Indicates the Item income and expenditure in time The observed value at time The moving average coefficient reflects the past The error term of the period For the current time No. Observed values ​​of income and expenditure The degree of impact, It's time The error term when It's time It can detect the error term when calculating the financial indicators, monitor the changing trends of various financial indicators, issue early warning information when abnormal fluctuations are found, including but not limited to a sharp increase in one of the expenditures, and make short-term forecasts of the financial trends for the next month.

[0052] Specifically, the financial forecasting model module integrates the multivariate linear regression model and the time series analysis model to conduct multi-dimensional forecasting and real-time monitoring of the community's financial revenue and expenditure. In the actual application of a certain smart community, historical financial data, management data such as occupancy rate, facility maintenance records, and external economic data such as the price index are used as independent variables to construct a multivariate linear regression equation. By analyzing the relationship between property fee income and occupancy rate and price index in the past three years, the property fee income for the next year is predicted, and the regression coefficient is solved using the least squares method. At the same time, a time series analysis of individual income and expenditure such as equipment maintenance fees and cleaning wages of the previous day is conducted in the early morning of each day. When it is found that the greening maintenance expenditure in a certain quarter has increased by 30% year-on-year, an early warning is automatically triggered, and the expenditure trend for the next month is predicted, realizing both annual and short-term dual forecasts of the community's finances, facilitating the improvement of the planning and scientific nature of the use of funds, and timely discovering and responding to abnormal expenditures through daily dynamic monitoring and abnormal warnings, preventing financial risks, and helping owners to transparently understand the community's financial operations, thereby improving the credibility of community management and owner satisfaction.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. 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 make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A smart community property management system, characterized in that: include: The dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and allocates tasks based on real-time monitoring data and the work status of grid workers; The dynamic integral calculation module uses the fuzzy comprehensive evaluation algorithm to comprehensively consider the grid workers' work performance and dynamically calculate the integral; The financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points; The intelligent data collection module is used to collect historical financial data, management data and external economic data of the community and integrate them into reference data; The financial forecast model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and monitors and analyzes them in real time.

2. A smart community property management system according to claim 1, characterized in that: The dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: Residents submit transaction information through the community management app or mini-program. The transaction information is transmitted to the task database in text form. The text is processed, including but not limited to word segmentation, part-of-speech tagging, and semantic understanding, to extract key information. The expression is: ， Where, Represents the transaction information text submitted by a given resident Extract key information under the conditions The conditional probability of Represents the key information sequence extracted from the transaction information text submitted by residents, Indicates the transaction information text submitted by residents. Represents key information sequence The number of key information elements contained in Is the index variable used to traverse the key information sequence For each element in Indicates key information sequence The Key information elements, Is the key information sequence Middle Key information elements, Indicates Extract relevant text from key information Part of the content, The conditional probability represents the key information extracted before the known and related content in the text Under the conditions of Key information The key information includes but is not limited to event type, urgency keywords, and geographic location description. At the same time, through the Internet of Things connection with the monitoring equipment, real-time monitoring data of the garbage dumping site is obtained in real time. The working status of the grid worker includes the grid worker's workload data and the grid worker's real-time location data.

3. A smart community property management system according to claim 2, characterized in that: The dynamic intelligent allocation module parses the transaction information text submitted by residents, extracts key information, and combines real-time monitoring data and the work status of grid workers to perform task allocation. The operational process includes: The real-time location data of the grid workers is obtained using the positioning function of the community management app on the grid workers' mobile phones. The workload data of the grid workers is obtained by recording the grid workers' completed tasks and the statistics of the assigned tasks. Based on the real-time monitoring data and the grid workers' working status, the suitability of each grid worker for handling the current task is calculated. The expression is: ， Where, Representative The suitability value of each grid worker to handle the current task, The weight coefficients correspond to the weights of the factors affecting distance, workload, task urgency, and task type matching in the formula. Indicates the The distance between each grid worker and the location where the current task occurs, yes , Representative The workload of each grid worker is Indicates the urgency of the current task. The matching coefficient of each grid worker is Representative The matching degree between each grid worker and the current task type is used to assign the task to the grid worker with the highest suitability. After receiving the task, the grid worker will go to process it and feedback the processing results through the mobile community management APP after completing the task.

4. A smart community property management system according to claim 3, characterized in that: The dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid worker's work performance. The operation process of dynamic settlement points includes: After the grid worker completes the task and submits the processing result, the timestamp is recorded and the points calculation process is automatically triggered. The fuzzy comprehensive evaluation algorithm obtains detailed information about the task from the task database, including the event type and processing time. At the same time, a satisfaction evaluation link is pushed to the resident who submitted the task. The resident can use the community management app or any of the mini-programs to score on a 5-level scale to construct a multi-dimensional evaluation index. The expression is: ， Where, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is a parameter used to control the steepness of the membership function curve. Corresponding to the input parameters and The weight coefficient of is the threshold parameter used to determine the center position of the membership function, is a natural constant that serves as the base of the exponential function, represents the processing efficiency value, Therefore The exponential function with base , is the actual processing time of the current task, is the average processing time of similar tasks in history, is the standard deviation of the processing time of similar tasks in history, It is the normalized difference between the current task processing time and the historical average processing time. represents the residents’ satisfaction score, It is The grade rating value, Resident rating For the first The membership of the grade rating, Represents the actual ratings given by residents.

