Smart community property management system and method

Abstract

The invention relates to the technical field of community property management, in particular to an intelligent community property management system and method, and the method comprises the following steps: obtaining the bill data of a user in an intelligent community property platform; based on the receivable data and the actual receipt data of the community users, outputting and obtaining a single paid risk value and a single unpaid risk value; outputting and obtaining a property management risk coefficient through a risk calculation model, comparing the property management risk coefficient with a corresponding threshold value, and generating a property management risk signal; based on the property management high risk signal, determining a tracking coefficient, and calculating to obtain the collection frequency of the unpaid user; according to the invention, the risk of the current intelligent community property management is regulated and processed, so that the risk degree of the intelligent community property management in the aspect of bill management can be effectively reduced.

Description

Technical Field

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

[0002] With the progress of society and the development of science and technology, people's living standards have improved, and they have higher requirements for the quality of property management services in residential communities. They not only need better services, but also need to make it convenient for owners to provide feedback and handle corresponding matters.

[0003] Chinese patent CN 111815288 B discloses a smart community property management system, including a property management platform, a personnel management subsystem, a transaction 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, and terminals with corresponding access and processing permissions respectively configured for personnel of different identities and positions;

[0004] The existing technology effectively improves the standardization and process of property management through the specific configuration design of the management platform and each subsystem, facilitates the communication between property management personnel and owners, facilitates the owners to handle various property management affairs and purchase the required goods, and improves the living experience of owners. In addition, through the commercial introduction of social retail, owners can enjoy the benefits of property fee reduction, which also increases the income of property companies, forming a mutually beneficial organic whole among property management personnel, owners and merchants.

[0005] However, property expense management in smart communities is also a rather thorny issue. It cannot accurately analyze and evaluate the risk factors of property management, which leads to the problem that properties cannot effectively coordinate and handle bill management. Summary of the invention

[0006] The purpose of the present invention is to provide a smart community property management system and method. The technical problem solved by the present invention is that the property expense management of a smart community is also a relatively thorny issue. It cannot accurately analyze and evaluate the property management risk factor, which leads to the problem that the property cannot be effectively coordinated in bill management.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A smart community property management method comprises the following steps:

[0009] Step 1: Obtain the billing data of users in the smart community property platform;

[0010] Among them, billing data includes receivable data and actual collection data;

[0011] Step 2: Based on the receivable data and actual payment data of community users, output the individual paid risk value and the individual unpaid risk value;

[0012] Step 3: Based on the paid risk value of the single unit and the unpaid risk value of the single unit, the property management risk coefficient is output through the risk calculation model, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal;

[0013] Step 4: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users;

[0014] Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient.

[0015] A smart community property management system, including the following modules:

[0016] Collection module: obtains the billing data of users in the smart community property platform;

[0017] Among them, billing data includes receivable data and actual collection data;

[0018] Processing module: Based on the receivable data and actual collection data of community users, output the paid risk value and unpaid risk value of each entity;

[0019] Evaluation module: Based on the paid risk value of each unit and the unpaid risk value of each unit, the risk calculation model is used to output the property management risk coefficient, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal;

[0020] Management module: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users;

[0021] Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient.

[0022] Beneficial effects of the present invention:

[0023] (1) The present invention obtains the bill data of users in the smart community property platform; based on the receivable data and actual data of the community users, outputs the individual paid risk value and the individual unpaid risk value; based on the individual paid risk value and the individual unpaid risk value, outputs the property management risk coefficient through a risk calculation model, compares the property management risk coefficient with the corresponding threshold value, and generates a property management risk signal; the present invention classifies the users included in the smart community property platform, and then calculates the corresponding risk level according to the classification, and finally calculates the risk level obtained by the classification through a model to obtain the property management risk coefficient, thereby achieving a more accurate judgment on the property in terms of bill management, so that the ability to control risks in a timely manner can be achieved based on the obtained risk signal;

[0024] (2) The present invention determines the tracking coefficient based on the high risk signal of property management and calculates the collection frequency CPj of the unpaid users; the present invention regulates and processes the current smart community property management risk based on the tracking coefficient obtained by further data processing of the above-mentioned single paid risk value and single unpaid risk value, thereby effectively reducing the risk level of the smart community property management in bill management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below in conjunction with the accompanying drawings.

