Running integral incentive method based on zero-knowledge proof and cross-chain carbon credit mapping

Abstract

The invention relates to a running integral incentive method based on zero-knowledge proof and cross-chain carbon credit mapping, which comprises the following steps of: modeling a running behavior into a semantic condition, and locally calculating and generating zero-knowledge proof in equipment; estimating individual energy output based on the physiological parameters and the motion type; comparing the carbon emission of the same traffic trip mode, and estimating an alternative emission reduction effect; a carbon reduction value is compared with intention and behavior, and energy and carbon are converted into a core model; introducing zero-knowledge proof to derive a carbon credit identity label on a chain, wherein the label is generated by two technologies of semantic proof and carbon estimation; after running is completed each time, the user obtains an anonymous carbon credit anchor point on the chain; the anchor point records a zero-knowledge proof abstract, a carbon reduction value, a timestamp and an intention label; a carbon credit atomic mapping protocol is adopted, and a carbon credit anchor point on a local chain is inseparably mapped to a mainstream carbon credit public chain at a time; the individual carbon emission reduction contribution is accurately quantified, and atomic cross-chain mapping and circulation of the carbon credit assets are realized.

Description

Technical Field

[0001] The present invention belongs to the field of regional chain service technology, and specifically relates to a running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping. Background Art

[0002] Existing technology 1 (CN117349897A) discloses a blockchain-based carbon quota trading privacy protection method, which has made certain explorations in privacy protection and carbon asset chain rights confirmation, and is particularly representative in achieving encrypted submission and on-chain verification of user carbon quota trading data through a zero-knowledge proof mechanism. This method is mainly aimed at protecting the privacy of sensitive parameters such as trading volume in the carbon quota trading market for enterprises or organizations. It generates transaction gradient information locally through the alternating direction multiplier method (ADMM), thereby avoiding users from directly disclosing transaction details during the carbon trading process, achieving data protection at the transaction volume level and ensuring authenticity. However, this method has a series of key deficiencies and structural limitations in its actual application in carbon incentive scenarios based on individual behavior.

[0003] First, the original design intention of existing technology 1 focused on the private transaction of existing carbon quotas, that is, based on a certain amount of carbon quotas owned by enterprises or institutions, these quotas are distributed and anonymously scheduled on the blockchain. Its application scenario is closer to the encryption processing of enterprise-level carbon market data, rather than the carbon asset generation process for individual low-carbon behaviors. Therefore, it cannot handle the stage of confirming the carbon value generated by individual behaviors; second, the ADMM algorithm adopted by existing technology 1 is mainly used to solve joint objective functions in distributed machine learning or optimization calculations. Its main advantage lies in processing high-dimensional numerical collaborative optimization, but it lacks direct applicability in carbon behavior semantic modeling, individual behavior intention recognition and its derived unstructured data such as cadence, displacement, rhythm, etc., especially in the construction of semantic assertions of motion behavior, data continuity verification, and path aperiodicity identification. This solution does not provide an effective strategy.

[0004] In terms of carbon credit asset generation, the zero-knowledge proof in existing technology 1 is used to store transaction results rather than an asset generation mechanism. Its logic still relies on submitting proof after the existing carbon quota has been traded, and does not support behavior-based carbon value derivation and title confirmation processes. The data structure it describes is a summary of transaction information and has not been extended to constructing an independently circulated carbon asset unit. In addition, the transaction process of existing technology 1 relies on the joint optimization and coordination of transaction parameters by a centralized server. Although it formally uses smart contracts to manage some operations, it does not truly achieve full-process automation of on-chain behavior generation → on-chain title confirmation → on-chain mapping → on-chain incentives, and does not have the ability to coordinate with the real-time carbon market. Finally, although the zero-knowledge proof mechanism relied on by the existing technology solves the problem of transaction privacy leakage, it does not provide identity desensitization and inter-chain composability mechanisms. In actual cross-platform or cross-chain operations, its transaction data cannot be directly connected to personal carbon accounts or points systems.

[0005] To sum up, although existing technologies have certain advantages in protecting the privacy of corporate carbon quotas, there are obvious technical gaps and mechanism deficiencies in individual-oriented, behavioral original carbon value generation, anchorable structure design, cross-chain composable asset mapping, and dynamic incentive closed-loop construction. Summary of the Invention

[0006] The purpose of the present invention is to provide a running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping, so as to solve some of the drawbacks and shortcomings pointed out in the background technology.

[0007] A running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping models running behavior as semantic conditions including duration, acceleration pattern, cadence, and displacement curves. Zero-knowledge proofs are calculated and generated locally on the device. Individual energy output is estimated based on physiological parameters and exercise type. Carbon emissions from the same transportation mode are compared to estimate carbon reduction values. This carbon reduction value is based on a core model that compares intention and behavior, and converts energy into carbon. A zero-knowledge proof-derived on-chain carbon credit identity is introduced, generated from semantic proofs and carbon valuations. After each run, users receive an anonymous on-chain carbon credit anchor that records the zero-knowledge proof summary, carbon reduction value, timestamp, and intention tag. Through the carbon credit atomic mapping protocol, the local on-chain carbon credit anchor is indivisibly mapped to the mainstream carbon credit public chain.

[0008] Furthermore, the method for locally calculating and generating zero-knowledge proofs involves: the user's terminal device collects running behavior data locally, including acceleration changes, displacement characteristics, cadence, and exercise duration; locally analyzes the running behavior data using semantic behavior modeling to generate a set of behavioral semantic assertions describing the continuity, naturalness, and rationality of the user's exercise behavior. These behavioral semantic assertions are then constructed into a zero-knowledge proof calculation circuit, and a set of zero-knowledge exercise behavior proofs is generated locally. These proofs are then submitted to a smart contract on the blockchain platform for verification, providing proof of the authenticity and validity of the behavior to the on-chain contract without exposing any plaintext behavior data. Based on the verified zero-knowledge exercise behavior proofs, the system combines user vital signs, exercise duration, and behavioral intention characteristics to estimate the individual carbon emission reduction value corresponding to the behavior in a local or trusted environment. The individual carbon emission reduction value, along with the behavior proof summary, generation timestamp, and intention tag, is then combined to form an on-chain carbon credit anchor (ZK-CreditAnchor), which serves as a unique low-carbon ownership record for the behavior.

