Metering excitation method, system and equipment for cross-domain tourism carbon footprint and medium

Abstract

The invention discloses a cross-domain tourism carbon footprint measurement excitation method, system and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: initializing a global carbon footprint model through a federal learning platform, and distributing model parameters to a plurality of data owners; training the global carbon footprint model by each data holder, generating update parameters, and uploading the update parameters to a federal learning platform; the federal learning platform aggregates the updated parameters and generates an updated global carbon footprint model; in response to a low-carbon behavior of the user, inputting behavior data of the low-carbon behavior to the global carbon footprint model, and calculating to obtain a carbon footprint saving amount; and based on the carbon footprint saving amount, issuing a corresponding number of carbon points to the user account through the block chain contract. Through adoption of the scheme, cooperative training of the cross-domain travel carbon footprint model is realized, the data island problem is effectively solved, the calculation precision of the carbon footprint saving amount is improved, and the enthusiasm of the user in participating in the low-carbon travel and the cooperative willingness of each data holder are remarkably improved in combination with the block chain contract.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, equipment, and medium for measuring and incentivizing the carbon footprint of cross-regional tourism. Background Technology

[0002] In the current field of tourism carbon footprint measurement and incentives, relevant parties need to calculate the total amount of greenhouse gas emissions directly or indirectly generated by tourists throughout their travels, and guide users to travel in a low-carbon manner through appropriate mechanisms.

[0003] However, current tourism carbon footprint measurement methods only use publicly available average emission factors to calculate the total carbon footprint of tourists during their trips, without considering specific data such as aircraft type, cabin class, load factor, or actual hotel energy consumption. This results in low measurement accuracy, failing to reflect the detailed differences in real-world scenarios and making it difficult to provide a reliable basis for individual environmental decisions. In the data management stage, airlines, railways, hotels, and scenic spots independently store and manage their own energy consumption data, which only circulates internally and is not exchanged with other related companies or third-party platforms, affecting the comprehensiveness and accuracy of carbon footprint calculation. In the incentive stage, only symbolic honors are awarded for relevant user behaviors, lacking effective and timely feedback incentive mechanisms. Existing incentives are not linked to users' real economic benefits, resulting in weak motivation for changing user behavior and low user participation. Summary of the Invention

[0004] This application provides a method, system, equipment, and medium for measuring and incentivizing cross-regional tourism carbon footprint, which addresses the problems of low accuracy and weak incentives in existing tourism carbon footprint measurement and incentive methods.

[0005] Firstly, this application provides a method for measuring and incentivizing the carbon footprint of cross-regional tourism, the method comprising: A global carbon footprint model is initialized through a federated learning platform, and model parameters are distributed to multiple data holders. Each of the data holders uses the data locally to train the global carbon footprint model, generates updated parameters for the local model, and uploads the updated parameters to the federated learning platform. The federated learning platform aggregates the updated parameters to generate an updated global carbon footprint model. In response to the user's low-carbon behavior, the behavioral data of the low-carbon behavior is input into the global carbon footprint model to calculate the carbon footprint savings. Based on the carbon footprint savings, a corresponding number of carbon credits are issued to user accounts via a blockchain contract, and these carbon credits are used to redeem benefits.

[0006] By adopting the above technical solution and leveraging the characteristics of federated learning, collaborative training of cross-regional tourism carbon footprint models can be achieved without acquiring the core business data of each data holder, effectively solving the data silo problem. At the same time, the accuracy of carbon footprint savings calculation is improved by using the optimized global carbon footprint model. Combined with the decentralized and tamper-proof characteristics of blockchain contracts, carbon footprint savings are directly linked to carbon credit issuance, significantly enhancing users' enthusiasm for participating in low-carbon tourism and the willingness of each data holder to collaborate.

[0007] In one specific implementation, the aggregation formula for the updated parameters includes: Among them, W global These are the updated parameters after aggregation, where N is the number of data holders, and W... k It is the update parameter for the kth data holder, n k It represents the amount of data held by the kth data holder, and n represents the total amount of data.

[0008] By adopting the above technical solutions, it is ensured that the aggregated global model parameters can fully integrate the scenario-based data characteristics of multiple data holders, further improving the measurement accuracy and adaptability of the global carbon footprint model in cross-domain scenarios.

[0009] In one specific implementation, the cropping process for adaptive processing of multiple images, before the input behavioral data is fed into the global carbon footprint model, further includes: The digital signature of the trusted behavior credential is verified by the federated learning platform or blockchain contract. The behavior data of the low-carbon behavior is generated by the data holder who provides the service, and is accompanied by a digital signature based on the data holder's private key to form the trusted behavior credential.

