CN117528448A - Thing networking security inspection system under 5G basic station environment - Google Patents
Thing networking security inspection system under 5G basic station environment Download PDFInfo
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- CN117528448A CN117528448A CN202311546652.0A CN202311546652A CN117528448A CN 117528448 A CN117528448 A CN 117528448A CN 202311546652 A CN202311546652 A CN 202311546652A CN 117528448 A CN117528448 A CN 117528448A
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- 238000007689 inspection Methods 0.000 title claims abstract description 24
- 230000006855 networking Effects 0.000 title claims description 3
- 238000004458 analytical method Methods 0.000 claims abstract description 71
- 238000001514 detection method Methods 0.000 claims abstract description 66
- 238000013475 authorization Methods 0.000 claims abstract description 9
- 238000012790 confirmation Methods 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims description 192
- 230000002159 abnormal effect Effects 0.000 claims description 76
- 238000012502 risk assessment Methods 0.000 claims description 65
- 239000000779 smoke Substances 0.000 claims description 46
- 238000012216 screening Methods 0.000 claims description 20
- 238000011156 evaluation Methods 0.000 claims description 8
- 230000005856 abnormality Effects 0.000 claims description 5
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 2
- 230000006399 behavior Effects 0.000 description 17
- 238000000034 method Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 3
- 230000007774 longterm Effects 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B19/00—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow
- G08B19/005—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow combined burglary and fire alarm systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses an internet of things security inspection system in a 5G base station environment, which relates to the technical field of internet of things security inspection, and comprises an intelligent door lock detection information acquisition module, an authorization confirmation module, an intelligent door lock indoor detection information acquisition module, an intelligent door lock indoor analysis module, an intelligent door lock outdoor analysis module, an execution terminal and a cloud database.
Description
Technical Field
The invention relates to the technical field of security inspection of the Internet of things, in particular to a security inspection system of the Internet of things in a 5G base station environment.
Background
With the continuous development of technology, 5G technology has become a hot topic in the world today. The 5G technology provides strong support for the development of the fields of the Internet of things, big data, artificial intelligence and the like. Today, the intelligent door lock can generally utilize the 5G technology to carry out security inspection to utilize the security around the information analysis intelligent door lock that detects, thereby provide more functions and possibility for intelligent door lock, if security analysis around the intelligent door lock is inaccurate, influence on the one hand can't provide accurate security aassessment for relevant personnel, thereby can't provide relevant value for relevant personnel, on the other hand reduces intelligent door lock's functionality, thereby is unfavorable for improving intelligent door lock's product competitiveness, consequently, security analysis around the intelligent door lock is extremely important.
The safety analysis around the intelligent door lock in the prior art can approximately meet the current requirements, but certain defects exist, and the safety analysis is specifically implemented in the following layers: (1) Most among the prior art is to the security analysis around the intelligent lock according to the video that intelligent lock detected, to intelligent lock and the linkage of district internal monitoring video, smog concentration sensor and temperature sensor not high, intelligent lock and district internal monitoring video, smog concentration sensor and temperature sensor's linkage can provide abundant data source to the security analysis's of area around the assurance intelligent lock accuracy, the neglect of this aspect of prior art leads to the security analysis result around the intelligent lock accuracy not high, thereby reduced security analysis's around the intelligent lock worth and the referential.
(2) Most of the prior art is used for analyzing the safety of the public area outside the intelligent door lock according to the fact that the analysis strength of the safety of the internal area of the intelligent door lock is not high, the safety of the internal area of the intelligent door lock is difficult to guarantee due to the neglect of the aspect of the prior art, so that the safety reference of the internal area of the intelligent door lock cannot be provided for related personnel, the functionality of the intelligent door lock is reduced, the sales of the intelligent door lock is reduced to a certain extent, and the long-term sustainable development of the intelligent door lock is not facilitated.
Disclosure of Invention
The invention aims to provide an Internet of things security inspection system in a 5G base station environment, which solves the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme: the invention provides an internet of things security inspection system in a 5G base station environment, which comprises: the intelligent door lock detection information acquisition module is used for acquiring outdoor monitoring video of the intelligent door lock to which the target resident belongs and acquiring record information corresponding to the intelligent door lock to which the target resident belongs.
The authorization confirmation module is used for analyzing the current behavior state corresponding to the target resident according to the record information corresponding to the intelligent door lock to which the target resident belongs, wherein the current behavior state comprises a door-out state and a door-out state, and if the current behavior state of the target resident is the door-out state, an intelligent door lock indoor monitoring request is sent to the target resident.
The intelligent door lock indoor detection information acquisition module is used for acquiring an indoor monitoring video of the intelligent door lock to which the target resident belongs and dividing the indoor monitoring video into a plurality of indoor monitoring sub-pictures according to a set frame number.
The intelligent door lock indoor analysis module is used for analyzing an indoor artificial risk assessment index and an indoor fire risk assessment index corresponding to a target resident.
The intelligent door lock outdoor analysis module is used for analyzing the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident according to the outdoor monitoring video of the intelligent door lock to which the target resident belongs and in combination with the monitoring video corresponding to the building to which the target resident belongs, which is stored in the cloud database.
The execution terminal is used for carrying out corresponding early warning on the target resident according to the indoor artificial risk assessment index and the indoor fire risk assessment index corresponding to the target resident, and carrying out corresponding early warning on the target resident according to the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident.
The cloud database is used for storing the monitoring video corresponding to the building to which the target resident belongs, storing the historical indoor monitoring pictures corresponding to each historical detection time point of the target resident, storing the body outline of each resident corresponding to the target resident, storing the fire color value interval, storing the indoor smoke safety concentration and storing the outdoor smoke safety concentration.
Further, the recorded information comprises states corresponding to each change of the intelligent door lock, wherein the states comprise unlocking and locking.
