Campus carbon footprint monitoring and carbon accounting method

Abstract

The invention discloses a campus carbon footprint monitoring and carbon accounting method, and particularly relates to the field of environmental information measurement and carbon emission responsibility evaluation, which comprises the following steps: in a campus carbon footprint monitoring and carbon accounting scene, constructing a behavior chain and identifying a non-dominant drop point chain by performing user dimension aggregation on an operation fragment set; potential carbon emission liability paths that have spatial offsets between user behavior triggers and device responses are extracted. A cross-space structure mapping relation between a behavior chain and a response chain is constructed, and a carbon emission affiliation correction mechanism is established based on non-physical variables such as path jump, response lag and spatial offset, so that carbon emission responsibility identification and accounting in scenes such as remote triggering, remote execution and behavior path breakage are realized; therefore, the problems of misliability and accounting distortion caused by inconsistency of a carbon emission generation place and a behavior triggering place in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental information measurement and carbon emission responsibility assessment, and more specifically, to a campus carbon footprint monitoring and carbon accounting method. Background Art

[0002] In current campus carbon footprint monitoring and accounting systems, widely used measurement methods typically allocate carbon emission responsibilities based on spatial energy consumption zoning, equipment electricity usage attribution, or physical area entrance information. For example, building meter data combined with foot traffic statistics are used as the basis for accounting to construct a carbon emission ledger for each unit or location.

[0003] However, with the widespread application of remote control, cloud-based task scheduling, and remote wake-up operations in teaching and research scenarios, the traditional carbon responsibility identification model based on physical space ownership is gradually becoming ineffective. Taking shared printing centers, computer rooms, or public experimental platforms as examples, equipment operation behaviors are often triggered remotely by users in different locations, or automatically executed by system timing rules. Although the energy consumption results fall into space A, the source of the behavior originates in space B or even more distant areas. In addition, some user behaviors are delayed in recovery after path interruption. For example, remotely submitting a simulation task in a dormitory will eventually run and complete on the laboratory server, resulting in a "behavior chain path jump" phenomenon.

[0004] The existing measurement system lacks the analysis and measurement logic for this type of "inconsistency between the place where the behavior occurs and the place where carbon emissions occur" situation, resulting in misaligned responsibility accounting and distorted carbon emission calculations; ultimately, the existing technology lacks a universal measurement mechanism based on non-physical variables, and is unable to establish a true responsibility path for carbon emissions from the user behavior chain, which constitutes the most core and unresolved structural problem in campus carbon footprint accounting. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a campus carbon footprint monitoring and carbon accounting method. By constructing a cross-space structural mapping relationship between the behavior chain and the response chain, and establishing a carbon emission attribution correction mechanism based on non-physical variables such as path jumping, response lag and spatial offset, it can realize the identification and accounting of carbon emission responsibilities in scenarios such as remote triggering, off-site execution and behavior path breakage, thereby solving the problems of responsibility dislocation and accounting distortion caused by the inconsistency between the place where carbon emissions occur and the place where the behavior is triggered in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a campus carbon footprint monitoring and carbon accounting method, comprising:

[0007] S1. In the campus carbon footprint monitoring and carbon accounting scenario, we construct a behavior chain by aggregating the user dimension of the operation fragment set and identify the non-explicit landing point chain, extracting the potential carbon emission responsibility path with spatial offset between user behavior trigger and device response;

[0008] S2. By constructing a response chain corresponding to the behavior chain and performing spatial mapping and scheduling association matching, we can identify unmappable response segments where there is an event gap between user behavior and device response.

[0009] S3. Measure the structural differences between the behavior chain and the response chain, extract the path regression segments of spatial span and temporal response offset, aggregate them into responsibility jump blocks, and construct a responsibility transfer measurement chain group that can be used for carbon emission attribution;

[0010] S4. Based on the responsibility transfer mapping chain group, perform carbon emission attribution adjustment measurement, generate a user-dimensional carbon emission correction table and reconstruct the responsibility allocation matrix to achieve carbon accounting conversion and attribution correction from the spatial dimension to the behavioral dimension.

[0011] In a preferred embodiment, S1 further includes: in a campus carbon footprint monitoring and carbon accounting scenario, uniformly formatting the operation records in the system interaction log into standard behavior units, and extracting the user identity, operation instructions, timestamps, and interface objects therein to form an operation fragment set;

[0012] Based on temporal continuity and operation relevance, the set of operation fragments is aggregated into behavior chains according to the user dimension. Each behavior chain represents a set of consecutive, related behavior triggering sequences. For each behavior chain, it is determined whether its terminal operation points to a physical device or a scheduling system interface. If the end of the operation chain does not contain an actual device mapping, it is marked as a non-explicit landing point chain.

