CN111369394A - Scenic spot passenger flow volume statistical evaluation system and method based on big data - Google Patents
Scenic spot passenger flow volume statistical evaluation system and method based on big data Download PDFInfo
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Abstract
The invention discloses a scenic spot passenger flow volume statistical evaluation system and a method based on big data, wherein the system comprises a scenic spot monitoring module, an image identification module, a regional classified statistical module, a total passenger flow volume checking module and a big data service platform, wherein the scenic spot monitoring module, the image identification module and the total passenger flow volume checking module are sequentially connected through an intranet, the regional classified statistical module and the total passenger flow volume checking module are connected through the intranet, and the scenic spot monitoring module, the image identification module, the regional classified statistical module and the total passenger flow volume checking module are respectively connected with the big data service platform through the intranet.
Description
Technical Field
The invention relates to the field of big data, in particular to a scenic spot passenger flow volume statistical evaluation system and method based on big data.
Background
Big data, an IT industry term, refers to a data set that cannot be captured, managed, and processed with a conventional software tool within a certain time range, and is a massive, high-growth-rate, and diversified information asset that needs a new processing mode to have stronger decision-making power, insight discovery power, and process optimization capability.
In the "big data era" written by vkto, mel, schenberger and kenius, cusk, the big data means that analysis processing is performed using all data without using a shortcut such as a random analysis method (sampling survey).
With the advent of the cloud era, big data has attracted more and more attention. Analyst teams believe that large data is often used to describe the large amount of unstructured and semi-structured data created by a company that can take excessive time and money to download to a relational database for analysis. Big data analysis is often tied to cloud computing because real-time large dataset analysis requires a MapReduce-like framework to distribute work to tens, hundreds, or even thousands of computers.
Large data requires special techniques to efficiently process large amounts of data that are tolerant of elapsed time. Technologies applicable to big data include Massively Parallel Processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the internet, and scalable storage systems. Data includes structured, semi-structured, and unstructured data, with unstructured data becoming an increasingly dominant part of the data. Survey reports by IDC show: 80% of the data in the enterprise is unstructured data, the data exponentially increases by 60% every year, the big data is a representation or a feature of the internet which is developed to the present stage, and the big data does not need to be mythically or worried about, and the data which is originally hard to collect and use is easy to utilize under the suspicion of the technical innovation large screen represented by cloud computing, and the big data can gradually create more value for human beings through continuous innovation of various industries.
At present, the passenger flow is calculated and only the number of the tickets sold in a time period is counted, so that the passenger flow in different time periods is judged.
Disclosure of Invention
The invention aims to provide a scenic spot passenger flow volume statistical evaluation system and method based on big data so as to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
scenic spot passenger flow volume statistics evaluation system based on big data, this system includes scenic spot monitoring module, image identification module, regional classification statistics module, total passenger flow volume checks module and big data service platform, wherein, scenic spot monitoring module, image identification module, total passenger flow volume check module loop through the intranet and connect, regional classification statistics module and total passenger flow volume check module pass through the intranet and connect, scenic spot monitoring module, image identification module, regional classification statistics module, total passenger flow volume check module respectively with big data service platform pass through the intranet and connect.
According to the technical scheme: scenic spot monitoring module includes a plurality of surveillance camera head and image acquisition submodule, each corner of the scenic spot that a plurality of surveillance camera head set up, the surveillance camera head is used for monitoring the personnel in each corner of scenic spot and gathers, the image acquisition submodule is used for carrying out the collection of scenic spot personnel's image according to the image of control shooting, surveillance camera head and big data service platform pass through wireless connection, the image acquisition submodule respectively with big data service platform, the image identification module passes through intranet connection.
According to the technical scheme: the image recognition module comprises a head feature extraction submodule, a body shape detection submodule and a detection data extraction submodule, the head feature extraction submodule is used for extracting head facial features and hair images, the body shape detection submodule is used for extracting body shapes and four limb images so as to be synthesized and judged with the head feature extraction submodule, the detection data extraction submodule is used for extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule, and the head feature extraction submodule and the body shape detection submodule are respectively connected with the scenic spot monitoring module through an internal network.
