An efficient solution for rural life cycle carbon management

Abstract

The application discloses a kind of efficient solution of rural full life cycle carbon management, to solve the problems such as rural carbon source dispersion, monitoring is difficult, accounting precision is low and system adaptability is poor, suitable for the whole process carbon management of administrative village level, first of all, this method is deployed in key carbon source point Embedded device, capture carbon activity signal and build event sequence library containing event type, intensity and time stamp;Then, the event priority weight is calculated by assignment algorithm, and the event queue with priority label is generated, to distinguish between immediate and delayed processing events;Again, different priority events are compressed differently based on compression protocol, combined with differential encoding and adjusted compression rate according to network conditions, to generate compressed data packets;Then, the accounting simulator is issued to the edge device by the cloud;The application realizes accurate monitoring of rural carbon source, efficient transmission and accurate accounting, improves the timeliness and adaptability of carbon management, and provides scientific support for carbon emission reduction strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental management, more particularly to an efficient solution for rural full life cycle carbon management. BACKGROUND

[0002] In actual application, the data from different sources differ in format, collection frequency and accuracy standard in the rural carbon management system. For example, the carbon concentration data collected by sensors in real time and the breeding scale data counted by artificial periodically are difficult to be efficiently fused, which is prone to data conflicts or omissions, thereby increasing the deviation of carbon accounting results. Meanwhile, the system lacks real-time performance, and the delay in data transmission and processing causes the lag of dynamic monitoring of carbon emissions, which may cause the discontinuity of carbon footprint accounting and fail to fully reflect the characteristics of rural full life cycle carbon emissions. The root cause of these technical deficiencies lies in the data processing paradigm adopted by traditional rural carbon management, which continues the centralized, high-frequency data transmission and calibration architecture. This architecture does not match the characteristics of rural environment, and the data heterogeneity in rural areas is high, and the activity laws of different carbon sources differ greatly. For example, agricultural production carbon emissions have seasonal variations, and life energy carbon emissions show intraday fluctuation characteristics, making it difficult for general data standardization and fusion algorithms to adapt. In addition, the dynamic changes of rural carbon sources are fast, such as temporary increase of breeding sheds, which require the data processing system to have fast response capability. However, the traditional centralized architecture needs to go through the complete process of data uploading, cloud processing and instruction issuing, which has high response delay.

[0003] In actual application scenarios, this mismatch between the architecture and the environment causes excessive coupling between data acquisition and processing. In order to pursue real-time accuracy of carbon accounting, the traditional system often uses high-frequency collection and transmission of raw data to try to improve the accuracy and real-time performance by increasing the amount of data. However, in a limited resource environment, this approach actually exacerbates transmission delay and data packet loss. A large amount of raw data is queued during transmission, and some data packets are discarded due to timeout. This not only fails to improve real-time performance, but also causes deviation between transmitted data and actual carbon emission status, leading to distorted monitoring data. Distorted monitoring data also leads to lag in carbon emission reduction planning and decision-making, such as failure to identify sudden high carbon emission events in time and take control measures, or deviation of reduction target setting from reality due to inaccurate accounting results, ultimately forming a vicious cycle of pursuing accuracy and real-time performance while reducing the reliability of accounting and the effectiveness of decision-making, which seriously hinders the landing effect of rural full life cycle carbon management. SUMMARY

[0004] In view of the problems existing in the prior art, the purpose of the present application is to provide an efficient solution for rural full life cycle carbon management, which can realize accurate monitoring, efficient transmission and accurate accounting of rural carbon sources, and improve the timeliness and adaptability of carbon management to provide scientific support for carbon emission reduction strategies.

[0005] To solve the above problems, the application adopts the following technical solutions:

[0006] An efficient solution for rural full life cycle carbon management includes the following steps:

[0007] Step 1, deploy embedded devices at key carbon source points in administrative villages, the embedded devices capture carbon activity signals through sensing and timing units and convert them into timestamp event streams, and build an event sequence library containing event type, event amplitude and timestamp;

[0008] Step 2, based on the event sequence library, calculate the priority weight according to the dynamic characteristics of the events by assigning algorithm, process and output the event queue with priority label using nonlinear function, and use the priority label to distinguish immediate processing events from delayed processing events;

[0009] Step 3, using the event queue with priority label, implement compact coding for events marked for immediate processing and feature-preserving compression for events marked for delayed processing through compression protocol, record the state change amount combined with differential coding, and dynamically adjust the compression rate according to the network condition to generate compressed event data packets;

[0010] Step 4, after the cloud system receives the compressed event data packets, it issues an accounting simulator to the edge device, the accounting simulator performs carbon accounting calculation based on local event data, generates carbon footprint results through the mapping relationship between event amplitude and emission factor, and the cloud system receives the carbon footprint results and abnormal summary;

[0011] Step 5, periodically compare the consistency of edge carbon accounting results and cloud accounting data, dynamically adjust the weight parameters of the assignment algorithm and the compression parameters of the compression protocol according to the difference between event capture output and carbon accounting results.

[0012] Further, the step 2 includes:

[0013] Step 21, map the time interval, signal change rate and amplitude fluctuation amplitude of each event in the event sequence library into a dynamic response characteristic vector;

[0014] Step 22, apply a feature amplitude sensitive nonlinear mapping function to the dynamic response characteristic vector to generate a priority weight;

[0015] Step 23, aggregate events according to the priority weight to form a hierarchical event queue, and mark immediate processing events and delayed processing events in the hierarchical event queue.

[0016] Further, the step 22 includes:

[0017] Step 221, introduce a nonlinear modulation coefficient to the local amplitude variation of each event in the dynamic response feature vector, and perform expansion processing on the event data satisfying the preset large amplitude fluctuation condition, and perform reduction processing on the event data satisfying the preset small amplitude fluctuation condition;

[0018] Step 222, segmentally and discontinuously map the dynamic response feature vector after local amplitude modulation along the amplitude interval, so that the weight gradient of the mapping result in the high amplitude interval is greater than that in the low amplitude interval;

[0019] Step 223, perform normalization based on the sequence dynamic range on the vector after segmental and discontinuous mapping to generate priority weights.

[0020] Further, the step 3 comprises:

[0021] Step 31, construct an event internal information sub-block for the event marked for immediate processing and structure compact coding with adaptive bit width;

[0022] Step 32, only retain key features for the event marked for delayed processing to form feature retention compression;

[0023] Step 33, perform differential coding on the state quantity variation of the continuous event, and record the relative variation quantity;

[0024] Step 34, dynamically adjust the compression rate of the immediate processing event and the feature retention ratio of the delayed processing event according to the current network condition;

[0025] Step 35, encapsulate the events after coding and compression processing to generate compressed event data packets, and the compressed event data packets contain event header identification, compressed data segment and state difference index.