[0026] Figure 1 is a flowchart of Embodiment 1 of the present invention;

[0027] Figure 2 is a flowchart of Embodiment 2 of the present invention;

[0028] Figure 3 It is a system block diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0029] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1

[0031] See also Figure 1 As shown, the present invention is a smart community property management system and method, comprising the following steps:

[0032] Step 1: Obtain the billing data of users in the smart community property platform;

[0033] Among them, billing data includes receivable data and actual collection data;

[0034] In some implementation schemes, the receivable data and actual collection data of community users are obtained through the smart community property platform;

[0035] It should be explained that the receivable data refers to the fees that users should pay and the payment time, which are marked as receivable fees and receivable time; the actual collection data refers to the fees and payment time actually paid by users, which are marked as actual collection fees and actual collection time;

[0036] Step 2: Based on the receivable data and actual payment data of community users, output the individual paid risk value and the individual unpaid risk value;

[0037] In some implementation schemes, the receivable data of each user is compared with the actually collected data based on the receivable data and the actually collected data of the community users;

[0038] If the receivable data is equal to the actual collected data, the user is marked as a paid user;

[0039] If the receivable data is greater than the actual collection data, the user will be marked as an unpaid user;

[0040] Analyze the paid users in the smart community property platform to obtain the paid risk value of each unit;

[0041] Analyze the non-payment users in the smart community property platform and obtain the single non-payment risk value;

[0042] First, specifically, the process of obtaining the paid-at-risk value of a single entity is as follows:

[0043] For each paid user, a monitoring period is set (the monitoring period can be set by the technical staff according to the actual situation, and can be half a year or one year), and the due time and the corresponding actual collection time are obtained during the monitoring period. The difference between the actual collection time and the due time is calculated to obtain the payment time difference;

[0044] Calculate the average of all payment time differences within the monitoring period to obtain the average payment time difference; then calculate the difference between each payment time difference and the average payment time difference to obtain the payment time difference deviation value;

[0045] Compare the payment time difference deviation value with the payment time difference deviation threshold;

[0046] If the payment time difference deviation value is greater than the payment time difference deviation threshold, it means that the average level of the payment time difference deviation is high, and a high deviation signal is generated;

[0047] If the payment time difference deviation value is less than the payment time difference deviation threshold, it means that the average level of the payment time difference deviation is low, and a low deviation signal is generated;

[0048] Count the number of high-drift signals, calculate the ratio of the number of high-drift signals to the total number of time differences in the monitoring period, and get the proportion of high-drift times;

[0049] And, calculating the mean of the time difference deviation values ​​corresponding to all high-excursion signals to obtain the total time difference deviation value, and calculating the ratio of the total time difference deviation value to the time difference mean to obtain the total time difference deviation ratio;

[0050] The proportion of high deviation times and the total ratio of payment time difference deviation are marked as BSy and BPy respectively. The single paid risk value ZFDJ is calculated by the formula ZFDJ=a1×BSy+a2×BPy, where a1 and a2 are weight ratio coefficients, a1+a2=1, a1 is 0.27, and a2 is 0.73;

[0051] Second, specifically, the process of obtaining the single unpaid risk value is as follows:

[0052] For each unpaid user, set the same monitoring period (the monitoring period can be set by the technical staff according to the actual situation, and can be half a year or one year), obtain the unpaid time within the monitoring period, calculate the ratio of the unpaid time to the monitoring period, and obtain the single unpaid risk value;

[0053] Step 3: Based on the paid risk value of the single unit and the unpaid risk value of the single unit, the property management risk coefficient is output through the risk calculation model, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal;

[0054] Among them, the property management risk signal includes a high property management risk signal or a low property management risk signal;

[0055] In some implementation schemes, according to the paid risk value of each individual, all paid risk values ​​ZFDJ in the smart community property platform are added together to obtain the total paid risk value ZFDJz;

[0056] According to the single unpaid risk value, all single unpaid risk values ​​in the smart community property platform are added together to obtain the total unpaid risk value ZFDWz;