[0009] The on-chain carbon credit anchor identifier is atomically mapped to the external carbon credit chain through the cross-chain oracle mechanism, thereby being converted into a tradable carbon asset, and the individual carbon emission reduction value is converted into a tradable carbon asset through the following integral incentive valuation function. Mapped to integral incentive value : Among them, the time interval [ , ] represents the time window for a running behavior anchor, is the behavior density function of the user in this interval, which measures the frequency of running behaviors that conform to the semantic model completed in unit time; function Expressing the carbon reduction benefits of behavior, It is a weighted function of behavioral intention, which is used to reflect the carbon value intensity of the behavior. Indicates the current circulation density of carbon credit assets on the chain, which is a dynamic function reflecting the market supply situation; It is an exponential attenuation factor to suppress the inflation of points caused by excess carbon assets. It is a constant that adjusts the attenuation sensitivity. When the supply of carbon assets is too high, the attenuation factor can ensure that the point rewards are automatically reduced to maintain the economic sustainability and anti-brush score ability of the incentive model.

[0010] Furthermore, the behavior semantic assertion includes one or more of a continuity assertion between cadence and acceleration, a non-periodic forgery detection assertion of displacement change, a high-frequency pseudo-vibration exclusion assertion of acceleration signal, and a behavior interruption and recovery interval judgment assertion.

[0011] Furthermore, the zero-knowledge proof is generated using the zk-SNARK or zk-STARK protocol, and the proof only encrypts and authenticates whether the behavioral semantic assertion is true or not, and does not contain any original behavioral data content; the carbon emission reduction estimation model is based on the user's weight, pace, energy output level and behavioral intention label, and compares the carbon emission baseline of alternative transportation modes to form an individual behavior carbon emission reduction equivalent.

[0012] Furthermore, the carbon credit anchor identifier includes a zero-knowledge proof summary hash, a carbon reduction valuation interval, a behavior timestamp, a user encrypted identity identifier, and a behavior intention label.

[0013] Furthermore, the method also includes point incentive scheduling, which dynamically adjusts the exchange ratio between running points and carbon assets based on the current on-chain carbon credit asset price and platform sports activity; the points obtained by users can be used to redeem platform resources, digital badges, equipment rights or return on-chain carbon credit tokens, forming a closed-loop path of user behavior, carbon value, and financial incentives.

[0014] Furthermore, the carbon credit anchor is an on-chain data structure generated based on the user's qualified running behavior, which contains the following irreversible and verifiable fields: (1) Zero-knowledge proof summary, which is used to record the hash summary of the zero-knowledge motion behavior proof generated locally by the user terminal and consistent with the behavioral semantics assertion; (2) Carbon reduction value, which is used to represent the equivalent carbon reduction estimate corresponding to the running behavior, and is generated by the local calculation module based on the user's motion parameters, physical data and alternative carbon emission benchmarks; (3) Timestamp, used to record the time when the anchor point is confirmed on the blockchain; (4) Behavior intention label, which is used to express the user's motivation characteristics when completing the behavior. It is generated by the behavior modeling module based on the user trajectory pattern and behavior context; (5) Anonymous identity identification, used to attribute the anchor point to the user's zero-knowledge identity system.

[0015] Furthermore, the zero-knowledge proof summary is a compressed hash value of the non-interactive zero-knowledge proof generated by the user's local device based on semantic behavior modeling. The summary can only be used by the on-chain contract to verify the compliance of the behavior and cannot be reversely deduced from the original behavior data.

[0016] Furthermore, the carbon reduction value is locally estimated based on the dynamic parameters of the user's weight, pace, cadence and behavior duration, combined with the carbon emission factor of the vehicle to be replaced. The anonymous identity is generated based on the user's zero-knowledge identity system, does not contain any plaintext identity information, and can be used across platforms for carbon credit asset attribution, point incentive redemption or cross-chain mapping.

[0017] Furthermore, after the anchor point is generated, it can be mapped across chains through smart contracts and oracles to generate tradable carbon assets, and the mapping process is atomic, irrevocable and one-time valid; the anchor point structure can be used to build a carbon credit asset portfolio, circulation certificate or carbon credit exchange system.

[0018] The present invention has the following beneficial effects: 1. This invention builds a closed loop of the entire process of behavior proof → carbon valuation → anchor identification → assetization → point mapping, and combines an integral function with the ability to perceive behavior density, evaluate carbon benefits, and adjust market feedback. It achieves the precise conversion and dynamic incentive of running behavior into tradable value units, and provides a safe, reliable, and quantifiable connection mechanism between individual low-carbon behavior and the green financial system.

[0019] 2. By locally generating zero-knowledge proof of exercise behavior, only the encrypted summary of the behavior semantic assertion result is submitted to the chain, avoiding uploading the user's original behavior data, location information or health parameters; by combining the user's weight, cadence, pace and behavior duration and other dynamic parameters, the system calculates its carbon emission reduction equivalent based on the behavior's real energy output and traffic substitution model, which is better than the traditional templated carbon valuation method; the carbon emission reduction valuation, intention label, timestamp and identity attribution of all behaviors are encapsulated in a unique anchor structure and recorded on the chain in an irreversible encrypted manner to ensure that the anchor structure cannot be tampered with, is verifiable and traceable, breaking the traditional carbon credit reliance on manual review and centralized registration model, and realizing the autonomous registration and trusted attribution of individual-level carbon assets.

[0020] 3. Through smart contracts and decentralized oracles, anchor points can be mapped to tradable carbon assets at one time between multiple mainstream blockchains, and ensure non-repeatable and irrevocable atomic operations, providing a standardized and automated path for the marketization of carbon credit assets, lowering transaction thresholds, and improving transparency and circulation efficiency; introducing a point valuation function, integrating parameters such as behavior density, carbon benefits, and on-chain asset market supply to dynamically adjust point rewards, enhance the anti-inflation ability and sustainability of the platform economic system, and guide users to increase running behavior when carbon value is higher or platform activity is insufficient, playing a role of dynamic balance and ecological incentives; opening up the full process closed loop of behavior-asset-incentive, and promoting individual participation in the global carbon market and green finance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping in the present invention; Figure 2 This is the core relationship diagram of the running behavior-carbon credit anchor point atom mapping of the present invention; Figure 3This is a flowchart of the generation and incentive of carbon credit assets for user running behavior in an embodiment of the present invention; Figure 4 This is a schematic diagram of the hierarchical structure of the carbon credit anchor points and asset mapping for users’ commuting and running activities according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further explained below with reference to the accompanying drawings; Combined with attachment Figure 1The present invention is based on a running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping. Through the device's local semantic behavior modeling and carbon effect estimation model, it realizes the full-chain trusted transformation of running behavior into carbon credit and points incentives. A set of local computing modules for semantic behavior recognition is deployed in user terminal devices such as mobile phones or smart watches. This module continuously collects the user's motion behavior data, including basic sensor information such as three-axis acceleration data, displacement changes, step frequency and rhythm, and establishes a motion behavior semantic model. A complete and effective running behavior is modeled as a combination of multiple continuity and structural semantic conditions. The combination includes at least the following elements: 1. The first is the continuity requirement of the behavior, that is, the behavior maintains a stable cadence and continuous displacement characteristics within a certain time window, and there must be no long-term interruptions; the second is acceleration pattern recognition, the system analyzes the acceleration change sequence during running, and determines whether there is an oscillation rhythm and acceleration fluctuation range consistent with natural running movement, and excludes simulated signals caused by non-human dominant movement; the third is the coupling of cadence and displacement curves, the system determines whether the cadence change matches the actual displacement, and prevents cheating behaviors such as abnormally high cadence but stagnant displacement; after the behavior modeling is completed, the terminal device will locally generate a zero-knowledge proof corresponding to the above semantic behavior assertion. The system is implemented by a lightweight zk-SNARK compilation circuit, which ensures that users can submit real and valid irreversible verification credentials of this behavior to the chain without uploading the original motion data. After completing the behavior confirmation, the system further retrieves the user's registered human parameters, including weight, age, height, and running behavior type, such as outdoor running, uphill and downhill running, interval running, etc., and calculates the individual energy output value during the behavior through the built-in energy estimation model. The energy output will be calculated in combination with the basal metabolic rate and the exercise thermal effect model, and then the value of the behavior in terms of carbon emission substitution will be modeled. Specifically, the system calls environmental data The carbon emission benchmark data in the database, combined with the user's average displacement, estimates the reference value of carbon emissions that would be generated if the behavior were instead completed by a means of transportation, and regards the energy output of the user's actual behavior as the effective output used to replace the transportation behavior. On this basis, the system constructs a carbon reduction assessment function, using behavioral intention and energy output as dual factors to form a composite model of behavioral comparison and energy-carbon conversion. After calculating the equivalent carbon emission reduction value, this model, together with the aforementioned zero-knowledge behavior proof summary, forms an on-chain carbon credit anchoring structure, which is ultimately written into the user's anonymous carbon credit certificate, and will subsequently serve as the basic certificate for carbon asset mapping, point incentive distribution, and carbon financial transactions.

