A network resilience prediction method, system and device based on digital twinning

Abstract

The application discloses a network resilience prediction method, system and device based on digital twinning, and relates to the technical field of ecological monitoring and network resilience modeling. The network resilience prediction method, system and device based on digital twinning comprise the following steps: S1, collecting multiple types of ecological monitoring data through an ecological monitoring device, and performing standardized preprocessing on the ecological monitoring data; S2, comprehensively evaluating the modeling difficulty of each ecological data, and realizing automatic filtering and hierarchical processing of the data quality; S3, performing state synchronization evaluation on the data to be modeled, and dynamically updating the running state of the corresponding ecological entity in the digital twinning based on the state evaluation result; and S4, identifying key fragile nodes and risk paths in each ecological entity, and outputting corresponding ecological risk grades and control suggestions. The problems that in the prior art, only a small amount of observation data is used for static threshold risk judgment, and the problems that the dynamic evolution characteristics and the multi-source heterogeneity existing in the ecological system cannot be effectively coped with are solved.

Description

Technical Field

[0001] This invention relates to the field of ecological monitoring and network resilience modeling technology, specifically to a network resilience prediction method, system, and device based on digital twins. Background Technology

[0002] In the field of natural ecological environment management and disaster early warning, structural modeling and operational status perception of ecological network systems are core tasks for achieving efficient monitoring and risk prevention. In recent years, with the development of multi-source sensing equipment and ecological monitoring technologies, operational data from various components of the ecosystem (such as meteorological, hydrological, water quality, soil, biological, and remote sensing data) have been collected in real time, providing a data foundation for networked perception and system prediction. However, most existing ecological monitoring platforms still rely primarily on decentralized data collection and static indicator assessment, lacking the ability to dynamically model the ecological network structure, temporal evolution, and risk propagation processes, making it difficult to support accurate resilience assessment and systemic early warning.

[0003] Traditional methods typically rely on static threshold assessments based on single-type or limited observational data, failing to effectively address the multi-source heterogeneity, high coupling, and dynamic evolutionary characteristics of ecosystems. Furthermore, different types of monitoring data exhibit significant differences in temporal resolution, field structure, and unit representation, lacking unified data standards and fusion mechanisms, making it difficult to comprehensively and accurately represent the overall system state. This problem is particularly pronounced when facing sudden ecological risk events such as extreme weather, pollution spread, or sudden changes in habitat, leading to delayed responses and inaccurate assessments.

[0004] Therefore, in view of the shortcomings of existing technologies, there is an urgent need for a network resilience prediction method, system and device based on digital twins. Summary of the Invention

[0005] Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a network resilience prediction method, system, and device based on digital twins, which solves the problems of complex multi-source data structures, lack of unified modeling mechanisms, inconsistent state representations, and inability to dynamically predict resilience fluctuations in existing ecological monitoring systems.

[0007] Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a network resilience prediction method, system, and apparatus based on digital twins, comprising: S1, collecting multiple types of ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and performing standardized preprocessing on the ecological monitoring data; S2, calculating modeling adaptation scores based on the standardized preprocessed ecological monitoring data, comparing the ecological data modeling evaluation results with the structural template, and marking them as data to be modeled; S3, performing state synchronization evaluation on the data to be modeled, calculating the state synchronization scores of each ecological entity, and dynamically updating the operating status of the corresponding ecological entity in the digital twin based on the digital twin state results; S4, calculating network resilience fluctuation values ​​based on the modeling adaptation scores and state synchronization scores of each ecological entity, identifying key vulnerable nodes and risk paths based on the fluctuation score results, and outputting corresponding ecological risk levels and control recommendations.

[0009] Furthermore, the specific steps for collecting ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and for standardizing and preprocessing the ecological monitoring data, are as follows: Meteorological data such as temperature, humidity, wind speed, wind direction, atmospheric pressure, and precipitation are collected through meteorological stations; hydrological and water quality data such as water level, water flow velocity, pH value, dissolved oxygen concentration, conductivity, and ammonia nitrogen concentration are collected through hydrological and water quality monitoring stations; soil data such as soil moisture, temperature, and salinity are collected through geological sensors; and species activity frequency and behavior are collected through infrared cameras, acoustic monitors, and biosensors. Ecological monitoring data including labels and estimated biomass density; remote sensing image data of surface temperature, vegetation index, and water area changes collected by remote sensing satellites and drones; standardized preprocessing of all collected data, including field format unification, timestamp alignment, missing value imputation, and unit conversion; collection of the number of valid fields received, entity connection strength, data update frequency per unit time, data update delay, and field nesting level for each ecological entity within the current time window; and recording of collected ecological monitoring data fields, standard fields, the length of time of missing ecological monitoring data during the collection process, and the total analysis time.