According to the technical scheme: regional classification statistics module includes ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule, wherein, ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule pass through the intranet with total passenger flow volume check module respectively and are connected, ticket window total ticketing statistics submodule is used for making statistics of the data that ticket window sells the scenic spot admission ticket, APP cloud ticketing statistics submodule is used for making statistics of network APP's ticketing data, scenic spot entry verification statistics submodule is used for getting into the scenic spot and verifies the number and make statistics, give all statistical data for total passenger flow volume check module.
According to the technical scheme: the total passenger flow checking module comprises a real-time data transmission module and a time-sharing passenger flow analysis module, the real-time data transmission module is respectively connected with the image identification module and the area classification statistical module through an intranet, the real-time data transmission module is used for receiving data detected and judged by the image identification module and the area classification statistical module in real time, and the time-sharing passenger flow analysis module is used for analyzing and evaluating passenger flows at different times.
According to the technical scheme: the real-time data transmission module comprises a passenger flow volume growth rate statistic submodule, the selling data counted by a ticket window total ticket selling statistic submodule is set as a first passenger flow volume I, the selling data counted by an APP cloud ticket selling statistic submodule is set as a second passenger flow volume J, ticket window ticket returning data I is counted, APP cloud ticket returning data is set as J, actual selling data of a window is set as I-I, actual selling data of an APP cloud ticket selling is set as J-J, actual selling data of the window and actual selling data of the APP cloud ticket are counted, the actual number of ticket selling of each day is divided into a plurality of fractional intervals, the actual selling data of the window and the actual selling data of the APP cloud ticket selling are sequenced from high to low according to different fractional intervals, intervals of which the actual selling data of the APP ticket cloud is higher than the actual selling data of the window are marked, and the difference value between the actual selling data of the window and the actual selling data of the APP cloud ticket selling, Δ x = | (I-I) - (J-J) |, and when Δ x is less than 100, drainage is performed at a proper ticket office.
According to the technical scheme: and the big data service platform is used for maintaining the data transmission inside the module.
The scenic spot passenger flow volume statistical evaluation method based on big data comprises the following steps:
s1: monitoring and acquiring personnel at each corner of a scenic spot by utilizing a monitoring camera arranged in a scenic spot monitoring module, and acquiring images of the personnel in the scenic spot by an image acquisition submodule according to the monitored and shot images;
s2: extracting head images of five sense organs and hair by using a head feature extraction submodule inside an image recognition module, extracting body shapes and four limbs images by using a body shape detection submodule so as to be synthesized and judged with the head feature extraction submodule, and extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule by using a detection data extraction submodule;
s3: the method comprises the steps that a general ticketing statistics submodule of a ticketing window in a regional classification statistics module is used for counting data of tickets sold in a scenic spot in the ticketing window, an APP cloud ticketing statistics submodule counts ticketing data of a network APP, a scenic spot entrance verification statistics submodule counts the number of verification of passengers entering the scenic spot, and all statistical data are sent to a general passenger flow volume check module;
s4: carrying out unified analysis on the passenger flow of the time division period by using a total passenger flow checking module;
s5: and the big data service platform is used for maintaining the data transmission inside the module.
According to the technical scheme: in step S4, the step of uniformly analyzing the passenger flow in the time division by using the total passenger flow check module further includes the following steps:
a1: the real-time data transmission module is used for receiving the data detected and judged by the image recognition module and the region classification statistical module in real time;
a2: comparing the received data of the image identification module and the region classification statistical module, and checking whether the comparison value exceeds a set threshold value;
a3: if the data exceeds the set threshold, returning the data to the image recognition module and the region classification statistical module, and screening and extracting again;
a4: and if the passenger flow volume is smaller than the set threshold, the time-interval passenger flow volume analysis module analyzes and evaluates the passenger flow volume at different time.
