Carbon emission data transmission accounting method, medium and device

Abstract

The application provides a carbon emission data transmission accounting method, medium and equipment, comprising collecting carbon emission activity data and scene labels on the terminal side, obtaining an activity state matrix based on the carbon emission activity data using a pyramid discretization strategy, and then generating standardized accounting segments through a fractal resolution function cluster; the terminal side receives the standardized accounting segments, intercepts problems in the standardized accounting segments based on a verification sampling strategy, then performs scene-based compensation calculation in combination with the scene labels, and generates regional-level accounting results through local aggregation; the cloud side receives the regional-level accounting results and constructs a global constraint network, performs an arc consistency algorithm on the global constraint network to shrink a candidate solution space, and obtains a carbon emission accounting interval based on the size of the candidate solution space through branch and bound or interval convergence, so that the terminal side has effective problem data recognition and interception capabilities under the premise that the original activity data does not go out of the domain, and the cloud side can complete accurate carbon emission accounting.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method, medium, and device for carbon emission data transmission and accounting. Background Technology

[0002] Existing edge-cloud collaborative carbon emission accounting methods typically follow a linear pipeline model: edge-side data collection and pre-calculation, edge-side aggregation and compensation, and cloud-side integration and calibration. In this linear pipeline model, to achieve carbon emission accounting, the standardized accounting segments generated on the edge still need to contain or carry specific activity data characteristics that can be reverse-analyzed, such as encoded energy consumption values. This means that once the data leaves the domain (i.e., the edge), it faces the risk of privacy leakage. Simultaneously, the edge can usually only perform format verification and simple statistical aggregation on the received data. Because it cannot access the original data, it lacks the technical means to effectively verify the authenticity and rationality of the data without compromising privacy. Once erroneous or tampered abnormal accounting segments enter the cloud through the edge, they will directly contaminate the accuracy of the system-level accounting results during subsequent aggregation and calibration processes, and are difficult to trace. Therefore, how to enable the edge to effectively identify and intercept problematic data while protecting data privacy and ensuring that the original activity data does not leave the domain, and to support accurate carbon emission accounting in the cloud, is a prominent challenge facing existing technologies. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a carbon emission data transmission accounting method, medium, and device.

[0004] The first aspect of this invention provides a method for carbon emission data transmission and accounting, comprising:

[0005] On the edge side, carbon emission activity data and corresponding scene labels are collected. The carbon emission activity data is discretized using a pyramid discretization strategy. The fractal resolution function clusters issued by the cloud side are used to incrementally resolve and encode the discretized activity state matrix to generate a standardized accounting fragment containing a global resolution vector and a template resolution vector. The standardized accounting fragment is then sent to the edge side.

[0006] On the edge, standardized accounting segments are received, and a grouping resolution vector is requested from the edge based on the verification sampling strategy issued by the cloud side. The standardized accounting segments are sampled and verified based on the grouping resolution vector to intercept problematic data. The standardized accounting segments that pass the verification are combined with scene tags to perform scene-based compensation calculation. The compensated standardized accounting segments are locally aggregated to generate regional-level accounting results and reported to the cloud side.

[0007] On the cloud side, a global constraint network is constructed based on the regional accounting results. An arc compatibility algorithm is executed on the global constraint network to shrink the candidate solution space. Based on the size of the candidate solution space, a branch and bound method or interval convergence is executed to obtain the carbon emission accounting interval, and then a source tracing analysis report is obtained.

[0008] According to a preferred embodiment, a pyramid discretization strategy is used to discretize carbon emission activity data, including:

[0009] A pyramid discretization structure is constructed to discretize carbon emission activity data in layers, forming an activity state matrix at multiple scales. The number of layers in the pyramid discretization structure is dynamically configured by the cloud side based on the accuracy requirements of the calculation, and the bottom layer is fine-grained discretization, while all other layers are coarse-grained discretization.

[0010] At the bottom layer of the pyramid discretization structure, continuous measurements of carbon emission activity data are mapped to preset ranges to generate a fine-grained activity state matrix.

[0011] In each layer of the pyramid discretization structure, except for the bottom layer, multiple adjacent gear intervals in the next layer are merged into the gear interval corresponding to the current layer, and continuous measurements of carbon emission activity data are mapped to the gear interval corresponding to the current layer to generate a corresponding coarse-grained activity state matrix.

[0012] According to a preferred embodiment, a cluster of fractal resolution functions distributed from the cloud side is used to incrementally resolve and encode the discretized activity state matrix, generating a standardized computational fragment containing a global resolution vector and a template resolution vector, including:

[0013] For the current accounting cycle, receive and deploy the fractal resolution function cluster and resolution parameter version number, wherein the fractal resolution function cluster is a hierarchical structure containing the first function, the second function and the third function;

[0014] For the activity state matrix at each scale, grouped resolution vectors, template resolution vectors, and global resolution vectors are generated respectively through a cluster of fractal resolution functions.

[0015] The global resolution vector corresponding to the coarsest-scale activity state matrix, the template resolution vector of the current cycle, the calculation cycle identifier, the timestamp, the scene label, and the resolution parameter version number are encapsulated into a standardized calculation fragment.

[0016] According to a preferred embodiment, for the activity state matrix at each scale, grouped resolution vectors, template resolution vectors, and global resolution vectors are generated respectively through a cluster of fractal resolution functions, including:

[0017] For each element in the activity state matrix, the row and column coordinates, timestamp, and discrete values ​​of the element are input into the first function, which outputs a fixed-length, one-way resolution vector. The one-wayness of the resolution vector is based on lattice cipher construction.

[0018] Based on the preset grouping template, the elements in the activity state matrix are divided into several groups. The second function is called to merge the resolution vectors of all elements in the group into a group resolution vector. At the same time, the third function is called to generate a global resolution vector based on the group resolution vector.

[0019] The current period's grouping resolution vector is incrementally fused with the cumulative grouping resolution vector updated in the previous accounting period to obtain the current period's cumulative grouping resolution vector.

[0020] The cumulative grouping resolution vectors of the current period are aggregated into template resolution vectors according to the grouping templates. The grouping templates include spatial grouping, temporal grouping, and cross-dimensional grouping, which are used to generate grouping resolution vectors with complementary perspectives.

[0021] According to a preferred embodiment, sampling verification of standardized accounting fragments based on grouped resolution vectors to intercept problematic data includes:

[0022] The receiving end responds to the sampling request uploaded group resolution vector and performs aggregation consistency verification on the group resolution vector. The aggregation consistency verification is used to obtain the template resolution vector in the standardized accounting segment that belongs to the same group template as the sampled group resolution vector, and compare the sampled group resolution vector with the template resolution vector. If the comparison result is inconsistent, the aggregation consistency verification is determined to fail; otherwise, it passes.

[0023] Simultaneously, a time continuity verification is performed on the grouped resolution vector. The time continuity verification is used to obtain the grouped resolution vectors of adjacent time groups with the same activity dimension in continuous time slices for the grouped resolution vectors under the time group, and calculate the vector difference between the two. If the vector difference is greater than the dynamic difference threshold, the time continuity verification is determined to be abnormal; otherwise, it passes.

[0024] Among them, the dynamic difference threshold is obtained by fitting the change magnitude of the grouping resolution vector of the time group corresponding to the same activity dimension in the historical accounting cycle on the end side;

[0025] Based on the results of sampling verification, a verification status marker is generated. Based on the verification status marker, problematic data is marked from the standardized accounting fragments on the edge side and intercepted. The verification status marker includes abnormal, passed, and questionable.

[0026] According to a preferred embodiment, the standardized accounting segments that have passed verification are combined with scene tags to perform scene-based compensation calculations. The compensated standardized accounting segments are then locally aggregated to generate regional-level accounting results, including:

[0027] The side maintains a scene compensation coefficient library, which stores the emission factor compensation coefficient and scene compensation coefficient version number corresponding to different scene labels;

[0028] Based on the scene labels reported by the edge, the corresponding scene compensation coefficient is retrieved. Utilizing the additive homomorphism of the template elimination vector, the template elimination vector in the standardized accounting segment that has passed the verification is homomorphically weighted with the corresponding scene compensation coefficient in the elimination value domain to generate the compensated template elimination vector.

[0029] All compensated template elimination vectors are locally aggregated according to the grouped templates to generate a region-level template elimination vector matrix;

[0030] The regional template resolution vector matrix and the verification status markers and scene compensation coefficient version numbers generated from the sampling verification results at each end are used as the regional accounting results.

[0031] According to a preferred embodiment, a global constraint network is constructed based on the regional-level accounting results, and an arc-compatible algorithm is performed on the global constraint network to shrink the candidate solution space, including:

[0032] Based on the regional template resolution vector matrix in the regional accounting results reported by the edge side, a global constraint network is constructed with the elements of the activity state matrix of each edge side at the corresponding scale as variables, and including the first and second constraints.

[0033] For edge sides whose reported verification status is marked as questionable, a constraint relaxation variable is introduced into the global constraint network.

[0034] For any two variables belonging to the same end side and the same group template, the first constraint is that the sum of the element elimination vectors equals the corresponding component in the template elimination vector.

[0035] For any two variables in the global constraint network that belong to the same end side and the same group template, if a certain value of one variable cannot satisfy the first constraint when the other variable takes any possible value, then determine whether to remove the value from the candidate value set of the variable by combining the constraint relaxation variable. Repeatedly scan all variable pairs with the first constraint until the candidate value set of all variables no longer changes.

[0036] The second constraint is that the sum of the grouping resolution vectors under the same grouping template equals the corresponding template resolution vector.

[0037] For a second constraint involving multiple variables, if a certain value of any variable cannot be found in the candidate value set of other variables to make the second constraint valid, then the constraint slack variable is used to determine whether to remove the value from the candidate value set of the variable, until the candidate value set of all variables no longer changes;

[0038] Once the set of candidate values ​​for all variables no longer changes, calculate the size of the candidate solution space.

[0039] According to a preferred embodiment, a branch-and-bound method or interval convergence is performed based on the size of the candidate solution space to obtain the carbon emission accounting interval, including:

[0040] If the candidate solution space is smaller than the preset solution threshold, the branch and bound method is used to obtain the carbon emission accounting interval;

[0041] If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the coarse-grained activity state matrix, then the cloud side requests the template resolution vector of the next finer scale from the corresponding end side through the edge side, reconstructs the global constraint network based on the elements of the activity state matrix of the next finer scale, and re-executes the arc compatibility algorithm until the solution space is less than the preset solution threshold.

