A digital renminbi incentive and settlement control system based on low-carbon behavior triggering
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU SUNING BANK CO LTD
- Filing Date
- 2026-03-16
- Publication Date
- 2026-07-21
Smart Images
Figure CN122434523A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of settlement control system technology, specifically to a digital RMB incentive and settlement control system based on low-carbon behavior triggering. Background Technology
[0002] With the continuous advancement of the digital economy and green and low-carbon development strategies, social governance and public management are gradually evolving from extensive management to digitalization, refinement, and quantification. Under the "dual carbon" goal, how to guide individuals to form low-carbon behaviors through technological means and effectively incentivize low-carbon behaviors has become an important issue in the construction of a low-carbon governance system.
[0003] As a typical closed environment with a high concentration of people, clear management boundaries, and well-defined behavioral rules, campuses have a solid foundation for implementing energy conservation, carbon emission reduction, and the promotion of green lifestyles. Currently, low-carbon governance on campuses mainly revolves around specific behaviors such as saving electricity, saving water, staggering meal times, and reducing the use of disposable items. However, these measures largely rely on institutional constraints or behavioral advocacy, lacking a systematic and technologically advanced incentive and feedback mechanism.
[0004] Meanwhile, with the development of the digital economy and digital financial system, the form of currency is gradually evolving from traditional account-based electronic money to digital and programmable legal digital currency. As an important practical form of my country's legal digital currency, the digital RMB has been piloted in multiple scenarios such as retail payments, public services, and transportation. It possesses technical characteristics such as controllable flow, programmable rules, and automatic clearing, providing a new technological foundation for building a refined incentive and settlement mechanism.
[0005] In campus settings, the digital yuan has been gradually piloted for everyday payment activities such as cafeteria spending, campus supermarkets, and public service payments. However, most existing applications remain at the level of "payment tool replacement," that is, simply integrating the digital yuan into the existing system as a new payment medium. This fails to effectively link with the goals of low-carbon governance on campus and fails to fully leverage the technological potential of the digital yuan in terms of incentive generation, usage control, and settlement management.
[0006] Therefore, how to systematically integrate the identification, quantification, and incentive mechanisms for low-carbon behaviors with the incentive issuance and settlement control capabilities of digital RMB in a campus setting, forming a set of technical solutions that can be automatically operated, audited, and promoted, has become a technical problem that needs to be solved in the current construction of low-carbon campuses. Summary of the Invention
[0007] The purpose of this invention is to address the shortcomings of existing technologies by providing a digital RMB incentive and settlement control system based on low-carbon behavior triggering.
[0008] To achieve the above objectives, this invention provides a digital RMB incentive and settlement control system based on low-carbon behavior triggering, comprising: The user identity and behavior entity binding module is used to complete the binding, unbinding, verification and permission management of user identity information and digital RMB wallet identifier, and generate a unified behavior entity identifier UID on the campus side; The Low-Carbon Behavior Collection and Event Abstraction module is used to collect raw data related to low-carbon behaviors from data sources and abstract them into standardized low-carbon behavior events. The low-carbon behavior quantification and evaluation module is used to perform baseline comparison and quantification calculations on low-carbon behavior events, and generate low-carbon behavior quantification results that can be used for incentive determination. The incentive triggering rule engine module is used to load and execute configurable low-carbon incentive rules, and determine whether to trigger incentives based on the quantification results of the low-carbon behavior. If so, it outputs the incentive event and incentive parameters. The digital RMB incentive generation and revenue sharing module is used to generate corresponding digital RMB incentive funds after an incentive event is triggered, and to logically separate the incentive funds from ordinary consumption funds through revenue sharing. The incentive settlement control module, which has limited uses, is used to set constraints on incentive funds and perform real-time verification and deduction control when spending them; The audit traceability and risk control module is used to record the entire chain of logs in the process of low-carbon behavior, incentive triggers, fund transfer and settlement, and supports anomaly identification and audit traceability; The merchant settlement, clearing and reconciliation module is used to automatically summarize, clear and reconcile the consumption of incentive funds and ordinary funds, and generate settlement results for auditing purposes.