[0013] The spatial trajectories of all non-explicit landing point chains are reconstructed to compare whether there is cross-spatial migration between the starting point of the behavior chain and the expected execution location, so as to identify potential carbon emission triggering dislocation paths.

[0014] In a preferred embodiment, S2 further includes: in a campus carbon footprint monitoring and carbon accounting scenario, extracting device operation logs and scheduling rule logs from the Internet of Things management system and the scheduling control system, parsing the records containing physical device identification, operation status change information, and scheduling trigger parameters, and constructing a response event set corresponding to the behavior chain;

[0015] Based on time sequence and scheduling consistency, the device operation fragments in the response event set are connected into response chains. Each response chain represents a set of continuous device operation trajectories controlled by logical rules in the system. A spatial mapping operation is performed on each response chain to determine the physical space area to which each device node belongs, forming a spatial mapping table of the response path.

[0016] Match the behavior chain and the response chain in the time dimension and the scheduling source field. If there is an event flow break between the behavior chain time starting point and the response chain starting device, it is marked as an unmappable response segment.

[0017] In a preferred embodiment, S3 further includes: performing path structure difference analysis on each pair of behavior chains and response chains with unmappable response segments, and constructing a path difference graph structure, wherein nodes represent behaviors or device events, and edges represent logical or physical connections;

[0018] Calculate the spatial span function in the path difference graph structure. The spatial span function is used to measure the lower limit of the path hop count between the spatial center of the device operation node in the response chain and the starting space of the behavior chain;

[0019] For path segments whose spatial span function values ​​exceed the preset threshold, a time overlap analysis is performed to determine whether the response node is activated within the time window after the end of the behavior chain. If the condition is met, the path segment is marked as a path return segment. All path return segments are aggregated into responsibility jump blocks, and the source node of the behavior chain is used as the logical starting point and the end node of the response chain is used as the physical end point to generate a responsibility transfer mapping chain group.

[0020] In a preferred embodiment, S4 further includes: defining a responsibility correction function for each responsibility transfer mapping chain in the responsibility transfer mapping chain group, taking the path span, the time misalignment degree, and the response chain device operation time as input items, and outputting an ownership adjustment factor for each node in the chain;

[0021] The original spatial carbon emission measurement value is used as the basic value, and is weighted and mapped with the attribution adjustment factor node by node to form the carbon emission allocation vector of the behavior node to the spatial area;

[0022] Aggregate the carbon emission allocation vectors in all responsibility transfer mapping chains according to the behavior nodes to form a user-dimensional carbon emission correction table. The user-dimensional carbon emission correction table is used to reflect the impact of each user's behavior chain on the actual carbon emission position after correction;

[0023] Using the user dimension carbon emission correction table as the main index, the responsibility allocation matrix in the carbon ledger is reconstructed to form a carbon accounting transformation from the spatial dimension to the behavioral dimension, completing the systematic correction of responsibility mismatch.

[0024] In a preferred embodiment, in S1, a behavior chain spatial misalignment potential function model is constructed based on the device mapping degree of the interface object in the operation segment, the time proportion of the operation segment, and the spatial offset between the start and end positions of the behavior. This is used to quantify the device carbon emission spatial offset risk caused by the user behavior chain. The behavior chain spatial misalignment potential function model is expressed as:

[0025]

[0026] in is the spatial dislocation potential value of the behavior chain β; n β is the number of operation segments in the behavior chain β; j is the interface object identifier in the j-th operation fragment; is χ j The structural complexity weight of is χ j The explicitness of the corresponding physical device mapping; τ j is the timestamp of the j-th operation; are the start and end timestamps of the behavior chain β respectively; is the position vector of the starting point of the behavior chain on the campus space topology map; The expected position vector of the logical target device pointed to by the interface object in the behavior chain; Represents the jump point distance function between two position vectors on the campus spatial topology map.

[0027] In a preferred embodiment, in S2, the coupling offset function between the behavior chain and the response chain is calculated by measuring the spatial overlap, response content difference and structural interruption effect between the behavior chain and the response chain:

[0028]

[0029] where ε β,r is the coupling offset rate between the behavior chain β and the response chain r in the whole cycle; T r The running time of the response chain r; is the position vector of the activated device in the spatial topology graph of the response chain at time t; is the reference position of the center of the behavior chain; d(·,·) is the jump point distance function in the topological space; Δ task (t) is the content difference rate between the behavior chain and the response chain in the task goal at time t; is a Boolean function that indicates whether the response chain has a structural break at time t, with a break being 1 and a continuity being 0. α1 is used to adjust the influence of the jump distance between the response device position and the spatial center of the behavior chain. α2 is used to adjust the matching difference between the response content and the behavior triggering task structure. α3 is used to adjust the degree of influence of the continuity of the response chain structure.