[0026] Further, the step 33 comprises:

[0027] Step 331, divide the state quantity variation of the continuous event into multiple dynamic intervals according to amplitude and rate;

[0028] Step 332, select a local starting point in each dynamic interval, and adaptively adjust the local starting point according to the state at the end of the previous dynamic interval;

[0029] Step 333, calculate the relative variation quantity with the local starting point as the reference, and encode through interval-sensitive nonlinear mapping;

[0030] Step 334, integrate the coding sequences of each dynamic interval into a global differential coding sequence, and record the starting point information of each dynamic interval through starting point index.

[0031] Further, the step 4 comprises:

[0032] Step 41, decode and nested index guided event reconstruction of compressed event data packets, recover event time, type and amplitude sequence;

[0033] Step 42, adaptively adjust the emission factor mapping corresponding to the event according to the event history amplitude fluctuation and local trend;

[0034] Step 43, perform carbon accounting on the edge device accounting simulator, accumulate the product of event amplitude and adaptive emission factor, generate local carbon contribution and aggregate to form carbon footprint results;

[0035] Step 44, perform nonlinear trend analysis on carbon footprint results to detect abnormal carbon emission patterns, and generate an abnormal summary;

[0036] Step 45, the cloud receives carbon footprint results and abnormal summary and integrates.

[0037] Further, the step 42 comprises:

[0038] Step 421, perform non-uniform segmented fluctuation analysis on the reconstructed event sequence, extract high amplitude mutation segment and low amplitude smooth segment;

[0039] Step 422, apply amplitude sensitive nonlinear weighted modulation to high amplitude mutation segment, and apply attenuation modulation to low amplitude smooth segment, to generate preliminary dynamic mapping emission factor;

[0040] Step 423, based on the local trend of adjacent time period of the event, correct the preliminary dynamic mapping emission factor;

[0041] Step 424, perform local amplitude priority normalization on the emission factor sequence after trend correlation correction, and perform amplitude enhancement on key high amplitude event segment, to form the final adaptive emission factor mapping sequence.

[0042] Further, the step 43 comprises:

[0043] Step 431, event weight modulation accumulation on the event sequence after adaptive emission factor mapping;

[0044] Step 432, divide the event sequence after weight modulation into local paragraphs and accumulate to form local carbon contribution value;

[0045] Step 433, apply nonlinear mapping to integrate local carbon contribution value;

[0046] Step 434, accumulate the integrated local contribution sequence to generate edge carbon accounting total result, and calibrate according to high priority event contribution.

[0047] Further, the step 5 comprises:

[0048] Step 51, the edge carbon accounting result and the cloud accounting data are subjected to multi-scale consistency analysis to form a difference amplitude and distribution consistency profile;

[0049] Step 52, generating an event difference factor according to the consistency profile;

[0050] Step 53, nonlinear dynamic reconstruction of the weight parameter of the assignment algorithm according to the event difference factor;

[0051] Step 54, difference-aware adjustment of the encoding strategy and compression rate of the compression protocol according to the event difference factor;

[0052] Step 55, verifying the adjustment effect in the next period of event capture and accounting, and forming feedback through the updated consistency profile.

[0053] Further, the step 52 comprises:

[0054] Step 521, decomposing the consistency profile into difference characteristics of time series deviation, local intensity anomaly and event priority offset;

[0055] Step 522, generating weight adjustment factors of the assignment algorithm and parameter adjustment factors of the compression protocol based on the difference characteristics;

[0056] Step 523, dynamic fusion of the weight adjustment factors and the parameter adjustment factors to form an event difference factor sequence.

[0057] Compared with the prior art, the present application has the following advantages:

[0058] (1) The present scheme realizes the accurate and real-time monitoring of rural carbon source activities by deploying embedded devices at key carbon source points in administrative villages, capturing carbon activity signals and constructing an event sequence library in combination with sensing and timing units. Compared with the traditional manual sampling monitoring method, the device can automatically capture the event type, intensity and timestamp information of carbon activities, avoiding the subjectivity and periodic limitations of manual monitoring, providing complete and continuous raw data support for subsequent carbon accounting, effectively improving the coverage and data accuracy of rural carbon source monitoring, and solving the problem of monitoring difficulty caused by the dispersion of rural carbon sources.

[0059] (2) The present scheme calculates the priority weight based on the event sequence library through the assignment algorithm, uses a nonlinear function to output an event queue with a priority label, realizes the differentiated processing of carbon activity events, marks high-priority events for immediate processing and low-priority events for delayed processing, avoids the waste of resources caused by indiscriminate processing of all events, and this differentiated strategy can make the system respond to key carbon activities first, improve the timeliness of carbon management, reasonably allocate computing and transmission resources, and ensure the overall operation efficiency of the system, solving the problem of unreasonable resource allocation in traditional management.

[0060] (3) The scheme implements differentiated compression on different priority events through compression protocol, records state change amount combined with differential encoding and adjusts compression rate according to network conditions, greatly optimizes the transmission efficiency of rural carbon data, high-priority event compact encoding preserves integrity, low-priority event feature preservation reduces redundancy, differential encoding reduces state amount storage, and network self-adaptive adjustment balances efficiency and quality. Compared with the fixed compression mode, in the unstable rural network condition scene, this strategy can not only reduce data transmission and save bandwidth, but also avoid key data loss, ensure efficient and reliable transmission of carbon data, and solve the problem of difficult data transmission caused by limited rural network bandwidth.