[0010] By adopting the above technical solutions, it is ensured that the behavioral data comes from the legitimate data holder and has not been tampered with, thus avoiding the distortion of carbon footprint savings calculation due to data authenticity issues. This provides a prerequisite guarantee for the reliability of subsequent carbon footprint measurement results and the fairness of carbon credit issuance.

[0011] In one specific implementation scheme, the method for calculating the carbon footprint savings includes: Extract one or more feature parameters from the verified behavioral data, the feature parameters including at least one of behavior type, service provider attribute, resource consumption baseline value and actual value; The feature parameters are input into the updated global carbon footprint model; Obtain the predicted carbon footprint savings output by the global carbon footprint model.

[0012] By adopting the above technical solution, multi-dimensional feature parameters are extracted from the verified behavioral data. Compared with the measurement method that relies on only a single emission factor, it can more comprehensively reflect the actual carbon emission influencing factors of low-carbon behavior, making the information input into the global carbon footprint model richer, and thus making the predicted carbon footprint savings output by the model more consistent with the actual emission reduction situation.

[0013] In one specific implementation scheme, the formula for calculating the carbon integral includes: I = ΔC × α; Where I is the number of carbon credits to be issued, ΔC is the amount of carbon footprint savings, and α is the preset exchange rate.

[0014] By adopting the above technical solution, a clear quantitative relationship is established between carbon footprint savings and carbon credits, ensuring that different users receive consistent carbon credit rewards for the same amount of carbon footprint savings, avoiding problems such as ambiguous incentive standards or subjective adjustments; at the same time, the calculation logic of the formula is simple and clear, and can be automatically executed by blockchain contracts without human intervention, improving the efficiency of carbon credit issuance and ensuring the transparency of the incentive process.

[0015] In one specific implementation, the method further includes: Construct an input-output table, which includes at least regional and industry dimensions, and carbon footprint coefficients for each dimension; determine the regional and industry dimensions of the behavioral data, and retrieve the corresponding carbon footprint coefficients from the input-output table; calculate the carbon footprint savings of the behavioral data based on the carbon footprint coefficients. The carbon footprint coefficient in the input-output table is updated based on the statistical data of each of the data holders.

[0016] By adopting the above technical solutions, an input-output table covering multiple regions and industries is constructed, providing exclusive carbon footprint coefficients for different regions and different tourism industries, solving the measurement deviation problem caused by regional carbon emission differences and industry characteristics; at the same time, the carbon footprint coefficients are updated regularly based on the statistical data of each data holder to ensure that the coefficients can dynamically adapt to changes in actual carbon emission levels, further improving the accuracy and timeliness of carbon footprint savings calculation, and providing a more accurate measurement basis for carbon credit issuance.

[0017] In one specific implementation scheme, the method for converting carbon credits includes: In response to a user's redemption request, verify the user's carbon credit balance; When the carbon credit balance is sufficient, the credits are deducted through the blockchain contract, and a unique rights redemption certificate is generated and recorded on the blockchain. The partner system provides users with corresponding products or services by querying the aforementioned rights redemption certificates.

[0018] By adopting the above technical solutions, the immutability of blockchain is used to prevent the forgery or reuse of credentials, ensuring the security of points deduction and rights redemption; at the same time, the redemption process is simplified, the efficiency of rights redemption is improved, and the recognition of the incentive system by users and partners is enhanced.

[0019] A second aspect of this application provides a measurement and incentive system for the carbon footprint of cross-regional tourism, the system comprising: The federated learning platform initializes the global carbon footprint model, distributes model parameters to multiple data holders, receives local model update parameters uploaded by each data holder, and aggregates these update parameters to generate an updated global carbon footprint model. The carbon footprint measurement module receives user low-carbon behavior data generated by data holders, verifies the legality of behavior data accompanied by digital signatures from the data holders' private keys, extracts feature parameters from verified behavior data, inputs them into the updated global carbon footprint model, and calculates carbon footprint savings by combining carbon footprint coefficients from multi-regional and multi-industry input-output tables. The blockchain incentive module, based on the carbon footprint savings output by the carbon footprint measurement module, issues corresponding carbon credits to user accounts through blockchain contracts at a preset exchange rate. It receives carbon credit exchange requests initiated by users, verifies the user account credit balance, and, when the balance is sufficient, completes the credit deduction through blockchain contracts and generates a unique rights exchange certificate stored in the blockchain distributed ledger.

