Community property intelligent management method and system based on Internet of Things

Abstract

The invention discloses a community property intelligent management method and system based on the Internet of Things, and relates to the technical field of property management. Comprising a data acquisition module which is used for collecting working data of community building power equipment, current and predicted future environment data of a building, historical working data of the community building power equipment, historical residential information data of building residents and real-time residential information data of the building residents in real time. The use loss of the equipment is researched according to requirements, real-time living information data of building residents are obtained, man-made influence values are analyzed, the loss of the electrical equipment is predicted in combination with man-made influence and environmental influence, and related technicians are helped to know the use state of the building electrical equipment in time. Related personnel can conveniently and reasonably formulate a maintenance technology of the equipment, idle time maintenance of the equipment is realized, the influence of equipment faults on normal life of residents is avoided, the use effect is good, and the method has a good use prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of property management, and in particular to an intelligent management method and system for residential properties based on the Internet of Things. Background Art

[0002] Property management refers to the activities in which property owners select and hire property service companies, and the owners and property service companies carry out repairs, upkeep and management of houses and supporting facilities and equipment and related venues in accordance with the property service contract, to maintain environmental sanitation and related order within the property management area.

[0003] The Internet of Things refers to connecting any object to the network through information sensing devices according to agreed protocols. Objects exchange and communicate information through information dissemination media to achieve intelligent identification, positioning, tracking, supervision and other functions.

[0004] With the progress of society, the power equipment in the community has gradually become intelligent. The property can directly obtain the data of the power equipment and supervise the power equipment accordingly. In order to better manage the community, people have invented some property management methods and systems based on the Internet of Things.

[0005] The existing patent number is CN106991624B, and the patent name is an invention patent of a property intelligent community management system and management method. It records that the management system includes a device and data management subsystem, a virtual agent subsystem, a device agent module, and electronic equipment. The device and data management subsystem is used for device registration and data registration of electronic equipment. The electronic equipment is linked to the server according to the device and data registration content; the virtual agent subsystem is used for one side of the virtual server interconnection, and the network equipment and management personnel are electronic equipment. The device and data registration and data sending and receiving, storage are completed through the device and data management subsystem; after the non-network electronic equipment is connected to the device agent module, it is connected to the community server through the device and data management subsystem to complete the device and data registration and data sending and receiving, storage. The management method includes the steps of device and data management, virtual agent, and device agent. The present invention has the characteristics of data compatibility and sharing, high degree of intelligence, high management efficiency, low operating cost, and easy maintenance;

[0006] The central idea of ​​the solution recorded in the above patent is to connect non-network electronic devices and object servers, network devices and community property management staff to the device and data management subsystem of the community server through the device proxy module and the virtual proxy subsystem, and obtain unique device identification and data identification as well as device node control structure and data control structure through device registration and data registration in the device and data management subsystem, thereby realizing effective collection and reliable connection of various electronic devices, management personnel and community servers on a single platform, and realizing data compatibility, sharing and automatic aggregation between each community server and the property company server, so as to achieve intelligent and real-time management within the community property and property management company.

[0007] However, in actual use, the above system plays the role of realizing the automatic collection, aggregation and sharing of data, mainly to facilitate the relevant personnel to view and analyze;

[0008] The above method has a certain effect in the management of daily pedestrians and infrequently used equipment. However, for building power equipment, especially power equipment affected by residents, such as elevators, floor induction lighting, building access control, etc., it is affected by the external environment and human factors, and it is used frequently for a long time, and is the focus of attention. The existing solution is used to simply monitor the power equipment, which cannot achieve the expected intelligent management effect. When in use, it still adopts a solution of fixed maintenance day maintenance and maintenance only when a fault occurs. It is impossible to fully understand the use status of the power equipment. Therefore, it is impossible to fully manage its use and cannot meet people's requirements. For this reason, we have developed an intelligent management method and system for community properties based on the Internet of Things. Summary of the invention

[0009] 1. Technical issues to be resolved

[0010] In view of the deficiencies in the prior art, the present invention provides an intelligent property management method and system for a residential area based on the Internet of Things, which collects the needs of residential residents, studies the usage loss of equipment according to the needs, obtains real-time residential information data of building residents, analyzes the human impact value, and predicts the loss of electrical equipment in combination with human impact and environmental impact. It can help relevant technical personnel to understand the usage status of building power equipment in a timely manner, facilitate relevant personnel to more reasonably formulate equipment maintenance technology, realize equipment maintenance during idle time, and effectively avoid the impact of equipment failure on residents' normal life. The overall use effect is good, it has a good use prospect, and effectively solves the problems raised in the background technology.

[0011] (II) Technical solution

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0013] The intelligent management system for residential properties based on the Internet of Things includes data acquisition module, demand analysis module, environment analysis module, risk estimation module and abnormality correction module:

[0014] Data acquisition module: used to collect real-time working data of community building power equipment, current and predicted future environmental data of the building, historical working data of community building power equipment, historical residential information data of building residents, and real-time residential information data of building residents;

[0015] Demand analysis module: used to sort out the collected historical working data of the community building power equipment and the historical residential information data of the building residents, and then conduct demand analysis and human impact analysis on the sorted data to calculate the equipment demand loss value and human impact value;

[0016] Environmental analysis module: used to pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. It also analyzes the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combines the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. It then compares the building loss value with the set maintenance interval, and executes the corresponding maintenance plan according to the comparison results;

[0017] Risk estimation module: used to analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range;

[0018] If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio;

[0019] If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold;

[0020] If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel;

[0021] If the work risk value is lower than the risk threshold, no action will be taken;

[0022] Abnormal correction module: used to obtain the data detected during the maintenance of building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, the equipment demand loss value is corrected, and the corrected equipment demand loss value replaces the original equipment demand loss value, and the data before and after the correction are recorded.

[0023] Furthermore, the environmental data includes temperature data, humidity data and air pollution data around the building power equipment, the working data of the building power equipment includes voltage data, current data, output power data, temperature speed, electric energy data and working noise data, and the historical living information data of the building residents includes the total number of building residents and the demand of the building residents for the operation of power equipment.

