CN117172970A - Wisdom property management platform - Google Patents
Wisdom property management platform Download PDFInfo
- Publication number
- CN117172970A CN117172970A CN202311099709.7A CN202311099709A CN117172970A CN 117172970 A CN117172970 A CN 117172970A CN 202311099709 A CN202311099709 A CN 202311099709A CN 117172970 A CN117172970 A CN 117172970A
- Authority
- CN
- China
- Prior art keywords
- elevator
- fire
- information
- calculation model
- obstacle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 claims abstract description 96
- 238000004458 analytical method Methods 0.000 claims abstract description 47
- 238000011156 evaluation Methods 0.000 claims abstract description 37
- 230000006698 induction Effects 0.000 claims abstract description 37
- 238000007726 management method Methods 0.000 claims description 40
- 230000002159 abnormal effect Effects 0.000 claims description 22
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 17
- 230000004044 response Effects 0.000 claims description 12
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 11
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 11
- 229910052760 oxygen Inorganic materials 0.000 claims description 11
- 239000001301 oxygen Substances 0.000 claims description 11
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 10
- 239000001569 carbon dioxide Substances 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 230000004888 barrier function Effects 0.000 claims description 6
- 238000013468 resource allocation Methods 0.000 claims description 5
- 239000000779 smoke Substances 0.000 claims description 5
- 230000004308 accommodation Effects 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 238000004148 unit process Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 5
- 238000013136 deep learning model Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 230000004438 eyesight Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Landscapes
- Maintenance And Inspection Apparatuses For Elevators (AREA)
Abstract
The application discloses an intelligent property management platform, which particularly relates to the technical field of property management, and comprises the following components: acquiring correct induction parameters of an elevator, elevator obstacle influence factors and air quality influence factors through an elevator information analysis module; calculating population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients through a living environment analysis module; acquiring a traffic smoothness index by using a traffic smoothness calculation model based on elevator information analysis and living environment analysis results, and outputting a traffic safety evaluation index in a safety calculation model based on elevator information analysis, living environment analysis results and the traffic smoothness index; the fault and the safety risk of the owner in the passing way are found through the Internet of things, the passing smoothness and the passing safety are obtained, the property management resources are distributed according to the passing safety, and the safety of the owner is improved with maximum efficiency.
Description
Technical Field
The application relates to the technical field of property management, in particular to an intelligent property management platform.
Background
Property management refers to that a professional organization accepts entrust of a property owner, specialized management is provided for the property owner based on the purpose of maintaining active operation of communities and parks, efficient and weekly service behaviors are provided for the property owner, and an intelligent property management platform is a platform which is convenient for the property organization to improve management efficiency of communities and parks, and management and control of communities and parks are realized based on the platform.
The method ensures that the owner passes smoothly and the passing safety is often placed at the most important position by a property manager, and a plurality of potential safety hazards exist in the process that the owner enters a room through an elevator or a safety channel, such as fire disaster, blocked safety channels, hidden elevator operation hazards and the like, and the existing property management platform tends to upload the passing problem to the property management platform based on the problem that the owner discovers and reports or the property staff patrols and acquires the problem affecting the passing safety. The property management platform mainly relies on manual inspection in the discovery of safety problems, is not intelligent enough, and can possibly cause the damage to the safety of owners under the conditions of large management range and insufficient property management staff: abnormal owner safety problems cannot be found in time, and property management resources cannot be effectively scheduled according to traffic problems, so that owners are not full, and life safety of the owners is seriously and even affected.
Based on the development of the Internet of things and big data, automatic mining of safety problems is achieved by utilizing the big data, and possibility is brought to intellectualization of property management.
Disclosure of Invention
(one) solving the technical problems
In order to overcome the defects in the prior art, the application provides an intelligent property management platform, which obtains abnormal environment problems and abnormal elevator operation problems based on image processing technology and data analysis by acquiring building information and elevator operation condition information, acquires traffic smoothness coefficient and traffic safety evaluation of a park, and allocates property management resources according to the traffic safety so as to solve the problems in the background technology.
(II) technical scheme
In order to achieve the above purpose, the present application provides the following technical solutions: an intelligent property management platform, comprising: a living environment information acquisition module, an elevator information acquisition module, a living environment analysis module, an elevator information analysis module, a traffic smoothness evaluation module, a traffic safety evaluation module and a response execution module,
the living environment information acquisition module is used for acquiring fire control information, living information and building information of living environment, the fire control information comprises actual fire control equipment quantity, the living information comprises population quantity and elevator quantity, and the building information comprises: building height, building service life, building electrical load, building floor area;
the living environment analysis module calculates population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients based on the population density parameter calculation model, the fire-fighting equipment guarantee parameter calculation model and the fire-fighting risk coefficient calculation model, and transmits calculation results to the traffic smoothness evaluation module;
the elevator information acquisition module is used for acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information, wherein the elevator basic information comprises the area of an elevator, the height of the elevator, the width of an elevator outlet, the waiting time of the elevator and the maximum number of people which can be accommodated in the elevator; the elevator induction information comprises the normal induction times and the abnormal induction times of the elevator; the elevator obstacle information comprises obstacle occupation area, obstacle height and obstacle position information, and the air quality information in the elevator comprises the following components: the method comprises the steps of transmitting collected information to an elevator information analysis module, wherein the carbon dioxide concentration, the carbon monoxide concentration, the oxygen concentration and the smoke concentration are respectively acquired;
the elevator information analysis module calculates elevator correct inductivity parameters, elevator obstacle influence factors and air quality influence factors based on an elevator correct inductivity parameter calculation model, an obstacle influence factor calculation model and an air quality influence factor calculation model, and transmits the acquired results to the traffic smoothness evaluation module;
the passing smoothness evaluation module inputs the results of the elevator information analysis module and the living environment analysis module into a passing smoothness calculation model and outputs a passing smoothness index;
the traffic safety evaluation module inputs the results of the living environment analysis module, the elevator information analysis module and the traffic smoothness evaluation module into a traffic safety calculation model, and outputs a traffic safety evaluation index;
the response execution module comprises a supervision resource allocation unit which allocates property supervision resources based on the traffic safety evaluation index.