[0020] A third aspect of this application provides an electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the above-described method steps.

[0021] A fourth aspect of this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a method for measuring and incentivizing the carbon footprint of cross-regional tourism, as provided in an embodiment of this application. Figure 2 This is another schematic diagram of a method for measuring and incentivizing the carbon footprint of cross-regional tourism provided in the embodiments of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] Please refer to Figure 1 This paper presents a flowchart and timing diagram of a method for measuring and incentivizing the carbon footprint of cross-regional tourism. This method can be implemented using a computer program, a microcontroller, or run on a cross-regional tourism carbon footprint measurement and incentive system. The computer program can be integrated into a computer device or run as a standalone application. Specifically, the method includes steps S100 to S500, as follows: S100. Initialize the global carbon footprint model through the federated learning platform and distribute the model parameters to multiple data holders. In this embodiment, the federated learning platform is a computer processing system with model initialization, parameter distribution, and parameter aggregation functions, which can be deployed on a cloud server or a distributed server cluster. The global carbon footprint model is a machine learning model used to predict the carbon footprint savings of users' cross-regional tourism low-carbon behavior. The input is the feature parameters of users' low-carbon behavior, and the output is the carbon footprint savings. The model type can be a deep learning model or a gradient boosting decision tree model, etc. Data holders are the entities that hold user behavior data and energy consumption data in the tourism service chain, and may include enterprises or institutions that provide tourism-related services, such as airlines, hotel operators, scenic spot managers, and railway operating companies.

[0027] In some embodiments, the federated learning platform determines the model infrastructure based on the sub-sectors of the tourism scenario. For example, the model for the transportation scenario includes a feature input layer representing flight information and travel distance, while the model for the accommodation scenario includes a feature input layer representing hotel class and number of nights, ensuring that the model structure adapts to the measurement needs of different scenarios.

[0028] Furthermore, the federated learning platform references publicly available carbon emission factor data from the tourism industry to assign initial values ​​to the parameters of each layer of the model, enabling the initial model to possess basic econometric capabilities. The federated learning platform establishes encrypted communication connections with multiple data holders through a transport layer security protocol to ensure the security of parameter transmission, and then sends the initialized global carbon footprint model parameters to each data holder, providing a unified model foundation for local training by each data holder.

[0029] S200: Each data holder uses the data locally to train the global carbon footprint model, generates updated parameters for the local model, and uploads the updated parameters to the federated learning platform. In some embodiments, the data holder filters valid data from a local database that matches the features of the model input. For example, the valid data filtered by an airline includes flight number, aircraft type, flight distance, actual fuel consumption, etc., where actual fuel consumption is used as label data; the valid data filtered by a hotel operator includes hotel rating, number of nights stayed, water and electricity consumption, etc., where water and electricity consumption is used as label data.

[0030] Furthermore, the data holder preprocesses the valid data, including: removing data with missing values ​​exceeding a preset proportion; and converting feature data of different magnitudes to the same numerical range to ensure that the data is suitable for model training requirements.

[0031] In some embodiments, the data holder may employ a gradient descent optimization algorithm to train an initial model using preprocessed local data, and obtain the minimum loss function between the predicted and actual values ​​by adjusting the model parameters. After a preset number of training rounds, the data holder obtains the updated parameters of the local model and uploads them to the federated learning platform via a previously established encrypted communication connection. During upload, the updated parameters are appended with a digital signature generated based on the data holder's private key, which is used by the federated learning platform to verify the legality and integrity of the parameter's origin.

[0032] S300 and the federated learning platform aggregate and update parameters to generate an updated global carbon footprint model; In some embodiments, the federated learning platform invokes the signature verification module to decrypt the digital signature using the public key of the corresponding data holder. If the decrypted parameter digest matches the digest calculated from the received parameter, the parameter is deemed valid; otherwise, the parameter is deemed invalid, and the data holder is required to re-upload it.

[0033] After verifying the validity of the parameters, the federated learning platform determines the weight of each updated parameter based on the amount of local training data held by each data holder. The larger the data holder's volume, the greater the weight of their updated parameters, ensuring that the aggregated model better reflects the characteristics of data with sufficient and representative data. Through this aggregation operation, the federated learning platform obtains new global carbon footprint model parameters. These parameters replace the initial model parameters, generating an updated global carbon footprint model with higher carbon footprint measurement accuracy, better adaptable to the tourism scenarios of different data holders.

