Intelligent carbon emission prediction method and system based on dark and peak seasons of scenic spot

Abstract

The invention relates to an intelligent carbon emission prediction method based on dark and peak seasons of a scenic spot, and the method specifically comprises the steps: determining the accounting boundary of carbon emission of different activity types according to the carbon emission source of a target scenic spot; based on the accounting boundaries of the carbon emissions of the different activity types, constructing a scenic spot carbon emission accounting model so as to obtain the carbon emissions of the different activity types of the target scenic spot in each month in a historical period; based on the carbon emission, establishing a scenic spot carbon emission prediction model by using an SARIMA model; and predicting the carbon emissions of different activity types of the target scenic spot in a future time period through a scenic spot carbon emission prediction model. Through the method, the carbon emission generated by different activity types in the scenic spot can be clearly and accurately calculated, the carbon emission generated by different activity types in the future scenic spot can be accurately obtained, and carbon management and planning of the scenic spot in advance are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of carbon data technology, and in particular to an intelligent carbon emission prediction method and system based on the off-season and peak-season of a scenic spot. Background Art

[0002] Tourism is a typical comprehensive industry, not an absolutely low-carbon industry. The development, maintenance, operation of scenic spots and various activities of tourists during the tour will generate a large amount of carbon emissions. In order to achieve the goal of carbon reduction, the carbon emission activities of scenic spots need to be reasonably managed and planned, which requires accurate and reliable carbon emission data. However, the carbon sources of tourist attractions are complex, the data are cumbersome and prone to overlap, and the existing technology lacks appropriate methods to accurately measure and estimate the carbon emissions of scenic spots. Summary of the invention

[0003] The purpose of the present invention is to improve the problem in the prior art that it is impossible to accurately calculate and predict the carbon emissions of scenic spots, and to provide an intelligent carbon emissions prediction method and system based on the off-season and peak-season of scenic spots.

[0004] In order to achieve the above-mentioned object of the invention, the embodiment of the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides an intelligent carbon emission prediction method based on the off-season and peak-season of a scenic spot, comprising:

[0006] Determine the accounting boundaries of carbon emissions of different activity types based on the carbon emission sources of the target scenic spot;

[0007] Based on the accounting boundaries of carbon emissions of different types of activities, a scenic spot carbon emissions accounting model is constructed to obtain the carbon emissions of different types of activities in the target scenic spot in each month during the historical period;

[0008] Based on the carbon emissions, a scenic spot carbon emissions prediction model is established using the SARIMA model;

[0009] The carbon emissions of different types of activities in the target scenic spot in the future time period are predicted through the scenic spot carbon emissions prediction model.

[0010] Furthermore, the sources of carbon emissions from the target scenic spot are generated by different types of activities, including tourist activities and scenic spot operation activities; the accounting boundaries of carbon emissions from tourist activities include carbon emissions from food and accommodation, carbon emissions from transportation, and carbon emissions from entertainment; the accounting boundaries of carbon emissions from scenic spot operation activities include basic electricity carbon emissions and garbage carbon emissions.

[0011] Furthermore, the steps of constructing a scenic spot carbon emission accounting model based on the accounting boundaries of carbon emissions of different activity types include:

[0012] According to the carbon emission accounting boundary of each type of activity, a corresponding scenic spot carbon emission accounting model is constructed, including:

[0013] The scenic area carbon emission accounting model for food and accommodation carbon emissions is constructed as follows:

[0014]

[0015] Among them, C SS Carbon emissions from providing food and accommodation services to tourists; N SS The number of entities providing food and accommodation services; C SSi is the carbon emission of the i-th food host; M SS The amount of energy used for food production activities; E ssj is the consumption of type j energy per unit time by the i-th food host; θ j is the carbon emission factor of energy j; T is the time for the i-th food host to use energy j;

[0016] The scenic area carbon emission accounting model constructed for the traffic carbon emission is:

[0017]

[0018] Among them, C JT Transportation carbon emissions generated by providing transportation services to tourists; N JT is the number of types of transportation; E JTi is the energy consumption per unit distance of vehicle i; θ i is the carbon emission factor corresponding to vehicle i; D i The total distance travelled by vehicle i;

