Forestry carbon sink capacity evaluation method and system based on forestry environment big data

Abstract

The application discloses a forestry carbon sink capacity evaluation method and system based on forestry environment big data, and belongs to the technical field of carbon sink monitoring. The method comprises the following steps: real-time monitoring of forestry environment data flow, generating an initial event trigger signal; obtaining baseline carbon sink data of the affected forestry small plots; generating emergency evaluation instance data; calculating the carbon sink difference value and generating an incremental event version; generating differentiated early warning information and corresponding intervention action suggestions; recalculating the carbon sink difference value until the carbon sink difference value converges within a predetermined threshold; archiving the incremental event version and all its iteration history records. The application realizes minute-level carbon sink evaluation and closed-loop disposal under emergencies by real-time capture of forestry environment data flow and driving online recalculation of carbon sink difference value by emergency evaluation instance, and solves the problem of long batch processing cycle leading to response lag and missed intervention opportunity in the prior art.

Description

Technical Field

[0001] This invention relates to the field of carbon sink monitoring technology, and in particular to a method and system for assessing forestry carbon sink capacity based on forestry environmental big data. Background Technology

[0002] With the continuous advancement of global climate change and carbon neutrality goals, the accurate monitoring and assessment of forest carbon sinks, as an important natural emission reduction channel, has become a key support for sustainable forestry management and policy formulation. Traditional forestry carbon sink assessments rely heavily on offline batch data processing and expert experience-based extrapolation, typically only obtaining information on changes in stand carbon storage within a fixed period. This makes it difficult to reflect the impact of unforeseen events (such as disasters, pests, or human disturbances) on carbon sink capacity in a timely manner.

[0003] In existing technologies, forestry carbon sequestration assessment systems aggregate changes in small-scale carbon storage through periodic batch processing and output results in monthly or quarterly reports. This model involves unified calculation after data collection, resulting in a closed process, long processing time, and significant shortcomings in responding to sudden or localized impact events. Summary of the Invention

[0004] This application provides a method and system for assessing forestry carbon sequestration capacity based on forestry environmental big data. This solves the problem of delayed response and missed intervention opportunities caused by long batch processing cycles in the prior art, and realizes minute-level online carbon sequestration assessment and closed-loop disposal in the event of emergencies.

[0005] This application provides a method for assessing forestry carbon sequestration capacity based on forestry environmental big data, including: real-time monitoring of forestry environmental data streams and generating initial event triggering signals based on a preset event triggering model;

[0006] Receive the initial event trigger signal to obtain baseline carbon sequestration data for the affected forestry sub-compartments;

[0007] Baseline carbon sink data of the affected forestry sub-compartments are locked and cloned, and emergency assessment instance data associated with the baseline carbon sink data are generated in an isolated computing environment.

[0008] In the isolated computing environment, the carbon sink difference is calculated using a preset carbon sink assessment model, and an incremental event version containing the carbon sink difference is generated.

[0009] Based on the carbon sequestration difference, differentiated early warning information and corresponding intervention action suggestions are generated and pushed to preset management terminals at all levels;

[0010] Receive and process on-site handling feedback instructions from the management terminals at all levels, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold.

[0011] The incremental event version and all its iteration history are archived, and the final incremental event version is merged into the baseline carbon sink data in the next regular accounting cycle.

[0012] Furthermore, based on the preset event triggering model, the steps for generating the initial event triggering signal include:

[0013] Continuous monitoring of forestry environmental data streams, which include meteorological data, soil moisture, vegetation index, fire monitoring data, pest and disease reports, and records of human activities;

[0014] When parameters in the forestry environment data stream exceed a preset threshold, an initial event trigger signal is automatically generated through the event triggering model.

[0015] Further steps to obtain baseline carbon sequestration data for the affected forestry sub-compartments include:

[0016] Based on the acquired initial event trigger signal, extract the event's geographical location and the radius of influence from the initial event trigger signal;

[0017] Retrieve boundary vector data and attribute information of all forestry sub-compartments from the historical database;

[0018] Obtain the coordinates of the geometric center point of each class and determine whether the coordinates of the geometric center point of each class are within the radius of influence of the event;

[0019] For small plots located within the event's influence radius, check whether the tree species, age group, and site type in the plot's attribute table are consistent with the sensitive type list output by the event triggering model;

[0020] When the geometric center point of a sub-compartment is within the influence radius and its attributes match the list of sensitive types, the sub-compartment is determined to be an affected forestry sub-compartment.