[0034] Based on the above embodiments, as another optional embodiment, the aggregation formula for updating parameters includes: Among them, W global These are the update parameters after aggregation, where N is the number of data holders and W is the number of data holders. k It is the update parameter for the kth data holder, n k It represents the amount of data held by the kth data holder, and n represents the total amount of data.

[0035] In some embodiments, the federated learning platform performs aggregation calculations to obtain W. global The aggregated parameters are used as new global carbon footprint model parameters to replace the original parameters and generate an updated global carbon footprint model.

[0036] S400: In response to the user's low-carbon behavior, input the behavior data of low-carbon behavior into the global carbon footprint model, and calculate the carbon footprint savings. Low-carbon behavior in this application embodiment refers to the actions taken by users to reduce greenhouse gas emissions during their travels, including choosing low-carbon flights, choosing public transportation, and reducing the generation of disposable waste in scenic spots; carbon footprint savings are the amount of greenhouse gas emissions reduced by users after implementing low-carbon behavior compared to conventional behavior.

[0037] In some embodiments, the presence of a user engaging in low-carbon behavior can be detected through a travel platform client or a data holder's service system. Once low-carbon behavior is detected, the data holder collects behavioral data related to that behavior. For example, airlines collect data such as flight number, aircraft type, and flight distance; hotels collect data such as the number of nights stayed, room number, and whether low-carbon services were selected.

[0038] After data collection is complete, the data holder transmits the behavioral data to the tourism platform server. The tourism platform server performs preliminary verification of the data, including checking its completeness and whether the data format meets the model's input requirements. If the verification passes, the tourism platform server inputs the behavioral data into the updated global carbon footprint model; if the verification fails, the data holder is required to retransmit the complete data. After receiving the behavioral data, the global carbon footprint model performs feature extraction and computational processing, predicts and calculates the carbon footprint savings, and outputs the predicted savings. The tourism platform server receives and stores these savings.

[0039] S401. Based on the above embodiments, as another optional embodiment, before inputting behavioral data into the global carbon footprint model, the following steps are also included: The digital signature of the trusted behavior credential is verified by a federated learning platform or blockchain contract; The behavioral data for low-carbon behaviors is generated by the data holder providing the service and is accompanied by a digital signature based on the data holder's private key to form a trusted behavioral credential.

[0040] In this application embodiment, the trusted behavior credential is a data carrier containing user low-carbon behavior data and the digital signature of the data holder, which can prove the legality and integrity of the source of the behavior data and ensure that the data has not been tampered with; the private key is the secret key used by the data holder to generate the digital signature, which is unique and confidential and is held only by the corresponding data holder; the blockchain contract is automated execution code deployed in the blockchain network, which can complete data verification, points issuance and other operations according to preset rules, and has the characteristics of decentralization, immutability and automatic execution.

[0041] In some embodiments, the data holder providing the service generates behavioral data on users' low-carbon behaviors. After generating the behavioral data, the data holder digitally signs the behavioral data using its own private key, including: hashing the behavioral data to obtain a hash value, and encrypting the hash value using the private key to obtain a digital signature. The data holder combines the behavioral data, the digital signature, and its own identity identifier to form a trusted behavioral credential. The data holder transmits the trusted behavioral credential to a federated learning platform or blockchain contract for digital signature verification.

[0042] If the trusted behavioral credential is verified by the federated learning platform, the platform obtains the corresponding public key based on the data holder's identity identifier in the credential, decrypts the digital signature using the public key to obtain the decryption hash value, and simultaneously re-hashes the behavioral data in the credential to obtain a new hash value. If the two hash values ​​match, the verification passes, and the behavioral data can be input into the global carbon footprint model; if they do not match, the verification fails, the federated learning platform refuses to accept the credential, and notifies the data holder.

[0043] If the trusted behavioral credentials are verified by a blockchain contract, the blockchain contract queries the public key pre-stored by the data holder from the blockchain distributed ledger, uses the public key to decrypt the digital signature to obtain the decryption hash value, and simultaneously performs a hash calculation on the behavioral data to obtain a new hash value. If the comparison matches, the verification is successful, and the behavioral data can be input into the global carbon footprint model; otherwise, the verification fails.

[0044] S402. Based on the above embodiments, as another optional embodiment, the method for calculating carbon footprint savings includes: Extract one or more feature parameters from verified behavioral data. The feature parameters include at least one of the following: behavior type, service provider attribute, baseline value of resource consumption and actual value. Input the feature parameters into the updated global carbon footprint model; Obtain the predicted carbon footprint savings output by the global carbon footprint model.