[0042] If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the fine-grained activity state matrix, then based on the entropy of the candidate value set of each element in the fine-grained activity state matrix, the group containing the variable to be converged is obtained by descending the entropy value. The cloud side requests the group resolution vector of the group from the corresponding end side through the edge side and adds it to the global constraint network. The arc compatibility algorithm is re-executed until the solution space is less than the preset solution threshold.

[0043] A second aspect of the present invention provides a non-volatile computer storage medium storing computer-executable instructions capable of performing the methods described in the embodiments.

[0044] A third aspect of the present invention provides a processing apparatus, the apparatus including a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the apparatus is triggered to perform the method described in the embodiment.

[0045] Based on the above, this application embodiment collects carbon emission activity data and corresponding scene tags on the edge side, uses a pyramid discretization strategy to discretize the carbon emission activity data, and uses a fractal resolution function cluster issued by the cloud side to incrementally resolve and encode the discretized activity state matrix, generating a standardized accounting fragment containing a global resolution vector and a template resolution vector, which is then sent to the edge side. This ensures from the source that the original activity data obtained by the edge side is not outside the domain, thus achieving privacy protection in the data transmission process.

[0046] On the other hand, by receiving standardized accounting segments at the edge, sampling and verifying them based on grouped resolution vectors to intercept problematic data, and combining the verified standardized accounting segments with scene labels to perform scene-based compensation calculations and local aggregation, regional accounting results are generated and reported to the cloud side. This enables the edge side to effectively identify and intercept abnormal data without touching the original data, preventing it from polluting the aggregation results.

[0047] Furthermore, by constructing a global constraint network on the cloud side based on regional-level accounting results, and performing an arc-compatible algorithm on the global constraint network to shrink the candidate solution space, and then performing a branch-and-bound method or interval convergence based on the size of the candidate solution space to obtain the carbon emission accounting range, the cloud can ultimately deduce the system-level carbon emissions with high precision by simply reversing the mathematical constraint relationships in the resolved value range. This achieves accurate and reliable carbon emission accounting while protecting data privacy throughout the process. Attached Figure Description

[0048] Figure 1 The execution flowchart of a carbon emission data transmission and accounting method of the present invention is shown.

[0049] Figure 2 The execution flowchart of the pyramid discretization structure in the carbon emission data transmission accounting method of the present invention is shown.

[0050] Figure 3 A schematic diagram of the processing equipment for a carbon emission data transmission and accounting method according to the present invention is shown. Detailed Implementation

[0051] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0054] like Figure 1 , Figure 2 As shown:

[0055] This embodiment provides a carbon emission data transmission accounting method, including:

[0056] Step S1: On the edge side, collect carbon emission activity data and corresponding scene labels, use the pyramid discretization strategy to discretize the carbon emission activity data, and use the fractal resolution function cluster issued by the cloud side to perform incremental resolution encoding on the discretized activity state matrix to generate a standardized accounting fragment containing a global resolution vector and a template resolution vector, and send the standardized accounting fragment to the edge side.

[0057] Step S2: On the edge, receive standardized accounting segments, request group resolution vectors from the edge based on the verification sampling strategy issued by the cloud side, sample and verify the standardized accounting segments based on the group resolution vectors to intercept problematic data, perform scenario-based compensation calculations on the standardized accounting segments that pass the verification in combination with the scenario tags, and perform local aggregation of the compensated standardized accounting segments to generate regional-level accounting results, which are then reported to the cloud side.

[0058] Step S3: On the cloud side, a global constraint network is constructed based on the regional accounting results. The arc compatibility algorithm is executed on the global constraint network to shrink the candidate solution space. Based on the size of the candidate solution space, the branch and bound method or interval convergence is executed to obtain the carbon emission accounting interval, and then the source tracing analysis report is obtained.

[0059] Among them, on the end side, it includes:

[0060] Step S1.1: Collect carbon emission activity data and corresponding scenario labels.

[0061] The edge side includes devices with data acquisition and processing capabilities deployed at various carbon emission source sites, such as embedded industrial control computers, smart gateways, or sensors with edge computing capabilities.

[0062] Specifically, on the edge side, multimodal sensing devices deployed in various carbon emission scenarios such as industrial production lines, buildings, and transportation hubs are connected to collect raw activity data that are directly or indirectly related to carbon emissions in real time or periodically. This carbon emission activity data typically includes continuous measurements in multiple dimensions, such as instantaneous power corresponding to the power load dimension, instantaneous flow rate corresponding to the fuel consumption dimension, input rate of key production materials, and auxiliary parameters that indirectly affect carbon emissions, such as equipment operating temperature and ambient temperature.

[0063] Furthermore, while collecting carbon emission activity data, the edge device automatically binds corresponding scene tags to the data source based on the preset configuration of the sensing device or data interface. The scene tag is used to uniquely identify the business scenario or process of the carbon emission activity data source. For example, in an industrial scenario, the scene tag could be steelmaking-electric furnace process; in a building scenario, it could be office building-central air conditioning system; and in an information technology scenario, it could be data center-server cluster.

[0064] Understandably, the scenario label is used to provide an index for retrieving scenario compensation coefficients in the subsequent scenario-based compensation calculation step S2.3. By applying the scenario compensation coefficients corresponding to the scenario label, it can be ensured that carbon emission activity data of the same dimension can be assigned differentiated accounting weights according to their actual carbon emission characteristics in different application scenarios, so that the generated carbon emission accounting interval can more accurately represent the actual carbon emission characteristics of a specific scenario.

[0065] Optionally, scene tags can be remotely updated by the cloud side via configuration update commands to adapt to dynamic changes in actual business scenarios on the client side.

[0066] Step S1.2: Discretize the carbon emission activity data using a pyramid discretization strategy.

[0067] It should be noted that after the edge device collects the original, continuous carbon emission activity data through step S1.1, in order to facilitate subsequent privacy-preserving encoding calculations and support multi-scale accurate accounting in the cloud, a pyramid discretization strategy is adopted to perform structured processing on the carbon emission activity data. This pyramid discretization strategy is used to map high-precision continuous measurement values ​​into a multi-level discrete interval structure from fine to coarse, thereby generating a series of activity state matrices at different scales, providing standardized input for subsequent fractal decomposition encoding.

[0068] Step S1.2.1: Construct a pyramid discretization structure to perform hierarchical discretization of carbon emission activity data, forming an activity state matrix at multiple scales. The number of layers in the pyramid discretization structure is dynamically configured by the cloud side based on the accuracy requirements of the calculation, and the bottom layer is fine-grained discretization, while all layers except the bottom layer are coarse-grained discretization.

[0069] Specifically, for each dimension of carbon emission activity data, such as electricity load, the edge device will construct a pyramid discretization structure locally. After comprehensively assessing the accuracy requirements of the cloud-based global carbon emission accounting task, the overall computational load of the system, and the requirements for privacy protection, a discretization configuration is dynamically generated and distributed to the edge device to construct the pyramid discretization structure. This discretization configuration includes at least the number of layers L of the pyramid discretization structure, the width of the tier interval of the first layer, and the merging strategy parameters used to guide the generation of tier intervals in each layer except the bottom layer. The merging strategy parameters are used to indicate that each tier interval in the current layer is formed by merging multiple adjacent tier intervals in the next layer.

[0070] The pyramid discretization structure consists of a first layer (layer 1) with fine-grained discretization, which divides the carbon emission activity data into the smallest intervals to best represent changes in carbon emission activity data and preserve detailed information. Layers 2 through L are coarse-grained discretizations, where intervals are not directly specified by the cloud side but generated by the edge side by merging adjacent intervals from the next layer according to merging strategy parameters. Therefore, the discretization granularity gradually increases compared to the next layer. The pyramid discretization strategy converts the original continuous measurement values ​​into L activity state matrices with different resolutions. In each activity state matrix, rows typically represent a continuous time-slice sequence, such as one time slice per minute, and columns represent a dimension of monitored carbon emission activity data or different monitoring points within the same dimension. The value filled in each position of the activity state matrix is ​​a unique identifier—the interval to which the carbon emission activity data at that time point and dimension is mapped.

[0071] In summary, for carbon emission activity data within the same time period, the endpoint synchronously generates a set of activity state matrices with progressively different scales, ranging from the most refined (layer 1 of the pyramid discretization structure) to the coarsest (layer L of the pyramid discretization structure). This forms the complete multi-scale data foundation required for fractal decomposition and encoding in the subsequent step S1.3.

[0072] Step S1.2.2: At the bottom layer of the pyramid discretization structure, the continuous measurement values ​​of carbon emission activity data are mapped to the preset level range to generate a fine-grained activity state matrix.

[0073] In the first layer of the pyramid discretization structure, the edge device, based on the discretization configuration issued by the cloud side, predefines a set of fine-grained, non-overlapping tier intervals for each dimension of carbon emission activity data, which can cover all reasonable value ranges of the data in that dimension. For example, for power load data in kilowatts, the bottom tier intervals can be preset as [0, 1), [1, 2), ..., [99, 100), [100, 110), etc. At each data acquisition moment, the edge device determines the bottom tier interval to which the acquired continuous measurement value belongs based on its numerical value, and writes the bottom tier identifier, such as the interval index number, into the corresponding position in the activity state matrix, that is, at the intersection of the row of the corresponding time slice and the column of the data dimension, and uses it as the fine-grained activity state matrix. The above mapping process converts continuous measurement values ​​into discrete digital identifiers.

[0074] Step S1.2.3: In each layer of the pyramid discretization structure, except for the bottom layer, merge multiple adjacent gear intervals in the next layer into the gear interval corresponding to the current layer, and map the continuous measurement values ​​of carbon emission activity data to the gear interval corresponding to the current layer to generate the corresponding coarse-grained activity state matrix.

[0075] Specifically, for each layer from layer 2 to layer L in the pyramid discretization structure, the endpoint determines the number of adjacent gear intervals that need to be merged in the next layer based on the merging strategy parameters in the discretization configuration issued by the cloud side. The endpoint traverses all gear intervals in the next layer and merges each specified number of adjacent gear intervals into a new, wider gear interval. The lower bound of this new interval is the minimum value of the merged intervals, and the upper bound is the maximum value of the merged intervals, thereby constructing a complete set of gear intervals corresponding to the current layer.

