User incentive and points management methods and systems for carbon emission reduction behavior

Abstract

This invention provides a user incentive and points management method and system for carbon emission reduction behavior, relating to the field of carbon emission reduction behavior management technology. The method includes: acquiring the user's original trajectory information in the target travel behavior, and simultaneously collecting corresponding environmental feature data and equipment motion status data; performing segmentation processing; performing anomaly identification processing on each trajectory segment; performing segment consistency reconstruction processing on trajectory segments containing anomaly segment markers; performing travel mileage calculation processing to obtain the user's actual travel mileage in the target travel behavior, and generating corresponding carbon emission reduction data; performing points generation processing based on the carbon emission reduction data, converting the carbon emission reduction data into user points records; and mapping user points to a preset incentive resource pool to generate incentive resource availability data. This invention improves the autonomy and accuracy of carbon emission reduction behavior management.

Description

Technical Field

[0001] This invention relates to the field of carbon emission reduction behavior management technology, and in particular to a user incentive and points management method and system for carbon emission reduction behavior. Background Technology

[0002] The closest existing technologies to this patent primarily rely on a "behavior collection + rule engine points" model. This involves collecting users' low-carbon behaviors (such as shared bicycle use, public transportation card swiping, and smart meter electricity consumption records) through third-party service platforms or city carbon account systems, and then calculating points based on preset rules in the backend. For example, some publicly available carbon account systems typically obtain users' walking distance or public transportation trips by calling transportation interfaces, calculate carbon emission reductions using a fixed conversion factor, and then map these carbon emission reductions to corresponding points. The platform further links these points to a redemption mall or a tiered system to form a closed loop of user incentives. The main characteristics of this type of system are: data directly comes from behavior records across multiple platforms; the points rules are centralized, cannot be dynamically adjusted, and mainly rely on a single indicator (such as distance, frequency, or energy consumption value) for conversion.

[0003] However, in specific urban green travel scenarios, such existing technologies may exhibit significant technical limitations. For example, in low-carbon travel incentive programs for shared bicycles, the system often relies on the "riding mileage" uploaded by the bicycle operators as the sole basis for points. When users ride in densely populated urban areas, GPS signal drift due to multiple paths often leads to overestimation or underestimation of mileage, and existing systems cannot automatically identify or correct abnormal trajectories. As a result, for the same 1.2-kilometer route from the subway station to the office building, some users may record it as 2.1 kilometers due to GPS offset, thus obtaining higher points, compromising the accuracy of point calculation. Since points are tied to carbon emission reductions, inaccurate mileage will directly lead to distorted carbon emission reduction data, thereby affecting the fairness of point distribution. This deficiency is not a general problem of "low efficiency" or "high cost," but rather stems from the current technology's reliance on a single data source and the lack of trajectory verification mechanisms, thus failing to ensure the credibility of carbon emission reduction calculations in the incentive system. Summary of the Invention

[0004] The purpose of this invention is to provide a user incentive and points management method and system for carbon emission reduction behavior, aiming to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a user incentive and points management method for carbon emission reduction behavior, the method comprising:

[0007] Obtain the user's original trajectory information during the target travel behavior, and simultaneously collect corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data;

[0008] The trajectory verification input data is segmented into multiple trajectory segments, and trajectory segment feature data is generated based on the environmental feature data and equipment motion state data corresponding to each trajectory segment.

[0009] Anomaly identification processing is performed on each trajectory segment based on the feature data of the trajectory segments. By comparing the consistency between speed changes, direction changes and environmental features, trajectory segment identification data containing anomaly segment markers is generated.

[0010] Based on the trajectory segment recognition data, segment consistency reconstruction processing is performed on the trajectory segments containing abnormal segment markers. By compensating for the turning point positions and correcting the segment lengths, corrected trajectory information is generated.

[0011] The travel mileage is calculated based on the corrected trajectory information to obtain the user's actual travel mileage in the target travel behavior, and corresponding carbon emission reduction data is generated based on the actual travel mileage.

[0012] Based on the carbon emission reduction data, perform the points generation process, convert the carbon emission reduction data into user points records, and write the user points records into user points accounts to form points account data;

[0013] Based on the points account data, user points are mapped to a preset incentive resource pool, generating incentive resource availability data.

[0014] Preferably, anomaly identification processing is performed on each trajectory segment based on the trajectory segment feature data. By comparing the consistency of velocity changes, direction changes, and environmental features, trajectory segment identification data containing anomaly segment markers is generated, including:

[0015] The velocity change information in the feature data of the trajectory segment is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated.

[0016] The directional change information in the feature data of the trajectory segment is compared with the directional change trend of the previous trajectory segment. When the directional change deviates from the preset offset angle, a directional anomaly marker is generated.

[0017] The environmental feature data in the trajectory segment feature data is compared with the environmental feature constraints of the target travel behavior. When the geographical location corresponding to the trajectory segment is inconsistent with the road morphology, ground structure or occlusion status, an environmental anomaly marker is generated.

[0018] The velocity anomaly marker, direction anomaly marker, and environmental anomaly marker are combined to form trajectory segment anomaly attribute data. Anomaly segment markers are then generated for the trajectory segments based on the trajectory segment anomaly attribute data, thus forming trajectory segment identification data.

[0019] Preferably, based on the trajectory segment identification data, segment consistency reconstruction processing is performed on the trajectory segments containing abnormal segment markers. By compensating for the inflection point positions and correcting the segment lengths, corrected trajectory information is generated, including:

[0020] Based on the trajectory segment recognition data, the adjacent normal trajectory segments before and after the abnormal segment are determined, and the logical turning point position of the abnormal segment is determined based on the directional change relationship of the adjacent normal trajectory segments, thus forming logical turning point data.

[0021] The logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, the original inflection point position is compensated according to the continuity of direction to generate the compensated inflection point position.

[0022] The path range of the abnormal segment is redefined based on the compensated turning point position, and the reasonable travel distance of the abnormal segment is calculated based on the speed stability interval of adjacent normal trajectory segments to form segment length correction data.

[0023] Based on the compensated turning point position and segment length correction data, the abnormal segments are continuously reconstructed to ensure that the reconstructed trajectory segments are consistent with the adjacent normal trajectory segments in terms of direction change and travel distance, thereby generating corrected trajectory information.

[0024] Preferably, the velocity change information in the trajectory segment feature data is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated, including:

[0025] Based on the velocity data of the previous trajectory segment, obtain the velocity stability interval and form velocity stability interval data;

[0026] Based on the velocity stability interval data, short-time jump comparison processing is performed on the velocity change information of the current trajectory segment. When the velocity change cannot form a sequential correspondence with the velocity stability interval in terms of temporal continuity, short-time jump judgment data is generated.

[0027] Based on the speed stability interval data, the speed change information of the current trajectory segment is compared with the trend. When the continuous direction of speed change is opposite to the change trend of the speed stability interval, trend deviation judgment data is generated.

[0028] Based on the equipment motion status data, a consistency check is performed on the speed change information. When the equipment motion status is displayed as stable but the speed change shows a sudden change, equipment contradiction judgment data is generated.

[0029] Based on short-term jump judgment data, trend deviation judgment data, and equipment contradiction judgment data, abnormal attribute assignment processing is performed on the current trajectory segment. The trajectory segment that meets any abnormal condition is marked as a speed abnormal segment, and speed abnormal labeling data for subsequent trajectory verification is generated.

[0030] Preferably, the directional change information in the trajectory segment feature data is compared with the directional change trend of the previous trajectory segment. When the directional change deviates from a preset offset angle, a directional anomaly marker is generated, including:

[0031] Based on the directional change data of the previous trajectory segment, the directional change trend is obtained, and directional change trend data is formed.

[0032] Based on the direction change trend data, the direction change information of the current trajectory segment is compared with the direction continuity data. When the direction change cannot form a continuous change relationship with the direction change trend, direction jump judgment data is generated.

[0033] Obtain the corresponding road orientation data based on the geographical location of the trajectory segment, and perform road consistency comparison processing on the direction change information based on the road orientation data. When the direction change deviates from the allowable range of road orientation, generate road deviation judgment data.

[0034] Based on the equipment motion status data, the direction change information is checked for consistency. When the equipment does not rotate but the direction change jumps, behavior anomaly judgment data is generated.

[0035] Based on the direction jump judgment data, road deviation judgment data, and behavior anomaly judgment data, the current trajectory segment is processed to determine the direction anomaly attribute. Trajectory segments that meet the offset conditions are marked as direction anomaly segments, and direction anomaly labeling data for trajectory recognition is generated.

