A rural leisure tourism area big data monitoring and analysis platform

Abstract

The application discloses a kind of rural leisure tourism area big data monitoring and analysis platform, including early warning area management unit, early warning data management unit, weather data management unit, people flow data management unit and traffic data management unit;The early warning area management unit is used to determine the area for early warning;Early warning data management unit is used for early warning data statistics;Weather data management unit is used to display and statistics to weather data;People flow data management unit is used to monitor and statistics to people flow;Traffic data management unit is used to display and statistics to traffic condition.The application is monitored in real time to people flow, traffic flow and weather lamp, and the pressure on the environment of monitoring area is judged by DPSIR model, so as to facilitate the condition of each regional environment to adjust overall people flow, reduce the pressure on the environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of software, in particular to a rural leisure tourism area big data monitoring and analysis platform. BACKGROUND

[0002] With the increasing popularity of rural tourism, more and more people like to go to the suburbs for rural tourism. However, the carrying capacity of the tourism area is limited, and too much traffic flow can cause congestion and other situations, and higher than the carrying capacity of the region can cause environmental damage. However, the passengers themselves do not know the flow situation of the location, and it is not convenient to guide and distribute the flow. The current flow judgment is usually through sensor signal judgment such as the patent CN106912070A discloses a business travel area flow density monitoring method and system based on iBeacon technology. However, it does not have monitoring of weather conditions, road condition detection of passengers arriving at the preset area, and statistics of each area. Therefore, it needs to be improved and improved. SUMMARY

[0003] To solve the above technical problems, the present application provides a rural leisure tourism area big data monitoring and analysis platform.

[0004] The purpose of the present application is achieved by the following technical solutions:

[0005] A rural leisure tourism area big data monitoring and analysis platform, characterized in that it comprises a warning area management unit, a warning data management unit, a weather data management unit, a flow data management unit and a traffic data management unit; the warning area management unit is used to determine the area for warning; the warning data management unit is used to statistically analyze the warning data; the weather data management unit is used to display and statistically analyze the weather data; the flow data management unit is used to monitor and statistically analyze the flow; and the traffic data management unit is used to display and statistically analyze the traffic conditions.

[0006] Further improvement, the warning area management unit comprises a warning area map module for displaying the warning area map, a warning area table module for displaying the warning area data, and a new warning area module for adding the warning area;

[0007] The map displayed by the warning area map module has zoom-in and zoom-out functions, and displays the area name and health status according to the position of the mouse movement, the health status is displayed by color, green represents good, yellow represents no data, and red represents warning; the health status includes the flow health status, the weather health status and the traffic health status.

[0008] The pre-warning area table module displays the data of the pre-warning area by a table, the table including area number, area name, area person in charge, area location, area health status, and operation column including view, edit, and delete buttons; the new pre-warning area module is used to add a new monitoring area on the map by point selection or by inputting area information, the area information including area name, belonging province, city, and district, and province, city, and district information; the point selection method is as follows: using a coordinate picker, recording the selected coordinate points on the map, connecting the coordinate points by straight lines, selecting the vertexes in anticlockwise order, forming a closed area, which is the monitoring area, and the diagonal distance of the outer rectangle of the polygon is not more than 2 kilometers, and each point is selected in order, and the center point of the closed figure is calculated as the positioning point.

[0009] Further improvement, the pre-warning data management unit includes a pre-warning message list module, a pre-warning proportion statistical module, and a pre-warning message statistical module; when the pre-warning message list module is clicked, detailed pre-warning information is displayed, the detailed pre-warning information including pre-warning state, pre-warning number, pre-warning type, pre-warning level, pre-warning area number, pre-warning area name, area person in charge, pre-warning theme, pre-warning content, reporter, reporting time, processing method, and processing time, and processing operation can be performed;

[0010] The pre-warning state includes unprocessed and processed; the operation includes view, processing, and deletion; the time, processing method, and processing time can be processed;

[0011] The detailed pre-warning information is automatically generated by the system, that is, the data is analyzed by using a multi-data source health running model, and the data is identified and generated, or the detailed pre-warning information can be generated by filling by the area person in charge; the pre-warning level of the detailed pre-warning information is divided into four levels: first level, second level, third level, and fourth level, which are respectively marked by red, orange, yellow, and blue;

[0012] The pre-warning proportion statistical module is used to view the data statistical situation in a preset time period; the preset time period includes the last year, the last month, and the last week; the data statistical situation is displayed in a pie chart according to the selected pre-warning level classification or pre-warning type classification; the pre-warning type includes weather, traffic, and people flow;

[0013] The pre-warning message statistical module is used to view the data statistical situation in a preset time period, and display a four-color column chart according to the detailed pre-warning information or a three-color column chart according to the pre-warning type classification.

