A land remediation type-based carbon effect quantification evaluation system and method

Abstract

The application relates to the technical field of carbon effect evaluation of land consolidation, and discloses a carbon effect quantitative evaluation system and method based on land consolidation types, which comprises a regional semantic layer module, a consolidation engineering semantic layer module and a parameter semantic layer module, and further comprises a parameter semantic processing module used for constructing a structured semantic representation of carbon factor parameters, wherein the carbon factor parameters are associated with an uncertainty descriptor ontology, and the uncertainty descriptor ontology is used for encapsulating metadata information representing statistical uncertainty of the carbon factor parameters and application range constraints. The carbon effect quantitative evaluation system and method based on land consolidation types establish a semantic mapping relationship between the regional semantic layer, the consolidation engineering semantic layer and the parameter semantic layer through a cross-layer semantic association module. The applicability state of the parameters can be accurately determined, evaluation deviation caused by parameter misuse is avoided, and the problems of low coverage of localized parameters and poor regional adaptability are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of land consolidation carbon effect assessment technology, and in particular to a system and method for quantitative assessment of carbon effects based on land consolidation type. Background Technology

[0002] Land consolidation activities significantly impact regional carbon cycles by altering land use patterns, soil physicochemical properties, and ecosystem structure. Scientifically quantifying the carbon effects of land consolidation projects has become a crucial technical step in assessing their ecological benefits and guiding low-carbon land consolidation planning.

[0003] The parameter recommendation process in existing land consolidation carbon effect assessment systems relies heavily on human experience and judgment, resulting in insufficient coverage of localized parameters and difficulty in adapting to my country's complex and diverse natural geographical zones and differentiated consolidation models. Furthermore, carbon effect parameters themselves have strict applicability limitations; for example, the carbon absorption coefficient of a specific crop is only applicable to specific climate zones and soil types. Existing systems generally lack automated verification capabilities for parameter applicability, leading to frequent parameter misuse. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology has the disadvantage of lacking automated verification capability for parameter applicability and frequent parameter misuse. To this end, we propose a carbon effect quantitative assessment system and method based on land consolidation type.

[0005] To achieve the above objectives, this application adopts the following technical solution: a carbon effect quantitative assessment system based on land consolidation type, comprising a regional semantic layer module, a consolidation project semantic layer module, and a parameter semantic layer module, and further comprising:

[0006] The parameter semantic processing module is used to construct a structured semantic representation of the carbon factor parameter. The carbon factor parameter is associated with an uncertainty descriptor ontology, which is used to encapsulate metadata information that characterizes the statistical uncertainty and applicable range constraints of the carbon factor parameter.

[0007] The cross-layer semantic association module is used to establish a semantic mapping relationship between the regional semantic layer module, the remediation project semantic layer module and the parameter semantic layer module. The cross-layer semantic association module is configured to: obtain the input target area features, target remediation type and target engineering measures, filter carbon factor parameters that match the target area features, target remediation type and target engineering measures based on the semantic mapping relationship, and generate a candidate parameter set.

[0008] The parameter applicability constraint reasoning module is used to verify the applicability of carbon factor parameters in the candidate parameter set based on a preset set of constraint rules. The set of constraint rules defines the applicability constraints of carbon factor parameters under specified regional attribute conditions. The parameter applicability constraint reasoning module determines the applicability status of each carbon factor parameter in the candidate parameter set based on the actual attribute values ​​of the target region features.

[0009] The applicability status determination result output by the parameter applicability constraint reasoning module is fed back to the cross-layer semantic association module, so that the cross-layer semantic association module can sort and adjust the candidate parameter set based on the applicability status determination result.

[0010] Preferably, the metadata information encapsulated in the uncertainty descriptor ontology includes confidence level attribute, sample size attribute, data source attribute, and applicable condition constraint attribute, wherein the confidence level attribute has a value range of 0 to 1.

[0011] Preferably, the semantic mapping relationship includes: a regional applicability mapping relationship for connecting the regional semantic layer module and the parameter semantic layer module; an engineering applicability mapping relationship for connecting the remediation engineering semantic layer module and the parameter semantic layer module; and a process source mapping relationship for connecting the carbon effect process entity and the parameter semantic layer module.

[0012] Preferably, each constraint rule in the constraint rule set defines a constraint type, a constraint condition expression, and a constraint strength, wherein the constraint strength includes two types: mandatory constraints and suggested constraints.

[0013] Preferably, the parameter applicability constraint reasoning module can determine the applicability status of each carbon factor parameter in the candidate parameter set based on the constraint rule set, and the applicability status is divided into three categories: applicable, inapplicable, and partially applicable.

[0014] Preferably, the cross-layer semantic association module includes a matching degree calculation submodule. The matching degree calculation submodule calculates the comprehensive matching degree of carbon factor parameters based on the regional feature matching degree and the engineering measure matching degree. The comprehensive matching degree is used to assist in the generation process of candidate parameter set.

