CN114897433A - Campus personnel flow monitoring and early warning method based on big data - Google Patents
Campus personnel flow monitoring and early warning method based on big data Download PDFInfo
- Publication number
- CN114897433A CN114897433A CN202210649960.5A CN202210649960A CN114897433A CN 114897433 A CN114897433 A CN 114897433A CN 202210649960 A CN202210649960 A CN 202210649960A CN 114897433 A CN114897433 A CN 114897433A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- area
- early warning
- personnel
- evaluation
- 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
- 238000012544 monitoring process Methods 0.000 title claims abstract description 105
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 26
- 238000011156 evaluation Methods 0.000 claims description 82
- 238000012795 verification Methods 0.000 claims description 40
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 238000005192 partition Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 4
- 230000001276 controlling effect Effects 0.000 claims description 3
- 238000004904 shortening Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 9
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Emergency Management (AREA)
- Alarm Systems (AREA)
Abstract
The invention relates to the technical field of personnel flow monitoring, and discloses a campus personnel flow monitoring and early warning method based on big data; the different areas of the park are subjected to modular processing, so that differentiated monitoring and early warning of the park can be realized, the different areas can be subjected to targeted regulation and control, the monitoring effects of the different areas can be effectively improved, each data collected and counted can be standardized and normalized, each processed data can be calculated and analyzed, whether the flow states of personnel in the different divided areas are normal or not can be obtained, and the overall effect of flow monitoring and early warning of the personnel in the park can be effectively improved; the invention can solve the technical problems of low accuracy of monitoring and early warning of the flowing of the personnel in the park and fuzzy early warning results in the existing scheme.
Description
Technical Field
The invention relates to the technical field of personnel flow monitoring, in particular to a campus personnel flow monitoring and early warning method based on big data.
Background
The park refers to a designated area of unified planning, and enterprises, companies and the like specially provided with certain specific industries and forms in the area are managed in a unified way, typically, such as an industrial park, a self-trade park, an industrial park and a cartoon park.
The current garden personnel flow monitoring early warning scheme is when implementing, just comes to monitor and early warning the personnel flow in the garden based on sensor or surveillance video, can not carry out the monitoring of differentiation to different functional areas in the garden, also can not verify the condition that appears unusually and verify, also can not confirm the reason that abnormal state appears simultaneously for the staff can not make the accuracy so that the pertinence handles, and the whole effect that leads to garden personnel flow monitoring early warning is not good.
Disclosure of Invention
The invention provides a campus personnel flow monitoring and early warning method and system based on big data, and mainly aims to solve the technical problems of low accuracy of flow monitoring and early warning of campus personnel and fuzzy early warning results in the existing scheme.
In order to achieve the purpose, the campus personnel flow monitoring and early warning method based on big data provided by the invention comprises the following steps:
modularizing all areas according to the names of different areas in the garden and the corresponding occupied areas thereof to obtain an area partition set comprising a plurality of partition areas;
monitoring and counting the number of personnel in different divided areas in the area divided set according to a preset monitoring time period to obtain an area monitoring set containing personnel monitoring data corresponding to a plurality of divided areas;
carrying out feature extraction and definition marking on a plurality of personnel monitoring data in the regional monitoring set to obtain a regional processing set containing a plurality of monitoring processing data;
analyzing and evaluating a plurality of monitoring processing data in the regional processing set in sequence, judging whether the personnel flow state of the corresponding divided region meets the monitoring standard, and obtaining a regional evaluation set containing a plurality of qualified personnel flow states and unqualified personnel flow states;
if the divided region which does not accord with the monitoring standard exists in the region evaluation set, setting the divided region as a selected region, and adjusting the monitoring time period to check the personnel flow state of the selected region to obtain a check and verification set;
and carrying out early warning and prompting on the personnel flow in the selected area in a self-adaptive manner according to the centralized verification result of the verification, and taking corresponding measures to evacuate or limit the flow.
Preferably, all areas are modularly processed according to the names of the different areas in the campus and their corresponding footprints, including:
acquiring names of different areas in the park, matching the acquired area names with a pre-constructed area name table to acquire corresponding area weight values, and setting the area weight values as first division values;
acquiring area occupied areas corresponding to different area names in the park, extracting the numerical value of the area occupied area and setting the numerical value as a second division value;
acquiring a product result of the first division value and the second division value, setting the product result as a division standard value, and setting a region corresponding to the division standard value as a division region;
and arranging the corresponding divided areas in a descending order according to the size of the dividing standard value to obtain an area division set.