[0036] Preferably, the adjacent normal trajectory segments before and after the abnormal segment are determined based on the trajectory segment recognition data, and the logical turning point position of the abnormal segment is determined based on the directional change relationship of the adjacent normal trajectory segments, forming logical turning point data, including:

[0037] Based on the trajectory segment recognition data, the range of abnormal segments is located, and the information on the directional changes of normal trajectory segments before and after the abnormal segments is obtained to form directional continuity input data;

[0038] The direction extension line generation process is performed based on the direction continuity input data to obtain the direction extension line data;

[0039] Based on the directional extension line data, position intersection analysis is performed in the map data. When the intersection of the directional extension lines falls on a road node where a turn may occur, candidate turning position data is generated.

[0040] Environmental consistency verification is performed on the candidate turning locations based on environmental feature data. When the terrain, occlusion, or road structure of the candidate location matches the turning behavior, environmental verification data is generated.

[0041] Based on the directional extension line data, turning candidate location data, and environmental verification data, the turning candidate locations are processed to confirm turning points. Candidate locations that simultaneously satisfy directional continuity, road structure characteristics, and environmental consistency are determined as logical turning points, and logical turning point data is generated.

[0042] Preferably, the logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, compensation processing is performed on the original inflection point position according to directional continuity to generate a compensated inflection point position, including:

[0043] Based on the offset relationship between the logical inflection point data and the original inflection point position data, offset distance analysis is performed to generate offset reference data;

[0044] Direction offset correction processing is performed based on the offset reference data and the direction change information of adjacent normal trajectory segments. When the direction change of the original turning point does not meet the direction continuity requirements, direction correction data is generated.

[0045] Road alignment processing is performed on the direction correction data based on the road structure data. When the position deviates from the center area of ​​the road due to the direction correction, road alignment data is generated.

[0046] Motion consistency verification is performed on the road alignment data based on the equipment motion status data. When the equipment does not make a turning motion but the trajectory turns, motion verification data is generated.

[0047] Based on the direction correction data, road alignment data, and motion verification data, the original turning point position is processed for position correction output. The turning point position after correction by the three conditions of direction adjustment, road constraint, and motion verification is determined as the compensated turning point position data.

[0048] Secondly, a user incentive and points management system for carbon emission reduction behavior, the system comprising:

[0049] The trajectory data acquisition module is used to acquire the user's original trajectory information during the target travel behavior, and synchronously collect the corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data;

[0050] The trajectory segment processing module is used to segment the trajectory verification input data, divide the original trajectory information into multiple trajectory segments, and generate trajectory segment feature data based on the environmental feature data and equipment motion state data corresponding to each trajectory segment.

[0051] The anomaly detection module is used to perform anomaly detection processing on each trajectory segment based on the feature data of the trajectory segment. By comparing the consistency between speed changes, direction changes and environmental features, it generates trajectory segment identification data containing anomaly segment markers.

[0052] The segment reconstruction module is used to perform segment consistency reconstruction processing on trajectory segments containing abnormal segment markers based on trajectory segment recognition data. It generates corrected trajectory information by compensating for the position of turning points and correcting the segment length.

[0053] The mileage calculation module is used to perform travel mileage calculation processing based on the correction trajectory information, obtain the user's actual travel mileage in the target travel behavior, and generate corresponding carbon emission reduction data based on the actual travel mileage.

[0054] The points generation module is used to perform points generation processing based on carbon emission reduction data, convert carbon emission reduction data into user points records, and write user points records into user points accounts to form points account data.

[0055] The incentive mapping module is used to map user points to a preset incentive resource pool based on points account data, and generate incentive resource availability data.

[0056] The above-described solution of the present invention has at least the following beneficial effects:

[0057] First, by simultaneously collecting environmental feature data and device motion status data while acquiring travel trajectories, trajectory recording no longer relies solely on a single source of location information, thus enabling a multi-dimensional description of the trajectory's authenticity. This approach overcomes the limitations of traditional systems that rely solely on trajectories uploaded by operators or simply on location signals, laying the foundation for subsequent trajectory validity verification.

[0058] Building upon this foundation, the present invention segments trajectory data into segments and generates trajectory segment feature data by combining the environmental characteristics and device status of each segment, thereby improving trajectory resolution from an overall level to multiple local areas. This processing method enables more precise anomaly detection in the trajectory, facilitating accurate identification of data offsets caused by occlusion, reflection, or short-term signal fluctuations, and helps solve the problem of existing technologies being unable to identify local trajectory anomalies.

[0059] Furthermore, this invention performs anomaly identification on trajectory segments through a comparison mechanism that compares speed changes, direction changes, and environmental characteristics, enabling abnormal segments to be clearly marked before actual user behavior occurs. Compared to traditional methods that rely solely on total distance for judgment, this identification mechanism can provide qualitative information at the location of the anomaly, thereby avoiding overall overestimation or underestimation of mileage.

[0060] After anomaly identification, this invention reconstructs the consistency of the abnormal segments by compensating for turning point locations and correcting segment lengths, ensuring that trajectory direction changes and travel distances are consistent with the road structure. Compared to prior art systems that do not provide correction capabilities, this invention can obtain a corrected trajectory that more closely matches the actual travel path, fundamentally reducing the accumulation of false mileage caused by GPS offset.

[0061] Based on the reconstructed and corrected trajectory, the actual travel mileage generated by this invention is closer to the real driving distance, allowing subsequent carbon emission reduction calculations to be based on a reliable mileage foundation. This avoids deviations such as recording a distance that should be 1.2 kilometers as 2.1 kilometers. The resulting carbon emission reduction data is more stable and reliable.

[0062] Finally, by generating points from carbon emission reduction data and mapping them to an incentive resource pool, this invention ensures that the points distribution process aligns with actual carbon emission reduction behavior. Users only receive corresponding incentive resources after completing actual emission reduction actions, which helps improve the fairness of the incentive mechanism and avoids unreasonable point accumulation due to location errors, thereby addressing the problem of low credibility in points distribution in the prior art. Attached Figure Description

[0063] Figure 1 This is a flowchart of a user incentive and points management method for carbon emission reduction behavior provided in an embodiment of the present invention. Detailed Implementation

[0064] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0065] like Figure 1 As shown, an embodiment of the present invention proposes a user incentive and points management method for carbon emission reduction behavior, the method comprising:

[0066] Obtain the user's original trajectory information during the target travel behavior, and simultaneously collect corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data;

[0067] The trajectory verification input data is segmented into multiple trajectory segments, and trajectory segment feature data is generated based on the environmental feature data and equipment motion state data corresponding to each trajectory segment.

[0068] Anomaly identification processing is performed on each trajectory segment based on the feature data of the trajectory segments. By comparing the consistency between speed changes, direction changes and environmental features, trajectory segment identification data containing anomaly segment markers is generated.

[0069] Based on the trajectory segment recognition data, segment consistency reconstruction processing is performed on the trajectory segments containing abnormal segment markers. By compensating for the turning point positions and correcting the segment lengths, corrected trajectory information is generated.

[0070] The travel mileage is calculated based on the corrected trajectory information to obtain the user's actual travel mileage in the target travel behavior, and corresponding carbon emission reduction data is generated based on the actual travel mileage.

[0071] Based on the carbon emission reduction data, perform the points generation process, convert the carbon emission reduction data into user points records, and write the user points records into user points accounts to form points account data;

[0072] Based on the points account data, user points are mapped to a preset incentive resource pool, generating incentive resource availability data.

[0073] In this embodiment of the invention, by simultaneously collecting raw trajectory information, environmental feature data, and device motion status data during the travel behavior recording process, relatively complete behavioral input information can be formed before trajectory verification begins. This provides the foundation for subsequent processing to make multi-dimensional judgments on the rationality of the trajectory. This approach avoids the problem of insufficient information that may result from relying solely on single trajectory data, and provides more sufficient data support for trajectory splitting and feature generation.

[0074] After segmenting the trajectory verification input data into segments and generating trajectory segment feature data, the trajectory can be locally processed from multiple dimensions such as velocity changes, direction changes, and environmental features, giving each trajectory segment a more granular behavioral description capability. This enables higher positioning accuracy when identifying abnormal segments, facilitating the timely detection of trajectory drift caused by environmental occlusion, short-term signal impacts, or equipment attitude fluctuations.

[0075] When performing anomaly identification on trajectory segment feature data, by comparing changes in speed, direction, and consistency with environmental features, segments in the trajectory that do not conform to actual travel characteristics can be identified from different perspectives, thus providing clear markers for abnormal segments in the subsequent reconstruction process. This enables the trajectory reconstruction process to have clear processing location, making trajectory correction more accurate.

[0076] After performing segment consistency reconstruction on trajectory segments containing abnormal segment markers, corrected trajectory information can be obtained while maintaining the trajectory direction change pattern and travel continuity, thus correcting deviations caused by drift or positioning anomalies in the trajectory. This ensures that the trajectory data used in subsequent mileage calculations is closer to the actual travel path, avoiding mileage errors caused by trajectory deviations.