[0019] The scenic spot carbon emission accounting model constructed for the tourism carbon emission is:

[0020] C Y W=C HX +C SB ,

[0021]

[0022]

[0023] Among them, C YW The carbon emissions of tourists during their travels; C HX Carbon emissions generated by tourists breathing; N YK is the total number of tourists; is the average daily respiratory carbon emission factor per capita; C SB Carbon emissions generated by tourists using recreational equipment; N SBis the number of recreational equipment in the scenic area; E SBi is the energy consumption of gaming device i per unit time; θ i is the carbon emission factor corresponding to the gaming device i; T i Working hours for gaming equipment i;

[0024] The scenic area carbon emission accounting model constructed for the basic electricity carbon emission is:

[0025] C JC =C GD +C WX ,

[0026] Among them, C JC Carbon emissions generated by powering and maintaining infrastructure for scenic spots; C GD Carbon emissions generated by powering the infrastructure of the scenic area; C WX Carbon emissions generated by infrastructure maintenance in scenic spots.

[0027] The carbon emission accounting model of the scenic area for garbage carbon emissions is constructed as follows:

[0028] C LJ =C TM +C RS ,

[0029] Among them, C LJ C is the carbon emissions generated by the scenic area in treating the garbage; TM Carbon emissions from landfill disposal; C RS Carbon emissions from burning waste.

[0030] Furthermore, based on the activity data and the carbon emissions, a scenic spot carbon emissions prediction model is established using the SARIMA model, including:

[0031] Preprocessing the carbon emissions based on the time sequence within the historical period to obtain a time-series historical carbon emissions time series data set;

[0032] Dividing the data set of the historical carbon emission time series into a training set, a validation set and a test set;

[0033] Stabilizing the historical carbon emission time series of the training set to obtain time-series historical carbon emission data;

[0034] Establish the SARIMA model SARIMA(p,d,q)(P,D,Q,m), use the time series historical carbon emission data to train the model SARIMA(p,d,q)(P,D,Q,m), and determine the model parameters d, p, q, D, P, Q, m; d is the number of non-seasonal differences, p is the number of non-seasonal autoregressive terms, q is the maximum lag order of the non-seasonal moving average operator, D is the number of seasonal differences, P is the number of seasonal autoregressive terms, Q is the maximum lag order of the seasonal moving average operator; m is the length of the forecast period;

[0035] After verification on the validation set, the scenic spot carbon emission prediction model was obtained.

[0036] On the other hand, an embodiment of the present invention further provides an intelligent carbon emission prediction system based on the off-season and peak-season of a scenic spot, comprising:

[0037] The carbon emission calculation module is used to determine the accounting boundaries of carbon emissions of different activity types according to the carbon emission sources of the target scenic spot; based on the accounting boundaries of carbon emissions of different activity types, a scenic spot carbon emission accounting model is constructed to obtain the carbon emissions of different activity types of the target scenic spot in each month during the historical period;

[0038] The carbon emission prediction module is used to establish a scenic spot carbon emission prediction model based on the carbon emissions using the SARIMA model; through the scenic spot carbon emission prediction model, the carbon emissions of different activity types in the target scenic spot in the future time period are predicted.

[0039] On the other hand, an embodiment of the present invention also provides a computer-readable storage medium including computer-readable instructions, which, when executed, enable a processor to perform the operations in the method described in the embodiment of the present invention.

[0040] On the other hand, an embodiment of the present invention also provides an electronic device, including: a memory, storing program instructions; a processor, connected to the memory, executing the program instructions in the memory, and implementing the steps in the method described in the embodiment of the present invention.

[0041] Compared with the existing technology, the method and system of the present invention divide the accounting boundaries of the scenic area's carbon emissions according to the characteristics and activity types of the scenic area activities, and can clearly and accurately calculate the carbon emissions generated by different types of activities in the scenic area. By using a machine learning model, the carbon emissions generated by different types of activities in the scenic area in the future can be accurately obtained. The scenic area management department can plan and manage the activities of the scenic area in advance according to the predicted carbon emissions to determine the scenic area's energy-saving and emission reduction plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 is an exemplary flow chart of the intelligent carbon emission prediction method based on the off-season and peak-season of the scenic spot described in the embodiment;

[0044] Figure 2 It is an exemplary structural diagram of the intelligent carbon emission prediction system based on the off-season and peak-season of the scenic spot described in the embodiment. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0046] Figure 1 is an exemplary flow chart of the intelligent carbon emission prediction method based on the off-season and peak-season of the scenic spot described in the embodiment, such as Figure 1 As shown, the intelligent carbon emission prediction method based on the off-season and peak-season of a scenic spot described in this embodiment specifically includes:

[0047] S1. Determine the accounting boundaries of carbon emissions of different activity types based on the carbon emission sources of the target scenic spot.