Preferably, the feature extraction and definition marking are performed on a plurality of person monitoring data in the regional monitoring set, and include:
according to the size of the dividing standard value, marking the number of the corresponding dividing regions as i, wherein i belongs to {1, 2, 3.., n }, and n is a positive integer and is expressed as the total number;
acquiring the total number of people, the number of entering people and the number of leaving people in the personnel monitoring data;
respectively taking values of the total number of people, the number of entering people and the number of leaving people in each divided area, and defining and marking the values as RZi, JRi and LRi;
sequentially arranging and combining the marked data items to obtain monitoring processing data corresponding to each divided area; the plurality of monitoring process data constitute a regional process set.
Preferably, the analysis and evaluation are sequentially performed on a plurality of monitoring processing data in the regional processing set, including:
acquiring total number RZi of people with value marks in the monitoring processing data corresponding to each divided region;
marking the dividing standard value corresponding to each divided area as HBi;
the total number RZi of the people marked correspondingly in each divided area and the dividing standard value HBi are combined, and the people estimated value RG of each divided area is obtained through formula calculation; the formula is:
RG＝HBi×(RZi-RZi0)
wherein RZi0 is the total number of standard persons corresponding to each divided region;
acquiring a human estimation threshold corresponding to each divided region according to the dividing standard value, judging that the human flow state of the divided region corresponding to the human estimation value smaller than the human estimation threshold is qualified, and generating a first human estimation signal; meanwhile, judging that the personnel flow state of the divided area corresponding to the people estimation value which is not less than the people estimation threshold value is unqualified and generating a second people estimation signal;
and the qualified personnel flow state and the corresponding first personnel estimation signals corresponding to the plurality of divided regions, and the unqualified personnel flow state and the corresponding second personnel estimation signals form a region evaluation set.
Preferably, adjusting the monitoring period to verify the flow status of the person in the selected area comprises:
shortening the monitoring duration of the monitoring time period according to a second personal estimation signal in the regional estimation set, setting the adjusted monitoring time period as a verification time period, and performing verification and analysis on the selected region by using the subsequent m verification time periods; m is a positive integer;
if the area evaluation set of the subsequent m verification time periods comprises m second personal evaluation signals, judging that continuous crowding exists in the selected area, generating a first early warning signal, setting the corresponding selected area as a target area according to the first early warning signal, and analyzing and evaluating the entering state and the leaving state of the target area to obtain an entering and exiting evaluation set;
if the area evaluation set of the subsequent m verification time periods contains k second personal evaluation signals, judging that discontinuous personal crowding exists in the selected area, generating second early warning signals, and setting the corresponding selected area as an observation area according to the second early warning signals; k is a positive integer less than m;
if the area evaluation sets of the subsequent m verification time periods contain 0 second personal evaluation signal, judging that short-time personal congestion exists in the selected area, and generating a third early warning signal;
the first early warning signal and the target area, the second early warning signal and the observation area, the third early warning signal and the access and exit evaluation set form a verification set.
Preferably, the analytical assessment of the entry and exit status of the target area comprises:
acquiring the total number of people, the number of people entering and the number of people leaving in the personnel monitoring data corresponding to the target area, respectively combining the total number of people RZi, the number of people entering JRi and the number of people leaving LRi marked by values in each divided area, and calculating and acquiring an access assessment value JCP of the target area through a formula; the formula is:
JCP＝HBi×(a1×JRi/RZi+a2×LRi/RZi)
wherein a1 and a2 are different proportionality coefficients which are larger than zero, and a1 is less than a 2;
acquiring an in-out evaluation threshold corresponding to each target area according to the division standard value;
if the access evaluation value is smaller than the access evaluation threshold, judging that the access state of the target area is abnormal and generating a first evaluation signal;
if the access evaluation value is not less than the access evaluation threshold and not more than p% of the access evaluation threshold, and p is a real number more than one hundred, judging that the leaving state of the target area is abnormal and generating a second evaluation signal;
if the entry and exit evaluation value is larger than p% of the entry and exit evaluation threshold value, judging that the entry state and the exit state of the target area are abnormal and generating a third evaluation signal;
the first evaluation signal, the second evaluation signal and the third evaluation signal form an in-out evaluation set.