[0023] Combined with attachment Figure 2After the user completes an effective running behavior, the local semantic behavior modeling and carbon valuation module jointly trigger the generation mechanism of the on-chain carbon credit identity, including: the mobile device worn by the user continuously collects multimodal motion data such as acceleration data, displacement changes, cadence and rhythm during the running process. The system analyzes the data in real time according to the preset behavioral semantic model locally to determine whether the behavior meets several behavioral logic conditions, including but not limited to the continuity, consistency and natural rhythm of the behavior. The model does not rely on specific numerical thresholds, but uses pattern recognition and behavioral semantic assertions to construct a computing circuit for behavioral compliance. If it is determined to be a valid behavior, the zero-knowledge proof generation module is triggered to construct a set of zero-knowledge proofs locally. The proof only authenticates whether the above-mentioned behavioral logic is valid or not, and does not contain any original data or location information. After the proof is generated in the form of zk-SNARK or an equivalent protocol, its digest hash value is used as the credibility basis of the first dimension; when the behavior is confirmed to be valid locally, the system retrieves physiological parameters such as weight, height, and exercise type in the user's registration file. The system then calculates the user's actual energy output during this run, combining dynamic data such as stride length, speed, and duration. The system then compares the average carbon emissions of an equivalent number of short-distance motor vehicle trips in an urban area. Combined with emission factors for corresponding scenarios in a carbon emissions benchmark database, the system calculates the equivalent carbon reduction achieved by the user's run compared to alternative transportation behaviors. This carbon valuation model is not only based on energy conversion but also incorporates subjective intention labels of the behavior, such as whether it constitutes daily commuting, sustainable transportation alternatives, healthy exercise, or recreational activities. Intent labels are inferred by the intent recognition module based on contextual factors such as the time period of the run, the path structure, and the user's set destination. Ultimately, the zero-knowledge proof summary generated for the run, the carbon reduction estimate, the behavior timestamp, and the intention label together constitute a unique on-chain anonymous carbon credit anchor. This anchor is bound to the user's zero-knowledge identity system without revealing their true identity. This anchor constitutes a minimal ownership structure for carbon credit units, ensuring verifiability, immutability, and anonymity.After on-chain ownership confirmation is completed, the system further calls the preset Carbon Proof Atom Mapping (CPAM) protocol, using the local carbon credit anchor point on the aforementioned chain as the input primitive. Through cryptographic signatures and inter-chain proof transmission modules, it achieves atomic, one-time mapping to the third-party carbon credit blockchain. The mapping process is indivisible, irrevocable, and valid once, avoiding the problems of double registration, carbon asset duplication, and anchor point reuse that exist in traditional cross-chain asset mapping. A tradable carbon credit asset or NFT certificate is generated on the target carbon credit public chain (such as Toucan Protocol, Flowcarbon, Celo, etc.). This asset has a complete one-to-one correspondence with the user's locally generated carbon anchor point and can be used for subsequent point redemption, carbon market transactions, or digital rights exchange, thus forming a complete path from privacy-protected behavior proof → dynamic carbon emission reduction valuation → anonymous carbon credit anchor point → main-chain carbon asset.