[0024] Furthermore, when arranging the collected historical working data of the community building power equipment and the historical residential information data of the building residents, the collected data are arranged in chronological order;

[0025] The steps for demand analysis of the sorted data are as follows:

[0026] Obtain requirements from building residents for electrical installation work;

[0027] Conduct experiments and tests on the obtained requirements, and evaluate the equipment loss value LRS caused by a single realization of the requirements;

[0028] The steps for human impact analysis on the collated data are as follows:

[0029] Obtain the total number of building residents recorded in the historical residence information data of the building residents and the number of times the power equipment in the community building was used N days after the historical residence information of the building residents was collected;

[0030] Calculate the human impact value. The specific calculation formula is as follows:

[0031]

[0032] In the formula, HFr is the calculated human impact value, M is the total number of residents in the building, and Ucs is i The number of times the power equipment in the residential building was used on the i-th day after the historical residence information of the building residents was collected;

[0033] Based on the results of demand analysis and human impact analysis, the total loss caused by the use of equipment in a single day in the future is predicted. The specific calculation formula is as follows:

[0034]

[0035] In the formula, LRr is the total loss caused by the use of the equipment in a single day in the future, K OU is the upper limit of normal daily usage, a and B are constant data, a>B>10, and ROUNDUP() is an upward integer function.

[0036] Furthermore, the environmental impact value includes a real-time environmental impact value calculated based on the environmental data of the building on that day and a future environmental impact value based on future environmental data:

[0037] The specific calculation formula is as follows:

[0038]

[0039] In the formula, EIs is the real-time environmental impact value, D is the number of categories of environmental data, and xs i is the impact coefficient of the i-th type of environmental data, Bee i Class I standard environmental data for building electrical equipment, is the average working environment data of the i-th category of building power equipment, G is the number of times the environmental data is monitored in a single working day, HJCS j is the environmental data monitored for the jth time;

[0040] EIw is the future environmental impact value, HJCSmax i is the predicted maximum value of the i-th working environment, HJCSmin i is the predicted minimum data of the i-th working environment, xzsj i The correction data of the i-th category working environment data and the actual working environment data;

[0041] The calculation formula for predicting future loss values ​​is as follows:

[0042] LRz=LRr+EIw

[0043] Where LRz is the predicted future loss value.

[0044] Furthermore, the steps for analyzing the historical loss value of building power equipment based on the historical working data of the community building power equipment are as follows:

[0045] Obtain maintenance data of residential building power equipment and historical work data after maintenance;

[0046] Extract equipment loss data from maintenance data;

[0047] Acquire and calculate historical working data to obtain energy utilization efficiency and equipment response time;

[0048] The historical loss value of building power equipment is calculated based on equipment loss data, energy utilization efficiency and equipment response time. The specific calculation formula is as follows:

[0049]

[0050] Where LRl is the historical loss value of building power equipment, ηx is the energy utilization efficiency, ηxs is the weighting coefficient of energy utilization efficiency, Tj is the equipment response time, Tb is the standard equipment response time, Txs is the weighting coefficient of the equipment response time change rate, F is the number of components of building power equipment, mscd i is the loss degree of the i-th accessory, bzxs i is the specific gravity coefficient of the i-th accessory.

[0051] Furthermore, the formula for predicting the building loss value on the next H day is as follows:

[0052]

[0053] In the formula, LR1H is the building loss value on the future Hth day, LRls is the historical loss value after the last maintenance of the building power equipment, R is the number of days from the last maintenance date to the current date, and LRr1 i EIs is the total loss caused by using the equipment on the i-th day after the last maintenance. i is the real-time environmental impact value on the ith day after the last maintenance, LRr j is the total loss caused by using the equipment on the jth day in the future, K i is the number of times the equipment works on the i-th day after the last maintenance, β is a constant coefficient, β>5.

[0054] Furthermore, the maintenance interval is set to (45%, 60%), the building loss value is compared with the set maintenance interval, and the corresponding maintenance plan is executed according to the comparison result as follows:

[0055] The predicted loss value LRlw1 of all building electrical equipment on the nearest maintenance day in the future and the predicted loss value LRlw2 of the building on the second maintenance day in the future;

[0056] Compare all building loss values ​​LRlw1 of the most recent maintenance day with the maintenance interval;

[0057] If LRlw1>60%, the corresponding building power equipment will be stopped and maintained immediately, and a list of building power equipment with LRlw2>45% will be obtained to form the maintenance plan for this building equipment;

[0058] If LRlw1>60% does not exist, but 60%>LRlw1>45% exists, the maintenance day will be advanced, and the LRlw1′ of the maintenance day after the advancement will satisfy LRlw1′≤45%. At the same time, a list of building power equipment with LRlw2>45% is obtained to form the maintenance plan for this building equipment.

[0059] If LRlw1≤45%, the maintenance day will not be adjusted and normal maintenance will be performed. The equipment to be maintained will be the list of building power equipment with LRlw2>45%.

[0060] Furthermore, the steps of analyzing the working status of the building power equipment and calculating the fluctuation value of the working data of the building power equipment are as follows:

[0061] Arrange the real-time collected working data of the community building power equipment;

[0062] Calculate the difference ratio RPD between a certain working data of the residential building power equipment and the quasi-working data. The specific calculation formula is as follows:

[0063]

[0064] In the formula, Pj is the working data of the power equipment in the residential building, and Pb is the set standard working data;

[0065] Obtain the maximum and minimum values ​​of the difference data, and calculate the working data fluctuation value RPDb=RPDmax-RPDmin of the building power equipment;

[0066] The set fluctuation range is [0, γ%], 5<γ<10;

[0067] If RPDb>γ%, calculate the distance ratio DS. The specific formula is as follows:

[0068]

[0069] If 0<DS<1, an online monitoring warning is issued to the power equipment maintenance personnel:

[0070] If 1≤DS, a shutdown detection warning is issued to the power equipment maintenance personnel:

[0071] The formula for calculating the work risk value is as follows:

[0072]

[0073] Where JHA is the work risk value, shxs is the influence coefficient of equipment loss, and bdxs is the influence coefficient of equipment work fluctuation.