[0061] (4) The scheme realizes accurate carbon footprint accounting and system continuous optimization through edge cloud cooperation and closed-loop adaptive optimization, and reduces cloud pressure through local accounting of edge devices, improves accounting accuracy through adaptive emission factor, and dynamically adjusts algorithm and compression parameters through closed-loop optimization. Compared with single cloud accounting, this mode reduces data transmission delay, improves accounting response speed, and through continuous optimization of parameters, adapts to the dynamic changes of rural carbon sources, and long-term guarantees the accuracy and efficiency of carbon management, provides scientific data support for rural carbon emission reduction strategy, and solves the problems of low accuracy and poor system adaptability of traditional accounting. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0063] Figure 1 The flow chart of an efficient solution method for rural full life cycle carbon management. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings; obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0065] Please refer to Figure 1 An efficient solution method for rural full life cycle carbon management includes the following operation steps:

[0066] Step 1: Deploy embedded devices at key carbon source points in administrative villages. These devices capture carbon activity signals through sensing and timing units and convert them into timestamp event streams. They build an event sequence library containing event types, event magnitudes, and timestamps. The specific operations are as follows:

[0067] In the administrative village carbon management system, embedded devices are prioritized in key areas with high carbon emissions and stable activity frequencies, such as large-scale livestock breeding areas, small-scale agricultural processing points, and centralized sewage treatment facilities. Their function is to capture, convert, and structure store carbon activity signals through integrated sensing and timing units. The sensing unit of the embedded device first undertakes the task of sensing carbon activity signals. Its working logic is based on the correlation between specific physical quantities and carbon activities. Different types of carbon source activities are accompanied by corresponding physical parameter changes. For example, biomass combustion causes temperature rise and specific gas concentration change, and sewage anaerobic treatment releases methane gas. The sensing unit integrates temperature sensors, gas concentration sensors (such as methane and carbon dioxide sensors), and flow sensors to convert these physical quantities directly related to carbon activities into continuous analog electrical signals. Due to the presence of noise interference and low amplitude of the original analog signal, the sensing unit internally amplifies the signal through an operational amplifier and then converts the amplified analog signal into a discrete digital signal through an analog-to-digital converter. The amplitude of the digital signal is positively correlated with the strength of the carbon activity, thus achieving preliminary quantification of carbon activity intensity.

[0068] The timing unit operates synchronously with the sensing unit. Its role is to add precise time markers to each set of captured carbon activity digital signals to establish a correspondence between signals and time. The timing unit usually uses a real-time clock module to generate a stable clock signal through an internal crystal oscillator, ensuring time accuracy within milliseconds. At the same time, the device regularly synchronizes time with the cloud or edge nodes to avoid time stamp deviations caused by device local clock drift, ensuring consistency in time dimension for carbon activity data captured by different carbon source points and different devices, and providing a reliable time reference for subsequent analysis of carbon activity time distribution characteristics, such as peak time periods within a day and seasonal variation patterns.

[0069] After obtaining the quantified carbon activity digital signal and the corresponding timestamp, the embedded device will integrate the two types of data in real time to form a timestamp event stream. During this process, the internal processor of the device will sample continuous digital signals at a predetermined time interval, such as 1 minute / time or 5 minutes / time, which can be adjusted according to the dynamic characteristics of the carbon source activity. Each sampling generates a basic data unit containing the sampling time and signal amplitude. Multiple consecutive basic data units are arranged in chronological order, which constitutes the timestamp event stream. This event stream not only records the time sequence information of carbon activity, but also reflects the real-time intensity of carbon activity through signal amplitude, avoiding the fragmentation of discrete data and ensuring the continuity and traceability of the carbon activity process.

[0070] The device processor will first determine the event type of each data unit in the timestamp event stream. The determination is based on the pre-set carbon source activity type rule. For example, if the temperature and carbon dioxide concentration increase simultaneously and exceed the threshold value, the event is determined as a fuel combustion event. If the methane concentration continues to rise and is accompanied by stable sewage flow, the event is determined as a sewage anaerobic treatment event. Subsequently, the processor will substitute the signal amplitude into the pre-set intensity calculation model to convert the original electrical signal amplitude into a carbon activity intensity value with actual physical meaning. For example, through the calibration curve of the gas concentration sensor, the electrical signal amplitude is converted into the gas emission amount per unit time, which is used as a quantitative indicator of event intensity. Finally, the device associates the event type, event intensity, and timestamp, and stores them in a structured format according to the database, such as using SQLite local database, to form an event sequence library containing complete event attributes. This library not only supports temporary caching of local data, but also can quickly extract event data of specified time period and type according to subsequent data transmission requirements.

[0071] Step 2: Based on the event sequence library, the priority weight is calculated according to the dynamic characteristics of the event through the assignment algorithm. Nonlinear function processing is used to output an event queue with priority labels. The priority label is used to distinguish between immediate processing events and delayed processing events. The specific operation is as follows:

[0072] Each event stored in the event sequence library contains basic information such as event type, intensity and timestamp. The first step of priority generation is to extract the key parameters directly related to the dynamic characteristics of the event from these basic information, namely the time interval, signal change rate and intensity fluctuation amplitude of the event. Then, through data mapping, these three parameters are converted into a calculable dynamic response feature vector. The time interval refers to the difference between the current event and the previous adjacent event on the time axis, reflecting the intensity of event occurrence. The signal change rate refers to the amount of change in event intensity signal per unit time, reflecting the speed of change in event intensity. The intensity fluctuation amplitude is the deviation of the current event intensity from the average intensity of the same type of event, representing the abnormality of the event intensity. The dynamic response feature vector composed of the three parameters can comprehensively depict the dynamic behavior of the event.

[0073] After obtaining the dynamic response feature vector, the priority weight needs to be generated through multiple steps. The first step is to introduce a nonlinear modulation coefficient to adjust the local amplitude of the vector. Since different dynamic characteristics have different effects on event priority, events with large fluctuations, such as sudden increase in carbon emissions, often require more priority processing. Therefore, nonlinear modulation needs to be applied to the local amplitude changes of each parameter in the dynamic response feature vector. For feature parameters with amplitude exceeding the preset threshold, the modulation coefficient is greater than 1, which stretches the vector and amplifies its contribution to the priority. For feature parameters with amplitude below the threshold, the modulation coefficient is between 0 and 1, which compresses the vector and reduces its impact on the priority. Through this non-uniform modulation, the vector is more consistent with the actual judgment logic of the emergency level of carbon management.

[0074] After completing the local amplitude modulation, the dynamic response feature vector needs to be segmented and non-continuously mapped. First, according to the historical data of rural carbon activities, the amplitude of the feature vector is divided into high, medium and low intervals. Different intervals correspond to different mapping functions. The high amplitude interval uses an exponential mapping function, which makes the weight corresponding to the feature vector in this interval rapidly increase with the amplitude, ensuring that high-intensity and fast-changing events can obtain higher weights. The medium amplitude interval uses a linear mapping function, and the weight increases smoothly with the amplitude. The low amplitude interval uses a logarithmic mapping function, and the weight increases slowly with the amplitude. This segmented non-continuous mapping breaks the limitation of the fixed proportion between weight and amplitude in traditional linear mapping, and better meets the actual needs of high-priority attention in carbon management.