[0030] In a preferred embodiment, in S3, a path return rate calculation model is constructed through the behavior response path structure. The path return rate calculation model is used to calculate whether there is a stable reverse attribution path between the behavior chain and the response chain, and output the path return rate to determine whether to generate a responsibility jump block; the path return rate calculation model is expressed as:

[0031]

[0032] in is the path regression rate of the behavior chain β and the response chain r; L reachable (β, r) represents the number of independent jump paths from the behavior chain node to the response chain node in the path difference graph; Δσ β,r is the lower limit difference of the number of path hops between the behavior chain and the response chain; is the positive triggering effect of the behavior chain β on the i-th item of the response chain r; is the jth reverse dependency of response chain r on behavior chain β; n is the total number of positive triggering effects generated by behavior chains on response chains; m is the total number of reverse dependencies formed by response chains on behavior chains; log2(·) is a logarithmic function.

[0033] In a preferred embodiment, a carbon emission attribution tensor model is constructed in S4 based on the number of path hops and response strength to project the behavior chain responsibility onto spatial nodes, forming a three-dimensional responsibility tensor and achieving the binding of the behavior dimension with spatial carbon accounting. The carbon emission attribution tensor model is expressed as:

[0034]

[0035]

[0036] in represents the carbon emission proportion of user u in the behavior chain β and the corresponding response chain r for the spatial node coordinate (x, y) at time point t; σ β,r is the lower limit of the number of hops between the behavior chain and the response chain; the behavior chain β′ represents other behavior chains except the current main path; the response chain r′ represents other response chains except the current main path; σ β′,r′ It refers to the lower limit of the number of hops in the path corresponding to the behavior chain β′ and the response chain r′; is the hop weight term; Δt β,r (t) is the interval from the end of the behavior chain to the running time t of the response chain; Δt β′,r′ (t) and ρ r′ (x, y, t) are the time offset and carbon emission intensity of each candidate path combination; ρ r (x, y, t) is the unit carbon emission intensity of the response chain r device in the space and time coordinates of (x, y, t); Emission r (t) is the total carbon emission of device r in the response chain at time t; is the final carbon accounting matrix after spatial reconstruction.

[0037] Technical effects and advantages of the present invention:

[0038] 1. By building a behavior chain structure and identifying non-explicit landing point chains, the path of user operation behavior from the trigger source to the non-explicit landing device can be tracked, thus solving the problem that traditional carbon emission accounting based on physical attribution cannot identify the responsible party in scenarios such as remote control and remote scheduling;

[0039] 2. By parsing equipment operation records into response chain structures and matching them with the dispatch trigger source fields in the dispatch system, the causal relationship between the event flow between the behavior chain and the response chain can be determined. This improves the system's ability to restore cross-regional carbon emission trigger paths and fills the accounting gap of the existing system in dispatch isolation scenarios.

[0040] 3. By constructing a path difference graph structure, based on the spatial span measurement and temporal overlap window analysis between the behavior chain and the response chain, the path return segment is determined and the responsibility jump block is formed. From a structural perspective, the attribution offset between the behavior initiation location and the carbon emission location is quantified, achieving structured identification of behavior-response asymmetry.

[0041] 4. By executing carbon emission attribution correction function modeling on the responsibility transfer mapping chain group, a carbon emission attribution ratio tensor of the behavior chain node to the spatial area is formed, and a carbon emission responsibility mapping table under the behavior dimension is effectively established, realizing a systematic transformation from physical space accounting to behavioral responsibility accounting, and providing a basis for building a real and reviewable carbon ledger. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of the framework of the method steps of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] Refer to the instruction manual Figure 1 The campus carbon footprint monitoring and carbon accounting method according to one embodiment of the present invention includes:

[0045] S1. In the campus carbon footprint monitoring and carbon accounting scenario, we construct a behavior chain by aggregating the user dimension of the operation fragment set and identify the non-explicit landing point chain, extracting the potential carbon emission responsibility path with spatial offset between user behavior trigger and device response;

[0046] S2. By constructing a response chain corresponding to the behavior chain and performing spatial mapping and scheduling association matching, we can identify unmappable response segments where there is an event gap between user behavior and device response.

[0047] S3. Measure the structural differences between the behavior chain and the response chain, extract the path regression segments of spatial span and temporal response offset, aggregate them into responsibility jump blocks, and construct a responsibility transfer measurement chain group that can be used for carbon emission attribution;

[0048] S4. Based on the responsibility transfer mapping chain group, perform carbon emission attribution adjustment measurement, generate a user-dimensional carbon emission correction table and reconstruct the responsibility allocation matrix to achieve carbon accounting conversion and attribution correction from the spatial dimension to the behavioral dimension.