[0076] In some possible embodiments, it is assumed that the power load data at the bottom layer, i.e., the first layer, is divided into 8 ranges: [0, 1), [1, 2), [2, 3), [3, 4), [4, 5), [5, 6), [6, 7), [7, 8). The merging strategy parameter instructs the second layer to merge every two adjacent ranges into one, so the ranges at the second layer are [0, 2), [2, 4), [4, 6), [6, 8), a total of 4 ranges. If the merging strategy parameter at the third layer instructs the third layer to merge every two adjacent ranges into one, then the ranges at the third layer are [0, 4), [4, 8), a total of 2 ranges. And so on, the larger the layer, the wider and fewer the ranges, and the data resolution decreases step by step.

[0077] Furthermore, after constructing the set of gear intervals for the current layer, the endpoint remaps the continuous measurements of carbon emission activity data to the corresponding gear intervals for the current layer according to their actual values ​​in each time slice, and writes the gear identifier into the corresponding position of the activity state matrix for the current layer, as the coarse-grained activity state matrix for the current layer. It can be understood that the mapping process of the coarse-grained activity state matrix is ​​completely symmetrical to the generation method of the fine-grained activity state matrix. Both map the same set of continuous measurements to gear intervals of different coarse and fine granularities, thereby forming a complete scale spectrum from fine to coarse.

[0078] It should be noted that there is a clear scale inheritance relationship between the elements of the coarse-grained activity state matrix and the fine-grained activity state matrix. Specifically, a certain level identifier in the coarse-grained activity state matrix is ​​logically equivalent to a set of consecutive and adjacent level identifiers in the fine-grained activity state matrix. The above scale inheritance relationship enables that when the scale refinement process of step S3.2.2 is executed on the cloud side, when it is necessary to switch from the coarse-scale activity state matrix to a finer scale, the candidate values ​​of the coarse-scale variables can be accurately expanded and mapped to the domain of the corresponding next-level finer-grained variables, thereby realizing the progression of constraints and the precise contraction of the solution space.

[0079] In summary, for carbon emission activity data within the same accounting period, L activity state matrices of different scales have been generated synchronously on the endpoint, including one fine-grained activity state matrix and L-1 coarse-grained activity state matrices, which constitute the complete data foundation required for multi-scale fractal decomposition and encoding in the subsequent step S1.3.

[0080] Step S1.3: Using the fractal resolution function cluster issued by the cloud side, incremental resolution encoding is performed on the discretized activity state matrix to generate a standardized accounting fragment containing global resolution vector and template resolution vector.

[0081] It should be noted that, in order to protect data privacy, after generating the multi-scale activity state matrix in step S1.2, the edge does not directly upload the activity state matrix of the data distribution that may leak the original carbon emission activity data. Instead, it uses a cluster of fractal resolution functions with unidirectional and homomorphic properties issued by the cloud to perform an irreversible transformation on the activity state matrix, i.e. resolution, to generate coded data, i.e. resolution vector, that can only be used for back-déjà vu under specific constraints and cannot be reversed back to the original data.

[0082] Step S1.3.1: For the current accounting cycle, receive and deploy the fractal resolution function cluster and resolution parameter version number, wherein the fractal resolution function cluster is a hierarchical structure containing the first function, the second function and the third function.

[0083] Specifically, at the beginning of each accounting cycle, the edge receives the current fractal resolution function cluster and the corresponding resolution parameter version number from the cloud. This fractal resolution function cluster is a set of functions with a three-level hierarchical structure. This cluster is used to support data aggregation and verification while protecting the one-way nature of data. Its three-level hierarchical structure includes an element resolution function for processing a single element (the first function), a group aggregation function for merging the processing results of a group of elements (the second function), and a global fusion function for generating a global summary (the third function). In addition, the edge needs to store and update the resolution parameter version number locally to identify the version of the currently effective fractal resolution function cluster, ensuring that all parties have a consistent understanding of the resolution rules in subsequent interactions with the edge and cloud.

[0084] Step S1.3.2: For the activity state matrix at each scale, generate grouped resolution vectors, template resolution vectors, and global resolution vectors respectively through the fractal resolution function cluster.

[0085] Step S1.3.2.1: For each element in the activity state matrix, input the row and column coordinates, timestamp, and discrete values ​​of the element into the first function, and output a fixed-length, one-way resolution vector, wherein the one-wayness of the resolution vector is based on lattice cipher construction.

[0086] Specifically, for each element in the activity state matrix of a given scale, the edge extracts three attributes of the element, including its row and column coordinates in the activity state matrix, the timestamp corresponding to its time slice, and the discrete value obtained after discretization, i.e., the gear identifier. The row and column coordinates identify the data dimension and time slice to which the element belongs.

[0087] Furthermore, the three attributes mentioned above, the start timestamp of the current accounting cycle determined by the local system clock on the edge, and the unique identifier inherent to the edge device, i.e., the edge identifier, are used together as inputs to the first function for calculation. The first function is constructed as a cryptographically one-way function. Its one-wayness is based on difficult problems in lattice ciphers, such as the Short Integer Solution Problem (SIS) or the Learning Error Problem (LWE). Therefore, the output can be efficiently calculated from the input. However, the original input cannot be deduced in polynomial time from the fixed-length numerical vector of the output, i.e., the resolution vector, under the assumption of difficult problems in lattice ciphers. The same carbon emission activity data will produce completely different resolution vectors in different accounting cycles and on different edges, thereby realizing the time binding of data and the isolation between edges, preventing problems such as pre-computation or cross-edge forgery.

[0088] Step S1.3.2.2: Based on the preset grouping template, the elements in the activity state matrix are divided into several groups. The second function is called to merge the resolution vectors of all elements in the group into a group resolution vector. At the same time, the third function is called to generate a global resolution vector based on the group resolution vector.

[0089] Specifically, for each scale of the activity state matrix generated in step S1.2, the edge device divides all elements of the activity state matrix of that scale into several logical groups according to the grouping template preset by the cloud device; the grouping template is used to organize data from different perspectives, including at least three complementary types: spatial grouping, temporal grouping, and cross-dimensional grouping.

[0090] Furthermore, each group is assigned a unique group identifier; the group identifier is generated by hashing and other calculations based on the type of the group template to which it belongs, the key parameters of the group, and the accounting cycle identifier, etc. For example, for a time group, its key parameter is the starting time slice number; for a spatial group, its key parameters are the time slice number and the set of dimension indices it contains; the generation rules for the group identifier are uniformly defined by the cloud side, ensuring that the edge side, the device side, and the cloud side can calculate the same group identifier based on the same input.

[0091] Specifically, spatial grouping involves grouping elements of carbon emission activity data from different dimensions within the same time slice into the same group; temporal grouping involves grouping elements of carbon emission activity data from the same dimension across several consecutive time slices, such as consecutive 5-minute intervals, into the same group; and cross-dimensional grouping involves grouping carbon emission activity data from different dimensions that are related to a process based on process knowledge. For example, in a specific process of a coal-fired boiler, elements of coal consumption and steam generation within the same time slice are grouped into the same group.

[0092] Specifically, for each group divided according to any of the above grouping templates, the end side calls the second function for processing; the second function is constructed to have additive homomorphism, and is used to directly add and merge the resolution vectors of each element in the group calculated by the first function in the vector space, and then output a single group resolution vector; the processing of the second function is carried out entirely in the resolution value domain, and the second function itself does not touch and cannot restore the gear identifier corresponding to any element.

[0093] The endpoint calls a third function; the third function receives the grouping resolution vectors of all groups within the current scale and current accounting period as input, and generates a global resolution vector to characterize the global data features of the current scale and current accounting period, i.e. the overall situation of all carbon emission activity data, through specific fusion calculations, such as vector addition or other homomorphic aggregation operations.

[0094] Step S1.3.2.3: Incrementally fuse the current period's grouping resolution vector with the cumulative grouping resolution vector updated in the previous accounting period to obtain the current period's cumulative grouping resolution vector.

[0095] It should be noted that, in order to achieve long-term and continuous accounting of carbon emission activity data, and to avoid repeated storage and calculation of all historical raw data, the edge side adopts an incremental calculation mechanism to maintain the cumulative state of the data. Specifically, the edge side persistently stores a set of cumulative grouping and resolution vectors locally. These cumulative grouping and resolution vectors fully represent the cumulative summary of all historical carbon emission activity data after resolution and encoding since the start of this accounting method up to the end of the previous accounting cycle.

[0096] Furthermore, in the current accounting cycle, after the endpoint generates the current cycle's group resolution vector through step S1.3.2.2, the incremental fusion mechanism is initiated. For each group, the endpoint retrieves the cumulative group resolution vector updated in the previous accounting cycle from local storage. Utilizing the additive homomorphism of the second function, the endpoint performs an addition operation between the current cycle's group resolution vector and the previous cycle's cumulative group resolution vector in the vector space. The result of this addition operation is the updated cumulative group resolution vector for the current cycle, representing all historical data up to the current cycle.

[0097] It should be noted that, from a mathematical equivalence perspective, the result of the above incremental fusion mechanism is exactly the same as the grouping resolution vector obtained by reconstructing the activity state matrix from all historical activity data up to the current accounting cycle and executing steps S1.3.2.1 and S1.3.2.2 in one complete run. However, this incremental fusion mechanism does not require backtracking or re-accessing any historical original data, nor does it require recalculating the historical resolution vectors already generated in the historical cycle, thereby improving processing efficiency and achieving an optimal balance between storage overhead, computing resources, and long-term data completeness.

[0098] Step S1.3.2.4: Aggregate the cumulative grouping resolution vectors of the current period into template resolution vectors according to the grouping templates. The grouping templates include spatial grouping, temporal grouping, and cross-dimensional grouping, which are used to generate complementary grouping resolution vectors.

[0099] It should be understood that after obtaining the cumulative grouping resolution vector for the current period through step S1.3.2.3, the end-side further aggregates the data across groups according to the grouping template type defined in step S1.3.2.2 to generate a higher-dimensional data summary, namely the template resolution vector. The template resolution vector includes spatial template resolution vector, temporal template resolution vector, and cross-dimensional template resolution vector.