[0075] After the segmented mapping, the vector needs to be subjected to sequence dynamic range perception normalization processing to eliminate the problem of non-comparable weights caused by the magnitude difference of characteristic parameters of different types of events. The normalization process takes the maximum amplitude and minimum amplitude of all dynamic response characteristic vectors in the current event queue as the benchmark, and through linear transformation, the amplitudes of all vectors are uniformly mapped to the [0, 1] interval while preserving the amplitude difference and non-linear jump characteristics formed after segmented mapping. That is, after normalization, the normalized value corresponding to the original high-amplitude interval vector is still significantly higher than that of the medium-low amplitude interval vector, and the difference in the normalized value in the high-amplitude interval is greater than that in the medium-low amplitude interval, ensuring that the normalized vector can still accurately reflect the dynamic difference of the event. The final generated value is the priority weight of the event. The closer the weight value is to 1, the higher the priority of the event.

[0076] After the priority weight is generated, the events need to be aggregated according to the weight value to form a layered event queue and mark the processing type. First, set two weight thresholds, high threshold and low threshold. Events with a weight value greater than the high threshold are classified as high-priority events, events with a weight value between the low threshold and the high threshold are classified as medium-priority events, and events with a weight value less than the low threshold are classified as low-priority events. The three types of events are arranged in order of priority from high to low to form a layered event queue. Among them, high-priority events, such as sudden large carbon emission events, are marked as immediate processing events and need to be transmitted to edge devices or the cloud for accounting and response in the shortest time after generation. Medium-low priority events, such as regular stable carbon emission events, are marked as delayed processing events and can be processed in batches later on the premise that they do not affect the processing of high-priority events. Through this layered marking, differentiated processing of carbon activity events is achieved, and the overall carbon management efficiency is improved.

[0077] In the entire priority weight calculation process, the mathematical relationship is reflected in the segmented non-continuous mapping link. Taking the exponential mapping function of the high-amplitude interval as an example, its expression is: ;

[0078] Among them, the derivation logic of the formula is based on the business requirement that high-amplitude characteristics need to be quickly converted into high weights. Through the non-linear growth characteristic of the exponential function, the weight in the high-amplitude interval is quickly enhanced. In the formula, represents the weight value corresponding to the characteristic vector in the high-amplitude interval; represents the amplitude of the dynamic response characteristic vector in the high-amplitude interval; is the weight coefficient, with a value range of 0.1~0.3, determined by the type of carbon activity; is the growth coefficient, with a value range of 1.5~2.5, controlling the weight growth rate; is the offset coefficient, with a value range of 0.2~0.4, ensuring that the minimum high-amplitude characteristic can also obtain a basic weight, and is adjusted by , , The value of the weight can be adapted to the event characteristics of different administrative village carbon source points, and the flexibility and accuracy of the weight calculation can be ensured.

[0079] Step 3, using the event queue with priority labels, compact encoding is implemented on events marked for immediate processing through the compression protocol, feature preservation compression is implemented on events marked for delayed processing, state change amount is recorded by combining differential encoding, and the compression rate is dynamically adjusted according to the network condition, and a compressed event data packet is generated. The specific operation is as follows:

[0080] For the compression processing of high-priority events, information sub-blocks need to be constructed first, and then adaptive bit-width structured compact encoding is implemented. High-priority events usually contain key information directly related to the emergency state of carbon activities, such as the intensity peak value, duration, and location-related parameters of sudden high-concentration carbon emissions. These information needs to be divided into independent information sub-blocks according to functional attributes, such as intensity parameter sub-block, time parameter sub-block, and location-related sub-block. Only the core data fields directly related to carbon accounting are retained in each sub-block, and unnecessary information such as repeated labels and redundant descriptions is removed to ensure the simplicity of the sub-block structure. In the encoding stage, the adaptive bit-width mechanism dynamically adjusts the encoding bit-width according to the data characteristics of each information sub-block. If the data value range in the sub-block is small, such as the number of minutes in the time parameter, which ranges from 0 to 59, an 8-bit binary code is used. If the data value range is large, such as the carbon emission intensity value, which may cover 0-10000mg / s, it is automatically expanded to 16-bit or 32-bit binary code to avoid space waste caused by fixed bit-width and achieve compact storage of data. At the same time, structured encoding rules are used to ensure the logical association of data in each sub-block.

[0081] The feature preservation of low-priority event compression focuses on balancing data volume reduction and key information preservation. Low-priority events are mostly routine and stable carbon activity data, such as daily household cooking carbon emissions and low-intensity carbon emissions from small agricultural equipment. The data characteristics of these events are periodic or smooth, and the proportion of redundant information is high. In processing, first, the key feature parameters of the event are identified through feature extraction algorithms, such as the period length, average intensity in the period, and upper limit of intensity fluctuation for periodic carbon emission events, and the average intensity and duration for stable carbon emission events. These parameters can fully reflect the contribution of the event to carbon accounting, and there is no need to retain the original data of each time node. Subsequently, redundant information such as instantaneous intensity values with a deviation of less than 5% from the average value in the period and repeated recording of stable state identifiers is removed, and only the key feature parameters are retained and encoded for storage. This significantly reduces the data volume while ensuring that the carbon activity information of the event can be restored through the key features in subsequent accounting, avoiding accounting bias caused by excessive compression.

[0082] The state quantity change of the continuous event is recorded by differential encoding to further optimize the data storage efficiency and retain the state change trend. First, the state quantity change of the continuous event is divided into multiple dynamic intervals according to the amplitude and rate. The division is based on historical state change data of rural carbon activities, for example, the carbon emission intensity change amplitude of 0-10 mg / s and the change rate of 0-0.5 mg / (s min) are divided into a low dynamic interval; the change amplitude of 10-50 mg / s and the change rate of 0.5-2 mg / (s min) are divided into a medium dynamic interval; and the change amplitude of >50 mg / s and the change rate of >2 mg / (s min) are divided into a high dynamic interval. Different intervals correspond to different differential processing strategies. In each dynamic interval, a local starting point is adaptively selected according to the last state value of the previous interval and the initial state value of the current interval. If the deviation between the last state value of the previous interval and the initial state value of the current interval is less than 3%, the initial state value of the current interval is taken as the local starting point. If the deviation is greater than 3%, the last state value of the previous interval is corrected and taken as the local starting point to ensure the continuity of the state transition between intervals.