[0077] By using corrected trajectory information to calculate actual travel mileage and generate carbon emission reduction data, the distortion of carbon data caused by positioning errors can be effectively avoided, making the carbon emission reduction records more reliable. Generating points records based on reliable carbon emission reductions ensures that the points align with actual emission reduction behavior, which is beneficial for building a more reasonable travel incentive system.

[0078] After mapping points accounts to incentive resource pools, incentive resource availability data can be generated based on users' actual emission reduction performance. This allows users to select available incentive resources based on their points, thus forming a complete closed loop from behavior collection, anomaly identification, trajectory verification to incentive distribution, enhancing the overall value of the mechanism.

[0079] For example, when a user uses a shared bicycle for a commute in the city, this method first collects information on the user's trajectory, road environment, and device status during the ride, and then breaks it down into multiple trajectory segments. If a segment experiences abnormal speed jumps, directional deviations, or a location inconsistent with the road surface, that segment is marked as abnormal. The trajectory is then corrected by compensating for turning points and adjusting segment lengths. The corrected trajectory is used to calculate the actual riding distance and the corresponding carbon emission reduction. Based on the carbon emission reduction, an integral record is generated, along with incentive resource availability data corresponding to that integral, thus completing a full trip behavior processing process.

[0080] In a preferred embodiment of the present invention, the trajectory verification input data is segmented into multiple trajectory segments, and trajectory segment feature data is generated based on the environmental feature data and device motion state data corresponding to each trajectory segment. Specifically, this includes:

[0081] The original trajectory information is initially segmented according to the collection time sequence, so that each trajectory segment covers a continuous collection period.

[0082] The path direction within a segment is determined by the distance changes between the collection points in the trajectory segment. When the path shows obvious broken lines or changes in direction, the broken line position is used as the segment division point, so that the segment has relatively uniform directional characteristics.

[0083] Based on the environmental feature data corresponding to the trajectory segments, road morphology, occlusion type, and ground structure information are extracted to enable the segments to describe the external scene;

[0084] Based on the equipment motion state data, extract speed stability, direction change amplitude, and equipment posture change characteristics to enable the segment to describe the user's motion state;

[0085] The aforementioned environmental feature information and equipment status information are bound to the trajectory sequence within the segment to generate trajectory segment feature data for subsequent anomaly identification.

[0086] In a preferred embodiment of the present invention, trip mileage calculation is performed based on the corrected trajectory information to obtain the user's actual trip mileage in the target trip behavior, and corresponding carbon emission reduction data is generated based on the actual trip mileage, specifically including:

[0087] The correction trajectory information is processed by connecting paths in chronological order so that every two consecutive collection points form a trajectory segment with a calculable distance.

[0088] The actual travel mileage of the user in the target travel behavior is obtained by summing the lengths of each trajectory segment.

[0089] Based on the type of the target travel behavior, call the corresponding reference parameters, such as the average energy consumption difference values ​​for scenarios such as walking, cycling, and public transportation.

[0090] The actual travel mileage and the emission difference value of the corresponding behavior type are matched and calculated in text form. That is, according to the principle that the longer the distance, the more obvious the substitution of high emission mode of transportation, the numerical expression of carbon emission reduction is obtained.

[0091] The aforementioned carbon emission reduction values ​​are solidified into carbon emission reduction data, which will then be used as input for subsequent integration generation.

[0092] In a preferred embodiment of the present invention, a points generation process is performed based on carbon emission reduction data to convert the carbon emission reduction data into user points records, and the user points records are written into user points accounts to form points account data, specifically including:

[0093] The system acquires data on carbon emission reductions during a user's trip and determines the point generation ratio based on a preset point conversion rule, so that different levels of carbon emission reduction activities correspond to different point values.

[0094] The carbon emission reduction data is converted based on the integral generation ratio to obtain the numerical expression of the integral record;

[0095] Points records are linked to users' unique identifiers to ensure that each points record can be correctly associated with the corresponding user's points account;

[0096] Perform an update operation on the user's existing points account data, write the newly generated points into the points account, and retain the account's accumulated points, available points, and historical points change status;

[0097] The updated points account data is stored in the user points database, enabling subsequent incentive resource matching to accurately obtain points status.

[0098] In a preferred embodiment of the present invention, mapping user points to a preset incentive resource pool and generating incentive resource availability data specifically includes:

[0099] Multiple types of incentive resources are pre-configured for the incentive resource pool. Each type of incentive resource has a corresponding resource identifier, resource availability conditions, points consumption rules, and validity period limit, so that different resources have different usage requirements.

[0100] Based on the characteristics of the incentive resource types, determine the minimum points requirement, cumulative points requirement, or continuous emission reduction behavior requirement for each resource, so that the way incentives are obtained and the behavior performance have a clear mapping relationship.

[0101] Based on the available points in the user's points account data, the points conditions for each incentive resource are matched and judged. When the user's points reach the requirements of a certain incentive resource, the availability of that resource is marked in the incentive resource pool.

[0102] Incentive resource availability data is generated based on resource availability, enabling users to select applicable incentive resources within the available resource range and use this availability data for subsequent redemption operations;

[0103] The resource pool is dynamically updated to keep the quantity and validity period of the resources continuously linked to the user's points, thus enabling the incentive system to operate stably in the long term.

[0104] In a preferred embodiment of the present invention, anomaly identification processing is performed on each trajectory segment based on trajectory segment feature data. By comparing the consistency of speed changes, direction changes, and environmental features, trajectory segment identification data containing anomaly segment markers is generated, including:

[0105] The velocity change information in the feature data of the trajectory segment is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated.

[0106] The directional change information in the feature data of the trajectory segment is compared with the directional change trend of the previous trajectory segment. When the directional change deviates from the preset offset angle, a directional anomaly marker is generated.

[0107] The environmental feature data in the trajectory segment feature data is compared with the environmental feature constraints of the target travel behavior. When the geographical location corresponding to the trajectory segment is inconsistent with the road morphology, ground structure or occlusion status, an environmental anomaly marker is generated.

[0108] The velocity anomaly marker, direction anomaly marker, and environmental anomaly marker are combined to form trajectory segment anomaly attribute data. Anomaly segment markers are then generated for the trajectory segments based on the trajectory segment anomaly attribute data, thus forming trajectory segment identification data.

[0109] In this embodiment of the invention, by comparing the consistency of velocity changes, direction changes, and environmental features in the trajectory segment feature data, the trajectory anomaly identification process can be judged from multiple independent dimensions. When the velocity change information is discontinuous with the velocity stability range, short-term velocity jumps can be identified, thereby detecting anomalies caused by signal fluctuations or instantaneous errors. When the direction change deviates from the existing directional trend or exceeds the allowable offset angle, an offset trajectory that does not conform to the actual travel direction can be detected, avoiding mistaking a sudden change in direction caused by positioning drift for a real turn. Through environmental feature constraints, it can be further determined whether the trajectory position is consistent with the road shape, ground structure, or occlusion conditions, thereby supplementing the external verification capabilities that pure trajectory data cannot provide. Various anomaly markers, when combined, can form more accurate anomaly attribute data, making the identification of anomaly segments more reliable, providing clear and reliable anomaly input data for subsequent trajectory reconstruction, and avoiding miscorrection or omission during trajectory correction.

[0110] In a preferred embodiment of the present invention, the environmental feature data in the trajectory segment feature data is compared with the environmental feature constraints of the target travel behavior. When the geographical location corresponding to the trajectory segment is inconsistent with the road morphology, ground structure, or occlusion status, an environmental anomaly marker is generated, specifically including:

[0111] Based on the geographical location of the trajectory segment, road morphology information such as road type, road width, road curvature, and road direction is extracted from the map data and used as the basic data for environmental feature comparison.

[0112] Based on information such as signal strength, ambient light, and height of surrounding buildings when the trajectory segment is collected, it is determined whether the trajectory segment is in an occluded environment, such as an area with tall buildings, a tunnel entrance, or under a bridge, and occlusion feature data is generated.

[0113] The ground structure information corresponding to the trajectory segment is compared with the preset environmental feature constraints. When the trajectory location is located in a building, green belt, water area or non-road area, the trajectory segment is identified as an environmental location anomaly.

[0114] Combine road morphology information to determine whether the trajectory segment is near the road centerline. When the trajectory position deviates from the road centerline and the degree of deviation exceeds the realistic feasible range, the trajectory segment is identified as a road structure anomaly.

[0115] Based on the occlusion feature data, determine whether the positioning of the trajectory segment is affected by the occlusion environment. When the trajectory position does not match the occlusion environment or does not meet the normal positioning range under occlusion conditions, the trajectory segment is identified as an occlusion inconsistency anomaly.

[0116] The detection results of the above-mentioned environmental location anomalies, road structure anomalies, and occlusion inconsistencies are merged into environmental anomaly markers and written into the trajectory segment anomaly attribute data for subsequent anomaly segment identification.