[0024] Example 1: Combined with attachment Figure 3The user is a 35-year-old male user, weighing 70 kg and 175 cm tall. His usual commuting distance is 2.8 kilometers. He is accustomed to running to complete his commute around 7:30 in the morning. He uses a smart watch and a mobile phone to record his behavior. The system first starts the local behavior collection module when the user starts exercising, and records the three-axis acceleration changes per second through the acceleration sensor. The relative displacement information is recorded through the GPS module but the absolute position is not stored. At the same time, the cadence and rhythm change curves are obtained. During the entire exercise process, the user continued running for about 18 minutes, with a total displacement of 2.85 kilometers. The system recognizes that his behavior is continuous, the rhythm is natural, and the acceleration curve is a typical running cycle type, which meets the local semantic behavior modeling rules. The continuity assertion detection is that the uninterrupted window is greater than 15 seconds, the acceleration pattern conforms to the stride-cadence linkage model, the average cadence is 165 steps / minute, and the displacement curve is a non-closed loop route, moving from the user's home location to the preset office location. The system marks it accordingly. The intention label is commuting replacement behavior. The above collected data is not uploaded to the chain, but is only used locally to generate a set of zero-knowledge proofs. The system uses the zk-SNARK protocol to generate a behavior proof of approximately 512 bytes, completes the circuit execution on the user side, and outputs the behavior summary hash. The summary is the on-chain credential for the authenticity of the behavior; then, the system retrieves the user's physical parameters and behavior process data to perform individual carbon emission reduction valuation. According to the known exercise metabolism model, the user runs at a speed of 165 steps / minute for 18 minutes, generating approximately 148 kcal of heat output, equivalent to approximately 619 kJ of energy. After conversion, its equivalent fuel carbon emissions are approximately 0.032 kg CO2e. If the user completes a 2.8-kilometer commute in a small gasoline car, based on the common emission factor of 0.176 kg / km, the estimated carbon emissions are 0.176×2.8≈0.4928 kg CO2e. Based on this, the system judges that the running behavior achieves 0.4928–0.032=0.4608 kg The CO2e substitution effect is evident in the following examples: After generating the behavior validity, timestamp (2025-05-28 07:33), carbon valuation, and intention tag for commuting substitution, the system combines these four fields to construct an anonymous on-chain carbon credit anchor identifier (ZK-CreditAnchor). This identifier does not contain the user's identity, but is bound only to their zero-knowledge identity (ZK-ID). The anchor is then submitted to the chain through a smart contract and successfully stored. After the anchor is completed, the system invokes the Carbon Credit Atomic Mapping Protocol (CPAM). This protocol, based on the consistency requirements of the on-chain anchor summary, the off-chain carbon valuation signature, and the time window, triggers a one-time cross-chain mapping process, converting the anchor into a carbon credit NFT. This NFT is successfully mapped to the carbon pool on the Polygon chain. The asset is named ZKCarbon_#910283 and the mapping was completed at 07:35. The market valuation of this asset is calculated based on the carbon credit unit price of ¥9.6 / kgCO2e on that day, which is approximately 9.6×0.4608≈¥4.42; The system then calculates the points that can be redeemed for the user's current behavior based on the point incentive valuation function. The formula is as follows: in, 7:30, The time window is 18 minutes, during which the user's behavior frequency is , because the behavior fully satisfies the semantic behavior assertion, the density function takes the full value; function middle, The carbon emission reduction is 0.4608, and the corresponding weight of the intention label commuting replacement is , so ; The current blockchain carbon credit asset market supply circulation density is , attenuation sensitivity coefficient , then the exponential term , the final integral is: Multiplying this value by the unit magnification factor of the points system will result in the user receiving approximately 121.7 RunPoints, which can be redeemed on the platform for digital badges, equipment discounts, or directly offset against part of the carbon credit handling fee. In addition, the points record will also be updated synchronously with the user's anonymous carbon credit anchor to form their personal low-carbon behavior asset chain. Through this method, users can completely convert a natural commuting behavior into low-carbon points and carbon assets available on the chain without exposing their location or original health data, providing an efficient, reliable, and privacy-protected technical path for individual carbon credit ownership, asset generation, and incentive issuance.

[0025] During the local analysis and confirmation of the behavior, the system not only collects basic acceleration and displacement data, but also comprehensively introduces a set of behavioral semantic assertion mechanisms to enhance the intelligent recognition capability of the credibility of running behavior and support the logical input of the zero-knowledge proof circuit. The behavioral semantic assertion mechanism consists of four types of assertion rules, namely: continuity assertion between cadence and acceleration, non-periodic forgery detection assertion of displacement changes, high-frequency pseudo-vibration exclusion assertion of acceleration signals, and behavior interruption and recovery interval judgment assertion. The system records acceleration information at a frequency of 50Hz per second during user exercise. The total running time is 18 minutes, and the theoretical number of sampling points is about 54,000. First, the system applies the cadence and acceleration continuity assertion rules for matching. The user's average cadence is 165 steps / minute, which is about 2.75 steps / second. In the pace range of 5'30'' / km, the system expects the acceleration peak to be close to the cadence. There is a linear response relationship between the frequencies, manifested as iso-rhythmic amplitude variations in the acceleration curve. The system extracts the local maximum peak intervals of the acceleration signal and matches it with the real-time cadence sequence, with an allowable error of ±0.15s. A cumulative error of no more than 10% is considered to meet the assertion criteria. The user's acceleration peaks consistently occur every 0.36 seconds, with an actual matching rate of 95.8%, thus passing the assertion. The system then invokes the aperiodic forgery detection assertion module to correlate and match the GPS displacement curve with the cadence sequence. If the displacement trajectory exhibits an equidistant, linear, repetitive pattern caused by mechanical drive, such as the vibrator and swing arm, it is identified as an unnatural trajectory. The system calculates the trajectory shape overlap using the dynamic time warping (DTW) algorithm, with a threshold set to no more than 85% overlap between three consecutive trajectory segments. The user's trajectory changes include path deformations such as acceleration and waiting to cross the road, resulting in a trajectory shape overlap of 66%.1%, which is consistent with the non-periodic assertion logic. Thirdly, the system analyzes the spectral structure of the acceleration signal to detect whether there are abnormal high-frequency vibration signals. This mechanism is used to identify situations where the cadence is mechanically forged. The upper limit of the filter is set to 15Hz. The system did not detect high-frequency continuous jump signals greater than 12Hz in the user data, and the energy was concentrated in the main frequency band of 3-6Hz, which is consistent with the natural movement frequency of the human body. Fourth, the behavior interruption and recovery interval judgment assertion requires that the system does not have a non-exercise state of more than 10 seconds in the behavior window. The user briefly stopped for about 5 seconds waiting for the red light at the 9th minute, and then quickly recovered. The system allows a maximum of one interruption of no more than 10 seconds in 60 seconds of exercise, and the cadence continuity is maintained at least 90% after recovery. The user's cadence before and after the interruption was 168 and 163 respectively, and the recovery continuity reached 97%, which is consistent with the assertion logic. At this point, all four semantic behavior assertions have passed, and the system uses the assertion result as a Boolean A vector form is embedded in a ZKP circuit to construct a logical statement that the behavior satisfies continuity, aperiodicity, a natural spectrum, and no spurious interruptions. This behavior is then compiled into a cryptographic proof by the zk-SNARK module. After the proof is generated, the user's local device only uploads the digest hash, which is accepted by the on-chain contract and becomes one of the trust foundations for their carbon credit anchor. Precisely because the assertion mechanism ensures the authenticity and credibility of the behavior data source, the system's subsequent estimated carbon emissions reduction (0.4608 kg CO2e) and the credit mapping value (121.7 points) are capable of on-chain ownership confirmation and ultimately successfully converted into the ZKCarbon_#910283 asset mapped to the Polygon chain. This process avoids the problems of data forgery, unverifiable behavior, and inflated asset values ​​found in traditional sports credit systems, achieving a complete trusted path from physical authenticity of the behavior to semantic confirmation of the behavior to construction of the proof circuit to on-chain ownership confirmation and asset generation.