[0045] In this embodiment, the feature parameters are key data extracted from behavioral data and used as input to the global carbon footprint model. They reflect the core characteristics of users' low-carbon behavior and affect the model's calculation results. The service provider attributes are the relevant attribute information of the data holder providing tourism services, reflecting the service type and low-carbon level of the service provider. The resource consumption benchmark value is the standard value of resource consumption of tourism services when users perform regular behaviors, determined based on industry standards or historical statistical data of the data holder. The actual resource consumption value is the actual resource consumption of tourism services when users perform low-carbon behaviors, collected by the data holder.

[0046] In some embodiments, the feature extraction module of the tourism platform server or the global carbon footprint model extracts feature parameters from the behavioral data verified by S401. The extracted feature parameters include at least one of the following: behavior type, service provider attribute, resource consumption baseline value and actual value.

[0047] After extracting feature parameters, these parameters are input into the updated global carbon footprint model. The global carbon footprint model processes the feature parameters, with the hidden layer performing weight allocation and data transformation according to the trained parameters, and the output layer predicting and calculating carbon footprint savings. The tourism platform server obtains the predicted carbon footprint savings output by the model and stores it in association with the user's identity information.

[0048] S403. Based on the above embodiments, as another optional embodiment, it includes: Construct an input-output table, which should include at least regional and industry dimensions, as well as the carbon footprint coefficient for each dimension; Determine the regional and industry dimensions of the behavioral data, and retrieve the corresponding carbon footprint coefficients from the input-output table; Calculate the carbon footprint savings based on the carbon footprint coefficient; The carbon footprint coefficient in the input-output table is updated based on the statistical data of each data holder.

[0049] In this embodiment of the application, the input-output table is a data table recording the correspondence between tourism services and carbon emissions in different regions and industries, providing a standardized coefficient reference for calculating carbon footprint savings. The regional dimension is a classification dimension in the input-output table divided by geographical region, reflecting the differences in carbon emission characteristics of tourism services in different regions. The industry dimension is a classification dimension in the input-output table divided by tourism service industry type, reflecting the differences in carbon emission levels of different tourism industries. The carbon footprint coefficient is a coefficient recorded in the input-output table used to calculate carbon emissions, representing the amount of carbon emissions generated per unit of tourism service volume.

[0050] In some embodiments, an input-output table is constructed by a federated learning platform or a tourism platform server based on data provided by regional tourism authorities, data holders, and third-party environmental organizations. The regional dimension of the input-output table can be divided by provincial administrative regions or geographic economic regions, and the industry dimension can be divided by sub-sectors of tourism, such as the air transportation industry, the hotel accommodation industry, and the scenic spot tour industry.

[0051] In some embodiments, the carbon footprint coefficient is calculated by dividing the total historical carbon emissions of the target industry in the target region by the total service volume.

[0052] After constructing the input-output table, the tourism platform server analyzes the regional information in the behavioral data to determine the regional dimension of the behavioral data, and analyzes the service type in the behavioral data to determine the industry dimension. The tourism platform server retrieves the carbon footprint coefficient from the input-output table, multiplies it by the service volume data in the behavioral data, and obtains the carbon footprint savings.

[0053] S500 issues a corresponding number of carbon credits to user accounts through blockchain contracts based on carbon footprint savings. These carbon credits can be used to redeem benefits.

[0054] In this embodiment of the application, carbon credits are virtual reward vouchers issued based on the carbon footprint savings of a user's low-carbon behavior, which can be used to redeem tourism-related products or services.

[0055] In some embodiments, the travel platform server obtains the user's carbon footprint savings and sends a carbon credit issuance request to the blockchain contract. The request includes the user's account identifier and the carbon footprint savings data of this low-carbon behavior.

[0056] After receiving an issuance request, the blockchain contract calculates the number of carbon credits to be issued according to preset rules. These rules are based on the exchange relationship between carbon footprint savings and carbon credits, ensuring that the number of credits matches the user's contribution to low-carbon behavior. Once the number of credits is calculated, the blockchain contract executes the issuance operation, adding the credits to the user's carbon credit balance and generating a carbon credit issuance record containing the user's account identifier, the number of credits issued, the carbon footprint savings, and the issuance time. This record is stored in the blockchain distributed ledger, leveraging the ledger's immutability and traceability to ensure transparency in the issuance process.