Preferably, the self-adaptive early warning and prompting of the personnel flow in the selected area according to the verification result in the verification set comprises the following steps:
and performing related early warning and prompting according to different early warning signals in the verification and verification set, and meanwhile, adaptively limiting entering personnel and regulating and controlling leaving personnel to leave quickly according to different assessment signals in the in-out assessment set.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, different areas of the park are subjected to modular processing, so that differentiated monitoring and early warning of the park can be realized, so that the different areas can be subjected to targeted regulation and control, the monitoring effects of the different areas can be effectively improved, each data can be standardized and normalized by processing each collected and counted data, whether the flow states of personnel in different divided areas are normal or not can be obtained by calculating and analyzing each processed data, and the overall effect of flow monitoring and early warning of the personnel in the park can be effectively improved.
According to the method and the device, the abnormal personnel flow state can be accurately and efficiently analyzed by analyzing and classifying the personnel flow states of different divided areas, whether the entering state and the leaving state of the target area are normal or not can be judged, so that the staff can accurately and efficiently maintain the abnormal personnel flow state, and compared with the prior scheme that fuzzy early warning prompt is carried out through a single number of people, the embodiment of the invention can realize more accurate and efficient early warning and regulation and control.
Drawings
Fig. 1 is a schematic flow chart of a campus personnel flow monitoring and early warning method based on big data according to an embodiment of the present invention.
The objects, features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1, a schematic flow chart of a campus personnel flow monitoring and early warning method based on big data according to an embodiment of the present invention is shown.
A campus personnel flow monitoring and early warning method based on big data comprises the following specific steps:
s1: modularizing all areas according to the names of different areas in the garden and the corresponding occupied areas thereof to obtain an area partition set comprising a plurality of partition areas; the method comprises the following steps:
acquiring names of different areas in the park, matching the acquired area names with a pre-constructed area name table to acquire corresponding area weight values, and setting the area weight values as first division values;
the domain name table is composed of a plurality of domain names and corresponding domain weight values, the corresponding domain weight values are preset for different domain names, and the specific numerical values of the domain weight values can be set according to the position of the domain or big data corresponding to the function;
acquiring area occupied areas corresponding to different area names in the park, extracting the numerical value of the area occupied area and setting the numerical value as a second division value; the size of the occupied area of different areas can indicate that the number of people accommodated correspondingly is different; the corresponding area floor area can also be obtained based on the big data of the garden;
acquiring a product result of the first division value and the second division value, setting the product result as a division standard value, and setting a region corresponding to the division standard value as a division region;
and arranging the corresponding divided areas in a descending order according to the size of the dividing standard value to obtain an area division set.
It should be noted that the purpose of performing modular processing on different areas of the park is to realize differentiated monitoring and early warning on the park, so that the different areas can be subjected to targeted regulation and control, the monitoring effects of the different areas can be effectively improved, and the purpose of setting the area weight values is to realize digital representation of the areas with different weights.
S2: monitoring and counting the number of personnel in different divided areas in the area divided set according to a preset monitoring time period to obtain an area monitoring set containing personnel monitoring data corresponding to a plurality of divided areas;
in the implementation of the invention, the number of people in the divided area can be monitored and counted by matching the counter with the monitoring equipment, the monitoring equipment is used for acquiring the video of the movement of the people in the divided area, and the number of people entering and leaving is determined by the movement direction of the people in the video; the preset monitoring time period may be 60 seconds;
the divided area may be rectangular, and if one direction is set as the entering direction, the direction is reversed to the leaving direction, when the person leaves the divided area, the corresponding number of the entering person and the number of the leaving person are added by one, and when the moving direction of the person is the leaving direction but still exists in the divided area, the counting is not performed.
S3: carry out feature extraction and definition mark to a plurality of personnel monitoring data in the regional monitoring set, obtain the regional processing set who contains a plurality of monitoring process data, include:
according to the size of the dividing standard value, marking the number of the corresponding dividing regions as i, wherein i belongs to {1, 2, 3.., n }, and n is a positive integer and is expressed as the total number;
acquiring the total number of people, the number of entering people and the number of leaving people in the personnel monitoring data;
respectively taking values of the total number of people, the number of entering people and the number of leaving people in each divided area, and defining and marking the values as RZi, JRi and LRi;
sequentially arranging and combining the marked data items to obtain monitoring processing data corresponding to each divided area; the monitoring processing data form a regional processing set;
in the embodiment of the invention, by processing the collected and counted data, the data can be standardized and normalized, and the subsequent simultaneous calculation of the data items is facilitated.