[0049] S1 also includes: in the campus carbon footprint monitoring and carbon accounting scenario, the operation records in the system interaction log are uniformly formatted into standard behavior units, and the user identity, operation instructions, timestamps and interface objects are extracted to form an operation fragment set;

[0050] Based on temporal continuity and operation relevance, the set of operation fragments is aggregated into behavior chains according to the user dimension. Each behavior chain represents a set of consecutive, related behavior triggering sequences. For each behavior chain, it is determined whether its terminal operation directly points to a physical device or scheduling system interface. If the end of the operation chain does not contain an actual device mapping, it is marked as a non-explicit landing point chain.

[0051] The spatial trajectories of all non-explicit landing point chains are reconstructed to compare whether there is cross-spatial migration between the starting point of the behavior chain and the expected execution location, so as to identify potential carbon emission triggering dislocation paths.

[0052] S2 also includes: in the campus carbon footprint monitoring and carbon accounting scenario, extracting equipment operation logs and scheduling rule logs from the IoT management system and scheduling control system, parsing records containing physical equipment identification, operation status change information and scheduling trigger parameters, and constructing a response event set corresponding to the behavior chain;

[0053] Based on time sequence and scheduling consistency, the device operation fragments in the response event set are connected into response chains. Each response chain represents a set of continuous device operation trajectories controlled by logical rules in the system. A spatial mapping operation is performed on each response chain to determine the physical space area to which each device node belongs, forming a spatial mapping table of the response path.

[0054] Match the behavior chain and the response chain in the time dimension and the scheduling source field. If there is a break in the event flow between the starting time of the behavior chain and the starting device of the response chain, it is marked as an unmappable response segment; the starting device of the response chain refers to the physical device node where the response event occurs earliest in a response chain, that is, the device that is first activated, turned on or scheduled to run during a certain device operation of the system.

[0055] S3 also includes: performing path structure difference analysis on each pair of behavior chains and response chains with unmappable response segments, and constructing a path difference graph structure, where nodes represent behaviors or device events and edges represent logical or physical connections;

[0056] Calculate the spatial span function in the path difference graph structure. The spatial span function is used to measure the lower limit of the path hop count between the spatial center of the device operation node in the response chain and the starting space of the behavior chain;

[0057] For path segments whose spatial span function values ​​exceed the preset threshold, a time overlap analysis is performed to determine whether the response node is activated within the time window after the end of the behavior chain. If the condition is met, the path segment is marked as a path return segment. All path return segments are aggregated into responsibility jump blocks, and the source node of the behavior chain is used as the logical starting point and the end node of the response chain is used as the physical end point to generate a responsibility transfer mapping chain group.

[0058] S4 also includes: for each responsibility transfer mapping chain in the responsibility transfer mapping chain group, defining a responsibility correction function, taking the path span, the time misalignment degree and the operation time of the response chain device as input items, and outputting the attribution adjustment factor of each node in the chain;

[0059] The original spatial carbon emission measurement value is used as the basic value, and is weighted and mapped with the attribution adjustment factor node by node to form the carbon emission allocation vector of the behavior node to the spatial area;

[0060] Aggregate the carbon emission allocation vectors in all responsibility transfer mapping chains according to the behavior nodes to form a user-dimensional carbon emission correction table. The user-dimensional carbon emission correction table is used to reflect the impact of each user's behavior chain on the actual carbon emission position after correction;

[0061] Using the user dimension carbon emission correction table as the main index, the responsibility allocation matrix in the carbon ledger is reconstructed to form a carbon accounting transformation from the spatial dimension to the behavioral dimension, completing the systematic correction of responsibility mismatch.

[0062] It should be noted that in the formula structure involved in this solution, dimensionless terms can serve as proportionality or structural adjustment factors. When combined with quantities with units, they only play a numerical scaling role and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system of expression. This combination of "dimensionless terms and units" can be understood as a composite structural expression commonly used in mathematical and physical modeling, conforming to the principle of dimensional consistency and having a clear physical interpretation basis.

[0063] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can be formed into a unified structure through function mapping, ratio combination or normalization adjustment. The units and meanings are clear, and the overall expression conforms to the principle of dimensional consistency and the common formula of engineering modeling.

[0064] In this solution, any design constants, weights, adjustment factors, threshold parameters, and proportional coefficients are adjustable control parameters for different application environments. Their values ​​depend on the target device configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are set within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have unique preset values, they have clear adjustment logic and calculation paths, and are part of the deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the solution is both universally adaptable, reproducible, and operable, without affecting its technical clarity and feasibility.