[0057] After issuance, the blockchain contract sends a successful issuance notification to the travel platform server. The travel platform server then sends a carbon credits arrival notification to the user via the travel platform client. The notification includes the number of credits received and the corresponding carbon footprint savings, ensuring the user is promptly informed of the reward. The carbon credits in the user's account can be used to redeem benefits later. Users can view the benefit list and initiate redemption requests through the travel platform client.

[0058] Based on the above embodiments, as another optional embodiment, the formula for calculating the carbon integral includes: I = ΔC × α; Where I is the number of carbon credits to be issued, ΔC is the carbon footprint savings, and α is the preset exchange rate.

[0059] In this embodiment, the exchange rate α is a preset proportional coefficient used to calculate the number of carbon credits, representing the number of carbon credits corresponding to a unit of carbon footprint savings.

[0060] In some embodiments, the exchange rate α is preset by the tourism platform or blockchain contract manager. When preset, factors such as the carbon credit value standard in the tourism industry, the equity cost of partners, and user incentive needs are taken into account to ensure that α can reflect the value of users' low-carbon behavior and ensure the feasibility of redeeming credits.

[0061] S501. Based on the above embodiments, as another optional embodiment, the carbon credit redemption method includes: in response to a user's redemption request, verifying the user's carbon credit balance; When the carbon credit balance is sufficient, the credits are deducted through a blockchain contract, and a unique redemption certificate is generated and recorded on the blockchain. The partner system provides users with corresponding products or services by querying the rights redemption certificate.

[0062] In some embodiments, users browse a list of benefits through the travel platform's client application. This list, obtained by the travel platform's server from partner systems, includes information such as benefit name, product or service content, required points, and validity period. After selecting a target benefit, the user initiates a redemption request through the client application. The request includes the user's account identifier, the target benefit identifier, and the required points.

[0063] The travel platform client transmits the redemption request to the travel platform server, which then forwards the request to the blockchain contract. The blockchain contract verifies the user's carbon credit balance. Based on the user's account identifier in the request, the blockchain contract queries the account's credit balance and compares the balance with the required number of credits. If the balance is greater than or equal to the required credits, it is determined that the balance is sufficient; if the balance is less than the required credits, it is determined that the balance is insufficient. The blockchain contract sends a notification of insufficient balance, and the server sends a redemption failure alert to the user through the client, informing them of the current balance and the required credits.

[0064] When the balance is sufficient, the blockchain contract executes the points deduction operation, deducting the required points from the user's account balance and updating the balance. After the deduction is completed, the blockchain contract generates a unique rights redemption voucher. The voucher contains information such as the user account identifier, the target rights identifier, the voucher's unique code, the number of points deducted, the redemption time, and the validity period. The contract stores the voucher in the blockchain distributed ledger to ensure immutability and traceability.

[0065] Furthermore, upon successful redemption via the blockchain contract, the server displays the voucher information to the user through the client and simultaneously sends the voucher query address to the partner system. The partner system accesses the blockchain ledger via the query address to retrieve the corresponding rights redemption voucher and verify its authenticity and validity.

[0066] Once verified, the partner system provides services to the user according to the agreed-upon benefits; for example, airlines may offer free upgrades, and scenic spots may offer free tickets. When enjoying the service, the user can present a unique voucher code displayed on the client. The partner system verifies the code again, and provides the service only after confirming its accuracy. After the service is completed, the partner system sends a service completion record to the travel platform's server, which stores the record for subsequent settlement with the partner.

[0067] refer to Figure 2 In some implementations, users book a trip from Beijing to Shanghai on a travel app. On the flight list page, each flight option displays not only the price and time, but also a new "Carbon Footprint" label and "Estimated Points" label. For example: XX Airlines direct flight | ¥1500 | 08:00-10:30 | Carbon footprint: 120kg | Points reward +80; YY Airlines connecting flight | ¥1400 | 08:00-14:00 | Carbon footprint: 180kg | Points reward +30. This intuitive comparison guides users to choose the direct flight with the lower carbon footprint for higher points rewards and environmental contribution.

[0068] In some embodiments, a user checks into a hotel participating in a green hotel program and selects the low-carbon option of not changing the sheets. The hotel's front desk system or mini-program will have a confirmation interface allowing the user to check the box indicating their participation in the low-carbon behavior. In the background, the hotel's energy consumption monitoring system will record the actual water and electricity savings for this stay.