[0100] Specifically, the spatial template resolution vector is obtained by summing all the cumulative grouping resolution vectors belonging to spatial grouping. The spatial template resolution vector represents the cumulative correlation between carbon emission activity data of different dimensions. The cumulative correlation specifically refers to the overall synergy or proportional relationship in the changing trends of carbon emission activity data of different dimensions in long-term operation. For example, suppose in a manufacturing workshop, data of dimension A, i.e., electricity consumption data and data of dimension B, i.e., natural gas consumption data, are monitored. Under ideal conditions, when the production line is running at full speed, both increase synchronously in a certain proportion. When production stops, both drop to the baseline level. Spatial grouping puts the data of dimension A and dimension B at the same moment together. The spatial template resolution vector obtained after long-term cumulative calculation has numerical characteristics that encode the correlation pattern of the overall synergistic change of electricity and natural gas consumption in history. If the reported data on a certain day shows a surge in electricity consumption while natural gas consumption remains unchanged, this abnormal correlation pattern may not be consistent with the constraints implied by the historically accumulated spatial template vector, and thus can be identified in subsequent verification or accounting.

[0101] Specifically, the time template resolution vector is obtained by summing all the cumulative grouping resolution vectors belonging to time grouping. The time template resolution vector represents the cumulative pattern of carbon emission activity data of the same dimension over time. The cumulative pattern is specifically the overall regularity, trend or periodicity of activity data of a single dimension in the long-term time series. For example, taking the single dimension of the power load of an office building as an example, its typical cumulative pattern is that the load is high during the day on weekdays and low at night and on weekends. Time grouping puts together the power data of multiple consecutive time points, such as 24 hours of a day. The time template resolution vector obtained by long-term accumulation encodes the typical daily periodic fluctuation pattern of the power load of the office building. If the 24-hour data curve reported on a certain day deviates from this historical pattern, for example, the night load is abnormally higher than the daytime load, then the time grouping vector generated will conflict with the cumulative pattern contained in the accumulated time template vector. This conflict can be used as a clue for data anomalies or in-depth verification.

[0102] Specifically, the cross-dimensional template resolution vector is obtained by summing all the cumulative grouping resolution vectors belonging to cross-dimensional grouping. The cross-dimensional template resolution vector represents the cumulative coupling relationship between carbon emission activity data of different dimensions with specific process associations. The cumulative coupling relationship is specifically a mathematical relationship determined by specific process associations, which reflects stable statistical characteristics in long-term historical data. For example, suppose that in a specific process of a coal-fired boiler, there is a positive correlation between dimension X data, i.e., coal consumption, and dimension Y data, i.e., steam production. Theoretically, constrained by boiler efficiency, the ratio of the two should be within a stable range. Cross-dimensional grouping will put dimension X data and dimension Y data collected at the same time into one group. The cross-dimensional template resolution vector obtained after long-term cumulative calculation encodes the stable range of the historical coupling relationship between coal consumption and steam production. If the ratio reflected by the resolution vectors of the two sets of data deviates significantly from this historical coupling range within a certain reporting period, it may indicate abnormal measurement equipment, sudden change in fuel quality, or data tampering, thereby triggering the generation of a question mark or causing the cloud to tighten the solution space of this part during the calculation.

[0103] Understandably, these template resolution vectors are a global summary of all historical carbon emission activity data up to the current moment from three complementary perspectives: space, time, and process correlation. The template resolution vectors are used in the edge processing of the subsequent step S2.2 to perform consistency comparison with the corresponding group resolution vectors obtained from the end-side sampling, thereby achieving problem data verification without touching the original data. On the other hand, the template resolution vectors can also be used in the cloud accounting of step S3.1 as the core constraint conditions for constructing the global constraint network, providing multi-angle mathematical relationships for reverse inference of carbon emissions.

[0104] Step S1.3.3: Encapsulate the global resolution vector corresponding to the coarsest scale activity state matrix, the template resolution vector of the current period, the calculation period identifier, the timestamp, the scene label, and the resolution parameter version number into a standardized calculation fragment.

[0105] Specifically, after completing the resolution encoding at each scale as described in step S1.3.2, the edge needs to selectively encapsulate the generated resolution vectors to generate the final reported standardized accounting fragments. The encapsulation follows the principles of data minimization and privacy protection, and by default only uploads summary information sufficient to support edge verification and initial reverse inference in the cloud, while keeping more detailed intermediate data locally on the edge.

[0106] Furthermore, the coarsest scale, namely the global resolution vector generated in step S1.3.2.2 corresponding to the activity state matrix of the Lth layer of the pyramid discretization structure, is selected and encapsulated into the standardized accounting fragment; this global resolution vector is the coarsest and most privacy-preserving global summary of the overall activity data of the current accounting cycle.

[0107] Furthermore, the spatial template resolution vector, temporal template resolution vector, and cross-dimensional template resolution vector generated in step S1.3.2.4 for the current period are encapsulated into a standardized accounting fragment.

[0108] Furthermore, metadata such as the unique accounting cycle identifier, the start timestamp of the current accounting cycle, the scenario tag obtained in step S1.1, and the resolution parameter version number obtained in step S1.3.1 are encapsulated into standardized accounting fragments. This metadata ensures the traceability and version consistency of the data flow.

[0109] It should be noted that the grouping resolution vectors at each scale, as well as the global resolution vectors and template resolution vectors at finer scales, are all stored locally on the edge and do not leave the domain. They are only uploaded on demand and in a targeted manner when the edge performs sampling verification in step S2.2, or when the cloud initiates a specific data request to shrink the solution space in step S3.2. Through the above encapsulation strategy, the standardized accounting fragments minimize the amount of data transmitted over the network while meeting the accounting requirements, and fundamentally cut off the privacy leakage risk caused by the original activity data and fine-grained encoded data leaving the domain.

[0110] Step S1.4: Send the standardized accounting fragment to the side.

[0111] Specifically, after the end-side device completes the encapsulation of the standardized accounting segment in step S1.3.3, it immediately sends the generated standardized accounting segment to the designated edge processing node through its network communication interface. This sending action signifies that the end-side device has completed the data collection, processing, and reporting tasks within the current accounting cycle. After receiving the standardized accounting segment, the edge-side device will initiate subsequent data verification, compensation, and aggregation processes based on its content. The structured format of the standardized accounting segment ensures that the edge-side device can correctly parse and process it.

[0112] Among them, on the side, it includes:

[0113] Step S2.1: Receive the standardized accounting segment and request the grouping and resolution vector from the end side based on the verification sampling strategy issued by the cloud side.

[0114] Specifically, the edge node, acting as an intermediate layer connecting the endpoint and the cloud, is responsible for receiving standardized accounting segments reported by one or more endpoints within its jurisdiction. Simultaneously, the edge node loads a verification sampling strategy issued by the cloud. This strategy defines the rules for sampling verification, and its parameters, such as sampling frequency and sampling weights for each group template, are dynamically generated and periodically updated by the cloud. When generating the verification sampling strategy, the cloud can comprehensively consider the historical data quality of the endpoint and the region, the uncertainty of regional accounting results, and the real-time load of the edge node. For example, the endpoint and... The historical data quality of a region may be reflected in a high proportion of verification status marks marked as abnormal or questionable on a certain end or within a certain region during historical periods. The uncertainty of the regional accounting results may be reflected in the large range of historical accounting results in that region, indicating that the constraints provided by the data are weak or there are contradictions. The real-time load of the edge nodes can be reflected in the utilization rate of the computing and communication resources of the edge nodes. Based on the above factors, the cloud side can dynamically adjust the verification sampling strategy, such as increasing the sampling frequency for ends or regions with poor data quality or high uncertainty, or appropriately reducing the overall sampling intensity when the system load is high.

[0115] It should be noted that the verification sampling strategy follows the principles of coverage, randomness, and low overhead. Specifically, the coverage principle ensures coverage of different group templates and different endpoints. The randomness principle randomly selects the sampled groups to prevent endpoint prediction and tampering. The low overhead principle controls the frequency and amount of sampling requests to balance verification effectiveness and communication overhead.

[0116] Furthermore, based on the aforementioned verification sampling strategy, the edge determines which edge and which specific groups under which group templates need to be verified within the current accounting cycle;

[0117] Subsequently, the edge sends a sampling request to the target end; the edge calculates the group identifier to be sampled based on the verification sampling strategy, the target end identifier, and the metadata in the standardized accounting fragment reported by it, according to the group identifier generation method disclosed in step S1.3.2.2; the sampling request includes at least the calculated group identifier and the corresponding resolution parameter version number; after receiving the sampling request, the end retrieves the requested group resolution vector from its local storage and uploads it to the edge.

[0118] Step S2.2: Sampling and verification of standardized accounting fragments based on grouping and resolution vectors to intercept problematic data.

[0119] After obtaining the local grouping resolution vector of the endpoint through step S2.1, the edge side performs sampling verification on the standardized accounting fragments reported by the endpoint in the resolution value range, including aggregation consistency verification and time continuity verification, without touching or restoring the original activity data, in order to identify and intercept problematic data.

[0120] Step S2.2.1: In response to the sampling request uploaded packet resolution vector, the receiving end performs an aggregation consistency verification on the packet resolution vector. The aggregation consistency verification is used to obtain the template resolution vector in the standardized accounting segment that belongs to the same packet template as the sampled packet resolution vector, and compare the sampled packet resolution vector with the template resolution vector. If the comparison result is inconsistent, the aggregation consistency verification is determined to have failed; otherwise, it passes.

[0121] It should be noted that the aggregation consistency verification is used to verify whether there is a mathematical contradiction between the intermediate results calculated and stored locally on the end side, namely the group resolution vector and its reported aggregation summary, namely the template resolution vector, and thus to detect calculation errors or tampering.

[0122] Specifically, the edge extracts the template resolution vector that belongs to the same group template type as the sampled group from the standardized accounting segment reported by the end side; for example, if the group resolution vector of a spatial group is sampled, the spatial template resolution vector in the standardized accounting segment is extracted.

[0123] Understandably, the second function used to generate the grouped resolution vectors and the template resolution vectors has additive homomorphism. Mathematically, the sum of all grouped resolution vectors under the same group template should equal the corresponding template resolution vector. Since the resolution vector is a fixed-length multidimensional vector, such as 256-dimensional, the value at each position is called a component. The first function is deterministic; after the elements of a specific activity state matrix are calculated by the first function, a resolution vector with definite components will be output. When the resolution vectors of multiple elements are aggregated by the second function, they are algebraically added at each position of the same component. Therefore, the above summation and equality are position-by-position relationships between the components of the resolution vector, that is, the i-th component of the template resolution vector should be equal to the algebraic sum of the i-th components of all its subordinate grouped resolution vectors.