[0010] Further, the specific steps for calculating the modeling fit score based on the standardized preprocessed ecological monitoring data and comparing the ecological data modeling evaluation results with the structural template, marking them as data to be modeled, are as follows: Extract the number of fields, field nesting levels, and number of unit types from the standardized ecological monitoring data, and calculate the field structure complexity; extract ecological monitoring data fields and standard fields to calculate the matching degree and obtain a semantic fit score; statistically analyze the missing time length and total analysis time length of the collected ecological monitoring data, and calculate the time coverage ratio; fuse the field structure complexity, semantic conflict ratio, and time coverage ratio to obtain the modeling fit score; compare the modeling fit score with the modeling fit threshold in real time, and then model... The adaptation thresholds include a first adaptation threshold and a second adaptation threshold: When the modeling adaptation score is less than the first adaptation threshold, it is judged as high-quality data with simple structure, consistent semantics, and complete time, and is directly included in the unified description model and enters the digital twin construction process; when the modeling adaptation score is greater than or equal to the first adaptation threshold and less than the second adaptation threshold, it is judged as medium-quality data with some structural complexity or semantic conflicts. After performing field mapping optimization, unit normalization, and missing data completion on the data, it is decided whether to include it in the twin model; when the modeling adaptation score is greater than or equal to the second adaptation threshold, it is judged as low-quality data with complex structure, chaotic field naming, or serious time missing, and is marked as data to be corrected and not included in the current twin construction process.

[0011] Furthermore, the specific method for calculating the modeling adaptation score is as follows: Sum the number of fields, the number of unit types, and the field nesting levels in the current ecological monitoring data, add one to the result, and take the logarithm to obtain the structural complexity score of the data; calculate the ratio of the ecological monitoring data fields to the standard fields, add one to this ratio, and take the reciprocal to obtain the semantic adaptation score; calculate the ratio of the missing time length of the ecological monitoring data within the target analysis period to the total analysis time length to obtain the time coverage ratio; add the structural complexity score, the semantic adaptation score, and the time coverage ratio to obtain the modeling adaptation score of the data source.

[0012] Further, the specific steps for performing state synchronization evaluation on the data to be modeled, calculating the state synchronization score of each ecological entity, and dynamically updating the operating status of the corresponding ecological entity in the digital twin based on the digital twin state results are as follows: Calculate the field integrity rate by statistically analyzing the number of valid fields received by each ecological entity within the current time window; obtain the modeling adaptation score and data update delay of the ecological entity; statistically analyze the data update delays of all ecological entities within the current time window, and select the data update delay of the ecological entity with the smallest data update delay as the optimal reference value for the data update cycle within the current time period; calculate the state synchronization score of the ecological entity based on the field integrity rate, modeling adaptation score, and data update delay; compare the state synchronization score and state threshold in real time, where the state threshold is divided into a first state threshold and a second state threshold; when the state synchronization score is greater than or equal to the first state threshold... When the state threshold is reached, the current ecological entity is in a highly consistent state, considered to have complete data, good timeliness, and good modeling adaptation. The state can be directly written into the digital twin and included in the current state view for subsequent resilience analysis and prediction. When the state synchronization score is greater than or equal to the second state threshold but less than the first state threshold, the current ecological entity is in an intermediate transition state and should be delayed in processing, not written into the main view. The current state is placed in a buffer, the state change trend is recorded, and attempts are made periodically to complete missing fields and realign timestamps. It is written into the twin only after the update conditions are met. When the state synchronization score is less than the second state threshold, the current ecological entity is in a low consistency state. The entity's data is considered distorted and the state is unreliable. Updates are blocked and anomalies are marked. The system will mark the entity as a "low-trust node" and initiate a data supplementation request. If the score is low for multiple consecutive periods, a structural correction or reconstruction process is triggered.