Preferably, the elevator information acquisition module acquires normal induction times and abnormal induction times of an elevator based on an elevator induction analysis model, and the elevator induction analysis model comprises:
and (3) data acquisition: acquiring the starting time t1 of elevator closing and the ending time t2 of elevator closing, if the interval time is larger than a preset value, indicating that an obstacle exists in the elevator closing process, acquiring an image frame of the elevator at the moment, for example, setting the normal closing time interval of the elevator as t 0 Acquiring the interval time of n times of elevator closing tasks through a sensor, screening to obtain elevator closing tasks with closing time intervals larger than t0, marking the elevator closing tasks as closing tasks to be analyzed, setting the closing tasks to be analyzed to have m times, and calling elevator image frames in the interval time of the closing tasks to be analyzed, wherein the elevator image frames are acquired through cameras arranged above an elevator entrance, and the acquisition time of the elevator image frames is from the starting time t1 to the ending time t2 of elevator closing, namely, in the process of closing an elevator door;
data analysis: judging whether an obstacle exists in the elevator closing process, for example, an object exists between elevator doors in an elevator image frame, and marking that the obstacle exists in the elevator closing process;
and (3) data judgment: judging whether the obstacle is extruded by the elevator, if so, recording the elevator closure as abnormal induction, for example, acquiring the real-time width of the elevator door and comparing the real-time width with the obstacle width, and if the real-time width of the elevator door is equal to the obstacle width, indicating that the obstacle is extruded by the elevator;
data statistics: and acquiring the total induction times of the elevator and the abnormal induction times of the elevator.
Preferably, the elevator information acquisition module acquires the floor area, the position information and the height of the obstacle based on an obstacle recognition model, and the obstacle recognition model may include:
and (3) image acquisition: acquiring elevator images according to a fixed frequency, wherein the fixed frequency is set as the time from the top to the bottom when an elevator runs empty;
image preprocessing: preprocessing the acquired image to improve the accuracy and efficiency of subsequent processing steps, such as denoising, image enhancement and size normalization;
feature extraction: extracting features related to the obstacle from the preprocessed image by using computer vision and image processing technology, wherein common features comprise the existence time, the occupied area, the color, the texture, the shape and the edge of the obstacle;
obstacle detection: analyzing and classifying the extracted features by using a deep learning model, and judging whether an obstacle exists in the image;
target positioning: if the obstacle detection is successful, further carrying out position location on the image, determining the position information of the obstacle in the image through boundary frame or pixel level segmentation, obtaining the coordinate information of the obstacle, and obtaining the occupied area and the height of the obstacle according to the coordinate information;
obstacle identification and classification: if more detailed identification and classification of the obstacle is required, the obstacle is identified and classified based on a machine learning algorithm or a deep learning model.