[0057] The property management risk coefficient ZFG is calculated through the risk calculation model ZFG = b1 × ZFDJz + b2 × ZFDWz; where b1 and b2 are weight ratio coefficients, b1 + a2 = 1, b1 is 0.19, and b2 is 0.81;

[0058] comparing the property management risk factor with a property management risk factor threshold;

[0059] If the property management risk coefficient is greater than or equal to the property management risk coefficient threshold, it means that during the monitoring period, the users included in the smart community property platform have a large receivables risk, and a high property management risk signal is generated;

[0060] If the property management risk coefficient is less than the property management risk coefficient threshold, it means that during the monitoring period, the users included in the smart community property platform have a small receivable risk, and a low property management risk signal is generated;

[0061] The technical solution of the embodiment of the present invention is as follows: obtaining the billing data of users in the smart community property platform; outputting the individual paid risk value and the individual unpaid risk value based on the community users' receivable data and the actual collected data; outputting the property management risk coefficient based on the individual paid risk value and the individual unpaid risk value through a risk calculation model, comparing the property management risk coefficient with the corresponding threshold value, and generating a property management risk signal; the present invention classifies the users included in the smart community property platform, and then calculates the corresponding risk level according to the classification, and finally calculates the risk level obtained by the classification through a model to obtain the property management risk coefficient, thereby achieving more accurate judgment on the property in terms of bill management, so that the ability to control risks in a timely manner can be achieved based on the obtained risk signal.

[0062] Example 2

[0063] See also Figure 2 As shown, the present invention is a smart community property management system and method, which also includes the following steps:

[0064] Step 4: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users;

[0065] Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient;

[0066] In some implementation schemes, when a high property management risk signal is obtained, the individual paid risk value ZFDJ of each paid user is obtained, and the individual paid risk value ZFDJ is compared with the total paid risk value ZFDJz to obtain the individual paid risk ratio coefficient;

[0067] And, obtain the timely proportion coefficient of each paid user, mark it as the single paid timely proportion coefficient, then record the single paid risk proportion coefficient and the single paid timely proportion coefficient as XJFi and XJJi, and calculate the paid tracking coefficient XZJi by the formula XZJi=XJFi×XJJi, where i represents the number of paid users;

[0068] Specifically, the process of obtaining the monomer paid-on-time proportion coefficient XJJ i is as follows:

[0069] Obtain each due time and the corresponding actual collection time within the monitoring period, as well as the collection time that occurs between the due time and the actual collection time, obtain the collection time closest to the due time, and calculate the difference with the actual collection time to obtain the collection time difference;

[0070] By formula The single paid on-time ratio coefficient XJJ i is calculated; among them, TC represents the time difference of collection, TS represents the collection time, and TY represents the actual collection time;

[0071] At the same time, the individual non-payment risk value of each non-payment user is obtained, and the individual non-payment risk value is compared with the total non-payment risk value ZFDWz to obtain the non-payment tracking coefficient XZWj, where j represents the number of non-payment users;

[0072] Get the total frequency of bill collection during the monitoring period, recorded as CP, through the formula Calculate the collection frequency CPj of the unpaid users;

[0073] The collection frequency CPj of the unpaid users is displayed visually;

[0074] The technical solution of the embodiment of the present invention: based on the high risk signal of property management, the tracking coefficient is determined, and the collection frequency CPj of the unpaid users is calculated; the present invention regulates and processes the current smart community property management risk based on the tracking coefficient obtained by further data processing of the above-mentioned single paid risk value and single unpaid risk value, thereby effectively reducing the risk level of the smart community property management in bill management.

[0075] Example 3

[0076] See also Figure 3 As shown, the present invention is a smart community property management system, comprising:

[0077] A smart community property management system, including the following modules:

[0078] Collection module: obtains the billing data of users in the smart community property platform;

[0079] Among them, billing data includes receivable data and actual collection data;

[0080] Processing module: Based on the receivable data and actual collection data of community users, output the paid risk value and unpaid risk value of each entity;

[0081] Specifically, the receivable data of each user is compared with the actual collection data;

[0082] If the receivable data is equal to the actual collected data, the user is marked as a paid user;

[0083] If the receivable data is greater than the actual collection data, the user will be marked as an unpaid user;