[0013] Furthermore, the specific method for calculating the state synchronization score of each ecological entity is as follows: divide the number of valid fields received by each ecological entity within the current time window by one and add the modeling adaptation score of the ecological entity to obtain the data integrity score of the ecological entity under the background of structural complexity; divide the data update delay of the ecological entity by the optimal reference value of the data update cycle in the current time period to calculate the time synchronization offset ratio, and subtract the time synchronization offset ratio by one to obtain the update timeliness score; multiply the data integrity score and the update timeliness score to obtain the state synchronization score of the ecological entity.

[0014] Further, the specific steps for calculating network resilience fluctuation values ​​based on the modeling adaptation score, state synchronization score, connection strength, and data update frequency of each ecological entity, and identifying key vulnerable nodes and risk paths based on these values, and outputting corresponding ecological risk levels and control recommendations, are as follows: Calculate the resilience fluctuation contribution value of each ecological entity by statistically analyzing the modeling adaptation score, state synchronization score, entity connection strength, and data update frequency per unit time for all ecological entities; Perform a weighted average of the resilience fluctuation contributions of each ecological entity to obtain the network resilience fluctuation value of the system at the current moment; Compare the network resilience fluctuation value with a risk threshold to determine the current system state. The risk threshold is divided into a first risk threshold, a second risk threshold, and a third risk threshold; When the network resilience fluctuation value is less than the third risk threshold, the system is determined to be in a high-resilience state, maintaining normal monitoring frequency and view update rhythm; When the network resilience fluctuation value is greater than or equal to the third risk threshold... When the network resilience fluctuation value is greater than or equal to the second risk threshold but less than the first risk threshold, the system is judged to be in a state of mild fluctuation. The update frequency of the state view is increased, and high-frequency data collection is performed on key path nodes to record the evolution trend. When the network resilience fluctuation value is greater than or equal to the second risk threshold but less than the first risk threshold, the system is judged to be in a state of resilience decay. Local intervention is carried out, the structural chain tracing algorithm is activated, key nodes are identified, the sampling frequency of relevant areas is increased, and data re-collection is triggered. Abnormal state nodes are locally isolated to suspend their impact on the global state. When the network resilience fluctuation value is higher than the first risk threshold, the system is judged to be in an unbalanced state. The system will mark the risk level as "high" and activate the resilience risk response process, including local structural isolation, data re-collection request, and path intervention suggestion generation and early warning output. At the same time, the ecological entities with the highest resilience contribution value are extracted as key vulnerable nodes, and the risk propagation path is identified based on the connection relationship between entities. A system-level risk map and regulation and intervention suggestions are output.

[0015] Furthermore, the specific method for calculating the network resilience fluctuation value is as follows: divide the modeling adaptation score of each ecological entity by one and add its state synchronization score to represent the modeling difficulty weight of the entity under the dual influence of structural complexity and data reliability; multiply this weight by the ratio of the connection strength of the ecological entity in the ecological network to the maximum connection strength among all ecological entities to reflect its structural criticality; multiply it by the ratio of the data update frequency of the ecological entity in the current time to the highest data update frequency among all ecological entities to reflect its activity level; sum the above multiplication results of all ecological entities and divide by the total number of entities to obtain the network resilience fluctuation value at the current moment, which is used to evaluate the overall operational stability and risk fluctuation level of the system.

[0016] A second aspect of this invention provides a network resilience prediction system based on digital twins, comprising: a data acquisition and preprocessing module for acquiring ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and performing standardized preprocessing on the ecological monitoring data; a standard modeling module for calculating modeling adaptation scores based on the standardized preprocessed ecological monitoring data, comparing the ecological data modeling evaluation results with a structural template, and marking them as data to be modeled; a digital twin construction module for performing state synchronization evaluation on the data to be modeled, calculating state synchronization scores for each ecological entity, and dynamically updating the operating state of the corresponding ecological entity in the digital twin based on the digital twin state results; and a resilience index mapping module for calculating network resilience fluctuation values ​​based on the modeling adaptation scores and state synchronization scores of each ecological entity, identifying key vulnerable nodes and risk paths based on the fluctuation score results, and outputting corresponding ecological risk levels and control recommendations.

