CN110969514A - Renting security method and system based on Internet of things - Google Patents
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Abstract
The embodiment of the application provides a renting security method and system based on the Internet of things. The method comprises the following steps: establishing an internet-of-things subnet in each rented house of the landlord by using the internet-of-things technology, collecting the internet-of-things subnets formed by each rented house of the landlord into one internet of things, and connecting the landlord terminal with the internet of things in a communication way; respectively acquiring the frequency data of personnel entering and exiting each rental house and the use data of household appliances in the rental houses in an internet of things subnet, and forming a personnel access data set and an appliance use data set; according to the specified time range, performing time series analysis on the personnel access data set and the electric appliance use data set, and extracting the constant population characteristics and the electric appliance use characteristics of each rental house; and detecting abnormal use conditions of the rented house by combining the population characteristics of the living quarters and the use characteristics of the electric appliances, and returning to the landlord. The method and the device improve the efficiency of traffic route planning through the prediction algorithm.
Description
Technical Field
The application relates to the field of Internet of things and community management, in particular to a renting security method and system based on the Internet of things.
Background
In the current community management, the landlord cannot supervise the state of a rented house in real time, and the information is asymmetric, so that a lot of community security risks appear.
For example, the tenants violate the contractual provisions, and privately transform the rented houses into the group houses, even the illegal business places, which causes huge security risks to the communities, and the landlords may also take on the responsibility without knowing;
therefore, there is a need for an efficient and accurate method and apparatus that addresses these problems during the rental housing installation process.
Disclosure of Invention
In view of this, the application aims to provide a renting room security method and system based on the internet of things, improve community management efficiency, and solve the technical problem that a homeowner cannot timely master the renting room condition in the existing renting room management process.
Based on the purpose, the application provides a renting room security method based on the Internet of things, which comprises the following steps:
establishing an internet-of-things subnet in each rented house of the landlord by using the internet-of-things technology, collecting the internet-of-things subnets formed by each rented house of the landlord into one internet of things, and connecting the landlord terminal with the internet of things in a communication way;
respectively acquiring the frequency data of personnel entering and exiting each rental house and the use data of household appliances in the rental houses in the internet of things subnet to form a personnel access data set and an appliance use data set;
according to a specified time range, performing time series analysis on the personnel access data set and the electric appliance use data set, and extracting the standing population characteristics and the electric appliance use characteristics of each rental house;
and detecting abnormal use conditions of the rented houses by combining the standing population characteristics and the electric appliance use characteristics, and returning to the landlord.
In some embodiments, detecting abnormal use of the rental housing in combination with the standing population characteristic and the appliance use characteristic comprises:
the standing population characteristics and the appliance usage characteristics are combined by the following formula:
u is obtained from α. IO + (1- α). E,
wherein, U is an abnormal confidence coefficient, IO is the confidence coefficient of the standing population characteristic, E is the confidence coefficient of the electric appliance use characteristic, and α is a weighting coefficient.
In some embodiments, the method further comprises:
and when the abnormal use condition of the rental house occurs, an abnormal alarm is also sent to the tenant, and confirmation is requested.
In some embodiments, an internet of things subnet is constructed in each rental housing of a landlord by using the technology of internet of things, and the internet of things subnets formed by each rental housing of the landlord are collected into one internet of things, and a landlord terminal is in communication connection with the internet of things, and the method includes the following steps:
the Internet of things subnets are connected through communication, and energy consumption standards of household appliances of the same type are exchanged and calibrated;
and sending the energy consumption standard to the landlord, and providing a list of household appliances with abnormal energy consumption.
In some embodiments, the method for forming the person access data set and the appliance usage data set includes the steps of respectively collecting the person frequency data entering and leaving each rented house and the usage data of the household appliances in the rented house in the internet of things subnet, and the steps include:
acquiring the frequency of the personnel entering and exiting each rental house through a face recognition technology, and carrying out anonymization treatment to identify the personnel entering and exiting each rental house by using identification codes;
the appliance usage data set is formed by recording the on and off times of each household appliance.
In some embodiments, performing time series analysis on the staff access data set and the appliance usage data set according to a specified time range to extract the standing population characteristics and the appliance usage characteristics of each rental housing, including:
in a specified time range, determining that the personnel who enter and exit the rental housing for times exceeding an entering and exiting frequency threshold and stay time exceeding a stay time threshold are effective entering and exiting personnel;
and performing time sequence analysis on the access data set formed by the effective personnel entering and exiting, and extracting the standing population characteristics of the rental housing.
