CN116471340A - Big data-based data transmission monitoring system and method - Google Patents
Big data-based data transmission monitoring system and method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
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- H—ELECTRICITY
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- H04L43/02—Capturing of monitoring data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The invention discloses a data transmission monitoring system and method based on big data, and relates to the technical field of data monitoring of big data. The system comprises a data acquisition module, a data processing module, a data monitoring module, a data feedback module and a data control module; the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the data monitoring module; the output end of the data monitoring module is connected with the input end of the data feedback module; the output end of the data feedback module is connected with the input end of the data control module; the invention also provides a method for implementing the system, which can effectively improve the condition of overlarge people flow in the target area and meet the requirement of protecting the life and property safety of people.
Description
Technical Field
The invention relates to the technical field of target area data monitoring safety, in particular to a data transmission monitoring system and method based on big data.
Background
The data monitoring of big data means that data are collected by means of scientific experiments, physical information and the like, meaningful data are subjected to specialized processing to obtain new data, and the new data are arranged, analyzed and utilized to realize that the data are added value to create more values.
In modern life, with the vigorous development of science and technology, big data is no longer an abstract word, and fills every corner of our life. In the prior art, big data monitoring can provide a more convenient and safe social environment for us, but a plurality of target areas are not used for reasonably monitoring the target data, so that the waste of human resources and the occurrence of safety accidents are caused, and in order to safely and effectively monitor the real-time data of the target areas, a big data monitoring system and method are urgently needed for solving the problems.
Disclosure of Invention
The invention aims to provide a data transmission monitoring system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the system comprises a data acquisition module, a data processing module, a data monitoring module, a data feedback module and a data control module;
the data acquisition module is used for acquiring the people flow in the target area; the data processing module is used for processing the data summarized by the data acquisition module; the data monitoring module is used for monitoring the flow of people entering and exiting the target area in unit time; the data feedback module is used for feeding back the condition of the traffic flow in the target area and giving an alarm when the traffic flow exceeds a threshold value; the data control module is used for reasonably controlling the flow of people entering the target area according to the data acquisition and monitoring results;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the data monitoring module; the output end of the data monitoring module is connected with the input end of the data feedback module; the output end of the data feedback module is connected with the input end of the data control module.
The data acquisition module comprises an identity recognition unit;
the identity recognition unit is used for recognizing the identities of people entering and exiting the target area.
The data processing module comprises a data receiving unit and a data processing unit;
the data receiving unit is used for receiving the data of the data acquisition module; the data processing unit is used for further finishing the data received by the data receiving unit;
the output end of the data receiving unit is connected with the input end of the data processing unit; the output end of the data processing unit is connected with the input end of the data monitoring module.
The data monitoring module comprises a database and a data prediction unit;
the database is used for storing related data of the people flow entering and exiting the target area; the data prediction unit is used for predicting the flow of people entering and exiting the target area in unit time;
the output end of the database is connected with the input end of the data prediction unit.
The data feedback module and the data control module respectively comprise a recording alarm unit and a receiving control unit;
the recording alarm unit is used for recording the monitored flow of people and alarming when the flow of people in the target area exceeds a threshold value; the receiving control unit is used for receiving the data transmitted by the data feedback module and controlling the flow of people entering the target area.
A data transmission monitoring method based on big data is characterized in that: the method comprises the following steps:
s1, calculating the maximum traffic volume which can be contained in a target area according to the area of the target area, feeding back the maximum traffic volume which can be contained in the target area to a target area manager, giving an alarm when the target area monitors that the difference between the current traffic volume and the maximum traffic volume is smaller than a threshold value, and setting a time node which receives early warning information each time as a target time node;
s2, setting a unit period in a historical time period from an opening time point of the target area to a target time node, and calculating the personnel flow speed of the unit period according to the personnel flow rate of each unit period entering and exiting the target area;
s3, calculating the average flow speed of the personnel in each group of historical time periods according to the personnel flow speed in the unit period;
s4, calculating the average flow speed of the personnel entering and exiting the target area according to the average flow speed of the personnel in each group of historical time periods in the step S3, and obtaining a history fitting curve equation;
s5, calculating the average flow speed of the personnel entering and exiting the target area according to the flow speed of the personnel in the current target area per unit period, and obtaining a current fitting curve equation;
s6, comparing the slope of the history fit curve equation in the step S5 with the slope of the current fit curve equation in the step S6, and if the ratio of the slope of the current fit curve equation to the slope of the history fit curve equation is greater than a threshold B, starting an alarm system to control the flow of people entering the target area.