Preferably, the elevator information analysis module comprises an elevator correct inductivity calculation unit, an obstacle influence factor calculation unit and an air quality influence factor calculation unit,
the elevator correct inductivity calculation unit is obtained by calculating an elevator correct inductivity parameter calculation model, and the calculation model meets the formulaWherein R is g Indicating correct inductivity of elevator, M 1 Indicating the correct sensing times of the elevator, M 2 The abnormal induction times of the elevator are represented;
the obstacle influence factor calculation unit is calculated by an obstacle influence factor calculation model, and the calculation model meets the formulaWherein R is z Representing obstacle influencing factors, representing DM 1 Represents the area of the elevator, zm represents the occupied area of the obstacle, zv represents the occupied volume of the space of the obstacle, h d Indicating the height of the elevator, using L 1 Indicating the width of the elevator exit after the obstacle occupies L 2 Representing the initial width, eta of the elevator exit 1 Representing the influence factor of the obstacle position, 0 < eta 1 ≤1；
The air quality influence factor calculation unit is obtained by calculating an air quality influence factor calculation model, and the calculation model meets the formulaWherein R is k Representing the air quality influencing factor, i.e. the air quality influencing factor in time t, a i Represents the carbon dioxide concentration in air at the ith time point, a 0 Representing the carbon dioxide concentration in a normal environment; bi represents the carbon monoxide concentration in the air at the ith time point, b 0 Represents the carbon monoxide concentration in a normal environment; ci represents the oxygen concentration in air at the ith time point, c 0 Represents the oxygen concentration in normal environment, w 1 Represents the influence weight of carbon dioxide on environmental quality, w 2 Represents the impact weight of carbon monoxide on environmental quality, w 3 Represents the impact weight of oxygen on environmental quality, and w 1 +w 2 +w 3 =100%, n represents the number of acquisition points, λ 1 The influence coefficient of smoke concentration in air is represented as 0-1]，λ 2 The influence coefficient of other harmful gases in the air is represented, D is a coefficient constant, and the value is 0-1]。
Preferably, the living environment analysis module comprises a population density parameter calculation unit, a fire protection equipment guarantee parameter calculation unit and a fire risk coefficient calculation unit,
the population density parameter calculating unit is obtained by calculation through a population density parameter calculating model, and the calculating model meets the formulaWherein R is 1 Indicating the total number of people S 1 Indicating the number of elevators r d Indicating the maximum number of accommodations of the elevator;
the fire-fighting equipment guarantee parameter calculation unit is obtained by calculating a fire-fighting equipment guarantee parameter calculation model, and the calculation model meets the formulaWherein X is 1 Indicating the number of actual fire-fighting equipments, h j Representing the height of a building, ZM 0 Representing the floor area of a building, +.>Representing the predicted number of fire-fighting devices required for a building, eta 1 The larger the value of the fire-fighting equipment guarantee parameter is, the stronger the fire-fighting equipment guarantee capability of the property is;
the fire risk coefficient calculation unit is calculated by a fire risk coefficient calculation model, and the calculation model meets the formulaWherein h is j Representing the height of the building, T representing the life of the building, DF representing the electrical load of the building, ZM 0 Representing the floor space of the building.
Preferably, the passing smoothness evaluation module calculates the passing smoothness through a passing smoothness evaluation calculation model, and the passing smoothness calculation model meets the formulaWherein TS represents the traffic smoothness, the larger the TS value is, the smoother the traffic is, and the value of TS is [0-1 ]]，T d Representing the influence coefficient of the elevator operation waiting time, tmax represents +.>Tmin represents +.>Is a minimum of (2).
Preferably, the traffic safety evaluation module calculates the traffic safety through a traffic safety evaluation calculation model, and the traffic safety calculation model meets the formulaWherein An represents the traffic safety, and a larger value of An indicates a safer traffic.
Preferably, the wisdom property management platform is including being used for obtaining warning information's early warning module, and early warning module includes the unusual early warning unit of elevator operation, the unusual early warning unit of living environment, the unusual early warning unit of elevator operation receives elevator correct inductivity, obstacle influence factor and air quality influence factor that elevator information analysis module obtained, and the unusual elevator operation of threshold value early warning according to predetermineeing includes:
a1, presetting an elevator correct inductivity threshold value tha, presetting an obstacle influence factor threshold value thb and presetting an air quality influence factor threshold value thc;
a2, when the actual correct inductivity of the elevator, the obstacle influence factor and the air quality influence factor exceed a preset threshold value, sending out warning information, wherein the warning information comprises the identity number of the elevator, the correct inductivity of the elevator, the obstacle influence factor and the air quality influence factor;
the abnormal living environment early warning unit receives the fire protection equipment guarantee parameters and the fire risk coefficients acquired by the living environment analysis module, and early warns the abnormal living environment according to a preset threshold value, and the abnormal living environment early warning unit comprises:
b1, presetting a fire-fighting equipment guarantee parameter threshold value thd and presetting a fire-fighting risk coefficient threshold value theta;
and B2, when the actual fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients exceed the preset threshold, sending warning information to the response execution module, wherein the warning information comprises the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients of the building.
Preferably, the early warning module includes a task priority calculating unit, the task priority calculating unit is configured to receive warning information transmitted by the elevator operation abnormality early warning unit and the living environment abnormality early warning unit, mark the warning information as a task to be processed, and calculate a priority of the task, and the method includes:
and C1, calculating an elevator running deviation index: inputting the correct inductivity, the obstacle influence factors and the air quality influence factors of the elevator into an elevator operation deviation index calculation model to obtain an elevator operation deviation index, and satisfying the formula:wherein DP represents the elevator running deviation index, w a Representing coefficient weight corresponding to correct inductivity of elevator, w b Representing the coefficient weight, w, corresponding to the obstacle influencing factor c Representing the coefficient weight corresponding to the air quality influence factor;
and C2, calculating a fire control deviation index of the living environment: inputting the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients into a living environment fire-fighting deviation index calculation model to obtain a living environment fire-fighting deviation index, and satisfying the formula:wherein JP represents the fire-fighting deviation index of living environment, w d Representing the coefficient weight, w, corresponding to the guarantee parameters of the fire-fighting equipment e Representing the coefficient weight corresponding to the fire risk coefficient;
and C3, calculating a task priority index: inputting the deviation index of the elevator operation and the deviation value of the fire protection deviation index of the living environment into a task priority index calculation model, wherein the elevator operation abnormality assessment index calculation model meets the following formula: yp=jp+dp, where YP represents a task priority index.
