Intelligent comprehensive management platform for rental housing

Abstract

An intelligent comprehensive management platform for a rental house belongs to the technical field of house management and comprises a data acquisition module, a data arrangement module, a data analysis module and an early warning module. Wherein the data acquisition module is used for receiving data transmitted by sensing equipment in real time and sending the received data to the data arrangement module; the data arrangement module is used for classifying and arranging the data sent by the data acquisition module according to the house units and the tenant information, and constructing time sequence data of the living behavior of each tenant; and the data analysis module calculates a tenant living frequency numerical value based on the time sequence data of each tenant living behavior, and compares and analyzes the calculated tenant living frequency numerical value with a preset threshold range. According to the invention, the residence behavior of the tenant can be accurately mastered, the abnormity can be timely found and processed, and the management decision is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of housing management, and more specifically, to an intelligent integrated management platform for rent-guaranteed housing. Background Art

[0002] In today's society, the application of intelligent monitoring technology in the field of housing management still has obvious limitations, which brings many challenges to the supervision of housing use. In terms of the supervision of the actual living conditions of public rental housing, many regions still rely mainly on the traditional method of regular manual door-to-door inspections. The purpose of the inspection is mainly to check whether there are bad behaviors such as illegal subletting or lending of public rental housing, or unauthorized change of the original use of the house. However, this method of manual inspection has inherent defects. Due to limited manpower, it is difficult to arrange high-frequency inspections, and it is impossible to achieve full-time coverage supervision without blind spots. This inevitably leads to loopholes in the supervision system, making some violations unable to be detected at the first time, and the violations can be hidden and continued, which destroys the rules and order of the reasonable allocation and use of public rental housing resources.

[0003] Although smart monitoring equipment, such as smart door locks and indoor and outdoor cameras, has considerable application potential in public rental housing supervision, in the real environment, its installation coverage is still relatively narrow, and a comprehensive and effective monitoring network has not been formed. What is more difficult is that there are huge challenges in how to properly use the data collected by these monitoring devices. On the one hand, it is necessary to ensure that the behavior of public rental housing residents can be effectively monitored through monitoring data and illegal behaviors can be discovered in a timely manner; on the other hand, the privacy rights of residents must be fully protected to avoid the abuse or leakage of monitoring data. Seeking a balance between the two has become a problem that needs to be solved in the current application of smart monitoring technology in the supervision of public rental housing.

[0004] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] In view of this, the present invention discloses an intelligent comprehensive management platform for public rental housing, which solves the technical problems that the installation coverage of intelligent monitoring equipment in the real environment is relatively narrow, comprehensive and effective monitoring cannot be achieved, and the behavior of public rental housing residents cannot be effectively supervised.

[0006] According to a first aspect of the present invention, there is provided an intelligent integrated management platform for rent-protected housing, comprising a data collection module, a data sorting module, a data analysis module and an early warning module; wherein: The data acquisition module is used to receive the data transmitted from the sensing devices in real time and send the received data to the data sorting module; The data sorting module is used to classify and sort the data sent by the data acquisition module according to the housing unit and tenant information, and construct the time series data of each tenant's living behavior; The data analysis module calculates the tenant residence frequency value based on the time series data of each tenant's living behavior, and compares and analyzes the calculated tenant residence frequency value with the preset threshold range; If the tenant residence frequency value is outside the preset threshold range, a warning message is sent through the warning module.

[0007] According to the present invention, the sensing devices include an intelligent door lock, a human body induction sensor and a door magnetic sensor. Among them, the information collected by the intelligent door lock includes the unlocking time, the locking time, the door opening method information, and the unlocking success information. The door opening method information includes, but is not limited to, password, fingerprint, and IC card; The human body induction sensor is used to collect the detection time and activity intensity information of indoor human activities; The door magnetic sensor is used to collect the opening and closing state of the door and its change time, and mutually verify the data collected by the intelligent door lock, so that the data collected by the door magnetic sensor and the intelligent door lock are consistent.