[0021] Baseline carbon sink data for each affected class was extracted from the baseline carbon sink historical database.

[0022] Furthermore, the step of generating emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment includes:

[0023] Based on the obtained list of affected forestry sub-compartments, the baseline carbon sink data for each forestry sub-compartment in the previous accounting period were obtained.

[0024] Apply a temporary read-only lock to the baseline carbon sink data records that are about to be acquired;

[0025] After successfully acquiring baseline carbon sink data, the data cloning process is initiated in a pre-defined sandbox computing environment that is completely isolated from the main production system network and storage.

[0026] The data cloning procedure creates a copy of the baseline carbon sink data for each affected class.

[0027] Once the replicas are created, a globally unique event identifier is generated for this emergency event, and this identifier is associated with all cloned data replicas to form an emergency assessment instance dataset.

[0028] Furthermore, the steps for calculating the carbon sink difference using a pre-defined carbon sink assessment model include:

[0029] The emergency assessment instance data containing the cloned baseline carbon sink data, along with the event type and intensity parameters carried in the initial event trigger signal, are loaded into the preset carbon sink assessment model as input.

[0030] The carbon sink assessment model dynamically calls dedicated computing components corresponding to the input event type from its internal model library based on the input event type.

[0031] By calling the components, the forest stand structure, biomass, and soil carbon pool information in the emergency assessment instance data are read, and the amount of carbon pool change caused by this event type to a specific carbon pool is calculated.

[0032] Once all relevant components have completed their calculations, the changes in carbon pool absorption output by each component are summed algebraically to obtain the total carbon sink value of the forestry sub-compartment.

[0033] The final carbon sink difference is derived based on the original baseline carbon sink value recorded in the emergency assessment case data.

[0034] Further steps to obtain the total carbon sequestration value of a forestry sub-compartment include:

[0035] The carbon pool change caused by an event type to a specific carbon pool is obtained through the carbon pool change calculation formula.

[0036] The formula for calculating the change in the carbon pool is:

[0037] ;

[0038] In the formula, For the first Changes in the carbon pool of each carbon pool The energy distribution function for event types. The forest stand impedance attenuation coefficient, For the first Basic parameters of a carbon pool. The carbon pool energy absorption constant, The energy intensity of the event;

[0039] The total carbon sequestration value of the forestry sub-compartment was obtained using the formula for calculating total carbon sequestration.

[0040] The formula for calculating the total carbon sequestration is as follows:

[0041] ;

[0042] In the formula, This represents the total carbon sequestration after the event. As a baseline carbon sequestration, For the number of carbon pools, This represents the sum of changes in all carbon pools.

[0043] Further steps to derive the final carbon margin include:

[0044] The final carbon sink difference is obtained through the carbon sink difference calculation formula;

[0045] The formula for calculating the carbon sink difference is as follows:

[0046] ;

[0047] In the formula, This is the carbon sink difference. As a baseline carbon sequestration, This represents the total carbon sequestration after the event. It is a restoring force delay operator;

[0048] in, ;

[0049] In the formula, For the restoring force time delay operator, To maximize recovery potential, The recovery rate constant is The duration after the event. It is a natural constant.

[0050] Furthermore, the steps of receiving and processing on-site handling feedback instructions from the management terminals at all levels include:

[0051] Receive on-site handling feedback instructions reported by management terminals at all levels. The on-site handling feedback instructions include the type of measures to be taken, the scope of implementation of the measures, the execution time of the measures, and the expected effect assessment.

[0052] The feedback instructions are categorized and processed to identify instructions for pest and disease control, vegetation restoration, soil remediation, and enhanced management.

[0053] The expected effects assessment in the on-site response feedback instructions will be quantified into carbon sequestration capacity recovery parameters.