[0009] Furthermore, the data sources include campus energy consumption systems, consumption systems, and equipment systems.
[0010] Furthermore, the quantification results of the low-carbon behavior include the contribution of low-carbon behavior or the equivalent carbon emission reduction.
[0011] Furthermore, the contribution of the low-carbon behavior is calculated as follows: C(e)=max(0,B(e)-A(e)) Where C(e) is the contribution of low-carbon behavior calculated based on low-carbon behavior event e, max() is the maximum value function, B(e) is the baseline consumption value, and A(e) is the actual consumption value.
[0012] Furthermore, the equivalent carbon emission reduction is calculated as follows: CO2(e) = C(e) × λ Wherein, CO2(e) is the equivalent carbon emission reduction calculated based on the low-carbon behavior event e, and λ is the carbon dioxide emission conversion coefficient corresponding to unit resource consumption.
[0013] Furthermore, the incentive triggering function in the low-carbon incentive rule is: Trigger(e) = I(CO2(e) ≥ δ) Where Trigger(e) is the excitation trigger function, δ is the set trigger threshold, and I(.) is the indicator function. If CO2(e)≥δ, then Trigger(e)=1; if CO2(e)<δ, then Trigger(e)=0.
[0014] Furthermore, the calculation method for the digital RMB incentive funds is as follows: R(e) = min(Rmax, α) CO2(e)) Where R(e) is the calculated digital RMB incentive fund, min(.) is the minimum value function, Rmax is the maximum reward amount for a single low-carbon behavior event e set by the system, and α is the incentive mapping coefficient.
[0015] Furthermore, the constraints include the scope of use, validity period, and non-withdrawal.
[0016] Furthermore, the scope of use and validity period are set as follows: Eligible(u,merchant,t)=I(merchant∈AllowSet(u)∧t <exp(u)) Wherein, Eligible(u,merchant,t) is the result of the availability judgment of incentive funds for user u and merchant merchant at the current time t, AllowSet(u) is the set of permitted uses of incentive funds, merchant is the current merchant, ∧ is AND, t is the current trading time, and exp(u) is the validity period of incentive funds.
[0017] Beneficial effects: 1. For the first time in a campus setting, this invention abstracts low-carbon behavior into calculable behavioral events, and by introducing a baseline model and comparing it with actual behavioral data, forms a quantitative model of low-carbon contribution and equivalent carbon dioxide emission reduction. Based on this, a digital RMB incentive account management mechanism triggered by low-carbon behavior is established. By dividing user accounts into ordinary fund accounts and incentive fund accounts at the system level, the low-carbon incentive funds have independent technical attributes in the process of generation, storage and use, providing a data and structural foundation for subsequent use control and settlement management. 2. This invention sets a low-carbon behavior trigger threshold to determine the low-carbon contribution. Incentives are only triggered when the equivalent carbon emission reduction generated by the behavior reaches the preset threshold, thus avoiding the problem of frequent triggering of incentives for minor behaviors. At the same time, this invention constructs an incentive calculation model based on the mapping relationship between carbon emission reduction and incentive amount, and uses an incentive upper limit control mechanism to cap the amount of a single incentive, thereby achieving a balance between incentive intensity and system controllability. 3. This invention proposes a settlement control mechanism for incentive funds based on restricted use of digital RMB. Before a consumption transaction occurs, the system determines the availability of incentive funds in real time. Incentive funds can only participate in settlement if the merchant belongs to the preset allowed set and the incentive funds are within the validity period. During the settlement and clearing stage, this invention forms independent accounting records and clearing channels for ordinary fund deductions and incentive fund deductions, realizing automated reconciliation, clearing and audit traceability, and technically ensuring the compliant use of incentive funds and the transparency of fund flow. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the structure of the digital RMB incentive and settlement control system based on low-carbon behavior triggering in an embodiment of the present invention. Detailed Implementation
[0019] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.