[0065] In S1, a behavior chain spatial misalignment potential function model is constructed based on the device mapping degree of interface objects in the operation segment, the time proportion of the operation segment, and the spatial offset between the start and end positions of the behavior. This is used to quantify the risk of device carbon emission spatial offset caused by the user behavior chain. The behavior chain spatial misalignment potential function model is expressed as:

[0066]

[0067] in is the spatial dislocation potential value of behavior chain β, which is used to quantify the intensity of its impact on the inconsistency of carbon emission locations; n β is the number of operation segments in the behavior chain β; j is the interface object identifier in the j-th operation fragment; is χ j The weight of structural complexity is high. In practical applications, such as remote services and high-concurrency scheduling, the weight of interfaces that can be applied is high. is χ j Corresponding to the explicitness of the physical device mapping, The range of is [0,1], The closer it is to 1, the clearer it is pointing to the actual device; τ j is the timestamp of the j-th operation; are the start and end timestamps of the behavior chain β respectively; is the position vector of the starting point of the behavior chain on the campus space topology map; The expected position vector of the logical target device pointed to by the interface object in the behavior chain; Represents the hop distance function (node ​​shortest path) between two position vectors on the campus spatial topology graph.

[0068] In S2, the coupling offset function between the behavior chain and the response chain is calculated by measuring the spatial overlap between the behavior chain and the response chain, the difference in response content, and the impact of structural interruption:

[0069]

[0070] where ε β,r is the coupling offset rate between the behavior chain β and the response chain r in the whole cycle. The coupling offset rate between the behavior chain β and the response chain r in the whole cycle indicates the degree of indirectness of the response to the behavior trigger; T r The running time of the response chain r (from start to end); is the position vector of the activated device in the spatial topology graph of the response chain at time t; is the reference position of the center of the behavior chain (the average node between the starting point and the end point); d(·,·) is the jump point distance function in the topological space; Δ task (t) is the content difference rate between the behavior chain and the response chain in the task target at time t (such as the number of devices and the difference in scheduling path length); is a Boolean function. The Boolean function in the above formula is used to indicate whether the response chain has a structural break at time t, with a break value of 1 and a continuity value of 0. α1 is used to adjust the influence intensity of the jump distance between the response device position and the spatial center of the behavior chain. The value of α1 can be adaptively set according to the density of the node distribution of the behavior chain in the spatial topology graph. The more discrete the distribution, the higher the weight given to α1, so as to enhance the recognition sensitivity of spatial offset. α2 is used to adjust the matching difference between the response content and the behavior triggering task structure. The value of α2 is calculated based on the matching ratio of the number of device types involved in the task structure to the type of operation. The more incomplete the matching, the higher α2 tends to be, highlighting the impact of poor task instruction coverage. α3 is used to adjust the influence degree of the continuity of the response chain structure. The value of α3 is dynamically adjusted according to the proportion of broken fragments in the response chain. The more frequent the chain interruption, the larger α3 is, so as to increase the measurement weight of the lack of structural stability in the offset calculation.

[0071] In S3, a path return rate calculation model is constructed through the behavior-response path structure. The path return rate calculation model is used to calculate whether there is a stable reverse attribution path between the behavior chain and the response chain, and output the path return rate to determine whether to generate a responsibility jump block. The path return rate calculation model is expressed as:

[0072]

[0073] in is the path return rate of the behavior chain β and the response chain r. The path return rate of the behavior chain β and the response chain r indicates whether there is a structural deflection in which the behavior is triggered but the carbon emission response falls into another domain. L reachable (β, r) represents the number of independent jump paths from the behavior chain node to the response chain node in the path difference graph; Δσ β,r is the lower limit difference of the number of path hops between the behavior chain and the response chain; is the positive triggering effect of behavior chain β on the i-th item of response chain r (such as device activation, log writing, etc.); is the jth reverse dependency of response chain r on behavior chain β (such as state reuse, logic complement chain, etc.); n is the total number of positive triggering effects generated by behavior chains on response chains; m is the total number of reverse dependencies formed by response chains on behavior chains; log2(·) is a logarithmic function, which is used to enhance the sensitivity of the regression rate.

[0074] In S4, a carbon emission attribution tensor model is constructed based on the number of path hops and response strength. This model is used to project the responsibility of the behavioral chain onto spatial nodes, forming a three-dimensional responsibility tensor and achieving the binding of the behavioral dimension with spatial carbon accounting. The carbon emission attribution tensor model is expressed as:

[0075]

[0076] in represents the carbon emission proportion of user u in the behavior chain β and the corresponding response chain r for the spatial node coordinate (x, y) at time point t; σ β,r is the lower limit of the number of path hops between the behavior chain and the response chain; the behavior chain β′ represents other behavior chains except the current main path, which is used to traverse all candidate behavior chain paths during the normalization process; the response chain r′ represents other response chains except the current main path, which is used to pair with β′ to form a path combination for normalization; σ β′,r′ It refers to the lower limit of the number of hops in the path corresponding to the behavior chain β′ and the response chain r′; is the hop weight term. The square in the hop weight term is used to emphasize the complexity of the path structure, and the +1 in the hop weight term is used to prevent the denominator from being zero. β,r (t) is the interval from the end of the behavior chain to the running time t of the response chain; Δt β′,r′ (t) and ρr′ (x, y, t) are the time offset and carbon emission intensity of each candidate path combination, which are used to normalize the denominator; ρ r (x, y, t) is the unit carbon emission intensity of the response chain r device in the space and time coordinates of (x, y, t); Emission r (t) is the total carbon emission of device r in the response chain at time t; is the final carbon accounting matrix after spatial reconstruction.