[0026] After the user completes the 18-minute, 2.85-kilometer morning commute run, the collected raw behavioral data, including the three-axis acceleration curve, cadence rhythm, displacement dynamics, timestamp sequence, etc., are all retained locally. The system does not upload any data in plain text to the server or chain, but instead starts the local zero-knowledge proof generation module. Based on the results of the four semantic assertions mentioned above, a logical input Boolean vector is constructed to indicate whether the behavior satisfies the continuous cadence-acceleration correspondence, the trajectory change is non-periodic, the acceleration spectrum is lower than the 15Hz high-frequency vibration threshold, and the interruption-recovery time window complies with the rules. The system uses a pre-deployed zk-SNARK protocol based on the ZK circuit template written in the Circom language. The path size is controlled within approximately 50,000 constraints to adapt to the computing power of mobile devices. The user's smart watch chip completes local proof generation through the zero-knowledge proof generation tool SnarkJS, which takes about 4.6 seconds. The generated ZKP size is 512 bytes, containing only the encrypted summary and circuit path that are valid for Boolean logic. It does not contain any original numerical data that can restore user behavior or information that can identify movement trajectories. Finally, the summary hash of the ZKP is verified to be valid through the on-chain smart contract, becoming the core trust basis for subsequent carbon anchor point evidence. After completing the confirmation of the validity of the behavior, the system enters the carbon emission reduction valuation stage, using an individualized dynamic estimation model that does not use a unified template parameter. The valuation process is driven by the user's physiological data, actual exercise intensity, and behavioral semantic labels. The user weighs 70 kg, runs at a pace of about 5'30" / km, and has an average speed of 10.9 km / h. According to the relevant recommended energy metabolism model, the energy output of men at this intensity is 0.173 kcal per kilogram of body weight per minute. Therefore, the user's energy output in 18 minutes is approximately 0.173×70×18≈217.98 kcal, equivalent to 912 kJ. This energy is regarded as an indicator of the metabolic intensity of the behavior. The system compares the output with the carbon emissions of alternative transportation behaviors. The user's commuting route is 2.85 km. If it is changed to a gasoline private car, the carbon emission coefficient of the fuel based on common models is 0.176kgCO2 e / km, the corresponding carbon emissions are 0.176×2.85≈0.5016kgCO2e. A behavioral intention weighting mechanism is introduced. Because the user's behavior is identified as a commuting substitution behavior, the weighting coefficient Γ=1.3. The system believes that the carbon reduction intention effect is enhanced, which is suitable for high-intensity weighting. Therefore, the final carbon reduction equivalent is calculated using the following logic: Carbon emission reduction value = (gasoline replacement emissions − own exercise carbon load) × intention weight = (0.5016−0.0413)×1.3≈0.5984kgCO2e, where the exercise carbon load of 0.0413kgCO2e is converted from its energy output of 912 kJ (1MJ≈0.112kgCO2e of food carbon emissions).5984kgCO2e is used as the carbon credit confirmed for this user's behavior in the carbon finance system. The system binds the aforementioned ZKP summary hash and timestamp to generate a unique carbon credit anchor, ZKCredit_Anchor#LM20250528, which is then mapped to the on-chain asset ZKCarbon#910283 through the carbon credit atomic mapping protocol. This process ensures the authenticity of user behavior, controllable privacy, and quantifiable carbon value. Without accessing the original movement information, the system achieves credible low-carbon behavior asset ownership. On the incentive side, the system calculates the RunPoints earned through the mapping integral function to be 124.5 points, approximately 28% higher than the points for typical recreational activities.

[0027] After the user completes a run of 18 minutes and a total mileage of 2.85 kilometers, the smart device he wears triggers the behavioral data collection and semantic modeling module locally. The behavioral data includes three-axis acceleration, displacement point series, cadence rhythm and activity time series. After passing the four tests of local semantic behavior assertion (i.e., cadence and acceleration continuity, non-periodic trajectory changes, high-frequency pseudo-vibration elimination, and behavior interruption recovery window control), the system calls the zk-SNARK protocol to generate a behavior proof circuit and constructs the Boolean statement logic of whether the predefined behavior structure is met. The device completes the proof generation in about 4.6 seconds locally and outputs the encrypted proof data. The data is summarized and compressed using the Blake2b hash algorithm to obtain the unique The 256-bit summary hash value of the carbon credit anchor is in the form of 0x8eac1f...fe4d, which becomes the zero-knowledge proof summary hash field of the carbon credit anchor identifier, which is used to verify the validity of the behavior on the chain without exposing the original behavior data; then, the system calls the local carbon emission reduction valuation model, and calculates it based on the user's weight (70kg), pace (about 5 minutes and 30 seconds / kilometer), behavior time (18 minutes) and intention label (commuting replacement). The user's behavior outputs a total of 912 kilojoules of energy, which is equivalent to a self-exercise carbon load of 0.0413kgCO2e, corresponding to the replacement of gasoline vehicle emissions of 0.176×2.85≈0.5016kgCO2e, the intention weighting coefficient is 1.3, and the converted carbon emission reduction equivalent is (0.5016–0.0413)×1.3≈0.5984kgCO2e. To prevent valuation errors from causing fluctuations in subsequent asset values, the system adopts a floating interval mechanism and sets the value to [0.5800,0.6150]kgCO2e. This interval becomes the second field carbon reduction valuation interval in the anchor identifier, representing the extent of the carbon credit value of the behavior. Next, the system records the UTC timestamp generated by the anchor point as 2025-05-28T07:48:12Z, and fills in the third field as the timestamp to ensure the uniqueness of the anchor point on the chain and the complete life cycle identification of the behavior. In terms of user privacy protection, real identity information is not used, and only the zkID generated when the user registers is used. The knowledge identity tag (e.g., ZKID_0x29c5...d3a7) serves as the user's encrypted identity. This tag not only confirms asset ownership but cannot be mapped back to any sensitive user data or personal information, ensuring that carbon credit allocation is completed under privacy protection conditions. Finally, the behavioral pattern recognition module comprehensively determines the behavioral intention as a commuting substitution behavior based on displacement characteristics, time semantics (7:30 am rush hour), route characteristics (starting from home address to office building), and historical commuting preference model. This behavior is marked with the tag code INT_TAG_COMMUTE, which serves as the fifth field of the anchor point's behavioral intention tag. In summary, the carbon credit anchor identifier (ZK-CreditAnchor) generated by the user's behavior has the following structure: 1. Zero-knowledge proof summary hash: 0x8eac1f...fe4d 2. Estimated carbon reduction range: [0.5800, 0.6150] kgCO2e 3. Action timestamp: 2025-05-28T07:48:12Z 4. User encrypted identity: ZKID_0x29c5...d3a7 5. Behavioral intent tag: INT_TAG_COMMUTE The logo is then atomically mapped to the Polygon blockchain through the carbon credit atomic mapping protocol CPAM, generating a carbon credit asset NFT numbered ZKCarbon_910283. Users complete behavioral rights confirmation, carbon value generation and asset mapping while maintaining privacy, and obtain 124.5 platform points through the system incentive function for subsequent redemption of carbon credit fees or digital badges.