Further, the specific analysis method for analyzing the current behavior state corresponding to the target resident comprises the following steps: acquiring the state corresponding to each change from the recorded information of the intelligent door lock to which the target resident belongs, acquiring the state corresponding to the last change of the intelligent door lock to which the target resident belongs, judging the current behavior state of the target resident as out-of-door if the state corresponding to the last change of the intelligent door lock to which the target resident belongs is out-of-door, and otherwise, judging the current behavior state of the target resident as out-of-door.
Further, the specific analysis method for analyzing the indoor artificial risk assessment index corresponding to the target resident comprises the following steps: extracting historical indoor monitoring pictures corresponding to each historical detection time point of a target resident from a cloud database, marking the historical indoor monitoring pictures as each historical monitoring picture corresponding to the target resident, accordingly, acquiring the outline of each object to which each historical monitoring picture corresponding to the target resident belongs, and acquiring the corresponding area S 'of each object' pj Wherein p is the number of each history monitoring picture, p=1, 2, & gt, q, q are any integers greater than 2, j is the number of each object to which the history monitoring picture belongs, j=1, 2, & gt, k, k are any integers greater than 2, and the outline of each object to which each indoor monitoring sub picture belongs corresponding to the target resident is obtained according to each indoor monitoring sub picture corresponding to the target resident.
Comparing the outline of each object of each indoor monitoring sub-picture corresponding to the target resident with the outline of each object of each history monitoring picture, thereby obtaining the overlapping area S of each object of each indoor monitoring sub-picture corresponding to the target resident and each object of each history monitoring picture impj Where i is the number of each indoor monitoring sub-picture, i=1, 2, nM is the number of each object to which the indoor monitoring sub-image belongs, m=1, 2, & gt, l, l is any integer greater than 2, and the similarity evaluation index of each object to which each indoor monitoring sub-image belongs and each object to which each history monitoring picture belongs corresponding to a target resident is analyzed according to the number of any integer greater than 2Screening various abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs, and counting the number M of the abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs i 。
Counting the number of objects to which each historical monitoring picture belongs corresponding to a target resident, counting the number of objects to which each indoor monitoring sub-picture belongs corresponding to the target resident, and analyzing the number of objects to which each indoor monitoring sub-picture corresponds to the target resident by the number of suitable coefficients mu i 。
Identifying whether personnel exist in each indoor monitoring sub-picture corresponding to the target resident according to each indoor monitoring sub-picture corresponding to the target resident, if personnel exist in each indoor monitoring sub-picture corresponding to the target resident, marking each indoor monitoring sub-picture corresponding to the target resident as an abnormal picture, thereby obtaining each abnormal picture corresponding to the target resident, and counting the number Y of the abnormal pictures corresponding to the target resident.
According to the different images corresponding to the target households, the body outlines of the people in the different images corresponding to the target households are obtained, the body outlines of the people in the different images corresponding to the target households are extracted from the cloud database, and further the body outline coincidence rate of the people in the different images corresponding to the target households and the body outlines of the people in the different images corresponding to the target households is analyzed, so that the risk images corresponding to the target households are screened accordingly.
And acquiring the time points of the first risk picture and the last risk picture corresponding to the target resident, thereby acquiring the personnel risk duration T corresponding to the target resident.
Counting the number Z of indoor monitoring sub-pictures corresponding to the target resident, thereby comprehensively analyzing the indoor artificial risk coefficient corresponding to the target residentWhere n is the number of indoor monitoring sub-pictures, M 'is the number of predefined allowable abnormal objects, and T' is the predefined personnel reference risk duration.
Further, the indoor fire risk assessment index corresponding to the target resident comprises the following specific analysis method: acquiring the chromaticity value of each detection point of each indoor monitoring sub-picture of the intelligent door lock of the target resident according to each indoor monitoring sub-picture of the intelligent door lock of the target resident, and extracting a fire chromaticity value interval in a cloud database, so as to screen each fire point of each indoor monitoring sub-picture of the intelligent door lock of the target resident, and counting the number D of the fire points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i 。
And acquiring indoor smoke concentration of each detection time point corresponding to the target resident from the internet security inspection background, comparing the indoor smoke concentration with the indoor smoke security concentration stored in the cloud database, screening each dangerous detection time point corresponding to the target resident, and counting the number G of the dangerous detection time points corresponding to the target resident.
Counting the number D of detection points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i 'and counting the number G' of detection time points corresponding to the target households, so as to analyze the indoor fire risk assessment index corresponding to the target households
Further, the specific analysis method for analyzing the outdoor artificial risk assessment index corresponding to the target resident comprises the following steps: acquiring all outdoor stay persons corresponding to the target residents according to the monitoring video corresponding to the building to which the target residents belong, acquiring the stay starting time point and the stay ending time point of all the outdoor stay persons corresponding to the target residents, and calculating the stay time of all the outdoor stay persons corresponding to the target residents according to the stay starting time point and the stay ending time point.
Comparing the stay time of each outdoor stay person corresponding to the target resident with a predefined stay time threshold TI', and marking each outdoor stay time corresponding to the stay time greater than or equal to the stay time threshold as each abnormal stay person, thereby obtaining each abnormal stay person corresponding to the target resident.
According to the intelligent door lock of the target resident, unlocking record data of the intelligent door lock of the target resident are obtained, wherein the unlocking record data comprise time points corresponding to each attempt to unlock and time points corresponding to each abnormal attempt to unlock, and further the abnormal risk assessment index sigma of the outdoor personnel behaviors corresponding to the target resident is analyzed according to the unlocking record data.
Acquiring the stay time TI of the target resident corresponding to each abnormal stay person according to the stay time of the target resident corresponding to each outdoor stay person h Where h is the number of each abnormal stay, h=1, 2, and g, g is any integer greater than 2, so as to comprehensively analyze the outdoor human risk assessment index corresponding to the target residentWhere g is the number of abnormally lingering people.