[0124] Furthermore, the side calculates a difference measure between the sampled grouped resolution vector and the template resolution vector extracted from the standardized accounting fragment. This difference measure is usually the norm of the difference between the two on the corresponding components, such as the L2 norm, or the sum of the absolute differences of each component.

[0125] Furthermore, the edge compares the difference metric with a preset tolerance threshold. This tolerance threshold accommodates minor numerical deviations caused by non-malicious factors such as inherent noise or errors in floating-point calculations and cryptographic operations. Its specific value is set and issued by the cloud side based on the error propagation characteristics of the fractal resolution function cluster used and statistical analysis of historical system data. If the difference metric is less than or equal to the tolerance threshold, the aggregation consistency verification of the current sampling location is deemed successful, indicating that the data reported by the edge is internally consistent within the allowable computational error range. If the difference metric is greater than the tolerance threshold, the aggregation consistency verification is deemed unsuccessful, indicating a contradiction between the sampled group resolution vector and the reported template resolution vector that cannot be explained by computational errors. This contradiction may stem from an error occurring when the edge locally generates the group resolution vector, or from the template resolution vector being tampered with, thus indicating that this portion of the data is unreliable.

[0126] Step S2.2.2: Simultaneously, perform time continuity verification on the grouped resolution vector. The time continuity verification is used to obtain the grouped resolution vectors of adjacent time groups with the same activity dimension in continuous time slices for the grouped resolution vectors under time groups, and calculate the vector difference between the two. If the vector difference is greater than the dynamic difference threshold, the time continuity verification is determined to be abnormal; otherwise, it passes.

[0127] Understandably, time continuity verification targets the grouping resolution vector under time grouping to detect whether data mutations in the same carbon emission activity data over time are within a reasonable range of historical fluctuations, thereby identifying abnormal patterns caused by equipment failure, drastic changes in operating conditions, or potential data tampering.

[0128] Specifically, the edge acquires the grouping resolution vectors of two consecutive time groups of carbon emission activity data in the same dimension, which are adjacent in time and on a continuous time slice. For example, for the electricity load dimension, the edge acquires the grouping resolution vector of the time group 9:00-9:05 in the current accounting period, as well as the grouping resolution vector of its previous adjacent time period 8:55-9:00.

[0129] Furthermore, the vector difference between two adjacent grouped resolution vectors is calculated on the side; this difference is a scalar value that measures the overall distance between the two, and the difference can be calculated by means of Euclidean distance, Manhattan distance, or the sum of the absolute differences of each dimension component.

[0130] Furthermore, the side compares the vector difference with the dynamic difference threshold; if the vector difference is less than or equal to the dynamic difference threshold, it is determined that the current data change is within the normal fluctuation range of history, and the time continuity verification is passed; if the vector difference is greater than the dynamic difference threshold, it is determined that the current data change has an abnormal jump, and the time continuity verification is abnormal.

[0131] Step S2.2.3: The dynamic difference threshold is obtained by fitting the change magnitude of the grouping resolution vector of the time group corresponding to the same activity dimension in the historical accounting cycle on the end side.

[0132] It should be noted that the dynamic difference threshold is calculated and distributed independently by the cloud side for each activity dimension of each end side, in order to adapt to the different operating characteristics of devices and the evolution of normal working conditions.

[0133] Specifically, the cloud side maintains the historical sequence of vector differences between the grouping resolution vectors of adjacent time groups under the carbon emission activity data of each end and each dimension, i.e., historical difference data; based on the historical difference data, the cloud side calculates the dynamic difference threshold through statistical methods; for example, the moving average μ and standard deviation σ of the historical difference data can be calculated, and μ+N*σ can be taken as the dynamic difference threshold, where N is a preset coefficient, such as 2 or 3, corresponding to a confidence interval of about 95% or 99.7%, or a certain high quantile of the historical difference data, such as the 95th or 99th quantile, can be directly taken as the dynamic difference threshold.

[0134] Understandably, the aforementioned method for obtaining the dynamic difference threshold ensures that it can cover a large amount of historical normal fluctuations. Similar to the verification sampling strategy, the specific value of the dynamic difference threshold is periodically re-analyzed, calculated, and updated by the cloud side based on the latest historical difference data, and then distributed to the edge side through a secure channel. This allows the threshold to adaptively adjust with the normal and slow evolution of the edge side's production or operation mode, avoiding false alarms for reasonable gradual changes while maintaining high sensitivity to real anomalies.

[0135] Step S2.2.4: Generate a verification status marker based on the sampling verification results, mark problematic data from the standardized accounting segment on the edge based on the verification status marker, and intercept it. The verification status marker includes abnormal, passed, and questionable.

[0136] Specifically, the results of the side-side integration steps S2.2.1 (aggregate consistency verification) and S2.2.2 (time continuity verification) generate verification status markers for the standardized accounting segments of the side-side in this accounting cycle, and perform corresponding data processing operations; the verification status markers define three states: abnormal, passed, and questionable.

[0137] It should be noted that once the aggregation consistency verification fails, it will be marked as abnormal regardless of the time continuity verification result. Only when all sampled aggregation consistency verifications pass will the result of the time continuity verification be used to determine whether it is marked as passed or questionable.

[0138] If the aggregation consistency verification fails, it indicates that there is a mathematical contradiction in the data reported by the edge that cannot be explained by calculation error, and there is a high risk of calculation error or malicious tampering. The edge will mark the standardized accounting segment as abnormal and intercept it. The standardized accounting segment will not participate in all subsequent edge processing procedures, including the scenario-based compensation calculation in step S2.3 and the regional aggregation in step S2.4, thereby preventing problematic data from polluting the regional and system-level accounting results.

[0139] If both the aggregation consistency verification and the time continuity verification pass, it indicates that the standardized accounting fragment is mathematically self-consistent and conforms to historical behavior patterns in the time dimension; the edge marks the standardized accounting fragment as passed and regards it as reliable data, allowing it to enter the subsequent edge processing flow.

[0140] If the aggregation consistency verification passes, but the time continuity verification fails, it indicates that the standardized accounting segment is mathematically correct, but the activity pattern it reflects has deviated from historical patterns in time. This deviation may be caused by real drastic changes in operating conditions, equipment failure, or more covert data tampering attempts. The edge side marks the segment as questionable, but allows it to proceed to the subsequent processing flow. The questionable verification status mark will be used as metadata and reported to the cloud side when the edge side generates regional accounting results in the subsequent step S2.4. The cloud side can adopt special processing strategies in the global accounting in step S3.1.2, such as introducing constraint relaxation variables, so that the global accounting results do not overly depend on this questionable data. At the same time, this situation is recorded for subsequent traceability analysis and auditing.

[0141] Step S2.3: Perform scene-based compensation calculations on the standardized accounting segments that have passed the verification, combined with scene labels.

[0142] Specifically, after completing the sampling verification in step S2.2, the side further performs differential correction of carbon emission accounting by combining its scenario label with the standardized accounting segments marked as passed. This process is called scenario-based compensation calculation. Scenario-based compensation calculation is used to directly weight the elimination vector in the elimination value domain according to the carbon emission characteristics of different scenarios without restoring the original carbon emission activity data.

[0143] Step S2.3.1: Maintain a scene compensation coefficient library on the side, storing the emission factor compensation coefficient and scene compensation coefficient version number corresponding to different scene labels.

[0144] Specifically, the side maintains a scenario compensation coefficient library locally; this scenario compensation coefficient library can be implemented by a mapping table or database, and each record contains at least fields such as scenario label, emission factor compensation coefficient, and scenario compensation coefficient version number.

[0145] The scene tags are completely consistent with the scene tags collected and reported by the terminal side in step S1.1, and are used for unique indexing, such as steelmaking-electric furnace process, office building-central air conditioning system, data center-server cluster, etc.

[0146] The emission factor compensation coefficient is a coefficient vector with the same dimension as the template elimination vector. Each component of the emission factor compensation coefficient is determined based on the specific process, equipment type, and geographical region corresponding to the scenario label, according to international or national greenhouse gas emission accounting guidelines, such as IPCC guidelines, industry-specific accounting standards, certified emission factor databases, or audit analysis results based on the historical energy consumption and output data of the scenario. The emission factor compensation coefficient is used to convert general activity data into scenario-specific carbon emission equivalents.

[0147] The scenario compensation coefficient version number is used to identify the current version of the scenario compensation coefficient library that is effective on the edge side. The scenario compensation coefficient library is uniformly managed, calculated and updated by the cloud side. When the emission factor standard on which it is based is updated, the scenario process changes, or the coefficient is optimized based on the latest research results and measured data, the cloud side generates a new generation of scenario compensation coefficient library and the corresponding new version number, and distributes it to all edge nodes through a secure channel to ensure the consistency of the global accounting standard.

[0148] Step S2.3.2: Based on the scene labels reported by the end side, retrieve the corresponding scene compensation coefficients. Utilize the additive homomorphism of the template elimination vectors to perform homomorphic weighting on the template elimination vectors in the standardized accounting segments that have passed the verification in the elimination value domain with the corresponding scene compensation coefficients to generate the compensated template elimination vectors.

[0149] Specifically, for each pair of standardized calculation segments marked as passed by the side, its scene label is read and used as an index to retrieve the corresponding emission factor compensation coefficient and the version number of the currently used scene compensation coefficient from the locally maintained scene compensation coefficient library.

[0150] Furthermore, the edge obtains the template elimination vector in the standardized accounting segment, including the spatial template elimination vector, the temporal template elimination vector, and the cross-dimensional template elimination vector. Since the template elimination vector is generated by the second and third functions with additive homomorphism, it supports direct linear operation in the encryption domain or the encoding domain. The edge performs homomorphic scalar multiplication on each component of the template elimination vector with the corresponding component of the emission factor compensation coefficient. The result of the above homomorphic scalar multiplication is mathematically equivalent to first weighting the emission factor compensation coefficient of the original carbon emission activity data according to the application scenario, and then performing elimination encoding. However, this scheme achieves the same correction effect by directly calculating in the elimination value domain, without needing to access any original carbon emission activity data throughout the process.

[0151] Furthermore, the result of the above homomorphic scalar multiplication is used as the compensated template resolution vector; it contains the encoding of the accumulated historical carbon emission activity information after scene-specific correction; the compensated template resolution vector is the direct input of the subsequent step S2.4 region aggregation, and its corresponding scene compensation coefficient version number will also be recorded and reported as metadata to ensure the traceability and auditability of the accounting process.