In some embodiments, performing time series analysis on the staff access data set and the appliance usage data set according to a specified time range to extract the standing population characteristics and the appliance usage characteristics of each rental housing, including:
within a specified time range, determining that the service life of each household appliance in each rental room exceeds a service life threshold value as an effective use record;
and performing time sequence analysis on the electric appliance use data set formed by the effective use records, and extracting the electric appliance use characteristics of the rental house.
Based on above-mentioned purpose, this application has still provided a rent room security protection system based on thing networking, includes:
the system comprises a building module, a system management module and a system management module, wherein the building module is used for building an internet of things subnet in each renting room of a landlord by using the technology of internet of things, and collecting the internet of things subnets formed by each renting room of the landlord into one internet of things, and the landlord terminal is in communication connection with the internet of things;
the acquisition module is used for respectively acquiring the frequency data of personnel entering and exiting each rental house and the use data of household appliances in the rental houses in the internet of things subnet, and forming a personnel access data set and an appliance use data set;
the analysis module is used for carrying out time series analysis on the personnel access data set and the electric appliance use data set according to a specified time range and extracting the standing population characteristics and the electric appliance use characteristics of each rental house;
and the detection module is used for detecting the abnormal use condition of the rented house by combining the population standing characteristics and the electric appliance use characteristics and returning the abnormal use condition to the landlord.
In some embodiments, the system further comprises:
and the alarm module is used for sending an abnormal alarm to the tenant and requesting confirmation when the abnormal use condition occurs in the rental house.
In some embodiments, the acquisition module comprises:
the personnel frequency acquisition unit is used for acquiring the frequency of personnel entering and exiting each rental house through a face recognition technology, carrying out anonymization treatment and identifying the personnel entering and exiting each rental house by using identification codes;
and the electric appliance use acquisition unit is used for forming an electric appliance use data set by recording the opening and closing time of each household electric appliance.
Generally speaking, the idea of the application is that each rented house of the landlord is connected through the internet of things, all networks are interconnected to form an integral network, and from the viewpoint of the landlord, the network spans communities and geographic areas and is a virtual large network. Each internet of things subnet can send the anonymous statistical state of the rented house to the landlord. And detecting abnormal use conditions of the rental houses by combining the standing population characteristics and the electric appliance use characteristics. For example, people living in a rental house suddenly increase, or a user who rents a house does not normally open a television, and the television is suddenly opened frequently, and the unusual use of the rental house can be more accurately determined by combining the contents of the two aspects. In addition, when the abnormal security protection condition occurs in the rented house, an abnormal alarm is sent to the tenant and the landlord at the same time, and confirmation is requested.
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In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 shows a flow chart of a renting room security method based on the internet of things according to an embodiment of the invention.
Fig. 2 shows a flow chart of a renting room security method based on the internet of things according to an embodiment of the invention.
Fig. 3 is a block diagram of a rental housing security system based on the internet of things according to an embodiment of the present invention.
Fig. 4 is a block diagram of a rental housing security system based on the internet of things according to an embodiment of the present invention.
Fig. 5 shows a configuration diagram of an acquisition module according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flow chart of a renting room security method based on the internet of things according to an embodiment of the invention. As shown in fig. 1, the renting security method based on the internet of things includes:
and S11, constructing an internet-of-things subnet in each rented house of the landlord by using the internet-of-things technology, collecting the internet-of-things subnets formed by each rented house of the landlord into one internet of things, and connecting the landlord terminal with the internet of things in a communication way.
Specifically, the internet of things can be constructed in the following manner: generally, as an owner of a rented house, at least one set of rented house is used for renting, so that an internet-of-things subnet is automatically formed in each set of rented house, the internet-of-things subnet is only used for sensing the state information of the set of rented house through internet of things, and the internet-of-things subnet provides the internet-of-things sensing state information of the rented house for a tenant and a landlord outwards at the same time, namely, the internet-of-things sensing state of the rented house is provided for the tenant through each internet-of-things subnet, so that the tenant can master whether the rented house is abnormal or not at any time, and the intelligent entrance experience of the tenant is improved; on the other hand, each internet of things subnet provides the owner with the sensing state of the internet of things of the rented house, so that the owner can master whether the rented house is abnormal at any time, and the owner can clearly master the safety state of the house.
In addition, all the internet of things subnets are connected to form an internet of things, and an access interface is provided for a landlord, so that the landlord can know the security protection condition of the rented house corresponding to each internet of things subnet, and can perform transverse or longitudinal comparative statistical analysis on the use condition of each rented house, thereby further understanding the state of the rented house.