Further, the outdoor personnel behavior abnormality risk assessment index corresponding to the target resident comprises the following specific analysis method: and acquiring unlocking record data of the intelligent door lock of the target resident according to the intelligent door lock of the target resident, wherein the unlocking record data comprises time points corresponding to each attempt to unlock and time points corresponding to each abnormal attempt to unlock.
According to the stay starting time points and the stay ending time points of all outdoor stay persons corresponding to the target households, further constructing a stay time range corresponding to all outdoor stay persons corresponding to the target households, and screening all attempt unlocking and all abnormal attempt unlocking within the stay time ranges of all outdoor stay persons corresponding to the target households by combining the time points of all attempt unlocking and all abnormal attempt unlocking corresponding to the intelligent door locks of the target households.
Counting the number of times C of unlocking trial to the target resident in the residence time range corresponding to each outdoor resident f And the number of abnormal attempts to unlock C' f Where f is the number of each outdoor stay, f=1, 2,..Number, thus comprehensively analyzing the outdoor personnel behavior abnormal risk assessment index corresponding to the target residentWherein C' is a predefined number of allowed attempts to unlock, and t is the number of people remaining outdoors.
Further, the specific analysis method for analyzing the outdoor fire risk assessment index corresponding to the target resident comprises the following steps: dividing an outdoor monitoring video of an intelligent door lock of a target resident into a plurality of outdoor monitoring sub-pictures according to a set frame number, so as to obtain all outdoor monitoring sub-pictures of the intelligent door lock of the target resident, and analyzing the quantity R 'of detection points of all outdoor monitoring sub-pictures of the intelligent door lock of the target resident according to the outdoor monitoring sub-pictures' b And number of fire points R b Where b is the number of each outdoor monitoring sub-picture, b=1, 2,..d, d is any integer greater than 2.
Similarly, analyzing the number A 'of detection points corresponding to each monitoring picture of the floor of the target resident according to the monitoring video corresponding to the building of the target resident' x And number of fire points A x Where x is the number of each monitor screen, x=1, 2,..y, y is any integer greater than 2.
And acquiring outdoor smoke concentration of the target householder at each arrangement time point from the internet security inspection background, comparing the outdoor smoke concentration with the outdoor smoke security concentration stored in the cloud database, and screening each smoke concentration abnormal arrangement time point corresponding to the target householder.
Based on the intelligent door lock of the target resident, the temperature of the target resident at each arrangement time point is obtained, and compared with the safe temperature stored in the cloud database, and each temperature abnormal arrangement time point corresponding to the target resident is screened.
Counting the number H of the corresponding layout time points of the target householder, the number E of the abnormal smoke concentration layout time points and the number F of the abnormal temperature layout time points, so as to analyze the outdoor fire risk assessment index corresponding to the target householderWhere d is the number of outdoor monitoring sub-pictures and y is the number of monitoring pictures.
The invention has the beneficial effects that: (1) According to the intelligent door lock detection information acquisition method, the outdoor monitoring video of the intelligent door lock to which the target resident belongs is acquired in the intelligent door lock detection information acquisition module, and the recorded information is acquired, so that data support is provided for subsequent intelligent door lock safety analysis.
(2) The intelligent door lock indoor monitoring system is used for establishing a foundation for subsequent indoor analysis of a target resident by sending an intelligent door lock indoor monitoring request to the target resident in the authorization confirmation module.
(3) According to the intelligent door lock indoor analysis module, the indoor artificial risk and the indoor fire risk of a target resident are analyzed, so that the defect that the analysis strength of the safety of the inner area of the intelligent door lock is low in the prior art is overcome, the safety of the inner area of the intelligent door lock is guaranteed, safety references of the inner area of the intelligent door lock are provided for related personnel, the functionality of the intelligent door lock is improved, sales of the intelligent door lock is increased to a certain extent, and long-term sustainable development of the intelligent door lock is facilitated.
(4) According to the intelligent door lock indoor analysis module, the indoor safety of the intelligent door lock is analyzed through the detection data of the intelligent door lock and the smoke concentration sensor in the target house, the outdoor safety of the intelligent door lock is analyzed through the detection data of the intelligent door lock and the built-in temperature sensor in the intelligent door lock outdoor analysis module and by combining the monitoring video of the building to which the target house belongs, so that the linkage of the intelligent door lock and the monitoring video, the smoke concentration sensor and the temperature sensor in the community is realized, the accuracy of safety analysis of the area around the intelligent door lock is ensured, the accuracy of the safety analysis result around the intelligent door lock is improved, and the value and the reference of the safety analysis around the intelligent door lock are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides an internet of things security inspection system in a 5G base station environment, including: the intelligent door lock detection information acquisition system comprises an intelligent door lock detection information acquisition module, an authorization confirmation module, an intelligent door lock indoor detection information acquisition module, an intelligent door lock indoor analysis module, an intelligent door lock outdoor analysis module, an execution terminal and a cloud database.
The intelligent door lock detection information acquisition module is connected with the authorization confirmation module and the intelligent door lock outdoor analysis module respectively, the authorization confirmation module is connected with the intelligent door lock indoor detection information acquisition module, the intelligent door lock indoor detection information acquisition module is connected with the intelligent door lock indoor analysis module, the intelligent door lock indoor analysis module and the intelligent door lock outdoor analysis module are connected with the execution terminal, and the cloud database is connected with the intelligent door lock indoor analysis module and the intelligent door lock outdoor analysis module respectively.
The intelligent door lock detection information acquisition module is used for acquiring outdoor monitoring video of the intelligent door lock to which the target resident belongs and acquiring record information corresponding to the intelligent door lock to which the target resident belongs.
In a specific embodiment of the present invention, the recorded information includes a state corresponding to each change to which the intelligent door lock belongs, where the state includes unlocking and locking.
According to the intelligent door lock detection information acquisition method, the outdoor monitoring video of the intelligent door lock to which the target resident belongs is acquired in the intelligent door lock detection information acquisition module, and the recorded information is acquired, so that data support is provided for subsequent intelligent door lock safety analysis.