[0152] Step S2.4: Locally aggregate the compensated standardized accounting fragments to generate regional accounting results.

[0153] Step S2.4.1: Locally aggregate all compensated template elimination vectors according to the grouped templates to generate a region-level template elimination vector matrix.

[0154] Specifically, the edge collects all compensated template resolution vectors reported by the end side that are marked as passed in the verification status within its jurisdiction; these compensated template resolution vectors contain the results of their respective end side's historical activity data after resolution encoding and scene correction.

[0155] Furthermore, based on the type of grouped template, the compensated template resolution vectors from different end sides are additively aggregated. The compensated spatial template resolution vectors from all end sides are added together to obtain a regional-level spatial template resolution vector, which represents the cumulative correlation between carbon emission activity data of different dimensions in the entire region. The compensated temporal template resolution vectors from all end sides are added together to obtain a regional-level temporal template resolution vector, which represents the cumulative pattern of carbon emission activity evolution over time in the entire region. The compensated cross-dimensional template resolution vectors from all end sides are added together to obtain a regional-level cross-dimensional template resolution vector, which represents the cumulative coupling relationship between different activity dimensions with specific process associations in the entire region.

[0156] Furthermore, the regional-level spatial template resolution vector, regional-level temporal template resolution vector, and regional-level cross-dimensional template resolution vector obtained above are organized into a structured dataset and used as a regional-level template resolution vector matrix. This regional-level template resolution vector matrix is ​​a multi-dimensional, coded summary of the cumulative historical carbon emission activities of all trusted endpoints within the jurisdiction up to the current accounting period from a regional global perspective.

[0157] Step S2.4.2: Use the regional template resolution vector matrix and the verification status marker and scene compensation coefficient version number generated by sampling verification results on each end side as the regional accounting result.

[0158] Specifically, the edge side encapsulates the regional template resolution vector matrix generated in step S2.4.1, the list of verification status markers of each edge side within its jurisdiction, and the version number of the scenario compensation coefficient used for scenario-based compensation calculation into a structured data packet, namely the regional calculation result, and reports it to the cloud side.

[0159] Among them, on the cloud side, are:

[0160] Step S3.1: Construct a global constraint network based on the regional-level accounting results, and perform an arc-compatible algorithm on the global constraint network to shrink the candidate solution space.

[0161] Specifically, after receiving the regional accounting results reported by all edge sides, the cloud side initiates the global accounting process. Step S3.1 is a reverse deduction, that is, instead of direct calculation, the regional template elimination vector matrix reported by the edge sides is regarded as a series of mathematical constraints that must be satisfied. By constructing a global constraint network and executing the arc compatibility algorithm, the original data value combinations that cannot satisfy all constraints are systematically eliminated, so as to reduce the candidate solution space and reduce the computational complexity of subsequent steps.

[0162] Step S3.1.1: Based on the regional template resolution vector matrix in the regional accounting results reported by the edge side, construct a global constraint network with the elements of the activity state matrix of each edge side at the corresponding scale as variables, and including the first constraint and the second constraint.

[0163] Specifically, the cloud side analyzes the accounting results of each region and extracts its region-level template resolution vector matrix. The cloud side then initiates reverse inference based on the current inference scale, with the initial value of the current inference scale being the coarsest scale used when the end side reports the standardized accounting fragment.

[0164] Furthermore, a global constraint network is constructed on the cloud side; in this global constraint network, each variable represents an element in the activity state matrix of an end side at its coarsest scale; the domain of the variable, i.e. the set of possible values, is the set of all possible gear identifiers that the carbon emission activity data represented by the element is mapped to at the current scale after pyramid discretization as described in step S1.2.

[0165] Step S3.1.1.1: For any two variables belonging to the same end side and the same group template, the sum of the element elimination vectors is equal to the corresponding component in the template elimination vector as the first constraint.

[0166] Specifically, for any two variables belonging to the same end side and the same group template, a first constraint is constructed based on their relevant mathematical facts. These relevant mathematical facts include: First, based on the regional accounting results, the template resolution vector corresponding to the group template is known; second, the template resolution vector is the sum of the group resolution vectors of all its subordinate groups; third, each group resolution vector is the sum of the element resolution vectors of all elements within its group. Therefore, for any two elements under this template, the component of their element resolution vector in any dimension is a partial contribution to the final template resolution vector. From this, a necessary binary inequality can be derived as the operable first constraint: the sum of the absolute values ​​of the components of the element resolution vectors of these two variables in any same dimension must be less than or equal to the absolute value of the component of the template resolution vector in the same dimension, which can be expressed as:

[0167]

[0168] in, This indicates the extraction of component values ​​of a resolution vector along a specific dimension. and These represent the element-wise elimination vectors corresponding to variables i and j, respectively. express and The template resolution vector corresponding to the group template.

[0169] Understandably, the first constraint is a lenient and conservative necessary condition; it stipulates that the joint contribution of any two variables belonging to the same end and the same group template cannot exceed the absolute value of the corresponding component of the known template elimination vector; this first constraint can be independently determined based on the values ​​of the two variables and the known template elimination vector, which meets the requirements of the arc-compatible algorithm for handling binary constraints; it can quickly filter out variable value combinations that violate the total constraint, thus narrowing the candidate solution space for subsequent processing of more refined multivariate constraints, i.e., the second constraint.

[0170] Step S3.1.1.2: The sum of the grouping resolution vectors under the same grouping template is equal to the corresponding template resolution vector as the second constraint.

[0171] Specifically, the sum of the grouping resolution vectors of all groups under the same grouping template must be equal to the corresponding template resolution vector, which is the second constraint. The second constraint is a direct manifestation of the grouping aggregation relationship in step S1.3.2.2. Although the cloud does not directly hold the grouping resolution vector, this constraint defines the aggregation relationship between the variable group, that is, all variables in the same group and a known vector, namely the template resolution vector.

[0172] Step S3.1.2: For edge sides whose reported verification status is marked as questionable, introduce constraint relaxation variables in the global constraint network.

[0173] Understandably, in order to improve the robustness of the accounting method to suspicious data, the cloud side checks the verification status markers in the accounting results of each region. For the end side marked as suspicious, the cloud side introduces constraint relaxation variables in the first and second constraints when constructing the global constraint network.

[0174] Specifically, the constraint relaxation variable is an adjustable tolerance parameter used to moderately relax the strict judgment conditions of the constraints imposed on the questionable data when executing the arc compatibility algorithm. For example, when judging whether a variable value should be deleted because it does not meet the constraints, if the constraint involves the questionable side, a small error range can be allowed. The above setting ensures that the constraints provided by the questionable data will not excessively affect the shrinkage process of the solution space, prevent large deviations in the overall calculation results due to single point of questionable data, and at the same time retain the effective information it may contain.

[0175] Step S3.1.3: For any two variables in the global constraint network that belong to the same end side and the same group template, if a certain value of one variable cannot satisfy the first constraint when the other variable takes any possible value, then determine whether to delete the value from the candidate value set of the variable in combination with the constraint relaxation variable. Repeatedly scan all variable pairs with the first constraint until the candidate value set of all variables no longer changes.

[0176] Specifically, for each pair of variables (R, T) with a first constraint, iterate through each value r in the current candidate value set of variable R; check if there exists at least one value t in the current entire candidate value set of variable T such that the pair (r, t) satisfies the first constraint connecting them; if no t can be found among all possible values ​​of T that can be paired with r to satisfy the constraint, the arc compatibility algorithm needs to determine whether to delete r; if the first constraint involves an end marked as questionable, the arc compatibility algorithm uses a more lenient decision condition based on constraint slack variables; for example, the inequality criterion for the first constraint is relaxed to:

[0177]

[0178] in, Let r be a small positive number defined by the constraint relaxation variable; r is deleted only if, even with the more relaxed decision condition, r cannot be paired with any t to satisfy the constraint; if no questionable side is involved, r is directly deleted from the candidate value set of variable R.

[0179] Furthermore, the above-mentioned checking and deletion operations are repeatedly and bidirectionally scanned among all variable pairs with the first constraint. When the set of candidate values ​​for a variable changes, i.e. a value is deleted, this change may affect other related variables. Therefore, it is necessary to rescan the first constraint affected by this change, as well as the second constraint that may be affected. The above process is iterated repeatedly until a complete scan is performed on all variable pairs and no variable value is deleted. Then, the propagation of the arc-compatible algorithm with respect to the first constraint reaches a stable state.

[0180] Step S3.1.4: For a second constraint involving multiple variables, if a certain value of any variable cannot be found in the candidate value set of other variables to make the second constraint valid, then determine whether to remove the value from the candidate value set of the variable in conjunction with the constraint slack variable, until the candidate value set of all variables no longer changes.

[0181] Specifically, after completing arc-compatible propagation based on the first constraint, the cloud side further processes the second constraint involving multiple variables; for each second constraint corresponding to a specific grouping template and its known template resolution vector, and the set of variables involved. First, for any variable in the variable set... Iterate through each value in its current candidate value set. ; Check all other variables Does the current set of candidate values ​​contain at least one complete set of values? , making when Furthermore, when other variables take the values ​​in this set, the sum of the element-wise resolution vectors corresponding to all variables is equal to the known template resolution vector corresponding to the second constraint.

[0182] Furthermore, if among all possible combinations of values ​​for other variables, no assignment can be found that makes the above constraint hold, then the arc compatibility algorithm needs to determine whether to assign the values ​​to the other variables. from The candidate value set is deleted; if the group involved in the second constraint belongs to a marked questionable end, the arc compatibility algorithm adopts a more lenient decision condition based on constraint slack variables; only if the more lenient decision condition is adopted, Only when no compatible assignment can be found should the assignment be... Delete; if it does not involve the questionable end, then directly delete. delete.

[0183] Understandably, the above checking and deletion operations will be performed repeatedly on all second constraints and all their variables. If a variable's value is deleted during the processing of any second constraint, this change may not only affect other second constraints but may also activate the associated first constraints, requiring them to be re-evaluated. Therefore, the arc compatibility algorithm needs to alternately and iteratively re-execute steps S3.1.3 and S3.1.4. This process is repeated until, in a complete traversal, executing steps S3.1.3 and this step no longer causes any change in the candidate value set of any variable. At this point, the arc compatibility algorithm as a whole reaches a stable state.