According to the technical scheme: in the step a4, if the passenger flow exceeds the set threshold, the time-share passenger flow analysis module analyzes and evaluates the passenger flow at different times, further comprising the following steps:
the time for setting and collecting the time-interval passenger flow is T1、T2、T3、...、Tn-1、TnWhen the time is the time, the image recognition module and the regional classification statistical module respectively detect the crowd inside the scenic region and check the verification number of the entrance to the garden at the entrance of the scenic region, the detection data extraction submodule is set to extract the judgment data synthesized by the head feature extraction submodule and the body shape detection submodule to obtain the value A1、A2、A3、...、An-1、AnThe scenic spot entrance verification statistical submodule carries out statistical value B on the verification number of the passengers entering the scenic spot1、B2、B3、...、Bn-1、BnExtracting scenic spot Tn-1~TnIn the time period, the data detected by the current detection data extraction submodule is An-1~AnAnd the data verified by the scenic spot entrance verification statistical submodule is Bn-1~BnSetting the data detected by the current time interval detection data extraction submodule to be C1, setting the data detected by the scenic spot entrance verification statistics submodule to be C2, and according to the formula:
setting the absolute value of the difference between C1 and C2 as | C1-C2|, if | C1-C2| is greater than a set threshold, returning the data to the image recognition module and the region classification statistical module, screening and extracting again, if | C1-C2| is less than or equal to the set threshold, adopting the value detected by the scenic spot entrance verification statistical submodule, setting the passenger flow per hour of the current time period as N, and according to the formula:
N=
(Unit: person/hour)
The passenger flow in the time period can be judged according to the formula, and the passenger flow in different time periods can be obtained for statistics by repeating the calculation.
Compared with the prior art, the invention has the beneficial effects that: the invention checks the flow of people from different cuts of the inside monitoring and entrance verification of the scenic spot, in order to count the passenger flow of different time;
monitoring and acquiring personnel at each corner of a scenic spot by utilizing a monitoring camera arranged in a scenic spot monitoring module, and acquiring images of the personnel in the scenic spot by an image acquisition submodule according to the monitored and shot images;
extracting head images of five sense organs and hair by using a head feature extraction submodule inside an image recognition module, extracting body shapes and four limbs images by using a body shape detection submodule so as to be synthesized and judged with the head feature extraction submodule, and extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule by using a detection data extraction submodule;
the method comprises the steps that a general ticketing statistics submodule of a ticketing window in a regional classification statistics module is used for counting data of tickets sold in a scenic spot in the ticketing window, an APP cloud ticketing statistics submodule counts ticketing data of a network APP, a scenic spot entrance verification statistics submodule counts the number of verification of passengers entering the scenic spot, and all statistical data are sent to a general passenger flow volume check module;
the total passenger flow volume checking module is used for carrying out unified analysis on the passenger flow volume of the time division period, and the real-time data transmission module is used for receiving the data detected and judged by the image identification module and the region classification statistical module in real time; comparing the received data of the image identification module and the region classification statistical module, and checking whether the comparison value exceeds a set threshold value; if the data exceeds the set threshold, returning the data to the image recognition module and the region classification statistical module, and screening and extracting again; if the passenger flow volume is smaller than the set threshold, the time-interval passenger flow volume analysis module analyzes and evaluates the passenger flow volume at different time;
and the big data service platform is used for maintaining the data transmission inside the module.
Drawings
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
FIG. 1 is a block diagram of a scenic spot passenger flow volume statistical evaluation system based on big data according to the present invention;
FIG. 2 is a diagram of the steps of the scenic spot passenger flow volume statistical evaluation method based on big data according to the present invention;
FIG. 3 is a detailed step diagram of step S4 of the method for statistical evaluation of scenic spot passenger flow based on big data according to the present invention;
fig. 4 is a schematic diagram of an implementation process of the scenic spot passenger flow volume statistical evaluation method based on big data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Referring to fig. 1 to 4, in the embodiment of the present invention, a scenic spot passenger flow volume statistics evaluation system and method based on big data includes a scenic spot monitoring module, an image recognition module, a regional classification statistics module, a total passenger flow volume check module, and a big data service platform, where the scenic spot monitoring module, the image recognition module, and the total passenger flow volume check module are sequentially connected via an intranet, the regional classification statistics module and the total passenger flow volume check module are connected via the intranet, and the scenic spot monitoring module, the image recognition module, the regional classification statistics module, and the total passenger flow volume check module are respectively connected via the intranet with the big data service platform.
According to the technical scheme: scenic spot monitoring module includes a plurality of surveillance camera head and image acquisition submodule, each corner of the scenic spot that a plurality of surveillance camera head set up, the surveillance camera head is used for monitoring the personnel in each corner of scenic spot and gathers, the image acquisition submodule is used for carrying out the collection of scenic spot personnel's image according to the image of control shooting, surveillance camera head and big data service platform pass through wireless connection, the image acquisition submodule respectively with big data service platform, the image identification module passes through intranet connection.