[0048] In some embodiments, the carbon emissions of the target scenic spot are generated by different types of activities, including tourist activities and scenic spot operation activities; the accounting boundaries of carbon emissions in tourist activities include carbon emissions from food and accommodation, carbon emissions from transportation, and carbon emissions from entertainment; the accounting boundaries of carbon emissions in scenic spot operation activities include basic electricity carbon emissions and garbage carbon emissions.

[0049] S2, based on the accounting boundaries of carbon emissions of different activity types, a scenic spot carbon emissions accounting model is constructed to obtain the carbon emissions of different activity types in the target scenic spot in each month during the historical period.

[0050] In some embodiments, based on the accounting boundaries of carbon emissions of different activity types, the steps of constructing a scenic spot carbon emissions accounting model specifically include:

[0051] According to the carbon emission accounting boundary of each type of activity, a corresponding scenic spot carbon emission accounting model is constructed. In detail, the scenic spot carbon emission accounting model for food and accommodation carbon emissions is constructed as follows:

[0052]

[0053] Among them, C SS N represents the carbon emissions from providing accommodation services to tourists; SS The number of entities providing food and accommodation services; C SSi is the carbon emission of the i-th food host; M SS The amount of energy used for food production activities; E ssj is the consumption of the jth type of energy by the i-th food host in unit time; θ j is the carbon emission factor of energy source j; T is the time that the i-th food host uses the j-th type of energy.

[0054] Specifically, when a host provides accommodation services to tourists, the types of energy used may include electricity, natural gas, coal gas, photovoltaics, etc. The carbon emission factors corresponding to each type of energy can be obtained by statistical calculation based on international standards, national standards or carbon emission factor software. The carbon emission factors corresponding to each type of energy may correspond to different values ​​in different seasons and regions. It should be noted that the defined unit time can be 1 hour, which can be adjusted according to specific circumstances.

[0055] The scenic area carbon emission accounting model for transportation carbon emissions is constructed as follows:

[0056]

[0057] Among them, C JT Transportation carbon emissions generated by providing transportation services to tourists; N JT is the number of types of transportation; E JTi is the energy consumption of the i-th vehicle per unit distance; θ i is the carbon emission factor corresponding to vehicle i; D i The total distance traveled by vehicle i.

[0058] Specifically, the means of transportation provided by scenic spots for tourists may include sightseeing buses, ferries, trams and other types. The carbon emission factors corresponding to each type of transportation are different. For example, the carbon emission factor of a sightseeing bus is 0.32t / Km (tons / kilometer), and the carbon emission factor of a tram is 0.16t / Km (tons / kilometer).

[0059] The scenic spot carbon emission accounting model constructed for tourism carbon emissions is:

[0060] C YW =C HX +C SB ,

[0061]

[0062] Among them, C YW The carbon emissions of tourists during their travels; C HX Carbon emissions generated by tourists breathing; N YK is the total number of tourists; is the average daily respiratory carbon emission factor per capita; C SB Carbon emissions generated by tourists using recreational equipment; N SB is the number of recreational equipment in the scenic area; E SBi is the energy consumption of the gaming device i in unit time; θ i is the carbon emission factor corresponding to the gaming device i; T i Working hours for gaming device i.

[0063] The scenic area carbon emission accounting model based on electricity carbon emission is constructed as follows:

[0064] C JC =C GD +C WX ,

[0065] Among them, C JC Carbon emissions generated by powering and maintaining infrastructure for scenic spots; C GD Carbon emissions generated by powering the infrastructure of the scenic area; C WX Carbon emissions generated by infrastructure maintenance in scenic spots.