5. A smart community property management system according to claim 4, characterized in that: The dynamic points calculation module uses a fuzzy comprehensive evaluation algorithm to comprehensively consider the grid worker's work performance. The operation process of dynamic settlement points includes: Dynamic settlement points are calculated based on the weight of event complexity (45%), processing time (25%), and resident satisfaction (30%). The expression is: ， Where, Represents the final points earned by the grid worker after completing this task. is the weight of the event complexity factor in the integral calculation, The fuzzy membership value that indicates the complexity of the event, are two input parameters that describe the complexity of the event. is the number of households involved in the incident, is the number of departments that need to be coordinated for the incident, is the fuzzy composition operator, is the weight of the processing time factor in the integral calculation, represents the processing efficiency value, is the weight of the resident satisfaction factor in the points calculation, It represents the residents' satisfaction score, calculates the points obtained by the grid worker for this task, and records the points into the grid worker's personal points account. In addition, a points mall is connected to the points account, and grid workers and volunteers can use their personal points to redeem corresponding products in the points mall.

6. A smart community property management system according to claim 5, characterized in that: The financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: The financial data includes income and expenditure information. When each piece of financial data is entered, it is automatically recorded in the distributed ledger of the blockchain, generating a unique hash value. The data aggregation algorithm is automatically executed every morning to publicize the financial information of the community account. The expression is: ， Where, Represents the financial balance of the community account, Representative Item income, Representative Item expenditure.

7. A smart community property management system according to claim 6, characterized in that: The financial transparency display module uses a distributed ledger based on blockchain technology to record the financial data of the community account and publicly display the financial information of the community account and the dynamic settlement points. The operation process includes: Residents can use the community management app or mini-program to view the financial information of the community account, including the total balance, income and expenditure. The financial information includes but is not limited to the purpose of purchasing items, the details of maintenance projects, invoice photos, and contract documents. Residents can verify the authenticity of the financial information by verifying the hash chain. The expression is: ， Where, Indicates the verification result of the authenticity of financial information, It is an indicative function. When the condition in the brackets is met, the function value is 1. When the condition is not met, the function value is 0. Indicates the Financial documents The hash value of represents a hash function, Indicates the Financial documents, It is Financial documents, It is The timestamp corresponding to each financial voucher is displayed. At the same time, the dynamic settlement points are made public. Residents can view the work content of grid workers and volunteers and the corresponding personal points obtained through any of the community management apps or mini-programs.

8. A smart community property management system according to claim 7, characterized in that: The intelligent data collection module is used to collect historical financial data, management data, and external economic data of the community, and the operational process of integrating them into reference data includes: A time series alignment interpolation function is set to unify the different timestamps and frequencies of the historical financial data, management data, and external economic data into a standard time scale. Z-score standardization is used to make the dimensions of the historical financial data, management data, and external economic data consistent. Through the multi-source data fusion mechanism, the data are fused into reference data. The expression is: ， Where, represents the reference data obtained after fusion, It is historical financial data The weight coefficient in the multi-source data fusion process, Represents historical financial data, It is management data The corresponding weight coefficient is, Represents management data, It is external economic data The weight coefficient of Representing external economic data, a Bayesian dynamic update mechanism is set to update the fusion parameters of the multi-source data fusion mechanism every time new data comes in.

9. A smart community property management system according to claim 8, characterized in that: The financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operational process of real-time monitoring and analysis includes: The multivariate linear regression model uses the reference data as the independent variable and the various income and expenditures in the next year as the dependent variable to perform training and prediction calculations, establish a multivariate regression equation, and use the least squares method to solve it to obtain the predicted values ​​of various income and expenditures in the next year. The expression is: ， Where, represents the independent variable matrix, express is a real number field OK A matrix of columns, is the sample size, is the number of independent variables, represents the dependent variable vector, yes The Each element corresponds to a specific income and expenditure. Indicates the The intercept term corresponding to the income and expenditure items represents the All are 0 hours The basic value of Indicates the Item income and expenditure corresponds to The regression coefficients of the independent variables, It is represented by the matrix of independent variables. Extract the independent variables, Indicates the The error term corresponding to the income and expenditure items, represents the estimated value vector of the regression coefficients, Represents the independent variable matrix The transposed matrix of Indicates calculation first and Then find the inverse matrix of the product matrix, represents the product matrix of the transposed independent variable matrix and the dependent variable vector, represents the predicted value vector of various revenue and expenditure in the next year, Represents the latest independent variable data matrix.

10. A smart community property management system according to claim 9, characterized in that: The financial forecasting model module uses a multiple linear regression model and a time series analysis model to predict the various income and expenditures of the next year based on the reference data, and the operational process of real-time monitoring and analysis includes: Using the time series analysis model, we conduct time series analysis on the financial data of the previous day in the early morning of each day, and fit a separate time series for each income and expenditure. The expression is: ， Where, Indicates the Item income and expenditure in time The observed value of It is used to distinguish different income and expenditure items. The autoregressive coefficient reflects the Item income and expenditure in time The data of the current time The impact of the data, Indicates the Item income and expenditure in time The observed value at time The moving average coefficient reflects the past The error term of the period For the current time No. Observed values ​​of income and expenditure The degree of impact, It's time The error term when It's time It can detect the error term when calculating the financial indicators, monitor the changing trends of various financial indicators, issue early warning information when abnormal fluctuations are found, including but not limited to a sharp increase in one of the expenditures, and make short-term forecasts of the financial trends for the next month.

Images