S4: analyzing and evaluating a plurality of monitoring processing data in the regional processing set in sequence, judging whether the personnel flow state of the corresponding divided region meets the monitoring standard, and obtaining a regional evaluation set containing a plurality of qualified personnel flow states and unqualified personnel flow states; the method comprises the following steps:
acquiring total number RZi of people with value marks in the monitoring processing data corresponding to each divided region;
marking the dividing standard value corresponding to each divided area as HBi;
the total number RZi of the people marked correspondingly in each divided area and the dividing standard value HBi are combined, and the people estimated value RG of each divided area is obtained through formula calculation; the formula is:
RG＝HBi×(RZi-RZi0)
wherein RZi0 is the total number of standard persons corresponding to each divided region;
the estimated value is a value for integrally evaluating the flow state of the people by combining the total number of the people in different divided areas with the dividing standard value; according to the dividing standard value, the monitoring standard of the personnel flow state corresponding to the divided areas can be obtained, whether the personnel flow state of different divided areas is normal or not can be obtained by analyzing the personnel estimation value, and the abnormal personnel flow state of different divided areas needs to be further analyzed and confirmed.
Acquiring a human estimation threshold corresponding to each divided region according to the dividing standard value, judging that the human flow state of the divided region corresponding to the human estimation value smaller than the human estimation threshold is qualified, and generating a first human estimation signal;
meanwhile, judging that the personnel flow state of the divided area corresponding to the people estimation value which is not less than the people estimation threshold value is unqualified and generating a second people estimation signal;
and the qualified personnel flow state and the corresponding first personnel estimation signals corresponding to the plurality of divided regions, and the unqualified personnel flow state and the corresponding second personnel estimation signals form a region evaluation set.
In the embodiment of the invention, the person flow states in different divided areas are analyzed and classified, so that the divided areas corresponding to abnormal person flow states can be accurately and efficiently regulated, and the overall effect of monitoring and early warning of the person flow in the garden can be effectively improved.
S5: if the divided region which does not accord with the monitoring standard exists in the region evaluation set, setting the divided region as a selected region, and adjusting the monitoring time period to check the personnel flow state of the selected region to obtain a check and verification set; the method comprises the following steps:
shortening the monitoring time of the monitoring time period according to a second personal estimation signal in the regional estimation set, setting the adjusted monitoring time period as a verification time period which can be 30 seconds, and verifying and analyzing the selected region by the subsequent m verification time periods; m is a positive integer and can take the value of 5;
if the area evaluation set of the subsequent m verification time periods contains m second personal evaluation signals, judging that continuous crowding exists in the selected area, generating a first early warning signal, and setting the corresponding selected area as a target area according to the first early warning signal;
the continuous crowd of people means that the overall speed of people moving is low;
analyzing and evaluating the entering state and the leaving state of the target area to obtain an entering and exiting evaluation set; the method comprises the following steps:
acquiring the total number of people, the number of people entering and the number of people leaving in the personnel monitoring data corresponding to the target area, respectively combining the total number of people RZi, the number of people entering JRi and the number of people leaving LRi marked by values in each divided area, and calculating and acquiring an access assessment value JCP of the target area through a formula; the formula is:
JCP＝HBi×(a1×JRi/RZi+a2×LRi/RZi)
in the formula, a1 and a2 are different proportionality coefficients which are larger than zero, a1 is more than a2, a1 can be 0.635, and a2 can be 3.254; the differential representation between a2 and a1 can make the weights of the corresponding data items different, so that the differential analysis evaluation can be accurately performed on the corresponding data items.
It should be noted that the access evaluation value is a numerical value used for integrally evaluating the access state of each data item in the target area by associating the data items; whether the entering state and the leaving state of the target area are normal or not can be judged based on the entering and exiting evaluation value, so that workers can accurately and efficiently maintain the target area.