[0184] Step S3.1.5: After the set of candidate values ​​for all variables no longer changes, calculate the size of the candidate solution space.

[0185] Understandably, when the arc compatibility algorithm described in steps S3.1.3 and S3.1.4 has been iterated and the candidate value set of all variables in the global constraint network no longer changes, it indicates that the constraint propagation has reached a stable compatibility state. At this time, the cloud side evaluates the current back-inference result and calculates the size of the candidate solution space.

[0186] Specifically, the candidate solution space is the set of all possible combinations of values ​​for each variable when it arbitrarily selects a value from its current candidate value set, provided that the first and second constraints are satisfied. Therefore, the product of the sizes of the current candidate value sets of all variables is taken as the size of the candidate solution space. For example, if there are M variables in the system, the current candidate value set of the i-th variable contains... If there are 2 possible gear identifiers, then the size of the candidate solution space is represented as:

[0187]

[0188] Understandably, the size of the candidate solution space quantifies the range of uncertainty that still exists in the original carbon emission activity data after constraint verification and propagation at the current scale.

[0189] Step S3.2: Based on the size of the candidate solution space, perform branch and bound method or interval convergence to obtain the carbon emission accounting interval.

[0190] Specifically, after calculating the size of the candidate solution space in step S3.1.5, the cloud side intelligently decides the subsequent processing path based on the comparison result with the preset solution threshold, so as to obtain a carbon emission accounting range with high confidence; the solution threshold is set by the cloud side by balancing the accounting accuracy and the computing cost.

[0191] Step S3.2.1: If the candidate solution space is less than the preset solution threshold, the branch and bound method is used to obtain the carbon emission accounting interval.

[0192] Specifically, when the size of the candidate solution space is less than the preset solution threshold, it indicates that after the constraint propagation at the current scale, the remaining possible data combinations are very limited and the uncertainty is low. At this time, the cloud side uses the branch and bound method to accurately search the global constraint network in order to determine the minimum and maximum possible values ​​of carbon emissions, and thus obtain the carbon emission accounting range.

[0193] Furthermore, the branch and bound method uses the calculation of total carbon emissions as the objective function; when the cloud side constructs the global constraint network in step S3.1, it simultaneously constructs a carbon emission contribution value lookup table; specifically, for each variable in the global constraint network, its corresponding carbon emission activity data dimension, during the discretization process in step S1.2, each level identifier uniquely corresponds to a preset numerical range, such as [0, 1) kilowatts; according to the principle of conservative accounting, the cloud side determines a specific activity data value for calculation for each level identifier, for example, using the upper limit of its corresponding numerical range; combined with the emission factor compensation coefficient obtained from the edge side and applied to this dimension, the cloud side calculates its carbon emission contribution value for each variable-level identifier combination through multiplication operation, the calculation formula is: carbon emission contribution value = emission factor compensation coefficient × activity data value; all pre-calculated carbon emission contribution values ​​are stored to form a carbon emission contribution value lookup table.

[0194] Furthermore, in the search tree naturally formed during the branch and bound process, each node represents a specific set of assignments to a subset of variables. For each node, the carbon emission contribution values ​​corresponding to the currently assigned variables are summed to obtain the carbon emission amount of the defined subset. For unassigned variables, the minimum carbon emission contribution value within the current candidate value set is taken from the carbon emission contribution value lookup table and summed to obtain the lower bound of the carbon emission amount achievable by that node. Here, the minimum carbon emission contribution value usually corresponds to the minimum tier identifier. Simultaneously, the carbon emission contribution values ​​corresponding to the currently assigned variables are summed to obtain the carbon emission amount of the defined subset. And for unassigned variables, the maximum carbon emission contribution value within the current candidate value set is taken from the carbon emission contribution value lookup table and summed to obtain the upper bound of the carbon emission amount achievable by that node. Here, the maximum carbon emission contribution value usually corresponds to the maximum tier identifier.

[0195] Furthermore, a global optimal lower bound and an optimal upper bound are maintained. The optimal lower bound is the minimum carbon emission among all current feasible solutions, and the optimal upper bound is the maximum carbon emission among all current feasible solutions. If the carbon emission lower bound of a node is greater than the current global optimal upper bound, or its upper bound is less than the current global optimal lower bound, then all descendant branches of that node cannot produce a better solution, and it is pruned and no longer explored.

[0196] Furthermore, the process of variable selection, branching, bound calculation, and pruning is repeated until the search space is fully explored. After the search is completed, the final global optimal lower bound and global optimal upper bound constitute the carbon emission accounting interval. This carbon emission accounting interval includes all possible values ​​of the actual carbon emissions under the premise of satisfying all constraints.

[0197] Step S3.2.2: If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the coarse-grained activity state matrix, then the cloud side requests the template resolution vector of the next finer scale from the corresponding end side through the edge side, reconstructs the global constraint network based on the elements of the activity state matrix of the next finer scale, and re-executes the arc compatibility algorithm until the solution space is less than the preset solution threshold.

[0198] Specifically, when the size of the candidate solution space is greater than or equal to the preset solution threshold, and the variables in the current global constraint network are constructed based on the elements of the coarse-grained activity state matrix, it indicates that the constraint information provided by the aggregated data at the current scale is still insufficient to narrow the uncertainty to a range that can be precisely enumerated. At this time, the cloud side initiates a scale refinement process to enhance the constraints by obtaining global summary data at a more refined scale.

[0199] Furthermore, based on the pyramid discretization structure constructed in step S1.2.1, the next finer scale of the current inference scale is determined; the cloud side sends a directional data request to the relevant end side through the edge side, requesting it to report the template resolution vector corresponding to the current calculation cycle and the finer scale.

[0200] Furthermore, after receiving the finer-scale template resolution vector forwarded from the edge side, the cloud side reconstructs the global constraint network based on the elements of the activity state matrix. Specifically, the domain of each variable in the global constraint network is updated to the narrower set of gear identifiers corresponding to the finer scale. Based on the newly acquired finer-scale template resolution vector, the first and second constraints are re-established according to the same rules defined in steps S3.1.1.1 and S3.1.1.2. The carbon emission contribution value lookup table in step S3.2.1 is updated according to the new gear identifiers and their corresponding activity data values, combined with the emission factor compensation coefficient.

[0201] Furthermore, after the new global constraint network and contribution value lookup table are constructed, the cloud side re-executes the arc compatibility algorithm described in step S3.1, i.e., steps S3.1.3 to S3.1.5. After the arc compatibility algorithm is executed, the size of the new candidate solution space is calculated and compared with the preset solution threshold again. If the size of the new candidate solution space is still greater than or equal to the solution threshold, step S3.2.2 is repeated to continue requesting the next finer-scale template elimination vector for iterative refinement. If the size of the new candidate solution space is less than the solution threshold, it indicates that the uncertainty has been reduced to a manageable range after the constraint propagation at a finer scale, and then the process returns to step S3.2.1, using the branch and bound method to obtain the accurate carbon emission accounting range.

[0202] Understandably, step S3.2.2 achieves an adaptive balance between accounting accuracy and computational overhead; by requesting data layer by layer from coarse to fine and on demand and tightening constraints, more refined information, which may also have a larger data volume, is introduced only when necessary, thereby optimizing the overall processing efficiency while ensuring the final accuracy.

[0203] Step S3.2.3: If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the fine-grained activity state matrix, then based on the entropy of the candidate value set of each element in the fine-grained activity state matrix, the group containing the variable to be converged is obtained by descending the entropy value. The cloud side requests the group resolution vector of the group from the corresponding end side through the edge side and adds it to the global constraint network. The arc compatibility algorithm is re-executed until the solution space is less than the preset solution threshold.

[0204] Specifically, when the size of the candidate solution space is greater than or equal to the preset solution threshold, and the variables in the current global constraint network are already elements of the fine-grained activity state matrix, it indicates that it is no longer possible to obtain more refined global constraints through scale refinement. At this time, the cloud side initiates targeted constraint supplementation, which applies stronger constraints by locating the local precise data that contributes the most to the uncertainty of the solution space.

[0205] Furthermore, the cloud side calculates the information entropy of the current candidate value set of each variable in the global constraint network; the higher the entropy value, the more uniform the possible value distribution of the variable, and the greater its uncertainty; then, the cloud side locates the single variable with the highest entropy value, and determines the unique group to which the variable belongs according to the grouping template defined in step S1.3.2.2; all variables in the group are regarded as a whole, and its overall uncertainty is determined as the target that needs to be prioritized for convergence.

[0206] Furthermore, the cloud side sends a directed data request to the endpoint to which the group belongs via the edge side, requesting it to report the group resolution vector of the specific group. After receiving the group resolution vector from the endpoint, the cloud side adds it as a new, strong constraint to the current global constraint network. Specifically, the group resolution vector is mathematically equal to the sum of the element resolution vectors corresponding to all variables in the group. This equation constitutes a new second constraint, thereby restricting the possible combinations of values ​​of the variables in the group.

[0207] Furthermore, after adding the new second constraint, the cloud side re-executes the arc compatibility algorithm described in step S3.1, i.e., steps S3.1.3 to S3.1.5. After the arc compatibility algorithm is completed, the new candidate solution space size is calculated and compared with the preset solution threshold again. If the new candidate solution space size is still greater than or equal to the solution threshold, step S3.2.3 is repeated. The cloud side will re-evaluate and locate the next group with the highest uncertainty based on the updated variable entropy value, request its group resolution vector, and perform iterative constraint supplementation. If the new candidate solution space size is less than the solution threshold, it indicates that after this directional constraint supplementation, the uncertainty has been reduced to a manageable range. Then, it proceeds to step S3.2.1, using the branch and bound method to obtain the accurate carbon emission accounting interval.

[0208] Step S3.3: Obtain the source tracing analysis report.

[0209] Specifically, after obtaining the system-level carbon emission accounting range through step S3.2, the cloud side automatically generates a source tracing analysis report; the source tracing analysis report is used to provide a traceable and auditable global perspective for the carbon emission accounting range.

[0210] Furthermore, the cloud side aggregates and structures the following key process data within this accounting cycle, and generates a report. This traceability analysis report may include carbon emission accounting intervals, data quality parameters, accounting traceability parameters, key decision parameters, and result confidence levels.