Preferably, the response execution module comprises a task processing unit and a supervision resource allocation unit, and the task processing unit processes the tasks to be processed according to the order of the task priority indexes from large to small; and the supervision resource allocation unit allocates property management resources based on the traffic safety evaluation index, and the lower the traffic safety evaluation index is, the more supervision resources are allocated.
Preferably, the intelligent property management platform comprises the following steps when in use:
step S01, acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information;
s02, acquiring fire information, living information and building information of living environment;
step S03, calculating correct induction parameters of the elevator, influence factors of the obstacle of the elevator and air quality influence factors based on the information acquired in the step S01;
step S04, calculating population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients based on the information acquired in the step S02;
s05, presetting a threshold value of an elevator correct inductivity parameter, an elevator obstacle influence factor, an air quality influence factor, a fire-fighting equipment guarantee parameter and a fire-fighting risk coefficient, marking information exceeding the threshold value as a task to be processed, and calculating task priority;
step S06, calculating the traffic smoothness based on the results of the step S03 and the step S04;
step S07, calculating the traffic safety degree based on the results of the step S03, the step S04 and the step S06;
and S08, distributing property management supervision resources based on traffic safety and distributing tasks to be processed based on task priority indexes.
(III) technical effects and advantages of the application:
(1) According to the method, the fault and the safety risk of the owners in the passing way are found through the Internet of things, the safety risk is analyzed, the passing smoothness and the passing safety are calculated, the property management resources are distributed according to the passing safety, and the safety of the owners is improved to the maximum efficiency.
(2) According to the intelligent property management platform provided by the application, the elevator warning information and the fire-fighting warning information of the living environment are obtained through the early warning module, the warning information is marked as a task to be processed, the task priority index of the task to be processed is calculated, the warning information is processed according to the task priority index, and the working efficiency is improved.
Drawings
Fig. 1 is a block diagram showing the overall structure of the present application.
Fig. 2 is a schematic structural diagram of a living environment analysis module according to the present application.
Fig. 3 is a schematic diagram of the elevator information analysis module according to the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
Embodiment 1 referring to fig. 1-3, the present application provides a smart property management platform as shown in fig. 1, comprising: the system comprises a living environment information acquisition module, an elevator information acquisition module, a living environment analysis module, an elevator information analysis module, a passing smoothness evaluation module, a passing safety evaluation module and a response execution module, wherein the elevator information analysis module comprises an elevator correct induction rate calculation unit, an obstacle influence factor calculation unit and an air quality influence factor calculation unit, and the living environment analysis module comprises a population density parameter calculation unit, a fire-fighting equipment guarantee parameter calculation unit and a fire-fighting risk coefficient calculation unit, wherein:
101. the living environment information acquisition module is used for acquiring fire control information, living information and building information of the living environment and transmitting the acquired information to the living environment analysis module;
it should be explained that the fire fighting information includes the actual number of fire fighting equipments, the living information includes the population number and the elevator number, and the building information includes: building height, building life, building electrical load, building floor space.
102. The elevator information acquisition module is used for acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information, and transmitting the acquired information to the elevator information analysis module;
the elevator basic information comprises the area of an elevator, the height of the elevator, the width of an elevator outlet, the waiting time of the elevator and the maximum number of people which can be accommodated by the elevator; the elevator induction information comprises elevator normal induction times and elevator abnormal induction times; the elevator obstacle information comprises obstacle occupation area, obstacle height and obstacle position information, and the air quality information in the elevator comprises the following components: carbon dioxide concentration, carbon monoxide concentration, oxygen concentration, smoke concentration.
The judgment rule of elevator induction failure is as follows: when the elevator door is blocked, the elevator door is not closed, whether the elevator door is blocked or not is identified based on an image identification technology, whether the elevator clamps the block or not is identified, and when the elevator has the block as a result, but the elevator clamps the block, the elevator is marked as one-time induction failure.
Further, the elevator information acquisition module acquires normal induction times and abnormal induction times of an elevator based on an elevator induction analysis model, and the elevator induction analysis model comprises:
and (3) data acquisition: acquiring the starting time t1 of elevator closing and the ending time t2 of elevator closing, if the interval time is larger than a preset value, indicating that an obstacle exists in the elevator closing process, acquiring an image frame of the elevator at the moment, for example, setting the normal closing time interval of the elevator as t 0 Acquiring the interval time of n times of elevator closing tasks through a sensor, screening to obtain elevator closing tasks with closing time intervals larger than t0, marking the elevator closing tasks as closing tasks to be analyzed, setting the closing tasks to be analyzed to have m times, and calling elevator image frames in the interval time of the closing tasks to be analyzed, wherein the elevator image frames are acquired through cameras arranged above an elevator entrance, and the time for acquiring the elevator image frames is at the beginning of elevator closingFrom time t1 to end time t2, i.e. during the closing of the elevator door;
data analysis: judging whether an obstacle exists in the elevator closing process, for example, an object exists between elevator doors in an elevator image frame, and marking that the obstacle exists in the elevator closing process;
and (3) data judgment: judging whether the obstacle is extruded by the elevator, if so, recording the elevator closure as abnormal induction, for example, acquiring the real-time width of the elevator door and comparing the real-time width with the obstacle width, and if the real-time width of the elevator door is equal to the obstacle width, indicating that the obstacle is extruded by the elevator;
data statistics: and acquiring the total induction times of the elevator and the abnormal induction times of the elevator.