[0117] In a preferred embodiment of the present invention, velocity anomaly markers, direction anomaly markers, and environmental anomaly markers are combined to form trajectory segment anomaly attribute data, and anomaly segment markers are generated for the trajectory segments based on the trajectory segment anomaly attribute data, specifically including:

[0118] Obtain the velocity anomaly marker, orientation anomaly marker, and environmental anomaly marker generated for the current trajectory segment, and input them as independent attributes;

[0119] The three abnormal attributes are merged according to the time order of the trajectory segments, so that each trajectory segment has a complete set of abnormal attributes;

[0120] A logical comparison is performed on the set of abnormal attributes. When any abnormal attribute exists, the trajectory segment is marked as an abnormal segment.

[0121] When multiple abnormal attributes occur simultaneously, the abnormal type of the abnormal segment is recorded as a combined abnormality, so that the segment can be corrected in more detail during the subsequent trajectory reconstruction process.

[0122] The trajectory segments marked as abnormal segments are written into the trajectory segment identification data, so that the trajectory segment identification data can accurately reflect the location and type of all abnormal segments, providing complete input for subsequent segment consistency reconstruction.

[0123] In a preferred embodiment of the present invention, segment consistency reconstruction processing is performed on trajectory segments containing abnormal segment markers based on trajectory segment identification data. Corrected trajectory information is generated by compensating for inflection point positions and correcting segment lengths, including:

[0124] Based on the trajectory segment recognition data, the adjacent normal trajectory segments before and after the abnormal segment are determined, and the logical turning point position of the abnormal segment is determined based on the directional change relationship of the adjacent normal trajectory segments, thus forming logical turning point data.

[0125] The logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, the original inflection point position is compensated according to the continuity of direction to generate the compensated inflection point position.

[0126] The path range of the abnormal segment is redefined based on the compensated turning point position, and the reasonable travel distance of the abnormal segment is calculated based on the speed stability interval of adjacent normal trajectory segments to form segment length correction data.

[0127] Based on the compensated turning point position and segment length correction data, the abnormal segments are continuously reconstructed to ensure that the reconstructed trajectory segments are consistent with the adjacent normal trajectory segments in terms of direction change and travel distance, thereby generating corrected trajectory information.

[0128] In this embodiment of the invention, the accuracy and continuity of trajectory correction are improved by performing segment consistency reconstruction on trajectory segments containing anomaly markers. After determining the normal trajectory segments before and after the anomaly segment, a continuous input of direction change can be generated, giving the anomaly segment a reasonable reference direction during the correction process. When generating logical turning points, the correspondence between direction relationships and road nodes can be used to determine the actual possible turning positions, thereby avoiding false turning points caused by signal drift. Comparing the logical turning points with the original turning points and compensating for the offset positions makes the trajectory turning more consistent with the actual route direction. After redefining the path range and calculating a reasonable travel distance, the reconstructed trajectory segments can maintain continuity in direction change and speed-distance, making the final generated corrected trajectory information more consistent with real travel behavior. This not only improves the accuracy of subsequent mileage calculations but also ensures the reliability of carbon emission reduction data.

[0129] In a preferred embodiment of the present invention, the path range of the abnormal segment is redefined based on the compensated turning point position, and the reasonable travel distance of the abnormal segment is calculated based on the velocity stability interval of adjacent normal trajectory segments to form segment length correction data, specifically including:

[0130] Based on the compensated turning point location, the start and end points of the abnormal segment are repositioned to form a trajectory segment after the path range is redefined.

[0131] Travel time data is generated based on the time intervals between trajectory points within the path range, and used as the time input for calculating the travel distance;

[0132] Based on the velocity stability intervals corresponding to adjacent normal trajectory segments, typical velocity values ​​in the velocity stability intervals are extracted and used as reference inputs for reasonable travel speeds.

[0133] Based on the correspondence between travel time data and reference speed, a reasonable travel distance expression is generated so that the length of the trajectory segment meets the range of real travel speed;

[0134] The trajectory segments after the reasonable travel distance and the path range are matched. By compressing, stretching or translating the spatial position in the trajectory segments, the trajectory length is made consistent with the reasonable travel distance, thus forming segment length correction data, which is used as input for trajectory reconstruction processing.

[0135] In a preferred embodiment of the present invention, the abnormal segment is continuously reconstructed based on the compensated turning point position and segment length correction data, so that the reconstructed trajectory segment maintains consistency with adjacent normal trajectory segments in terms of direction change and travel distance, generating corrected trajectory information, specifically including:

[0136] The starting direction of the abnormal segment is determined based on the position of the compensated turning point, so that the change in its direction can smoothly connect to the ending direction of the previous normal trajectory segment.

[0137] The target length trajectory segment is generated based on the segment length correction data, and the trajectory segment is inserted at the compensated turning point position so that the trajectory length meets the reasonable travel distance requirements.

[0138] Based on the directional changes at both ends of the abnormal segment, directional transition data for directional smoothing is generated, so that the trajectory direction gradually transitions to the initial direction of the next normal trajectory segment.

[0139] Applying directional transition data to adjust the position of trajectory points ensures that changes in trajectory direction conform to the directional continuity of a normal trajectory, preventing abrupt changes or reverse shifts.

[0140] The length correction data is fused with the direction transition data to generate the final reconstructed trajectory segment. This segment is then used to replace the position of the abnormal segment in the trajectory sequence, forming complete corrected trajectory information for subsequent mileage calculation and integration generation.

[0141] In a preferred embodiment of the present invention, the velocity change information in the trajectory segment feature data is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated, including:

[0142] Based on the velocity data of the previous trajectory segment, obtain the velocity stability interval and form velocity stability interval data;

[0143] Based on the velocity stability interval data, short-time jump comparison processing is performed on the velocity change information of the current trajectory segment. When the velocity change cannot form a sequential correspondence with the velocity stability interval in terms of temporal continuity, short-time jump judgment data is generated.

[0144] Based on the speed stability interval data, the speed change information of the current trajectory segment is compared with the trend. When the continuous direction of speed change is opposite to the change trend of the speed stability interval, trend deviation judgment data is generated.

[0145] Based on the equipment motion status data, a consistency check is performed on the speed change information. When the equipment motion status is displayed as stable but the speed change shows a sudden change, equipment contradiction judgment data is generated.

[0146] Based on short-term jump judgment data, trend deviation judgment data, and equipment contradiction judgment data, abnormal attribute assignment processing is performed on the current trajectory segment. The trajectory segment that meets any abnormal condition is marked as a speed abnormal segment, and speed abnormal labeling data for subsequent trajectory verification is generated.

[0147] In this embodiment of the invention, by forming a stable velocity range from the velocity data of the previous trajectory segment, a stable reference range can be provided for subsequent velocity change comparisons. When the velocity change of the current trajectory segment cannot continuously correspond to the stable velocity range, short-term jump phenomena can be identified in a timely manner, thereby discovering unreasonable velocity increases or decreases caused by signal drift. During trend comparison, when the continuous direction of velocity change does not conform to the change trend of the previous segment, long-term offset behavior can be further identified, supplementing the deficiencies of short-term jump detection. Combined with equipment motion state data, consistency checks can be performed on velocity changes, filtering out cases where the equipment has not moved but the trajectory velocity changes abruptly, and generating contradictory equipment data. By assigning the above judgment results as anomaly attributes, the velocity anomaly marking can be made more accurate, and velocity changes can be verified from multiple perspectives. This processing method can effectively reduce false judgments, improve the reliability of anomaly segment detection, and provide highly reliable anomaly input for subsequent trajectory reconstruction.

[0148] In a preferred embodiment of the present invention, obtaining a stable velocity interval based on the velocity data of the previous trajectory segment to form stable velocity interval data specifically includes:

[0149] The velocities of each collection point in the previous trajectory segment are arranged in the order of collection time to obtain a velocity change sequence;

[0150] Perform variation amplitude analysis on the velocity change sequence, and statistically analyze the highest and lowest values ​​of velocity change as well as the velocity change amplitude between adjacent sampling points to clarify the velocity change pattern;

[0151] Based on multiple consecutive sampling points where the velocity change is most stable in the velocity sequence, the main concentrated interval of velocity change is determined, and this interval serves as the basic range of the velocity stability interval.

[0152] Based on the upper and lower boundaries of the basic range, and combined with the device motion state of the previous trajectory segment, it is determined whether there is slight shaking. The possible shaking effects are added to the speed stability range, so that the speed range is more in line with the natural fluctuations of the travel process.

[0153] The speed stability interval formed by the above processing is defined as the speed stability interval data, which will be used for subsequent speed change comparison and judgment.