[0020] like Figure 1 As shown, this embodiment of the invention provides a digital RMB incentive and settlement control system based on low-carbon behavior triggering, including a user identity and behavior subject binding module 1, a low-carbon behavior collection and event abstraction module 2, a low-carbon behavior quantification and evaluation module 3, an incentive triggering rule engine module 4, a digital RMB incentive generation and distribution module 5, a restricted-use incentive settlement control module 6, an audit traceability and risk control module 7, and a merchant settlement, clearing and reconciliation module 8.
[0021] The user identity and behavior entity binding module 1 is used to bind, unbind, verify, and manage permissions between user identity information and digital RMB wallet identifiers, generating a unified behavior entity identifier (UID) on the campus side. Specifically, the aforementioned users include students and faculty members, and the aforementioned identity information includes student ID, employee ID, campus card, and real-name information.
[0022] The Low-Carbon Behavior Collection and Event Abstraction Module 2 is used to collect raw data related to low-carbon behaviors from data sources and abstract it into standardized low-carbon behavior events. Specifically, the aforementioned data sources include campus energy consumption systems, consumption systems, and equipment systems, etc.
[0023] The Low-Carbon Behavior Quantification and Assessment Module 3 is used to perform baseline comparisons and quantitative calculations of low-carbon behavior events, generating low-carbon behavior quantification results that can be used for incentive determination. Specifically, the aforementioned low-carbon behavior quantification results include the contribution of low-carbon behavior or equivalent carbon emission reductions. The calculation method for the contribution of low-carbon behavior is as follows: C(e)=max(0,B(e)-A(e)) Wherein, C(e) is the low-carbon behavior contribution calculated based on low-carbon behavior event e, representing the effective energy saving or emission reduction contribution generated by the low-carbon behavior event, and is the core quantitative indicator used in this invention for incentive triggering and monetary calculation. The aforementioned low-carbon behavior event e represents a low-carbon behavior event that has been identified and quantified by the system, such as energy-saving electricity use or staggered mealtimes. max(.) is the function for finding the maximum value, and B(e) is the baseline consumption value, representing the reference consumption level of the event under the corresponding scenario, time, or device when no low-carbon behavior occurs. This baseline value can be determined in the following ways: historical average energy consumption of the same type; group average value in the same time period; preset standard energy consumption model. B(e) is not a fixed constant, but a reference value dynamically associated with behavior type, scenario, and time dimension, used to characterize "how much resource should be consumed under normal circumstances". A(e) is the actual consumption value, representing the real resource consumption value collected by the system when the low-carbon behavior event e actually occurs. For example: actual electricity consumption; actual energy consumption power; actual number of resource uses.
[0024] The equivalent carbon emission reduction is calculated as follows: CO2(e) = C(e) × λ Wherein, CO2(e) is the equivalent carbon emission reduction calculated based on low-carbon behavior event e, and λ is the carbon dioxide emission conversion coefficient corresponding to unit resource consumption.
[0025] The incentive triggering rule engine module 4 is used to load and execute configurable low-carbon incentive rules. It determines whether to trigger an incentive based on the quantification results of low-carbon behavior; if so, it outputs the incentive event and incentive parameters. Specifically, taking the equivalent carbon emission reduction CO2(e) as an example, the incentive triggering function in the low-carbon incentive rule is: Trigger(e) = I(CO2(e) ≥ δ) Among them, Trigger(e) is the incentive trigger function, and δ is the set trigger threshold, indicating how much equivalent carbon dioxide emission reduction at least needs to be generated by a low-carbon behavior to be eligible for incentives. This threshold can be configured by the management side, such as setting δ = 0.1 kg CO2 or δ = 1.0 kg CO2, etc. I(.) is the indicator function. When the condition inside the parentheses holds, the function value is 1; when the condition does not hold, the function value is 0. That is, if CO2(e) ≥ δ, then Trigger(e) = 1; if CO2(e) < δ, then Trigger(e) = 0.