The authorization confirmation module is used for analyzing the current behavior state corresponding to the target resident according to the record information corresponding to the intelligent door lock to which the target resident belongs, wherein the current behavior state comprises a door-out state and a door-out state, and if the current behavior state of the target resident is the door-out state, an intelligent door lock indoor monitoring request is sent to the target resident.
In a specific embodiment of the present invention, the specific analysis method for analyzing the current behavior state corresponding to the target resident includes: acquiring the state corresponding to each change from the recorded information of the intelligent door lock to which the target resident belongs, acquiring the state corresponding to the last change of the intelligent door lock to which the target resident belongs, judging the current behavior state of the target resident as out-of-door if the state corresponding to the last change of the intelligent door lock to which the target resident belongs is out-of-door, and otherwise, judging the current behavior state of the target resident as out-of-door.
The intelligent door lock indoor monitoring system is used for establishing a foundation for subsequent indoor analysis of a target resident by sending an intelligent door lock indoor monitoring request to the target resident in the authorization confirmation module.
The intelligent door lock indoor detection information acquisition module is used for acquiring an indoor monitoring video of the intelligent door lock to which the target resident belongs and dividing the indoor monitoring video into a plurality of indoor monitoring sub-pictures according to a set frame number.
The intelligent door lock indoor analysis module is used for analyzing an indoor artificial risk assessment index and an indoor fire risk assessment index corresponding to a target resident.
In a specific embodiment of the present invention, the method for analyzing the indoor artificial risk assessment index corresponding to the target resident includes: extracting historical indoor monitoring pictures corresponding to each historical detection time point of a target resident from a cloud database, marking the historical indoor monitoring pictures as each historical monitoring picture corresponding to the target resident, accordingly, acquiring the outline of each object to which each historical monitoring picture corresponding to the target resident belongs, and acquiring the corresponding area S 'of each object' pj Where p is the number of each history monitor picture, p=1,2, q, q is any integer greater than 2, j is the number of each object to which the history monitoring picture belongs, j=1, 2, k, k is any integer greater than 2, and the outline of each object to which each indoor monitoring sub-picture corresponds to the target resident is obtained according to each indoor monitoring sub-picture corresponding to the target resident.
Comparing the outline of each object of each indoor monitoring sub-picture corresponding to the target resident with the outline of each object of each history monitoring picture, thereby obtaining the overlapping area S of each object of each indoor monitoring sub-picture corresponding to the target resident and each object of each history monitoring picture impj Where i is the number of each indoor monitoring sub-picture, i=1, 2,..n, n is any integer greater than 2, m is the number of each object to which the indoor monitoring sub-picture belongs, m=1, 2,..l, l is any integer greater than 2, and accordingly, the similarity evaluation index of each object to which the target resident corresponds to each indoor monitoring sub-picture and each object to which each history monitoring picture corresponds is analyzedScreening various abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs, and counting the number M of the abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs i 。
The specific analysis method of the abnormal objects of the indoor monitoring sub-pictures corresponding to the screening target households is as follows: comparing the similarity evaluation indexes of the objects of the indoor monitoring sub-pictures corresponding to the target resident and the objects of the historical monitoring pictures corresponding to the target resident with a predefined similarity evaluation index threshold, if the similarity evaluation indexes of the object of the indoor monitoring sub-pictures corresponding to the target resident and the objects of the historical monitoring pictures corresponding to the target resident are smaller than the similarity evaluation index threshold, marking the object as an abnormal object, and counting the abnormal objects of the indoor monitoring sub-pictures corresponding to the target resident according to the similarity evaluation indexes.
Counting the number of the objects of each historical monitoring picture corresponding to the target resident, counting the number of the objects of each indoor monitoring sub-picture corresponding to the target resident, and analyzing the target according to the numberThe number of objects corresponding to each indoor monitoring sub-picture corresponding to resident is suitable for coefficient mu i 。
The method for analyzing the object quantity suitability coefficients corresponding to the indoor monitoring sub-pictures corresponding to the target households comprises the following steps: comparing the number of the objects corresponding to each indoor monitoring sub-picture of the target resident with the number of the objects corresponding to each history monitoring sub-picture, if the number of the objects corresponding to a certain indoor monitoring sub-picture of the target resident is equal to the number of the objects corresponding to a certain history monitoring picture, marking the suitability coefficient of the number of the objects corresponding to the indoor monitoring sub-picture of the target resident as beta, otherwise marking the suitability coefficient as beta', and counting the suitability coefficient mu of the number of the objects corresponding to each indoor monitoring sub-picture of the target resident according to the suitability coefficient beta i Wherein mu i =β or β'.
Identifying whether personnel exist in each indoor monitoring sub-picture corresponding to the target resident according to each indoor monitoring sub-picture corresponding to the target resident, if personnel exist in each indoor monitoring sub-picture corresponding to the target resident, marking each indoor monitoring sub-picture corresponding to the target resident as an abnormal picture, thereby obtaining each abnormal picture corresponding to the target resident, and counting the number Y of the abnormal pictures corresponding to the target resident.
According to the different images corresponding to the target households, the body outlines of the people in the different images corresponding to the target households are obtained, the body outlines of the people in the different images corresponding to the target households are extracted from the cloud database, and further the body outline coincidence rate of the people in the different images corresponding to the target households and the body outlines of the people in the different images corresponding to the target households is analyzed, so that the risk images corresponding to the target households are screened accordingly.
The body contour coincidence rate of the person and the resident person in the analysis target resident corresponding to each abnormal picture is described in the following concrete analysis method: the body contour of the person in each abnormal picture corresponding to the target resident is overlapped with the body contour of each resident corresponding to the person, so that the overlapped area of the person in each abnormal picture corresponding to the target resident and the body contour of each resident corresponding to the person is obtained, the body contour area of each resident corresponding to the target resident is obtained, the overlapped area of the person in each abnormal picture corresponding to the person and the body contour area of the person is divided by the body contour area of the person, and the body contour overlapping rate of the person in each abnormal picture corresponding to the target resident and the body contour of each resident is obtained.