In one embodiment, the method for establishing an internet of things subnet in each rented house of a landlord by using the internet of things technology and collecting the internet of things subnets formed by each rented house of the landlord into one internet of things, wherein a landlord terminal is in communication connection with the internet of things, comprises the following steps:
the Internet of things subnets are connected through communication, and energy consumption standards of household appliances of the same type are exchanged and calibrated;
and sending the energy consumption standard to the landlord, and providing a list of household appliances with abnormal energy consumption.
Specifically, due to errors of the sensors of the internet of things sensor network or different use conditions of the household appliances, certain errors may be generated in the estimation of the energy consumption level of each household appliance. For example, the landlord generally uses the same type of household appliances, which are also type a household appliances, in different rented houses, and the energy consumption levels of the landlord are different due to different purchase ages, so that the energy consumption evaluation standard of the type a household appliances in each internet of things subnet can be calibrated through communication between the internet of things subnets, and thus energy consumption rule discovery is performed on each rented house more accurately.
In addition, the energy consumption level of the household appliances in each rented house is evaluated, and abnormal energy consumption is immediately sent to the landlord when being found. For example, when all the household appliances in the rental house X are turned off during working hours on a weekday and the energy consumption is at a low level, the house owner should be notified immediately when the household appliances in the rental house X are all turned on and the energy consumption is at a peak level suddenly in one working day.
And step S12, acquiring the frequency data of the personnel entering and exiting each rental house and the use data of the household appliances in the rental houses in the internet of things subnet respectively, and forming a personnel access data set and an appliance use data set.
For example, the frequency of the people entering the rental housing can be acquired by accessing an access control system of the community and adopting a face recognition technology. It should be noted that, for privacy protection, after the information of each person is identified by the face recognition technology, the frequency of the person can be calculated only according to the information of each person, and the data returned to the landlord does not include any data related to personal privacy.
In one embodiment, the method for forming the person access data set and the appliance usage data set includes the steps of respectively collecting the person frequency data entering and leaving each rented house and the usage data of the household appliances in the rented house in the internet of things subnet, and comprises the following steps:
acquiring the frequency of the personnel entering and exiting each rental house through a face recognition technology, and carrying out anonymization treatment to identify the personnel entering and exiting each rental house by using identification codes;
the appliance usage data set is formed by recording the on and off times of each household appliance.
That is to say, whether the abnormal condition occurs in the rented house can be analyzed from two aspects of the frequency of the personnel and the service condition of the household appliance: from the aspect of frequency, if the frequency of the personnel entering the rental housing changes suddenly, abnormal use conditions may exist in the rental housing, for example, 3 persons enter the rental housing every day and stay overnight at the early stage of rental, but 10 persons enter the rental housing every day and stay overnight after 3 months, which indicates that the rental housing is possibly rented out; from the use condition of the household appliances, if the use condition of the household appliances changes, abnormal use conditions may exist in the rented room, for example, the television is not turned on every day in the early stage of renting, but after 3 months, the television is turned on every day for a long time, which indicates that the tenant in the rented room may change.
And step S13, according to the specified time range, performing time series analysis on the personnel access data set and the electric appliance use data set, and extracting the standing population characteristics and the electric appliance use characteristics of each rental house.
Specifically, after a time range is specified, for example, for the time range of the last week, time sequence analysis of the personnel access data set and the electric appliance use data set is respectively carried out, and the standing population characteristics and the electric appliance use characteristics of each rental house are extracted. The standing population characteristics include the identity of the people who stay in the rental housing on average each day during the time period. That is, in order to protect the privacy of the tenant, each person who enters the rental house through face recognition has a unique identification record. From the landlord's perspective, he can only see how often he enters a rental housing, without knowing who the person is specifically. The standing population characteristics comprise the identification of each person and the corresponding access frequency of each identification.
In one embodiment, the time-series analysis of the personnel access data set and the electrical appliance usage data set according to a specified time range to extract the standing population characteristics and the electrical appliance usage characteristics of each rental housing includes:
in a specified time range, determining that the personnel who enter and exit the rental housing for times exceeding an entering and exiting frequency threshold and stay time exceeding a stay time threshold are effective entering and exiting personnel;
and performing time sequence analysis on the access data set formed by the effective personnel entering and exiting, and extracting the standing population characteristics of the rental housing.