[0026] The digital RMB incentive generation and account splitting module 5 is used to generate corresponding digital RMB incentive funds after the incentive event is triggered, and logically isolate the incentive funds from the ordinary consumption funds through account splitting. Specifically, the calculation method of the digital RMB incentive funds is as follows: R(e)=min(Rmax,α CO2(e)) Among them, R(e) is the calculated digital RMB incentive funds, min(.) is the minimum value function, Rmax is the maximum reward amount for a single low-carbon behavior event e set by the system, which is used to prevent excessive incentive amounts caused by abnormal data or extreme behaviors. α is the incentive mapping coefficient, which is used to describe "how much incentive amount corresponds to each unit of carbon dioxide emission reduction". It can be summarized as: the incentive amount is proportional to the low-carbon contribution, but is restricted by the system's safety upper limit. Specifically, it can be expressed as: When α CO2 (e)<Rmax → The incentive amount increases linearly according to the contribution; When α CO2 (e)≥Rmax → The incentive amount is capped at Rmax.
[0027] The digital RMB incentive generation and account splitting module 5 defines the account structure for any user u: Ordinary fund balance: Bp(u,t), which represents the digital RMB balance that the user can freely use at time t.
[0028] Incentive fund balance: Br(u,t), which represents the digital RMB incentive fund balance generated by the system due to the user's low-carbon behavior.
[0029] The incentive funds are only allowed to participate in the settlement when the usage rules are met. Among them, t represents the current moment or the moment when the transaction occurs, which is used to describe the dynamic change state of the account balance.
[0030] The restricted-use incentive settlement control module 6 is used to set constraints on incentive funds and perform real-time verification and deduction control during consumption. These constraints include the scope of use, validity period, and non-withdrawal. Setting the scope of use and validity period can be expressed as follows: Eligible(u,merchant,t)=I(merchant∈AllowSet(u)∧t <exp(u)) Wherein, Eligible(u,merchant,t) represents the availability of incentive funds for user u at the current time t and merchant merchant, AllowSet(u) represents the set of permitted uses of incentive funds, indicating the set of merchants or scenarios where incentive funds are allowed to be used. Merchant is the current merchant, ∧ represents ∧, t is the current transaction time, and exp(u) is the validity period of the incentive funds, which is generally the latest time that the incentive funds can be used. After this time, the incentive funds will automatically expire or enter an expired state.
[0031] The Audit Traceability and Risk Control Module 7 records the entire process of low-carbon behavior, incentive triggers, fund transfers, and settlement, supporting anomaly identification and audit traceability. It also performs consistency checks according to the settlement cycle, and anomalies are processed through the risk control procedure.
[0032] The Merchant Settlement, Clearing, and Reconciliation Module 8 is used to automatically summarize, clear, and reconcile the consumption of incentive funds and ordinary funds, generating settlement results for auditing purposes. The system outputs dual transaction records for each transaction: ordinary fund transaction record and incentive fund transaction record.
[0033] The system of the present invention includes the following steps when in use: Step 1: Identity Binding and Behavioral Subject Initialization 1. The actor submits his / her identity information and initiates a binding request through the unified campus portal; 2. The system verifies the legitimacy of the identity and establishes a binding relationship with the digital RMB wallet identifier; 3. Generate a unique identifier (UID) for each entity and initialize its fund allocation structure and risk control parameters.
[0034] Step 2: Low-carbon behavior data collection and event generation 1. The system collects raw behavioral data from data sources such as energy-consuming devices and consumption systems; 2. Based on the preset behavior mapping rules, the raw data is converted into low-carbon behavior events LCB_Event; 3. Each event must include at least the subject of the action, the type of action, the time of occurrence, the scene identifier, and the original indicator data.