[0015] Preferably, the parameter applicability constraint reasoning module further includes an applicability scoring submodule, which is used to quantify and score the carbon factor parameters and output an applicability score value.

[0016] Preferably, it also includes a feedback optimization subsystem, which dynamically adjusts the confidence attribute corresponding to the carbon factor parameter based on the deviation between the measured carbon effect data and the recommended value of the carbon factor parameter.

[0017] A method for quantitatively assessing the carbon effects based on land consolidation types includes the following steps:

[0018] Construct a regional semantic layer, a remediation project semantic layer, and a parameter semantic layer, and associate carbon factor parameters with an uncertainty descriptor ontology that encapsulates metadata on statistical uncertainty and applicable scope constraints;

[0019] Establish a semantic mapping relationship between the regional semantic layer, the remediation project semantic layer, and the parameter semantic layer. Based on the input target region characteristics, target remediation type, and target engineering measures, filter matching carbon factor parameters based on the semantic mapping relationship to generate a candidate parameter set.

[0020] The applicability of carbon factor parameters in the candidate parameter set is verified based on a preset set of constraint rules, and the applicability status of each carbon factor parameter is determined based on the actual attribute values ​​of the target region features.

[0021] The applicability status determination results are fed back to the sorting process of the candidate parameter set. The candidate parameter set is sorted and adjusted based on the applicability status, and a list of recommended parameters is output.

[0022] Preferably, the actual attribute values ​​of the target region features are matched and verified with the constraint condition expressions of each constraint rule in the constraint rule set; if all mandatory constraints corresponding to the carbon factor parameter are satisfied, it is determined to be applicable; if at least one mandatory constraint is not satisfied, it is determined to be inapplicable; if all mandatory constraints are satisfied but at least one suggested constraint is not satisfied, it is determined to be partially applicable.

[0023] The technical effects and advantages of this invention are as follows:

[0024] In this invention, a semantic mapping relationship is established between the regional semantic layer, the remediation project semantic layer, and the parameter semantic layer through a cross-layer semantic association module. Based on the characteristics of the target area, the remediation type, and the specific conditions of the engineering measures, matching carbon factor parameters can be intelligently selected. Next, the applicability of candidate parameters is verified through a parameter applicability constraint reasoning module, automatically detecting whether the parameters are suitable for the target area based on a set of constraint rules. Each constraint rule defines the constraint type, constraint condition expression, and constraint strength, accurately determining the applicability status of the parameters and avoiding evaluation bias caused by parameter misuse. This effectively solves the problems of low coverage and poor regional adaptability of localized parameters. Attached Figure Description

[0025] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0026] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0027] Figure 2 This is a structural diagram of the uncertainty descriptor ontology of the present invention;

[0028] Figure 3 This is a flowchart illustrating the reasoning process for the parameter applicability constraints of this invention. Detailed Implementation

[0029] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0030] Reference Figure 1-3 As shown, the present invention provides a technical solution: a carbon effect quantitative assessment system based on land consolidation type, including a regional semantic layer module, a consolidation project semantic layer module and a parameter semantic layer module, and further including a parameter semantic processing module, a cross-layer semantic association module and a parameter applicability constraint reasoning module.

[0031] The system begins with the user inputting target assessment conditions. Through the system's interactive interface, the user specifies the geographical location of the target area or directly enters characteristic data such as climate, soil, and topography of the area. At the same time, the user selects the target remediation type, such as high-standard farmland construction or land reclamation, and checks the specific engineering measures list.

[0032] After receiving the target region input, the regional semantic layer module constructs a hierarchical semantic profile for the target region using pre-built regional entity ontology classes, climate zone ontology classes, soil type ontology classes, and vegetation zone ontology classes. The construction process is accomplished through three associations: the climate association connects the target region to the corresponding climate zone entity (e.g., associating a region with an average annual temperature of 17.5 degrees Celsius and annual precipitation of 1300 millimeters to the "subtropical monsoon climate" zone); the soil association connects the target region to the corresponding soil type entity (e.g., associating a red soil region to the "red soil" ontology); and the vegetation association connects the target region to the corresponding vegetation zone entity. Through these associations, the target region is represented as a composite semantic object carrying climate zone identifiers, soil type identifiers, and vegetation zone identifiers. This object also encapsulates specific numerical attributes such as average annual temperature, annual precipitation, soil organic carbon content, and terrain slope.

[0033] Simultaneously, the semantic layer module of the remediation project responds to the input of the target remediation type and engineering measures. Internally, the semantic layer module pre-defines the inclusion relationship between the remediation type ontology class and the engineering measure ontology class, as well as the triggering relationship between the engineering measure ontology class and the carbon effect process ontology class. The semantic layer module first determines the set of engineering measures covered by the target remediation type based on the inclusion relationship, and then activates the corresponding engineering measure entity by taking the intersection of this set with the user-selected measure list. Subsequently, it traverses along the triggering relationships to deduce which carbon effect processes each activated engineering measure will trigger.