[0054] Based on the quantified carbon sequestration capacity recovery parameters, update the relevant parameters in the emergency assessment instance data;

[0055] The carbon sink assessment model is re-initiated, and the carbon sink difference is recalculated based on the updated parameters.

[0056] Furthermore, the step of archiving the incremental event version and all its iteration history includes:

[0057] Collect complete data for incremental event versions, which is recorded as archived data. The complete data includes initial event data, calculation data for each iteration, handling feedback data, and final calculation result data.

[0058] Each incremental event version is assigned a unique event identifier code, which includes information on the event occurrence time, event type, and scope of impact.

[0059] Establish an iteration history table to record the input parameters, calculation process, and output results for each calculation;

[0060] Archived data is categorized and stored according to chronological order and event type;

[0061] At the start of the next regular accounting cycle, extract the calculation results of the final incremental event version;

[0062] The calculation results of the final incremental event version are merged and updated with the baseline carbon sequestration data of the corresponding class to form new baseline data.

[0063] This application provides a forestry carbon sink capacity assessment system based on forestry environmental big data, which is used to implement a forestry carbon sink capacity assessment method based on forestry environmental big data, including: an event trigger monitoring module, a baseline data acquisition module, an emergency assessment instance generation module, an incremental version generation module, a push module, an iterative calculation module, and a baseline data merging module;

[0064] The event triggering monitoring module is used to monitor forestry environment data streams in real time and generate initial event triggering signals based on a preset event triggering model.

[0065] The baseline data acquisition module is used to receive the initial event trigger signal and obtain the baseline carbon sink data of the affected forestry sub-compartment;

[0066] The emergency assessment instance generation module is used to lock and clone the baseline carbon sink data of the affected forestry sub-compartments, and generate emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment.

[0067] The incremental version generation module is used in the isolated computing environment to calculate the carbon sink difference using a preset carbon sink assessment model, and generate an incremental event version containing the carbon sink difference.

[0068] The push module is used to generate differentiated early warning information and corresponding intervention action suggestions based on the carbon sink difference value, and push them to preset management terminals at all levels.

[0069] The iterative calculation module is used to receive and process on-site handling feedback instructions from the management terminals at all levels, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold.

[0070] The baseline data merging module is used to archive the incremental event version and all its iteration history, and merge the final incremental event version into the baseline carbon sink data in the next regular accounting cycle.

[0071] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0072] 1. By monitoring forestry environmental data streams in real time and generating emergency assessment instances immediately upon event triggering, monthly batch processing is transformed into minute-level online response, thereby enabling zero-delay quantitative assessment of the impact of sudden weather or forestry management events on carbon sequestration. This effectively solves the problems of long assessment cycles and missed optimal intervention opportunities in existing technologies.

[0073] 2. By locking, cloning, and generating independent emergency assessment instance data for the affected sub-classes in an isolated computing environment, the data security of the main production system is ensured and multi-instance parallel processing is supported, thereby significantly improving the processing efficiency of carbon sink assessment.

[0074] 3. By pushing differentiated early warning information and receiving on-site handling feedback from multi-level management terminals, combined with iterative recalculation and convergence control of carbon sink difference, a closed-loop management of carbon sink assessment and intervention is achieved, thereby optimizing the dynamic monitoring process of forestry carbon sink capacity. Attached Figure Description

[0075] Figure 1 A flowchart of a forestry carbon sequestration capacity assessment method based on forestry environmental big data provided in this application embodiment;

[0076] Figure 2 This is a schematic diagram of the structure of the forestry carbon sequestration capacity assessment system based on forestry environmental big data provided in the embodiments of this application. Detailed Implementation

[0077] This application provides a method and system for assessing forestry carbon sequestration capacity based on forestry environmental big data. This solves the problem of delayed response and missed intervention opportunities caused by long batch processing cycles in the prior art. By monitoring the data stream in real time and triggering emergency assessment instances, minute-level carbon sequestration difference calculation and closed-loop processing are completed in an isolated environment, realizing minute-level online carbon sequestration assessment and precise intervention under emergencies.