Further, the elevator information acquisition module acquires the floor area, the position information and the height of the obstacle based on an obstacle recognition model, and the obstacle recognition model may include:
and (3) image acquisition: acquiring elevator images according to a fixed frequency, wherein the fixed frequency is set as the time from the top to the bottom when an elevator runs empty;
image preprocessing: preprocessing the acquired image to improve the accuracy and efficiency of subsequent processing steps, such as denoising, image enhancement and size normalization;
feature extraction: extracting features related to the obstacle from the preprocessed image by using computer vision and image processing technology, wherein common features comprise the existence time, the occupied area, the color, the texture, the shape and the edge of the obstacle;
obstacle detection: analyzing and classifying the extracted features by using a deep learning model, and judging whether an obstacle exists in the image;
target positioning: if the obstacle detection is successful, further carrying out position location on the image, determining the position information of the obstacle in the image through boundary frame or pixel level segmentation, obtaining the coordinate information of the obstacle, and obtaining the occupied area and the height of the obstacle according to the coordinate information;
obstacle identification and classification: if more detailed identification and classification of the obstacle is required, the obstacle is identified and classified based on a machine learning algorithm or a deep learning model.
103. The living environment analysis module is used for calculating population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients, and transmitting calculation results to the early warning module and the traffic smoothness evaluation module;
it should be explained that population density parameters satisfy the formulaWherein R is 1 Indicating the total number of people S 1 Indicating the number of elevators r d Indicating the maximum number of accommodations of the elevator; fire-fighting equipment guarantee parameter meeting formulaWherein X is 1 Indicating the number of actual fire-fighting equipments, h j Representing the height of a building, ZM 0 Representing the floor area of a building, +.>Representing the predicted number of fire-fighting devices required for a building, eta 1 The larger the value of the fire-fighting equipment guarantee parameter is, the stronger the fire-fighting equipment guarantee capability of the property is; fire risk coefficient satisfies the formula->Wherein h is j Representing the height of the building, T representing the life of the building, DF representing the electrical load of the building, ZM 0 Representing the floor space of the building.
104. The elevator information analysis module is used for calculating correct induction parameters of an elevator, elevator obstacle influence factors and air quality influence factors, and transmitting calculation results to the early warning module and the passing smoothness evaluation module;
it should be explained that the correct inductivity parameter of the elevator satisfies the formulaWherein R is g Indicating correct inductivity of elevator, M 1 Indicating the correct sensing times of the elevator, M 2 The abnormal induction times of the elevator are represented; elevator obstacle influencing factor satisfying the formula +.>Wherein R is z Representing obstacle influencing factors, representing DM 1 Represents the area of the elevator, zm represents the occupied area of the obstacle, zv represents the occupied volume of the space of the obstacle, h d Indicating the height of the elevator, using L 1 Indicating the width of the elevator exit after the obstacle occupies L 2 Representing the initial width, eta of the elevator exit 1 Representing the influence factor of the obstacle position, 0 < eta 1 Is less than or equal to 1; the air quality influence factor satisfies the formulaWherein R is k Representing the air quality influencing factor, i.e. the air quality influencing factor in time t, a i Represents the carbon dioxide concentration in air at the ith time point, a 0 Representing the carbon dioxide concentration in a normal environment; bi represents the carbon monoxide concentration in the air at the ith time point, b 0 Represents the carbon monoxide concentration in a normal environment; ci represents the oxygen concentration in air at the ith time point, c 0 Represents the oxygen concentration in normal environment, w 1 Represents the influence weight of carbon dioxide on environmental quality, w 2 Represents the impact weight of carbon monoxide on environmental quality, w 3 Represents the impact weight of oxygen on environmental quality, and w 1 +w 2 +w 3 =100%, n represents the number of acquisition points, λ 1 The influence coefficient of smoke concentration in air is represented as 0-1]，λ 2 The influence coefficient of other harmful gases in the air is represented, D is a coefficient constant, and the value is 0-1]。
105. The traffic smoothness evaluation module acquires traffic smoothness based on a traffic smoothness calculation model;
it should be explained that the traffic smoothness calculation model satisfies the formulaWherein TS represents the traffic smoothness, the larger the TS value is, the smoother the traffic is, and the value of TS is [0-1 ]]，T d Representing the influence coefficient of the elevator operation waiting time, tmax represents +.>Tmin represents +.>Is a minimum of (2).
106. The traffic safety evaluation module acquires traffic safety based on a traffic safety calculation model;
it should be explained that the traffic safety calculation model satisfies the formulaWherein An represents the traffic safety, and a larger value of An indicates a safer traffic.
107. The response execution module allocates property management resources based on the traffic safety assessment index.
It should be explained that the response execution module includes a supervision resource allocation unit that allocates property management resources based on the traffic safety evaluation index, and the lower the traffic safety evaluation index, the more supervision resources are allocated.