In step S1, the steps of calculating the maximum traffic and alarming that can be accommodated by the target area are as follows:
s7-1, calculating the maximum people flow which can be contained in the target area, and according to the formula:
d is the maximum flow of people which can be accommodated in the target area; s is the active area of the target area; h is the average footprint of an adult;
s7-2, setting a system threshold A, alarming when the difference value between the current people flow and the maximum people flow is smaller than the threshold value, and recording the time point of each time of receiving the early warning information as a target time node.
In step S2, the personnel flow rate per unit cycle is calculated according to the formula:
wherein K is i For the flow rate of people per unit period, R Feeding in R is the flow of people entering a target area per unit period Out of The flow rate of people leaving the target area in each unit period;
in step S3, according to the personnel flow speed of the unit period, the average personnel flow speed of each group of historical time periods is calculated, and according to the formula:
wherein S is i For average flow velocity, K, of personnel for each set of historical time periods invoked i The personnel flow speed per unit period is i is the unit period number selected in each group of historical time period;
in step S4, the average flow velocity of the person entering and exiting the target area is calculated according to the average flow velocity of the person in each group of historical time periods, and the formula is given by:
wherein K is Flat 1 For average flow velocity of person entering or exiting target area S i For the average flow speed of the personnel in each group of the historical time periods, n is the number of the selected historical time periods;
according to the average flow speed of the personnel entering and exiting the target area, a history fitting curve equation is obtained, and according to the formula:
y 1 ＝K flat 1 X+C
Wherein y is 1 And C is the total current people flow of the target area after the prediction, and C is the current people flow of the target area entering the target area before the prediction.
In step S5, the step of obtaining a new fitted curve equation is as follows:
s9-1, calculating the personnel flow speed of the current unit period according to the formula:
wherein K is p For the flow rate of people in the current unit period, R Inlet 1 R is the current flow of people entering the target area per unit period Go out 1 The flow rate of people leaving the target area in each unit period is the current flow rate;
s9-2, calculating the average flow speed of people entering and exiting the target area according to the flow speed of people in the current unit period, and according to the formula:
wherein K is Flat 2 For the average flow velocity, K, of the person currently entering and exiting the target area p The personnel flow speed in the current unit period is the personnel flow speed in the current unit period, and p is the current unit time number;
s9-3, obtaining a current fitting curve equation according to the average flow speed of people currently entering and exiting the target area, and according to the formula:
y 2 ＝K flat 2 X+C
Wherein y is 2 K is the total current people flow in the current target area after prediction Flat 2 And C is the average flow speed of the current personnel entering and exiting the target area, and C is the existing flow of the personnel entering the target area before the target area is opened to prediction.
In step S6, comparing the slope of the history fitted curve equation with the slope of the current fitted curve equation, and if the ratio of the slope of the current fitted curve equation to the slope of the history fitted curve equation is greater than the threshold B, starting the alarm system to control the flow of people entering the target area.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can reduce the number of security personnel in the target area, effectively control the entrance, save human resources and better meet the actual situation;
2. according to the invention, big data and data monitoring can be integrated, the total amount of real-time people flow in the target area is monitored from multiple aspects, and a foundation is provided for controlling the flow and speed of personnel;
3. according to the invention, the people flow leaving the target area can be recorded from the outlet of the target area, so that the total real-time people flow in the target area can be obtained more accurately, and the situation that people always enter and leave is avoided.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a data transmission monitoring system based on big data according to the present invention;
fig. 2 is a schematic diagram of steps of a data transmission monitoring method based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the system comprises a data acquisition module, a data processing module, a data monitoring module, a data feedback module and a data control module;
the data acquisition module is used for acquiring the people flow in the target area; the data processing module is used for processing the data summarized by the data acquisition module; the data monitoring module is used for monitoring the flow of people entering and exiting the target area in unit time; the data feedback module is used for feeding back the condition of the traffic flow in the target area and giving an alarm when the traffic flow exceeds a threshold value; the data control module is used for reasonably controlling the flow of people entering the target area according to the data acquisition and monitoring results;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the data monitoring module; the output end of the data monitoring module is connected with the input end of the data feedback module; the output end of the data feedback module is connected with the input end of the data control module.
The data acquisition module comprises an identity recognition unit;
the identity recognition unit is used for recognizing the identities of people entering and exiting the target area.
The data processing module comprises a data receiving unit and a data processing unit;
the data receiving unit is used for receiving the data of the data acquisition module; the data processing unit is used for further finishing the data received by the data receiving unit;
the output end of the data receiving unit is connected with the input end of the data processing unit; the output end of the data processing unit is connected with the input end of the data monitoring module.