According to the technical scheme: the image recognition module comprises a head feature extraction submodule, a body shape detection submodule and a detection data extraction submodule, the head feature extraction submodule is used for extracting head facial features and hair images, the body shape detection submodule is used for extracting body shapes and four limb images so as to be synthesized and judged with the head feature extraction submodule, the detection data extraction submodule is used for extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule, and the head feature extraction submodule and the body shape detection submodule are respectively connected with the scenic spot monitoring module through an internal network.
According to the technical scheme: regional classification statistics module includes ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule, wherein, ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule pass through the intranet with total passenger flow volume check module respectively and are connected, ticket window total ticketing statistics submodule is used for making statistics of the data that ticket window sells the scenic spot admission ticket, APP cloud ticketing statistics submodule is used for making statistics of network APP's ticketing data, scenic spot entry verification statistics submodule is used for getting into the scenic spot and verifies the number and make statistics, give all statistical data for total passenger flow volume check module.
According to the technical scheme: the total passenger flow checking module comprises a real-time data transmission module and a time-sharing passenger flow analysis module, the real-time data transmission module is respectively connected with the image identification module and the area classification statistical module through an intranet, the real-time data transmission module is used for receiving data detected and judged by the image identification module and the area classification statistical module in real time, and the time-sharing passenger flow analysis module is used for analyzing and evaluating passenger flows at different times.
According to the technical scheme: the real-time data transmission module comprises a passenger flow volume growth rate statistic submodule, the selling data counted by a ticket window total ticket selling statistic submodule is set as a first passenger flow volume I, the selling data counted by an APP cloud ticket selling statistic submodule is set as a second passenger flow volume J, ticket window ticket returning data I is counted, APP cloud ticket returning data is set as J, actual selling data of a window is set as I-I, actual selling data of an APP cloud ticket selling is set as J-J, actual selling data of the window and actual selling data of the APP cloud ticket are counted, the actual number of ticket selling of each day is divided into a plurality of fractional intervals, the actual selling data of the window and the actual selling data of the APP cloud ticket selling are sequenced from high to low according to different fractional intervals, intervals of which the actual selling data of the APP ticket cloud is higher than the actual selling data of the window are marked, and the difference value between the actual selling data of the window and the actual selling data of the APP cloud ticket selling, Δ x = | (I-I) - (J-J) |, and when Δ x is less than 100, drainage is performed at a proper ticket office.
According to the technical scheme: and the big data service platform is used for maintaining the data transmission inside the module.
The scenic spot passenger flow volume statistical evaluation method based on big data comprises the following steps:
s1: monitoring and acquiring personnel at each corner of a scenic spot by utilizing a monitoring camera arranged in a scenic spot monitoring module, and acquiring images of the personnel in the scenic spot by an image acquisition submodule according to the monitored and shot images;
s2: extracting head images of five sense organs and hair by using a head feature extraction submodule inside an image recognition module, extracting body shapes and four limbs images by using a body shape detection submodule so as to be synthesized and judged with the head feature extraction submodule, and extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule by using a detection data extraction submodule;
s3: the method comprises the steps that a general ticketing statistics submodule of a ticketing window in a regional classification statistics module is used for counting data of tickets sold in a scenic spot in the ticketing window, an APP cloud ticketing statistics submodule counts ticketing data of a network APP, a scenic spot entrance verification statistics submodule counts the number of verification of passengers entering the scenic spot, and all statistical data are sent to a general passenger flow volume check module;
s4: carrying out unified analysis on the passenger flow of the time division period by using a total passenger flow checking module;
s5: and the big data service platform is used for maintaining the data transmission inside the module.
According to the technical scheme: in step S4, the step of uniformly analyzing the passenger flow in the time division by using the total passenger flow check module further includes the following steps:
a1: the real-time data transmission module is used for receiving the data detected and judged by the image recognition module and the region classification statistical module in real time;
a2: comparing the received data of the image identification module and the region classification statistical module, and checking whether the comparison value exceeds a set threshold value;
a3: if the data exceeds the set threshold, returning the data to the image recognition module and the region classification statistical module, and screening and extracting again;
a4: and if the passenger flow volume is smaller than the set threshold, the time-interval passenger flow volume analysis module analyzes and evaluates the passenger flow volume at different time.