Specifically, in order to improve the efficiency of the time sequence analysis, before the time sequence analysis is performed, an entry and exit frequency threshold and a retention time threshold may be specified, and in a specified time range, only the persons who enter and exit the rental housing more than the frequency threshold and stay in the rental housing for a time longer than the retention time threshold are reserved. For example, although a courier, a food delivery person, or the like may enter and exit a rental housing, they are not considered to be in an abnormal state because they enter and exit the rental housing frequently and have a short staying time.
In one embodiment, the time-series analysis of the personnel access data set and the electrical appliance usage data set according to a specified time range to extract the standing population characteristics and the electrical appliance usage characteristics of each rental housing includes:
within a specified time range, determining that the service life of each household appliance in each rental room exceeds a service life threshold value as an effective use record;
and performing time sequence analysis on the electric appliance use data set formed by the effective use records, and extracting the electric appliance use characteristics of the rental house.
For example, although the tenant does not turn on the television for a long time, it may be turned off immediately after accidentally touching the switch several times, and the turning on and turning off of the records of the television immediately may not prove that the rental house is abnormal, and therefore, the records need to be rejected, thereby improving the accuracy of the timing analysis.
And step S14, detecting abnormal use conditions of the rented house by combining the population features of the living quarters and the electric appliance use features, and returning to the landlord.
In one embodiment, the detecting abnormal use of the rental housing in combination with the standing population characteristic and the appliance use characteristic comprises:
the standing population characteristics and the appliance usage characteristics are combined by the following formula:
u is obtained from α. IO + (1- α). E,
wherein, U is an abnormal confidence coefficient, IO is the confidence coefficient of the standing population characteristic, E is the confidence coefficient of the electric appliance use characteristic, and α is a weighting coefficient.
Fig. 2 shows a flow chart of a renting room security method based on the internet of things according to an embodiment of the invention. As shown in fig. 2, the renting security method based on the internet of things further includes:
and step S15, when abnormal use condition occurs in the rental house, sending abnormal alarm to the tenant and requesting confirmation.
Specifically, when abnormal use of the rented house occurs, an abnormal alarm is sent to the tenant, so that the tenant can check whether the abnormal use of the rented house occurs or not at the first time and communicate with the landlord, and therefore the generation of contradiction is avoided.
Fig. 3 is a block diagram of a rental housing security system based on the internet of things according to an embodiment of the present invention. As shown in fig. 3, the whole renting security system based on the internet of things can be divided into:
the building module 31 is used for building an internet of things subnet in each rented house of the landlord by using the internet of things technology, and collecting the internet of things subnets formed by each rented house of the landlord into one internet of things, wherein the landlord terminal is in communication connection with the internet of things;
the acquisition module 32 is used for respectively acquiring the frequency data of the personnel entering and exiting each rental house and the use data of the household appliances in the rental houses in the internet of things subnet, and forming a personnel access data set and an appliance use data set;
the analysis module 33 is configured to perform time series analysis on the staff access data set and the electrical appliance usage data set according to a specified time range, and extract the standing population characteristics and the electrical appliance usage characteristics of each rental housing;
and the detection module 34 is used for detecting abnormal use conditions of the rented houses by combining the population standing characteristics and the electric appliance use characteristics and returning the abnormal use conditions to the landlord.
Fig. 4 is a block diagram of a rental housing security system based on the internet of things according to an embodiment of the present invention. As shown in fig. 4, the renting security system based on the internet of things further includes:
and the alarm module 35 is configured to send an abnormal alarm to the tenant and request confirmation when the abnormal use condition occurs in the rental house.
Fig. 5 shows a configuration diagram of an acquisition module according to an embodiment of the present invention. As shown in fig. 5, the collection module 32 of the renting room security system based on the internet of things includes:
the personnel frequency acquisition unit 321 is used for acquiring the frequency of personnel entering and exiting each rental house through a face recognition technology, performing anonymization processing, and identifying the personnel entering and exiting each rental house by using an identification code;
an appliance usage acquisition unit 322 for forming the appliance usage data set by recording the on and off time of each household appliance.