[0017] A third aspect of this invention provides a network resilience prediction device based on digital twins, comprising: an ecological monitoring terminal, used to collect various types of ecological environment data such as meteorological, hydrological, water quality, soil, biological activity and remote sensing images in real time within a target monitoring area, to obtain field information, location coordinates, sampling time and device status of each ecological entity, and to upload the collected data to a data processor via wired or wireless means; and a data processor, used to standardize the field format, align the timestamps and convert the units of the received ecological monitoring data, calculate the modeling adaptation score and status synchronization score of each ecological entity, and transmit the score results and processed data to the twin construction module; The digital twin construction unit is used to map ecological entities to corresponding objects in the twin model based on the scoring results and standardized data provided by the data processor. It also updates the operating status and connection relationships of each ecological entity in the twin in real time according to the entity status synchronization score, forming a virtual mapping that evolves synchronously with the real ecosystem. The resilience assessment and early warning unit is used to calculate the overall resilience fluctuation score of the system based on the modeling adaptation score, status synchronization score, connection strength and data update frequency of each ecological entity in the status view. It also judges the system risk level based on the score results, identifies key vulnerable nodes and risk propagation paths, and generates risk early warning information and regulation and intervention suggestions.

[0018] Beneficial effects

[0019] The present invention has the following beneficial effects:

[0020] (1) The network resilience prediction method, system and device based on digital twins, by constructing a unified description model of cross-level and multi-source heterogeneous data, realizes the standardized processing of ecological monitoring data in terms of structural expression, semantic matching and time alignment, and effectively solves the problems of inconsistent data formats and difficulty in fusion of heterogeneous data in the existing technology.

[0021] (2) The network resilience prediction method, system and device based on digital twins improves the system’s ability to identify and manage real-time state changes and data quality differences of ecological entities by introducing a dynamic evaluation mechanism that combines state synchronization scoring and modeling adaptation scoring, and effectively solves the problem of monitoring misjudgment caused by data lag and state distortion in the existing technology.

[0022] (3) The network resilience prediction method, system and device based on digital twins realizes continuous quantification of system resilience level and risk warning by using a resilience fluctuation scoring model based on connection strength, data update frequency and entity state fluctuation, which effectively solves the problems of lack of dynamism in resilience assessment and inaccurate risk identification in the prior art.

[0023] (4) The network resilience prediction method, system and device based on digital twins dynamically generate risk paths and control suggestions by combining resilience fluctuation scores and vulnerable node identification, thereby achieving precise control of key nodes and high-risk areas, effectively solving the problem of general and untargeted control strategies in the existing technology. Attached Figure Description

[0024] Figure 1 This is a flowchart of the network resilience prediction method based on digital twins of the present invention;

[0025] Figure 2 This is a structural diagram of the network resilience prediction system based on digital twins of the present invention;

[0026] Figure 3 This is a bar chart showing the contribution of each ecological entity involved in this invention to the network resilience fluctuation value. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figures 1-3This invention provides a technical solution: a network resilience prediction method, system, and device based on digital twins, comprising: S1, collecting multiple types of ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and performing standardized preprocessing on the ecological monitoring data; S2, calculating modeling adaptation scores based on the standardized preprocessed ecological monitoring data, comparing the ecological data modeling evaluation results with the structural template, and marking them as data to be modeled; S3, performing state synchronization evaluation on the data to be modeled, calculating the state synchronization scores of each ecological entity, and dynamically updating the operating status of the corresponding ecological entity in the digital twin based on the digital twin state results; S4, calculating network resilience fluctuation values ​​based on the modeling adaptation scores and state synchronization scores of each ecological entity, identifying key vulnerable nodes and risk paths based on the fluctuation score results, and outputting corresponding ecological risk levels and control recommendations.

[0029] Specifically, the steps for collecting various types of ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity and remote sensing images, through ecological monitoring equipment, and standardizing and preprocessing the collected data are as follows: key meteorological parameters such as temperature, humidity, wind speed, wind direction, atmospheric pressure and precipitation are collected through meteorological stations to form a basic observation dataset of regional climate environment, and the dataset is continuously updated at a frequency of minutes or hours.

[0030] Hydrological and water quality monitoring stations deployed at river cross-sections, reservoir outlets, and wetland ecological zones collect indicators such as water level, water flow velocity, pH value, dissolved oxygen concentration, conductivity, and ammonia nitrogen concentration to reflect the water body's operational status and water quality ecological condition.

[0031] By acquiring soil environmental factors such as soil moisture, temperature and salinity through geological sensors deployed in the soil layer, we can support the analysis and modeling of surface water migration, agricultural suitability and underground ecological processes.