[0014] Further improvement, the weather data management unit includes the day weather condition module, the future seven-day weather condition module and the special data statistics module; the day weather condition module is used to display the day weather condition of the selected monitoring area and; the day weather condition includes the highest temperature, the minimum temperature, the weather condition of each hour from 0 to 24 hours of the day, humidity, wind power, wind direction, air pressure, air quality; the future seven-day weather condition module is used to display the future seven-day weather condition of the selected monitoring area, and the future seven-day weather condition includes the highest temperature, the minimum temperature, the weather condition and the date, week information of the future seven days; the special data statistics is used to view the weather data in the selected monitoring area according to the input time period and display in the form of a line chart.

[0015] Further improvement, the people flow data management unit includes the real-time data details module, the real-time data statistics module, the people number fluctuation statistics module and the people flow type statistics module; the real-time data details module is used to display the total data and the daily data of the selected monitoring area;

[0016] The total data includes the total people flow, the total people flow of the current year, the total people flow of the current month and the total people flow of yesterday; the daily data includes the current number of people in the area, the maximum number of people in the day and the average value of the historical data of the area;

[0017] The real-time data statistics module is used to display the people number change line chart of the monitoring area in the province, city and district of the day in 15-minute units or the double-line statistics chart of the entering and leaving people number of the monitoring area in the province, city and district of the day in 15-minute units;

[0018] The people number fluctuation statistics module is used to view the people flow data statistics according to the selected monitoring area and time period, and the people flow data statistics includes the change line chart of the total people flow of the monitoring area and the change line chart of the maximum number of people in the monitoring area; the people flow type statistics module is used to display the people flow source column chart according to the selected monitoring area and time period.

[0019] Further improvement, the traffic data management unit includes the surrounding road condition map module, the surrounding road condition statistics module and the vehicle statistics module; the surrounding road condition map module is used to display the traffic road condition in the monitoring area on the map by using the API interface; the surrounding road condition statistics module is used to perform line chart statistics on the road condition of the monitoring area in the day, refreshes every 10 minutes, the abscissa is time, the ordinate is type, the dynamic display of the line is performed from 0 to 4, and pie chart statistics is performed on the road condition type of the previous day; the vehicle statistics module is used to count the number of vehicles entering the park and the total number of vehicles in the park, display the vehicle type statistical column chart and display the vehicle number change curve chart in the park.

[0020] Further improvement, the health running model of the multiple data sources is a DPSIR model, and population density representing population pressure is selected as a factor of the population pressure index in the DPSIR model:

[0021] PD=PQ / LA

[0022] D is population density; PQ is the population quantity in the region; and LA is the total land area;

[0023] Tourism environmental capacity saturation (%) = total number of tourists / environmental capacity;

[0024] The population density measurement method is as follows:

[0025] (1) Area volume method: C=A*D / a

[0026] C is daily environmental capacity, unit: person-time;

[0027] a is reasonable touring area per tourist, unit: square meter / person;

[0028] A is the touring area, unit: square meter / person

[0029] D is the turnover rate, D=8 hours of opening time of the scenic spot / time required for touring the scenic spot;

[0030] The driving force index in the DPSIR model includes the tourism number growth rate and the GDP growth rate; the tourism number growth rate=(tourism number of the current year-tourism number of the last year) / tourism number of the last year; and the GDP growth rate=(GDP of the current year-GDP of the last year) / GDP of the last year;

[0031] The influence index in the DPSIR model includes the protected area coverage rate and the environmental protection investment proportion of GDP.

[0032] The present application has the beneficial effects that:

[0033] The present application can monitor the human flow, vehicle flow and weather light in real time, and judge the pressure on the environment of the monitoring area through the DPSIR model, so as to adjust the overall human flow according to the situation of each regional environment and reduce the pressure on the environment. BRIEF DESCRIPTION OF DRAWINGS

[0034] The present application is further described by using the drawings, but the content in the drawings does not constitute any limitation on the present application.