The functions of the modules in the systems in the embodiments of the present application may refer to the corresponding descriptions in the above methods, and are not described herein again.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A renting room security method based on the Internet of things is characterized by comprising the following steps:
establishing an internet-of-things subnet in each rented house of the landlord by using the internet-of-things technology, collecting the internet-of-things subnets formed by each rented house of the landlord into one internet of things, and connecting the landlord terminal with the internet of things in a communication way;
respectively acquiring the frequency data of personnel entering and exiting each rental house and the use data of household appliances in the rental houses in the internet of things subnet to form a personnel access data set and an appliance use data set;
according to a specified time range, performing time series analysis on the personnel access data set and the electric appliance use data set, and extracting the standing population characteristics and the electric appliance use characteristics of each rental house;
and detecting abnormal use conditions of the rented houses by combining the standing population characteristics and the electric appliance use characteristics, and returning to the landlord.
2. The method of claim 1, wherein detecting abnormal use of a rental housing in combination with the standing population characteristic and the appliance use characteristic comprises:
the standing population characteristics and the appliance usage characteristics are combined by the following formula:
u is obtained from α. IO + (1- α). E,
wherein, U is an abnormal confidence coefficient, IO is the confidence coefficient of the standing population characteristic, E is the confidence coefficient of the electric appliance use characteristic, and α is a weighting coefficient.
3. The method of claim 1, further comprising:
and when the abnormal use condition of the rental house occurs, an abnormal alarm is also sent to the tenant, and confirmation is requested.
4. The method of claim 1, wherein the establishing of the subnet of the internet of things in each rental housing of the landlord by using the technology of the internet of things and the grouping of the subnet of the internet of things formed by each rental housing of the landlord into one internet of things are performed, and the landlord terminal is in communication connection with the internet of things, and the method comprises the following steps:
the Internet of things subnets are connected through communication, and energy consumption standards of household appliances of the same type are exchanged and calibrated;
and sending the energy consumption standard to the landlord, and providing a list of household appliances with abnormal energy consumption.
5. The method according to claim 1, wherein the collecting of the frequency data of the persons entering and exiting each rented house and the usage data of the household appliances in the rented house in the internet of things subnet respectively forms a person access data set and an appliance usage data set, and comprises the following steps:
acquiring the frequency of the personnel entering and exiting each rental house through a face recognition technology, and carrying out anonymization treatment to identify the personnel entering and exiting each rental house by using identification codes;
the appliance usage data set is formed by recording the on and off times of each household appliance.
6. The method of claim 1, wherein the time-series analysis of the personnel access data set and the appliance usage data set according to a specified time range to extract the standing population characteristics and the appliance usage characteristics of each rental housing comprises:
in a specified time range, determining that the personnel who enter and exit the rental housing for times exceeding an entering and exiting frequency threshold and stay time exceeding a stay time threshold are effective entering and exiting personnel;
and performing time sequence analysis on the access data set formed by the effective personnel entering and exiting, and extracting the standing population characteristics of the rental housing.
7. The method of claim 1, wherein the time-series analysis of the personnel access data set and the appliance usage data set according to a specified time range to extract the standing population characteristics and the appliance usage characteristics of each rental housing comprises:
within a specified time range, determining that the service life of each household appliance in each rental room exceeds a service life threshold value as an effective use record;
and performing time sequence analysis on the electric appliance use data set formed by the effective use records, and extracting the electric appliance use characteristics of the rental house.
8. The utility model provides a rent house security protection system based on thing networking which characterized in that includes:
the system comprises a building module, a system management module and a system management module, wherein the building module is used for building an internet of things subnet in each renting room of a landlord by using the technology of internet of things, and collecting the internet of things subnets formed by each renting room of the landlord into one internet of things, and the landlord terminal is in communication connection with the internet of things;
the acquisition module is used for respectively acquiring the frequency data of personnel entering and exiting each rental house and the use data of household appliances in the rental houses in the internet of things subnet, and forming a personnel access data set and an appliance use data set;
the analysis module is used for carrying out time series analysis on the personnel access data set and the electric appliance use data set according to a specified time range and extracting the standing population characteristics and the electric appliance use characteristics of each rental house;
and the detection module is used for detecting the abnormal use condition of the rented house by combining the population standing characteristics and the electric appliance use characteristics and returning the abnormal use condition to the landlord.
9. The system of claim 8, further comprising:
and the alarm module is used for sending an abnormal alarm to the tenant and requesting confirmation when the abnormal use condition occurs in the rental house.
10. The system of claim 8, wherein the acquisition module comprises:
the personnel frequency acquisition unit is used for acquiring the frequency of personnel entering and exiting each rental house through a face recognition technology, carrying out anonymization treatment and identifying the personnel entering and exiting each rental house by using identification codes;
and the electric appliance use acquisition unit is used for forming an electric appliance use data set by recording the opening and closing time of each household electric appliance.
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