[0074] Furthermore, the formula for calculating the difference LRCZ between the real-time loss value and the predicted loss value is as follows:

[0075]

[0076] Where Tw is the number of days between this maintenance and the last maintenance, and LRlj is the real-time loss value of the building power equipment calculated based on the maintenance data;

[0077] When correcting the human impact value, first calculate the total human loss value Then input the total artificial loss value LRrz into the following formula:

[0078]

[0079] Calculate the corrected equipment demand loss value LRS′.

[0080] Furthermore, the intelligent management method of community property based on the Internet of Things includes the following steps:

[0081] Real-time collection of working data of power equipment in residential buildings, current and predicted future environmental data of buildings, historical working data of power equipment in residential buildings, historical residential information data of building residents, and real-time residential information data of building residents;

[0082] The collected historical working data of the community building power equipment and the historical residential information data of the building residents are sorted out, and then the demand analysis and human impact analysis are carried out on the sorted data to calculate the equipment demand loss value and the human impact value;

[0083] Pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. Analyze the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combine the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. Then compare the building loss value with the set maintenance interval, and execute the corresponding maintenance plan according to the comparison result;

[0084] Analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range;

[0085] If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio;

[0086] If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold;

[0087] If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel;

[0088] If the work risk value is lower than the risk threshold, no action will be taken;

[0089] Obtain the data detected during the maintenance of the building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, correct the equipment demand loss value, and replace the original equipment demand loss value with the corrected equipment demand loss value, and record the data before and after the correction.

[0090] (III) Beneficial effects

[0091] The present invention provides a community property intelligent management method and system based on the Internet of Things, which has the following beneficial effects:

[0092] 1. The present invention provides an intelligent property management method and system for a residential area based on the Internet of Things. The method collects the needs of residential residents, studies the use loss of equipment according to the needs, obtains real-time residential information data of building residents, analyzes the human impact value, and predicts the loss of electrical equipment in combination with human impact and environmental impact. It can help relevant technical personnel to understand the use status of building power equipment in a timely manner, facilitate relevant personnel to formulate equipment maintenance technology more reasonably, realize equipment maintenance during idle time, and effectively avoid the impact of equipment failure on residents' normal life. The overall use effect is good and has a good prospect for use.

[0093] 2. The present invention provides an intelligent property management method and system for a residential area based on the Internet of Things. The method and system can also obtain the volatility of equipment operation, and analyze the risk value of equipment operation in combination with the equipment loss and the volatility data of equipment operation. The risk value can help relevant technical personnel directly determine the status of power equipment and whether the power equipment can continue to operate, thereby effectively reducing the risks caused by power equipment failures and improving the safety of building power equipment. The overall use effect is good and has a good prospect for use.

[0094] 3. The present invention provides an intelligent property management method and system for a residential area based on the Internet of Things, which can obtain data before and after the maintenance of power equipment, and further analyze the data, further adjust the loss data of the normal use of the electrical equipment, and make real-time adjustments to make subsequent predictions of power equipment more accurate and with relatively high prediction accuracy. The system realizes automatic supervision and prediction of building power equipment during operation, and can also analyze the risks of equipment operation and help formulate maintenance plans. It realizes automated supervision of building power equipment, with good overall effect and good prospects for use. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1This is a flowchart of the intelligent management system of residential property based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0096] 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.

[0097] Since the central idea of ​​the existing property management system based on the Internet of Things is to connect non-network electronic devices through the device proxy module and the object server, network equipment and community property management staff through the virtual proxy subsystem to the device and data management subsystem of the community server, and obtain unique device identification and data identification as well as device node control structure and data control structure through device registration and data registration in the device and data management subsystem, it can realize the effective collection and reliable link of various electronic devices, management personnel and community servers on a single platform, and realize the compatible sharing and automatic aggregation of data between each community server and the property company server, so as to achieve intelligent and real-time management within the community property and property management company.

[0098] The function of the above system is to realize automatic data collection and aggregation without manual data transcription, thereby reducing the workload of relevant property personnel.

[0099] However, in actual use, the above system only realizes the automatic collection, aggregation and sharing of data, which is mainly convenient for relevant personnel to view and analyze, but cannot realize the automatic management of power equipment;

[0100] The above method has a certain effect in the management of daily pedestrians and infrequently used equipment (ventilation equipment, which will only be activated in high humidity weather). However, for building power equipment, especially power equipment affected by residents, such as elevators, induction lighting at the bottom of the building (the induction lighting on each floor corridor has a lower manpower impact due to the small number of people passing by), building access control, etc., it is affected by the external environment and human factors, and its long-term and frequent use is the focus of attention. The existing solution is used to simply monitor the power equipment, which cannot achieve the expected intelligent management effect. When using building power equipment, fixed maintenance days and maintenance only when a fault occurs are still used. The use status of the power equipment cannot be fully understood. Therefore, its use cannot be fully managed and cannot meet people's requirements. For this reason, we have developed an intelligent management method and system for community properties based on the Internet of Things.

[0101] Therefore, the design system is aimed at the existing management systems, most of which are simple data collection and organization methods. A few systems set upper and lower thresholds, compare the working status of the equipment, and issue warnings when the equipment working data is outside the upper and lower thresholds. The effect is relatively simple, mainly to assist in viewing. However, equipment management is not only about managing the operation of the equipment, but also requires predicting equipment risks and formulating equipment maintenance plans.

[0102] Therefore, during the research and development process, the initial research and development idea was how to better predict the status of power equipment, determine whether the power equipment needs maintenance based on the prediction results, and formulate the corresponding maintenance period. After analysis, the use of fixed daily loss analysis can improve the accuracy of equipment loss prediction to a certain extent.

[0103] However, in actual use, it is found that the number of residents is different, the number of times it is used is different, and the loss caused is also different. Therefore, the effect predicted by the above scheme is not very accurate and can only serve as a reference.

[0104] Therefore, in the middle stage of research and development, the prediction plan was re-examined, the number of residents was analyzed, the corresponding number of usages was reasonably and scientifically calculated, and then the loss of power equipment was calculated based on the number of usages, so as to predict more accurate loss data.