The data monitoring module comprises a database and a data prediction unit;
the database is used for storing related data of the people flow entering and exiting the target area; the data prediction unit is used for predicting the flow of people entering and exiting the target area in unit time;
the output end of the database is connected with the input end of the data prediction unit.
The data feedback module and the data control module respectively comprise a recording alarm unit and a receiving control unit;
the recording alarm unit is used for recording the monitored flow of people and alarming when the flow of people in the target area exceeds a threshold value; the receiving control unit is used for receiving the data transmitted by the data feedback module and controlling the flow of people entering the target area.
A data transmission monitoring method based on big data is characterized in that: the method comprises the following steps:
s1, calculating the maximum traffic volume which can be contained in a target area according to the area of the target area, feeding back the maximum traffic volume which can be contained in the target area to a target area manager, giving an alarm when the target area monitors that the difference between the current traffic volume and the maximum traffic volume is smaller than a threshold value, and setting a time node which receives early warning information each time as a target time node;
s2, setting a unit period in a historical time period from an opening time point of the target area to a target time node, and calculating the personnel flow speed of the unit period according to the personnel flow rate of each unit period entering and exiting the target area;
s3, calculating the average flow speed of the personnel in each group of historical time periods according to the personnel flow speed in the unit period;
s4, calculating the average flow speed of the personnel entering and exiting the target area according to the average flow speed of the personnel in each group of historical time periods in the step S3, and obtaining a history fitting curve equation;
s5, calculating the average flow speed of the personnel entering and exiting the target area according to the flow speed of the personnel in the current target area per unit period, and obtaining a current fitting curve equation;
s6, comparing the slope of the history fit curve equation in the step S5 with the slope of the current fit curve equation in the step S6, and if the ratio of the slope of the current fit curve equation to the slope of the history fit curve equation is greater than a threshold B, starting an alarm system to control the flow of people entering the target area.
In step S1, the steps of calculating the maximum traffic and alarming that can be accommodated by the target area are as follows:
s7-1, calculating the maximum people flow which can be contained in the target area, and according to the formula:
d is the maximum flow of people which can be accommodated in the target area; s is the active area of the target area; h is the average footprint of an adult;
s7-2, setting a system threshold A, alarming when the difference value between the current people flow and the maximum people flow is smaller than the threshold value, and recording the time point of each time of receiving the early warning information as a target time node.
In step S2, the personnel flow rate per unit cycle is calculated according to the formula:
wherein K is i For the flow rate of people per unit period, R Feeding in R is the flow of people entering a target area per unit period Out of The flow rate of people leaving the target area in each unit period;
in step S3, according to the personnel flow speed of the unit period, the average personnel flow speed of each group of historical time periods is calculated, and according to the formula:
wherein S is i For average flow velocity, K, of personnel for each set of historical time periods invoked i The personnel flow speed per unit period is i is the unit period number selected in each group of historical time period;
in step S4, the average flow velocity of the person entering and exiting the target area is calculated according to the average flow velocity of the person in each group of historical time periods, and the formula is given by:
wherein K is Flat 1 For average flow velocity of person entering or exiting target area S i For average flow velocity of people for each set of historical time periods invoked, n is the historical time selectedA number of segments;
according to the average flow speed of the personnel entering and exiting the target area, a history fitting curve equation is obtained, and according to the formula:
y 1 ＝K flat 1 X+C
Wherein y is 1 And C is the total current people flow of the target area after the prediction, and C is the current people flow of the target area entering the target area before the prediction.
In step S5, the step of obtaining a new fitted curve equation is as follows:
s9-1, calculating the personnel flow speed of the current unit period according to the formula:
wherein K is p For the flow rate of people in the current unit period, R Inlet 1 R is the current flow of people entering the target area per unit period Go out 1 The flow rate of people leaving the target area in each unit period is the current flow rate;
s9-2, calculating the average flow speed of people entering and exiting the target area according to the flow speed of people in the current unit period, and according to the formula:
wherein K is Flat 2 For the average flow velocity, K, of the person currently entering and exiting the target area p The personnel flow speed in the current unit period is the personnel flow speed in the current unit period, and p is the current unit time number;
s9-3, obtaining a current fitting curve equation according to the average flow speed of people currently entering and exiting the target area, and according to the formula:
y 2 ＝K flat 2 X+C
Wherein y is 2 K is the total current people flow in the current target area after prediction Flat 2 For the average flow velocity of the personnel currently entering and exiting the target area, C is the opening of the target area toThe existing traffic of people previously entering the target area is predicted.
In step S6, comparing the slope of the history fitted curve equation with the slope of the current fitted curve equation, and if the ratio of the slope of the current fitted curve equation to the slope of the history fitted curve equation is greater than the threshold B, starting the alarm system to control the flow of people entering the target area.