[0083] After calculating the relative change amount based on the local starting point, the interval sensitive nonlinear mapping is used for encoding. The formula of the mapping relationship is:

[0084] ;

[0085] According to the requirement of different dynamic intervals to differentiate and retain change details, the logarithmic function, linear function and square root function are combined in sections to realize the compressed storage of small changes and the detail retention of large changes. In the formula, represents the encoded value; represents the relative change amount of the state quantity of the continuous event; and are the division thresholds of the low-medium and medium-high dynamic intervals, which are set according to actual carbon activity data, such as = 10 mg / s, = 50 mg / s; and are linear mapping coefficients, such as = 0.2, = 3; is a square root mapping coefficient, such as = 2. For small changes in the low dynamic interval, the logarithmic function can compress a larger range of into a smaller encoded value; for medium changes in the medium dynamic interval, the linear function ensures stable correspondence between the change amount and the encoded value; and for large changes , the square root function can slow down the growth rate of the encoding value while preserving the change details; finally, the nonlinear difference encoding sequence of each interval is integrated in chronological order into a global difference encoding sequence, and the local starting point information of each interval is recorded by the starting point index nested, and the index corresponds to the encoding sequence one by one, ensuring that the starting point of each interval can be quickly located according to the index during decoding, and the complete state quantity change process of the continuous event can be accurately reconstructed.

[0086] During compression, the compression parameters need to be dynamically adjusted according to the current network conditions to balance the transmission efficiency and data quality. The network condition monitoring module will collect three core parameters in real time, including transmission bandwidth, packet loss rate and delay. If the monitoring bandwidth is sufficient, such as >10Mbps, packet loss rate <1%, and delay <50ms, it means that the network condition is good. At this time, the compression rate of high-priority events can be appropriately reduced, such as from 80% to 50%, to retain more event details. At the same time, the feature retention rate of low-priority events can be increased, such as from 60% to 80%, to reduce feature loss. If the monitoring bandwidth is insufficient, such as <2Mbps, packet loss rate >5%, and delay >200ms, the network condition is poor, and the compression rate of high-priority events needs to be increased, such as to 90%, to ensure that data can be quickly transmitted. At the same time, the feature retention rate of low-priority events can be reduced, such as to 40%, to further reduce the data volume and avoid loss of critical data due to network congestion.

[0087] The final compressed event data packet needs to integrate all processed information to form a standardized data structure. The data packet first contains the event header identification, which consists of 8 bytes. The first 2 bytes record the data packet generation time, the middle 2 bytes identify the event priority type contained in the data packet, such as 01 representing only high-priority events, 10 representing only low-priority events, and 11 representing mixed priority events, and the last 4 bytes record the total length of the data packet, which is convenient for the receiving end to quickly identify the basic information of the data packet. After the header identification, there is a compression data segment, which contains high-priority event data encoded by adaptive bit width, low-priority event data encoded by feature retention, and global difference encoding sequence. Each type of data is arranged in the order of high-priority, then low-priority, and finally difference sequence, and each data type has a 2-byte length identifier in front of it, which is convenient for data segmentation during decoding; finally, there is a state difference index, which records the local starting point value, interval number, and encoding length of each dynamic interval in table form, which corresponds to the difference sequence in the compression data segment, ensuring that the receiving end can accurately decode and reconstruct the event data to form a complete and usable compressed event data packet.

[0088] Step 4, after receiving the compressed event data packet, the cloud system issues a calculation simulator to the edge device, which performs carbon accounting calculation based on local event data, generates carbon footprint results through the mapping relationship between event amplitude and emission factors, and receives carbon footprint results and anomaly summaries. The specific operation is as follows:

[0089] The cloud system first performs decoding and event reconstruction operations on the received compressed event data packet. This process relies on the state difference index and structured encoding rules in the data packet. In the decoding stage, the cloud separates the compact encoding data of high-priority events, the feature-preserving encoding data of low-priority events, and the global difference encoding sequence according to the priority type and data length information in the data packet header identification. Then, corresponding decoding algorithms are called for different types of data. For example, for the adaptive bit-width encoding data of high-priority events, the original information sub-block is inversely parsed according to the preset bit-width mapping table. For the feature encoding data of low-priority events, the key data fields are completed through the feature parameter restoration model. Event reconstruction is guided by nested indexes and performs reverse mapping and accumulation operations on the global difference encoding sequence according to the local starting points of each dynamic interval recorded by the indexes, gradually restoring the complete state quantity changes of continuous events. At the same time, the decoded high and low priority event data are sorted by timestamp, and a structured data set containing the complete time, type, and intensity sequence of events is formed, ensuring the completeness and time sequence consistency of the data relied on by subsequent emission factor mapping and carbon accounting.

[0090] The adaptive mapping of event emission factors needs to be dynamically adjusted in combination with the fluctuation characteristics and trend relevance of event sequences to improve carbon accounting accuracy. First, non-uniform segmented fluctuation analysis is performed on the reconstructed event sequence. The standard deviation and change rate of event intensity within the window are calculated through the sliding window algorithm. When both the standard deviation and change rate exceed the preset threshold, the window is marked as a high-amplitude mutation segment, otherwise it is marked as a low-amplitude smooth segment. All marked segments are arranged in chronological order to form a local fluctuation profile. Based on this profile, amplitude-sensitive nonlinear weighting modulation is applied to high-amplitude mutation segments. The greater the event intensity in the mutation segment, the greater the corresponding modulation coefficient, making the emission factor rise nonlinearly with the growth of event intensity to highlight the contribution of high-intensity carbon activities to total emissions. For low-amplitude smooth segments, attenuation modulation is applied to reduce the sensitivity of emission factors to small intensity fluctuations, avoiding accounting bias caused by smooth segment data noise, thereby generating preliminary dynamically mapped emission factors.

[0091] The preliminary emission factor also needs to be associated with the local trend of the adjacent time period to make correction. The correction logic is based on the characteristics of carbon activities with time continuity. If the current event segment is a stable segment and the previous event segment is a mutation segment, it means that the carbon activity is transitioning from high intensity to low intensity. The emission factor of the current segment needs to be linearly adjusted according to the trend slope at the end of the previous mutation segment, so that it is smoothly connected with the emission factor of the previous stage. If the current event segment and the previous event segment are both mutation segments and the intensity change direction is consistent, the change amplitude of the emission factor is enhanced through the trend amplification coefficient, to reflect the continuous strengthening or weakening of carbon activities. The sequence of the corrected emission factor also needs to be normalized in local amplitude priority to eliminate the problem of non-comparison of factors caused by the difference in emission base of different carbon source types, such as livestock breeding and straw burning. The normalization process takes the maximum and minimum values of the historical emission factors of the whole village as the benchmark, maps all factors to the interval [0.1, 1.5], 0.1 is the lower limit of the baseline emission factor, and 1.5 is the upper limit of the factor of high intensity events. At the same time, the emission factor of the key high amplitude event segment is additionally strengthened by 1.2 times of the amplitude, to ensure that the finally generated adaptive emission factor mapping sequence can not only reflect the fluctuation characteristics of the event itself, but also reflect the time correlation of carbon activities.