[0078] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0079] like Figure 1 The diagram shows a flowchart of a forestry carbon sequestration capacity assessment method based on forestry environmental big data provided in this application embodiment. This method is applied to a forestry carbon sequestration capacity assessment system based on forestry environmental big data. The method includes the following steps: real-time monitoring of forestry environmental data stream; based on a preset event triggering model, identifying and capturing sudden environmental events or forestry management instructions that may have a potential impact on the carbon storage of forest plots from the data stream; and generating an initial event triggering signal.

[0080] The system receives the initial event trigger signal and obtains the baseline carbon sink data of the affected forestry sub-compartments. The criteria for determining whether a sub-compartment is affected are: spatial overlay analysis is performed based on the "influence radius r (km)" output by the event trigger model and the "sub-compartment boundary vector data". Only when the geometric center of the sub-compartment falls within the radius r and the "tree species-age group-site type" in the sub-compartment attribute table matches the "sensitive type list" given by the event trigger model, is the sub-compartment considered to be affected.

[0081] Baseline carbon sink data of the affected forestry sub-compartments are locked and cloned, and emergency assessment instance data associated with the baseline carbon sink data are generated in an isolated computing environment.

[0082] The criteria for determining whether emergency assessment instance data is correlated with baseline carbon sink data are as follows:

[0083] It is established through the following three unique identifiers:

[0084] Small class spatial identification - using a vector boundary file (small class ID, geometric coordinates) consistent with the baseline;

[0085] Time stamp – Inheriting the timestamp from the baseline data as the starting point of the evaluation;

[0086] Attribute identifiers – replicate the tree species, age group, volume, and soil carbon pool fields of the most recent record in the same plot before the event was triggered from the baseline data.

[0087] Only when all three indicators match the baseline record exactly is the emergency assessment instance data considered to be 'directly associated' with the baseline carbon sink data. If any indicator does not match, it is considered not associated and is discarded.

[0088] In the isolated computing environment, a carbon sink assessment model is used to calculate the carbon sink difference caused by a sudden environmental event or forestry management instruction based on emergency assessment instance data and initial event triggering signals, and an incremental event version containing the carbon sink difference is generated.

[0089] Based on the carbon sequestration difference, differentiated early warning information and corresponding intervention action suggestions are generated and pushed to preset management terminals at all levels;

[0090] Receive and process on-site handling feedback instructions from the management terminals at all levels. The feedback instructions trigger a status update of the emergency assessment instance data, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold.

[0091] The incremental event version and all its iteration history are archived, and the final incremental event version is merged into the baseline carbon sink data in the next regular accounting cycle.

[0092] Furthermore, continuous monitoring of forestry environmental data streams, which include meteorological data, soil moisture, vegetation index, fire monitoring data, pest and disease reports, and records of human activities;

[0093] The pre-defined event triggering model consists of clearly defined thresholds and conditions, which are set based on historical data and expert knowledge, and are used to identify anomalous events that may affect forestry carbon sequestration capacity.

[0094] When a parameter in the forestry environment data stream exceeds a preset threshold, an initial event trigger signal is automatically generated through an event triggering model; the initial event trigger signal includes information on event type, occurrence time, geographical location, and influence radius.

[0095] For example, when temperatures in a region remain above normal and precipitation decreases significantly, the model may generate a drought event signal. Or, when vegetation indices decline rapidly in a region, the model may generate a pest and disease outbreak signal.

[0096] The event triggering model is as follows:

[0097] ;

[0098] In the formula, This is an abnormal temperature quantity. This is a vegetation condition index. This represents the number of anomaly reports. , , These are the weighting coefficients for the corresponding factors. This is the trigger threshold.

[0099] Further steps to obtain baseline carbon sequestration data for the affected forestry sub-compartments include:

[0100] Based on the acquired initial event trigger signal, extract the event's geographical location and the radius of influence from the initial event trigger signal;

[0101] Retrieve boundary vector data and attribute information of all forestry sub-compartments from the historical database;

[0102] Obtain the coordinates of the geometric center point of each class and determine whether the coordinates of the geometric center point of each class are within the radius of influence of the event;

[0103] For small plots located within the event's influence radius, check whether the tree species, age group, and site type in the plot's attribute table are consistent with the sensitive type list output by the event triggering model;

[0104] If the geometric center point of a small class is within the influence radius and its attributes match the list of sensitive types, then the small class is determined to be an affected small class.