[0028] After completing the 2.85km run, the user generated a carbon credit asset, ZKCarbon_910283, which is valued at 0.5984kgCO2e. Based on the average real-time transaction price of carbon assets on the Polygon chain in the third-party carbon credit market on that day, the system calculated the price of carbon credit per kilogram to be RMB 9.45. The system read the market data through the on-chain oracle and recorded its fluctuation as a steady increase. The platform's point incentive scheduling module is in the middle and high range according to this price range (RMB 9.00-10.00 / kg), and also refers to the current platform activity index. The index is calculated by the number of valid ZKP behaviors submitted by all users in the past 24 hours and the active ratio of total registered users. The current value is 0.42, which is below the median level of platform activity. Based on this, the incentive scheduling strategy module determines that the current point allocation strategy of "encouraging behavioral incentives to increase" should be adopted, that is, increasing the exchange ratio of points to carbon credits to stimulate the activity of healthy behaviors on the platform. The system will increase the default point conversion factor from 100 points / 0.5kgCO2e to 125 points / 0.5kgCO2e, that is, the user's carbon credit valuation this time is 0.5984kgCO According to the current strategy, 2e can be exchanged for approximately 149.6 platform points RunPoints, which are synchronized to the user's on-chain wallet account; the user then enters the platform points mall page. When viewing the redemption options, the platform gives a special benefit reminder based on his intention label commuting alternative behavior, reminding him that if he meets the behavior standard three times this week, he can obtain a Green City Pioneer digital badge. In addition to being used for display and check-in, the badge can also increase the discount rate of carbon credit redemption fees by 10% in the future. The user chooses to use 100 points of the points earned this time to redeem a limited-time equipment benefit anti-sweat running The 50% off coupon for the boom bag, and the remaining 49.6 points are not used for the time being, and are reserved for the next carbon asset subsidy return. They will be used as a fee subsidy deduction when the next anchor asset is generated. The system records the points distribution path and displays the complete closed-loop trajectory in its behavior data view, namely: behavior confirmation → carbon credit valuation → asset generation → incentive distribution → equity exchange → subsequent subsidies. Users can intuitively view the positive impact of their past behavior on the environment (cumulative carbon emission reduction of 2.35kg), the asset equity obtained (a total of 3 equipment exchanged and 1 carbon token return) and the platform contribution (top 5% this month).

[0029] Example 2: Combined with attachment Figure 4Based on Example 1, after a user wearing a smartwatch starts running from their residence at 7:30 a.m. along their designated commuting route at a pace of approximately 5 minutes and 30 seconds per kilometer for 2.85 kilometers, the system sequentially activates the acceleration acquisition, cadence recording, displacement tracking, and semantic behavior modeling modules on their local terminal. After completing behavior continuity assertion, aperiodic trajectory determination, high-frequency vibration elimination, and interruption window evaluation, the system confirms that the behavior meets the set semantic behavior standards and initiates the zero-knowledge proof generation process. The embedded zk-SNARK module is called to construct a proof circuit, which does not contain the original behavior data and only cryptographically verifies the establishment of the behavior's logical conditions. The generated proof is approximately 512 bytes in size and takes approximately 4.6 seconds to generate. Finally, the system uses the Blake2b hash algorithm to compress the ZKP into a unique summary hash 0xab47e1...c8df, which serves as the first field of the behavior anchor: the zero-knowledge proof summary. The system then reads the basic physical parameters such as weight (70kg) and age (35 years old) provided by the user when registering on the platform. Combined with the average pace (5'30" / km), total duration (18 minutes), and displacement distance (2.85 kilometers) collected during the behavior, the energy output is calculated to be approximately 217.98 kcal (912 kJ). Based on the food metabolism carbon emission factor of 0.112 kgCO2e / MJ, the exercise load is estimated to be 0.0413 kgCO2e. In contrast, if the behavior is completed by a gasoline car for 2.85 kilometers, the energy output is about 217.98 kcal (912 kJ). The carbon emissions required for commuting are approximately 0.176kg / km×2.85km=0.5016kgCO2e, so the actual carbon emission reduction is (0.5016–0.0413)=0.4603kgCO2e. The valuation tolerance mechanism is supported, and the value is mapped to a floating interval of approximately 0.45–0.48kgCO2e as the second field of the anchor point: carbon reduction value. The system then calls the on-chain clock service to obtain the current UTC timestamp 2025-05-28T07:49:02Z and records it as the third field: timestamp, which is used to identify the generation time of the carbon credit anchor point on the chain and ensure uniqueness and traceability. In the behavioral intention analysis module, the system analyzes the starting point of the user's behavior to set the location for their long-term commuting. The system combines the user's behavior records and commuting preferences over the past 30 days to determine that this behavior is a commuting replacement behavior. The system adds the label code INT_COMMUTE as the fourth field: behavioral intention label, which is used as a reference for subsequent carbon credit mapping and incentive weight scheduling. Finally, the system uses the zero-knowledge identity identifier ZKID_0x29c5f3...d3a7 generated by the user during initial registration as the attribution credential, binds all information of this behavior anchor to their anonymous identity, and forms the fifth field: anonymous identity identifier. The resulting carbon credit anchor ZK-CreditAnchor_003928 has the following structure: (1) Zero-knowledge proof summary: 0xab47e1...c8df, (2) Carbon reduction value: [0.45–0.48] kgCO2e, (3) Timestamp: 2025-05-28T07:49:02Z, (4) Behavioral intention label: INT_COMMUTE, (5) Anonymous identity: ZKID_0x29c5f3...d3a7, This anchor structure realizes the on-chain confirmation of the authenticity of low-carbon behavior without exposing any original movement data. At the same time, it provides a complete trusted data unit for subsequent cross-chain mapping to the carbon credit chain, point incentive issuance and user environmental contribution portrait. When users view their behavior records on the platform, they can clearly see the structural summary, valuation level and redeemable status of the anchor. In the weekly carbon asset assessment, the platform issues RunPoints and badge rights to them based on the ZKCarbon assets generated by the anchor, indicating that the anchor has completed the entire process from behavior collection → semantic verification → anonymous confirmation → value mapping.