[0092] The carbon accounting work of the edge device is completed by the accounting simulator issued by the cloud. The accounting process needs to achieve priority differentiation and result precision through multi-step processing. The first step is event weight modulation accumulation. The accounting simulator assigns weights according to the priority label and intensity fluctuation characteristics of the event. The default weight coefficient of high-priority events is 1.5, and the default weight coefficient of low-priority events is 0.8. At the same time, an additional 0.3 fluctuation weight is added to events with intensity fluctuation amplitude exceeding 30%. The weight value is multiplied by the corresponding event intensity and adaptive emission factor, and then accumulated, so that high-priority and strong-fluctuation events naturally have a higher contribution ratio in the accumulation process. The second step is local carbon contribution value calculation. The simulator dynamically adjusts the segmentation threshold according to the event density. When the number of events in a unit time exceeds the threshold, the time period is divided into multiple short paragraphs, otherwise it is combined into a long paragraph. The weighted and modulated accumulated value in each paragraph is taken as the local carbon contribution value of the paragraph, realizing the matching of accounting granularity and event activity. The third step is the nonlinear mapping integration of local contribution value. The local contribution value is compressed and adjusted by S-shaped function, and the abnormally high value, such as local contribution exceeding 3 times the historical average, is reduced by a certain proportion, and the abnormally low value, such as local contribution less than 1 / 3 of the historical average, is appropriately improved, to control the total accumulation deviation. Finally, the integrated local contribution sequence is accumulated in chronological order to generate the total edge carbon accounting result, and the high-priority event contribution ratio is fine-tuned. If the high-priority event contribution ratio exceeds 40% of the total result, a 1.05 times calibration coefficient is applied to the total result, otherwise the original result is kept, to ensure that the total carbon footprint accurately reflects the actual impact of event priority difference on carbon emissions.

[0093] While generating the carbon accounting result, the edge device also needs to perform nonlinear trend analysis on the result to detect abnormal carbon emission patterns. The analysis process uses a combination of sliding window and trend fitting. The first derivative of the accounting result within the window is used to calculate the carbon emission trend. When the trend value exceeds the preset positive abnormal threshold, it is determined to be a sudden increase in carbon emissions, which may correspond to fuel leakage, equipment failure, etc. When the trend value is lower than the negative abnormal threshold, it is determined to be a sudden decrease in carbon emissions, which may correspond to the suspension of carbon source activities, monitoring equipment failure, etc. For the determined abnormal patterns, the edge device needs to extract key information such as abnormal occurrence time, duration, amplitude, and corresponding event type, form a structured abnormal summary, and upload it to the cloud synchronously with the carbon accounting result.

[0094] After receiving the edge carbon footprint results and anomaly summary, the cloud performs an abnormal priority integration operation. In the integration stage, the cloud first classifies and summarizes the accounting results from different edge devices, i.e., corresponding to different carbon source points, according to carbon source types, calculates the proportion of carbon emissions of each carbon source type in the village and the total emission amount; at the same time, the priority of the anomaly summary is sorted, the sudden increase in carbon emissions is listed as the highest priority, the sudden decrease in carbon emissions is listed as the medium priority, and an anomaly handling list is generated in order of priority; finally, the cloud integrates the village carbon emission summary data and the anomaly handling list into a visual monitoring report, which provides data support for subsequent carbon emission reduction strategy making and clear guidance for rapid disposal of abnormal situations, realizing the whole-process closed loop from carbon activity monitoring to management decision-making.

[0095] In the calculation link of carbon accounting, the nonlinear mapping integration of local carbon contribution value can be realized by S-type function, and its formula is:

[0096] ;

[0097] According to the business nature that the carbon contribution value has a physical upper limit and the abnormal value needs to be controlled, the emission capacity of the carbon source is limited by objective conditions such as equipment power and raw material consumption, and the local carbon contribution value will not increase indefinitely, so the physical upper limit is set through the denominator ; at the same time, the linear preservation of the intermediate interval data and the compression of the two end extreme values are realized through the exponential term; in the formula, represents the adjusted local carbon contribution value after mapping integration, and the unit is consistent with the original contribution value, such as mgCO2; represents the theoretical maximum value of the local carbon contribution value of this type of carbon source, which is determined by the carbon source type, such as 5000mgCO2 / section for livestock breeding area and 2000mgCO2 / section for life energy area; represents the steepness coefficient of the curve, and the value range is 0.001~0.01, the larger the value, the steeper the slope of the curve in the middle interval, and the higher the discrimination degree of the data; represents the original local carbon contribution value before mapping; represents the median of the local carbon contribution value, which is obtained by statistical analysis of the historical accounting data of the carbon source, to ensure that the symmetric center of the curve is consistent with the actual data distribution center.

[0098] In practical application, the mapping integration process of S-type function needs to be implemented in three steps, the first step is parameter calibration, the accounting simulator of the edge device will call the preset parameter library according to the current carbon source type, if it is a new carbon source such as a temporary straw handling point, the median of the original local contribution value of the first three accounting is calculated , and the and The values are initialized to ensure that the parameters match the actual characteristics of the carbon source; the second step is abnormal value identification and mapping, the simulator will map the original local carbon contribution value When , if it exceeds times, the exponential term tends to 0, tends to , achieving the ceiling compression of abnormally high values; when , if it is less than times, the exponential term tends to infinity, tends to 0, avoiding the interference of small abnormal low values on the total result; when is in the normal interval , and are approximately linear, fully preserving normal fluctuation information; the third step is mapping result verification, the simulator will compare the deviation rate of the data before and after mapping, if the deviation rate exceeds 10%, such as the deviation after abnormal high value compression, the deviation reason will be recorded and marked for that local segment, which is convenient for tracing back during subsequent cloud consistency verification, ensuring that the mapping integration not only controls abnormal values but also does not lose key data characteristics; this S-shaped function-based nonlinear mapping integration has the advantage of smoother adjustment of abnormal values compared to traditional linear truncation methods, avoiding data mutation caused by hard truncation, while dynamically adapting parameters to different carbon source types, balancing the universality and accuracy of accounting, providing an important mathematical tool for edge devices to generate reliable local carbon contribution data.