According to the technical scheme: in the step a4, if the passenger flow exceeds the set threshold, the time-share passenger flow analysis module analyzes and evaluates the passenger flow at different times, further comprising the following steps:
the time for setting and collecting the time-interval passenger flow is T1、T2、T3、...、Tn-1、TnWhen the time is the time, the image recognition module and the regional classification statistical module respectively detect the crowd inside the scenic region and check the verification number of the entrance to the garden at the entrance of the scenic region, the detection data extraction submodule is set to extract the judgment data synthesized by the head feature extraction submodule and the body shape detection submodule to obtain the value A1、A2、A3、...、An-1、AnThe scenic spot entrance verification statistical submodule carries out statistical value B on the verification number of the passengers entering the scenic spot1、B2、B3、...、Bn-1、BnExtracting scenic spot Tn-1~TnIn the time period, the data detected by the current detection data extraction submodule is An-1~AnAnd the data verified by the scenic spot entrance verification statistical submodule is Bn-1~BnSetting the data detected by the current time interval detection data extraction submodule to be C1, setting the data detected by the scenic spot entrance verification statistics submodule to be C2, and according to the formula:
setting the absolute value of the difference between C1 and C2 as | C1-C2|, if | C1-C2| is greater than a set threshold, returning the data to the image recognition module and the region classification statistical module, screening and extracting again, if | C1-C2| is less than or equal to the set threshold, adopting the value detected by the scenic spot entrance verification statistical submodule, setting the passenger flow per hour of the current time period as N, and according to the formula:
N=
(Unit: person/hour)
The passenger flow in the time period can be judged according to the formula, and the passenger flow in different time periods can be obtained for statistics by repeating the calculation.
Example 1: limiting conditions, extracting 11: 00-15: 00 time periods of a scenic spot, setting data detected by a current detection data extraction submodule to be 3640-7800, setting data verified by a scenic spot entrance verification statistics submodule to be 3680-7845, setting data detected by the current time period detection data extraction submodule to be C1, setting data detected by the scenic spot entrance verification statistics submodule to be C2, and according to a formula:
calculating to obtain:
setting absolute values of differences between C1 and C2 as | C1-C2|, | C1-C2| =5, setting a threshold value as 100, | C1-C2| less than or equal to the set threshold value, adopting a value detected by the scenic spot entrance verification statistic submodule, setting the hourly passenger flow volume of the current time period as N, and according to the formula: n =
(unit: person/hour), calculated to give: n =
Approximately equals 1041 person/hour, thereby judging the scenic spot to be 11: 00-15Time period 00 passenger flow per hour.
Example 2: and (3) limiting conditions, extracting the time periods of 8: 00-18: 00 of the scenic spot, setting the data detected by the current detection data extraction submodule to be 720-11383, setting the data verified by the scenic spot entrance verification statistic submodule to be 734-11464, setting the data detected by the current time period detection data extraction submodule to be C1, setting the data detected by the scenic spot entrance verification statistic submodule to be C2, and according to the formula:
calculating to obtain:
setting absolute values of differences between C1 and C2 as | C1-C2|, | C1-C2| =67, setting a threshold value as 100, | C1-C2| less than or equal to the set threshold value, adopting a value detected by the scenic spot entrance verification statistic submodule, setting the hourly passenger flow volume of the current time period as N, and according to the formula: n =
(unit: person/hour), calculated to give: n =
And =1073 persons/hour, so as to judge the passenger flow per hour in the scenic spot in the time period of 8: 00-18: 00.
Example 3: and (3) limiting conditions, extracting the time periods of 7: 00-10: 00 of the scenic spot, setting the data detected by the current detection data extraction submodule to be 83-987, setting the data verified by the scenic spot entrance verification statistics submodule to be 97-1022, setting the data detected by the current time period detection data extraction submodule to be C1, setting the data detected by the scenic spot entrance verification statistics submodule to be C2, and according to a formula:
calculating to obtain:
the absolute value of the difference between C1 and C2 is | C1-C2|, | C1-C2| =21Setting the threshold value to be 100, | C1-C2| is less than or equal to the set threshold value, adopting the numerical value detected by the scenic spot entrance verification statistic submodule, setting the passenger flow per hour of the current time period to be N, and according to the formula: n =
(unit: person/hour), calculated to give: n =
And the passenger flow is approximately equal to 314 persons/hour, so that the passenger flow per hour in the scenic spot time period of 7: 00-10: 00 is judged.