[0034] For example, land leveling projects trigger carbon emissions from mechanical operations and soil organic carbon mineralization; irrigation and drainage projects trigger carbon emissions from energy consumption; and farmland shelterbelt projects trigger carbon absorption by the shelterbelt biomass. Thus, the system has completed semantic parsing of the input information, obtaining multidimensional feature attributes of the target area and clarifying the list of carbon effect processes corresponding to the target engineering measures.

[0035] After completing the above analysis, the cross-layer semantic association module initiates the four-dimensional coupling parameter filtering process. Internally, the cross-layer semantic association module establishes three types of semantic mapping relationships to connect the regional semantic layer, the remediation project semantic layer, and the parameter semantic layer.

[0036] The first type is the regional applicability mapping relationship, which connects regional entities with carbon factor parameters to indicate what combination of climate, soil and vegetation conditions a certain parameter is applicable to.

[0037] The second type is the engineering applicability mapping relationship, which connects engineering measure entities with carbon factor parameters to indicate which type of engineering measure a certain parameter is applicable to;

[0038] The third type is the process source mapping relationship, which connects the carbon effect process entity with the carbon factor parameter to indicate the source of a parameter from which carbon emission or carbon absorption process.

[0039] The cross-layer semantic association module traverses all carbon factor parameters stored in the parameter semantic layer module, performing a triple matching check on each parameter: first, it verifies whether the parameter's applicable region identifier is compatible with the climate zoning and soil type of the target region; second, it verifies whether the parameter's applicable engineering measure identifier includes the target engineering measure; and finally, it verifies whether the parameter's process source association falls within the previously derived carbon effect process list. Only parameters that pass all three matching checks consecutively are included in the candidate parameter set. For example, the "red soil region land leveling machinery carbon emission factor" stored in the parameter library passes all checks successfully because its regional applicability points to the subtropical red soil region, its engineering applicability points to land leveling engineering, and its process source points to the carbon emission process of mechanical operations; while the "North China alluvial soil region farmland soil carbon pool change factor" is excluded in the first check stage because its regional applicability points to the warm temperate alluvial soil region, which is clearly inconsistent with the subtropical red soil conditions of the target region.

[0040] The parameter semantic processing module provides structured metadata support for parameters during the screening process. Within this module, each carbon factor parameter is associated with an uncertainty descriptor ontology, which encapsulates confidence, sample size, data source, and applicability constraint attributes. The confidence attribute, ranging from zero to one, represents the statistical reliability of the parameter; the sample size attribute records the sample size upon which the parameter estimation is based; the data source attribute identifies whether the parameter originates from field measurements, literature reviews, or model simulations; and the applicability constraint attribute stores the preconditions for parameter use in a structured format. Once the uncertainty descriptor is abstracted into an independent ontology, it can be independently accessed, transmitted, and updated during parameter matching and inference processes, providing a structural foundation for subsequent uncertainty propagation calculations.

[0041] Carbon factor parameters are quantitative assessment parameters used to characterize the carbon effect corresponding to a unit of activity, unit area, unit of energy consumption, unit of fuel consumption, unit of biomass change, or unit of soil carbon pool change in land consolidation activities. Carbon effects include carbon emission effects, carbon absorption effects, and soil carbon pool change effects. In other words, carbon factor parameters are not arbitrary environmental parameters, but rather factor-type parameters that can establish a correspondence with the characteristics of the target area, consolidation engineering measures, and carbon effect processes, and can be systematically invoked to quantify the carbon effects of land consolidation.

[0042] Each carbon factor parameter is stored as a structured parameter record in the parameter semantic layer module. This structured parameter record includes at least the parameter name, parameter type, parameter value, unit of measurement, activity field, applicable regional conditions, applicable engineering measures, corresponding carbon effect process, calculation formula identifier, and uncertainty descriptor ontology. Parameter types include carbon emission factors, carbon absorption factors, and soil carbon pool change factors. The parameter value represents the specific value of the carbon factor parameter; the unit of measurement defines the physical quantity unit corresponding to the parameter value; the activity field indicates the input data type that the carbon factor parameter should correspond to; and the calculation formula identifier indicates the type of formula called by the system during quantitative calculation.

[0043] When the carbon factor parameter is a carbon emission factor, it is used to characterize the carbon emissions per unit of activity during land remediation construction, mechanical operations, energy consumption, transportation operations, or soil disturbance. The unit of measurement for the carbon emission factor can be... , , or According to the formula Determine the first The carbon emissions corresponding to carbon emission processes, of which, Indicates the first Carbon emissions from carbon-emission processes Indicates the first The amount of engineering activity corresponding to carbon emission processes. This represents the carbon emission factor corresponding to the amount of activity in this project. This represents the unit conversion factor. Using this formula, activity quantities such as diesel consumption, electricity consumption, construction area, or transportation turnover can be correlated with corresponding carbon emission factors, thus forming a quantitative result for that type of carbon emission process.