[0077] It should be further explained that this solution stems from a fundamental optimization of the existing campus carbon footprint accounting system. In traditional solutions, carbon emission responsibility is mostly divided based on physical space ownership, energy consumption zoning, or equipment electricity consumption statistics, assuming that the location of carbon emissions is highly consistent with the location of behavioral triggers. However, in the current teaching and research environment, this assumption no longer holds true. The widespread existence of remote operation, scheduled scheduling, remote control, and cross-terminal linkage often leads to a separation between the starting point of the behavioral chain and the end point of carbon emissions, resulting in a "misaligned responsibility path" problem.

[0078] Therefore, starting from the measurement dimension of non-physical variables, this scheme remodels the carbon emission accounting path for the first time as a multi-level matching mechanism based on the behavior chain structure, response chain structure and spatial difference map;

[0079] In S1, behavior chain construction and non-explicit landing point identification:

[0080] This phase is the starting point of the entire carbon footprint measurement logic. By performing structured extraction of system interaction logs, a user-level set of operation snippets is established. Each operation snippet contains key information such as user identity, operation time, and interface object, and is aggregated into a behavior chain through temporal continuity and coupling with logical instructions. In particular, the solution introduces the concept of "non-explicit landing point chains" at this stage. These chains are behavior chains whose endpoints do not explicitly point to physical equipment or scheduling systems, representing the starting point of a potential mismatch between carbon emission locations and behavior locations.

[0081] This identification mechanism ensures that it not only focuses on whether the device itself is running, but also traces back to whether the behavioral intention is actually implemented at the spatial node, building a risk foundation for the spatial dislocation path;

[0082] In S2, the relationship between response chain construction and scheduling mapping is analyzed:

[0083] This section corresponds to the system-level modeling of physical device scheduling and operational responses. By parsing the operation logs of the IoT platform and scheduling system, fragments containing device ID, operating status, and scheduling trigger fields are extracted to form a response event set, and then constructed into a response chain based on time and logical coherence.

[0084] Through spatial mapping, the physical location corresponding to each response event is clearly identified. Combined with the behavior chain structure, a one-to-one comparison is performed on the timeline and scheduling source fields. If a lack of event causal flow or spatial continuity is found between the behavior chain and the response chain, it is marked as an "unmappable response segment." This mechanism lays the boundary conditions for subsequent path structure difference analysis.

[0085] In S3, path difference measurement and responsibility jump path generation:

[0086] Based on the combination of behavior chain and response chain, a path difference graph is constructed. The spatial span function and the time overlap function are jointly measured in the graph structure to construct the "path return segment";

[0087] By calculating the lower limit of the number of hops between the starting point of the behavior chain and the device node of the response chain, and superimposing time lag judgment, we extract those segments that are spatially deviated but temporally responsive but have a weak causal relationship with the behavior chain, and then construct a "responsibility transfer mapping chain group." This is a structural set that represents all potential paths where user behavior triggers carbon emissions but is not located in the space where the behavior occurs, forming the backbone logic of spatially dislocated responsibility transfer.

[0088] In S3, responsibility correction and carbon emission matrix reconstruction:

[0089] This section focuses on the core question of "how to reverse-correct identified responsibility mismatches back to the user's actual behavior chain." For each responsibility transfer mapping chain, a responsibility correction function is established that incorporates path complexity, response time offset, and device carbon emission intensity, and an attribution adjustment factor is calculated.

[0090] A tensor structure is used to construct the user-path-space-time carbon emission distribution ratio, mapping all carbon emissions from the physical device space to the behavioral space at the starting point of the behavior chain, forming a user-dimensional carbon emission correction table. This table serves as an index for the reconstructed accounting matrix, replacing the original attribution model based on electricity meters or room numbers, completing the accounting paradigm shift from the physical dimension to the behavioral dimension.

[0091] This solution, centered on a "behavior chain-response chain" path structure, is driven by the need to model the structural dislocations of real-world operational links. Compared to traditional static models based on energy consumption zoning, this solution proactively identifies the impact of dynamic factors such as remote triggering, path hopping, and indirect scheduling on carbon emission attribution.