[0154] In a preferred embodiment of the present invention, short-time jump comparison processing is performed on the velocity change information of the current trajectory segment based on the velocity stability interval data. When the velocity change cannot form a sequential correspondence with the velocity stability interval in terms of temporal continuity, short-time jump judgment data is generated, specifically including:

[0155] The velocity change sequence of the current trajectory segment is compared with the velocity stability interval in time so that each velocity point can find a corresponding reference interval;

[0156] Determine whether the velocity in the current trajectory segment suddenly increases or decreases within a very short period of time, and check whether the change significantly deviates from the upper or lower boundaries of the velocity stability range;

[0157] When a sudden increase or decrease in speed value is detected, and the speed value before and after the change cannot form a continuous trend compared with the stable speed range, it is identified as a short-term jump.

[0158] Short-term jump judgment data is generated based on the location and duration of the short-term jump, so that subsequent anomaly identification can clearly identify the specific segment where the jump occurred;

[0159] The short-term jump judgment data is stored in the anomaly analysis input set for use in subsequent multi-dimensional judgment processes.

[0160] In a preferred embodiment of the present invention, trend comparison processing is performed on the velocity change information of the current trajectory segment based on the velocity stability interval data. When the continuous direction of velocity change is opposite to the change trend of the velocity stability interval, trend deviation judgment data is generated, specifically including:

[0161] Use the speed change trend in the stable speed range as a reference trend, such as a steady increase, a steady decrease, or a basically stable state.

[0162] The velocity change direction of each acquisition point in the current trajectory segment is continuously extracted to form a comparative trend sequence;

[0163] The trend sequence is compared with the trend of the stable speed interval segment by segment. When the current trend shows a completely opposite direction to the reference trend in multiple consecutive collection points, for example, the reference is a stable decline while the current trend is a stable rise, it is judged as a trend deviation.

[0164] Trend deviation judgment data is generated based on the starting point, duration, and degree of trend reversal of the trend.

[0165] Data records used to determine trend deviations are used as one of the important inputs for subsequent assignment of abnormal attributes.

[0166] In a preferred embodiment of the present invention, consistency verification processing is performed on the speed change information based on the equipment motion state data. When the equipment motion state is displayed as stable but the speed change shows a sudden change, equipment contradiction judgment data is generated, specifically including:

[0167] Extract attitude change information of the equipment from the equipment motion state data, including tilt degree, acceleration stability, and equipment rotation amplitude;

[0168] Determine whether the device is in a stable state within the current acquisition range, such as having almost constant acceleration, no obvious shaking, and no large-scale movement;

[0169] When the device status is displayed as stable, the speed change of the current trajectory segment is compared. If the speed value suddenly rises or falls abnormally but the device status does not show a corresponding motion change, it is determined that the speed change does not conform to the actual behavior of the device.

[0170] The above inconsistencies are recorded as equipment contradiction judgment data to indicate that the speed change may be caused by positioning drift rather than actual behavior;

[0171] The equipment conflict judgment data, short-term jump judgment data, and trend deviation judgment data are jointly provided to the abnormal attribute assignment step.

[0172] In a preferred embodiment of the present invention, based on short-term jump judgment data, trend deviation judgment data, and equipment contradiction judgment data, abnormal attribute assignment processing is performed on the current trajectory segment. Trajectory segments that meet any abnormal condition are marked as speed abnormal segments, and speed abnormality marking data for subsequent trajectory verification is generated. Specifically, this includes:

[0173] The short-term jump judgment data, trend deviation judgment data, and equipment contradiction judgment data are combined into the speed anomaly assessment input;

[0174] Perform item-by-item verification on the current trajectory segment. If any judgment data shows that the segment is abnormal, the segment will be identified as a segment with abnormal speed.

[0175] When multiple anomalies occur simultaneously, the anomaly type of the segment is defined as a multiple anomaly, so that subsequent processing can adopt a more cautious trajectory reconstruction method.

[0176] Generate velocity anomaly marker data for trajectory segments identified as velocity anomalies, and record the interval and attribute type where the anomaly occurred;

[0177] The velocity anomaly marker data is output to the anomaly identification process, so that it can be combined with orientation anomalies and environmental anomalies to form complete trajectory segment identification data.

[0178] In a preferred embodiment of the present invention, the direction change information in the trajectory segment feature data is compared with the direction change trend of the previous trajectory segment. When the direction change deviates from a preset offset angle, a direction anomaly marker is generated, including:

[0179] Based on the directional change data of the previous trajectory segment, the directional change trend is obtained, and directional change trend data is formed.

[0180] Based on the direction change trend data, the direction change information of the current trajectory segment is compared with the direction continuity data. When the direction change cannot form a continuous change relationship with the direction change trend, direction jump judgment data is generated.

[0181] Obtain the corresponding road orientation data based on the geographical location of the trajectory segment, and perform road consistency comparison processing on the direction change information based on the road orientation data. When the direction change deviates from the allowable range of road orientation, generate road deviation judgment data.

[0182] Based on the equipment motion status data, the direction change information is checked for consistency. When the equipment does not rotate but the direction change jumps, behavior anomaly judgment data is generated.

[0183] Based on the direction jump judgment data, road deviation judgment data, and behavior anomaly judgment data, the current trajectory segment is processed to determine the direction anomaly attribute. Trajectory segments that meet the offset conditions are marked as direction anomaly segments, and direction anomaly labeling data for trajectory recognition is generated.

[0184] In this embodiment of the invention, by organizing and analyzing the directional change data of the previous trajectory segment, directional change trend data can be formed, enabling the directional deviation of the current trajectory segment to be correlated with existing directional behavior. When the directional change of the current trajectory segment cannot maintain a continuous relationship with the directional change trend, directional jumps caused by positioning drift can be detected, preventing them from being mistaken for genuine turning behavior. Combining the road orientation data obtained from the location of the trajectory segment, it is possible to further determine whether the directional change conforms to the actual orientation of the road structure, thereby identifying abnormal directional changes that deviate from the road shape. Using equipment motion state data to perform motion consistency verification on directional changes can identify the non-authenticity of sudden directional changes when the equipment has not rotated, improving the accuracy of directional anomaly marking. Unifying the judgment results of directional jumps, road deviations, and motion inconsistencies for directional anomaly attribute determination allows for verification of directional changes from different angles, helping to establish a more reliable directional anomaly identification capability and making the data foundation upon which subsequent trajectory reconstruction depends more accurate.

[0185] In a preferred embodiment of the present invention, obtaining the direction change trend based on the direction change data of the previous trajectory segment to form direction change trend data specifically includes:

[0186] The direction values ​​of all collected points in the previous trajectory segment are arranged in chronological order to form a continuous sequence of direction changes;

[0187] Based on the sequence of direction changes, determine the overall trend of the direction change, such as maintaining a basically straight line, slowly shifting to the left, slowly shifting to the right, etc., so that the direction change has a describable trend;

[0188] A range analysis is performed on the directional difference between the sampling points in the directional change sequence to extract the allowable offset range of directional change under normal travel conditions, so that the directional change trend has reference upper and lower limits.

[0189] Perform a continuity check on the direction change sequence to eliminate isolated direction jumps caused by acquisition errors, so that the direction change trend reflects the actual direction of travel;

[0190] The above trend, directional offset range, and continuity results are combined to form directional change trend data, which serves as the basis for directional comparison of the current trajectory segment.

[0191] In a preferred embodiment of the present invention, direction continuity comparison processing is performed on the direction change information of the current trajectory segment based on direction change trend data. When the direction change cannot form a continuous change relationship with the direction change trend, direction jump judgment data is generated, specifically including:

[0192] The direction change sequence of the current trajectory segment is compared with the direction change trend data in chronological order so that each direction value can be matched with the corresponding trend segment.

[0193] Determine whether the direction change of the current trajectory segment deviates significantly from the trend of direction change in a short period of time, such as a sudden large shift in direction value followed by a return to the original direction range at the next acquisition point;

[0194] When the magnitude of the directional change significantly exceeds the acceptable range of the directional change trend, the location is identified as a directional anomaly point.

[0195] Determine whether a directional jump is caused by the number of directional anomalies, their location, and their continuity. For example, if two or more consecutive sampling points show abnormal offsets.

[0196] The start point, end point, and transition characteristics of the directional transition are recorded as directional transition judgment data for subsequent abnormal attribute processing.

[0197] In a preferred embodiment of the present invention, corresponding road orientation data is obtained based on the geographical location of the trajectory segment, and road consistency comparison processing is performed on the direction change information based on the road orientation data. When the direction change deviates from the allowable range of road orientation, road deviation judgment data is generated, specifically including:

[0198] Based on the actual geographical location of the trajectory segment, road centerline information, including road direction, road curvature, road width, etc., is extracted from the map data to form road orientation data;

[0199] The direction change information in the current trajectory segment is compared one-to-one with the road direction data to determine whether the current direction is within the reasonable direction range of the road.

[0200] When the road direction is relatively fixed (such as a straight road), determine whether the change in direction exceeds the allowable deviation range of the straight direction, for example, if the deviation angle exceeds the turning range allowed by the road structure.