[0008] According to the present invention, a door lock usage analysis model is set inside the intelligent door lock. The door lock usage analysis model performs real-time analysis on the door opening time, unlocking frequency data, and tenant identity information. If there is an abnormal usage situation within the preset time, a warning message is sent.

[0009] According to the present invention, the intelligent door lock, the human body induction sensor and the door magnetic sensor are all connected to the backend management system through a wireless communication protocol. The intelligent door lock, the human body induction sensor and the door magnetic sensor transmit data to the backend management system at different frequencies; among them, the door magnetic sensor transmits data immediately when the state of the door changes, and the human body induction sensor transmits data at a set time interval.

[0010] According to the present invention, the intelligent door lock has a time recording module, and the time recording module is used to record the time of each unlocking. The backend management system is provided with a database for storing the unlocking time, unlocking frequency data, and tenant identity information data, and classifying and storing them according to the tenant number and house number.

[0011] According to the present invention, the human body induction sensor is installed in the set activity area of the public rental housing. When the human body induction sensor detects human activities, it triggers a recording event, records time information, and transmits the data to the same back-end management system connected to the intelligent door lock. The back-end management system combines and analyzes the unlocking data of the intelligent door lock to determine whether the tenant is in the house and the duration of activities in the house.

[0012] According to the present invention, in the data analysis module, the time series data of each tenant's living behavior is fused and analyzed, and machine learning algorithms are used to train and learn historical data and real-time data to establish a normal usage behavior model. When new data is input, it is compared and analyzed with the normal behavior model; if the data deviates from the normal range and exceeds the set threshold, the early warning mechanism is automatically triggered, and decisions are made based on the deviation situation and data characteristics.

[0013] According to the present invention, during the process of establishing the normal usage behavior model, for missing data points, different filling methods are adopted according to different degrees of missingness, including: if it is the first quantity of missing values in the time series, it is filled according to the linear interpolation method of the front and back data; if it is the second quantity of missing values in the time series and the data has periodic characteristics, it is filled according to the historical data of the same period, where the first quantity of missing values is less than the second quantity of missing values.

[0014] According to the present invention, in the step of automatically sending out early warning information through the early warning module, the early warning information is pushed to relevant personnel through set channels, and the set channels include but are not limited to push notifications of mobile applications of property management personnel, email reminders, and pop-up prompts in the property management system.

[0015] According to the present invention, the induction device further includes dome cameras installed in the public areas of each floor in the public rental housing. The dome cameras use a zoom lens of 2.5 - 10 mm, and each dome camera is horizontally transmitted to the nearest floor weak current room through category 5e or category 6 network cables.

[0016] Technical effect: Through the close cooperation of the data acquisition module and the induction device, various types of data related to the use of public rental housing can be continuously obtained, and with the help of the data sorting module, accurate classification is carried out according to housing unit and tenant information, thereby constructing detailed and accurate tenant living behavior time series data. This enables the platform to comprehensively and meticulously understand the activities of each tenant in the public rental housing, including daily entry and exit times, usage time nodes of various facilities, etc., providing a solid data foundation for subsequent in-depth analysis.

[0017] The data analysis module calculates the tenant residence frequency value based on time series data, transforming the originally complex and discrete residence behaviors into intuitive quantitative indicators. This helps to quickly grasp the residence stability and activity of tenants from a macro level, providing a clear basis for the housing management department to evaluate the tenant residence patterns.

[0018] Comparing and analyzing the calculated tenant residence frequency value with the preset threshold range is the core mechanism for quickly locating abnormal residence behaviors. Once it is found that the tenant residence frequency value is outside the preset threshold range, the warning module will quickly send out a warning message to ensure that the housing management department can learn about potential abnormal situations in a timely manner.

[0019] The entire platform is based on a closed-loop process of data collection, collation, analysis, and warning, realizing the transformation of housing management from traditional manual experience judgment to a data-driven intelligent management mode. Through the comprehensive, accurate, and real-time data and analysis results provided by the platform, it is possible to understand the overall usage situation of public rental housing and the individual behavior characteristics of each tenant more scientifically and objectively, so as to make more accurate and reasonable management decisions.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present invention. According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present invention will be clearer. Brief Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can also be obtained based on these drawings; Figure 1 Shows a schematic diagram of an intelligent integrated management platform for public rental housing according to an embodiment of the present invention. Detailed Description of the Embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0023] The technical solution of the present invention will be described in detail below with specific embodiments. These several specific embodiments can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments.