[0105] However, in the actual use process, it is found that the equipment is not only affected by the loss during use, but also by the stability. The loss degree analysis alone is not enough to formulate a more perfect plan. Moreover, with the use of power equipment, the loss of single use of power equipment is gradually increasing, and frequent use will also accelerate the rate of increase in loss. Therefore, the above-mentioned solution has certain defects when used and needs to be further improved.

[0106] Therefore, in the later stage of research and development, a comprehensive analysis was conducted and an intelligent property management system for residential areas based on the Internet of Things was developed, which can help relevant personnel better supervise and maintain power equipment, and at the same time realize the prediction of the loss value and risk of power equipment, which can help relevant technical personnel better manage the power equipment in residential buildings.

[0107] Embodiment 1:

[0108] The hardware part of this IoT-based community property intelligent management system is based on the existing property server, and the collected data is also directly obtained from the existing property management system. There is no need to add new data collection devices separately. Therefore, the cost of its application is relatively low.

[0109] The software part of this community property intelligent management system based on the Internet of Things includes data acquisition module, demand analysis module, environment analysis module, risk estimation module and abnormal correction module. For its specific usage process, please refer to Figure 1 ,

[0110] The implementation of this system is based on the need for sufficient data as support. Therefore, the first step in the operation of this system is to collect the required data, and this step is based on the data acquisition module. The data acquisition module is connected to the existing property management system to obtain the required data from the existing property management system.

[0111] Data acquisition module: used to collect real-time working data of community building power equipment, current and predicted future environmental data of the building, historical working data of community building power equipment, historical living information data of building residents, and real-time living information data of building residents.

[0112] In actual use, in order to improve the overall security and data accuracy and non-tamperability, blockchain technology can be combined to make data collection more accurate and ensure data security and transparency. As for blockchain technology, its distributed storage characteristics, non-tamperability, and secure and reliable centralized distributed design applications are relatively common and belong to conventional technologies in this field. Therefore, no excessive description will be given.

[0113] Environmental data include temperature data, humidity data and air pollution data around the building's electrical equipment. The working data of the building's electrical equipment include voltage data, current data, output power data, temperature speed, electric energy data and working noise data. The historical residential information data of the building's residents include the total number of building residents and the building's residents' demand for the operation of electrical equipment.

[0114] The temperature and humidity data around the building's power equipment are obtained through the corresponding sensors that come with the power equipment or the corresponding sensors installed on each floor, while the air pollution data is obtained through local official detection data, or through the Internet of Things if there are corresponding detection equipment.

[0115] The requirements of building residents for the operation of electrical facilities are obtained through questionnaire surveys, such as the speed of elevators, the brightness of lighting fixtures, etc. Different requirements require different equipment operating powers, and the equipment losses are naturally different. Based on the user's needs, the survey and analysis can determine the equipment losses, which will be more accurate overall.

[0116] After obtaining the required data, it is necessary to further analyze and process the data. The first thing to analyze is the user's needs. The loss of the power equipment is analyzed based on the user's needs, and the process is based on the demand analysis module.

[0117] Demand analysis module: used to organize the collected historical working data of community building power equipment and historical living information data of building residents, and then conduct demand analysis and human impact analysis on the organized data to calculate the equipment demand loss value and human impact value.

[0118] When arranging the collected historical working data of the community building power equipment and the historical residential information data of the building residents, the collected data are arranged in chronological order;

[0119] Arranging them in chronological order allows you to view the data clearly and intuitively, making analysis easier.

[0120] The steps for demand analysis of the sorted data are as follows:

[0121] Obtain requirements from building residents for electrical installation work;

[0122] Conduct experiments and tests on the obtained requirements, and evaluate the equipment loss value LRS caused by a single realization of the requirements;

[0123] This step is obtained through multiple tests (more than 100 times) or through data provided by the manufacturer;

[0124] The steps for human impact analysis on the collated data are as follows:

[0125] Obtain the total number of building residents recorded in the historical residence information data of the building residents and the number of times the power equipment in the community building was used N days after the historical residence information of the building residents was collected;

[0126] Calculate the human impact value. The specific calculation formula is as follows:

[0127]

[0128] In the formula, HFr is the calculated human impact value, M is the total number of residents in the building, and Ucs is i The number of times the power equipment in the residential building was used on the i-th day after the historical residence information of the building residents was collected;

[0129] For example: N is 5, the total number of residents in the building M is 150, and the following table shows the number of elevator operations in 5 days: 122, 118, 131, 125 and 129. After calculation, the human impact value HFr = 0.82 is obtained.

[0130] When the total number of residents M in a building changes by more than 5%, HFr will be recalculated to ensure the accuracy of subsequent predictions.

[0131] Based on the results of demand analysis and human impact analysis, the total loss caused by the use of equipment in a single day in the future is predicted. The specific calculation formula is as follows:

[0132]

[0133] In the formula, LRr is the total loss caused by the use of the equipment in a single day in the future, K OU is the upper limit of normal daily usage, a and B are constant data, a>B>10, and ROUNDUP() is an upward integer function.

[0134] HFr×M+2≤K OU For normal use within the day, HFr×M+2>K OU This indicates frequent use within a single day.

[0135] And K OU It is determined by the quality of the product.

[0136] For example, the elevator of Unit 1, Building 5 in a certain community has a K OU If the number of uses exceeds 100 in one day, it will be over-operated, which will aggravate the loss and the loss will exceed the loss of normal use.

[0137] For example, frequent use of elevators will cause their mechanical parts to start and stop frequently, thus accelerating wear and tear. In particular, parts such as elevator doors and elevator door locks will be more damaged. Frequent use will also increase the burden on the elevator control system, which may cause the control system components to age and wear and tear, shortening the service life of the elevator. Frequent use will cause the elevator control system to receive a large number of repeated instructions, affecting the normal operation of the elevator, thereby increasing the wear and tear of the elevator.