[0069] In some embodiments, after a user completes a flight or hotel check-in, the user's app will push a message: "Congratulations! Your low-carbon behavior has saved you 5.2kg of carbon footprint, and +52 carbon credits have been credited to your account. Thank you for your contribution to the planet!"

[0070] In some embodiments, a carbon footprint wallet is set up on the travel app, including a carbon footprint dashboard. The carbon footprint dashboard displays the user's historical carbon footprint changes in a line graph or bar chart and compares them with the industry average, giving the user a sense of accomplishment.

[0071] In some embodiments, a points detail is set up on the travel app, clearly listing the source, time, and corresponding carbon savings for each point, ensuring the transparency and credibility of the system.

[0072] In some embodiments, a points mall is set up on the travel app, which offers a variety of immediate benefits for users to redeem. For example, 100 points can be redeemed for a ¥10 taxi voucher; 500 points can be redeemed for one airport VIP lounge experience; and 1000 points can be redeemed for one ticket to a popular attraction.

[0073] Based on the above embodiments, as another optional embodiment, this application also provides a measurement and incentive system for cross-regional tourism carbon footprint, the system comprising: The federated learning platform initializes the global carbon footprint model, distributes model parameters to multiple data holders, receives local model update parameters uploaded by each data holder, and aggregates these update parameters to generate an updated global carbon footprint model. The carbon footprint measurement module receives user low-carbon behavior data generated by data holders, verifies the legality of behavior data accompanied by digital signatures from the data holders' private keys, extracts feature parameters from verified behavior data, inputs them into the updated global carbon footprint model, and calculates carbon footprint savings by combining carbon footprint coefficients from multi-regional and multi-industry input-output tables. The blockchain incentive module, based on the carbon footprint savings output by the carbon footprint measurement module, issues corresponding carbon credits to user accounts through blockchain contracts at a preset exchange rate. It receives carbon credit exchange requests initiated by users, verifies the user account credit balance, and, when the balance is sufficient, completes the credit deduction through blockchain contracts and generates a unique rights exchange certificate stored in the blockchain distributed ledger.

[0074] In some embodiments, the system further includes: The behavioral data collection module is used to connect with the service systems and IoT devices of various data holders. It collects flight and aircraft type data and actual fuel consumption data from airline ticketing systems, room water and electricity consumption data from hotel energy management systems, and user travel and transportation selection data from scenic area service systems. The collected raw data is organized in a unified format and then transmitted to the carbon footprint measurement module. This module ensures the comprehensiveness and timeliness of low-carbon behavior data through multi-source data connection. The user interaction module is used to display carbon footprint measurement results and carbon credit information to users, including presenting the user's historical carbon footprint change trend in the form of a line graph, displaying the credit details and redeemable rights list in the form of a list, receiving carbon credit redemption requests initiated by users and forwarding them to the blockchain incentive module, receiving user requests to share low-carbon behavior achievements through social platforms and generating shareable behavior achievement cards. This module enhances user participation through intuitive information display and convenient operation access. The input-output table management module is used to jointly build multi-regional and multi-industry input-output tables with regional tourism authorities and third-party environmental protection agencies. It receives statistical data reported by data holders annually, updates the carbon footprint coefficients of each region and industry in the input-output table based on the statistical data, and responds to the call requests of the carbon footprint measurement module, providing it with the corresponding carbon footprint coefficients for the corresponding region and industry. This module ensures that the carbon footprint coefficients are consistent with the actual carbon emission levels through regular updates. The data holder collaboration module manages the registration information and permissions of each data holder, assigns a unique identity to each data holder and stores it in the blockchain distributed ledger, receives cross-domain collaboration verification requests initiated by data holders, associates the behavioral data of multiple data holders under the same user and the same trip through the user's anonymous unique identifier and trip association identifier, and transmits the associated behavioral data package to the carbon footprint measurement module to support cross-domain carbon footprint cumulative calculation. This module ensures the collaboration efficiency of multiple data holders through identity management and data association.

[0075] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0076] Based on the above embodiments, as another optional embodiment, the present application embodiment may further include a computer storage medium, which may store multiple instructions adapted for loading by a processor and executing a method of the above embodiments. For the specific execution process, please refer to the detailed description of the above embodiments, which will not be repeated here.

[0077] Based on the above embodiments, as another optional embodiment, this application embodiment may further include an electronic device. The electronic device may include: at least one processor, at least one communication bus, a user interface, at least one network interface, and a memory.

[0078] The communication bus is used to enable communication between these components.

[0079] The user interface may include a display screen and a camera. Optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0080] The network interface may include standard wired interfaces and wireless interfaces (such as Wi-Fi interfaces).