[0035] Step 3: Quantitative Calculation of Low-Carbon Behaviors 1. The system loads a corresponding baseline model for each type of low-carbon behavior; 2. Compare the actual indicators of the behavioral events with the baseline values to calculate the low-carbon contribution; 3. Convert the low-carbon contribution into an equivalent emission reduction value or behavioral score as an input for incentive triggering.
[0036] Step 4: Excitation Trigger Determination and Parameter Generation 1. The incentive rule engine reads the quantitative results of low-carbon behaviors; 2. Determine whether the stimulus triggering conditions (threshold, frequency, time window, etc.) are met; 3. If the conditions are met, an incentive event is generated, and the incentive amount, validity period, and usage rules are determined.
[0037] Step 5: Digital RMB Incentive Generation and Revenue Separation 1. The system generates corresponding digital RMB incentive funds based on incentive events; 2. Incentive funds are not included in the regular consumption balance, but are written into a separate incentive account; 3. At the same time, the incentive funds are bound to a usage restriction attribute.
[0038] Step 6: Consumption Initiation and Incentive Settlement Control 1. The actor initiates a consumer transaction in a campus setting; 2. The system reads the transaction context and determines whether incentive revenue sharing is allowed; 3. If permitted, incentive funds will be deducted according to the preset strategy; otherwise, only ordinary funds will be used.
[0039] Step 7: Settlement and Reconciliation 1. The system summarizes transaction records according to the settlement cycle; 2. Separate settlement procedures should be established for ordinary funds and incentive funds; 3. Generate merchant settlement statements and execute automatic clearing; 4. The reconciliation module performs multi-party consistency verification.
[0040] For example: (I) Application Scenario Description This embodiment uses a senior high school in Nanjing as an application scenario. The school implements a closed campus management system, with a large number of students, and daily learning, living, and consumption activities mainly take place within the campus. The campus is uniformly equipped with a student canteen, a campus supermarket, study classrooms, and public activity areas, and is also equipped with energy consumption monitoring equipment, a consumption system, and a campus information management system.
[0041] With the advancement of the "dual carbon" goals, the school hopes to guide students to adopt low-carbon behaviors such as energy-saving electricity use, staggered meal times, and reduced resource waste through technological means. Furthermore, it aims to introduce quantifiable, verifiable, and traceable incentive mechanisms without altering students' existing consumption habits. To this end, the school has deployed and applied the system of this invention, deeply integrating low-carbon behaviors with the digital RMB incentive mechanism.
[0042] (II) System Deployment and Account Initialization Based on its existing campus information system, the school deployed the low-carbon behavior incentive and digital RMB settlement control system of this invention and completed the following system integrations: Campus energy consumption monitoring system; Student Identity and Access Control System; Campus Merchant Management System; Digital RMB payment and wallet interface.
[0043] The specific implementation steps include: 1. After enrollment or system upgrade, students complete identity verification through the unified campus portal (campus app or self-service terminal) and bind their student ID and real-name information to their digital RMB wallet; 2. The system generates a unique campus user identifier for each student, which is used for recording low-carbon behavior and calculating incentives; 3. At the account level, a digital RMB revenue sharing structure is established for each student, including: a general fund sharing account, used to record the digital RMB that students independently recharge or spend; and an incentive fund sharing account, used to record the digital RMB incentive amount generated due to low-carbon behavior. 4. At the same time, configure basic risk control parameters for the account, including the single incentive limit, the daily incentive limit, the available scenarios for incentive funds, and the validity period.
[0044] (III) Low-carbon behavior data collection and quantitative modeling In this embodiment, the school collects and models various low-carbon behaviors on campus, such as: Non-essential lights in the teaching building should be turned off at night; Students eat during off-peak hours; Dormitory electricity consumption is below baseline levels.