[0138] In addition to human influence, power equipment is also affected by the external environment during normal use. Therefore, a separate environmental analysis is required to quickly understand the impact of the environment on the service life of the equipment. This process is based on the environmental analysis module.

[0139] The present invention provides a community property intelligent management method and system based on the Internet of Things, which collects the needs of community residents, studies the use loss of equipment according to the needs, obtains real-time residential information data of building residents, analyzes the human impact value, and predicts the loss of electrical equipment in combination with human impact and environmental impact. It can help relevant technical personnel to understand the use status of building power equipment in a timely manner, facilitate relevant personnel to more reasonably formulate equipment maintenance technology, realize equipment maintenance during idle time, and effectively avoid the impact of equipment failure on residents' normal life. The overall use effect is good and has a good use prospect.

[0140] Environmental analysis module: used to pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. It also analyzes the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combines the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. It then compares the building loss value with the set maintenance interval, and executes the corresponding maintenance plan according to the comparison results;

[0141] The environmental impact value includes the real-time environmental impact value calculated based on the environmental data of the building on that day and the future environmental impact value based on future environmental data:

[0142] The real-time environmental impact value calculates past impacts based on monitored data, while the future environmental impact value based on future environmental data predicts future impacts based on predicted data.

[0143] The specific calculation formula is as follows:

[0144]

[0145] In the formula, EIs is the real-time environmental impact value, D is the number of categories of environmental data, and xs i is the impact coefficient of the i-th type of environmental data, Bee i Class I standard environmental data for building electrical equipment, is the average working environment data of the i-th category of building power equipment, G is the number of times the environmental data is monitored in a single working day, HJCS j is the environmental data monitored for the jth time;

[0146] For example, if data is collected once every half an hour on average, then G is 48, and the number of categories of environmental data is 3: temperature, humidity, and air pollution index. The analysis of environmental factors is achieved through the changes in the 3 types of data in one day.

[0147] The above analysis is applicable to areas with smaller temperature differences, that is, areas where the difference between the highest and lowest temperatures of the day does not exceed 12°C.

[0148] For areas with large temperature differences, calculate the collected data and Bee i -5 or Bee i +5, take the minimum of the two distances, then calculate the average of the 48 groups of distances and replace the average with Thus, the real-time environmental impact value can be obtained.

[0149] EIw is the future environmental impact value, HJCSmax i is the predicted maximum value of the i-th working environment, HJCSmin iis the predicted minimum data of the i-th working environment, xzsj i The correction data of the i-th category working environment data and the actual working environment data;

[0150] The EIw calculated above is also for areas with small temperature differences. For areas with large temperature differences, the predicted HJCSmax i and HJCSmin i Calculate and Bee i -5 or Bee i +5, take the minimum of the two distances, then calculate the average of the remaining 2 groups of distances and replace the average with Thus, the real-time environmental impact value can be obtained.

[0151] When D is 3 groups, Bee i There are 3, and they are completely different. They are the optimal usage environment data obtained by the manufacturer's tests.

[0152] As for xzsj i , is calculated through previous data. For example, the elevator equipment in a certain area is located inside the wall. Therefore, the temperature feedback from the internal sensor is 5-10℃ higher than that of the outside. i The temperature compensation difference is between 5-10 (different in different seasons).

[0153] The calculation formula for predicting future loss values ​​is as follows:

[0154] LRz=LRr+EIw

[0155] Where LRz is the predicted future loss value

[0156] The future loss value of this step is the loss value of a single day in the future. For example, the loss value of the day three days later is equal to the sum of the human impact value and the environmental impact value of the day.

[0157] The steps to analyze the historical loss value of building power equipment based on the historical working data of community building power equipment are as follows:

[0158] Obtain maintenance data of residential building power equipment and historical work data after maintenance;

[0159] Extract equipment loss data from maintenance data;

[0160] Acquire and calculate historical working data to obtain energy utilization efficiency and equipment response time;

[0161] The historical loss value of building power equipment is calculated based on equipment loss data, energy utilization efficiency and equipment response time. The specific calculation formula is as follows:

[0162]

[0163] Where LRl is the historical loss value of building power equipment, ηx is the energy utilization efficiency, ηxs is the weighting coefficient of energy utilization efficiency, Tj is the equipment response time, Tb is the standard equipment response time, Txs is the weighting coefficient of the equipment response time change rate, F is the number of components of building power equipment, mscd i is the loss degree of the i-th accessory, bzxs i is the specific gravity coefficient of the i-th accessory.

[0164] The above calculation method can clearly and clearly reflect the overall status of the equipment.

[0165] The existing solution is to compare single data separately, that is, compare the data with the set threshold one by one. When it does not exceed the set threshold range, it is determined that there is no problem. This method does not take into account the accumulation of equipment loss. Therefore, it often cannot reflect the actual situation of the equipment, and therefore, sudden equipment failure may occur.

[0166] The formula for predicting the building loss value on the next H day is as follows:

[0167]

[0168] In the formula, LR1H is the building loss value on the future Hth day, LRls is the historical loss value after the last maintenance of the building power equipment, R is the number of days from the last maintenance date to the current date, and LRr1 i EIs is the total loss caused by using the equipment on the i-th day after the last maintenance. i is the real-time environmental impact value on the ith day after the last maintenance, LRr j is the total loss caused by using the equipment on the jth day in the future, K i is the number of times the equipment works on the i-th day after the last maintenance, β is a constant coefficient, β>5.

[0169] For example, if the last maintenance was 10 days ago and the building loss value needs to be predicted 5 days later, then R is 10 and H is 5. This method combines actual data with future data. It does not predict all the building loss values ​​for the entire future cycle after maintenance. This solution will make predictions based on all the data after maintenance, which greatly improves its accuracy and provides better results.