Example 4: limiting conditions, extracting time periods of 11: 00-13: 00 of scenic spots, setting data detected by a current detection data extraction submodule to be 12314-15878, setting data verified by a scenic spot entrance verification statistic submodule to be 13414-16400, setting data detected by the current time period detection data extraction submodule to be C1, setting data detected by the scenic spot entrance verification statistic submodule to be C2, and according to a formula:
calculating to obtain:
setting absolute values of differences between C1 and C2 as | C1-C2|, | C1-C2| =578, | C1-C2| to be larger than a set threshold value 100, returning data to the image recognition module and the region classification statistical module, and carrying out screening extraction again.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. Scenic spot passenger flow volume statistics evaluation system based on big data, its characterized in that: the system comprises a scenic spot monitoring module, an image identification module, a regional classification statistical module, a total passenger flow checking module and a big data service platform, wherein the scenic spot monitoring module, the image identification module and the total passenger flow checking module are sequentially connected through an intranet, the regional classification statistical module and the total passenger flow checking module are connected through the intranet, and the scenic spot monitoring module, the image identification module, the regional classification statistical module and the total passenger flow checking module are respectively connected with the big data service platform through the intranet.
2. The big-data based scenic spot passenger flow statistics evaluation system of claim 1, wherein: scenic spot monitoring module includes a plurality of surveillance camera head and image acquisition submodule, each corner of the scenic spot that a plurality of surveillance camera head set up, the surveillance camera head is used for monitoring the personnel in each corner of scenic spot and gathers, the image acquisition submodule is used for carrying out the collection of scenic spot personnel's image according to the image of control shooting, surveillance camera head and big data service platform pass through wireless connection, the image acquisition submodule respectively with big data service platform, the image identification module passes through intranet connection.
3. The big-data based scenic spot passenger flow statistics evaluation system of claim 1, wherein: the image recognition module comprises a head feature extraction submodule, a body shape detection submodule and a detection data extraction submodule, the head feature extraction submodule is used for extracting head facial features and hair images, the body shape detection submodule is used for extracting body shapes and four limb images so as to be synthesized and judged with the head feature extraction submodule, the detection data extraction submodule is used for extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule, and the head feature extraction submodule and the body shape detection submodule are respectively connected with the scenic spot monitoring module through an internal network.
4. The big-data based scenic spot passenger flow statistics evaluation system of claim 1, wherein: regional classification statistics module includes ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule, wherein, ticket window total ticketing statistics submodule, APP cloud ticketing statistics submodule and scenic spot entry verification statistics submodule pass through the intranet with total passenger flow volume check module respectively and are connected, ticket window total ticketing statistics submodule is used for making statistics of the data that ticket window sells the scenic spot admission ticket, APP cloud ticketing statistics submodule is used for making statistics of network APP's ticketing data, scenic spot entry verification statistics submodule is used for getting into the scenic spot and verifies the number and make statistics, give all statistical data for total passenger flow volume check module.
5. The big-data based scenic spot passenger flow statistics evaluation system of claim 1, wherein: the total passenger flow checking module comprises a real-time data transmission module and a time-sharing passenger flow analysis module, the real-time data transmission module is respectively connected with the image identification module and the area classification statistical module through an intranet, the real-time data transmission module is used for receiving data detected and judged by the image identification module and the area classification statistical module in real time, and the time-sharing passenger flow analysis module is used for analyzing and evaluating passenger flows at different times.
6. The big-data based scenic spot passenger flow volume statistical evaluation system according to claim 4 or 5, wherein: the real-time data transmission module comprises a passenger flow volume growth rate statistic submodule, the selling data counted by a ticket window total ticket selling statistic submodule is set as a first passenger flow volume I, the selling data counted by an APP cloud ticket selling statistic submodule is set as a second passenger flow volume J, ticket window ticket returning data I is counted, APP cloud ticket returning data is set as J, actual selling data of a window is set as I-I, actual selling data of an APP cloud ticket selling is set as J-J, actual selling data of the window and actual selling data of the APP cloud ticket are counted, the actual number of ticket selling of each day is divided into a plurality of fractional intervals, the actual selling data of the window and the actual selling data of the APP cloud ticket selling are sequenced from high to low according to different fractional intervals, intervals of which the actual selling data of the APP ticket cloud is higher than the actual selling data of the window are marked, and the difference value between the actual selling data of the window and the actual selling data of the APP cloud ticket selling, Δ x = | (I-I) - (J-J) |, and when Δ x is less than 100, drainage is performed at a proper ticket office.