Acquiring an in-out evaluation threshold corresponding to each target area according to the division standard value;
if the access evaluation value is smaller than the access evaluation threshold, judging that the access state of the target area is abnormal and generating a first evaluation signal;
if the access evaluation value is not less than the access evaluation threshold and not more than p% of the access evaluation threshold, wherein p is a real number greater than one hundred, and can be 150, judging that the leaving state of the target area is abnormal and generating a second evaluation signal;
if the entry and exit evaluation value is larger than p% of the entry and exit evaluation threshold value, judging that the entry state and the exit state of the target area are abnormal and generating a third evaluation signal;
the first evaluation signal, the second evaluation signal and the third evaluation signal form an in-out evaluation set;
if the area evaluation set of the subsequent m verification time periods contains k second personal evaluation signals, judging that discontinuous personal crowding exists in the selected area, generating second early warning signals, and setting the corresponding selected area as an observation area according to the second early warning signals; k is a positive integer less than m, and can take the value of 3;
the intermittent crowding refers to the intermittent crowding which is not crowded for a while;
if the area evaluation set of the subsequent m verification time periods contains 0 second personal evaluation signal, judging that short personal congestion exists in the selected area, and generating a third early warning signal;
the temporary crowding of people means that crowding occurs only once;
the first early warning signal and the target area, the second early warning signal and the observation area, the third early warning signal and the in-out evaluation set form a verification set.
In the embodiment of the invention, the purpose of checking the abnormal divided areas is to improve the accuracy of the monitoring and analyzing result, adaptively shorten the monitoring time period so as to be more efficient to verify, judge the state of the congestion of the corresponding divided areas, and eliminate the defect that the error in the single monitoring result influences the whole early warning effect in the existing scheme.
S6: according to the verification, the centralized verification result is verified, the personnel flow in the selected area is self-adaptively pre-warned and prompted, and corresponding measures are taken for evacuation or current limiting, and the method comprises the following steps:
and performing related early warning and prompting according to different early warning signals in the verification and verification set, and meanwhile, adaptively limiting entering personnel and regulating and controlling leaving personnel to leave quickly according to different assessment signals in the in-out assessment set.
In the embodiment of the invention, the specific abnormal state of the target area is analyzed and verified, so that the staff can be accurately and efficiently arranged to process the specific abnormal state, the target area in the abnormal state can be timely and effectively solved, and compared with the prior scheme in which the regulation and control are carried out only by the number of people, the embodiment of the invention can realize better processing effect of monitoring and early warning of staff flow.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (8)
1. Campus personnel flow monitoring and early warning method based on big data is characterized by comprising the following steps:
modularizing all areas according to the names of different areas in the garden and the corresponding occupied areas thereof to obtain an area partition set comprising a plurality of partition areas;
monitoring and counting the number of personnel in different divided areas in the area divided set according to a preset monitoring time period to obtain an area monitoring set containing personnel monitoring data corresponding to a plurality of divided areas;
carrying out feature extraction and definition marking on a plurality of personnel monitoring data in the regional monitoring set to obtain a regional processing set containing a plurality of monitoring processing data;
analyzing and evaluating a plurality of monitoring processing data in the regional processing set in sequence, judging whether the personnel flow state of the corresponding divided region meets the monitoring standard, and obtaining a regional evaluation set containing a plurality of qualified personnel flow states and unqualified personnel flow states;
if the divided region which does not accord with the monitoring standard exists in the region evaluation set, setting the divided region as a selected region, and adjusting the monitoring time period to check the personnel flow state of the selected region to obtain a check and verification set;
and carrying out early warning and prompting on the personnel flow in the selected area in a self-adaptive manner according to the centralized verification result of the verification, and taking corresponding measures to evacuate or limit the flow.
2. The big data based campus people flow monitoring and pre-warning method as claimed in claim 1, wherein all areas are treated modularly according to names of different areas in the campus and their corresponding floor areas, including:
acquiring names of different areas in the park and corresponding area weight values of the names;
acquiring area occupied areas corresponding to different area names in a park;
multiplying the area weight value and the area occupied area to obtain a dividing standard value of the area, and setting the area corresponding to the dividing standard value as a divided area;
and arranging the corresponding divided areas in a descending order according to the size of the dividing standard value to obtain an area division set.