[0081] The processor may include one or more processing cores. It connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various server functions and process data. Optionally, the processor may be implemented using at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0082] The memory may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. As a computer storage medium, the memory may include an operating system, a network communication module, a user interface module, and an application program of one method.

[0083] In electronic devices, the user interface is primarily used to provide an input interface for users and to acquire user input data; while the processor can be used to call an application program stored in memory that represents a method. When executed by one or more processors, this causes the electronic device to perform one or more methods as described in the above embodiments. It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps can be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0084] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0085] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0089] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0090] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for measuring and incentivizing the carbon footprint of cross-regional tourism, characterized in that, The method includes: A global carbon footprint model is initialized through a federated learning platform, and model parameters are distributed to multiple data holders. Each of the data holders uses the data locally to train the global carbon footprint model, generates updated parameters for the local model, and uploads the updated parameters to the federated learning platform. The federated learning platform aggregates the updated parameters to generate an updated global carbon footprint model. In response to the user's low-carbon behavior, the behavioral data of the low-carbon behavior is input into the global carbon footprint model to calculate the carbon footprint savings. Based on the carbon footprint savings, a corresponding number of carbon credits are issued to user accounts via a blockchain contract, and these carbon credits are used to redeem benefits.

2. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 1, characterized in that, The aggregation formula for the updated parameters includes: Among them, W global These are the updated parameters after aggregation, where N is the number of data holders, and W... k It is the update parameter for the kth data holder, n k It represents the amount of data held by the kth data holder, and n represents the total amount of data.

3. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 1, characterized in that, Before the input behavioral data is fed into the global carbon footprint model, the following is also included: The digital signature of the trusted behavior credential is verified by the federated learning platform or blockchain contract. The behavior data of the low-carbon behavior is generated by the data holder who provides the service, and is accompanied by a digital signature based on the data holder's private key to form the trusted behavior credential.

4. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 1, characterized in that, The method for calculating the carbon footprint savings includes: Extract one or more feature parameters from the verified behavioral data, the feature parameters including at least one of behavior type, service provider attribute, resource consumption baseline value and actual value; The feature parameters are input into the updated global carbon footprint model; Obtain the predicted carbon footprint savings output by the global carbon footprint model.

5. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 4, characterized in that, The formula for calculating the carbon integral includes: I = ΔC × α; Where I is the number of carbon credits to be issued, ΔC is the amount of carbon footprint savings, and α is the preset exchange rate.

6. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 5, characterized in that, include: Construct an input-output table, which includes at least regional and industry dimensions, and carbon footprint coefficients for each dimension; Determine the regional and industry dimensions of the behavioral data, and retrieve the corresponding carbon footprint coefficients from the input-output table; calculate the carbon footprint savings of the behavioral data based on the carbon footprint coefficients. The carbon footprint coefficient in the input-output table is updated based on the statistical data of each of the data holders.

7. The method for measuring and incentivizing the carbon footprint of cross-regional tourism according to claim 6, characterized in that, The methods for redeeming carbon credits include: In response to a user's redemption request, verify the user's carbon credit balance; When the carbon credit balance is sufficient, the credits are deducted through the blockchain contract, and a unique rights redemption certificate is generated and recorded on the blockchain. The partner system provides users with corresponding products or services by querying the aforementioned rights redemption certificates.

8. A measurement and incentive system for the carbon footprint of cross-regional tourism, characterized in that, The system includes: The federated learning platform initializes the global carbon footprint model, distributes model parameters to multiple data holders, receives local model update parameters uploaded by each data holder, and aggregates these update parameters to generate an updated global carbon footprint model. The carbon footprint measurement module receives user low-carbon behavior data generated by data holders, verifies the legality of behavior data accompanied by digital signatures from the data holders' private keys, extracts feature parameters from verified behavior data, inputs them into the updated global carbon footprint model, and calculates carbon footprint savings by combining carbon footprint coefficients from multi-regional and multi-industry input-output tables. The blockchain incentive module, based on the carbon footprint savings output by the carbon footprint measurement module, issues corresponding carbon credits to user accounts through blockchain contracts at a preset exchange rate. It receives carbon credit exchange requests initiated by users, verifies the user account credit balance, and, when the balance is sufficient, completes the credit deduction through blockchain contracts and generates a unique rights exchange certificate stored in the blockchain distributed ledger.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted to be loaded by a processor and executed as described in any one of claims 1-7.

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