[0084] Analyze the paid users in the smart community property platform to obtain the paid risk value of each unit;

[0085] Analyze the non-payment users in the smart community property platform and obtain the single non-payment risk value;

[0086] The process of obtaining the paid-at-risk value of a single entity is as follows:

[0087] For each paid user, a monitoring period is set to obtain each due time and the corresponding actual time collected during the monitoring period, and the difference between the actual time collected and the due time is calculated to obtain the payment time difference;

[0088] Calculate the average of all payment time differences within the monitoring period to obtain the average payment time difference; then calculate the difference between each payment time difference and the average payment time difference to obtain the payment time difference deviation value;

[0089] If the payment time difference deviation value is greater than the payment time difference deviation threshold, it means that the average level of the payment time difference deviation is high, and a high deviation signal is generated;

[0090] Count the number of high-drift signals, calculate the ratio of the number of high-drift signals to the total number of time differences in the monitoring period, and get the proportion of high-drift times;

[0091] And, calculating the mean of the time difference deviation values ​​corresponding to all high-excursion signals to obtain the total time difference deviation value, and calculating the ratio of the total time difference deviation value to the time difference mean to obtain the total time difference deviation ratio;

[0092] The proportion of high deviation times and the total ratio of payment time difference deviation are marked as BSy and BPy respectively, and the single paid risk value ZFDJ is calculated by the formula ZFDJ=a1×BSy+a2×BPy, where a1 and a2 are weight ratio coefficients;

[0093] The process of obtaining the single unpaid risk value is as follows:

[0094] For each unpaid user, set the same monitoring period, obtain the unpaid duration within the monitoring period, calculate the ratio of the unpaid duration to the monitoring period, and obtain the single unpaid risk value.

[0095] Evaluation module: Based on the paid risk value of each unit and the unpaid risk value of each unit, the risk calculation model is used to output the property management risk coefficient, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal;

[0096] Management module: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users;

[0097] Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient;

[0098] Specifically, the process of obtaining the collection frequency CPj of unpaid users is as follows:

[0099] When a high signal of property management risk is obtained, the individual paid risk value ZFDJ of each paid user is obtained, and the individual paid risk value ZFDJ is compared with the total paid risk value ZFDJz to obtain the individual paid risk ratio coefficient;

[0100] And, obtain the timely proportion coefficient of each paid user, mark it as the single paid timely proportion coefficient, then record the single paid risk proportion coefficient and the single paid timely proportion coefficient as XJFi and XJJi, and calculate the paid tracking coefficient XZJi by the formula XZJi=XJFi×XJJi, where i represents the number of paid users;

[0101] At the same time, the individual non-payment risk value of each non-payment user is obtained, and the individual non-payment risk value is compared with the total non-payment risk value ZFDWz to obtain the non-payment tracking coefficient XZWj, where j represents the number of non-payment users;

[0102] Get the total frequency of bill collection during the monitoring period, recorded as CP, through the formula Calculate the collection frequency CPj of the unpaid users;

[0103] The process of obtaining the monomer paid-on-time proportion coefficient XJJ i is as follows:

[0104] By formula The single paid on-time ratio coefficient XJJ i is calculated; among them, TC represents the collection time difference, TS represents the collection time, and TY represents the actual collection time.

[0105] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0106] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A smart community property management method, characterized in that: The following steps are involved: Step 1: Obtain the billing data of users in the smart community property platform; Among them, billing data includes receivable data and actual collection data; Step 2: Based on the receivable data and actual payment data of community users, output the individual paid risk value and the individual unpaid risk value; Step 3: Based on the paid risk value of the single unit and the unpaid risk value of the single unit, the property management risk coefficient is output through the risk calculation model, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal; Step 4: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users; Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient.

2. A smart community property management method according to claim 1, characterized in that: In step 2, the receivable data of each user is compared with the actual collection data; If the receivable data is equal to the actual collected data, the user is marked as a paid user; If the receivable data is greater than the actual collection data, the user will be marked as an unpaid user; Analyze the paid users in the smart community property platform to obtain the paid risk value of each unit; Analyze the unpaid users in the smart community property platform and obtain the individual unpaid risk value.