It should also be noted that, the specific analysis method for screening each risk picture corresponding to the target resident includes: comparing the body contour coincidence rate of the person corresponding to each abnormal picture of the target resident with a predefined body contour coincidence rate threshold, and if the body contour coincidence rate of the person corresponding to each abnormal resident in a certain abnormal picture of the target resident is smaller than the body contour coincidence rate threshold, marking the abnormal picture as a risk picture so as to obtain each risk picture corresponding to the target resident.
And acquiring the time points of the first risk picture and the last risk picture corresponding to the target resident, thereby acquiring the personnel risk duration T corresponding to the target resident.
Counting the number Z of indoor monitoring sub-pictures corresponding to the target resident, thereby comprehensively analyzing the indoor artificial risk coefficient corresponding to the target residentWhere n is the number of indoor monitoring sub-pictures, M 'is the number of predefined allowable abnormal objects, and T' is the predefined personnel reference risk duration.
In a specific embodiment of the present invention, the indoor fire risk assessment index corresponding to the target resident is specifically analyzed by the following method: acquiring the chromaticity value of each detection point of each indoor monitoring sub-picture of the intelligent door lock of the target resident according to each indoor monitoring sub-picture of the intelligent door lock of the target resident, and extracting a fire chromaticity value interval in a cloud database, so as to screen each fire point of each indoor monitoring sub-picture of the intelligent door lock of the target resident, and counting the number D of the fire points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i 。
The specific analysis method of each fire point of each indoor monitoring sub-picture of the intelligent door lock of the screening target resident comprises the following steps: comparing the chromaticity value of each detection point of each indoor monitoring sub-picture of the intelligent door lock of the target resident with the fire chromaticity value interval, and if the chromaticity value of a detection point of a certain indoor monitoring sub-picture of the intelligent door lock of the target resident is in the fire chromaticity value interval, marking the detection point as a fire point, thereby obtaining each fire point of each indoor monitoring sub-picture of the intelligent door lock of the target resident.
And acquiring indoor smoke concentration of each detection time point corresponding to the target resident from the internet security inspection background, comparing the indoor smoke concentration with the indoor smoke security concentration stored in the cloud database, screening each dangerous detection time point corresponding to the target resident, and counting the number G of the dangerous detection time points corresponding to the target resident.
The specific analysis method of each dangerous detection time point corresponding to the screening target resident is as follows: comparing the indoor smoke concentration of each detection time point corresponding to the target resident with the indoor smoke safety concentration, and if the indoor smoke concentration of a certain detection time point corresponding to the target resident is greater than the indoor smoke safety concentration, marking the detection time point as a dangerous detection time point, thereby obtaining each dangerous detection time point corresponding to the target resident.
Counting the number D of detection points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i 'and counting the number G' of detection time points corresponding to the target households, so as to analyze the indoor fire risk assessment index corresponding to the target households
According to the intelligent door lock indoor analysis module, the indoor artificial risk and the indoor fire risk of a target resident are analyzed, so that the defect that the analysis strength of the safety of the inner area of the intelligent door lock is low in the prior art is overcome, the safety of the inner area of the intelligent door lock is guaranteed, safety references of the inner area of the intelligent door lock are provided for related personnel, the functionality of the intelligent door lock is improved, sales of the intelligent door lock is increased to a certain extent, and long-term sustainable development of the intelligent door lock is facilitated.
The intelligent door lock outdoor analysis module is used for analyzing the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident according to the outdoor monitoring video of the intelligent door lock to which the target resident belongs and in combination with the monitoring video corresponding to the building to which the target resident belongs, which is stored in the cloud database.
In a specific embodiment of the present invention, the method for analyzing the outdoor artificial risk assessment index corresponding to the target resident includes: acquiring all outdoor stay persons corresponding to the target residents according to the monitoring video corresponding to the building to which the target residents belong, acquiring the stay starting time point and the stay ending time point of all the outdoor stay persons corresponding to the target residents, and calculating the stay time of all the outdoor stay persons corresponding to the target residents according to the stay starting time point and the stay ending time point.
It should be noted that, the calculating the residence time of each outdoor residence person corresponding to the target resident specifically includes: subtracting the stay starting time point from the stay ending time point of each outdoor stay person corresponding to the target resident, so as to obtain the stay time of each outdoor stay person corresponding to the target resident.
Comparing the stay time of each outdoor stay person corresponding to the target resident with a predefined stay time threshold TI', and marking each outdoor stay time corresponding to the stay time greater than or equal to the stay time threshold as each abnormal stay person, thereby obtaining each abnormal stay person corresponding to the target resident.
According to the intelligent door lock of the target resident, unlocking record data of the intelligent door lock of the target resident are obtained, wherein the unlocking record data comprise time points corresponding to each attempt to unlock and time points corresponding to each abnormal attempt to unlock, and further the abnormal risk assessment index sigma of the outdoor personnel behaviors corresponding to the target resident is analyzed according to the unlocking record data.
Acquiring the stay time TI of the target resident corresponding to each abnormal stay person according to the stay time of the target resident corresponding to each outdoor stay person h Where h is the number of each abnormal stay, h=1, 2, and g, g is any integer greater than 2, so as to comprehensively analyze outdoor artifacts corresponding to the target resident Risk assessment indexWhere g is the number of abnormally lingering people.
In a specific embodiment of the present invention, the outdoor personnel behavior abnormality risk assessment index corresponding to the target resident is specifically analyzed by: and acquiring unlocking record data of the intelligent door lock of the target resident according to the intelligent door lock of the target resident, wherein the unlocking record data comprises time points corresponding to each attempt to unlock and time points corresponding to each abnormal attempt to unlock.