[0170] The maintenance interval is set to (45%, 60%). The building loss value is compared with the set maintenance interval. The corresponding maintenance plan is executed according to the comparison result as follows:

[0171] The predicted loss value LRlw1 of all building electrical equipment on the nearest maintenance day in the future and the predicted loss value LRlw2 of the building on the second maintenance day in the future;

[0172] Compare all building loss values ​​LRlw1 of the most recent maintenance day with the maintenance interval;

[0173] If LRlw1>60%, the corresponding building power equipment will be stopped and maintained immediately, and a list of building power equipment with LRlw2>45% will be obtained to form the maintenance plan for this building equipment;

[0174] For example, if the LRlw1 of a certain device is 60.4%, it needs immediate maintenance. At this time, obtain the LRlw2 of other devices. When LRlw2>45%, obtain the corresponding power equipment name and perform maintenance together to avoid the situation where multiple maintenance is required within one maintenance cycle.

[0175] LRlw1>60% means the equipment may be damaged at any time and the degree of damage is serious.

[0176] If LRlw1>60% does not exist, but 60%>LRlw1>45% exists, the maintenance day will be advanced, and the LRlw1′ of the maintenance day after the advancement will satisfy LRlw1′≤45%. At the same time, a list of building power equipment with LRlw2>45% is obtained to form the maintenance plan for this building equipment.

[0177] For example, if the LRlw1 of a certain device is 45.1%, all predicted future building loss values ​​are retrieved to check the dates when the future building loss values ​​are less than or equal to 45%, and then a date is selected from the middle for maintenance. At this time, the LRlw2 of other devices is obtained. When LRlw2>45%, the corresponding power equipment name is obtained and maintenance is performed together.

[0178] 60%>LRlw1>45% means that the equipment is in a period of accelerated loss and needs to be maintained as soon as possible to extend the service life of the power equipment.

[0179] If LRlw1≤45%, the maintenance day will not be adjusted and normal maintenance will be performed. The equipment to be maintained will be the list of building power equipment with LRlw2>45%.

[0180] This situation indicates that the power equipment is normal and can be maintained according to the normal maintenance plan.

[0181] Using the above solution to maintain power equipment can greatly extend the service life of the power equipment.

[0182] After analyzing the impact of the environment and human factors on the operation of power equipment, in order to further improve the safety of equipment operation, it is also necessary to conduct a corresponding assessment of the safety of equipment operation to determine whether the equipment can operate stably and safely. This process is based on the risk assessment module.

[0183] Risk estimation module: used to analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range;

[0184] If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio;

[0185] If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold;

[0186] If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel;

[0187] If the work risk value is lower than the risk threshold, no action will be taken;

[0188] Risk thresholds are set based on the quality and type of equipment.

[0189] The steps for analyzing the working status of building power equipment and calculating the fluctuation value of working data of building power equipment are as follows:

[0190] Arrange the real-time collected working data of the community building power equipment;

[0191] Calculate the difference ratio RPD between a certain working data of the residential building power equipment and the quasi-working data. The specific calculation formula is as follows:

[0192]

[0193] In the formula, Pj is the working data of the power equipment in the residential building, and Pb is the set standard working data;

[0194] The above steps are for calculating the fluctuation ratio of the working data.

[0195] Obtain the maximum and minimum values ​​of the difference data, and calculate the working data fluctuation value RPDb=RPDmax-RPDmin of the building power equipment;

[0196] Although the equipment has a normal working fluctuation range, which is generally an up-and-down floating ratio, the existence of sudden up-and-down changes indicates that the equipment is in an unstable state and may be abnormal.

[0197] The set fluctuation range is [0, γ%], 5<γ<10;

[0198] For example, the normal fluctuation range of general equipment is (-5%, 5%), but there are cases where it fluctuates from upper to lower limits. This situation is obviously abnormal and requires separate analysis and judgment.

[0199] For example, when γ in the above formula is 6, the data fluctuates from 4% to -3%, which is abnormal, but it is still within the normal fluctuation range (-5%, 5%). This situation needs to be analyzed separately.

[0200] If RPDb>γ%, calculate the distance ratio DS. The specific formula is as follows:

[0201]

[0202] If 0<DS<1, an online monitoring warning is issued to the power equipment maintenance personnel:

[0203] This situation is a fluctuation, but the fluctuation is still controllable. The data can be monitored online to determine whether there is any abnormality in the power equipment.

[0204] If 1≤DS, a shutdown detection warning is issued to the power equipment maintenance personnel:

[0205] This situation indicates that a major fault has occurred in the device.

[0206] The working data calculated in this step is the data in a constant state during use, such as the lighting brightness of a lamp, and does not include the data in the on and off states.

[0207] The formula for calculating the work risk value is as follows:

[0208]

[0209] Where JHA is the work risk value, shxs is the influence coefficient of equipment loss, and bdxs is the influence coefficient of equipment work fluctuation.

[0210] The work risk value is calculated by combining the equipment fluctuation data with the equipment loss data, which can more accurately reflect the status of the equipment, and the data reflected by it as an indicator is clearer.

[0211] The present invention provides a community property intelligent management method and system based on the Internet of Things, which can also obtain the volatility of equipment operation, and analyze the risk value of equipment operation in combination with the equipment loss and equipment operation volatility data. The risk value can help relevant technical personnel directly judge the status of power equipment and whether the power equipment can continue to operate, effectively reduce the risk caused by power equipment failure, and improve the safety of building power equipment. The overall use effect is good and has a good prospect for use.

[0212] During daily use of the system, when the power equipment is used to a certain extent, the loss of normal use will increase. At this time, if the original solution is used, it will not be possible to accurately judge the loss of the power equipment. Therefore, continuous correction is required, and the correction process is based on the abnormal correction module.

[0213] Abnormal correction module: used to obtain the data detected during the maintenance of building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, the equipment demand loss value is corrected, and the corrected equipment demand loss value replaces the original equipment demand loss value, and the data before and after the correction are recorded.

[0214] The formula for calculating the difference LRCZ between the real-time loss value and the predicted loss value is as follows:

[0215]

[0216] Where Tw is the number of days between this maintenance and the last maintenance, and LRlj is the real-time loss value of the building power equipment calculated based on the maintenance data;

[0217] Abnormal correction is started after each maintenance of the electric equipment, therefore there is a statement of this maintenance and the last maintenance.

[0218] When correcting the human impact value, first calculate the total human loss value Then input the total artificial loss value LRrz into the following formula:

[0219]

[0220] Calculate the corrected equipment demand loss value LRS′.