Further, the intelligent property management platform comprises an early warning module, wherein the early warning module is used for early warning abnormal information, marking the abnormal information as a task to be processed, calculating a task priority index, and transmitting the task to be processed and the task priority index to the response execution module;
the utility model provides a need explanation, early warning module includes the unusual early warning unit of elevator operation, the unusual early warning unit of living environment, the unusual early warning unit of elevator operation receives elevator correct inductivity, obstacle influence factor and air quality influence factor that elevator information analysis module obtained, and the unusual elevator operation of early warning according to the threshold value of predetermineeing includes:
a1, presetting an elevator correct inductivity threshold value tha, presetting an obstacle influence factor threshold value thb and presetting an air quality influence factor threshold value thc;
a2, when the actual correct inductivity of the elevator, the obstacle influence factor and the air quality influence factor exceed a preset threshold value, sending out warning information, wherein the warning information comprises the identity number of the elevator, the correct inductivity of the elevator, the obstacle influence factor and the air quality influence factor;
further, the living environment abnormality pre-warning unit receives the fire protection parameters and the fire risk coefficients of the fire protection equipment acquired by the living environment analysis module, pre-warns the abnormal living environment according to a preset threshold value, and comprises the following steps:
b1, presetting a fire-fighting equipment guarantee parameter threshold value thd and presetting a fire-fighting risk coefficient threshold value theta;
and B2, when the actual fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients exceed the preset threshold, sending warning information to the response execution module, wherein the warning information comprises the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients of the building.
Further, the early warning module includes a task priority calculating unit, the task priority calculating unit is configured to receive warning information transmitted by the elevator operation abnormality early warning unit and the living environment abnormality early warning unit, mark the warning information as a task to be processed, and calculate a priority of the task, and the method includes:
and C1, calculating an elevator running deviation index: inputting the correct inductivity, the obstacle influence factors and the air quality influence factors of the elevator into an elevator operation deviation index calculation model to obtain an elevator operation deviation index, and satisfying the formula:wherein DP represents the elevator running deviation index, w a Representing coefficient weight corresponding to correct inductivity of elevator, w b Representing the coefficient weight, w, corresponding to the obstacle influencing factor c Representing the coefficient weight corresponding to the air quality influence factor;
and C2, calculating a fire control deviation index of the living environment: inputting the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients into a living environment fire-fighting deviation index calculation model to obtain livingThe environment fire control deviation index satisfies the formula:wherein JP represents the fire-fighting deviation index of living environment, w d Representing the coefficient weight, w, corresponding to the guarantee parameters of the fire-fighting equipment e Representing the coefficient weight corresponding to the fire risk coefficient;
and C3, calculating a task priority index: inputting the deviation index of the elevator operation and the deviation value of the fire protection deviation index of the living environment into a task priority index calculation model, wherein the elevator operation abnormality assessment index calculation model meets the following formula: yp=jp+dp, where YP represents a task priority index.
Further, the response execution module processes the tasks to be processed according to the order of the task priority indexes from large to small.
Further, referring to fig. 3, the intelligent property management platform includes the following steps when applied:
step S01, acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information;
s02, acquiring fire information, living information and building information of living environment;
step S03, calculating correct induction parameters of the elevator, influence factors of the obstacle of the elevator and air quality influence factors based on the information acquired in the step S01;
step S04, calculating population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients based on the information acquired in the step S02;
s05, presetting a threshold value of an elevator correct inductivity parameter, an elevator obstacle influence factor, an air quality influence factor, a fire-fighting equipment guarantee parameter and a fire-fighting risk coefficient, marking information exceeding the threshold value as a task to be processed, and calculating task priority;
step S06, calculating the traffic smoothness based on the results of the step S03 and the step S04;
step S07, calculating the traffic safety degree based on the results of the step S03, the step S04 and the step S06;
and S08, distributing property management supervision resources based on traffic safety and distributing tasks to be processed based on task priority indexes.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (9)
1. An wisdom property management platform, its characterized in that: comprising the following steps:
the elevator information acquisition module is used for acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information, and transmitting the acquired information to the elevator information analysis module;
the elevator information analysis module calculates elevator correct inductivity parameters, elevator obstacle influence factors and air quality influence factors based on an elevator correct inductivity parameter calculation model, an obstacle influence factor calculation model and an air quality influence factor calculation model, and transmits the acquired results to the traffic smoothness evaluation module;
the living environment information acquisition module is used for acquiring fire control information, living information and building information of the living environment and transmitting the acquired information to the living environment analysis module;
the living environment analysis module calculates population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients based on the population density parameter calculation model, the fire-fighting equipment guarantee parameter calculation model and the fire-fighting risk coefficient calculation model, and transmits calculation results to the traffic smoothness evaluation module;
the passing smoothness evaluation module inputs the results of the elevator information analysis module and the living environment analysis module into a passing smoothness calculation model and outputs a passing smoothness index;
the traffic safety evaluation module inputs the results of the living environment analysis module, the elevator information analysis module and the traffic smoothness evaluation module into a traffic safety calculation model, and outputs a traffic safety evaluation index;
the response execution module comprises a supervision resource allocation unit which allocates property supervision resources based on the traffic safety evaluation index.