[0044] When the carbon factor parameter is the carbon absorption factor, it is used to characterize the amount of carbon absorbed per unit area or per unit of biomass during vegetation restoration, shelterbelt construction, crop biomass growth, or ecological restoration. The unit of measurement for the carbon absorption factor can be... , or According to the formula ; Determine the first The amount of carbon absorbed corresponding to carbon-like absorption processes, among which... Indicates the first The amount of carbon absorbed by carbon-like absorption processes. Indicates the first The area, biomass change, or crop planting area corresponding to carbon absorption processes. This represents the carbon absorption factor corresponding to this carbon absorption process. Indicates the accounting period. This represents the unit conversion factor. Using this formula, the area of ​​protective forests, the area of ​​vegetation restoration, or the change in biomass can be correlated with the corresponding carbon absorption factor, thus forming a quantitative result for this type of carbon absorption process.

[0045] When the carbon factor parameter is a soil carbon pool change factor, it is used to characterize the change in soil organic carbon storage per unit area before and after land reclamation, according to the formula... Determine the first Changes in soil carbon pools, among which, Indicates the first Changes in soil carbon pool Indicates the area affected by land consolidation. This represents the factor representing the change in soil carbon pool per unit area. Indicates the accounting period. This represents the conversion factor between carbon element and carbon dioxide equivalent. Take 44 / 12. Using this formula, the area affected by land consolidation can be correlated with soil carbon pool change factors, thus forming a quantitative result of the soil carbon pool change process;

[0046] After the system completes the screening and applicability verification of carbon emission factors, carbon absorption factors, and soil carbon pool change factors, the comprehensive carbon effect of the target land remediation project satisfies the formula:

[0047] ,in, This indicates the overall carbon effect of the target land consolidation project. This represents the sum of carbon absorbed in all carbon absorption processes. This represents the sum of changes in the entire soil carbon pool. This represents the sum of carbon emissions from all carbon-emitting processes. When When, it indicates that the target land consolidation project exhibits a net carbon sink effect during the accounting period; when When, it indicates that the target land consolidation project exhibits a net carbon emission effect within the accounting period; when When the carbon emissions of the target land consolidation project are offset by the carbon absorption and the increase in the soil carbon pool during the accounting period;

[0048] To facilitate system identification and retrieval, the numerical values ​​and units of measurement for carbon factor parameters can be determined based on project-measured data, publicly available literature, regional database data, calculation parameters issued by relevant authorities, or model simulation results. These parameters, along with their applicable regional conditions, applicable engineering measures, and corresponding carbon effect processes, are stored in the parameter semantic layer module. When performing applicability verification, the parameter applicability constraint inference module first determines whether the carbon factor parameters meet the applicable conditions of the target region and target engineering measures. Then, it outputs the carbon factor parameters that meet the applicability requirements as recommended parameters. Therefore, the carbon factor parameters in this system have clear parameter meanings, parameter types, parameter values, units of measurement, applicable objects, and formula calling relationships, enabling the quantitative assessment of the carbon effects of land consolidation.

[0049] To enhance the ranking of candidate parameters, a matching degree calculation submodule is included in the cross-layer semantic association module. This submodule calculates the comprehensive matching degree for each carbon factor parameter in the candidate parameter set: first, it calculates the overlap between the target area's average annual temperature and annual precipitation and the parameter's applicable climate range as the climate matching degree; second, it calculates the semantic similarity between the target area's soil type and the parameter's applicable soil type as the soil matching degree; and third, it calculates the ontological semantic distance between the target engineering measure and the parameter's applicable engineering measure as the engineering measure matching degree. Then, it performs a weighted sum of the individual scores for these three dimensions to obtain a comprehensive matching degree ranging from zero to one. A higher comprehensive matching degree indicates a higher overall fit between the parameter and the current evaluation scenario. Based on this, the candidate parameter set is initially sorted in descending order.

[0050] The candidate parameter set is then sent to the parameter applicability constraint inference module for applicability verification. This module maintains a set of constraint rules, each containing three fields: constraint type, constraint condition expression, and constraint strength. Constraint types include temperature constraints, precipitation constraints, soil organic carbon constraints, soil type constraints, and terrain slope constraints. The constraint condition expression defines the applicable boundaries of the parameter in the form of a logical expression, such as "annual average temperature ≥ 10℃" or "annual precipitation ∈ [400, 800] mm". Constraint strength is divided into mandatory constraints and suggested constraints. Mandatory constraints are necessary conditions for the parameter's applicability, while suggested constraints are optimization conditions for achieving better results. The parameter applicability constraint inference module extracts the actual characteristic attribute values ​​of the target region. For each carbon factor parameter in the candidate set, it extracts all associated constraint rules and substitutes the actual attribute values ​​of the target region into the constraint condition expressions for verification.