[0201] When a road has curvature (such as a curve), the change in direction is compared with the range of changes in direction formed by the curvature of the road to determine whether there is a change in direction that deviates from the curve trajectory.

[0202] Locations where the directional change is significantly inconsistent with the road structure are recorded as road deviation judgment data to locate possible trajectory drift sections.

[0203] In a preferred embodiment of the present invention, action consistency verification processing is performed on the direction change information based on the device motion state data. When the device does not rotate but the direction change jumps, behavior anomaly judgment data is generated, specifically including:

[0204] Extract attitude information such as angular velocity, rotation angle change, and tilt direction change from the equipment motion state data;

[0205] Determine whether the device is significantly rotating within the current acquisition range, such as a continuous change in angular velocity or a stable shift in the device's orientation;

[0206] When the device motion status display shows that the device remains stable or does not rotate, the change in the direction of the current trajectory segment is compared.

[0207] If the change in direction is obvious and abrupt, such as a sudden and large change in the direction value, but the device's motion does not show a corresponding rotation or tilt, then the change in direction is judged to have no actual behavioral basis.

[0208] The aforementioned inconsistencies are recorded as behavioral anomaly judgment data, which will be used to form subsequent directional anomaly attributes.

[0209] In a preferred embodiment of the present invention, based on direction jump judgment data, road deviation judgment data, and behavior anomaly judgment data, direction anomaly attribute determination processing is performed on the current trajectory segment, trajectory segments that meet the offset conditions are marked as direction anomaly segments, and direction anomaly marking data for trajectory recognition is generated, specifically including:

[0210] Using direction change judgment data, road deviation judgment data, and behavior anomaly judgment data as input data sources, multi-condition comparison is performed on the current trajectory segment;

[0211] When a trajectory segment meets any of the abnormal conditions, the segment can be identified as a direction abnormal segment;

[0212] If a trajectory segment simultaneously satisfies two or more of the following conditions: direction jump, road deviation, or behavior anomaly, the segment is recorded as a composite direction anomaly so that a more stringent correction method can be adopted in the subsequent reconstruction stage.

[0213] Generate orientation anomaly marker data, which includes the type, location range, and triggering cause of the orientation anomaly, for use by the trajectory segment recognition module;

[0214] By using the directional anomaly marker data as the final output, the trajectory anomaly identification system is equipped with the ability to verify directional changes.

[0215] In a preferred embodiment of the present invention, adjacent normal trajectory segments before and after the abnormal segment are determined based on trajectory segment recognition data, and the logical turning point position of the abnormal segment is determined based on the directional change relationship of the adjacent normal trajectory segments, forming logical turning point data, including:

[0216] Based on the trajectory segment recognition data, the range of abnormal segments is located, and the information on the directional changes of normal trajectory segments before and after the abnormal segments is obtained to form directional continuity input data;

[0217] The direction extension line generation process is performed based on the direction continuity input data to obtain the direction extension line data;

[0218] Based on the directional extension line data, position intersection analysis is performed in the map data. When the intersection of the directional extension lines falls on a road node where a turn may occur, candidate turning position data is generated.

[0219] Environmental consistency verification is performed on the candidate turning locations based on environmental feature data. When the terrain, occlusion, or road structure of the candidate location matches the turning behavior, environmental verification data is generated.

[0220] Based on the directional extension line data, turning candidate location data, and environmental verification data, the turning candidate locations are processed to confirm turning points. Candidate locations that simultaneously satisfy directional continuity, road structure characteristics, and environmental consistency are determined as logical turning points, and logical turning point data is generated.

[0221] In this embodiment of the invention, by determining the adjacent normal trajectory segments before and after the abnormal segment based on trajectory segment identification data, the position of the abnormal segment in the trajectory continuity can be clearly identified, providing a traceable directional reference for the correction process. After analyzing the directional changes of normal segments, directional extension lines can be generated, allowing the abnormal segment to be inferred from the directional trend of the normal trajectory. Position intersection analysis of the directional extension lines in map data can identify possible actual turning points at road nodes, thus avoiding incorrectly identifying offset positions as turning points. Combining environmental feature data with environmental consistency verification of candidate positions can determine whether candidate turning points conform to terrain changes, road bifurcations, or occlusion conditions, further improving the authenticity of turning points. Logical turning points jointly confirmed by direction, road nodes, and environmental conditions can serve as a reliable basis for trajectory compensation processing, ensuring that the trajectory maintains directional continuity and environmental rationality after correction.

[0222] In a preferred embodiment of the present invention, the abnormal segment range is located based on the trajectory segment recognition data, and the directional change information of the normal trajectory segments before and after the abnormal segment is obtained to form directional continuity input data, specifically including:

[0223] Based on the abnormal segment markers contained in the trajectory segment identification data, determine the start and end positions of the abnormal segment in the trajectory sequence;

[0224] Extract normal trajectory segments before and after the abnormal segment from the trajectory sequence, ensuring that neither of these trajectory segments is marked as abnormal;

[0225] Based on the direction data in the normal trajectory segment, the direction change sequence of the previous normal trajectory segment and the direction change sequence of the next normal trajectory segment are formed;

[0226] The two directional change sequences mentioned above are processed continuously to clarify the starting and ending points of the directional changes, thereby providing a reference direction for the directional trend of abnormal segments;

[0227] The two directional change sequences are combined to form directional continuity input data, providing a basic reference direction for the subsequent generation of directional extension lines.

[0228] In a preferred embodiment of the present invention, a direction extension line generation process is performed based on the direction continuity input data to obtain direction extension line data, specifically including:

[0229] Based on the direction of the end of the previous normal trajectory segment before the abnormal segment, take that direction as the starting direction for the extension;

[0230] Based on the starting direction of the normal trajectory segment following the abnormal segment, that direction is taken as the extended target direction;

[0231] Perform directional trend analysis on the starting direction and the target direction to determine whether there is a straight continuation, a slow turn, or a significant change in direction between them.

[0232] Based on this directional trend, directional reference lines are extended along the starting and target directions in the map space, so that the two reference lines can represent the possible directions of the trajectory before and after the abnormal segment.

[0233] The spatial information of the two directional reference lines is recorded as directional extension line data, which is used for subsequent calculation of possible turning points.

[0234] In a preferred embodiment of the present invention, position intersection analysis is performed on map data based on directional extension line data. When the intersection of the directional extension lines falls on a road node where a turn may occur, candidate turning position data is generated, specifically including:

[0235] Obtain road node information corresponding to the area where the directional extension line is located from the map data, including intersections, curve locations, and forks;

[0236] Spatial matching is performed between the aforementioned directional extension line data and road node data to identify the intersection points between the directional extension lines and road nodes;

[0237] When an intersection is located near a road node and meets the turning conditions allowed by the road structure, the intersection is considered a possible turning candidate location.

[0238] If multiple locations matching road nodes appear on the directional extension line, nodes that do not conform to the actual travel path are filtered out by judging the accessibility and path rationality of the road nodes.

[0239] Intersections that meet the criteria are recorded as candidate turning points for further environmental consistency verification.

[0240] In a preferred embodiment of the present invention, environmental consistency verification processing is performed on the candidate turning location data based on environmental feature data. When the terrain, occlusion, or road structure of the candidate location matches the turning behavior, environmental verification data is generated, specifically including:

[0241] Based on the geographical location of the candidate turning point, obtain the corresponding environmental feature data, including the distribution of obstructions, ground type, road width, road connection, etc.

[0242] Determine whether the candidate turning position is in a road structure that allows for normal turning, such as whether the road forms an intersection or whether the road width can support turning;

[0243] The location signal is likely to be distorted based on the distribution of obstructions. If the obstruction environment does not match the logic of the turning behavior, the candidate location is marked as unsuitable.

[0244] Determine whether the ground structure at the location allows vehicles or cycling equipment to turn, such as whether the road is continuous or whether the ground is a passable surface;

[0245] Candidate locations that meet the road structure, shading conditions, and terrain conditions are marked as having passed environmental verification and recorded as environmental verification data.

[0246] In a preferred embodiment of the present invention, based on directional extension line data, turning candidate position data, and environmental verification data, a turning point confirmation process is performed on the turning candidate positions. Candidate positions that simultaneously satisfy directional continuity, road structure characteristics, and environmental consistency are determined as logical turning points, and logical turning point data is generated. Specifically, this includes:

[0247] Compare the positions of all candidate turning points that have passed environmental verification with the data of the directional extension line to determine whether the position is within the reasonable extension range of the directional extension line.

[0248] If a candidate position is both on the extension path of the direction extension line and meets the structural characteristics of a road node, then it is further determined whether it satisfies the direction change logic in the direction continuity input data.

[0249] When a candidate position enables a natural connection between the directional changes of the preceding and following normal trajectory segments, it is identified as a logical turning point.