3. A smart community property management method according to claim 2, characterized in that: The process of obtaining the paid-at-risk value of a single entity is as follows: For each paid user, a monitoring period is set to obtain each due time and the corresponding actual time collected during the monitoring period, and the difference between the actual time collected and the due time is calculated to obtain the payment time difference; Calculate the average of all payment time differences within the monitoring period to obtain the average payment time difference; then calculate the difference between each payment time difference and the average payment time difference to obtain the payment time difference deviation value; If the payment time difference deviation value is greater than the payment time difference deviation threshold, it means that the average level of the payment time difference deviation is high, and a high deviation signal is generated; Count the number of high-drift signals, calculate the ratio of the number of high-drift signals to the total number of time differences in the monitoring period, and get the proportion of high-drift times; And, calculating the mean of the time difference deviation values ​​corresponding to all high-excursion signals to obtain the total time difference deviation value, and calculating the ratio of the total time difference deviation value to the time difference mean to obtain the total time difference deviation ratio; The proportion of high deviation times and the total ratio of payment time difference deviation are marked as BSy and BPy respectively, and the single paid risk value ZFDJ is calculated.

4. A smart community property management method according to claim 3, characterized in that: The process of obtaining the single unpaid risk value is as follows: For each unpaid user, set the same monitoring period, obtain the unpaid duration within the monitoring period, calculate the ratio of the unpaid duration to the monitoring period, and obtain the single unpaid risk value.

5. A smart community property management method according to claim 1, characterized in that: In step 3, the calculation process of the property management risk factor ZFG is: Add up all the paid risk values ​​ZFDJ of the individual entities in the smart community property platform to get the total paid risk value ZFDJz; According to the single unpaid risk value, all single unpaid risk values ​​in the smart community property platform are added together to obtain the total unpaid risk value ZFDWz; The property management risk coefficient ZFG is calculated through the risk calculation model.

6. A smart community property management method according to claim 5, characterized in that: If the property management risk coefficient is greater than or equal to the property management risk coefficient threshold, a high property management risk signal is generated.

7. A smart community property management method according to claim 1, characterized in that: In step 4, the process of obtaining the collection frequency CPj of the unpaid users is as follows: When a high signal of property management risk is obtained, the individual paid risk value ZFDJ of each paid user is obtained, and the individual paid risk value ZFDJ is compared with the total paid risk value ZFDJz to obtain the individual paid risk ratio coefficient; And, obtain the timely proportion coefficient of each paid user, mark it as the single paid timely proportion coefficient, then record the single paid risk proportion coefficient and the single paid timely proportion coefficient as XJF i and XJJ i, and obtain the paid tracking coefficient XZJ i by calculation; At the same time, obtain the individual non-payment risk value of each non-payment user, compare the individual non-payment risk value with the total non-payment risk value ZFDWz, and obtain the non-payment tracking coefficient XZWj; The total frequency of bill collection during the monitoring period is obtained, recorded as CP, and the collection frequency CPj of unpaid users is obtained by calculation.

8. A smart community property management method according to claim 7, characterized in that: The process of obtaining the monomer paid-on-time proportion coefficient XJJ i is as follows: The monomer paid-on-time proportion coefficient XJJ i is obtained by calculation.

9. A smart community property management method according to claim 8, characterized in that: The process of obtaining the collection time difference is as follows: Obtain each due date and the corresponding actual collection date within the monitoring period, as well as the collection date that occurs between the due date and the actual collection date, obtain the collection date that is closest to the due date, and calculate the difference between the due date and the actual collection date to obtain the collection date difference.

10. A smart community property management system, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and comprises the following modules: Collection module: obtains the billing data of users in the smart community property platform; Among them, billing data includes receivable data and actual collection data; Processing module: Based on the receivable data and actual collection data of community users, output the paid risk value and unpaid risk value of each entity; Evaluation module: Based on the paid risk value of each unit and the unpaid risk value of each unit, the risk calculation model is used to output the property management risk coefficient, and the property management risk coefficient is compared with the corresponding threshold to generate a property management risk signal; Management module: Based on the high risk signal of property management, determine the tracking coefficient and calculate the collection frequency CPj of the unpaid users; Among them, the tracking coefficient includes the paid tracking coefficient and the unpaid tracking coefficient.

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