[0211] Furthermore, the cloud side first lists the verification status markers and their distribution statistics of all endpoints, identifying potential problematic data sources as data quality parameters; secondly, it records the details of steps S3.2.2 (scale refinement) and S3.2.3 (directional constraint supplementation) triggered to shrink the solution space, including the requested scale, grouping, and corresponding endpoint identifiers as accounting traceability parameters; then, it records the application of constraint relaxation variables, including which questionable endpoint data constraints were relaxed and the extent of relaxation as key decision parameters; preferably, based on the shrinkage process of the candidate solution space and the constraint satisfaction, a quantitative assessment can be made of the accounting results, i.e., the confidence level or uncertainty range of the carbon emission accounting interval, as the result confidence level.

[0212] Understandably, this report does not contain any raw activity data, but based on the elimination vector and process metadata, it provides a complete chain of evidence and quality assessment for carbon emission accounting results, meeting the needs of regulatory audits and internal optimization.

[0213] Step S3.4: Adaptive evolution of cloud-side parameters.

[0214] Specifically, in order to continuously optimize the accuracy, efficiency and robustness of the entire system's accounting, the cloud side periodically evaluates and adaptively updates the core parameters and configurations it issues based on historical accounting results, traceability reports and system performance indicators.

[0215] Furthermore, the adaptive evolution of cloud-side parameters mainly targets the following key configurations generated and distributed by the cloud side, including accounting parameters and verification compensation parameters. Among them, the accounting parameters include the number of layers and tier width of the pyramid discretization structure, the fractal resolution function cluster and the solution threshold, and its evolution goal is to balance accounting accuracy and computational cost. The verification compensation parameters include verification sampling strategy, dynamic difference threshold and scenario compensation coefficient library, and its evolution goal is to improve the problem data recognition rate and optimize compensation accuracy.

[0216] Furthermore, the basis for the adaptive evolution of cloud-side parameters may include the historical trend of changes in the width of the accounting interval, the statistical regularity of the verification status markers in each region, the efficiency of the constraint network solution, and the latest industry emission factor standards. Based on these bases, the cloud side generates a new generation of parameter versions through offline analysis or machine learning models, and distributes them uniformly to all edge and end-side nodes through a secure channel.

[0217] This embodiment also provides a non-volatile computer storage medium storing computer-executable instructions that can perform the methods described in the embodiment.

[0218] like Figure 3 As shown:

[0219] This embodiment also provides a processing device, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method described in the embodiment.

[0220] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A carbon emission data transmission accounting method, characterized in that, The method includes: On the edge side, carbon emission activity data and corresponding scene labels are collected. A pyramid discretization strategy is used to discretize the carbon emission activity data. Then, using a cluster of fractal resolution functions distributed from the cloud side, incremental resolution encoding is performed on the discretized activity state matrix to generate a standardized accounting fragment containing a global resolution vector and a template resolution vector. This standardized accounting fragment is sent to the edge side, including: For the current accounting cycle, receive and deploy the fractal resolution function cluster and resolution parameter version number, wherein the fractal resolution function cluster is a hierarchical structure containing the first function, the second function and the third function; For the activity state matrix at each scale, grouped resolution vectors, template resolution vectors, and global resolution vectors are generated respectively using a cluster of fractal resolution functions, i.e.: For each element in the activity state matrix, its row and column coordinates, timestamp, and discrete values ​​are input into a first function, which outputs a fixed-length, unidirectional resolution vector. The unidirectionality of the resolution vector is based on lattice cipher construction. Based on a preset grouping template, the elements in the activity state matrix are divided into several groups. A second function is called to merge the resolution vectors of all elements within each group into a grouped resolution vector. Simultaneously, a third function is called to generate a global resolution vector based on the grouped resolution vectors. The grouped resolution vector of the current period is incrementally fused with the cumulative grouped resolution vector updated in the previous accounting period to obtain the cumulative grouped resolution vector of the current period. The cumulative grouped resolution vector of the current period is aggregated into a template resolution vector according to the grouping template. The grouping template includes spatial grouping, temporal grouping, and cross-dimensional grouping, which are used to generate complementary grouped resolution vectors. The global resolution vector corresponding to the coarsest scale activity state matrix, the template resolution vector of the current cycle, the calculation cycle identifier, the timestamp, the scene label, and the resolution parameter version number are encapsulated into a standardized calculation fragment; On the edge, standardized accounting segments are received, and a grouping resolution vector is requested from the edge based on the verification sampling strategy issued by the cloud side. The standardized accounting segments are sampled and verified based on the grouping resolution vector to intercept problematic data. The standardized accounting segments that pass the verification are combined with scene tags to perform scene-based compensation calculation. The compensated standardized accounting segments are locally aggregated to generate regional-level accounting results and reported to the cloud side. On the cloud side, a global constraint network is constructed based on the regional-level accounting results. An arc-compatible algorithm is then applied to this global constraint network to shrink the candidate solution space. Based on the size of the candidate solution space, a branch-and-bound method or interval convergence is performed to obtain the carbon emission accounting interval, thereby generating a source tracing analysis report, including: Based on the regional template resolution vector matrix in the regional accounting results reported by the edge side, a global constraint network is constructed with the elements of the activity state matrix of each edge side at the corresponding scale as variables, and including the first and second constraints. For edge sides whose reported verification status is marked as questionable, a constraint relaxation variable is introduced into the global constraint network. For any two variables belonging to the same end side and the same group template, the first constraint is that the sum of the element elimination vectors equals the corresponding component in the template elimination vector. For any two variables in the global constraint network that belong to the same end side and the same group template, if a certain value of one variable cannot satisfy the first constraint when the other variable takes any possible value, then determine whether to remove the value from the candidate value set of the variable by combining the constraint relaxation variable. Repeatedly scan all variable pairs with the first constraint until the candidate value set of all variables no longer changes. The second constraint is that the sum of the grouping resolution vectors under the same grouping template equals the corresponding template resolution vector. For a second constraint involving multiple variables, if a certain value of any variable cannot be found in the candidate value set of other variables to make the second constraint valid, then the constraint slack variable is used to determine whether to remove the value from the candidate value set of the variable, until the candidate value set of all variables no longer changes; Once the set of candidate values ​​for all variables no longer changes, calculate the size of the candidate solution space.

2. The method of claim 1, wherein, A pyramid discretization strategy is used to discretize carbon emission activity data, including: A pyramid discretization structure is constructed to discretize carbon emission activity data in layers, forming an activity state matrix at multiple scales. The number of layers in the pyramid discretization structure is dynamically configured by the cloud side based on the accuracy requirements of the calculation, and the bottom layer is fine-grained discretization, while all other layers are coarse-grained discretization. At the bottom layer of the pyramid discretization structure, continuous measurements of carbon emission activity data are mapped to preset ranges to generate a fine-grained activity state matrix. In each layer of the pyramid discretization structure, except for the bottom layer, multiple adjacent gear intervals in the next layer are merged into the gear interval corresponding to the current layer, and continuous measurements of carbon emission activity data are mapped to the gear interval corresponding to the current layer to generate a corresponding coarse-grained activity state matrix.

3. The method of claim 1, wherein, Sampling and validation of standardized accounting fragments based on grouped resolution vectors to intercept problematic data, including: The receiving end responds to the sampling request uploaded group resolution vector and performs aggregation consistency verification on the group resolution vector. The aggregation consistency verification is used to obtain the template resolution vector in the standardized accounting segment that belongs to the same group template as the sampled group resolution vector, and compare the sampled group resolution vector with the template resolution vector. If the comparison result is inconsistent, the aggregation consistency verification is determined to fail; otherwise, it passes. Simultaneously, a time continuity verification is performed on the grouped resolution vector. The time continuity verification is used to obtain the grouped resolution vectors of adjacent time groups with the same activity dimension in continuous time slices for the grouped resolution vectors under the time group, and calculate the vector difference between the two. If the vector difference is greater than the dynamic difference threshold, the time continuity verification is determined to be abnormal; otherwise, it passes. Among them, the dynamic difference threshold is obtained by fitting the change magnitude of the grouping resolution vector of the time group corresponding to the same activity dimension in the historical accounting cycle on the end side; Based on the results of sampling verification, a verification status marker is generated. Based on the verification status marker, problematic data is marked from the standardized accounting fragments on the edge side and intercepted. The verification status marker includes abnormal, passed, and questionable.

4. The method of claim 1, wherein, The standardized accounting fragments that pass the verification are combined with scene tags to perform scene-based compensation calculations. The compensated standardized accounting fragments are then locally aggregated to generate regional-level accounting results, including: The side maintains a scene compensation coefficient library, which stores the emission factor compensation coefficient and scene compensation coefficient version number corresponding to different scene labels; Based on the scene labels reported by the edge, the corresponding scene compensation coefficient is retrieved. Utilizing the additive homomorphism of the template elimination vector, the template elimination vector in the standardized accounting segment that has passed the verification is homomorphically weighted with the corresponding scene compensation coefficient in the elimination value domain to generate the compensated template elimination vector. All compensated template elimination vectors are locally aggregated according to the grouped templates to generate a region-level template elimination vector matrix; The regional template resolution vector matrix and the verification status markers and scene compensation coefficient version numbers generated from the sampling verification results at each end are used as the regional accounting results.

5. The method of claim 1, wherein, Based on the size of the candidate solution space, branch and bound or interval convergence methods are performed to obtain the carbon emission accounting interval, including: If the candidate solution space is smaller than the preset solution threshold, the branch and bound method is used to obtain the carbon emission accounting interval; If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the coarse-grained activity state matrix, then the cloud side requests the template resolution vector of the next finer scale from the corresponding end side through the edge side, reconstructs the global constraint network based on the elements of the activity state matrix of the next finer scale, and re-executes the arc compatibility algorithm until the solution space is less than the preset solution threshold. If the candidate solution space is greater than or equal to the preset solution threshold, and the variables of the current global constraint network belong to the fine-grained activity state matrix, then based on the entropy of the candidate value set of each element in the fine-grained activity state matrix, the group containing the variable to be converged is obtained by descending the entropy value. The cloud side requests the group resolution vector of the group from the corresponding end side through the edge side and adds it to the global constraint network. The arc compatibility algorithm is re-executed until the solution space is less than the preset solution threshold.

6. A non-transitory computer storage medium storing computer-executable instructions that, when executed, cause a computer to perform: The computer-executable instructions are capable of performing the method described in any one of claims 1-5.

7. A processing device, characterized by The device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to perform the method according to any one of claims 1-5.

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