[0051] If all the mandatory constraints of a parameter are satisfied, its applicability status is determined to be "applicable";

[0052] If at least one mandatory constraint is not met, the condition is determined to be "not applicable".

[0053] If all mandatory constraints are satisfied but at least one suggested constraint is not satisfied, it is determined to be "partially applicable", and those suggested constraints that were not satisfied are recorded simultaneously.

[0054] Based on this, the applicability scoring submodule within the module will also quantify and weight the degree to which each parameter's constraints are met, and finally output an applicability score value between zero and one hundred.

[0055] After completing all verification work, the parameter applicability constraint inference module feeds back the applicability status, applicability score, and information on unmet constraints for each parameter to the cross-layer semantic association module. Upon receiving this feedback, the cross-layer semantic association module performs a secondary sorting adjustment on the candidate parameter set: parameters with an applicability status of "applicable" are moved to the front of the list, and then further sorted by applicability score from highest to lowest; parameters with an applicability status of "partially applicable" are placed at the back of the list after being annotated with specific explanations of unmet constraints; parameters with an applicability status of "not applicable" are moved to the end of the list or filtered out. The system ultimately presents the user with a list of carbon factor parameters arranged by recommendation priority and accompanied by applicability descriptions. Users can directly select the "applicable" parameters at the top, or consider those marked "partially applicable" parameters after fully understanding the risks.

[0056] In a preferred embodiment, the system further includes a feedback optimization subsystem. After the land consolidation project is completed and measured carbon effect data such as soil organic carbon change and greenhouse gas flux are obtained, this subsystem calculates the deviation rate between the measured values ​​and the recommended values ​​of the parameters used in the assessment phase. Simultaneously, it performs a consistency analysis based on the applicability score obtained during the applicability verification phase. If the deviation rate is small and the applicability score is high, the system will appropriately increase the confidence attribute of the parameter; if the deviation rate is large and there is a significant deviation from the applicability score, the system will decrease the confidence attribute of the parameter or trigger a parameter review reminder. Through this closed-loop feedback mechanism, the confidence attributes in the parameter library can be continuously and dynamically optimized based on the measured engineering data, and the quality of parameter recommendations in subsequent assessment tasks will gradually improve.

[0057] Example 1:

[0058] The following example uses a high-standard farmland construction project organized and implemented by a natural resources department in the hilly areas of southern China to demonstrate the coordination relationship and execution steps between various modules within the system, thereby verifying the completeness and operability of the technical solution. The specific values ​​appearing in the embodiments are only illustrative examples and do not constitute a limitation on the scope of protection of this invention.

[0059] Users first input the basic information required for the assessment through the system's graphical user interface. In the area settings panel, users select the climate zone type and soil type of the target area from the drop-down menu, and fill in the characteristic data of the area in the corresponding numerical input boxes, including average annual temperature, annual precipitation, soil organic carbon content, and average topographic slope. After switching to the engineering settings panel, users select the target remediation type, and the interface then expands to display all the preset engineering measures options under that remediation type. Users select the specific engineering measures that need to be included in the assessment according to the actual situation of the project. After confirming that the input information is correct, users click the "Start Assessment" button, and a series of processing steps within the system are then initiated.

[0060] The first module to respond to user input in the system is the regional semantic layer module. Internally, this module pre-constructs a hierarchical regional ontology system, defining regional entity ontology classes, climate zone ontology classes, soil type ontology classes, and vegetation zone ontology classes, and pre-setting the semantic relationships between them. Upon receiving the regional information submitted by the user, the regional semantic layer module immediately creates a regional semantic object instance representing the target region and injects semantic attributes into it through the three relationships.

[0061] The first is the climate association, which points the newly created regional instance to the corresponding climate zone entity. The climate zone entity has a set of pre-defined structured attribute fields to describe the typical characteristics of the climate type, including the upper and lower limits of the annual average temperature, the upper and lower limits of the annual precipitation, the limit of the average temperature of the coldest month, the limit of the average temperature of the hottest month, and the description of monsoon characteristics.

[0062] The second is the soil association, which connects regional instances with corresponding soil type entities. Each soil type entity also carries a set of structured typical feature fields, including soil pH range, clay content range, organic matter content level, base saturation, and main clay mineral types.

[0063] The third element is vegetation association, which assigns regional instances to corresponding vegetation zone entities. Simultaneously, the specific numerical values ​​input by the user are filled into the attribute slots of the regional instances. Through this series of operations, the originally abstract target region is transformed into a composite semantic object that simultaneously possesses climate type, soil type, vegetation zone, and multiple quantitative attribute values. This object will continue to function as a constraint condition for the regional dimension in subsequent parameter matching processes.