The attempt to unlock is to use a fingerprint or a face, and the abnormal attempt to unlock is to use violence to unlock.
The intelligent door lock is also provided with an acceleration sensor which is used for detecting violent attempt unlocking.
According to the stay starting time points and the stay ending time points of all outdoor stay persons corresponding to the target households, further constructing a stay time range corresponding to all outdoor stay persons corresponding to the target households, and screening all attempt unlocking and all abnormal attempt unlocking within the stay time ranges of all outdoor stay persons corresponding to the target households by combining the time points of all attempt unlocking and all abnormal attempt unlocking corresponding to the intelligent door locks of the target households.
Counting the number of times C of unlocking trial to the target resident in the residence time range corresponding to each outdoor resident f And the number of abnormal attempts to unlock C' f Wherein f is the number of each outdoor stay person, f=1, 2, t, t is any integer greater than 2, so as to comprehensively analyze the outdoor person behavior abnormality risk assessment index corresponding to the target residentWherein C' is a predefined number of allowed attempts to unlock, and t is the number of people remaining outdoors.
In a specific embodiment of the present invention, the outdoor fire risk assessment index corresponding to the target resident is analyzed,the specific analysis method comprises the following steps: dividing an outdoor monitoring video of an intelligent door lock of a target resident into a plurality of outdoor monitoring sub-pictures according to a set frame number, so as to obtain all outdoor monitoring sub-pictures of the intelligent door lock of the target resident, and analyzing the quantity R 'of detection points of all outdoor monitoring sub-pictures of the intelligent door lock of the target resident according to the outdoor monitoring sub-pictures' b And number of fire points R b Where b is the number of each outdoor monitoring sub-picture, b=1, 2,..d, d is any integer greater than 2.
The specific analysis method for analyzing the number of detection points and the number of fire points of each outdoor monitoring sub-picture of the intelligent door lock of the target resident includes: according to each outdoor monitoring sub-picture of the intelligent door lock to which the target resident belongs, the chromaticity value of each detection point of each outdoor monitoring sub-picture of the intelligent door lock to which the target resident belongs is randomly obtained, and the chromaticity value interval of fire stored in the cloud database is combined, if the chromaticity value of a detection point of a certain outdoor monitoring sub-picture of the intelligent door lock to which the target resident belongs is in the fire chromaticity value interval, the detection point of the outdoor monitoring sub-picture is marked as a fire point, so that each fire point of each outdoor monitoring sub-picture of the intelligent door lock to which the target resident belongs is obtained, and the number of detection points and the number of fire points of each outdoor monitoring sub-picture of the intelligent door lock to which the target resident belongs are counted according to the chromaticity value.
Similarly, analyzing the number A 'of detection points corresponding to each monitoring picture of the floor of the target resident according to the monitoring video corresponding to the building of the target resident' x And number of fire points A x Where x is the number of each monitor screen, x=1, 2,..y, y is any integer greater than 2.
And acquiring outdoor smoke concentration of the target householder at each arrangement time point from the internet security inspection background, comparing the outdoor smoke concentration with the outdoor smoke security concentration stored in the cloud database, and screening each smoke concentration abnormal arrangement time point corresponding to the target householder.
The specific analysis method of the abnormal arrangement time points of the smoke concentration corresponding to the screening target households is as follows: comparing the outdoor smoke concentration of the target resident at each layout time point with the outdoor smoke safety concentration, and if the outdoor smoke concentration of the target resident at a certain layout time point is greater than the outdoor smoke safety concentration, marking the layout time point as an abnormal smoke concentration layout time point, thereby obtaining each abnormal smoke concentration layout time point corresponding to the target resident.
Based on the intelligent door lock of the target resident, the temperature of the target resident at each arrangement time point is obtained, and compared with the safe temperature stored in the cloud database, and each temperature abnormal arrangement time point corresponding to the target resident is screened.
The analysis method of each smoke concentration abnormal arrangement time point corresponding to the screening target resident is consistent, and each temperature abnormal arrangement time point corresponding to the screening target resident is screened.
Counting the number H of the corresponding layout time points of the target householder, the number E of the abnormal smoke concentration layout time points and the number F of the abnormal temperature layout time points, so as to analyze the outdoor fire risk assessment index corresponding to the target householderWhere d is the number of outdoor monitoring sub-pictures and y is the number of monitoring pictures.
According to the intelligent door lock indoor analysis module, the indoor safety of the intelligent door lock is analyzed through the detection data of the intelligent door lock and the smoke concentration sensor in the target house, the outdoor safety of the intelligent door lock is analyzed through the detection data of the intelligent door lock and the built-in temperature sensor in the intelligent door lock outdoor analysis module and by combining the monitoring video of the building to which the target house belongs, so that the linkage of the intelligent door lock and the monitoring video, the smoke concentration sensor and the temperature sensor in the community is realized, the accuracy of safety analysis of the area around the intelligent door lock is ensured, the accuracy of the safety analysis result around the intelligent door lock is improved, and the value and the reference of the safety analysis around the intelligent door lock are improved.
The execution terminal is used for carrying out corresponding early warning on the target resident according to the indoor artificial risk assessment index and the indoor fire risk assessment index corresponding to the target resident, and carrying out corresponding early warning on the target resident according to the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident.
The method is characterized in that the target resident is correspondingly pre-warned according to the indoor artificial risk assessment index and the indoor fire risk assessment index corresponding to the target resident, and correspondingly pre-warned according to the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident, and the specific method comprises the following steps: comparing the indoor artificial risk assessment index corresponding to the target resident with a predefined indoor artificial risk assessment index threshold, if the indoor artificial risk assessment index corresponding to the target resident is greater than or equal to the indoor artificial risk assessment index threshold, carrying out indoor artificial risk early warning on the target resident, and similarly, carrying out indoor fire risk early warning on the target resident according to the indoor fire risk assessment index corresponding to the target resident, and respectively carrying out outdoor artificial risk early warning and outdoor fire risk threshold according to the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident.