[0221] The results in this patent are all obtained through computer calculations.

[0222] The present invention provides a community property intelligent management method and system based on the Internet of Things, which can obtain data before and after the maintenance of power equipment, and further analyze the data, further adjust the loss data of the normal use of the electrical equipment, and make real-time adjustments to make subsequent predictions of the power equipment more accurate and with relatively high prediction accuracy. The system realizes automatic supervision and prediction of building power equipment during operation, and can also analyze the risks of equipment operation and help formulate maintenance plans. It realizes automated supervision of building power equipment, has a good overall effect, and has a good prospect for use.

[0223] The weight coefficient involved in the above formula is determined by the coefficient of variation method. The coefficient of variation method is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of an indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator to obtain the weight of the indicator through calculation, and therefore is objective.

[0224] Example 2

[0225] The intelligent management method of community property based on the Internet of Things includes the following steps:

[0226] Real-time collection of working data of power equipment in residential buildings, current and predicted future environmental data of buildings, historical working data of power equipment in residential buildings, historical residential information data of building residents, and real-time residential information data of building residents;

[0227] The collected historical working data of the community building power equipment and the historical residential information data of the building residents are sorted out, and then the demand analysis and human impact analysis are carried out on the sorted data to calculate the equipment demand loss value and the human impact value;

[0228] Pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. Analyze the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combine the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. Then compare the building loss value with the set maintenance interval, and execute the corresponding maintenance plan according to the comparison result;

[0229] Analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range;

[0230] If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio;

[0231] If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold;

[0232] If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel;

[0233] If the work risk value is lower than the risk threshold, no action will be taken;

[0234] Obtain the data detected during the maintenance of the building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, correct the equipment demand loss value, and replace the original equipment demand loss value with the corrected equipment demand loss value, and record the data before and after the correction.

[0235] In the application, the several formulas involved are all calculated by taking their numerical values ​​after removing the dimensions, and the formulas are established by collecting a large amount of data and performing software simulation to obtain a formula for the most recent actual situation. Some coefficients or weights in the formulas are set by technical personnel in this field according to actual conditions, so they will not be elaborated here.

[0236] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0237] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0238] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. The intelligent management system of community property based on the Internet of Things is characterized by: include: Data acquisition module: used to collect real-time working data of community building power equipment, current and predicted future environmental data of the building, historical working data of community building power equipment, historical residential information data of building residents, and real-time residential information data of building residents; Demand analysis module: used to sort out the collected historical working data of the community building power equipment and the historical residential information data of the building residents, and then conduct demand analysis and human impact analysis on the sorted data to calculate the equipment demand loss value and human impact value; Environmental analysis module: used to pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. It also analyzes the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combines the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. It then compares the building loss value with the set maintenance interval, and executes the corresponding maintenance plan according to the comparison results; Risk estimation module: used to analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range; If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio; If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold; If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel; If the work risk value is lower than the risk threshold, no action will be taken; Abnormal correction module: used to obtain the data detected during the maintenance of building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, the equipment demand loss value is corrected, and the corrected equipment demand loss value replaces the original equipment demand loss value, and the data before and after the correction are recorded.

2. The intelligent property management system for residential areas based on the Internet of Things according to claim 1 is characterized by: Environmental data include temperature data, humidity data and air pollution data around the building's electrical equipment. The working data of the building's electrical equipment include voltage data, current data, output power data, temperature speed, electric energy data and working noise data. The historical residential information data of the building's residents include the total number of building residents and the building's residents' demand for the operation of electrical equipment.

3. The intelligent property management system for residential areas based on the Internet of Things according to claim 2 is characterized by: When arranging the collected historical working data of the community building power equipment and the historical residential information data of the building residents, the collected data are arranged in chronological order; The steps for demand analysis of the sorted data are as follows: Obtain requirements from building residents for electrical installation work; Conduct experiments and tests on the obtained requirements, and evaluate the equipment loss value LRS caused by a single realization of the requirements; The steps for human impact analysis on the collated data are as follows: Obtain the total number of building residents recorded in the historical residence information data of the building residents and the number of times the power equipment in the community building was used N days after the historical residence information of the building residents was collected; Calculate the human impact value. The specific calculation formula is as follows: In the formula, HFr is the calculated human impact value, M is the total number of residents in the building, and Ucs is i The number of times the power equipment in the residential building was used on the i-th day after the historical residence information of the building residents was collected; Based on the results of demand analysis and human impact analysis, the total loss caused by the use of equipment in a single day in the future is predicted. The specific calculation formula is as follows: In the formula, LRr is the total loss caused by the use of the equipment in a single day in the future, K OU is the upper limit of normal daily usage, a and B are constant data, a>B>10, and ROUNDUP() is an upward integer function.

4. The intelligent property management system for residential areas based on the Internet of Things according to claim 3 is characterized by: The environmental impact value includes the real-time environmental impact value calculated based on the environmental data of the building on that day and the future environmental impact value based on future environmental data: The specific calculation formula is as follows: In the formula, EIs is the real-time environmental impact value, D is the number of categories of environmental data, and xs i is the impact coefficient of the i-th type of environmental data, Bee i Class I standard environmental data for building electrical equipment, is the average working environment data of the i-th category of building power equipment, G is the number of times the environmental data is monitored in a single working day, HJCS j is the environmental data monitored for the jth time; EIw is the future environmental impact value, HJCSmax i is the predicted maximum value of the i-th working environment, HJCSmin i is the predicted minimum data of the i-th working environment, xzsj i The correction data of the i-th category working environment data and the actual working environment data; The calculation formula for predicting future loss value is as follows: LRz=LRr+EIw Where LRz is the predicted future loss value.