3. The big data based campus people flow monitoring and early warning method as claimed in claim 1, wherein the feature extraction and definition marking are performed on a plurality of people monitoring data in a regional monitoring set, and the method comprises the following steps:
numbering and marking a plurality of corresponding divided areas according to the size of the dividing standard value;
acquiring the total number of people, the number of entering people and the number of leaving people in the personnel monitoring data, and respectively taking values and defining marks;
sequentially arranging and combining the marked data items to obtain monitoring processing data corresponding to each divided area; the plurality of monitoring process data constitute a regional process set.
4. The big data based campus people flow monitoring and early warning method as claimed in claim 1, wherein analyzing and evaluating several monitoring process data in regional process set in turn comprises:
the total number of the personnel correspondingly marked in each divided area and the dividing standard value are combined to obtain the estimated value of each divided area;
acquiring a human estimation threshold corresponding to each divided region according to the dividing standard value, judging that the human flow state of the divided region corresponding to the human estimation value smaller than the human estimation threshold is qualified, and generating a first human estimation signal; meanwhile, judging that the personnel flow state of the divided area corresponding to the people estimation value which is not less than the people estimation threshold value is unqualified and generating a second people estimation signal;
and the qualified personnel flow state and the corresponding first personnel estimation signals corresponding to the plurality of divided regions, and the unqualified personnel flow state and the corresponding second personnel estimation signals form a region evaluation set.
5. The big data based campus people flow monitoring and pre-warning method as claimed in claim 1, wherein adjusting the monitoring time period to check the people flow status of the selected area comprises:
shortening the monitoring duration of the monitoring time period according to a second personal estimation signal in the regional estimation set, setting the adjusted monitoring time period as a verification time period, and performing verification and analysis on the selected region by using the subsequent m verification time periods; m is a positive integer;
if the area evaluation set of the subsequent m verification time periods comprises m second personal evaluation signals, generating a first early warning signal, setting a corresponding selected area as a target area according to the first early warning signal, and analyzing and evaluating the entering state and the leaving state of the target area to obtain an entering and exiting evaluation set;
if the region evaluation set of the subsequent m verification time periods contains k second personal evaluation signals, generating second early warning signals, and setting the corresponding selected region as an observation region according to the second early warning signals; k is a positive integer less than m;
if the regional evaluation set of the subsequent m verification time periods contains 0 second personal evaluation signal, generating a third early warning signal;
the first early warning signal and the target area, the second early warning signal and the observation area, the third early warning signal and the access and exit evaluation set form a verification set.
6. The big data based campus people flow monitoring and forewarning method of claim 5 wherein the analysis and assessment of the entering and leaving status of the target area comprises:
acquiring the total number of people, the number of people entering and the number of people leaving in the personnel monitoring data corresponding to the target area, and simultaneously acquiring the access evaluation value of the target area by carrying out the total number of people, the number of people entering and the number of people leaving marked by the values in each divided area;
and obtaining an access evaluation threshold corresponding to each target area according to the division standard value, and performing matching analysis on the access evaluation threshold and the access evaluation value to obtain an access evaluation set containing a first evaluation signal, a second evaluation signal and a third evaluation signal.
7. The campus personnel flow monitoring and early warning method based on big data as claimed in claim 6, wherein if the access assessment value is smaller than the access assessment threshold, it is determined that the access status of the target area is abnormal and a first assessment signal is generated;
if the access evaluation value is not less than the access evaluation threshold and not more than p% of the access evaluation threshold, and p is a real number more than one hundred, judging that the leaving state of the target area is abnormal and generating a second evaluation signal;
and if the in-out evaluation value is larger than p% of the in-out evaluation threshold value, judging that the entering state and the leaving state of the target area are abnormal and generating a third evaluation signal.