[0035] Figure 1 The figure is the interface schematic diagram of the platform of the present application;

[0036] Figure 2 The figure is the index schematic diagram of the DPSIR model. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and examples.

[0038] As shown in Figure 1 a rural leisure tourism area big data monitoring and analysis platform, comprising a warning area management unit, a warning data management unit, a weather data management unit, a people flow data management unit and a traffic data management unit;

[0039] The warning area management unit comprises a warning area map module for displaying a warning area map, a warning area table module for displaying warning area data and a new warning area module for adding a warning area;

[0040] The map displayed by the warning area map module has zoom-in and zoom-out functions, and displays the area name and health status according to the position moved to by the mouse, the health status is displayed by color, green represents good, yellow represents no data, and red represents warning; the health status includes people flow health status, weather health status and traffic health status.

[0041] The warning area table module displays the data of the warning area through a table, the table includes area number, area name, area responsible person, area location, area health status and operation column, the operation column includes view, edit and delete buttons; the new warning area module is used to add a new monitoring area on the map by selecting or inputting area information, the area information includes area name, belonging province, city and district, and province, city and district information; the selection method is as follows: using a coordinate picker, recording the selected coordinate points on the map, connecting the coordinate points by straight lines, selecting the vertices in anticlockwise order, forming a closed area, which is the monitoring area, and the diagonal distance of the outer rectangle of the polygon is not more than 2 kilometers, each point is selected in order, and the center point of the closed figure is calculated as the positioning point.

[0042] The warning data management unit comprises a warning message list module, a warning proportion statistical module and a warning message statistical module; when the warning message list module is clicked, detailed warning information is displayed, the detailed warning information includes warning state, warning number, warning type, warning level, warning area number, warning area name, area responsible person, warning theme, warning content, reporter, reporting time, processing method and processing time, and processing operation can be performed;

[0043] The warning state includes unprocessed and processed; the operation includes view, processing and deletion; the time, processing method and processing time can be processed;

[0044] The detailed early warning information is automatically generated by the system, i.e., the health operation model analyzes data from multiple data sources to generate the early warning information, or is generated by filling in by the regional person in charge; the early warning level of the detailed early warning information is divided into four levels: first level, second level, third level and fourth level, which are respectively marked by red, orange, yellow and blue;

[0045] The early warning proportion statistical module is used to view the data statistical situation in a preset time period; the preset time period includes the last year, the last month and the last week; the data statistical situation is displayed in a pie chart according to the selected early warning level classification or early warning type classification; the early warning type includes weather, traffic and people flow;

[0046] The early warning message statistical module is used to view the data statistical situation in a preset time period, and display the data statistical situation in a four-color column chart according to the detailed early warning information or in a three-color column chart according to the early warning type classification.

[0047] The weather data management unit includes a current day weather condition module, a future seven-day weather condition module and a special data statistical module; the current day weather condition module is used to display the current day weather condition of the selected monitoring area; the current day weather condition includes the maximum temperature, the minimum temperature, the weather condition of each hour from 0 to 24 hours, the humidity, the wind power, the wind direction, the air pressure and the air quality; the future seven-day weather condition module is used to display the future seven-day weather condition of the selected monitoring area; the future seven-day weather condition includes the maximum temperature, the minimum temperature, the weather condition and the date and week information of the next seven days; the special data statistical module is used to view the weather data in the selected monitoring area according to the input time period and display the weather data in the form of a line chart.

[0048] The people flow data management unit includes a real-time data detail module, a real-time data statistical module, a people flow fluctuation statistical module and a people flow type statistical module; the real-time data detail module is used to display the total data and the current day data of the selected monitoring area;

[0049] The total data includes the total people flow, the total people flow in the current year, the total people flow in the current month and the total people flow yesterday; the current day data includes the current number of people in the area, the maximum number of people in the current day and the average value of the historical data of the area;

[0050] The real-time data statistical module is used to display the people flow change line chart of the monitoring area in the current day and province, city and district in 15-minute units, or display the double-line statistical chart of the entering and leaving people flow of the monitoring area in the current day and province, city and district in 15-minute units;

[0051] The people flow fluctuation statistical module is used to view the people flow data statistical situation according to the selected monitoring area and time period; the people flow data statistical situation includes the change line chart of the total people flow of the monitoring area and the change line chart of the maximum number of people in the monitoring area; the people flow type statistical module is used to display the people flow source column chart according to the selected monitoring area and time period.