[0066] Specifically, the basic electricity of a scenic spot can include the electricity consumed for supplying power and maintaining the scenic spot’s infrastructure. The scenic spot’s infrastructure can include electricity used for lights and air conditioners in public buildings. GD The calculation formula is:

[0067] C GD =(N L (1-α L )W L ×H L +N A (1-α A )W A ×H A )×E e ,

[0068] Among them, N L With N A are the number of electric lights and air conditioners in the public facilities in the scenic area, α L With α A are the proportion of electric lights and air conditioners powered by renewable energy, W L With W A The power of each light and air conditioner, H L With H A are the monthly usage hours of electric lights and air conditioners, E e is the carbon emission factor of the power system.

[0069] In the process of over-maintenance of infrastructure, the main source of greenhouse gases is the release of sulfur hexafluoride conversion. The carbon emissions of infrastructure maintenance are C WX The calculation formula is:

[0070]

[0071] Among them, N WX Refers to the amount of infrastructure that needs repair, REP i Refers to the sulfur hexafluoride capacity during the maintenance of infrastructure, GWP SF6 Refers to the greenhouse gas potential of sulfur hexafluoride.

[0072] The carbon emission accounting model of the scenic area for garbage carbon emissions is constructed as follows:

[0073] C LJ =C TM +C RS ,

[0074] Among them, C LJ C is the carbon emissions generated by the scenic area in treating the garbage; TM Carbon emissions from landfill disposal; C RS Carbon emissions from burning waste.

[0075] Specifically, solid waste treatment mainly includes landfill and incineration. A large amount of greenhouse gases will be generated in the process of treating waste. Different treatment methods have different corresponding carbon emission factors. TM The carbon emission factor of landfill waste is obtained by multiplying the amount of landfill waste; the carbon emission generated by the waste treatment by burning is C RS Obtained by multiplying the carbon emission factor of burning garbage by the amount of garbage burned. Carbon emissions generated by landfilling garbage C TM The calculation formula is:

[0076] C TM =(WTM ×L0-R)×(1-OX),

[0077] Among them, W TM is the amount of solid waste landfilled; L0 is the methane production potential of the landfill; R is the amount of methane recovered; OX is the oxidation factor.

[0078] Carbon emissions from burning waste RS The calculation formula is:

[0079] C RS =W RS ×CCW×FCF×EF,

[0080] Among them, W RS is the amount of solid waste burned; CCW is the carbon content ratio in solid waste; FCF is the proportion of mineral carbon in solid waste in the total carbon content; EF is the combustion efficiency of the solid waste incinerator.

[0081] S3, based on the carbon emissions of all activity types, a scenic spot carbon emissions prediction model was established using the SARIMA model;

[0082] In some embodiments, the step of establishing a scenic spot carbon emissions prediction model using a SARIMA model based on the carbon emissions of all activity types includes:

[0083] Preprocessing the carbon emissions based on the time sequence within the historical period to obtain a time-series historical carbon emissions time series data set;

[0084] Dividing the data set of the historical carbon emission time series into a training set, a validation set and a test set;

[0085] Stabilizing the historical carbon emission time series of the training set to obtain time-series historical carbon emission data;

[0086] Establish the SARIMA model SARIMA(p,d,q)(P,D,Q,m), use the time series historical carbon emission data to train the model SARIMA(p,d,q)(P,D,Q,m), and determine the model parameters d, p, q, D, P, Q, m; d is the number of non-seasonal differences, p is the number of non-seasonal autoregressive terms, q is the maximum lag order of the non-seasonal moving average operator, D is the number of seasonal differences, P is the number of seasonal autoregressive terms, Q is the maximum lag order of the seasonal moving average operator; m is the length of the forecast period;

[0087] After verification on the validation set, the scenic spot carbon emission prediction model was obtained.

[0088] In some embodiments, the SARIMA model (Seasonal Autoregressive Integrated Moving Average Model) is a method for time series forecasting and analysis, which can more accurately capture and predict seasonal changes in time series data by adding seasonal differences, seasonal autoregression, and seasonal moving average terms to the ARIMA model. In this embodiment, the SARIMA model is fitted using historical carbon emission data to determine model parameters, where m is the length of the forecast period, which is at least 1 month and can be set to 3 months or half a year, etc., as needed.