[0064] While the regional semantic layer module constructs regional semantic objects, the remediation project semantic layer module also processes the remediation type and engineering measure information input by the user in parallel. The remediation project semantic layer module also has a pre-built complete remediation project ontology network, among which the two most critical types of semantic relationships are:

[0065] The first type is the inclusion relationship between the remediation type ontology class and the engineering measures ontology class, which defines which engineering measures categories are typically covered by a certain remediation type.

[0066] The second category is the triggering relationship between engineering measure ontology and carbon effect process ontology. This clarifies which specific carbon emission or carbon absorption processes a particular engineering measure will trigger during its implementation. The remediation engineering semantic layer module first retrieves all associated engineering measures based on the user-selected remediation type identifier, following the inclusion relationship to obtain a complete set of engineering measures. Then, the remediation engineering semantic layer module compares this set with the specific engineering measures selected by the user, taking the intersection. The engineering measure entities corresponding to the engineering measures in the intersection are activated one by one, forming engineering instances in a working state.

[0067] With specific engineering examples, the remediation engineering semantic layer module continues along the triggering relation network to deduce which carbon-effect processes each engineering example will trigger. For each activated engineering measure entity, the triggering relation connects it to one or more carbon-effect process entities, which describe the carbon emission or carbon absorption mechanisms triggered by the engineering measure during the construction or operation phase. At this point, the remediation engineering semantic layer module generates a list containing several specific carbon-effect processes, which will become the "process source" constraint for subsequent parameter selection.

[0068] After the regional semantic layer module and the remediation project semantic layer module complete their respective parsing tasks, the cross-layer semantic association module takes over control and begins executing the core steps of the system. The first type is the regional applicability mapping relationship, which binds regional entity identifiers with carbon factor parameter identifiers, expressing the key information of which regional type a parameter is applicable to. The second type is the engineering applicability mapping relationship, which connects engineering measure entity identifiers with carbon factor parameter identifiers, answering the question of whether a parameter is applicable to a specific engineering measure. The third type is the process source mapping relationship, which connects carbon effect process entity identifiers with carbon factor parameter identifiers, explaining which carbon effect process the value of a parameter originates from. These three types of mapping relationships together constitute a three-dimensional semantic network, providing a path for the precise positioning of parameters.

[0069] The cross-layer semantic association module then begins to traverse all carbon factor parameters stored in the parameter semantic layer module. For each parameter in the parameter library, the cross-layer semantic association module must perform three checks in sequence, and only parameters that pass all three checks consecutively can enter the candidate list.

[0070] The first check focuses on regional applicability. The cross-layer semantic association module extracts the applicable region identifier field of the current parameter. This field has been labeled by domain experts based on the parameter's original literature or measured locations when the parameter was entered into the database, recording the applicable climate zone type and soil type. The cross-layer semantic association module compares the content of this field with the climate type identifier and soil type identifier carried by the target region semantic object to determine whether the two are compatible. If the applicable region of the parameter matches the actual type of the target region, the first check passes; if they are incompatible, the parameter is directly rejected and will not proceed to the subsequent check process.

[0071] Parameters that pass the first check then face the second check: engineering applicability check. The cross-layer semantic association module extracts the applicable engineering measure identifier field of the parameter and verifies whether any of the engineering measure types listed in this field are included in the list selected by the user. If so, the second check passes.

[0072] The third check targets the process source. The cross-layer semantic association module extracts the process source identifier field from the parameters and determines whether the carbon effect process entity pointed to by this field belongs to the carbon effect process list previously derived by the remediation project semantic layer module. If it does, the third check passes.

[0073] After such a rigorous screening process, only the parameters that pass all three checks consecutively are retained, and they together form a set of candidate parameters.

[0074] While generating the candidate parameter set, the matching degree calculation submodule embedded in the cross-layer semantic association module also starts working simultaneously. The task of this matching degree calculation submodule is to calculate a comprehensive matching degree value for each parameter in the set, so as to distinguish the priority in subsequent ranking. The calculation process unfolds from three dimensions. The first dimension is climate matching degree. The matching degree calculation submodule compares the actual annual average temperature and annual precipitation of the target area with the upper and lower limits of the applicable climate range of the parameter, calculating the degree of overlap between the actual values ​​and the applicable range. The second dimension is soil matching degree. The matching degree calculation submodule calculates the semantic distance between the soil type of the target area and the soil type applicable to the parameter in the ontology semantic network; the closer the distance, the higher the matching degree. The third dimension is engineering measure matching degree. The matching degree calculation submodule examines the correspondence between the target engineering measure and the engineering measure applicable to the parameter. The scores of the three dimensions are weighted and summed according to a preset weight ratio to obtain the comprehensive matching degree of the carbon factor parameter. The higher the comprehensive matching degree value, the higher the overall fit of the parameter with the current evaluation scenario. The candidate parameter set is then sorted in descending order based on the overall matching degree in the first round.