[0024] Embodiment

[0025] Figure 1 The figure shows a schematic diagram of an intelligent integrated management platform for affordable rental housing according to an embodiment of the present invention. As shown in the figure, the platform includes a data collection module, a data sorting module, a data analysis module, and an early warning module; among them: The data collection module is used to receive the data transmitted from the sensing devices in real time and send the received data to the data sorting module; specifically, the sensing devices can be various, such as intelligent door locks installed on the doors of affordable rental housing, intelligent water and electricity meter measuring devices, and human infrared sensors indoors.

[0026] The data sorting module is used to classify and sort the data sent by the data collection module according to housing units and tenant information, and construct time series data of each tenant's living behavior; constructing time series data of each tenant's living behavior means arranging various living-related behavior data generated by the same tenant at different time points in chronological order to form a complete data chain.

[0027] The data analysis module calculates the tenant's living frequency value based on the time series data of each tenant's living behavior, and compares and analyzes the calculated tenant's living frequency value with a preset threshold range; specifically, the living frequency is calculated according to the number of days the tenant enters the house, that is, the total number of days with records of the tenant entering the house within a certain period (such as within a month) is counted as the living frequency value. Then, the calculated tenant's living frequency value is compared and analyzed with the preset threshold range. The preset threshold range is obtained through statistical analysis of the normal living conditions of a large number of past affordable rental housing tenants, and is a reference interval for measuring whether the tenant's living behavior is normal. For example, in the case of normal living, the number of days the tenant lives within a month is between 15 - 25 days, and this interval is the preset threshold range.

[0028] If the tenant's living frequency value is outside the preset threshold range, an early warning message is sent through the early warning module. This may mean that there are abnormalities in the tenant's living situation, such as not living here for a long time, subletting, or leaving the house idle.

[0029] In the embodiment of the present invention, the sensing devices include an intelligent door lock, a human body induction sensor, and a door magnetic sensor. Among them, the intelligent door lock is used to collect information such as the door opening time, door closing time, door opening method, and whether the door opening is successful. The door opening method information includes but is not limited to password, fingerprint, and IC card; The human body induction sensor is used to collect the detection time and activity intensity information of human activities indoors; The door magnetic sensor is used to collect the opening and closing state of the door and its change time, and mutually verify the data collected by the intelligent door lock. Through the mutual verification mechanism, it is ensured that the data collected by the door magnetic sensor about the opening and closing of the door and the data related to the door collected by the intelligent door lock (such as the opening time recorded by the intelligent door lock) are consistent, avoiding data deviation or inaccurate data caused by reasons such as the failure of a certain sensor. Based on the abnormal opening information collected by the intelligent door lock (opening the door at abnormal times, opening the door with multiple password errors, etc.), the abnormal door state changes of the door magnetic sensor, and the situation that the human body induction sensor detects activities during the time when there should be no one, it is possible to comprehensively judge whether there are security risks such as illegal intrusion, and realize security functions such as timely alarm; it is also possible to perform intelligent scenario linkage based on these data, automatically turn on the indoor lights and adjust the indoor temperature according to the person entering the door, etc., to improve the convenience and intelligence level of life or work.

[0030] In the embodiment of the present invention, a door lock usage analysis model is set inside the intelligent door lock. The door lock usage analysis model performs real-time analysis on the unlocking time, unlocking frequency data, and tenant identity information. If there is an abnormal usage situation within the preset time, a warning message is sent. For example, the preset time is set to 1 day, 12 hours, etc. Within this set time interval, if the data in terms of the above unlocking time, unlocking frequency, tenant identity, etc. shows abnormal situations that do not conform to the normal usage logic after analysis, the intelligent door lock system will actively send a warning message.