2. The intelligent property management platform of claim 1, wherein: the elevator information analysis module comprises an elevator correct inductivity calculation unit, an obstacle influence factor calculation unit and an air quality influence factor calculation unit,
the elevator correct inductivity calculation unit is obtained by calculating an elevator correct inductivity parameter calculation model, and the calculation model meets the formulaWherein R is g Indicating correct inductivity of elevator, M 1 Indicating the correct sensing times of the elevator, M 2 The abnormal induction times of the elevator are represented;
the obstacle influence factor calculation unit is calculated by an obstacle influence factor calculation model, and the calculation model meets the formulaWherein R is z Representing obstacle influencing factors, representing DM 1 Represents the area of the elevator, zm represents the occupied area of the obstacle, zv represents the occupied volume of the space of the obstacle, h d Indicating the height of the elevator, using L 1 Indicating the width of the elevator exit after the obstacle occupies L 2 Representing the initial width, eta of the elevator exit 1 Represents the obstacle position influencing factor, and 0 < eta 1 ≤1；
The air quality influence factor calculation unit is obtained by calculating an air quality influence factor calculation model, and the calculation model meets the formulaWherein R is k Representing the air quality influencing factor, i.e. the air quality influencing factor in time t, a i Represents the carbon dioxide concentration in air at the ith time point, a 0 Representing a normal environmentCarbon dioxide concentration in (a); bi represents the carbon monoxide concentration in the air at the ith time point, b 0 Represents the carbon monoxide concentration in a normal environment; ci represents the oxygen concentration in air at the ith time point, c 0 Represents the oxygen concentration in normal environment, w 1 Represents the influence weight of carbon dioxide on environmental quality, w 2 Represents the impact weight of carbon monoxide on environmental quality, w 3 Represents the impact weight of oxygen on environmental quality, and w 1 +w 2 +w 3 =100%, n represents the number of acquisition points, λ 1 The influence coefficient of smoke concentration in air is represented as 0-1]，λ 2 The influence coefficient of other harmful gases in the air is represented, D is a coefficient constant, and the value is 0-1]。
3. The intelligent property management platform of claim 2, wherein: the living environment analysis module comprises a population density parameter calculation unit, a fire-fighting equipment guarantee parameter calculation unit and a fire risk coefficient calculation unit,
the population density parameter calculating unit is obtained by calculation through a population density parameter calculating model, and the calculating model meets the formulaWherein R is 1 Indicating the total number of people S 1 Indicating the number of elevators r d Indicating the maximum number of accommodations of the elevator;
the fire-fighting equipment guarantee parameter calculation unit is obtained by calculating a fire-fighting equipment guarantee parameter calculation model, and the calculation model meets the formulaWherein X is 1 Indicating the number of actual fire-fighting equipments, h j Representing the height of a building, ZM 0 Representing the floor area of a building, +.>Representing predicted building needsNumber of fire-fighting equipments eta 1 Representing coefficient constants;
the fire risk coefficient calculation unit is calculated by a fire risk coefficient calculation model, and the calculation model meets the formulaWherein h is j Representing the height of the building, T representing the life of the building, DF representing the electrical load of the building, ZM 0 Representing the floor space of the building.
4. A smart property management platform as claimed in claim 3, wherein: the passing smoothness evaluation module calculates the passing smoothness through a passing smoothness evaluation calculation model, and the passing smoothness calculation model meets the formulaWherein TS represents the traffic smoothness, the larger the TS value is, the smoother the traffic is, and the value of TS is [0-1 ]]，T d Representing the influence coefficient of the elevator operation waiting time, tmax represents +.>Tmin represents +.>Is a minimum of (2).
5. The intelligent property management platform of claim 4, wherein: the traffic safety evaluation module calculates the traffic safety through a traffic safety evaluation calculation model, and the traffic safety calculation model meets the formulaWherein An represents the traffic safety.
6. The intelligent property management platform of claim 5, wherein: the system comprises an early warning module for acquiring warning information, wherein the early warning module comprises an elevator operation abnormity early warning unit and a living environment abnormity early warning unit, the elevator operation abnormity early warning unit receives the elevator correct inductivity, the obstacle influence factor and the air quality influence factor which are acquired by the elevator information analysis module, and the abnormal elevator operation is early warned according to a preset threshold value to acquire elevator warning information; the living environment abnormality early-warning unit receives the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients acquired by the living environment analysis module, and early-warns the abnormal living environment according to a preset threshold value to acquire living environment fire-fighting warning information.