[0105] Baseline carbon sink data for each affected class was extracted from the historical baseline carbon sink database. The baseline carbon sink data includes indicators such as carbon storage and carbon sink growth rate, and was compiled into a dataset for subsequent use.

[0106] Furthermore, the step of generating emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment includes:

[0107] Based on the obtained list of affected forestry plots, the baseline carbon sink data of each forestry plot in the previous accounting period was obtained by making a precise data retrieval request to the core carbon sink database.

[0108] While data retrieval is underway, a temporary read-only lock is applied to the baseline carbon sink data records to be acquired. This lock ensures that the original baseline data is not modified by any other process throughout the entire emergency assessment.

[0109] After successfully acquiring baseline carbon sink data, the data cloning process is initiated in a pre-defined sandbox computing environment that is completely isolated from the main production system network and storage.

[0110] The data cloning procedure creates a copy of the baseline carbon sink data for each affected class.

[0111] After the replicas are created, a globally unique event identifier is generated for this emergency event, and this identifier is associated with all cloned data replicas to form a logically unified emergency assessment instance dataset dedicated to the assessment of this event.

[0112] The isolation process ensures that all subsequent simulations, parameter adjustments, and result iterations occur only on this instance of data, thus guaranteeing the absolute security and data consistency of the authoritative baseline carbon sink database at both the physical and logical levels.

[0113] Furthermore, the steps for calculating the carbon sink difference using a pre-defined carbon sink assessment model include:

[0114] The emergency assessment instance data containing the cloned baseline carbon sink data, along with the event type and intensity parameters carried in the initial event trigger signal, are loaded into the preset carbon sink assessment model as input.

[0115] The carbon sink assessment model is as follows:

[0116] ;

[0117] In the formula, The energy distribution function for event types. The forest stand impedance attenuation coefficient, For the first The basic carbon parameters of a carbon library The carbon pool energy absorption constant, The energy intensity of the event. The total number of carbon pools, This is the sum of changes in all carbon pools.

[0118] The carbon sink assessment model dynamically calls dedicated computing components corresponding to the input event type from its internal model library based on the input event type.

[0119] For example, when the event type is fire, the system will call the fire-damaged wood biomass loss assessment component and the soil organic matter combustion release component; when the event type is pests and diseases, it will call the canopy loss leading to decreased photosynthetic efficiency component and the affected wood death decomposition component.

[0120] By calling the components, the forest stand structure, biomass, and soil carbon pool information in the emergency assessment instance data are read, and the amount of carbon pool change caused by this event type to a specific carbon pool is calculated.

[0121] Once all relevant components have completed their calculations, the changes in carbon pool absorption output by each component are summed algebraically to obtain the total carbon sink value of the forestry sub-compartment.

[0122] The final carbon sink difference is derived based on the original baseline carbon sink value recorded in the emergency assessment case data.

[0123] Further steps to obtain the total carbon sequestration value of a forestry sub-compartment include:

[0124] The carbon pool change caused by an event type to a specific carbon pool is obtained through the carbon pool change calculation formula.

[0125] The formula for calculating the change in the carbon pool is:

[0126] ;

[0127] In the formula, For the first Changes in the carbon pool of each carbon pool The energy distribution function for event types. The forest stand impedance attenuation coefficient, For the first Basic parameters of a carbon pool. The carbon pool energy absorption constant, The energy intensity of the event;

[0128] The total carbon sequestration value of the forestry sub-compartment was obtained using the formula for calculating total carbon sequestration.

[0129] The formula for calculating the total carbon sequestration is as follows:

[0130] ;

[0131] In the formula, This represents the total carbon sequestration after the event. As a baseline carbon sequestration, For the number of carbon pools, This represents the sum of changes in all carbon pools.