7. The big-data based scenic spot passenger flow statistics evaluation system of claim 1, wherein: and the big data service platform is used for maintaining the data transmission inside the module.
8. The scenic spot passenger flow volume statistical evaluation method based on big data is characterized by comprising the following steps:
s1: monitoring and acquiring personnel at each corner of a scenic spot by utilizing a monitoring camera arranged in a scenic spot monitoring module, and acquiring images of the personnel in the scenic spot by an image acquisition submodule according to the monitored and shot images;
s2: extracting head images of five sense organs and hair by using a head feature extraction submodule inside an image recognition module, extracting body shapes and four limbs images by using a body shape detection submodule so as to be synthesized and judged with the head feature extraction submodule, and extracting judgment data synthesized by the head feature extraction submodule and the body shape detection submodule by using a detection data extraction submodule;
s3: the method comprises the steps that a general ticketing statistics submodule of a ticketing window in a regional classification statistics module is used for counting data of tickets sold in a scenic spot in the ticketing window, an APP cloud ticketing statistics submodule counts ticketing data of a network APP, a scenic spot entrance verification statistics submodule counts the number of verification of passengers entering the scenic spot, and all statistical data are sent to a general passenger flow volume check module;
s4: carrying out unified analysis on the passenger flow of the time division period by using a total passenger flow checking module;
s5: and the big data service platform is used for maintaining the data transmission inside the module.
9. The big-data based scenic spot passenger flow volume statistical evaluation method according to claim 1, wherein: in step S4, the step of uniformly analyzing the passenger flow in the time division by using the total passenger flow check module further includes the following steps:
a1: the real-time data transmission module is used for receiving the data detected and judged by the image recognition module and the region classification statistical module in real time;
a2: comparing the received data of the image identification module and the region classification statistical module, and checking whether the comparison value exceeds a set threshold value;
a3: if the data exceeds the set threshold, returning the data to the image recognition module and the region classification statistical module, and screening and extracting again;
a4: and if the passenger flow volume is smaller than the set threshold, the time-interval passenger flow volume analysis module analyzes and evaluates the passenger flow volume at different time.
10. The big-data based scenic spot passenger flow volume statistical evaluation method according to claim 1, wherein: in the step a4, if the passenger flow exceeds the set threshold, the time-share passenger flow analysis module analyzes and evaluates the passenger flow at different times, further comprising the following steps:
the time for setting and collecting the time-interval passenger flow is T1、T2、T3、...、Tn-1、TnWhen the time is the time, the image recognition module and the regional classification statistical module respectively detect the crowd inside the scenic region and check the verification number of the entrance to the garden at the entrance of the scenic region, the detection data extraction submodule is set to extract the judgment data synthesized by the head feature extraction submodule and the body shape detection submodule to obtain the value A1、A2、A3、...、An-1、AnThe scenic spot entrance verification statistical submodule carries out statistical value B on the verification number of the passengers entering the scenic spot1、B2、B3、...、Bn-1、BnExtracting scenic spot Tn-1~TnIn the time period, the data detected by the current detection data extraction submodule is An-1~AnAnd the data verified by the scenic spot entrance verification statistical submodule is Bn-1~BnSetting the data detected by the current time interval detection data extraction submodule to be C1, setting the data detected by the scenic spot entrance verification statistics submodule to be C2, and according to the formula:
setting the absolute value of the difference between C1 and C2 as | C1-C2|, if | C1-C2| is greater than a set threshold, returning the data to the image recognition module and the region classification statistical module, screening and extracting again, if | C1-C2| is less than or equal to the set threshold, adopting the value detected by the scenic spot entrance verification statistical submodule, setting the passenger flow per hour of the current time period as N, and according to the formula:
N=
(Unit: person/hour)
The passenger flow in the time period can be judged according to the formula, and the passenger flow in different time periods can be obtained for statistics by repeating the calculation.
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