[0045] The system connects with energy consumption monitoring equipment and the consumption system to collect actual energy consumption data before and after a behavior occurs. It then combines this data with historical averages or a population baseline model to calculate the low-carbon contribution corresponding to each low-carbon behavior. Based on this, the system further converts the low-carbon contribution into equivalent carbon dioxide emission reductions, which serve as the core technical indicator for incentive triggering and incentive amount calculation.
[0046] (iv) Incentive rule configuration and triggering execution The school administration system configures campus-specific incentive rules through the incentive rule engine of this invention, for example: 1. Incentive triggering condition: The incentive is triggered when the equivalent carbon dioxide emission reduction generated by a single low-carbon behavior is not lower than a preset threshold. 2. Incentive Amount Calculation Rules: The incentive amount is proportional to the carbon emission reduction, and a maximum limit is set for the single incentive amount; 3. Behavioral linkage strategy: Provide higher incentive coefficients for sustained low-carbon behaviors; 4. Restrictions on the use of incentive funds: Incentive funds can only be used for consumption in the school canteen and on-campus supermarkets; incentive funds cannot be withdrawn or transferred to off-campus accounts; incentive funds have an expiration date and will automatically expire after the expiration date.
[0047] 5. The system automatically completes the incentive trigger determination and incentive amount calculation according to the above rules, and records the generated incentive amount into the student's incentive fund account.
[0048] (v) Settlement and control process of campus consumption and incentive funds When students make purchases on campus, the system determines the availability of incentive funds in real time. The specific process is as follows: 1. Students can initiate payment requests using digital RMB in campus cafeterias or supermarkets; 2. The system obtains contextual information such as the merchant type, transaction amount, and transaction time. 3. The system determines whether the transaction meets the conditions for using incentive funds, including whether the merchant is within the permitted scope and whether the incentive funds are within the validity period; 4. If the usage conditions are met, the system will use the incentive fund allocation to deduct the amount according to the preset rules, either firstly or proportionally. Any shortfall will be deducted from the ordinary fund allocation. 5. If the usage conditions are not met, the system will only deduct the payment from the regular fund account. 6. After the transaction is completed, the system will synchronously update the balance of each sub-account and generate a complete transaction and sub-account flow record.
[0049] (vi) Merchant settlement and automatic clearing The system aggregates transaction data from the school cafeteria and supermarkets according to a preset settlement cycle (such as daily settlement) and performs separate statistics: The consumption amount corresponding to ordinary fund splitting; The deduction amount corresponding to the incentive fund distribution.
[0050] In this embodiment, the incentive funds are centrally settled through a low-carbon special fund pool established by the school. The system automatically generates settlement and clearing instructions to achieve the following: 1. Automatic settlement of accounts receivable for merchants on campus; 2. Multi-caliber settlement of general funds and incentive funds; 3. The reconciliation results are automatically written into the audit and ledger module for subsequent auditing and verification.
[0051] (vii) Technical effects and application results Through pilot deployment and operation at a senior high school in Nanjing, this invention has achieved at least the following technical effects: 1. It has achieved automatic identification, quantification, and incentive triggering of low-carbon behaviors on campus, avoiding manual statistics; 2. The use of incentive funds for digital RMB is restricted to ensure that incentive resources are not abused; 3. The incentive generation, settlement, and clearing processes are highly automated, significantly reducing management costs; 4. Without changing students' original payment and consumption habits, a deep integration of low-carbon governance and the application of digital RMB has been achieved.