[0031] In the embodiment of the present invention, the intelligent door lock, the human body induction sensor, and the door magnetic sensor are all connected to the backend management system through a wireless communication protocol. The intelligent door lock, the human body induction sensor, and the door magnetic sensor transmit data to the backend management system at different frequencies; among them, the door magnetic sensor transmits data immediately when the state of the door changes, and the human body induction sensor transmits data at a set time interval.

[0032] In the embodiment of the present invention, the intelligent door lock has a time recording module. The time recording module is used to record the time of each unlocking. The backend management system is provided with a database for storing the unlocking time, unlocking frequency data, and tenant identity information data, and classifying and storing them according to the tenant number and house number. For example, through the tenant number, all the unlocking-related data of the door locks of all houses involved by a certain tenant can be quickly queried, and through the house number, it can be known which tenants have opened the door lock of the corresponding house, etc., which is convenient for accurately managing and statistically analyzing the usage of door locks for different tenants and different houses.

[0033] In the embodiment of the present invention, the human body induction sensor is installed in the set activity area of the public rental housing. When the human body induction sensor detects human activities, it triggers a recording event, records the time information, and transmits the data to the same backend management system connected to the intelligent door lock. The backend management system combines and analyzes the data with the unlocking data of the intelligent door lock to determine whether the tenant is in the house and the duration of activities in the house. By setting thresholds and rules to detect abnormal situations. For example, if the human body induction sensor does not detect human activities for a long time, but the door lock record shows that the tenant has entered the house, the system may trigger an alarm to indicate possible safety hazards or that the tenant needs help. On the contrary, if the door lock record shows that the tenant has left the house, but the human body induction sensor still detects human activities, the system will also trigger an alarm to check for problems such as unauthorized personnel intrusion.

[0034] In the embodiment of the present invention, in the data analysis module, the time series data of each tenant's living behavior is fused and analyzed. Machine learning algorithms are used to train and learn the historical data and real-time data to establish a normal usage behavior model. Specifically, for the various data collected by the data collection module, abnormal data caused by equipment failures, network problems, etc. is removed to ensure the uniformity of the timestamps of all devices, which is convenient for time series analysis. Useful features for model establishment are extracted from the original data, such as the daily door opening times, the distribution of door opening time periods, the indoor activity frequency, etc. Through methods such as correlation analysis and chi-square test, the features that contribute the most to the model prediction effect are selected, and the features with large differences in numerical ranges are scaled. Recurrent neural networks such as LSTM and GRU are used to analyze the time series data. The historical data is divided into a training set and a test set, and the training set data is used to train the model. The model performance is optimized by adjusting the model parameters. Real-time data stream processing technologies (such as Apache Kafka, Apache Flink, etc.) are used to process the real-time data transmitted from the devices. For the newly generated data, the model is updated in an online learning manner.

[0035] When new data is input, it is compared and analyzed with the normal behavior model. If the data deviates from the normal range and exceeds the set threshold, the early warning mechanism is automatically triggered, and decisions are made based on the deviation situation and data characteristics. It should be noted that by comprehensively using historical data and real-time data, with the powerful automatic learning and pattern recognition capabilities of machine learning algorithms, after repeated training, parameter adjustment and other processes, a normal usage behavior model is finally constructed. Through the normal usage behavior model, the normal range of water and electricity usage, the reasonable frequency and time distribution of entering and leaving the house, etc. in a certain season and a certain time period are clarified, which becomes an important reference standard for subsequent judgment of whether the tenant's living behavior is normal.

[0036] Specifically, when the new data is compared with the normal behavior model and it is found that it deviates from the normal range and exceeds the set threshold, it is determined that the living behavior of the current tenant has abnormal conditions. For example, if the water and electricity consumption of a certain tenant suddenly exceeds the normal consumption in the corresponding period of the normal behavior model by 50% and lasts for several consecutive days, and at the same time, the frequency of entering and leaving the house late at night also far exceeds the normal threshold, the abnormal determination condition is met in this case.