[0132] Further steps to derive the final carbon margin include:

[0133] The final carbon sink difference is obtained through the carbon sink difference calculation formula;

[0134] The formula for calculating the carbon sink difference is as follows:

[0135] ;

[0136] In the formula, This is the carbon sink difference. As a baseline carbon sequestration, This represents the total carbon sequestration after the event. It is a restoring force delay operator;

[0137] in, ;

[0138] In the formula, For the restoring force time delay operator, To maximize recovery potential, The recovery rate constant is The duration after the event. It is a natural constant.

[0139] Furthermore, the steps of receiving and processing on-site handling feedback instructions from the management terminals at all levels include:

[0140] Receive on-site handling feedback instructions reported by management terminals at all levels. The on-site handling feedback instructions include the type of measures to be taken, the scope of implementation of the measures, the execution time of the measures, and the expected effect assessment.

[0141] The feedback instructions are categorized and processed to identify instructions for pest and disease control, vegetation restoration, soil remediation, and enhanced management.

[0142] The expected effects assessment in the on-site response feedback instructions will be quantified into carbon sequestration capacity recovery parameters.

[0143] The quantification steps are as follows: identify the type of treatment measures and retrieve the corresponding effect coefficients from the preset measure effect database; calculate the benefit area based on the scope of measure implementation and convert it into an area weight coefficient; obtain the time decay coefficient based on the measure implementation time and set a time correction factor considering the lag period of measure effectiveness; convert the qualitative expected effect description into numerical values, such as converting significant improvement into a recovery coefficient of 0.7 to 0.9 and slight improvement into a recovery coefficient of 0.2 to 0.4; and finally generate carbon sequestration capacity recovery parameters by comprehensively considering the area weight time factor effect intensity.

[0144] Based on the quantified carbon sequestration capacity recovery parameters, the relevant parameters in the emergency assessment instance data are updated to reflect the impact of the response measures;

[0145] The carbon sink assessment model is re-initiated, and the carbon sink difference is recalculated based on the updated parameters.

[0146] Furthermore, the step of archiving the incremental event version and all its iteration history includes:

[0147] Collect complete data for incremental event versions, which is recorded as archived data. The complete data includes initial event data, calculation data for each iteration, handling feedback data, and final calculation result data.

[0148] Each incremental event version is assigned a unique event identifier code, which includes information on the event occurrence time, event type, and scope of impact.

[0149] Establish an iteration history table to record the input parameters, calculation process, and output results for each calculation;

[0150] Archived data is categorized and stored according to chronological order and event type;

[0151] At the start of the next regular accounting cycle, extract the calculation results of the final incremental event version;

[0152] The calculation results of the final incremental event version are merged and updated with the baseline carbon sequestration data of the corresponding class to form new baseline data.

[0153] like Figure 2The diagram shown is a structural schematic of the forestry carbon sequestration capacity assessment system based on forestry environmental big data provided in this application embodiment. It is used to implement the forestry carbon sequestration capacity assessment method based on forestry environmental big data. The forestry carbon sequestration capacity assessment system based on forestry environmental big data provided in this application embodiment includes: an event trigger monitoring module, a baseline data acquisition module, an emergency assessment instance generation module, an incremental version generation module, a push module, an iterative calculation module, and a baseline data merging module.

[0154] The event triggering monitoring module is used to monitor forestry environment data streams in real time and generate initial event triggering signals based on a preset event triggering model.

[0155] The baseline data acquisition module is used to receive the initial event trigger signal and obtain the baseline carbon sink data of the affected forestry sub-compartment;

[0156] The emergency assessment instance generation module is used to lock and clone the baseline carbon sink data of the affected forestry sub-compartments, and generate emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment.

[0157] The incremental version generation module is used in the isolated computing environment to calculate the carbon sink difference using a preset carbon sink assessment model, and generate an incremental event version containing the carbon sink difference.

[0158] The push module is used to generate differentiated early warning information and corresponding intervention action suggestions based on the carbon sink difference value, and push them to preset management terminals at all levels.

[0159] The iterative calculation module is used to receive and process on-site handling feedback instructions from the management terminals at all levels, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold.

[0160] The baseline data merging module is used to archive the incremental event version and all its iteration history, and merge the final incremental event version into the baseline carbon sink data in the next regular accounting cycle.