[0052] The traffic data management unit includes a surrounding traffic map module, a surrounding traffic statistics module, and a vehicle statistics module. The surrounding traffic map module displays traffic conditions within the monitored area on a map using an API interface. The surrounding traffic statistics module generates a line graph of the daily traffic conditions in the monitored area, refreshing every 10 minutes. The horizontal axis represents time, and the vertical axis represents type, dynamically displaying the line graph from 0 to 4. It also generates a pie chart of the previous day's traffic conditions. The vehicle statistics module counts the number of vehicles entering the park and the total number of vehicles in the park, displays a bar chart of vehicle models, and displays a curve showing the change in the number of vehicles in the park.

[0053] Among them, the health operation model of multiple data sources is the DPSIR model. The various indicators in the DPSIR model are as follows: Figure 2 As shown:

[0054] Driving force analysis: These are the potential causes of environmental change; social, economic, and demographic factors are the main driving force indicators.

[0055] Stress indicator: Human disturbance activities put stress on the ecosystem.

[0056] State indicators are the changes in the ecosystem caused by stress indicators, reflecting the current status of the ecosystem in the study area. State indicators are the result of driving and stress indicators, and also the basis for the analysis of influence and response indicators.

[0057] Impact indicator: The impact of ecological environment status on socio-economic structure.

[0058] Response indicators: Countermeasures taken by humans to address various impacts on the environment.

[0059] Driving force indicators:

[0060] Tourist number growth rate: (Number of tourists in the current year - Number of tourists in the previous year) / Number of tourists in the previous year

[0061] GDP growth rate: (Current year's GDP - Previous year's GDP) / Previous year's GDP

[0062] Stress indicators:

[0063] Population density: Population density is calculated as the ratio of the population to the total land area within a region. The higher the population density, the greater the population pressure on the region and the greater the stress on the ecosystem.

[0064] PD = PQ / LA

[0065] In the formula, PD represents population density; PQ represents the population size within the region; and LA represents the total land area.

[0066] Tourism environment capacity saturation (%): total number of tourists / environmental capacity (daily change)

[0067] (1) Area capacity method: C=A×D / a

[0068] In the formula: C---daily environmental capacity, unit: person-time;

[0069] a---each tourist should occupy the reasonable area of the tour, unit: square meters / person;

[0070] A---tourable area, unit: square meters / person;

[0071] D---turnover rate (D=8 hours of opening time of scenic spots / time required for visiting scenic spots).

[0072] (2) Using the existing research in the region of daily environmental capacity,

[0073] State indicators:

[0074] Forest coverage rate (%): directly obtained from statistical yearbook

[0075] Tourism per capita consumption level: total tourism revenue / tourism population

[0076] Air comprehensive pollution (%): (total days-environmental excellent days) / total days

[0077] Impact indicators:

[0078] Tourism direct employment contribution rate (%): number of direct tourism employment / total number of employment at the end of the year

[0079] Tourism total income and GDP ratio (%): total tourism income / total income of the region

[0080] Per capita GDP: total income / total population

[0081] Response indicators:

[0082] Protected area coverage: statistical data

[0083] Environmental protection investment accounts for the proportion of GDP: environmental protection investment / GDP value

[0084] Determine the health grade classification standard of each indicator:

[0085]

[0086]

[0087]

[0088] Determine the health evaluation standard

[0089]

[0090]

[0091] 5、Calculate the weight of each index

[0092] Entropy method to calculate the weight of the index

[0093] 1、The index data are normalized by min-max standardization:

[0094] Positive index:

[0095]

[0096] Where, y i represents the normalized results of each index data, i represents the ith index, n represents a total of n indexes, x i represents the value of the ith index, min{x i} represents the minimum value of each index data, and max{x i} represents the maximum value of each index data.

[0097] Negative index:

[0098]

[0099] Where, max represents the maximum value of each index data, and min represents the minimum value of each index data.

[0100] 2、Calculate the proportion of each index in each year:

[0101]

[0102] p ij represents the proportion of the jth index in the ith year, i represents the ith year, j represents the jth index, and x ij represents the value of the jth index in the ith year, and m represents a total of m years.

[0103] 3、Calculate the entropy value of each index:

[0104]

[0105] H j represents the entropy value of the jth index, j represents the jth index, ln represents the natural logarithm, and K represents the Boltzmann constant.