5. The intelligent property management system for residential areas based on the Internet of Things according to claim 4 is characterized by: The steps to analyze the historical loss value of building power equipment based on the historical working data of community building power equipment are as follows: Obtain maintenance data of residential building power equipment and historical work data after maintenance; Extract equipment loss data from maintenance data; Acquire and calculate historical working data to obtain energy utilization efficiency and equipment response time; The historical loss value of building power equipment is calculated based on equipment loss data, energy utilization efficiency and equipment response time. The specific calculation formula is as follows: Where LRl is the historical loss value of building power equipment, ηx is the energy utilization efficiency, ηxs is the weighting coefficient of energy utilization efficiency, Tj is the equipment response time, Tb is the standard equipment response time, Txs is the weighting coefficient of the equipment response time change rate, F is the number of components of building power equipment, mscd i is the loss degree of the i-th accessory, bzxs i is the specific gravity coefficient of the i-th accessory.

6. The intelligent property management system for residential areas based on the Internet of Things according to claim 5 is characterized by: The formula for predicting the building loss value on the next H day is as follows: In the formula, LR1H is the building loss value on the future Hth day, LRls is the historical loss value after the last maintenance of the building power equipment, R is the number of days from the last maintenance date to the current date, and LRr1 i EIs is the total loss caused by using the equipment on the i-th day after the last maintenance. i is the real-time environmental impact value on the ith day after the last maintenance, LRr j is the total loss caused by using the equipment on the jth day in the future, K i is the number of times the equipment works on the i-th day after the last maintenance, β is a constant coefficient, β>5.

7. The intelligent property management system for residential areas based on the Internet of Things according to claim 6 is characterized by: The maintenance interval is set to (45%, 60%). The building loss value is compared with the set maintenance interval. The corresponding maintenance plan is executed according to the comparison result as follows: The predicted loss value LRlw1 of all building electrical equipment on the nearest maintenance day in the future and the predicted loss value LRlw2 of the building on the second maintenance day in the future; Compare all building loss values ​​LRlw1 of the most recent maintenance day with the maintenance interval; If LRlw1>60%, the corresponding building power equipment will be stopped and maintained immediately, and a list of building power equipment with LRlw2>45% will be obtained to form the maintenance plan for this building equipment; If LRlw1>60% does not exist, but 60%>LRlw1>45% exists, the maintenance day will be advanced, and the LRlw1′ of the maintenance day after the advancement will satisfy LRlw1′≤45%. At the same time, a list of building power equipment with LRlw2>45% is obtained to form the maintenance plan for this building equipment. If LRlw1≤45%, the maintenance day will not be adjusted and normal maintenance will be performed. The equipment to be maintained will be the list of building power equipment with LRlw2>45%.

8. The intelligent property management system for residential areas based on the Internet of Things according to claim 7 is characterized by: The steps for analyzing the working status of building power equipment and calculating the fluctuation value of working data of building power equipment are as follows: Arrange the real-time collected working data of the community building power equipment; Calculate the difference ratio RPD between a certain working data of the residential building power equipment and the quasi-working data. The specific calculation formula is as follows: In the formula, Pj is the working data of the power equipment in the residential building, and Pb is the set standard working data; Obtain the maximum and minimum values ​​of the difference data, and calculate the working data fluctuation value RPDb=RPDmax-RPDmin of the building power equipment; The set fluctuation range is [0, γ%], 5<γ<10; If RPDb>γ%, calculate the distance ratio DS. The specific formula is as follows: If 0<DS<1, an online monitoring warning is issued to the power equipment maintenance personnel: If 1≤DS, a shutdown detection warning is issued to the power equipment maintenance personnel: The formula for calculating the work risk value is as follows: Where JHA is the working risk value, shxs is the influence coefficient of equipment loss, and bdxs is the influence coefficient of equipment working fluctuation.

9. The intelligent property management system for residential areas based on the Internet of Things according to claim 8 is characterized by: The formula for calculating the difference LRCZ between the real-time loss value and the predicted loss value is as follows: Where Tw is the number of days between this maintenance and the last maintenance, and LRlj is the real-time loss value of the building power equipment calculated based on the maintenance data; When correcting the human impact value, first calculate the total human loss value Then input the total artificial loss value LRrz into the following formula: Calculate the corrected equipment demand loss value LRS′.

10. A community property intelligent management method based on the Internet of Things, using any system described in claims 1 to 9, characterized in that: The steps include: Real-time collection of working data of power equipment in residential buildings, current and predicted future environmental data of buildings, historical working data of power equipment in residential buildings, historical residential information data of building residents, and real-time residential information data of building residents; The collected historical working data of the community building power equipment and the historical residential information data of the building residents are sorted out, and then the demand analysis and human impact analysis are carried out on the sorted data to calculate the equipment demand loss value and the human impact value; Pre-process the current and future environmental data of the building, calculate the environmental impact value, and then combine the equipment demand loss value and the human impact value to predict the future loss value. Analyze the historical loss value of the building power equipment based on the historical working data of the community building power equipment, and combine the historical loss value and future loss value of the building power equipment to predict the building loss value in the future. Then compare the building loss value with the set maintenance interval, and execute the corresponding maintenance plan according to the comparison result; Analyze the real-time collected working data of the community building power equipment, analyze the working status of the building power equipment, calculate the working data fluctuation value of the building power equipment, and compare the working data fluctuation value of the building power equipment with the set fluctuation range; If the working data fluctuation value is outside the set fluctuation range, the distance ratio of the working data fluctuation value to the set fluctuation range is calculated, and the corresponding risk control strategy is executed according to the distance ratio; If the working data fluctuation value is within the set fluctuation range, the working risk value is predicted by combining the working data fluctuation value and the current building loss value, and then the working risk value is compared with the preset risk threshold; If the work risk value is higher than the risk threshold, the equipment will be shut down for maintenance, an early warning will be issued, and the equipment location data will be sent to the property management personnel; If the work risk value is lower than the risk threshold, no action will be taken; Obtain the data detected during the maintenance of the building power equipment, analyze the real-time loss value of the building power equipment at this time, and compare it with the predicted loss value of the building power equipment, calculate the difference between the real-time loss value and the predicted loss value, and compare the difference with the upper limit of the error. If the difference is higher than the upper limit of the error, correct the equipment demand loss value, and replace the original equipment demand loss value with the corrected equipment demand loss value, and record the data before and after the correction.

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