[0092] Furthermore, in practical applications, the solution does not rely on device-specific physical indicators. Instead, it builds measurement quantities based entirely on a general structure that can be extracted from logs, scheduling rules, and system behavior. This solution is portable across systems and spatial topologies, and its technical approach avoids subordination or overlap conflicts with existing methods.

[0093] In response to the trend of green campus and digital carbon verification, the original solution centered on space sharing can no longer meet the operational scenario requirements of distance learning, distributed computing platforms, and cross-space shared equipment; therefore, building a "path chain behavior measurement-behavior-based attribution conversion logic" has become a feasible technical path to achieve "identification of actual responsible persons" and "generation of reproducible carbon ledgers."

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Campus carbon footprint monitoring and carbon accounting method, characterized by: include: S1. In the campus carbon footprint monitoring and carbon accounting scenario, we construct a behavior chain by aggregating the user dimension of the operation fragment set and identify the non-explicit landing point chain, extracting the potential carbon emission responsibility path with spatial offset between user behavior trigger and device response; S2. By constructing a response chain corresponding to the behavior chain and performing spatial mapping and scheduling association matching, we can identify unmappable response segments where there is an event gap between user behavior and device response. S3. Measure the structural differences between the behavior chain and the response chain, extract the path regression segments of spatial span and temporal response offset, aggregate them into responsibility jump blocks, and construct a responsibility transfer measurement chain group that can be used for carbon emission attribution; S4. Based on the responsibility transfer mapping chain group, perform carbon emission attribution adjustment measurement, generate a user-dimensional carbon emission correction table and reconstruct the responsibility allocation matrix to achieve carbon accounting conversion and attribution correction from the spatial dimension to the behavioral dimension.

2. The campus carbon footprint monitoring and carbon accounting method according to claim 1, characterized in that: S1 also includes: in the campus carbon footprint monitoring and carbon accounting scenario, the operation records in the system interaction log are uniformly formatted into standard behavior units, and the user identity, operation instructions, timestamps and interface objects are extracted to form an operation fragment set; Based on temporal continuity and operation relevance, the set of operation fragments is aggregated into behavior chains according to the user dimension. Each behavior chain represents a set of consecutive, related behavior triggering sequences. For each behavior chain, it is determined whether its terminal operation points to a physical device or a scheduling system interface. If the end of the operation chain does not contain an actual device mapping, it is marked as a non-explicit landing point chain. The spatial trajectories of all non-explicit landing point chains are reconstructed to compare whether there is cross-spatial migration between the starting point of the behavior chain and the expected execution location, so as to identify potential carbon emission triggering dislocation paths.

3. The campus carbon footprint monitoring and carbon accounting method according to claim 2, characterized in that: S2 also includes: in the campus carbon footprint monitoring and carbon accounting scenario, extracting equipment operation logs and scheduling rule logs from the IoT management system and scheduling control system, parsing records containing physical equipment identification, operation status change information and scheduling trigger parameters, and constructing a response event set corresponding to the behavior chain; Based on time sequence and scheduling consistency, the device operation fragments in the response event set are connected into response chains. Each response chain represents a set of continuous device operation trajectories controlled by logical rules in the system. A spatial mapping operation is performed on each response chain to determine the physical space area to which each device node belongs, forming a spatial mapping table of the response path. Match the behavior chain and the response chain in the time dimension and the scheduling source field. If there is an event flow break between the behavior chain time starting point and the response chain starting device, it is marked as an unmappable response segment.

4. The campus carbon footprint monitoring and carbon accounting method according to claim 3 is characterized by: S3 also includes: performing path structure difference analysis on each pair of behavior chains and response chains with unmappable response segments, and constructing a path difference graph structure, where nodes represent behaviors or device events and edges represent logical or physical connections; Calculate the spatial span function in the path difference graph structure. The spatial span function is used to measure the lower limit of the path hop count between the spatial center of the device operation node in the response chain and the starting space of the behavior chain; For path segments whose spatial span function values ​​exceed the preset threshold, a time overlap analysis is performed to determine whether the response node is activated within the time window after the end of the behavior chain. If the condition is met, the path segment is marked as a path return segment. All path return segments are aggregated into responsibility jump blocks, and the source node of the behavior chain is used as the logical starting point and the end node of the response chain is used as the physical end point to generate a responsibility transfer mapping chain group.