[0161] Those skilled in the art should understand that the above formulas are all dimensionless numerical calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0162] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0163] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0164] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0165] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0166] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for assessing forestry carbon sequestration capacity based on forestry environmental big data, characterized in that, Includes the following steps: Real-time monitoring of forestry environmental data streams; generating initial event trigger signals based on a pre-defined event triggering model. Receive the initial event trigger signal to obtain baseline carbon sequestration data for the affected forestry sub-compartments; Baseline carbon sink data of the affected forestry sub-compartments are locked and cloned, and emergency assessment instance data associated with the baseline carbon sink data are generated in an isolated computing environment. In the isolated computing environment, the carbon sink difference is calculated using a preset carbon sink assessment model, and an incremental event version containing the carbon sink difference is generated. Based on the carbon sequestration difference, differentiated early warning information and corresponding intervention action suggestions are generated and pushed to preset management terminals at all levels; Receive and process on-site handling feedback instructions from the management terminals at all levels, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold. The incremental event version and all its iteration history are archived, and the final incremental event version is merged into the baseline carbon sink data in the next regular accounting cycle.

2. The forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in claim 1, characterized in that, Based on a preset event triggering model, the steps for generating the initial event triggering signal include: Continuous monitoring of forestry environmental data streams, which include meteorological data, soil moisture, vegetation index, fire monitoring data, pest and disease reports, and records of human activities; When parameters in the forestry environment data stream exceed a preset threshold, an initial event trigger signal is automatically generated through the event triggering model.

3. The forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in claim 1, characterized in that, The steps to obtain baseline carbon sequestration data for affected forestry sub-compartments include: Based on the acquired initial event trigger signal, extract the event's geographical location and the radius of influence from the initial event trigger signal; Retrieve boundary vector data and attribute information of all forestry sub-compartments from the historical database; Obtain the coordinates of the geometric center point of each class and determine whether the coordinates of the geometric center point of each class are within the radius of influence of the event; For small plots located within the event's influence radius, check whether the tree species, age group, and site type in the plot's attribute table are consistent with the sensitive type list output by the event triggering model; When the geometric center point of a sub-compartment is within the influence radius and its attributes match the list of sensitive types, the sub-compartment is determined to be an affected forestry sub-compartment. Baseline carbon sink data for each affected class was extracted from the baseline carbon sink historical database.

4. The forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in claim 1, characterized in that, The steps for generating emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment include: Based on the obtained list of affected forestry sub-compartments, the baseline carbon sink data for each forestry sub-compartment in the previous accounting period were obtained. Apply a temporary read-only lock to the baseline carbon sink data records that are about to be acquired; After successfully acquiring baseline carbon sink data, the data cloning process is initiated in a pre-defined sandbox computing environment that is completely isolated from the main production system network and storage. The data cloning procedure creates a copy of the baseline carbon sink data for each affected class. Once the replicas are created, a globally unique event identifier is generated for this emergency event, and this identifier is associated with all cloned data replicas to form an emergency assessment instance dataset.

5. The method for assessing forestry carbon sequestration capacity based on forestry environmental big data as described in claim 1, characterized in that, The steps for calculating the carbon sink difference using a pre-defined carbon sink assessment model include: The emergency assessment instance data containing the cloned baseline carbon sink data, along with the event type and intensity parameters carried in the initial event trigger signal, are loaded into the preset carbon sink assessment model as input. The carbon sink assessment model dynamically calls dedicated computing components corresponding to the input event type from its internal model library based on the input event type. By calling the components, the forest stand structure, biomass, and soil carbon pool information in the emergency assessment instance data are read, and the amount of carbon pool change caused by this event type to a specific carbon pool is calculated. Once all relevant components have completed their calculations, the changes in carbon pool absorption output by each component are summed algebraically to obtain the total carbon sink value of the forestry sub-compartment. The final carbon sink difference is derived based on the original baseline carbon sink value recorded in the emergency assessment case data.