[0075] After the initial sorting, the candidate parameter set is completely handed over to the parameter applicability constraint inference module for a more rigorous applicability verification process. The parameter applicability constraint inference module internally stores a set of constraint rules corresponding to each carbon factor parameter. Each rule consists of three fields: the constraint type field indicates whether the rule is subject to temperature, precipitation, soil organic carbon, soil type, or topographic slope constraints; the constraint condition expression field provides the specific numerical boundary conditions in a structured form; and the constraint strength field clearly distinguishes between "mandatory" and "suggested." The former represents the hard prerequisites for the parameter to be used, while the latter represents the soft optimization conditions for the parameter to achieve better results.

[0076] The parameter applicability constraint reasoning module first extracts the specific attribute values ​​that have been recorded from the semantic object of the target region. Then, for each parameter in the candidate set, the parameter applicability constraint reasoning module loads the associated constraint rules one by one, substitutes the actual attribute values ​​of the target region into the constraint condition expression of each rule, and verifies whether they are satisfied one by one.

[0077] The verification logic is as follows:

[0078] If all mandatory constraints associated with a parameter are satisfied by the actual attribute values ​​of the target area, the applicability status of the parameter is determined to be "applicable"; if at least one mandatory constraint is not satisfied, it is determined to be "not applicable"; if all mandatory constraints are satisfied, but at least one suggested constraint is not satisfied, it is determined to be "partially applicable", and the specific information of those unsatisfied suggested constraints is recorded simultaneously as explanatory content to be prompted to the user.

[0079] The parameter applicability constraint reasoning module performs constraint verification on each parameter in the candidate set, counting the number of parameters for each applicability state. Based on this, the applicability scoring submodule within the parameter applicability constraint reasoning module further quantifies and scores each parameter. This applicability scoring submodule converts the degree of satisfaction of each constraint rule into a satisfaction score, and then, according to the principle of assigning different weights to mandatory constraints and suggested constraints, performs a weighted summation of the satisfaction scores of each rule to finally obtain the parameter's applicability score. A higher applicability score indicates that the parameter is more sufficiently applicable under the conditions of the target region.

[0080] After the verification phase, the parameter applicability constraint reasoning module packages the applicability status, applicability score, and explanations of any unmet constraints for each parameter into a feedback report and sends it back to the cross-layer semantic association module. Upon receiving this report, the cross-layer semantic association module immediately begins a second round of sorting and adjustment of the candidate parameter set. This adjustment is based on the applicability status and applicability score from the verification feedback.

[0081] The adjustment strategy is as follows:

[0082] All parameters with an "Applicable" status are moved to the top of the list, and then further sorted by their applicability score from highest to lowest. Parameters with a "Partially Applicable" status are placed after the "Applicable" parameters, with each item accompanied by a description explaining the specific constraints that are not met and the suggested verification measures for the user. Parameters with a "Not Applicable" status are moved to the bottom of the list, displayed as unselectable, and accompanied by relevant warning messages.

[0083] In a preferred embodiment, the system further includes a feedback optimization subsystem. Once the project is completed and measured carbon effect data is acquired, the feedback optimization subsystem automatically starts operating. The subsystem retrieves all carbon factor parameters used in the evaluation phase and calculates the deviation rate between the recommended and measured values ​​for each parameter. For parameters with deviation rates within a preset acceptable range, the feedback optimization subsystem appropriately increases their confidence level. For parameters with large deviation rates, the subsystem performs a consistency analysis based on the applicability score obtained during the applicability verification phase, determining whether to maintain, lower, or trigger a parameter review reminder. Simultaneously, the measured data from this project is added to the parameter's sample size record. Through this closed-loop feedback mechanism, the parameter confidence level and sample size in the parameter library are continuously optimized based on engineering practice data, providing a more reliable parameter foundation for subsequent evaluation work in similar areas.