[0030] The user starts running at 7:30 am on their smart wearable device, recording their behavioral data throughout the entire process, including three-axis acceleration (used to determine cadence and body vibration rhythm), displacement trajectory (used to verify the authenticity and non-periodic characteristics of the route), behavior time (used to determine continuity and rhythm), and interruption recovery characteristics (such as the logical consistency of short stops and rapid recovery). The above raw data is stored in encrypted form on the user's local device and is used to run the deployed semantic behavior modeling module without uploading. This module breaks down the behavior into a set of structured semantic assertions, such as whether the cadence range is continuous and stable (the user averages 165 steps / minute, with a standard error of less than ±3 %), whether the peak acceleration change conforms to the natural running rhythm (the average peak interval is 0.36 seconds, which conforms to the corresponding relationship between the cadence and rhythm), whether the displacement path is a non-closed-loop linear extension (from the starting residence to the end office area), whether there is a single interruption of no more than 10 seconds and the recovery speed is higher than the original average speed, etc. All assertions are Boolean logic and are wired as inputs to the ZK proof circuit. The zero-knowledge proof circuit built in the Circom language runs locally to generate a non-interactive proof structure (using the Groth16 protocol). The proof process does not require interaction with the chain. After the user's smart watch completes the proof construction and calculation generation, it outputs 512 bytes of proof data, which is then processed by the local The Blake2b algorithm performs summary processing to form a unique zero-knowledge proof summary hash, such as 0xf63aef218b...95c7d. This hash is the only encrypted summary of whether the behavior is compliant and trustworthy. It does not contain any original information about the movement distance, speed, trajectory or time, and has no reverse deconstruction capability. Even if an attacker obtains the hash summary, they cannot restore the user's behavior process, ensuring the absolute security of behavioral privacy. This summary is written into the blockchain as a key field in the carbon credit anchoring structure. The preset smart contract logic is only used to determine whether the summary corresponds to a valid proof constructed by the platform's standard semantic modeling template. If the smart contract has built-in If the verification key can successfully verify the validity of the proof corresponding to the summary, the behavior is considered to have been confirmed on the chain. Otherwise, it will be rejected from writing to the on-chain anchor. The summary submitted by the user is matched under contract verification, successfully verified with the on-chain behavior assertion template, and registered in block number #23127498. Its zero-knowledge summary hash 0xf63aef218b...95c7d is officially recorded as the zero-knowledge proof summary field of the ZKCreditAnchor structure. The platform subsequently generates the carbon credit asset ZKCarbon_910283 based on this anchor and issues 149 RunPoints to the user to redeem carbon subsidy preferential qualifications.

[0031] The user put on his smartwatch at 7:30 in the morning and started his daily commute run. The system collected his behavioral data in real time, recording a total duration of 18 minutes, a pace of 5 minutes and 30 seconds per kilometer, a total distance of 2.85 kilometers, and an average cadence of 165 steps per minute. According to the set carbon emission reduction estimation model, the system combined the user's weight parameter of 70 kg, and calculated his energy output rate per unit time based on the pace and cadence. The exercise metabolism model converted it into a total energy output of about 218 kcal in this running behavior, which is equivalent to 912 Kilojoules, and the carbon emission load factor used in the present invention is set to 0.112kgCO2eMJ, corresponding to the user's own metabolic carbon emission of this exercise is about 0.0413kgCO2e; then the system identifies the behavior as a commuting replacement behavior through the user's behavioral intention model. According to its non-closed-loop route, daily behavior patterns, trajectory start and end characteristics, etc., it is determined that if the user originally used a gasoline private car to travel, the corresponding fuel consumption will produce carbon emissions. The system calls the carbon emission standard of a small gasoline car as 0.176kg according to the local city emission factor library. CO2e / km, multiplied by the commuting distance of 2.85 km, estimates that the emission of alternative transportation behavior is about 0.5016kgCO2e. Based on the alternative emission reduction valuation logic in this invention, the system performs a difference operation to obtain the carbon emission reduction value of this behavior as 0.5016 minus 0.0413, that is, 0.4603kgCO2e. To ensure that the actual valuation is consistent with the floating range of on-chain assets, the system generates an estimated range of [0.45, 0.48]kgCO2e for this value as an anchor field. During the construction phase of the carbon credit anchor identifier, The user-generated zero-knowledge proof digest 0xf63aef...95c7d and the behavior timestamp 2025-05-28T07:49:02Z are used together to construct a trusted data structure. The user's identity is not based on conventional sensitive fields such as real name or mobile phone number. Instead, the user uses the established zero-knowledge identity management system. During the registration phase, the zk-ID generation module generates an anonymous, non-decryptable identity. The user's identity in this behavior anchoring is ZKID_0x29c5f3a8...d3a7. This identifier does not contain any real-world identity information, but it can be authenticated and transferred across multiple platforms using a zero-knowledge signature mechanism, ensuring trustworthy asset ownership and cross-chain mapping capabilities. This anonymous identity is strongly bound to all fields in the ZK-CreditAnchor to form a traceable but unidentifiable carbon credit anchor. This anchor is then atomically mapped to a third-party carbon credit asset pool on the Polygon chain, generating a carbon asset NFT numbered ZKCarbon_910283. Ownership is controlled by the user's anonymous identity. Furthermore, the platform's points system uses this anonymous identity as the point record. The user's 149 RunPoints earned from this activity can be redeemed in the platform's redemption mall using their anonymous ID for benefits such as carbon credit fee discount coupons, discounts on sports equipment, and limited-edition digital badges. The entire process, from carbon reduction value estimation and anchor confirmation to point incentives and on-chain asset ownership, is closed-loop without exposing user plaintext information. This ensures the system combines trustworthiness, security, user privacy protection, and compliant carbon asset attribution, making it widely adaptable to multi-platform green finance ecosystems.

[0032] After the user completed the compliant running behavior, the platform completed the semantic behavior assertion recognition and zero-knowledge proof generation locally, and output the digest hash 0xf63aef...95c7d. The system confirmed that it met all the behavioral credibility conditions. Combining its metabolic carbon load and commuting substitution behavior model, it evaluated the effective carbon reduction value of this time to be approximately 0.4603kgCO2e. The system encapsulated this value together with the ZK identity ZKID_0x29c5f3a8...d3a7, the behavior timestamp 2025-05-28T07:49:02Z, and the behavior intention label INT_COMMUTE` into the ZK-CreditAnchor structure, forming an on-chain data anchor number Z that cannot be tampered with and has a trusted source. K-CreditAnchor_003928 is generated and recorded in the platform's private chain main account book. Once the anchor is generated, the cross-chain mapping process is triggered, and cross-chain transmission is achieved using an atomic smart contract control mechanism and a decentralized oracle collaboration architecture. The mapping steps include hashing and signing the anchor structure, submitting a mapping request to the carbon credit bridge contract CarbonBridge.sol, and synchronizing the oracle node to verify whether the behavior hash matches the platform behavior model template. At the same time, it checks whether the ZK-ID has been bound to the hash. If the match is successful, the one-time Mint function is triggered, and a carbon asset numbered ZKCarbon_910283 is issued to the user's anonymous ZK identity address on the target chain Polygon. NFT, its metadata encapsulates the anchor hash, carbon reduction valuation, timestamp and behavior summary fields, and the system marks the anchor status on the source chain as mapped, that is, it enters an irrevocable state. Any repeated mapping operation will be rejected by the CarbonBridge contract, ensuring that the carbon credit of this behavior can only generate assets in one pass path, with absolute uniqueness and economic anti-cheating properties. Once generated, the NFT obtains circulation properties compatible with other standard carbon assets. Users can choose to place orders, trade or combine issuances in open markets that support carbon credit circulation. For example, if a user completes commuting substitution behavior for seven consecutive days, the system will recognize that he holds seven consecutively numbered ZK-CreditAnchorNFT assets. If the total carbon emission reduction If the weight exceeds 3.2kg, users can choose to have the platform's smart asset contract issue a combined carbon credit certificate, ZKCarbonPack_0131, which can be combined into multiple behavioral combined credit assets that can be used for tax deductions or carbon neutrality declarations. This asset has a higher grade and reputation weight, and enjoys priority purchase rights in the corporate carbon neutrality alliance. At the same time, the data model supported by the anchor structure can also be embedded in the platform's carbon credit redemption system. Users' RunPoints accounts on the platform have accumulated 432 points. The newly acquired 149 points come from the ZKCarbon_910283 asset mapping behavior. The platform's points engine allocates points based on the user's behavioral intention type (commuting), the mapped chain value (the current average price of carbon assets on the Polygon chain is 9.3 yuan / kg), and the anchor carbon emission reduction value (0.4603kgCO2e) dynamically adjust the point conversion rate, granting users the right to redeem points for carbon subsidy exemption coupons within the platform, equipment discounts, or future anchor minting fee reductions. The anchor structure simultaneously records the ownership ID and asset structure, allowing for seamless asset migration and maintaining complete historical traceability when users change devices, migrate across platforms, or synchronize identities between multiple chains, ensuring the integrity of user assets and the stability of the point value conversion path.