6. The forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in claim 5, characterized in that, The steps to obtain the total carbon sequestration value of a forest sub-compartment include: The carbon pool change caused by an event type to a specific carbon pool is obtained through the carbon pool change calculation formula. The formula for calculating the change in the carbon pool is: ； In the formula, For the first Changes in the carbon pool of each carbon pool The energy distribution function for event types. The forest stand impedance attenuation coefficient, For the first Basic parameters of a carbon pool. The carbon pool energy absorption constant, The energy intensity of the event; The total carbon sequestration value of the forestry sub-compartment was obtained using the formula for calculating total carbon sequestration. The formula for calculating the total carbon sequestration is as follows: ； In the formula, This represents the total carbon sequestration after the event. As a baseline carbon sequestration, For the number of carbon pools, This represents the sum of changes in all carbon pools.

7. The forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in claim 6, characterized in that, The steps to derive the final carbon margin include: The final carbon sink difference is obtained through the carbon sink difference calculation formula; The formula for calculating the carbon sink difference is as follows: ； In the formula, This is the carbon sink difference. As a baseline carbon sequestration, This represents the total carbon sequestration after the event. It is a restoring force delay operator; in, ; In the formula, For the restoring force time delay operator, To maximize recovery potential, The recovery rate constant is The duration after the event. It is a natural constant.

8. The method for assessing forestry carbon sequestration capacity based on forestry environmental big data as described in claim 1, characterized in that, The steps for receiving and processing on-site handling feedback instructions from the management terminals at all levels include: Receive on-site handling feedback instructions reported by management terminals at all levels. The on-site handling feedback instructions include the type of measures to be taken, the scope of implementation of the measures, the execution time of the measures, and the expected effect assessment. The feedback instructions are categorized and processed to identify instructions for pest and disease control, vegetation restoration, soil remediation, and enhanced management. The expected effects assessment in the on-site response feedback instructions will be quantified into carbon sequestration capacity recovery parameters. Based on the quantified carbon sequestration capacity recovery parameters, update the relevant parameters in the emergency assessment instance data; The carbon sink assessment model is re-initiated, and the carbon sink difference is recalculated based on the updated parameters.

9. The method for assessing forestry carbon sequestration capacity based on forestry environmental big data as described in claim 1, characterized in that, The steps for archiving the incremental event version and all its iteration history include: Collect complete data for incremental event versions, which is recorded as archived data. The complete data includes initial event data, calculation data for each iteration, handling feedback data, and final calculation result data. Each incremental event version is assigned a unique event identifier code, which includes information on the event occurrence time, event type, and scope of impact. Establish an iteration history table to record the input parameters, calculation process, and output results for each calculation; Archived data is categorized and stored according to chronological order and event type; At the start of the next regular accounting cycle, extract the calculation results of the final incremental event version; The calculation results of the final incremental event version are merged and updated with the baseline carbon sequestration data of the corresponding class to form new baseline data.

10. A forestry carbon sequestration capacity assessment system based on forestry environmental big data, used to implement the forestry carbon sequestration capacity assessment method based on forestry environmental big data as described in any one of claims 1-9, characterized in that, include: Event trigger monitoring module, baseline data acquisition module, emergency assessment instance generation module, incremental version generation module, push module, iterative calculation module, and baseline data merging module; The event triggering monitoring module is used to monitor forestry environment data streams in real time and generate initial event triggering signals based on a preset event triggering model. The baseline data acquisition module is used to receive the initial event trigger signal and obtain the baseline carbon sink data of the affected forestry sub-compartment; The emergency assessment instance generation module is used to lock and clone the baseline carbon sink data of the affected forestry sub-compartments, and generate emergency assessment instance data associated with the baseline carbon sink data in an isolated computing environment. The incremental version generation module is used in the isolated computing environment to calculate the carbon sink difference using a preset carbon sink assessment model, and generate an incremental event version containing the carbon sink difference. The push module is used to generate differentiated early warning information and corresponding intervention action suggestions based on the carbon sink difference value, and push them to preset management terminals at all levels. The iterative calculation module is used to receive and process on-site handling feedback instructions from the management terminals at all levels, and then recalculate the carbon sink difference until the carbon sink difference converges to within a predetermined threshold. The baseline data merging module is used to archive the incremental event version and all its iteration history, and merge the final incremental event version into the baseline carbon sink data in the next regular accounting cycle.

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