[0089] S4, predicting the carbon emissions of different types of activities in the target scenic spot in the future time period through a scenic spot carbon emissions prediction model.

[0090] Figure 2 : is an exemplary structural diagram of the intelligent carbon emission prediction system based on the off-season and peak-season of the scenic spot described in the embodiment. Figure 2 As shown, an embodiment of the present invention provides an intelligent carbon emission prediction system based on the off-season and peak-season of a scenic spot, which specifically includes:

[0091] The carbon emission calculation module 210 is used to determine the accounting boundaries of carbon emissions of different activity types based on the carbon emission sources of the target scenic spot; based on the accounting boundaries of carbon emissions of different activity types, a scenic spot carbon emission accounting model is constructed to obtain the carbon emissions of different activity types of the target scenic spot in each month during the historical period.

[0092] In some embodiments, the carbon emissions of the target scenic spot are generated by different types of activities, including tourist activities and scenic spot operation activities; the accounting boundaries of carbon emissions in tourist activities include carbon emissions from food and accommodation, carbon emissions from transportation, and carbon emissions from entertainment; the accounting boundaries of carbon emissions in scenic spot operation activities include basic electricity carbon emissions and garbage carbon emissions.

[0093] Furthermore, the carbon emission calculation module 210 is used to construct a scenic spot carbon emission accounting model based on the accounting boundaries of carbon emissions of different activity types, so as to obtain the carbon emissions of different activity types of the target scenic spot in each month during the historical period, specifically including:

[0094] According to the carbon emission accounting boundary of each type of activity, a corresponding scenic spot carbon emission accounting model is constructed.

[0095]

[0096] Among them, C SS Carbon emissions from providing food and accommodation services to tourists; N SS The number of entities providing food and accommodation services; C SSiis the carbon emission of the i-th food host; M SS The amount of energy used for food production activities; E ssj is the consumption of type j energy per unit time by the i-th food host; θ j is the carbon emission factor of energy j; T is the time that the i-th food host uses energy j.

[0097] Specifically, when a host provides accommodation services to tourists, the energy types used may include electricity, natural gas, coal gas, photovoltaics, etc. The carbon emission factors corresponding to each type of energy can be obtained by statistical calculation based on international standards, national standards or carbon emission factor software. The carbon emission factors corresponding to each type of energy may correspond to different values ​​depending on the season and region.

[0098] The scenic area carbon emission accounting model for transportation carbon emissions is constructed as follows:

[0099]

[0100] Among them, C JT Transportation carbon emissions generated by providing transportation services to tourists; N JT is the number of types of transportation; E JTi is the energy consumption per unit distance of vehicle i; θ i is the carbon emission factor corresponding to vehicle i; D i The total distance traveled by vehicle i.

[0101] Specifically, the means of transportation provided by scenic spots for tourists may include sightseeing buses, ferries, trams and other types. The carbon emission factors corresponding to each type of transportation are different. For example, the carbon emission factor of a sightseeing bus is 0.32t / Km, and the carbon emission factor of a tram is 0.16t / Km.

[0102] The scenic spot carbon emission accounting model constructed for tourism carbon emissions is:

[0103] C YW =C HX +C SB ,

[0104]

[0105]

[0106] Among them, C YW The carbon emissions of tourists during their travels; C HX Carbon emissions generated by tourists breathing; N YK is the total number of tourists; is the average daily respiratory carbon emission factor per capita; C SBCarbon emissions generated by tourists using recreational equipment; N SB is the number of recreational equipment in the scenic area; E SBi is the energy consumption of gaming device i per unit time; θ i is the carbon emission factor corresponding to the gaming device i; T i Working hours for gaming device i.

[0107] The scenic area carbon emission accounting model based on electricity carbon emission is constructed as follows:

[0108] C JC =C GD +C WX ,

[0109] Among them, C JC Carbon emissions generated by powering and maintaining infrastructure for scenic spots; C GD Carbon emissions generated by powering the infrastructure of the scenic area; C WX Carbon emissions generated by infrastructure maintenance in scenic spots.

[0110] Specifically, the basic electricity of a scenic area may include the electricity consumed for supplying and maintaining the scenic area's infrastructure. The scenic area's infrastructure may include street lights, electricity for public construction lights, electricity for air conditioners, etc.