[0033] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping, characterized by: The following steps are involved: Model running behavior as semantic conditions including persistence, acceleration pattern, cadence, and displacement curves, and calculate and generate zero-knowledge proofs locally on the device; Estimate individual energy output based on physiological parameters and exercise types; compare carbon emissions of the same transportation mode and estimate the emission reduction effect of substitution; The carbon reduction value is based on the comparison between intention and behavior, and the conversion of energy and carbon into a core model; A zero-knowledge proof-derived on-chain carbon credit identity is introduced, and the identity is jointly generated by semantic proof and carbon valuation; after each run is completed, the user obtains an anonymous on-chain carbon credit anchor, which is used to record the zero-knowledge proof summary, carbon reduction value, timestamp, intention label, and uses the carbon credit atomic mapping protocol to map the carbon credit anchor on the local chain to the mainstream carbon credit public chain in an indivisible way at one time.

2. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 1 is characterized by: The method for calculating and generating zero-knowledge proof locally on the device is: The user's terminal device collects running behavior data locally, including acceleration changes, displacement characteristics, cadence rhythm, and exercise duration; Performing structured analysis of the running behavior data locally using semantic behavior modeling to generate a set of behavioral semantic assertions that describe the continuity, naturalness, and rationality of the user's exercise behavior; Constructing the semantic assertion into a zero-knowledge proof computation circuit to locally generate a set of zero-knowledge motion behavior proofs; Submitting the zero-knowledge proof to a smart contract on the blockchain platform for verification; estimating the carbon emission reduction corresponding to the running behavior in a local or trusted environment based on the verified running behavior proof, combined with the user's physical parameters, exercise duration, and behavioral intention characteristics; The carbon emission reduction amount is bound to the running behavior to generate an on-chain carbon credit anchor identifier as the unique identification information of the carbon credit unit; the carbon credit identifier is atomically mapped to the third-party carbon credit chain through the cross-chain oracle mechanism to generate a tradable carbon asset, and the carbon asset is mapped and exchanged with the platform points through point incentive scheduling.

3. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as described in claim 2 is characterized by: The behavioral semantic assertions include one or more of the following: Continuity assertion between cadence and acceleration; Aperiodic forgery detection assertion for displacement changes; High-frequency pseudo-vibration exclusion assertion of acceleration signals; Behavior interruption and recovery interval judgment assertion.

4. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping according to claim 1 or 2, characterized in that: The zero-knowledge proof is generated using the zk-SNARK or zk-STARK protocol, and the proof only encrypts and authenticates whether the behavioral semantic assertion is true or not, and does not contain any original behavioral data content; the carbon emission reduction estimation model is based on the user's weight, pace, energy output level and behavioral intention label, and compares the carbon emission baseline of alternative transportation modes to form the individual behavior carbon emission reduction equivalent.

5. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 2 is characterized by: The carbon credit anchor identifier includes the following fields: Zero-knowledge proof digest hash; Estimated range of carbon reduction; Behavior timestamp; User encrypted identity; Behavioral intention label.

6. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 2 is characterized by: The point incentive scheduling dynamically adjusts the exchange ratio between running points and carbon assets based on the current on-chain carbon credit asset price and platform sports activity; the points earned by users can be used to redeem platform resources, digital badges, equipment rights or return on-chain carbon credit tokens, forming a closed-loop path of user behavior, carbon value and financial incentives.

7. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 1 is characterized by: The carbon credit anchor is an on-chain data structure generated based on the user's qualified running behavior, which contains the following irreversible and verifiable fields: (1) Zero-knowledge proof summary, which is used to record the hash summary of the zero-knowledge motion behavior proof generated locally by the user terminal and consistent with the behavioral semantics assertion; (2) Carbon reduction value, which is used to represent the equivalent carbon reduction estimate corresponding to the running behavior, and is generated by the local calculation module based on the user's motion parameters, physical data and alternative carbon emission benchmarks; (3) Timestamp, used to record the time when the anchor point is confirmed on the blockchain; (4) Behavior intention label, which is used to express the user's motivation characteristics when completing the behavior. It is generated by the behavior modeling module based on the user trajectory pattern and behavior context; (5) Anonymous identity identification, used to attribute the anchor point to the user's zero-knowledge identity system.

8. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 7 is characterized by: The zero-knowledge proof summary is a compressed hash value of the non-interactive zero-knowledge proof generated by the user's local device based on semantic behavior modeling. The summary can only be used by the on-chain contract to verify the compliance of the behavior and cannot be reversed to derive the original behavior data.

9. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping as claimed in claim 7, characterized in that: The carbon reduction value is estimated locally based on the dynamic parameters of the user's weight, pace, cadence and behavior duration, combined with the carbon emission factor of the vehicle to be replaced. The anonymous identity is generated based on the user's zero-knowledge identity system, does not contain any plaintext identity information, and can be used across platforms for carbon credit asset attribution, point incentive redemption or cross-chain mapping.

10. The running points incentive method based on zero-knowledge proof and cross-chain carbon credit mapping according to claim 1 or 7, characterized in that: After the carbon credit anchor point is generated, it can be mapped across chains through smart contracts and oracles to generate tradable carbon assets, and the mapping process is atomic, irrevocable and one-time valid; the anchor point structure can be used to build a carbon credit asset portfolio, circulation certificate or carbon credit exchange system.

Images