In this embodiment:
taking a scenic spot area as a target area, and respectively counting the people flow of different entrances of the scenic spot and the people flow of different exits leaving the scenic spot in the same unit period at the entrance and the exit of the scenic spot by adopting an identity recognition technology by a data acquisition module so as to obtain the current people flow in the scenic spot at the moment;
in this example, the scenic spot can accommodate the maximum upper limit of tourists, according to the formula:
intercepting different unit periods in a historical time period from the initial opening time of a scenic spot to the early warning sending, intercepting 8:30-9:00;9:30-10:00;10:30:11:00 three unit periods, calculating the personnel flow speed of intercepting the unit periods, and recording the personnel flow in and out of the scenic spot of the three unit periods as follows:
data one: an inlet (381, 217, 248); an outlet (183, 233, 125);
data two: an inlet (290, 320, 332); an outlet (321, 121, 172);
data three: an inlet (342, 295, 275); an outlet (212, 287, 153);
according to the formula:
the personnel flow speeds of the three unit periods entering and exiting the scenic spot are calculated to be K respectively 1 ≈1.56、K 2 ≈1.53、K 3 ≈1.40；
According to the personnel flow speeds of the three unit periods entering and exiting the scenic spot, calculating the personnel average flow speed of the first group of historical data, and according to the formula:
calculating the average flow speed S of the personnel in the first group of historical data 1 ≈1.50
Repeatedly selecting the other two sets of historical data, repeating the steps to calculate the average personnel flow speed of the second set of historical data and the third set of historical data, and recording as S 1 And S is 2 Calculating to obtain S 2 ＝2.10，S 3 ＝1.80；
According to the obtained data, calculating the average flow speed of people entering and exiting the scenic spot, and according to the formula:
obtaining the average flow velocity K of people entering and exiting the scenic spot Flat 1 =1.8, resulting in a history-fit curve equation,
y 1 ＝K flat 1 X+C
According to step S5, a current fitting curve equation is calculated, wherein the personnel flow speed of the current unit period is K respectively p1 ＝2.28，K p2 ＝2.45，K p3 =2.52; according to the personnel flow speed of the current unit period, the personnel flow speed K of the current in-out target area is obtained Flat 2 =1.75, where K Flat 2 Slope of the curve equation is fitted currently;
slope K of the current fitted curve equation Flat 2 =1.75, slope K of curve equation fitted to history Flat 1 The ratio of =1.8 isAt the moment, the ratio of the slope of the current fitting curve to the slope of the history fitting curve is greater than the system thresholdB=90%, the alarm system starts the alarm, the data control module works, the number of tourists existing in the scenic spot is fed back to scenic spot management personnel, the flow of people entering the scenic spot is controlled, and a comfortable and safe tourism environment is provided for the tourists.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The utility model provides a data transmission monitoring system based on big data which characterized in that: the system comprises a data acquisition module, a data processing module, a data monitoring module, a data feedback module and a data control module;
the data acquisition module is used for acquiring the people flow in the target area; the data processing module is used for processing the data summarized by the data acquisition module; the data monitoring module is used for monitoring the flow of people entering and exiting the target area in unit time; the data feedback module is used for feeding back the condition of the traffic flow in the target area and giving an alarm when the traffic flow exceeds a threshold value; the data control module is used for reasonably controlling the flow of people entering the target area according to the data acquisition and monitoring results;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the data monitoring module; the output end of the data monitoring module is connected with the input end of the data feedback module; the output end of the data feedback module is connected with the input end of the data control module.
2. The big data based data transmission monitoring system of claim 1, wherein: the data acquisition module comprises an identity recognition unit;
the identity recognition unit is used for recognizing the identities of people entering and exiting the target area.
3. The big data based data transmission monitoring system of claim 1, wherein: the data processing module comprises a data receiving unit and a data processing unit;
the data receiving unit is used for receiving the data of the data acquisition module; the data processing unit is used for further finishing the data received by the data receiving unit;
the output end of the data receiving unit is connected with the input end of the data processing unit; the output end of the data processing unit is connected with the input end of the data monitoring module.
4. The big data based data transmission monitoring system of claim 1, wherein: the data monitoring module comprises a database and a data prediction unit;
the database is used for storing related data of the people flow entering and exiting the target area; the data prediction unit is used for predicting the flow of people entering and exiting the target area in unit time;
the output end of the database is connected with the input end of the data prediction unit.