[0111] The carbon emission accounting model of the scenic area for garbage carbon emissions is constructed as follows:

[0112] C LJ =C TM +C RS ,

[0113] Among them, C LJ C is the carbon emissions generated by the scenic area in treating the garbage; YM Carbon emissions from landfill disposal; C RS Carbon emissions from burning waste.

[0114] Specifically, solid waste treatment mainly includes landfill and incineration. A large amount of greenhouse gases will be generated in the process of treating waste. Different treatment methods have different corresponding carbon emission factors. YM The carbon emission factor of landfill waste is obtained by multiplying the amount of landfill waste; the carbon emission generated by the waste treatment by burning is C RS It is obtained by multiplying the carbon emission factor of burning garbage by the amount of burning garbage.

[0115] The carbon emission prediction module 220 is used to establish a scenic spot carbon emission prediction model based on the carbon emissions of all activity types using the SARIMA model; through the scenic spot carbon emission prediction model, the carbon emissions of different activity types of the target scenic spot in the future time period are predicted.

[0116] In some embodiments, based on the activity data and the carbon emissions, establishing a scenic spot carbon emissions prediction model using a SARIMA model includes:

[0117] Preprocessing the carbon emissions based on the time sequence within the historical period to obtain a time-series historical carbon emissions time series data set;

[0118] Dividing the data set of the historical carbon emission time series into a training set, a validation set and a test set;

[0119] Stabilizing the historical carbon emission time series of the training set to obtain a data set of the historical carbon emission time series in a time series format;

[0120] Establish the SARIMA model SARIMA(p,d,q)(P,D,Q,m), use the time series historical carbon emission data to train the model SARIMA(p,d,q)(P,D,Q,m), and determine the model parameters d, p, q, D, P, Q, m; d is the number of non-seasonal differences, p is the number of non-seasonal autoregressive terms, q is the maximum lag order of the non-seasonal moving average operator, D is the number of seasonal differences, P is the number of seasonal autoregressive terms, Q is the maximum lag order of the seasonal moving average operator; m is the length of the forecast period;

[0121] After verification on the validation set, the scenic spot carbon emission prediction model was obtained.

[0122] An embodiment of the present invention further provides a computer-readable instruction, wherein when the instruction is executed in an electronic device, the program causes the electronic device to execute the operation steps included in the method of the present invention.

[0123] An embodiment of the present invention further provides a storage medium storing computer-readable instructions, wherein the computer-readable instructions enable an electronic device to execute the operation steps included in the method of the present invention.

[0124] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0126] Those of ordinary skill in the art will appreciate that the modules of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the constituent modules and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent carbon emission prediction method based on the off-season and peak-season of scenic spots, characterized in that: include: Determine the accounting boundaries of carbon emissions of different activity types based on the carbon emission sources of the target scenic spot; Based on the accounting boundaries of carbon emissions of different types of activities, a scenic spot carbon emissions accounting model is constructed to obtain the carbon emissions of different types of activities in the target scenic spot in each month during the historical period; Based on the carbon emissions of all types of activities, the SARIMA model is used to establish a carbon emissions prediction model for scenic spots; The carbon emissions of different types of activities in the target scenic spot in the future time period are predicted through the scenic spot carbon emissions prediction model.

2. The intelligent carbon emission prediction method based on the off-season and peak-season of scenic spots according to claim 1 is characterized in that: The sources of carbon emissions of the target scenic spot are generated by different types of activities, including tourist activities and scenic spot operation activities; the accounting boundaries of carbon emissions in tourist activities include carbon emissions from food and accommodation, carbon emissions from transportation, and carbon emissions from entertainment; the accounting boundaries of carbon emissions in scenic spot operation activities include basic electricity carbon emissions and garbage carbon emissions.