The cloud database is used for storing the monitoring video corresponding to the building to which the target resident belongs, storing the historical indoor monitoring pictures corresponding to each historical detection time point of the target resident, storing the body outline of each resident corresponding to the target resident, storing the fire color value interval, storing the indoor smoke safety concentration and storing the outdoor smoke safety concentration.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (8)
1. The utility model provides an thing networking security inspection system under 5G basic station environment which characterized in that includes:
the intelligent door lock detection information acquisition module is used for acquiring outdoor monitoring video of the intelligent door lock to which the target resident belongs and acquiring record information corresponding to the intelligent door lock to which the target resident belongs;
the authorization confirmation module is used for analyzing the current behavior state corresponding to the target resident according to the record information corresponding to the intelligent door lock to which the target resident belongs, wherein the current behavior state comprises a door-out state and a door-out state, and if the current behavior state of the target resident is the door-out state, an intelligent door lock indoor monitoring request is sent to the target resident;
The intelligent door lock indoor detection information acquisition module is used for acquiring an indoor monitoring video of an intelligent door lock to which a target resident belongs and dividing the indoor monitoring video into a plurality of indoor monitoring sub-pictures according to a set frame number;
the intelligent door lock indoor analysis module is used for analyzing an indoor artificial risk assessment index and an indoor fire risk assessment index corresponding to a target resident;
the intelligent door lock outdoor analysis module is used for analyzing an outdoor artificial risk assessment index and an outdoor fire risk assessment index corresponding to a target resident according to an outdoor monitoring video of an intelligent door lock to which the target resident belongs and in combination with a monitoring video corresponding to a building to which the target resident belongs, which is stored in the cloud database;
the execution terminal is used for carrying out corresponding early warning on the target resident according to the indoor artificial risk assessment index and the indoor fire risk assessment index corresponding to the target resident, and carrying out corresponding early warning on the target resident according to the outdoor artificial risk assessment index and the outdoor fire risk assessment index corresponding to the target resident;
the cloud database is used for storing the monitoring video corresponding to the building to which the target resident belongs, storing the historical indoor monitoring pictures corresponding to each historical detection time point of the target resident, storing the body outline of each resident corresponding to the target resident, storing the fire color value interval, storing the indoor smoke safety concentration and storing the outdoor smoke safety concentration.
2. The internet of things security inspection system under a 5G base station environment according to claim 1, wherein the recorded information includes a state corresponding to each change to which the intelligent door lock belongs, and the state includes unlocking and locking.
3. The internet of things security inspection system in a 5G base station environment according to claim 2, wherein the specific analysis method for analyzing the current behavior state corresponding to the target resident is as follows: acquiring the state corresponding to each change from the recorded information of the intelligent door lock to which the target resident belongs, acquiring the state corresponding to the last change of the intelligent door lock to which the target resident belongs, judging the current behavior state of the target resident as out-of-door if the state corresponding to the last change of the intelligent door lock to which the target resident belongs is out-of-door, and otherwise, judging the current behavior state of the target resident as out-of-door.
4. The internet of things security inspection system in a 5G base station environment according to claim 1, wherein the specific analysis method of the indoor artificial risk assessment index corresponding to the analysis target resident is as follows:
extracting historical indoor monitoring pictures corresponding to each historical detection time point of a target resident from a cloud database, marking the historical indoor monitoring pictures as each historical monitoring picture corresponding to the target resident, accordingly, acquiring the outline of each object to which each historical monitoring picture corresponding to the target resident belongs, and acquiring the corresponding area S 'of each object' pj Wherein p is the number of each history monitoring picture, p=1, 2, & gt, q, q are any integers greater than 2, j is the number of each object to which the history monitoring picture belongs, j=1, 2, & gt, k, k are any integers greater than 2, and the outline of each object to which each indoor monitoring sub picture corresponds to the target resident is obtained according to each indoor monitoring sub picture corresponding to the target resident;
comparing the outline of each object of each indoor monitoring sub-picture corresponding to the target resident with the outline of each object of each history monitoring picture, thereby obtaining the overlapping area S of each object of each indoor monitoring sub-picture corresponding to the target resident and each object of each history monitoring picture impj Where i is the number of each indoor monitoring sub-picture, i=1, 2, n, n is any integer greater than 2, m is the number of each object to which the indoor monitoring sub-picture belongs, m=1, 2,..The analysis target resident corresponds to the similarity evaluation index of each object of each indoor monitoring sub-picture and each object of each history monitoring pictureScreening various abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs, and counting the number M of the abnormal objects to which each indoor monitoring sub-picture corresponding to the target resident belongs i ；
Counting the number of objects to which each historical monitoring picture belongs corresponding to a target resident, counting the number of objects to which each indoor monitoring sub-picture belongs corresponding to the target resident, and analyzing the number of objects to which each indoor monitoring sub-picture corresponds to the target resident by the number of suitable coefficients mu i ；
Identifying whether personnel exist in each indoor monitoring sub-picture corresponding to the target resident according to each indoor monitoring sub-picture corresponding to the target resident, if personnel exist in each indoor monitoring sub-picture corresponding to the target resident, marking each indoor monitoring sub-picture corresponding to the target resident as an abnormal picture, thereby obtaining each abnormal picture corresponding to the target resident, and counting the number Y of the abnormal pictures corresponding to the target resident;
according to the different images corresponding to the target households, acquiring the body outlines of the people in the different images corresponding to the target households, extracting the body outlines of the people in the common living corresponding to the target households from the cloud database, and further analyzing the body outline coincidence rate of the people in the different images corresponding to the target households and the people in the common living, so that the risk images corresponding to the target households are screened accordingly;
acquiring a time point of a target resident corresponding to a first risk picture and a last risk picture, thereby acquiring a personnel risk duration T corresponding to the target resident;
Counting the number Z of indoor monitoring sub-pictures corresponding to the target resident, thereby comprehensively analyzing the indoor artificial risk coefficient corresponding to the target residentWhere n is the number of indoor monitoring sub-pictures and M' is a predefined allowable anomalyThe number of objects, T', is a predefined person reference risk duration.