[0106] 4、Calculate the difference coefficient of each index:

[0107] g j = 1-H j

[0108] g j Dj represents the difference coefficient of the jth index;

[0109] 5. Obtain the weight of each index:

[0110]

[0111] ω j ωj represents the weight of the jth index;

[0112] 6. Evaluation by fuzzy comprehensive evaluation method

[0113] 6.1. Calculation of index membership degree:

[0114] 6.1.1. Positive index:

[0115] Calculation of morbid membership function:

[0116]

[0117] y1 represents the morbid membership degree, S1 represents the lowest lower limit value from morbid to complete morbid, and S2 represents the highest upper limit value from morbid to normal state;

[0118] Intermediate:

[0119]

[0120] y m y2 represents the membership degree of the intermediate state, S2 represents the lower limit value of the intermediate state, and S3 represents the upper limit value of the intermediate state; m S represents the limit value, and X represents the data of each index;

[0121] Very healthy:

[0122]

[0123] y5 represents the membership degree of the very healthy state, S5 represents the highest upper limit value from healthy to very healthy, and S4 represents the lowest lower limit value from very healthy to healthy; m5

[0124] 6.1.2. Negative index:

[0125] Morbid:

[0126]

[0127] Intermediate:

[0128]

[0129] Very healthy:

[0130]

[0131] ​6.2 Calculation:

[0132] Make a comprehensive decision for each index layer

[0133] Calculate for each criterion:

[0134] (1) Get the fuzzy relation matrix

[0135] Calculate its membership function and establish the fuzzy relation matrix of the criterion layer:

[0136] The membership degree refers to the belonging degree of an index to an evaluation grade.

[0137]

[0138] a ij Rij represents the membership degree of the ith index on the criterion to the jth ecological health grade; R Z R represents the fuzzy relation matrix of the region, and Z represents the region;

[0139] (2) Get the fuzzy evaluation result

[0140] Fuzzy synthesis of the fuzzy relation matrix and the weight vector of the index calculated above:

[0141]

[0142]

[0143] H Z H represents the fuzzy evaluation result, W n W represents the weight of index n;

[0144] Get the final fuzzy comprehensive evaluation matrix:

[0145]

[0146] hij = (h1, h2, h3, h4, h j ), j = 5.

[0147]

[0148] a ij hij represents the score of the ith index belonging to the jth ecological health grade.

[0149] Calculate the ecological health index:

[0150] Get the total score by fuzzy barycenter method:

[0151]

[0152] h ja fuzzy score representing the jth ecological health class.