[0099] Step 5, regularly compare the consistency of edge carbon accounting results and cloud accounting data, dynamically adjust the weight parameters of the assignment algorithm and the compression parameters of the compression protocol according to the differences between event capture output and carbon accounting results, the specific operations are as follows:

[0100] The first step of edge cloud closed-loop adaptive optimization is to analyze the multi-scale consistency of edge carbon accounting results and cloud data, which covers time scale and data dimension. On the time scale, it is divided into short-term, medium-term and long-term intervals according to hours, days and weeks, and the absolute and relative errors of edge and cloud accounting results in different intervals are calculated; on the data dimension, it is classified and counted according to event types such as breeding carbon source and combustion carbon source, priority levels such as high, medium and low, and intensity intervals such as high, medium and low; error distribution; through multi-scale analysis, the error data is arranged in matrix form according to time interval and data dimension, forming a multi-dimensional consistency profile containing difference amplitude such as maximum, minimum and average error, and difference distribution such as error proportion in each dimension, which intuitively presents the deviation characteristics of edge and cloud data.

[0101] Generating event difference factor based on multi-dimensional consistency profile is the core step connecting difference analysis and parameter adjustment. The process needs to decompose difference characteristics and then complete factor mapping and fusion. First, the consistency profile is decomposed into four types of multi-dimensional difference characteristics, including time series deviation, local intensity anomaly, event priority deviation and abnormal event density. Time series deviation refers to the error fluctuation trend of edge and cloud data on the time axis. Local intensity anomaly is the error peak value in a specific intensity interval. Event priority deviation is the error difference of events with different priorities. Abnormal event density is the proportion of the number of events with error exceeding the standard. Then, a mapping model is constructed for each type of difference characteristic to convert it into a local weight sensitive factor. For example, the larger the time series deviation, the higher the value of the corresponding weight sensitive factor. The higher the peak value of local intensity anomaly, the more significant the increase of the sensitive factor. According to the event priority and local fluctuation characteristics, the assignment algorithm weight adjustment factor and compression protocol parameter adjustment factor are split from the local weight sensitive factor. The adjustment factor weight of high-priority events is higher than that of low-priority events, and the sensitivity of adjustment factor in strong fluctuation interval is higher than that in stable interval. Finally, all local weight sensitive factors are dynamically fused by weighted summation. The event priority information is embedded in the fusion process, so that the difference of high-priority events accounts for a higher proportion in the fusion result. Finally, the event difference factor sequence that changes dynamically with time and event characteristics is formed, which is directly used as the quantitative basis for driving parameter adjustment.

[0102] After the generation of the event difference factor sequence, it is first applied to the nonlinear dynamic reconstruction of the weight parameters of the event priority assignment algorithm. The reconstruction logic follows the principle of enhancing the response of high-difference events and keeping the stability of low-difference events. For the weight parameters related to time interval, signal change rate and intensity fluctuation amplitude in the assignment algorithm, the parameter weight is adjusted according to the value of the event difference factor. If the difference factor of a certain type of event, such as high-priority combustion carbon source event, exceeds the preset threshold, it means that the priority judgment of this type of event deviates greatly from the actual calculation result, and the weight parameters of signal change rate and intensity fluctuation amplitude need to be increased, so that the priority calculation of this type of event in the future is more sensitive, and it is marked as an immediate processing event. If the difference factor of a certain type of event is lower than the threshold, it means that the priority judgment is accurate, and only a small adjustment or no adjustment is made to the weight parameter. In the reconstruction process, a nonlinear adjustment function is used to make the change amplitude of the weight parameter and the difference factor nonlinearly positively correlated. The larger the difference factor, the more significant the parameter adjustment amplitude, avoiding parameter oscillation caused by linear adjustment, and ensuring that the assignment algorithm can quickly adapt to data differences and improve the accuracy of priority judgment.

[0103] Meanwhile, the event difference factor sequence is also used for difference-aware adjustment of the compression protocol parameters, and the adjustment range covers high and low priority event encoding strategies, differential record depth, and compression rate. For high priority events, if the corresponding difference factor shows that there is an abnormal difference segment, such as a significant accounting error of high priority events in a certain period, the encoding strategy needs to be adjusted from the original adaptive bit width encoding to more detailed variable length encoding, and at the same time, the compression rate is reduced, such as from 90% to 70%, to reduce information loss in the data compression process; the differential record depth is increased by 1-2 levels, which more carefully records the state quantity change, and ensures that the decoded data can accurately restore the original event characteristics; for low priority events, the compression rate and feature retention rate are adjusted adaptively according to the fluctuation of the difference factor. If the difference factor is within the normal range, a higher compression rate and a lower feature retention rate can be maintained to save bandwidth; if the difference factor rises slightly, the compression rate is appropriately reduced and the feature retention rate is increased, which controls the data volume while reducing the difference expansion. Through such differentiated adjustment, the integrity of high priority event data is guaranteed, and the transmission efficiency of low priority event data is also considered.

[0104] After completing the assignment algorithm and adjusting the compression protocol parameters, the adjustment effect needs to be verified in the event capture and accounting process of the next period. The verification link needs to repeatedly perform multi-scale consistency analysis to generate a new multi-dimensional consistency profile, and compare it with the profile before adjustment to analyze whether the difference amplitude is reduced and the difference distribution is more uniform; if the new profile shows that the consistency of the edge and the cloud data is significantly improved, it means that the parameter adjustment is effective, and the current parameters can be used as the reference parameters for the next period; if the consistency does not improve significantly or even deteriorates, the parameters need to be fine-tuned again according to the new event difference factor sequence. Through the cycle of adjustment, verification, feedback, and final adjustment, a continuous adaptive closed loop is formed, so that the cooperation between the edge device and the cloud system is always in a dynamic optimization state, and the accuracy and efficiency of rural full life cycle carbon management are continuously improved.

[0105] In the parameter adjustment process, the quantitative calculation is reflected in the mapping relationship between the event difference factor and the compression rate, and the calculation formula is:

[0106] ;

[0107] The formula is based on the business requirement that the larger the difference factor, the more the compression rate needs to be reduced to reduce information loss, and the linear mapping establishes the correlation between the difference factor and the adjustment amplitude of the compression rate; in the formula, is the adjusted compression rate; is the basic compression rate before adjustment; is the compression rate adjustment coefficient, with a value range of 0.1-0.3, 0.3 for high priority events, and 0.1 for low priority events; is an event difference factor, the value range is 0~1, the larger the value represents the more significant difference; for example, when the base compression rate of high priority event =90%, event difference factor =0.8, adjustment coefficient =0.3, the adjusted compression rate =90%×(1−0.3×0.8)=90%×0.76=68.4%, by reducing the compression rate to reduce the compression loss of high difference event, ensure data integrity.