5. The internet of things security inspection system in a 5G base station environment according to claim 1, wherein the indoor fire risk assessment index corresponding to the target resident comprises the following specific analysis method:
acquiring the chromaticity value of each detection point of each indoor monitoring sub-picture of the intelligent door lock of the target resident according to each indoor monitoring sub-picture of the intelligent door lock of the target resident, and extracting a fire chromaticity value interval in a cloud database, so as to screen each fire point of each indoor monitoring sub-picture of the intelligent door lock of the target resident, and counting the number D of the fire points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i ；
Acquiring indoor smoke concentration of each detection time point corresponding to a target resident from an internet security inspection background, comparing the indoor smoke concentration with indoor smoke security concentration stored in a cloud database, screening each dangerous detection time point corresponding to the target resident, and counting the number G of the dangerous detection time points corresponding to the target resident;
Counting the number D of detection points of each indoor monitoring sub-picture of the intelligent door lock of the target resident i 'and counting the number G' of detection time points corresponding to the target households, so as to analyze the indoor fire risk assessment index corresponding to the target households
6. The internet of things security inspection system in a 5G base station environment according to claim 1, wherein the specific analysis method of the outdoor artificial risk assessment index corresponding to the analysis target resident is as follows:
acquiring all outdoor stay persons corresponding to the target resident according to the monitoring video corresponding to the building to which the target resident belongs, acquiring the stay starting time point and the stay ending time point of all the outdoor stay persons corresponding to the target resident, and calculating the stay time of all the outdoor stay persons corresponding to the target resident according to the stay starting time point and the stay ending time point;
comparing the stay time of each outdoor stay person corresponding to the target resident with a predefined stay time threshold TI', and marking each outdoor stay time corresponding to the stay time greater than or equal to the stay time threshold as each abnormal stay person, thereby obtaining each abnormal stay person corresponding to the target resident;
acquiring unlocking record data of the intelligent door lock of the target resident according to the intelligent door lock of the target resident, wherein the unlocking record data comprise time points corresponding to each attempt to unlock and time points corresponding to each abnormal attempt to unlock, and further analyzing an outdoor personnel behavior abnormal risk assessment index sigma corresponding to the target resident according to the unlocking record data;
Acquiring the stay time TI of the target resident corresponding to each abnormal stay person according to the stay time of the target resident corresponding to each outdoor stay person h Where h is the number of each abnormal stay, h=1, 2, and g, g is any integer greater than 2, so as to comprehensively analyze the outdoor human risk assessment index corresponding to the target residentWhere g is the number of abnormally lingering people.
7. The internet of things security inspection system in a 5G base station environment according to claim 6, wherein the outdoor personnel behavior abnormality risk assessment index corresponding to the target resident comprises the following specific analysis method:
acquiring unlocking record data of an intelligent door lock of a target resident according to the intelligent door lock of the target resident, wherein the unlocking record data comprises a time point corresponding to each attempt to unlock and a time point corresponding to each abnormal attempt to unlock;
according to the stay starting time points and the stay ending time points of all outdoor stay personnel corresponding to the target resident, further constructing a stay time range corresponding to all outdoor stay personnel corresponding to the target resident, and screening all attempt unlocking and all abnormal attempt unlocking within the stay time range corresponding to all outdoor stay personnel of the target resident by combining the time points of all attempt unlocking and all abnormal attempt unlocking corresponding to the intelligent door lock of the target resident;
Counting the number of times C of unlocking trial to the target resident in the residence time range corresponding to each outdoor resident f And the number of abnormal attempts to unlock C' f Wherein f is the number of each outdoor stay person, f=1, 2, t, t is any integer greater than 2, so as to comprehensively analyze the outdoor person behavior abnormality risk assessment index corresponding to the target residentWherein C' is a predefined number of allowed attempts to unlock, and t is the number of people remaining outdoors.
8. The internet of things security inspection system in a 5G base station environment according to claim 1, wherein the specific analysis method of the outdoor fire risk assessment index corresponding to the analysis target resident is as follows:
dividing an outdoor monitoring video of an intelligent door lock of a target resident into a plurality of outdoor monitoring sub-pictures according to a set frame number, so as to obtain all outdoor monitoring sub-pictures of the intelligent door lock of the target resident, and analyzing the quantity R 'of detection points of all outdoor monitoring sub-pictures of the intelligent door lock of the target resident according to the outdoor monitoring sub-pictures' b And number of fire points R b Wherein b is the number of each outdoor monitoring sub-picture, b=1, 2, d, d is any integer greater than 2;
similarly, analyzing the number A 'of detection points corresponding to each monitoring picture of the floor of the target resident according to the monitoring video corresponding to the building of the target resident' x And number of fire points A x Wherein x is the number of each monitoring picture, x=1, 2,..y, y is any integer greater than 2;
acquiring outdoor smoke concentration of a target resident at each layout time point from an internet security inspection background, comparing the outdoor smoke concentration with outdoor smoke security concentration stored in a cloud database, and screening each smoke concentration abnormal layout time point corresponding to the target resident;
acquiring the temperature of the target resident at each layout time point based on the intelligent door lock of the target resident, comparing the temperature with the safe temperature stored in the cloud database, and screening each temperature abnormal layout time point corresponding to the target resident;
counting the number H of the corresponding layout time points of the target householder, the number E of the abnormal smoke concentration layout time points and the number F of the abnormal temperature layout time points, so as to analyze the outdoor fire risk assessment index corresponding to the target householderWhere d is the number of outdoor monitoring sub-pictures and y is the number of monitoring pictures.
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