[0153] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and not to limit the protection scope of the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A rural leisure tourism area big data monitoring and analysis platform, characterized in that, The early warning system comprises an early warning area management unit, an early warning data management unit, a weather data management unit, a people flow data management unit and a traffic data management unit; The early warning area management unit is used for determining the area for early warning; The early warning data management unit is used for early warning data statistics; The weather data management unit is used for displaying and statistics of weather data; The people flow data management unit is used for monitoring and statistics of people flow; The traffic data management unit is used for displaying and statistics of traffic conditions; The early warning area management unit comprises an early warning area map module for displaying an early warning area map, an early warning area table module for displaying early warning area data and an early warning area adding module for adding an early warning area; The map displayed by the early warning area map module has zoom-in and zoom-out functions, and displays the area name and health status according to the position moved to by the mouse, and the health status is displayed by color, green represents good, yellow represents no data and red represents early warning; The health status comprises people flow health status, weather health status and traffic health status; The early warning area table module displays the data of the early warning area through a table, and the table comprises area number, area name, area person in charge, area position, area health status and an operation column, the operation column comprises view, edit and delete buttons; the early warning area adding module is used for adding a new monitoring area on the map or in the form of inputting area information, and the area information comprises area name, belonging province, city and district, and the point selection method is as follows: a coordinate picker is used to record the selected coordinate points on the map, the straight lines between the coordinate points are connected, the vertex order is selected in anticlockwise direction, a closed area is formed, the circumscribed rectangle of the polygon has a diagonal distance of not more than 2 kilometers, each point is selected in order, and the center point of the closed figure is calculated as a positioning point; The health running model of the multiple data sources is a DPSIR model, and the population density is selected as a population pressure index factor in the DPSIR model: ； PD is population density; PQ is the number of people in the area; and LA is total land area; Tourism environmental capacity saturation (%) = total number of tourists / environmental capacity; The population density measurement method is as follows: (1) Area-volume method: ; C is daily environmental capacity, unit: person-time; A is the area that can be toured, unit: square meters / person D is turnover rate, D = 8 hours of opening time of the scenic spot / time required for touring the scenic spot; The driving force index of the DPSIR model comprises a tourism number growth rate and a GDP growth rate; the tourism number growth rate = (tourism number of the current year - tourism number of the last year) / tourism number of the last year; and the GDP growth rate = (GDP of the current year - GDP of the last year) / GDP of the last year; The influence index of the DPSIR model comprises a protected area coverage rate and an environmental protection investment proportion of GDP. The early warning data management unit comprises an early warning message list module, an early warning proportion statistics module and an early warning message statistics module; 2.The agro-recreation tourism area big data monitoring and analyzing platform of claim 1, wherein, ​ Click to view the early warning message list module, display detailed warning information, detailed warning information contains early warning state, early warning number, early warning type, early warning level, early warning area number, early warning area name, area responsible person, early warning theme, early warning content, reporter, report time, processing method, processing time, can be handled operation; Early warning state includes unprocessed, processed; Operation includes viewing, processing, deleting; Time, processing method, processing time, can be handled operation; Detailed early warning information is automatically generated by the system, that is, the health running model analyzes the data to identify and generate various data, or it can be generated by the area responsible person; The early warning level of detailed early warning information is divided into four levels: first, second, third and fourth, which are marked with red, orange, yellow and blue respectively; The early warning proportion statistical module is used to view the data statistics in the preset time period; The preset time period includes the last year, the last month and the last week; The data statistics is displayed in a pie chart according to the selected early warning level classification or early warning type classification; Early warning type includes weather, traffic, and people flow; The early warning message statistical module is used to view the data statistics in the preset time period, and display the four-color column chart according to the detailed early warning information or the three-color column chart according to the early warning type classification. 3.The agro-tourism area big data monitoring and analyzing platform of claim 1, wherein, The weather data management unit includes the current day weather condition module, the future seven-day weather condition module and the special data statistical module; The current day weather condition module is used to display the current day weather condition of the selected monitoring area; The current day weather condition includes the maximum temperature, the minimum temperature, the weather condition of each hour from 0 to 24 hours, humidity, wind power, wind direction, air pressure and air quality; The future seven-day weather condition module is used to display the future seven-day weather condition of the selected monitoring area; The future seven-day weather condition includes the maximum temperature, the minimum temperature, the weather condition and the date and week information of the next seven days; The special data statistical module is used to view the weather data in the selected monitoring area according to the input time period and display the data in the form of a line chart. 4.The agro-tourism area big data monitoring and analyzing platform of claim 1, wherein, The people flow data management unit includes the real-time data detail module, the real-time data statistical module, the people number fluctuation statistical module and the people flow type statistical module; The real-time data detail module is used to display the total data and the current day data of the selected monitoring area; The total data includes the total people flow, the total people flow in the current year, the total people flow in the current month and the total people flow yesterday; The current day data includes the current number of people in the area, the maximum number of people in the current day and the average value of the historical data of the area; The real-time data statistical module is used to display the number of people in the monitoring area in the form of a line chart or the double-line statistical chart of the number of people entering and leaving the monitoring area in the form of a line chart in 15-minute units in the current day, province, city and district; The people number fluctuation statistical module is used to view the people flow data statistics according to the selected monitoring area and time period; The people flow data statistics includes the change line chart of the total people flow in the monitoring area and the change line chart of the maximum number of people in the monitoring area; The people flow type statistical module is used to display the people flow source column chart according to the selected monitoring area and time period. 5.The agro-tourism area big data monitoring and analyzing platform of claim 1, wherein, The traffic data management unit comprises a surrounding road condition map module, a surrounding road condition statistics module, and a vehicle statistics module; the surrounding road condition map module is used for displaying the traffic road condition in the monitoring area on a map by using an API interface; the surrounding road condition statistics module is used for performing a line graph statistics on the road condition of the monitoring area on the same day, refreshing once every 10 minutes, with the abscissa being time and the ordinate being type, performing a dynamic display of a line graph from 0 to 4, and performing a pie chart statistics on the road condition type of the previous day; and the vehicle statistics module is used for counting the number of vehicles entering the park, counting the total number of vehicles in the park, displaying a vehicle model statistical column chart, and displaying a vehicle number change curve chart in the park.

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