8. The big-data-based campus people flow monitoring and early warning method as claimed in claim 1, wherein the self-adaptive early warning and prompting of people flow in the selected area according to the verification result in the verification and verification set comprises:
and performing related early warning and prompting according to different early warning signals in the verification and verification set, and meanwhile, adaptively limiting entering personnel and regulating and controlling leaving personnel to leave quickly according to different assessment signals in the in-out assessment set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210649960.5A CN114897433A (en) | 2022-06-09 | 2022-06-09 | Campus personnel flow monitoring and early warning method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210649960.5A CN114897433A (en) | 2022-06-09 | 2022-06-09 | Campus personnel flow monitoring and early warning method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114897433A true CN114897433A (en) | 2022-08-12 |
Family
ID=82727542
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210649960.5A Pending CN114897433A (en) | 2022-06-09 | 2022-06-09 | Campus personnel flow monitoring and early warning method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114897433A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116030607A (en) * | 2023-03-27 | 2023-04-28 | 中国电力工程顾问集团西南电力设计院有限公司 | Intelligent power plant safety supervision reminding and early warning system |
| CN116091272A (en) * | 2023-04-13 | 2023-05-09 | 内江市感官密码科技有限公司 | Campus abnormal activity monitoring method, device, equipment and medium |
| CN117689119A (en) * | 2024-02-01 | 2024-03-12 | 浙江蓝宸信息科技有限公司 | Intelligent building site safety supervision method and system based on Internet of things |
-
2022
- 2022-06-09 CN CN202210649960.5A patent/CN114897433A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116030607A (en) * | 2023-03-27 | 2023-04-28 | 中国电力工程顾问集团西南电力设计院有限公司 | Intelligent power plant safety supervision reminding and early warning system |
| CN116030607B (en) * | 2023-03-27 | 2023-06-09 | 中国电力工程顾问集团西南电力设计院有限公司 | Intelligent power plant safety supervision reminding and early warning system |
| CN116091272A (en) * | 2023-04-13 | 2023-05-09 | 内江市感官密码科技有限公司 | Campus abnormal activity monitoring method, device, equipment and medium |
| CN116091272B (en) * | 2023-04-13 | 2023-06-20 | 内江市感官密码科技有限公司 | Campus abnormal activity monitoring method, device, equipment and medium |
| CN117689119A (en) * | 2024-02-01 | 2024-03-12 | 浙江蓝宸信息科技有限公司 | Intelligent building site safety supervision method and system based on Internet of things |
| CN117689119B (en) * | 2024-02-01 | 2024-05-03 | 浙江蓝宸信息科技有限公司 | Intelligent building site safety supervision method and system based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114897433A (en) | Campus personnel flow monitoring and early warning method based on big data | |
| CN110493816B (en) | Real-time prediction method for rail transit subway station passenger flow volume | |
| CN100489870C (en) | Method and multidimensional system for statistical process control | |
| CN103065121B (en) | The engine driver's method for monitoring state analyzed based on video human face and device | |
| CN116300758A (en) | Cloud computing-based intelligent manufacturing industrial production data acquisition and analysis system and method | |
| CN113850535B (en) | Intelligent construction site personnel management method based on wearable equipment | |
| CN103425884B (en) | A kind of fuzzy prediction method of subway transfer channel traffic state | |
| CN106295513B (en) | People counting method and device based on residence time probability distribution | |
| CN116342344B (en) | Intelligent pension service management system based on Internet of things | |
| CN115331425B (en) | Traffic early warning method, device and system | |
| CN115550609B (en) | Building internet of things monitoring system capable of realizing automatic adaptation | |
| CN115736902A (en) | Constructor management system based on intelligent wearable equipment | |
| CN113099178B (en) | Intelligent community safety online real-time monitoring and early warning method based on video remote monitoring | |
| CN108287327A (en) | Metering automation terminal fault diagnostic method based on Bayes's classification | |
| CN110658544A (en) | Equipment personnel state monitoring and predicting method based on nuclear power plant access monitoring system | |
| CN114936801A (en) | Distribution network dispatching operation management method based on big data | |
| CN107145986B (en) | Charging electric quantity prediction method and device | |
| CN116596722A (en) | Smart community risk prevention and control system and risk prevention and control method | |
| CN110488188A (en) | The three-dimensional healthy method for quantitatively evaluating of unit based on dynamic threshold | |
| CN115358492B (en) | Passenger flow prediction method, device and system | |
| CN117053323A (en) | Air purification system based on indoor air proportion self-balancing | |
| CN116452370A (en) | Intelligent building environment safety management system based on multidimensional data | |
| CN112163774B (en) | Escalator people flow evaluation model building method, people flow analysis method and device | |
| CN109615275A (en) | A kind of power telecom network equipment health degree integrated evaluating method | |
| CN115328986A (en) | Power plant safety early warning data analysis processing method and system |
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 |