[0250] If multiple candidate positions meet the above conditions, the position that connects most naturally with the preceding and following trajectories and has the smallest offset is selected as the final logical turning point.

[0251] The identified logical turning points are recorded as logical turning point data and output to the subsequent turning point compensation steps.

[0252] In a preferred embodiment of the present invention, the logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, the original inflection point position is compensated according to directional continuity to generate a compensated inflection point position, including:

[0253] Based on the offset relationship between the logical inflection point data and the original inflection point position data, offset distance analysis is performed to generate offset reference data;

[0254] Direction offset correction processing is performed based on the offset reference data and the direction change information of adjacent normal trajectory segments. When the direction change of the original turning point does not meet the direction continuity requirements, direction correction data is generated.

[0255] Road alignment processing is performed on the direction correction data based on the road structure data. When the position deviates from the center area of ​​the road due to the direction correction, road alignment data is generated.

[0256] Motion consistency verification is performed on the road alignment data based on the equipment motion status data. When the equipment does not make a turning motion but the trajectory turns, motion verification data is generated.

[0257] Based on the direction correction data, road alignment data, and motion verification data, the original turning point position is processed for position correction output. The turning point position after correction by the three conditions of direction adjustment, road constraint, and motion verification is determined as the compensated turning point position data.

[0258] In this embodiment of the invention, by analyzing the offset relationship between logical turning point data and the original turning point position, offset reference data can be obtained, providing a quantitative basis for turning point compensation. In the direction offset correction process, when the direction change of the original turning point is discontinuous with the direction change of adjacent normal trajectory segments, the turning position can be adjusted using direction correction data to conform to the natural trend of trajectory direction change. In the road alignment process, road structure data can be used to bring the corrected turning point closer to the center area of ​​the road, avoiding abnormal positions deviating from the road structure due to positioning drift. Combining the device motion state data with motion consistency verification can determine whether the device has actually performed a turning action, thereby avoiding mistakenly correcting a trajectory that has not actually turned as a turning path. Through the combined processing of direction correction, road alignment, and motion verification, the compensated turning point position can be finally generated, ensuring that the trajectory remains consistent between direction changes and the actual road structure, providing a reliable foundation for subsequent path range delineation and trajectory reconstruction.

[0259] In a preferred embodiment of the present invention, offset distance analysis is performed based on the offset relationship between logical inflection point data and original inflection point position data to generate offset reference data, specifically including:

[0260] Obtain the geographic coordinates of the logical turning points based on the logical turning point data, and extract the original turning point positions from the original trajectory;

[0261] By spatially mapping the logical turning point position to the original turning point position, the relative offset direction and offset magnitude of the two points can be accurately identified.

[0262] Determine whether the offset belongs to common positioning drift based on the trend of the distance change between two points, such as whether it presents an offset pattern along the edge of a building or one side of a tall building;

[0263] Confirm the duration of the offset process to ensure that the offset reference data includes the offset start point, offset end point, and the overall offset path;

[0264] The above three pieces of information—offset direction, offset magnitude, and duration—are integrated into offset reference data to provide a basis for subsequent directional offset correction.

[0265] In a preferred embodiment of the present invention, direction offset correction processing is performed based on the offset reference data and the direction change information of adjacent normal trajectory segments. When the direction change of the original turning point does not meet the direction continuity requirement, direction correction data is generated, specifically including:

[0266] Based on the offset direction in the offset reference data, determine whether the directional change of the original turning point position is significantly discontinuous with the directional change of the preceding and following normal trajectory segments.

[0267] If there is a sudden change in direction, the direction at the end of the previous normal trajectory segment is used as the starting direction for direction correction, and the direction at the beginning of the next normal trajectory segment is used as the target direction for direction correction.

[0268] A directional transition curve is generated between the starting direction and the target direction to gradually stabilize the directional change without sudden deviation.

[0269] The directional transition curve is applied to the position correction logic of the original turning point to form directional correction data after continuous adjustment;

[0270] The direction correction data is recorded as input for the next stage of road alignment processing, making direction correction the first adjustment basis for trajectory correction.

[0271] In a preferred embodiment of the present invention, road alignment processing is performed on the direction correction data based on the road structure data. When the position deviates from the center area of ​​the road due to the direction correction, road alignment data is generated, specifically including:

[0272] Obtain the road centerline information of the road where the logical turning point is located from the map data, including the spatial location of the road center, the width of the road, and its lateral distribution structure;

[0273] Based on the direction correction data, determine whether the corrected position deviates from the road centerline, such as whether it falls in the road edge area or the outer area of ​​the road;

[0274] If a deviation occurs, an alignment route is generated from the direction correction position to the road centerline based on the centerline direction in the road structure data.

[0275] The position is fine-tuned along the alignment route so that the corrected turning point position returns to a reasonable range near the center of the road, avoiding discontinuous paths that cross the edge of the road.

[0276] The adjusted location information is recorded as road alignment data for use in the next step of equipment motion verification.

[0277] In a preferred embodiment of the present invention, motion consistency verification is performed on road alignment data based on device motion state data. When the device does not perform a turning motion but the trajectory shows a turning point, motion verification data is generated, specifically including:

[0278] The changes in angular velocity, rotation amplitude, and motion posture of the equipment during the corresponding time segment of the abnormal segment are obtained from the equipment motion status data.

[0279] Determine whether the device exhibits a rotational movement consistent with the turning behavior within that time interval, such as whether there is a significant angular velocity or a shift in the device's direction;

[0280] If the device is in a stable posture and does not rotate, it is determined that the positional change after the previous road alignment may be environmental drift rather than a real turning behavior.

[0281] Record motion verification information based on the inconsistency between the device posture and the road alignment, indicating that although the trajectory position is aligned with the road, it has not passed the actual motion verification.

[0282] Output the motion verification data so that the direction correction and road alignment may need further fine-tuning during the final correction process.

[0283] In a preferred embodiment of the present invention, based on direction correction data, road alignment data, and motion verification data, position correction output processing is performed on the original turning point position. The turning point position after correction by the three conditions of direction adjustment, road constraint, and motion verification is determined as the compensated turning point position data, specifically including:

[0284] Using the direction correction data as the basis for position correction, the compensated trajectory direction can continue the direction change pattern of the previous segment.

[0285] The position of the direction correction result is adjusted based on the road alignment data to keep the trajectory within the allowable range of the road center and avoid deviation from the road structure.

[0286] The above correction results are combined with motion verification data for consistency checks. When the trajectory changes are inconsistent with the equipment movement, the turning point positions are fine-tuned again to make them conform to the actual behavior of the equipment.

[0287] The final turning point compensation result is the position after direction adjustment, road constraints, and motion consistency verification.

[0288] The final compensated position is output as the compensated turning point position data, which is then used for subsequent path range delineation and trajectory reconstruction steps.

[0289] Embodiments of the present invention also provide a user incentive and points management system for carbon emission reduction behavior, the system comprising:

[0290] The trajectory data acquisition module is used to acquire the user's original trajectory information during the target travel behavior, and synchronously collect the corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data;

[0291] The trajectory segment processing module is used to segment the trajectory verification input data, divide the original trajectory information into multiple trajectory segments, and generate trajectory segment feature data based on the environmental feature data and equipment motion state data corresponding to each trajectory segment.

[0292] The anomaly detection module is used to perform anomaly detection processing on each trajectory segment based on the feature data of the trajectory segment. By comparing the consistency between speed changes, direction changes and environmental features, it generates trajectory segment identification data containing anomaly segment markers.

[0293] The segment reconstruction module is used to perform segment consistency reconstruction processing on trajectory segments containing abnormal segment markers based on trajectory segment recognition data. It generates corrected trajectory information by compensating for the position of turning points and correcting the segment length.

[0294] The mileage calculation module is used to perform travel mileage calculation processing based on the correction trajectory information, obtain the user's actual travel mileage in the target travel behavior, and generate corresponding carbon emission reduction data based on the actual travel mileage.

[0295] The points generation module is used to perform points generation processing based on carbon emission reduction data, convert carbon emission reduction data into user points records, and write user points records into user points accounts to form points account data.

[0296] The incentive mapping module is used to map user points to a preset incentive resource pool based on points account data, and generate incentive resource availability data.