5. The campus carbon footprint monitoring and carbon accounting method according to claim 4, characterized in that: S4 also includes: for each responsibility transfer mapping chain in the responsibility transfer mapping chain group, defining a responsibility correction function, taking the path span, the time misalignment degree and the operation time of the response chain device as input items, and outputting the attribution adjustment factor of each node in the chain; The original spatial carbon emission measurement value is used as the basic value, and is weighted and mapped with the attribution adjustment factor node by node to form the carbon emission allocation vector of the behavior node to the spatial area; Aggregate the carbon emission allocation vectors in all responsibility transfer mapping chains according to the behavior nodes to form a user-dimensional carbon emission correction table. The user-dimensional carbon emission correction table is used to reflect the impact of each user's behavior chain on the actual carbon emission position after correction; Using the user dimension carbon emission correction table as the main index, the responsibility allocation matrix in the carbon ledger is reconstructed to form a carbon accounting transformation from the spatial dimension to the behavioral dimension, completing the systematic correction of responsibility mismatch.

6. The campus carbon footprint monitoring and carbon accounting method according to claim 5, characterized in that: In S1, a behavior chain spatial misalignment potential function model is constructed based on the device mapping degree of interface objects in the operation segment, the time proportion of the operation segment, and the spatial offset between the start and end positions of the behavior. This is used to quantify the risk of device carbon emission spatial offset caused by the user behavior chain. The behavior chain spatial misalignment potential function model is expressed as: in is the spatial dislocation potential value of the behavior chain β; n β is the number of operation segments in the behavior chain β; j is the interface object identifier in the j-th operation fragment; is χ j The structural complexity weight of is χ j The explicitness of the corresponding physical device mapping; τ j is the timestamp of the j-th operation; are the start and end timestamps of the behavior chain β respectively; is the position vector of the starting point of the behavior chain on the campus space topology map; The expected position vector of the logical target device pointed to by the interface object in the behavior chain; Represents the jump point distance function between two position vectors on the campus spatial topology map.

7. The campus carbon footprint monitoring and carbon accounting method according to claim 6, characterized in that: In S2, the coupling offset function between the behavior chain and the response chain is calculated by measuring the spatial overlap between the behavior chain and the response chain, the difference in response content, and the impact of structural interruption: where ε β,r is the coupling offset rate between the behavior chain β and the response chain r in the whole cycle; T r The running time of the response chain r; is the position vector of the activated device in the spatial topology graph of the response chain at time t; is the reference position of the center of the behavior chain; d(·,·) is the jump point distance function in the topological space; Δ task (t) is the content difference rate between the behavior chain and the response chain in the task goal at time t; is a Boolean function that indicates whether the response chain has a structural break at time t, with a break being 1 and a continuity being 0. α1 is used to adjust the influence of the jump distance between the response device position and the spatial center of the behavior chain. α2 is used to adjust the matching difference between the response content and the behavior triggering task structure. α3 is used to adjust the degree of influence of the continuity of the response chain structure.

8. The campus carbon footprint monitoring and carbon accounting method according to claim 7, characterized in that: In S3, a path return rate calculation model is constructed through the behavior-response path structure. The path return rate calculation model is used to calculate whether there is a stable reverse attribution path between the behavior chain and the response chain, and output the path return rate to determine whether to generate a responsibility jump block. The path return rate calculation model is expressed as: in is the path regression rate of the behavior chain β and the response chain r; L reachable (β, r) represents the number of independent jump paths from the behavior chain node to the response chain node in the path difference graph; Δσ β,r is the lower limit difference of the number of path hops between the behavior chain and the response chain; is the positive triggering effect of the behavior chain β on the i-th item of the response chain r; is the jth reverse dependency of response chain r on behavior chain β; n is the total number of positive triggering effects generated by behavior chains on response chains; m is the total number of reverse dependencies formed by response chains on behavior chains; log2(·) is a logarithmic function.

9. The campus carbon footprint monitoring and carbon accounting method according to claim 8, characterized in that: In S4, a carbon emission attribution tensor model is constructed based on the number of path hops and response intensity. This model is used to project the responsibility of the behavioral chain onto spatial nodes, forming a three-dimensional responsibility tensor and achieving the binding of the behavioral dimension with spatial carbon accounting. The carbon emission attribution tensor model is expressed as: in represents the carbon emission proportion of user u in the behavior chain β and the corresponding response chain r for the spatial node coordinate (x, y) at time point t; σ β,r is the lower limit of the number of hops between the behavior chain and the response chain; the behavior chain β′ represents other behavior chains except the current main path; the response chain r′ represents other response chains except the current main path; σ β′,r′ It refers to the lower limit of the number of hops in the path corresponding to the behavior chain β′ and the response chain r′; is the hop weight term; Δt β,r (t) is the interval from the end of the behavior chain to the running time t of the response chain; Δt β′,r′ (t) and ρ r′ (x, y, t) are the time offset and carbon emission intensity of each candidate path combination; ρ r (x, y, t) is the unit carbon emission intensity of the response chain r device in the space and time coordinates of (x, y, t); Emission r (t) is the total carbon emission of device r in the response chain at time t; is the final carbon accounting matrix after spatial reconstruction.

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