[0037] In the embodiment of the present invention, during the process of establishing the normal usage behavior model, for the missing data points, different filling methods are adopted according to different degrees of missing, including: if it is the first quantity of missing values in the time series, it is filled according to the linear interpolation method of the front and back data; if it is the second quantity of missing values in the time series and the data has periodic characteristics, it is filled according to the historical data in the same period, where the first quantity of missing values is less than the second quantity of missing values. Taking the electricity meter data of public rental housing as an example, assume that a certain tenant has five consecutive days of missing electricity meter data (the second quantity of missing values) in the second week of a certain month, and the electricity meter data shows periodicity every week (the usage varies regularly on weekdays and weekends). At this time, the electricity meter data in the same period of the second week of the past few months of this tenant can be checked to find a similar usage pattern to fill the current missing data. If the average electricity meter reading for these five days in the past same period was 20 degrees per day, then 20 degrees can be used to fill the current daily electricity meter reading.

[0038] In the embodiment of the present invention, in the step of automatically sending a warning message through the warning module, the warning message is pushed to relevant personnel through set channels, and the set channels include but are not limited to push notifications of the mobile application of property management personnel, email reminders, and pop-up prompts in the property management system.

[0039] In the embodiment of the present invention, the induction device further includes dome cameras installed in the public areas on each floor of the public rental housing. The dome cameras use a zoom lens of 2.5 - 10mm, and each dome camera is horizontally transmitted to the nearest floor weak current room through a category 5e or category 6 network cable. In the public rental housing monitoring system, selecting this category 5e or category 6 network cable can ensure that the video data collected by the dome cameras can be transmitted stably and quickly. "Horizontal transmission" means that the data of the camera is transmitted on the same floor, rather than a wiring method across floors. The weak current room is a place where various weak current devices (such as network switches, monitoring storage devices, etc.) are centrally placed, and it can be used as a node for data aggregation and management. By transmitting the data of the camera here, it is convenient to centrally process the video data.

[0040] Through the coordinated cooperation of each module, the intelligent integrated management platform for affordable rental housing demonstrates remarkable technical effects in accurately grasping tenants' living behaviors, promptly detecting and handling anomalies, optimizing management decisions, and ensuring the living environment, strongly promoting the development of housing management towards the direction of intelligence, standardization, and high efficiency.

[0041] The intelligent integrated management platform for affordable rental housing has the following advantages: Through the close cooperation between the data collection module and the sensing devices, various types of data related to the use of public rental housing can be continuously obtained, and with the help of the data sorting module, accurate classification can be carried out based on housing unit and tenant information, thus constructing detailed and accurate time series data of tenants' living behaviors. This enables the platform to comprehensively and meticulously understand the activities of each tenant in the public rental housing, including daily entry and exit times, usage time nodes of various facilities, etc., providing a solid data foundation for subsequent in-depth analysis.

[0042] The data analysis module calculates the tenant residence frequency value based on the time series data, transforming the originally complex and discrete living behaviors into intuitive quantitative indicators. This helps to quickly grasp the living stability and activity of tenants from a macroscopic level, providing a clear basis for the housing management department to evaluate the living patterns of tenants.

[0043] Comparing and analyzing the calculated tenant residence frequency value with the preset threshold range is the core mechanism for quickly locating abnormal living behaviors. Once it is found that the tenant residence frequency value is outside the preset threshold range, the warning module promptly issues a warning message to ensure that the housing management department can be informed of potential anomalies in a timely manner.

[0044] The entire platform is based on the closed-loop process of data collection, sorting, analysis, and warning, realizing the transformation of housing management from traditional manual experience judgment to a data-driven intelligent management mode. Through the comprehensive, accurate, and real-time data and analysis results provided by the platform, it is possible to more scientifically and objectively understand the overall usage situation of public rental housing and the individual behavior characteristics of each tenant, thereby making more accurate and reasonable management decisions.

[0045] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and without departing from the said principles, the embodiments of the present invention can have any deformation or modification.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent integrated management platform for rent-protected housing, characterized in that: It includes data collection module, data sorting module, data analysis module and early warning module; among which: The data acquisition module is used to receive data transmitted from the sensing device in real time and send the received data to the data sorting module; The data sorting module is used to classify and sort the data sent by the data collection module according to the housing unit and tenant information, and construct the time series data of each tenant's living behavior; The data analysis module calculates the tenant's residence frequency value based on the time series data of each tenant's residence behavior, and compares and analyzes the calculated tenant's residence frequency value with a preset threshold range; If the tenant's occupancy frequency value is outside the preset threshold range, an early warning message is issued through the early warning module.