7. The intelligent property management platform of claim 6, wherein: the early warning module comprises a task priority calculating unit, wherein the task priority calculating unit is used for receiving elevator warning information and living environment fire control warning information transmitted by the elevator operation abnormity early warning unit and the living environment abnormity early warning unit, marking the warning information as a task to be processed and calculating the priority of the task, and the early warning module comprises the following components:
calculating an elevator running deviation index: the correct inductivity, the obstacle influence factor and the air quality influence factor of the elevator are input into an elevator operation deviation index calculation model to obtain an elevator operation deviation index, and the formula is satisfiedWherein DP represents the elevator running deviation index, w a Representing coefficient weight corresponding to correct inductivity of elevator, w b Representing the coefficient weight, w, corresponding to the obstacle influencing factor c Representing the coefficient weight corresponding to the air quality influence factor;
calculating a fire protection deviation index of the living environment: inputting the fire-fighting equipment guarantee parameters and the fire-fighting risk coefficients into a living environment fire-fighting deviation index calculation model to obtain a living environment fire-fighting deviation index, and satisfying a formulaWherein JP represents the fire-fighting deviation index of living environment, w d Representing the coefficient weight, w, corresponding to the guarantee parameters of the fire-fighting equipment e Representing the coefficient weight corresponding to the fire risk coefficient;
calculating a task priority index: the deviation index of the elevator operation and the deviation value of the fire protection deviation index of the living environment are input into a task priority index calculation model, and the elevator operation abnormality evaluation index calculation model satisfies the formula yp=jp+dp, wherein YP represents the task priority index.
8. The intelligent property management platform of claim 7, wherein: the response execution module comprises a task processing unit, and the task processing unit processes tasks to be processed according to the order of the task priority indexes from large to small.
9. The intelligent property management platform of claim 1, wherein: the method comprises the following steps when in application:
step S01, acquiring elevator basic information, elevator induction information, elevator barrier information and elevator inner air quality information;
s02, acquiring fire information, living information and building information of living environment;
step S03, calculating correct induction parameters of the elevator, influence factors of the obstacle of the elevator and air quality influence factors based on the information acquired in the step S01;
step S04, calculating population density parameters, fire-fighting equipment guarantee parameters and fire-fighting risk coefficients based on the information acquired in the step S02;
s05, presetting a threshold value of an elevator correct inductivity parameter, an elevator obstacle influence factor, an air quality influence factor, a fire-fighting equipment guarantee parameter and a fire-fighting risk coefficient, marking information exceeding the threshold value as a task to be processed, and calculating task priority;
step S06, calculating the traffic smoothness based on the results of the step S03 and the step S04;
step S07, calculating the traffic safety degree based on the results of the step S03, the step S04 and the step S06;
and S08, distributing property management supervision resources based on traffic safety and distributing tasks to be processed based on task priority indexes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311099709.7A CN117172970A (en) | 2023-08-29 | 2023-08-29 | Wisdom property management platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311099709.7A CN117172970A (en) | 2023-08-29 | 2023-08-29 | Wisdom property management platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117172970A true CN117172970A (en) | 2023-12-05 |
Family
ID=88944110
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311099709.7A Pending CN117172970A (en) | 2023-08-29 | 2023-08-29 | Wisdom property management platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117172970A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117724367A (en) * | 2023-12-21 | 2024-03-19 | 广东全芯半导体有限公司 | Property management system with time sequence control function main control chip |
-
2023
- 2023-08-29 CN CN202311099709.7A patent/CN117172970A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117724367A (en) * | 2023-12-21 | 2024-03-19 | 广东全芯半导体有限公司 | Property management system with time sequence control function main control chip |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3680206B1 (en) | System and method for detecting elevator maintenance behaviors in elevator hoistway | |
| CN100595121C (en) | Target place optimized dispatching method and system | |
| CN111564015B (en) | Method and device for monitoring perimeter intrusion of rail transit | |
| CN117172970A (en) | Wisdom property management platform | |
| CN112884325A (en) | Method and system for application analysis and health condition evaluation of customer station equipment | |
| CN113158752A (en) | Intelligent safety management and control system for electric power staff approach operation | |
| CN110304513B (en) | Risk avoiding method and device, terminal equipment and storage medium | |
| CN112733668B (en) | Video deep learning-based detection method for individual elevator taking of pets in elevator | |
| CN116386120B (en) | A noninductive control management system for wisdom campus dormitory | |
| CN115196455A (en) | Elevator control method, elevator and computer readable storage medium | |
| CN113269111B (en) | Video monitoring-based elevator abnormal behavior detection method and system | |
| CN112487976B (en) | Monitoring method, device and storage medium based on image recognition | |
| CN114912678A (en) | Online automatic detection and early warning method and system for abnormal operation of power grid regulation and control | |
| CN116443682B (en) | Intelligent elevator control system | |
| CN107331007A (en) | A kind of wagon flow logistics supervisory systems and method based on video acquisition | |
| CN115359416A (en) | Intelligent early warning system for railway freight yard sky eye | |
| CN112801072B (en) | Elevator non-flat-layer door opening fault recognition device and method based on computer vision | |
| CN114701934A (en) | Security control method, device and system for elevator, cloud platform and storage medium | |
| CN114332695A (en) | Method and device for identifying opening and closing of elevator door and storage medium | |
| CN114408691A (en) | Method, device and equipment for detecting elevator detained personnel and storage medium | |
| CN114359839A (en) | Method and system for identifying entrance of electric vehicle into elevator | |
| CN113903005A (en) | Big data-based smart campus security data processing system | |
| CN114140849A (en) | Electric power infrastructure field personnel state management method and system based on expression recognition | |
| CN114538234B (en) | Automatic construction system and method for safe operation standard AI of big data elevator of Internet of things | |
| CN117902415B (en) | Intelligent elevator monitoring method and device based on Internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |