Garden intelligent management system based on Internet of Things

Abstract

The invention discloses an intelligent garden management system based on the Internet of Things, relates to the technical field of garden management, and aims to realize optimization adjustment and monitoring of a greenhouse garden through the Internet of Things technology and the intelligent technology. Which scenic spots are more welcomed by tourists can be known through data analysis; the greenhouse garden adjustment and management module is used for adjusting the occupied area of the scenic spot according to the popularity degree of the scenic spot; the air quality detection frequency of the scenic spots is managed in combination with the popularity degree and the growth environment of plants in the scenic spots, so that optimization of different scenic spots is improved; according to the scheme, the greenhouse garden can be optimized and adjusted, resources can be reasonably distributed, and resource waste is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of garden management. Specifically, it relates to an intelligent garden management system based on the Internet of Things. Background Art

[0002] The actual needs of contemporary society are complementary to ecological civilization. With the continuous improvement of people's living standards, people's ecological needs and taste for natural scenery are also constantly improving; greenhouse gardens can bring together various plants with different growth environments, and through greenhouse technology, sustainable development of gardens can be achieved.

[0003] However, the penetration rate of Internet of Things technology and intelligent technology is still relatively low in the garden field, and the cost is relatively high. In most areas, the monitoring of plants in the garden still relies on manual experience and manual inspections. The manual inspection method has low efficiency and high cost, and the ability to take improvement measures for problems occurring during inspections needs to be improved; through Internet of Things technology, data analysis of tourists' visiting preferences can be realized, and the garden can be reasonably adjusted according to tourists' preferences, which can achieve good economic benefits. And this solution provides an automated air detection method, which can set different detection frequencies for different scenic spots, and can effectively improve the air quality of each scenic spot in the garden. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent garden management system based on the Internet of Things, which solves the shortcomings of subjectivity and non-representativeness in the decisions made during the transformation and normal operation of greenhouse gardens in the prior art. This solution analyzes the popularity of different scenic spots through researching tourists' preferences, and then adjusts the floor area of the scenic spots accordingly. By detecting the air quality in the scenic spots and combining the popularity, the air quality detection frequency applicable to different scenic spots is adjusted, which can effectively improve the air quality in the scenic spots and achieve reasonable resource allocation.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An intelligent garden management system based on the Internet of Things, the system includes:

[0007] A tourist data analysis module, which obtains the tourist density and tourist viewing time of each scenic spot in the greenhouse garden;

[0008] A greenhouse garden adjustment and management module, which analyzes the popularity of the scenic spots by combining the tourist density and tourist viewing time of the scenic spots, and adjusts the floor area of each scenic spot according to the popularity of the scenic spots; adjusts the air quality detection frequency of the scenic spots by combining the popularity and the growth environment of the plants in the scenic spots;

[0009] The plant growth environment monitoring module monitors the growth environment of plants in the scenic area, identifies abnormal growth environments, and the greenhouse gardening adjustment and management module executes the air quality optimization program;

[0010] The data storage module stores the data obtained from the analysis of this solution and provides numerical extraction for the control module;

[0011] The control module is the hub for communication and data transmission between modules and processes the calculation and analysis steps described in this solution.

[0012] As a further solution of the present invention, the growth environment of the plant is the air quality pollution index associated with the plant during its growth process.

[0013] As a further solution of the present invention, the tourist data analysis module includes:

[0014] The mobile gardening mini-program provides online ticket purchasing. Tourists can enter and exit the scenic spots in the garden by swiping the ticket purchase program code;

[0015] The identification access card allows tourists to enter and exit the scenic spots in the garden by swiping the card;

[0016] The scenic spot access control obtains the time when tourists enter and exit the scenic spot and records the number of tourists in the scenic spot in real time.

[0017] As a further solution of the present invention, the method for the tourist data analysis module to obtain the tourist density and tourist viewing time of each scenic spot in the greenhouse garden is as follows:

[0018] S41. When a tourist enters the scenic spot, the scenic spot access control records the time t1, and the count R of the number of tourists in the scenic spot is incremented by one. When the greenhouse garden closes, R is reset to 0, and the data is uploaded and stored regularly;

[0019] S42. When a tourist leaves the scenic spot, the scenic spot access control records the time t2, and the count R of the number of tourists is decremented by one. Use t3 = t2 - t1 to obtain the stay time t3 of this tourist;

[0020] S43. Calculate the total number of tourists uploaded to the scenic spot on the same day, divide it by the number of uploads, and obtain the average number of tourists;

[0021] Then divide the average number of tourists by the floor area of the scenic spot to obtain the tourist density on the same day;

[0022] S44. Calculate the average stay time of all tourists in the scenic spot on the same day as the tourist viewing time of the scenic spot on the same day;

[0023] S45. During a traceability period, calculate the average daily tourist density of the corresponding scenic spot as the tourist density ρ of the corresponding scenic spot during the traceability period; at the same time, calculate the average daily scenic viewing time of tourists at the corresponding scenic spot as the scenic viewing time T of tourists at the corresponding scenic spot during the traceability period.

[0024] As a further solution of the present invention, the method for the greenhouse garden adjustment and management module to analyze the popularity of a scenic spot by combining the tourist density and tourist scenic viewing time of the scenic spot is as follows:

[0025] S51. Obtain the critical comfort tourist density of each scenic spot, denoted as C;

[0026] S52. Construct the formula:

[0027]

[0028] Calculate the popularity index P of the corresponding scenic spot, where T0 is the preset ideal tourist scenic viewing time, k is the growth rate parameter and can be regarded as a known value, is the crowding penalty term, indicating the decline in experience caused by overcrowding. Determine the value of the crowding penalty term by calculating and setting the adjustment coefficient γ, and the value range of γ is from 0 to 1; is the core calculation factor, reflecting the basic tourist aggregation effect;

[0029] S53. According to the method described in S51 - S52, calculate the popularity indexes of each scenic spot, denoted as P1, P2,..., P i , where i represents the total number of scenic spots in the greenhouse garden.

[0030] As a further solution of the present invention, the method for the greenhouse garden adjustment and management module to adjust the scenic spots according to the popularity is as follows:

[0031] Obtain the sequence of scenic spot floor areas S1, S2,..., S i during a traceability period, and record the popularity indexes P ’ 1, P ’ 2,..., P ’ i ;

[0032] Obtain the sum of the areas of each scenic spot in the greenhouse garden, denoted as S all ;

[0033] Allocate the areas of each scenic spot according to the ratio between the popularity indexes of each scenic spot, and sort them in ascending order of the scenic spot floor areas to obtain the adjusted sequence of scenic spot floor areas S1 ‘ , S2 ‘ ,..., S i‘ , and S1 ‘ +S2 ‘ +...+S i ‘ =S all ;

[0034] Continuously monitor the popularity index of the scenic spots until the next traceability period;

[0035] Obtain the popularity index of each scenic spot within the next traceability period, and record them in ascending order of the floor area of the scenic spots as P1 " , P2 ‘’ ,..., P i ‘’ Correspond to the sequence of the floor area of the scenic spots S1 ‘ , S2 ‘ ,..., S i ‘ Correspond;

[0036] Calculate the difference rate U of the popularity index of the same scenic spot in two consecutive traceability periods. The calculation formula is as follows:

[0037]

[0038] Set a buffer for the difference rate U. If U satisfies -10% < U < 10%, no adjustment is made;

[0039] If U does not satisfy the buffer and the calculated result U is positive, increase U based on the floor area of the current corresponding scenic spot;

[0040] If U does not satisfy the buffer and the calculated result U is negative, decrease U based on the floor area of the current corresponding scenic spot.

[0041] As a further solution of the present invention, the method for the greenhouse garden adjustment and management module to manage the scenic spots in combination with the popularity and the growth environment of the plants in the scenic spots is as follows:

[0042] Obtain the air quality pollution index associated with the growth environment of the plants in the corresponding scenic spot and monitor it. If the air quality pollution index is higher than the preset air quality pollution index threshold, the greenhouse garden adjustment and management module executes the air quality optimization program to optimize the air quality in the scenic spot;

[0043] Then, in combination with the popularity of the corresponding scenic spot, adjust the detection frequency of the scenic spot.

[0044] As a further solution of the present invention, the method for the greenhouse garden adjustment and management module to execute the air quality optimization program to optimize the air quality in the scenic spot includes the following steps:

[0045] S81. The plant growth environment monitoring module obtains the carbon dioxide concentration within the scenic spot Harmful gas concentration Temperature Humidity

[0046] S82. Obtain the average maximum carbon dioxide concentration that the plants within the corresponding scenic spot can withstand during the growth process Average maximum harmful gas concentration Average maximum temperature Average maximum humidity As the maximum safety threshold;

[0047] S83. According to the air quality pollution index calculation formula:

[0048]

[0049] Calculate the air quality pollution index API of the current scenic spot, where α is an adjustment coefficient, and the specific value is set by the operator;

[0050] S84. Set different air quality pollution index thresholds for each scenic spot. When the calculated API exceeds the air quality pollution index threshold of the scenic spot, the air quality optimization program needs to be executed.

[0051] As a further solution of the present invention, the method for the greenhouse garden adjustment and management module to manage plants in combination with the popularity and the growth environment of the plants within the scenic spot further includes;

[0052] Obtain the basic detection frequency F for detecting the air quality of the scenic spot, and this detection frequency is set by the staff in combination with the actual situation;

[0053] Sort the scenic spots in descending order according to the popularity index, and select the scenic spots with the popularity index in the top β%. Respectively increase the detection frequency, and the value of β is determined by the staff. The method for increasing the detection frequency is as follows:

[0054] Obtain the number of times that the air quality pollution index API of the scenic spots with the popularity index in the top β% exceeds the air quality pollution index threshold under the basic detection frequency F within a traceability period, and record them as M1, M2,..., M n , where n represents the total number of scenic spots with the popularity index in the top β%;

[0055] Obtain the popularity index of each scenic spot, and record them as P1, P2,..., P n , and P o corresponds to M o , where o is a counting index, starting from 1, representing any one of 1 - n;

[0056] For the first scenic spot, calculate the detection frequency feature V1 of this scenic spot using V1 = M1 × P1, and calculate other scenic spots using this method to obtain V1, V2, ..., V n Arrange the obtained detection frequency features in ascending order according to the values, and record them as the detection frequency feature sequence V1 ‘ , V2 ‘ , ..., V n ‘ ;

[0057] Starting from the scenic spot corresponding to the first detection frequency feature, increase the number of detections by G times on the basis of the basic detection frequency F. For each subsequent scenic spot, increase the number of detections by G times on the basis of the detection frequency of the previous scenic spot. The G is a fixed value pre-set by the staff.

[0058] Advantages of the present invention:

[0059] (1) The present invention provides a method for deeply understanding the distribution and tour preferences of tourists at different scenic spots by real-time monitoring the tourist density and viewing time of each scenic spot, providing an accurate data basis for subsequent analysis operations. This solution provides a method for special processing of the tour data of tourists with too short stay time, which can effectively eliminate abnormal data generated for various reasons, and ensure the effectiveness and representativeness of the tourist viewing time and tourist density data used for analysis;

[0060] (2) Based on the analysis and calculation of the actual data of tourists, the present invention avoids the limitations of subjective judgment and experience decision-making in traditional management methods, making the decision-making more scientific, objective and effective; and by continuously monitoring and analyzing the popularity index and difference rate of each scenic spot, it provides a continuous optimization and improvement plan for the greenhouse garden management party. With the continuous accumulation of data and the continuous optimization of decision-making, the greenhouse garden can achieve better management level and operation effect;

[0061] (3) By introducing the critical comfortable tourist density and the crowding penalty term, the present invention can effectively avoid overcrowding of scenic spots. When the tourist density exceeds the critical value, the impact of the experience decline is reflected in the calculation of the popularity index; at the same time, the adjustment coefficient is reasonably set according to the size of the scenic spot space, and a larger penalty is given to the scenic spots with narrow space to guide tourists to divert, ensuring that tourists can have a more comfortable tour experience at each scenic spot. This enables the greenhouse garden management layer to focus on the actual needs and interests of tourists, optimize and expand the popular scenic spots, so as to meet the diverse tour needs of tourists;

[0062] (4) The present invention provides a method that can effectively and timely grasp the internal environmental conditions of a greenhouse garden by monitoring the air quality pollution index in different scenic spots, set different thresholds for the air quality pollution index, and start an optimization program when the standard is exceeded, which helps to maintain suitable growth conditions and promote the healthy growth of plants; adjusting the air quality detection frequency in combination with the popularity index provided by the present invention not only ensures that high-traffic areas receive more frequent attention, but also ensures the effective allocation of resources and avoids unnecessary waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below with reference to the accompanying drawings.

[0064] Figure 1 is a schematic structural diagram of the garden intelligent management system based on the Internet of Things of the present invention;

[0065] Figure 2 is a schematic flow diagram of the method described in Embodiment 2 of the present invention;

[0066] Figure 3 is a schematic flow diagram of the method described in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Embodiment 1

[0069] The garden intelligent management system based on the Internet of Things, as Figure 1 shown, specifically includes the following:

[0070] A tourist data analysis module, which is used to collect data of tourists visiting scenic spots in real time, mainly obtaining the tourist density and tourist residence time at each scenic spot in the greenhouse garden;

[0071] The tourist data analysis module mainly obtains the tourist density and tourist residence time according to the following three sub-modules:

[0072] A mobile garden mini-program, which provides an online ticket purchase channel for online users. Online tourists can purchase tickets through the mobile garden mini-program and obtain the corresponding ticket purchase program code. Tourists can scan the code to enter and exit the scenic spots in the garden;

[0073] An identification access card, which provides an offline method for tourists who have not purchased tickets online. Tourists can swipe the identification access card to enter and exit the scenic spots in the greenhouse garden;

[0074] Scenic spot access control is set at the entrance and exit of each scenic spot, used to obtain the start time when tourists enter the scenic spot and the end time when they leave the scenic spot, and can record the number of tourists in the corresponding scenic spot in real time.

[0075] Greenhouse garden adjustment and management module analyzes the popularity of the scenic spot by combining the tourist density and the tourist viewing time in the scenic spot, calculates the popularity index of the current scenic spot, and optimizes and adjusts the corresponding scenic spot according to the popularity and popularity index of the scenic spot; combines the popularity and the growth environment of the plants in the scenic spot to optimize the management of the plants;

[0076] Plant growth environment monitoring module monitors the growth environment of the plants in the scenic spot through relevant monitoring instruments;

[0077] The growth environment of the plant is the air quality pollution index associated with the plant during its growth process;

[0078] The monitoring instrument mainly detects the air components, and the main detection targets are: carbon dioxide, harmful gases, temperature, humidity, and the harmful gases include: sulfur dioxide, nitrogen dioxide, ammonia;

[0079] The air quality pollution index is calculated from carbon dioxide, harmful gases, temperature, humidity and the air quality pollution index calculation formula.

[0080] Data storage module stores the data obtained from the analysis of this solution and provides numerical extraction for the control module.

[0081] Control module is used to coordinate and process data communication and data transmission between various modules.

[0082] It should be noted that for the greenhouse garden described in this solution, before the specific implementation of this solution, there need to be various scenic spots that have been planned in area, and subsequent adjustment operations are carried out on this basis, and different scenic spots are separated from each other to ensure that the environments between adjacent scenic spots will not affect each other. For example, a desert scenic spot and a tropical rainforest scenic spot, adjust the scenic spot environment according to the growth conditions required by different plants, and ensure that the environments between the desert scenic spot and the tropical rainforest scenic spot will not affect each other, so it is necessary to make partitions between scenic spots.

[0083] Embodiment 2

[0084] This embodiment discloses a method for adjusting the floor area of a scenic spot according to the popularity index of each scenic spot in a greenhouse garden, as Figure 2 shown, the method specifically includes the following:

[0085] First, it is necessary to clarify the popularity of each scenic spot. The popularity of a scenic spot can be reflected by calculating the popularity index of the scenic spot. In this solution, a method for calculating the popularity index by statistically analyzing the tourist density and the time tourists spend enjoying the scenery at each scenic spot is provided. The specific steps are as follows:

[0086] For each scenic spot in the greenhouse garden involved, a one-person-one-vote-one-gate system is adopted. Tourists purchase tickets online through the mobile garden applet, obtain the online program code, and can scan the code to enter and exit the scenic spot access control. Tourists who purchase tickets offline use the uniformly issued identification access control card for entering the scenic spot and swipe the card to enter and exit the scenic spot access control;

[0087] Take one operation cycle of the greenhouse garden as the tracing cycle. One tracing cycle represents the time when the management staff of the greenhouse garden regularly adjusts the scenic spots and the plants in the scenic spots;

[0088] During one tracing cycle, obtain the tourist density and the time tourists spend enjoying the scenery at any scenic spot during the business hours of any day. The detailed steps are as follows:

[0089] First, record the time t1 recorded by the scenic spot access control of the corresponding scenic spot when any tourist enters the corresponding scenic spot. At the same time, the access control of this scenic spot increments the count R of the number of tourists recorded by 1. At the end of the business hours of the greenhouse garden (at this time, the number of people in the scenic spot should be 0, and there may be unavoidable special situations such as tourists who maliciously evade tickets, resulting in the number of tourists R in the scenic spot not being 0, and R needs to be reset), the value of R is reset to 0, and the real-time number of tourists recorded at this scenic spot is uploaded to the data storage module every preset period of time;

[0090] Then, obtain the time t2 recorded by the scenic spot access control when the corresponding tourist leaves the corresponding scenic spot (identifying the tourist's identity through the online program code or the identification access control card for offline ticket purchase). At this time, decrement the count R of the number of tourists associated with this scenic spot by 1, indicating that this tourist leaves the current scenic spot and the number of people in the scenic spot decreases by 1. Then, according to the formula t3 = t2 - t1, obtain the staying time t3 of this tourist in the corresponding scenic spot;

[0091] Set different basic staying times t4 for each scenic spot. If the staying time t3 of a tourist in this scenic spot < t4, it is considered that this tourist's visit is abnormal. When this tourist leaves the scenic spot, the count R of the associated number of tourists is decremented by 2, considering that this tourist has not visited this scenic spot, and the staying time t3 of this tourist in the scenic spot is deleted;

[0092] Then, obtain the number of tourists uploaded by this scenic spot on that day, sum up the uploaded number of tourists, and divide the sum by the total number of uploads to obtain the average number of tourists;

[0093] Divide the average number of visitors by the floor area of the scenic spot to obtain the visitor density of the scenic spot on that day;

[0094] Obtain the stay time t3 of each visitor in the scenic spot on that day, average the stay times of all visitors, and use the averaged result as the viewing time of visitors in the scenic spot on that day;

[0095] Take the average of the visitor densities of the corresponding scenic spot for each day within a tracing period as the visitor density of the scenic spot within that tracing period, denoted as ρ; take the average of the viewing times of visitors of the corresponding scenic spot for each day within a tracing period as the viewing time of visitors of the scenic spot within that tracing period, denoted as T;

[0096] Then obtain the critical comfortable visitor density of the corresponding scenic spot, denoted as C. If ρ is greater than C, it means that the number of visitors in the scenic spot is too large, the visitors are crowded, and the experience of visitors decreases. The critical comfortable visitor density C means the standard of the maximum number of visitors that can be accommodated to ensure the quality of the visitor experience and the requirements of environmental protection. Generally, it is related to the design and floor area of the scenic spot, and the maximum value is generally 2 people per square meter;

[0097] According to the trend that the popularity of the scenic spot increases with the increase of the viewing time of visitors and the trend that the popularity increases with the increase of the number of visitors in the scenic spot, construct the following formula:

[0098]

[0099] Calculate the popularity index P of the corresponding scenic spot; where, T0 is the preset ideal viewing time of visitors, representing the shortest time spent visiting all areas in the scenic spot, k is the growth rate parameter, which can be set as a fixed value and affects the steepness of the curve in the two-dimensional coordinate system, but does not affect the calculation results involved in this solution. is the crowding penalty term, indicating that the experience of visitors decreases due to overcrowding. Determine the value of the crowding penalty term by calculating and setting the value of the adjustment coefficient γ, and the value range is from 0 to 1. is the core calculation factor, reflecting the basic visitor aggregation effect. The greater the density, the more popular the scenic spot;

[0100] Obtain the popularity index of each scenic spot in the greenhouse garden according to the popularity index calculation formula, denoted as P1, P2,..., P i , where i represents the total number of scenic spots in the greenhouse garden. The popularity of the scenic spot is proportional to the popularity index P of the scenic spot. The larger the value of P, the more popular the scenic spot.

[0101] The popularity index calculation formula involves determining the value of the adjustment coefficient γ. The specific method for determining the value of the adjustment coefficient γ includes the following:

[0102] For scenic spots with narrow spaces and a high likelihood of congestion, set the adjustment coefficient γ to a relatively large value. For scenic spots with broad spaces and a low likelihood of congestion, set the adjustment coefficient γ to a relatively small value. Based on this principle, the following steps are carried out:

[0103] Obtain the floor areas of all scenic spots and arrange them in ascending order of floor area to obtain the scenic spot floor area sequence, denoted as S1, S2,..., S i ;

[0104] For S with the largest floor area i and S1 with the smallest floor area, perform special processing: Take the scenic spot S with the largest floor area among them i , consider it as the least likely to cause congestion in this greenhouse garden, and set the adjustment coefficient γ to 0 at this time. For the scenic spot S1 with the smallest floor area, consider it as the most likely to cause congestion in this greenhouse garden, and set the adjustment coefficient γ to 1 at this time;

[0105] Divide the value range [0 - 1] of the adjustment coefficient γ into i - 1 value intervals. The i - 1 value intervals correspond to i boundary values, that is, the maximum and minimum values corresponding to a value interval. Sort the i boundary values from large to small to obtain the adjustment coefficient γ sequence, denoted as γ1, γ2,..., γ i , where γ1 is 1, γ i is 0;

[0106] It should be noted that there is a non - inclusive relationship between two adjacent intervals of these i - 1 value intervals. For example, when i = 3, divide [0 - 1] into 3 - 1 = 2 segments, that is, divide it into [0 - 0.55] and [0.55 - 1] two segments, obtaining four boundary values 0, 0.55, 0.55, 1. Remove the repeated 0.55 to obtain three boundary values 0, 0.55, 1. Then the adjustment coefficient γ sequence is 0, 0.55, 1, corresponding to i = 3, that is, corresponding to three scenic spots;

[0107] Then, make the scenic spot floor area sequence and the adjustment coefficient γ sequence correspond one by one. S j corresponds to γ j , where j is the counting index, and its value range is from 1 to i.

[0108] Then, adjust the scenic spots according to the popularity of the scenic spots. The adjustment steps are as follows:

[0109] First, obtain the sum of the areas of each scenic spot in this greenhouse garden as the total area, denoted as S all , to ensure that the total area after adjusting the scenic spot areas does not exceed S all ;

[0110] Then obtain the floor area sequences S1, S2,..., S of each scenic spot within a traceability period, i which are arranged in ascending order of floor area;

[0111] Record the popularity indices of the corresponding scenic spots in sequence according to the order of the scenic spot floor area sequence, denoted as P ’ 1, P ’ 2,..., P ’ i ;

[0112] Calculate the ratios between the popularity indices of each scenic spot in this greenhouse garden, that is, P ’ 1:P ’ 2:...:P ’ i And allocate the adjusted floor area of each scenic spot according to this ratio, and sort each scenic spot after allocation again in ascending order of scenic spot floor area to obtain the adjusted scenic spot floor area sequence, denoted as S1 ‘ , S2 ‘ ,..., S i ‘ , and S1 ‘ +S2 ‘ +...+S i ‘ =S all , if the sum of the total areas after adjustment is higher than S all , then a total area verification is required to ensure that S all can meet the allocation requirements;

[0113] Continuously monitor the popularity indices of the scenic spots adjusted in the current traceability period until the next traceability period;

[0114] Calculate the new popularity indices of each scenic spot in the next traceability period, and sort them in ascending order of the numerical value of the scenic spot floor area, and record them in sequence as P1 " , P2 ‘’ ,..., P i ‘’ , which correspond one by one to the scenic spot floor area sequences S1 ‘ , S2 ‘ ,..., S i ‘ ;

[0115] Calculate the difference rate U of the popularity indices of the same scenic spot in two traceability periods. The calculation formula is as follows:

[0116]

[0117] A buffer is set for the difference rate U. If U satisfies -10% < U < 10%, it is considered that the current scenic spot is within the buffer area and no adjustment is made;

[0118] The set buffer area is not a fixed value range. Here, -10% < U < 10% is only an example value, and the staff can adjust the range of the buffer area according to the actual situation;

[0119] If U does not satisfy the buffer area and the calculated difference rate U is positive, it means that the previous adjustment to this scenic spot has a positive effect, and the popularity has been improved after adjustment. And the following operations are carried out: continue to increase the floor area of U on the basis of the current floor area of the corresponding scenic spot;

[0120] If U does not satisfy the buffer area and the calculated difference rate U is negative, it means that the previous adjustment to this scenic spot has a negative effect, and the popularity has decreased after adjustment. And the following operations are carried out: reduce U on the basis of the current floor area of the corresponding scenic spot;

[0121] Both the operation of reducing the floor area and the operation of increasing the floor area need to be verified by the total area to ensure that the area after adjustment will not exceed the total area;

[0122] Continuously monitor each traceability cycle to form a systematic adjustment plan for the floor area of scenic spots;

[0123] Exemplarily, there is a scenic spot. The popularity index in the previous traceability cycle was 20, and the floor area after adjustment in the previous traceability cycle was 100 square meters. The popularity index of this scenic spot this time is 22. Then, according to the formula provided by the plan, the difference rate U can be calculated as 10%;

[0124] Compared with the previous traceability cycle, the growth rate of the popularity index of this scenic spot this time is 10%. Then, increase it by 10% on the basis of the original area, and the adjusted floor area of the scenic spot is 110 square meters;

[0125] After the areas of all scenic spots are adjusted in this traceability cycle, verification with the total area is required to prevent the situation that the areas of multiple scenic spots generally need to be increased, resulting in the sum of the areas of the scenic spots after adjustment exceeding the existing total area of the scenic spots;

[0126] If this situation occurs, calculate the percentage increase of all scenic spots and normalize it, and distribute the adjustable area (the area left over after other scenic spots are reduced) according to the normalized ratio of each scenic spot.

[0127] Example 3

[0128] This embodiment discloses a method for managing plants by combining the popularity of scenic spots and the growth environment of plants in the scenic spots. As Figure 3 shown, the method includes the following:

[0129] Obtain the air quality pollution index associated with the growth environment of plants in the corresponding scenic spot. The air quality pollution index needs to measure the air components in the scenic spot. This solution provides a plant growth environment monitoring module, and uses relevant instruments for air quality detection to obtain the carbon dioxide concentration in the current scenic spot environment Concentration of harmful gases Temperature Humidity The harmful gases include: sulfur dioxide, nitrogen dioxide, ammonia. The content of harmful gases is relatively scarce. This solution summarizes the three harmful gases into one parameter, namely: concentration of harmful gases

[0130] Obtain the average maximum carbon dioxide concentration that plants in the scenic spot can withstand during the growth process Average maximum concentration of harmful gases Average maximum temperature Average maximum humidity Collectively as the maximum safety threshold;

[0131] Construct an air quality pollution index calculation formula:

[0132]

[0133] Calculate the air quality pollution index API of the current scenic spot. The higher the API value, the more serious the air quality pollution in the current scenic spot. Among them, α is an adjustment coefficient, and the specific value is set by the staff;

[0134] Set the air quality pollution index thresholds for different scenic spots. The air quality pollution index thresholds are set by the staff in combination with the different adaptabilities of the plant growth environments in each scenic spot. When the calculated air quality pollution index API exceeds the air quality pollution index threshold of the scenic spot, the greenhouse garden adjustment and management module will execute the air quality optimization program to optimize the air quality in the scenic spot;

[0135] The air quality optimization program is some conventional methods adopted by the current existing technologies, including: installing air purifiers, setting up spray dust suppression systems, carbon dioxide supplementation, etc. During the execution of the air quality optimization program, it is necessary to monitor the air quality pollution index API in real time. If it is detected that the air quality pollution index API in the current greenhouse scenic spot meets the standards of the plants in the current greenhouse scenic spot, stop; otherwise, continue to implement the air quality optimization program.

[0136] Adjust the detection frequency of air quality for a scenic spot in combination with the popularity of the corresponding scenic spot to ensure that the air quality within the scenic spot meets the safety threshold;

[0137] Obtain the basic detection frequency F for detecting the air quality of scenic spots set by the staff according to the actual situation. The detection frequency of any scenic spot is not lower than the basic detection frequency F;

[0138] Sort the scenic spots of the current greenhouse garden in descending order according to the obtained popularity index, and select the scenic spots with the popularity index in the top β%. The value of β is determined by the staff;

[0139] For the scenic spots with the popularity index in the top β%, obtain the number of times that the air quality pollution index exceeds the air quality pollution index threshold under the basic detection frequency F for all scenic spots in a traceability period, and record them as M1, M2,..., M n , where n represents the total number of scenic spots with the popularity index in the top β%;

[0140] Obtain the popularity index of each scenic spot, denoted as P1, P2,..., P n , and P o Corresponding to M o , where o is the counting index, starting from 1, representing any one of the n scenic spots;

[0141] For the first scenic spot, calculate the detection frequency feature V1 of this scenic spot by using V1 = M1 × P1, and calculate all the remaining scenic spots according to this calculation method. Finally, obtain V1, V2,..., V n , re - sort the obtained detection frequency features in ascending order of value, and record the sorted result as the detection frequency feature sequence V1‘, V2‘,..., V n ‘;

[0142] The detection frequency feature takes into account the scenic spot popularity index and the number of times of air quality exceeding the standard. For those scenic spots that are both popular and prone to air quality problems, more detection times are allocated, so that limited detection resources are concentrated in the most needed places, improving the resource utilization efficiency. The specific allocation method is as follows:

[0143] Starting from the scenic spot corresponding to the first detection frequency feature, within a traceability period, increase the detection times by G times on the basis of the basic detection frequency F. For each subsequent scenic spot, increase the detection frequency by G times on the basis of the detection frequency after the previous scenic spot has been increased until the last scenic spot completes the increase in detection frequency. G is a fixed value preset by the staff; Repeat this step in the next traceability period to adjust the detection frequency of the scenic spots, and gradually form an increasingly perfect detection frequency optimization system solution.

[0144] The above content is merely an example and illustration of the present invention. Those skilled in the art to which this technology belongs can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the invention or exceed the scope defined by this claim book, they shall fall within the protection scope of the present invention.

[0145] It should be stated that all user data collected in this application is collected with the consent and authorization of the users. Moreover, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations and standards of the relevant regions.

Claims

1. The intelligent garden management system based on the Internet of Things is characterized by: This management system includes: A tourist data analysis module that obtains the tourist density and the time tourists spend enjoying the scenery at each scenic spot in the greenhouse garden; A greenhouse garden adjustment and management module that analyzes the popularity of scenic spots by combining the tourist density and the time tourists spend enjoying the scenery at the scenic spots, and adjusts the floor area of each scenic spot according to the popularity of the scenic spots; combines the popularity and the growth environment of the plants in the scenic spots to adjust the air quality detection frequency of the scenic spots; A plant growth environment monitoring module that monitors the growth environment of the plants in the scenic spots, identifies abnormal growth environments, and the greenhouse garden adjustment and management module executes an air quality optimization program; A data storage module that stores the data obtained from the analysis of this solution and provides numerical extraction for the control module; A control module, which is the hub for communication and data transmission between modules and processes the calculation and analysis steps described in this solution.

2. The garden intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The growth environment of the plants is the air quality pollution index associated with the plants during their growth process.

3. The garden intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The tourist data analysis module includes: A mobile garden mini-program that provides online ticket purchasing. Tourists enter and exit the scenic spots in the garden by swiping the ticket purchase program code; An identification access card. Tourists enter and exit the scenic spots in the garden by swiping the identification access card; Scenic spot access control, which obtains the time when tourists enter and exit the scenic spots and records the number of tourists in the scenic spots in real time.

4. The garden intelligent management system based on the Internet of Things according to claim 3 is characterized in that: The method by which the tourist data analysis module obtains the tourist density and the time tourists spend enjoying the scenery at each scenic spot in the greenhouse garden is as follows: S41. When a tourist enters a scenic spot, the scenic spot access control records the time t1, and the count R of the number of tourists in the scenic spot is incremented by one. When the greenhouse garden closes for business, R is reset to 0, and the data is uploaded and stored regularly; S42. When a tourist leaves a scenic spot, the scenic spot access control records the time t2, the count R of the number of tourists is decremented by one, and t3=t2 - t1 is used to obtain the stay time t3 of this tourist; S43. Calculate the total number of tourists uploaded at this scenic spot on the same day, divide it by the number of uploads, and obtain the average number of tourists; Then divide the average number of tourists by the floor area of this scenic spot to obtain the tourist density on the same day; S44. Calculate the average stay time of all tourists in the scenic spot on the same day as the time tourists spend enjoying the scenery at this scenic spot on the same day; S45. During a traceability period, calculate the average value of the tourist density of the corresponding scenic spot every day as the tourist density ρ of the corresponding scenic spot during this traceability period; at the same time, calculate the average value of the time tourists spend enjoying the scenery of the corresponding scenic spot every day as the time tourists spend enjoying the scenery T of the corresponding scenic spot during this traceability period.

5. The garden intelligent management system based on the Internet of Things according to claim 4 is characterized in that: The method by which the greenhouse garden adjustment and management module analyzes the popularity of scenic spots by combining the tourist density and the time tourists spend enjoying the scenery at the scenic spots is as follows: S51. Obtain the critical comfortable tourist density of each scenic spot, denoted as C; S52. Construct the formula: Calculate the popularity index P of the corresponding scenic spot, where T0 is the preset ideal tourist viewing time, k is the growth rate parameter, which can be regarded as a known value, is the congestion penalty term, indicating the degradation of experience caused by excessive congestion. The value of the congestion penalty term is determined by calculating and setting the adjustment coefficient γ. The value range of γ is 0 to 1. It is the core calculation factor, reflecting the basic tourist aggregation effect; S53, according to the method described in S51-S52, calculate the popularity index of each scenic spot, which is recorded as P1, P2, ..., P i , where i represents the total number of scenic spots in the greenhouse garden.

6. The garden intelligent management system based on the Internet of Things according to claim 5 is characterized in that: The method by which the greenhouse garden adjustment and management module adjusts the scenic spots according to the popularity is as follows: Get the area sequence S1, S2, ..., S of the scenic spots within a traceability period i , and record the popularity index P of the scenic spots in the order of the area occupied by the scenic spots ’ 1,P ’ 2,...,P ’ i ; Get the sum of the areas of the greenhouse garden attractions, denoted as S all ; The area of ​​each scenic spot is allocated according to the ratio of the popularity index between the scenic spots, and the scenic spots are sorted in order from the smallest to the largest area, and the adjusted scenic spot area sequence S1 is obtained. ‘ ,S2 ‘ ,...,S i ‘ , and S1 ‘ +S2 ‘ +...+S i ‘ =S all ; Continuously monitor the popularity index of the scenic spot until the next traceability period; Get the popularity index of each scenic spot in the next traceability cycle, and record it in order of the area of ​​the scenic spot from small to large as P1 " ,P2 ‘’ ,...,P i ‘’ Sequence S1 with scenic spots covering area ‘ ,S2 ‘ ,...,S i ‘ correspond; Calculate the difference rate U of the popularity index of the same scenic spot in two consecutive traceability periods, and the calculation formula is as follows: Set a buffer for the difference rate U. If U satisfies -10% < U < 10%, no adjustment is made; If U does not satisfy the buffer and the calculated result U is positive, then increase it by U on the basis of the current floor area of the corresponding scenic spot; If U does not satisfy the buffer zone and if the calculated result of U is negative, U will be adjusted down based on the current area of ​​the corresponding scenic spot.

7. The garden intelligent management system based on the Internet of Things according to claim 6 is characterized in that: The greenhouse garden adjustment and management module manages the scenic spot in accordance with the popularity and the growth environment of the plants in the scenic spot as follows: Obtain the air quality pollution index associated with the growth environment of the plants in the corresponding scenic spot and monitor it. If the air quality pollution index is higher than the preset air quality pollution index threshold, the greenhouse garden adjustment and management module executes the air quality optimization program to optimize the air quality in the scenic spot; Based on the popularity of the corresponding scenic spot, adjust the detection frequency of the scenic spot.

8. The garden intelligent management system based on the Internet of Things according to claim 7 is characterized in that: The greenhouse garden adjustment and management module executes the air quality optimization program. The method for optimizing the air quality in the scenic spot includes the following steps: S81, plant growth environment monitoring module obtains carbon dioxide concentration in the scenic spot Harmful gas concentration temperature humidity S82. Obtain the average maximum carbon dioxide concentration that the plants in the corresponding scenic spot can withstand during their growth Average maximum concentration of harmful gases Average maximum temperature Average maximum humidity As the maximum safety threshold; S83. According to the air quality pollution index calculation formula: Calculate the air quality pollution index API of the current scenic spot, where α is the adjustment coefficient, and the specific value is set by the operator; S84. Set a different air quality pollution index threshold for each scenic spot. When the calculated API exceeds the air quality pollution index threshold of the scenic spot, an air quality optimization program needs to be executed.

9. The garden intelligent management system based on the Internet of Things according to claim 8 is characterized in that: The greenhouse garden adjustment and management module further includes a method for managing plants in combination with popularity and the growth environment of plants in the scenic spot; Obtain the basic detection frequency F for detecting the air quality of the scenic spot, which is set by the staff based on the actual situation; Scenic spots are sorted from largest to smallest according to popularity index, and the attractions with popularity index in the top β% are selected to increase the detection frequency. The value of β is determined by the staff. The method of increasing the detection frequency is as follows: Get the number of times the air quality pollution index API exceeds the air quality pollution index threshold at the basic detection frequency F for the attractions with the popularity index in the top β% within a traceability period, and record them as M1, M2, ..., M n , where n represents the total number of attractions whose popularity index is in the top β%; Get the popularity index of each attraction, denoted as P1, P2, ..., P n , and P o Corresponding to M o , where o is a counting index, starting from 1, representing any one from 1 to n; For the first scenic spot, V1=M1×P1 is used to calculate the detection frequency feature V1 of the scenic spot, and the same method is used to calculate other scenic spots, and V1, V2, ..., V n , sort the obtained detection frequency features according to the value from small to large, and record them as the detection frequency feature sequence V1 ‘ ,V2 ‘ ,...,V n ‘ ; Starting from the first scenic spot corresponding to the detection frequency feature, the number of detections is increased by G times on the basis of the basic detection frequency F, and each subsequent scenic spot increases the number of detections by G times on the basis of the detection frequency of the previous scenic spot, where G is a fixed value pre-set by the staff.

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