[0084] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A carbon effect quantitative assessment system based on land consolidation type, comprising a regional semantic layer module, a consolidation project semantic layer module, and a parameter semantic layer module, characterized in that, Also includes: The parameter semantic processing module is used to construct a structured semantic representation of carbon factor parameters, which are quantitative assessment parameters used to convert land consolidation activity volume, land area, energy consumption, fuel consumption, biomass change or soil property change into carbon emissions, carbon absorption or soil carbon pool change. The carbon factor parameters include parameter type, parameter value, unit of measurement, activity field, applicable conditions, corresponding carbon effect process, calculation formula identifier and uncertainty descriptor ontology. The parameter types include carbon emission factor, carbon absorption factor and soil carbon pool change factor. The calculation formula identifier is used to call the formula. , , or To calculate carbon emissions, carbon uptake, soil carbon pool changes, or overall carbon effect respectively; the uncertainty descriptor ontology is used to encapsulate metadata information characterizing the statistical uncertainty and applicable scope constraints of carbon factor parameters. The cross-layer semantic association module is used to establish a semantic mapping relationship between the regional semantic layer module, the remediation project semantic layer module and the parameter semantic layer module. The cross-layer semantic association module is configured to: obtain the input target area features, target remediation type and target engineering measures, filter carbon factor parameters that match the target area features, target remediation type and target engineering measures based on the semantic mapping relationship, and generate a candidate parameter set. The parameter applicability constraint reasoning module is used to verify the applicability of carbon factor parameters in the candidate parameter set based on a preset set of constraint rules. The set of constraint rules defines the applicability constraints of carbon factor parameters under specified regional attribute conditions. The parameter applicability constraint reasoning module determines the applicability status of each carbon factor parameter in the candidate parameter set based on the actual attribute values ​​of the target region features. The applicability status determination result output by the parameter applicability constraint reasoning module is fed back to the cross-layer semantic association module, so that the cross-layer semantic association module can sort and adjust the candidate parameter set based on the applicability status determination result.

2. The carbon effect quantitative assessment system based on land consolidation type according to claim 1, characterized in that: The metadata information encapsulated in the uncertainty descriptor ontology includes confidence level attribute, sample size attribute, data source attribute, and applicable condition constraint attribute, wherein the confidence level attribute has a value range of 0 to 1.

3. The carbon effect quantitative assessment system based on land consolidation type according to claim 1, characterized in that: The semantic mapping relationships include: a regional applicability mapping relationship for connecting the regional semantic layer module and the parameter semantic layer module; an engineering applicability mapping relationship for connecting the remediation engineering semantic layer module and the parameter semantic layer module; and a process source mapping relationship for connecting the carbon effect process entity and the parameter semantic layer module.

4. The carbon effect quantitative assessment system based on land consolidation type according to claim 1, characterized in that: Each constraint rule in the constraint rule set defines a constraint type, constraint condition expression, and constraint strength. The constraint strength includes two types: mandatory constraints and suggested constraints.

5. The carbon effect quantitative assessment system based on land consolidation type according to claim 1, characterized in that: The parameter applicability constraint reasoning module can determine the applicability status of each carbon factor parameter in the candidate parameter set based on the constraint rule set. The applicability status is divided into three categories: applicable, inapplicable, and partially applicable.

6. The carbon effect quantitative assessment system based on land consolidation type according to claim 3, characterized in that: The cross-layer semantic association module includes a matching degree calculation submodule. The matching degree calculation submodule calculates the comprehensive matching degree of carbon factor parameters based on the regional feature matching degree and the engineering measure matching degree. The comprehensive matching degree is used to assist in the generation process of candidate parameter set.

7. The carbon effect quantitative assessment system based on land consolidation type according to claim 5, characterized in that: The parameter applicability constraint reasoning module also includes an applicability scoring submodule, which is used to quantify and score the carbon factor parameters and output the applicability score value.

8. The carbon effect quantitative assessment system based on land consolidation type according to claim 7, characterized in that: It also includes a feedback optimization subsystem, which dynamically adjusts the confidence attribute of the carbon factor parameter based on the deviation between the measured carbon effect data and the recommended value of the carbon factor parameter.

9. A method for quantitatively assessing the carbon effect based on land consolidation type, characterized in that, The method is applied to the carbon effect quantitative assessment system based on land consolidation type as described in any one of claims 1 to 8, and the method includes the following steps: Construct a regional semantic layer, a remediation project semantic layer, and a parameter semantic layer, and associate carbon factor parameters with an uncertainty descriptor ontology that encapsulates metadata on statistical uncertainty and applicable scope constraints; Establish a semantic mapping relationship between the regional semantic layer, the remediation project semantic layer, and the parameter semantic layer. Based on the input target region characteristics, target remediation type, and target engineering measures, filter matching carbon factor parameters based on the semantic mapping relationship to generate a candidate parameter set. The applicability of carbon factor parameters in the candidate parameter set is verified based on a preset set of constraint rules, and the applicability status of each carbon factor parameter is determined based on the actual attribute values ​​of the target region features. The applicability status determination results are fed back to the sorting process of the candidate parameter set. The candidate parameter set is sorted and adjusted based on the applicability status, and a list of recommended parameters is output.

10. The method for quantitative assessment of carbon effects based on land consolidation type according to claim 9, characterized in that: The actual attribute values ​​of the target region features are matched and verified with the constraint condition expressions of each constraint rule in the constraint rule set. If all mandatory constraints corresponding to the carbon factor parameter are satisfied, it is determined to be applicable. If at least one mandatory constraint is not satisfied, it is determined to be inapplicable. If all mandatory constraints are satisfied but at least one suggested constraint is not satisfied, it is determined to be partially applicable.

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