[0052] The above description is merely a preferred embodiment of the present invention. It should be noted that for those skilled in the art, other parts not specifically described are existing technology or common knowledge. Several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A digital RMB incentive and settlement control system based on low-carbon behavior triggering, characterized in that, include: The user identity and behavior entity binding module is used to complete the binding, unbinding, verification and permission management of user identity information and digital RMB wallet identifier, and generate a unified behavior entity identifier UID on the campus side; The Low-Carbon Behavior Collection and Event Abstraction module is used to collect raw data related to low-carbon behaviors from data sources and abstract them into standardized low-carbon behavior events. The low-carbon behavior quantification and evaluation module is used to perform baseline comparison and quantification calculations on low-carbon behavior events, and generate low-carbon behavior quantification results that can be used for incentive determination. The incentive triggering rule engine module is used to load and execute configurable low-carbon incentive rules, and determine whether to trigger incentives based on the quantification results of the low-carbon behavior. If so, it outputs the incentive event and incentive parameters. The digital RMB incentive generation and revenue sharing module is used to generate corresponding digital RMB incentive funds after an incentive event is triggered, and to logically separate the incentive funds from ordinary consumption funds through revenue sharing. The incentive settlement control module, which has limited uses, is used to set constraints on incentive funds and perform real-time verification and deduction control when spending them; The audit traceability and risk control module is used to record the entire chain of logs in the process of low-carbon behavior, incentive triggers, fund transfer and settlement, and supports anomaly identification and audit traceability; The merchant settlement, clearing and reconciliation module is used to automatically summarize, clear and reconcile the consumption of incentive funds and ordinary funds, and generate settlement results for auditing purposes.
2. The digital RMB incentive and settlement control system based on low-carbon behavior triggering as described in claim 1, characterized in that, The data sources include the campus energy consumption system, consumption system, and equipment system.
3. The digital RMB incentive and settlement control system based on low-carbon behavior triggering as described in claim 1, characterized in that, The quantitative results of low-carbon behavior include the contribution of low-carbon behavior or the equivalent carbon emission reduction.
4. The digital RMB incentive and settlement control system based on low-carbon behavior triggering according to claim 3, characterized in that, The contribution of the low-carbon behavior is calculated as follows: C(e)=max(0,B(e)-A(e)) Where C(e) is the contribution of low-carbon behavior calculated based on low-carbon behavior event e, max() is the maximum value function, B(e) is the baseline consumption value, and A(e) is the actual consumption value.
5. A digital RMB incentive and settlement control system based on low-carbon behavior triggering as described in claim 4, characterized in that, The equivalent carbon emission reduction is calculated as follows: CO2(e) = C(e) × λ Wherein, CO2(e) is the equivalent carbon emission reduction calculated based on the low-carbon behavior event e, and λ is the carbon dioxide emission conversion coefficient corresponding to unit resource consumption.
6. A digital RMB incentive and settlement control system based on low-carbon behavior triggering, as described in claim 5, is characterized in that... The incentive trigger function in the low-carbon incentive rule is: Trigger(e) = I(CO2(e) ≥ δ) Where Trigger(e) is the excitation trigger function, δ is the set trigger threshold, and I(.) is the indicator function. If CO2(e)≥δ, then Trigger(e)=1; if CO2(e)<δ, then Trigger(e)=0.
7. A digital RMB incentive and settlement control system based on low-carbon behavior triggering, as described in claim 5, is characterized in that... The calculation method for the digital RMB incentive funds is as follows: R(e)=min(Rmax,α CO2(e)) Where R(e) is the calculated digital RMB incentive fund, min(.) is the minimum value function, Rmax is the maximum reward amount for a single low-carbon behavior event e set by the system, and α is the incentive mapping coefficient.
8. A digital RMB incentive and settlement control system based on low-carbon behavior triggering according to claim 6, characterized in that, The constraints include the scope of use, validity period, and non-withdrawal.
9. A digital RMB incentive and settlement control system based on low-carbon behavior triggering according to claim 8, characterized in that, The scope of use and validity period are set as follows: Eligible(u,merchant,t)=I(merchant∈AllowSet(u)∧t <exp(u)) Wherein, Eligible(u,merchant,t) is the result of the availability judgment of incentive funds for user u and merchant merchant at the current time t, AllowSet(u) is the set of permitted uses of incentive funds, merchant is the current merchant, ∧ is AND, t is the current trading time, and exp(u) is the validity period of incentive funds.