3. The intelligent carbon emission prediction method based on the off-season and peak-season of scenic spots according to claim 2 is characterized in that: The steps of constructing a scenic spot carbon emission accounting model based on the accounting boundaries of carbon emissions of different activity types include: According to the carbon emission accounting boundary of each type of activity, a corresponding scenic spot carbon emission accounting model is constructed, including: The scenic area carbon emission accounting model for food and accommodation carbon emissions is constructed as follows: Among them, C SS Carbon emissions from providing food and accommodation services to tourists; N SS The number of entities providing food and accommodation services; C SSi is the carbon emission of the i-th food host; M SS The amount of energy used for food production activities; E ssj is the consumption of type j energy per unit time by the i-th food host; θ j is the carbon emission factor of energy j; T is the time for the i-th food host to use energy j; The scenic area carbon emission accounting model constructed for the traffic carbon emission is: Among them, C JT Transportation carbon emissions generated by providing transportation services to tourists; N JT is the number of types of transportation; E JTi is the energy consumption per unit distance of vehicle i; θ i is the carbon emission factor corresponding to vehicle i; D i The total distance travelled by vehicle i; The scenic spot carbon emission accounting model constructed for the tourism carbon emission is: C YW =C HX +C SB , Among them, C YW The carbon emissions of tourists during their travels; C HX Carbon emissions generated by tourists breathing; N YK is the total number of tourists; is the average daily respiratory carbon emission factor per capita; C SB Carbon emissions generated by tourists using recreational equipment; N SB is the number of recreational equipment in the scenic area; E SBi is the energy consumption of gaming device i per unit time; θ i is the carbon emission factor corresponding to the gaming device i; T i Working hours for gaming equipment i; The scenic area carbon emission accounting model constructed for the basic electricity carbon emission is: C JC =C GD +C WX , Among them, C JC Carbon emissions generated by powering and maintaining infrastructure for scenic spots; C GD Carbon emissions generated by powering the infrastructure of the scenic area; C WX Carbon emissions generated by infrastructure maintenance for scenic spots; The carbon emission accounting model of the scenic area for garbage carbon emissions is constructed as follows: C LJ =C TM +C RS , Among them, C LJ C is the carbon emissions generated by the scenic area in treating the garbage; TM Carbon emissions from landfill disposal; C RS Carbon emissions from burning waste.

4. The intelligent carbon emission prediction method based on the off-season and peak-season of scenic spots according to claim 1 is characterized in that: Based on the activity data and the carbon emissions, the SARIMA model is used to establish a scenic spot carbon emissions prediction model, including: Preprocessing the carbon emissions based on the time sequence within the historical period to obtain a time-series historical carbon emissions time series data set; Dividing the data set of the historical carbon emission time series into a training set, a validation set and a test set; Stabilizing the historical carbon emission time series of the training set to obtain time-series historical carbon emission data; Establish the SARIMA model SARIMA(p,d,q)(P,D,Q,m), use the time series historical carbon emission data to train the model SARIMA(p,d,q)(P,D,Q,m), and determine the model parameters d, p, q, D, P, Q, m; where d is the number of non-seasonal differences, p is the number of non-seasonal autoregressive terms, q is the maximum lag order of the non-seasonal moving average operator, D is the number of seasonal differences, P is the number of seasonal autoregressive terms, Q is the maximum lag order of the seasonal moving average operator, and m is the length of the forecast period; After verification on the validation set, the scenic spot carbon emission prediction model was obtained.

5. An intelligent carbon emission prediction system based on the off-season and peak-season of a scenic spot, used to execute the method described in claims 1-4, characterized in that: include: The carbon emission calculation module is used to determine the accounting boundaries of carbon emissions of different activity types based on the carbon emission sources of the target scenic spot; Based on the accounting boundaries of carbon emissions of different types of activities, a scenic spot carbon emissions accounting model is constructed to obtain the carbon emissions of different types of activities in the target scenic spot in each month during the historical period; The carbon emission prediction module is used to establish a scenic spot carbon emission prediction model based on the carbon emissions using the SARIMA model; through the scenic spot carbon emission prediction model, the carbon emissions of different activity types in the target scenic spot in the future time period are predicted.

6. A computer-readable storage medium comprising computer-readable instructions, characterized in that: When the computer readable instructions are executed, the processor is caused to perform the operations in any one of the methods of claims 1-4.

7. An electronic device, characterized in that: The device comprises: Memory, which stores program instructions; A processor is connected to the memory and executes program instructions in the memory to implement the steps in any one of the methods of claims 1-4.

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