2. The intelligent integrated management platform for rent-protected housing according to claim 1 is characterized in that: The sensing device includes a smart door lock, a human body sensing sensor and a door magnetic sensor, wherein the information collected by the smart door lock includes unlocking time, locking time, door opening method information, and whether the unlocking is successful. The door opening method information includes but is not limited to password, fingerprint, and IC card; The human body sensing sensor is used to collect the detection time and activity intensity information of indoor human activities; The door magnetic sensor is used to collect the opening and closing status of the door and the change time thereof, and mutually verify the data collected by the smart door lock so that the data collected by the door magnetic sensor and the smart door lock are consistent.

3. The intelligent integrated management platform for rent-protected housing according to claim 2 is characterized in that: A door lock usage analysis model is set inside the smart door lock, and the door opening time, unlocking frequency data, and tenant identity information are analyzed in real time through the door lock usage analysis model. If abnormal usage occurs within a preset time, an early warning message is sent.

4. The intelligent integrated management platform for rent-protected housing according to claim 2 is characterized in that: The smart door lock, the human body sensing sensor and the door magnetic sensor are all connected to the back-end management system through a wireless communication protocol. The smart door lock, the human body sensing sensor and the door magnetic sensor transmit data to the back-end management system at different frequencies; wherein the door magnetic sensor transmits data immediately when the state of the door changes, and the human body sensing sensor transmits data at a set time interval.

5. The intelligent integrated management platform for rent-protected housing according to claim 4 is characterized in that: The smart door lock has a time recording module, which is used to record the time of each unlocking. The back-end management system is provided with a database for storing unlocking time, unlocking frequency data, tenant identity information data, and classified storage according to tenant number and house number.

6. The intelligent integrated management platform for rent-protected housing according to claim 2 is characterized in that: The human body sensing sensor is installed in the set activity area of ​​the public rental housing. When the human body sensing sensor detects human activity, it triggers the recording of the event, records the time information, and transmits the data to the same back-end management system connected to the smart door lock. The back-end management system combines and analyzes the unlocking data of the smart door lock to determine whether the tenant is in the house and the duration of the activity in the house.

7. The intelligent integrated management platform for rent-protected housing according to claim 1 is characterized in that: In the data analysis module, the time series data of each tenant's living behavior is integrated and analyzed, and the historical data and real-time data are trained and learned using a machine learning algorithm to establish a normal use behavior model; When new data is input, it is compared and analyzed with the normal behavior model; if the data deviates from the normal range and exceeds the set threshold, the early warning mechanism is automatically triggered, and decisions are made based on the deviation and data characteristics.

8. The intelligent integrated management platform for rent-protected housing according to claim 7 is characterized in that: In the process of establishing the normal usage behavior model, different filling methods are used for missing data points according to different degrees of missingness, including: if it is a first number of missing values ​​in the time series, it is filled according to the linear interpolation method of the previous and next data; if it is a second number of missing values ​​in the time series and the data has periodic characteristics, it is filled according to the historical data of the same period, where the first number of missing values ​​is less than the second number of missing values.

9. The intelligent integrated management platform for rent-protected housing according to claim 1 is characterized in that: In the step of automatically issuing warning information through the warning module, the warning information is pushed to relevant personnel through set channels, and the set channels include but are not limited to mobile phone application push notifications, email reminders and pop-up prompts in the property management system of the property management personnel.

10. The intelligent integrated management platform for rent-protected housing according to claim 2 is characterized in that: The sensing equipment also includes hemispherical cameras installed in the public areas of each floor of the public rental housing. The hemispherical cameras use a 2.5~10mm zoom lens. Each hemispherical camera transmits horizontally to the weak current room on the nearest floor via a Category 5e or Category 6 network cable.

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