5. The big data based data transmission monitoring system of claim 1, wherein: the data feedback module and the data control module respectively comprise a recording alarm unit and a receiving control unit;
the recording alarm unit is used for recording the monitored flow of people and alarming when the flow of people in the target area exceeds a threshold value; the receiving control unit is used for receiving the data transmitted by the data feedback module and controlling the flow of people entering the target area.
6. A data transmission monitoring method based on big data is characterized in that: the method comprises the following steps:
s1, calculating the maximum traffic volume which can be contained in a target area according to the area of the target area, feeding back the maximum traffic volume which can be contained in the target area to a target area manager, giving an alarm when the target area monitors that the difference between the current traffic volume and the maximum traffic volume is smaller than a threshold value, and setting a time node which receives early warning information each time as a target time node;
s2, setting a unit period in a historical time period from an opening time point of the target area to a target time node, and calculating the personnel flow speed of the unit period according to the personnel flow rate of each unit period entering and exiting the target area;
s3, calculating the average flow speed of the personnel in each group of historical time periods according to the personnel flow speed in the unit period;
s4, calculating the average flow speed of the personnel entering and exiting the target area according to the average flow speed of the personnel in each group of historical time periods in the step S3, and obtaining a history fitting curve equation;
s5, calculating the average flow speed of the personnel entering and exiting the target area according to the flow speed of the personnel in the current target area per unit period, and obtaining a current fitting curve equation;
s6, comparing the slope of the history fit curve equation in the step S5 with the slope of the current fit curve equation in the step S6, and if the ratio of the slope of the current fit curve equation to the slope of the history fit curve equation is greater than a threshold B, starting an alarm system to control the flow of people entering the target area.
7. The big data based data transmission monitoring method of claim 6, wherein: in step S1, the steps of calculating the maximum traffic and alarming that can be accommodated by the target area are as follows:
s7-1, calculating the maximum people flow which can be contained in the target area, and according to the formula:
d is the maximum flow of people which can be accommodated in the target area; s is the active area of the target area; h is the average footprint of an adult;
s7-2, setting a system threshold A, alarming when the difference value between the current people flow and the maximum people flow is smaller than the threshold value, and recording the time point of each time of receiving the early warning information as a target time node.
8. The big data based data transmission monitoring method of claim 6, wherein: in step S2, the personnel flow rate per unit cycle is calculated according to the formula:
wherein K is i For the flow rate of people per unit period, R Feeding in R is the flow of people entering a target area per unit period Out of The flow rate of people leaving the target area in each unit period;
in step S3, according to the personnel flow speed of the unit period, the average personnel flow speed of each group of historical time periods is calculated, and according to the formula:
wherein S is i For average flow velocity, K, of personnel for each set of historical time periods invoked i The personnel flow speed per unit period is i is the unit period number selected in each group of historical time period;
in step S4, the average flow velocity of the person entering and exiting the target area is calculated according to the average flow velocity of the person in each group of historical time periods, and the formula is given by:
wherein K is Flat 1 For average flow velocity of person entering or exiting target area S i For the average flow speed of the personnel in each group of the historical time periods, n is the number of the selected historical time periods;
according to the average flow speed of the personnel entering and exiting the target area, a history fitting curve equation is obtained, and according to the formula:
y 1 ＝K flat 1 X+C
Wherein y is 1 And C is the total current people flow of the target area after the prediction, and C is the current people flow of the target area entering the target area before the prediction.
9. The big data based data transmission monitoring method of claim 6, wherein: in step S5, the step of obtaining a new fitted curve equation is as follows:
s9-1, calculating the personnel flow speed of the current unit period according to the formula:
wherein K is p For the flow rate of people in the current unit period, R Inlet 1 R is the current flow of people entering the target area per unit period Go out 1 The flow rate of people leaving the target area in each unit period is the current flow rate;
s9-2, calculating the average flow speed of people entering and exiting the target area according to the flow speed of people in the current unit period, and according to the formula:
wherein K is Flat 2 For the average flow velocity, K, of the person currently entering and exiting the target area p The personnel flow speed in the current unit period is the personnel flow speed in the current unit period, and p is the current unit time number;
s9-3, obtaining a current fitting curve equation according to the average flow speed of people currently entering and exiting the target area, and according to the formula:
y 2 ＝K flat 2 X+C
Wherein y is 2 K is the total current people flow in the current target area after prediction Flat 2 And C is the average flow speed of the current personnel entering and exiting the target area, and C is the existing flow of the personnel entering the target area before the target area is opened to prediction.
In step S6, the slope of the history fitted curve is compared with the slope of the current fitted curve, and if the ratio of the slope of the current fitted curve equation to the slope of the history fitted curve equation is greater than the threshold B, the alarm system is started to control the flow of people entering the target area.
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