[0108] The above is only the preferred specific embodiments of the present application; however, the protection scope of the present application is not limited thereto. Any skilled person in the art, according to the technical solution of the present application and its improvement concept, within the technical range disclosed by the present application, equivalent replacement or change, should be covered within the protection scope of the present application.

Claims

1. An efficient solution for rural life cycle carbon management characterized in that, The method comprises the following steps: Step 1, deploying embedded devices at key carbon source points in administrative villages, the embedded devices capturing carbon activity signals through sensing and timing units and converting them into a timestamp event stream, and building an event sequence library containing event type, event amplitude and timestamp; Step 2, based on the event sequence library, calculating priority weights according to the dynamic characteristics of events by assigning algorithms, processing and outputting an event queue with priority labels using nonlinear functions, the priority labels being used to distinguish immediate processing events from delayed processing events, and further comprising: Step 21, mapping the time interval, signal change rate and amplitude fluctuation amplitude of each event in the event sequence library into a dynamic response characteristic vector; Step 22, applying a characteristic amplitude sensitive nonlinear mapping function to the dynamic response characteristic vector to generate a priority weight; Step 23, aggregating events according to the priority weight to form a hierarchical event queue, and marking immediate processing events and delayed processing events in the hierarchical event queue; Step 3, using the event queue with priority labels, implementing compact coding on events marked for immediate processing and feature retention compression on events marked for delayed processing through a compression protocol, recording state change amounts by differential coding, and dynamically adjusting the compression rate according to network conditions to generate compressed event data packets, and further comprising: Step 31, constructing an event information sub-block for events marked for immediate processing and structurally compact coding according to adaptive bit width; Step 32, retaining only key features for events marked for delayed processing to form feature retention compression; Step 33, performing differential coding on the state amount changes of consecutive events to record relative change amounts, and further comprising: Step 331, dividing the state amount changes of consecutive events into multiple dynamic intervals according to amplitude and rate; Step 332, selecting a local starting point in each dynamic interval and adaptively adjusting the local starting point according to the state at the end of the previous dynamic interval; Step 333, calculating the relative change amount with the local starting point as the reference and encoding it through interval sensitive nonlinear mapping; Step 334, integrating the coding sequences of each dynamic interval into a global differential coding sequence and recording the starting point information of each dynamic interval through starting point index; Step 34, dynamically adjusting the compression rate of immediate processing events and the feature retention ratio of delayed processing events according to the current network conditions; Step 35, encapsulating the events after coding and compression to generate compressed event data packets, the compressed event data packets containing event header identification, compressed data segment and state differential index; Step 4, after receiving the compressed event data packets, the cloud system issues an accounting simulator to the edge device, the accounting simulator performs carbon accounting calculation based on local event data, generates carbon footprint results through the mapping relationship between event amplitude and emission factors, and the cloud system receives carbon footprint results and abnormal summaries, and further comprising: Step 41, decoding and nested index guided event reconstruction of compressed event data packets to restore the time, type and amplitude sequence of events; Step 42, adaptively adjusting the emission factor mapping corresponding to the event according to the event history amplitude fluctuation and local trend; Step 43, performing carbon accounting on the edge device's accounting simulator, accumulating the product of event amplitude and adaptive emission factor, generating local carbon contribution and aggregating to form carbon footprint results; Step 44, performing nonlinear trend analysis on carbon footprint results to detect abnormal carbon emission patterns and generate an abnormal summary; Step 45, the cloud receives carbon footprint results and abnormal summary and integrates them; Step 5, periodically compare the consistency of edge carbon accounting results and cloud accounting data, dynamically adjust the weight parameters of the assignment algorithm and the compression parameters of the compression protocol according to the difference between event capture output and carbon accounting results.

2. The efficient solution for rural life cycle carbon management according to claim 1, characterized in that, The step 22 comprises: Step 221, introducing a nonlinear modulation coefficient to the local amplitude change of each event in the dynamic response feature vector, expanding the event data that meets the preset large amplitude fluctuation condition, and reducing the event data that meets the preset small amplitude fluctuation condition; Step 222, segmenting the dynamic response feature vector after local amplitude modulation along the amplitude interval for non-continuous mapping, so that the weight gradient of the mapping result in the high amplitude interval is greater than that in the low amplitude interval; Step 223, performing normalization based on the sequence dynamic range on the segmented non-continuous mapped vector to generate priority weights.

3. The efficient solution for rural life cycle carbon management according to claim 2, characterized in that, The step 42 comprises: Step 421, performing non-uniform segmented fluctuation analysis on the reconstructed event sequence to extract high amplitude mutation segments and low amplitude stable segments; Step 422, applying amplitude-sensitive nonlinear weighted modulation to the high amplitude mutation segment and applying attenuation modulation to the low amplitude stable segment to generate the preliminary dynamic mapping emission factor; Step 423, based on the local trend of adjacent time periods of events, correlation correction is performed on the preliminary dynamic mapping emission factor; Step 424, performing local amplitude priority normalization on the trend correlation corrected emission factor sequence, and performing amplitude enhancement on the key high amplitude event segment to form the final adaptive emission factor mapping sequence.

4. The efficient solution for rural life cycle carbon management according to claim 3, characterized in that, The step 43 comprises: Step 431, event weight modulation accumulation is performed on the event sequence after adaptive emission factor mapping; Step 432, the event sequence after weight modulation is divided into local paragraphs and accumulated to form local carbon contribution values; Step 433, applying nonlinear mapping to integrate the local carbon contribution values; Step 434, accumulating the integrated local contribution sequence to generate the total edge carbon accounting result, and calibrating according to the high priority event contribution.

5. The efficient solution for rural life cycle carbon management according to claim 4, characterized in that, The step 5 comprises: Step 51, performing multi-scale consistency profiling on the edge carbon accounting results and cloud accounting data to form a consistency profile of difference amplitude and distribution; Step 52, generating an event difference factor according to the consistency profile; Step 53, nonlinear dynamic reconstruction of the weight parameters of the assignment algorithm according to the event difference factor; Step 54, difference-aware adjustment of the encoding strategy and compression rate of the compression protocol according to the event difference factor; Step 55, verify the adjustment effect in the next period of event capture and accounting, and form feedback through the updated consistency profile.

6. The efficient solution for rural life cycle carbon management according to claim 5, wherein, The step 52 comprises: Step 521, decomposing the consistency profile into difference characteristics of time series deviation, local intensity anomaly and event priority offset; Step 522, based on the difference characteristics, generating the assignment algorithm weight adjustment factor and the compression protocol parameter adjustment factor; Step 523, dynamically fusing the weight adjustment factor and the parameter adjustment factor to form the event difference factor sequence.

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