[0297] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0298] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0299] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0300] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles 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 user incentive and points management method for carbon emission reduction behavior, characterized in that, The method includes: Obtain the user's original trajectory information during the target travel behavior, and simultaneously collect corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data; The trajectory verification input data is segmented into multiple trajectory segments, and trajectory segment feature data is generated based on the environmental feature data and equipment motion state data corresponding to each trajectory segment. Anomaly identification processing is performed on each trajectory segment based on the feature data of the trajectory segments. By comparing the consistency between speed changes, direction changes and environmental features, trajectory segment identification data containing anomaly segment markers is generated. Based on the trajectory segment recognition data, segment consistency reconstruction processing is performed on the trajectory segments containing abnormal segment markers. By compensating for the turning point positions and correcting the segment lengths, corrected trajectory information is generated. The travel mileage is calculated based on the corrected trajectory information to obtain the user's actual travel mileage in the target travel behavior, and corresponding carbon emission reduction data is generated based on the actual travel mileage. Based on the carbon emission reduction data, perform the points generation process, convert the carbon emission reduction data into user points records, and write the user points records into user points accounts to form points account data; Based on the points account data, user points are mapped to a preset incentive resource pool to generate incentive resource availability data; Based on the trajectory segment identification data, segment consistency reconstruction processing is performed on trajectory segments containing abnormal segment markers. By compensating for inflection point positions and correcting segment lengths, corrected trajectory information is generated, including: Based on the trajectory segment recognition data, the adjacent normal trajectory segments before and after the abnormal segment are determined, and the logical turning point position of the abnormal segment is determined based on the directional change relationship of the adjacent normal trajectory segments, thus forming logical turning point data. The logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, the original inflection point position is compensated according to the continuity of direction to generate the compensated inflection point position. The path range of the abnormal segment is redefined based on the compensated turning point position, and the reasonable travel distance of the abnormal segment is calculated based on the speed stability interval of adjacent normal trajectory segments to form segment length correction data. Based on the compensated turning point position and segment length correction data, the abnormal segments are continuously reconstructed to ensure that the reconstructed trajectory segments are consistent with the adjacent normal trajectory segments in terms of direction change and travel distance, thereby generating corrected trajectory information.

2. The user incentive and points management method for carbon emission reduction behavior according to claim 1, characterized in that, Anomaly detection processing is performed on each trajectory segment based on its feature data. By comparing the consistency of velocity changes, direction changes, and environmental features, trajectory segment identification data containing anomaly segment markers is generated, including: The velocity change information in the feature data of the trajectory segment is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated. The directional change information in the feature data of the trajectory segment is compared with the directional change trend of the previous trajectory segment. When the directional change deviates from the preset offset angle, a directional anomaly marker is generated. The environmental feature data in the trajectory segment feature data is compared with the environmental feature constraints of the target travel behavior. When the geographical location corresponding to the trajectory segment is inconsistent with the road morphology, ground structure or occlusion status, an environmental anomaly marker is generated. The velocity anomaly marker, direction anomaly marker, and environmental anomaly marker are combined to form trajectory segment anomaly attribute data. Anomaly segment markers are then generated for the trajectory segments based on the trajectory segment anomaly attribute data, thus forming trajectory segment identification data.

3. The user incentive and points management method for carbon emission reduction behavior according to claim 2, characterized in that, The velocity change information in the trajectory segment feature data is compared with the velocity stability interval of the previous trajectory segment. When the velocity change exceeds the change range corresponding to the preset velocity stability interval, a velocity anomaly marker is generated, including: Based on the velocity data of the previous trajectory segment, obtain the velocity stability interval and form velocity stability interval data; Based on the velocity stability interval data, short-time jump comparison processing is performed on the velocity change information of the current trajectory segment. When the velocity change cannot form a sequential correspondence with the velocity stability interval in terms of temporal continuity, short-time jump judgment data is generated. Based on the speed stability interval data, the speed change information of the current trajectory segment is compared with the trend. When the continuous direction of speed change is opposite to the change trend of the speed stability interval, trend deviation judgment data is generated. Based on the equipment motion status data, a consistency check is performed on the speed change information. When the equipment motion status is displayed as stable but the speed change shows a sudden change, equipment contradiction judgment data is generated. Based on short-term jump judgment data, trend deviation judgment data, and equipment contradiction judgment data, abnormal attribute assignment processing is performed on the current trajectory segment. The trajectory segment that meets any abnormal condition is marked as a speed abnormal segment, and speed abnormal labeling data for subsequent trajectory verification is generated.

4. The user incentive and points management method for carbon emission reduction behavior according to claim 2, characterized in that, The directional change information in the trajectory segment feature data is compared with the directional change trend of the previous trajectory segment. When the directional change deviates from a preset offset angle, a directional anomaly marker is generated, including: Based on the directional change data of the previous trajectory segment, the directional change trend is obtained, and directional change trend data is formed. Based on the direction change trend data, the direction change information of the current trajectory segment is compared with the direction continuity data. When the direction change cannot form a continuous change relationship with the direction change trend, direction jump judgment data is generated. Obtain the corresponding road orientation data based on the geographical location of the trajectory segment, and perform road consistency comparison processing on the direction change information based on the road orientation data. When the direction change deviates from the allowable range of road orientation, generate road deviation judgment data. Based on the equipment motion status data, the direction change information is checked for consistency. When the equipment does not rotate but the direction change jumps, behavior anomaly judgment data is generated. Based on the direction jump judgment data, road deviation judgment data, and behavior anomaly judgment data, the current trajectory segment is processed to determine the direction anomaly attribute. Trajectory segments that meet the offset conditions are marked as direction anomaly segments, and direction anomaly labeling data for trajectory recognition is generated.

5. The user incentive and points management method for carbon emission reduction behavior according to claim 1, characterized in that, Based on the trajectory segment recognition data, adjacent normal trajectory segments before and after the abnormal segment are determined. Then, based on the directional change relationship between the adjacent normal trajectory segments, the logical turning point position of the abnormal segment is determined, forming logical turning point data, including: Based on the trajectory segment recognition data, the range of abnormal segments is located, and the information on the directional changes of normal trajectory segments before and after the abnormal segments is obtained to form directional continuity input data; The direction extension line generation process is performed based on the direction continuity input data to obtain the direction extension line data; Based on the directional extension line data, position intersection analysis is performed in the map data. When the intersection of the directional extension lines falls on a road node where a turn may occur, candidate turning position data is generated. Environmental consistency verification is performed on the candidate turning locations based on environmental feature data. When the terrain, occlusion, or road structure of the candidate location matches the turning behavior, environmental verification data is generated. Based on the directional extension line data, turning candidate location data, and environmental verification data, the turning candidate locations are processed to confirm turning points. Candidate locations that simultaneously satisfy directional continuity, road structure characteristics, and environmental consistency are determined as logical turning points, and logical turning point data is generated.

6. The user incentive and points management method for carbon emission reduction behavior according to claim 1, characterized in that, The logical inflection point data is compared with the original inflection point position of the abnormal segment. When the original inflection point position deviates from the logical inflection point data, compensation processing is performed on the original inflection point position based on directional continuity to generate a compensated inflection point position, including: Based on the offset relationship between the logical inflection point data and the original inflection point position data, offset distance analysis is performed to generate offset reference data; Direction offset correction processing is performed based on the offset reference data and the direction change information of adjacent normal trajectory segments. When the direction change of the original turning point does not meet the direction continuity requirements, direction correction data is generated. Road alignment processing is performed on the direction correction data based on the road structure data. When the position deviates from the center area of ​​the road due to the direction correction, road alignment data is generated. Motion consistency verification is performed on the road alignment data based on the equipment motion status data. When the equipment does not make a turning motion but the trajectory turns, motion verification data is generated. Based on the direction correction data, road alignment data, and motion verification data, the original turning point position is processed for position correction output. The turning point position after correction by the three conditions of direction adjustment, road constraint, and motion verification is determined as the compensated turning point position data.

7. A user incentive and points management system for carbon emission reduction behavior, characterized in that, The system, used in the method of any one of claims 1 to 6, comprises: The trajectory data acquisition module is used to acquire the user's original trajectory information during the target travel behavior, and synchronously collect the corresponding environmental feature data and device motion status data based on the original trajectory information to form trajectory verification input data; The trajectory segment processing module is used to segment the trajectory verification input data, divide the original trajectory information into multiple trajectory segments, and generate trajectory segment feature data based on the environmental feature data and equipment motion state data corresponding to each trajectory segment. The anomaly detection module is used to perform anomaly detection processing on each trajectory segment based on the feature data of the trajectory segment. By comparing the consistency between speed changes, direction changes and environmental features, it generates trajectory segment identification data containing anomaly segment markers. The segment reconstruction module is used to perform segment consistency reconstruction processing on trajectory segments containing abnormal segment markers based on trajectory segment recognition data. It generates corrected trajectory information by compensating for the position of turning points and correcting the segment length. The mileage calculation module is used to perform travel mileage calculation processing based on the correction trajectory information, obtain the user's actual travel mileage in the target travel behavior, and generate corresponding carbon emission reduction data based on the actual travel mileage. The points generation module is used to perform points generation processing based on carbon emission reduction data, convert carbon emission reduction data into user points records, and write user points records into user points accounts to form points account data. The incentive mapping module is used to map user points to a preset incentive resource pool based on points account data, and generate incentive resource availability data.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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