[0032] The frequency of species occurrence is recorded by infrared cameras, the intensity of biological calls is captured by acoustic monitors, behavioral tags and activity patterns are identified by biosensor chips, and biological density is estimated by combining location data to form multi-scale ecological behavior data.

[0033] Remote sensing image data such as surface temperature, vegetation index changes and water area expansion trends are collected by remote sensing satellite and UAV systems to support macro-level assessment of regional geomorphology, vegetation cover and water resource evolution;

[0034] Standardized preprocessing operations are performed on all collected multi-source heterogeneous data. Preprocessing includes standardizing field naming formats, converting physical units, aligning timestamp precision, and handling null and outlier values ​​to ensure that all types of data can be aligned and modeled on a unified platform.

[0035] Simultaneously, the system collects the number of valid fields received by each ecological entity within the current time window, the structural connection strength between the entity and other nodes, the data update frequency per unit time, the latency during data transmission, and the maximum nesting level of the field structure, which are used for subsequent data quality assessment, structural modeling, and state synchronization score calculation.

[0036] In this implementation plan, by extracting the number of fields, the nesting level of fields, and the type of units from the standardized preprocessed ecological monitoring data, the system achieves a quantitative expression of the structural complexity of each data source. Combined with the proportion of semantic conflicts and the proportion of time coverage, a unified modeling evaluation index system applicable to multi-source ecological data is constructed. The extracted structural and temporal feature parameters are substituted into the modeling scoring function to calculate the modeling fit score, ensuring a comprehensive evaluation of the data across three dimensions: structural expression, semantic consistency, and time coverage integrity. By comparing the scoring results with the set modeling fit thresholds in real time, the system can quickly identify the difficulty of data standardization and the quality level of access, providing a screening basis for subsequent digital twin construction. Data with lower scores is judged as high-quality data with clear structure and strong timeliness, and is directly used for digital twin construction; data with medium scores undergoes field semantic optimization and time alignment processing, and is included in the modeling process after completion; data with high scores is marked as low-fit data and is not immediately included in the subsequent processing flow, reducing the risk of system modeling bias. This step, by introducing a structural scoring mechanism, enables the estimation of processing costs and control of structural complexity of ecological data sources, thereby improving the data manageability and modeling efficiency of the system's input layer.

[0037] Specifically, based on standardized preprocessed ecological monitoring data, a modeling fit score is calculated, and the ecological data modeling evaluation results are compared with the structural template and marked as data to be modeled. The specific steps are as follows: extract structural indicators such as the number of fields, field nesting level, and physical unit types of each data source from the standardized ecological monitoring data to measure the complexity of the data structure, and then calculate the field structure complexity score to reflect the difficulty of the data source in the model building process; further extract the naming method of all fields in the collected data and compare it with the standard field names in the preset unified ecological data model. By calculating the semantic matching ratio and the conflict field ratio, a semantic fit score is obtained to evaluate the standardization difficulty of the data at the semantic expression level.

[0038] At the same time, the actual effective duration and missing duration of various types of data within the specified analysis time window are statistically analyzed, and the time coverage ratio is calculated to reflect the time continuity and sampling integrity of the data source.

[0039] The scoring results from the above three dimensions are integrated to generate a modeling adaptation score for the data source, which serves as a comprehensive indicator to determine whether it meets the conditions for accessing the unified modeling process. The modeling adaptation score is then compared in real time with the modeling adaptation thresholds set by the system. These thresholds include a first adaptation threshold and a second adaptation threshold, which are used to classify the data source and select the processing path.

[0040] When the modeling fit score is less than the first fit threshold, it indicates that the data structure is clear, the semantics are standardized, and the time coverage is complete. This data is considered high-quality and can be directly incorporated into the unified data description model, simultaneously advancing to the digital twin construction process for state view generation and resilience analysis calculations. When the modeling fit score is greater than or equal to the first fit threshold but less than the second fit threshold, it indicates that the data has some deviations in structural design, field naming, or time continuity. This data is considered medium-quality and the system will perform field mapping optimization, physical unit normalization, and missing segment completion operations before deciding whether to include it in the digital twin model. When the modeling fit score is greater than or equal to the second fit threshold, it indicates that the data source has problems such as overly complex structure, semantic confusion, or severe missing sampling time. This data is considered low-quality and the system marks it as pending correction, excluding it from the current cycle's digital twin construction process. It is simultaneously transferred to data calibration or backsampling tasks to avoid affecting subsequent modeling quality and system stability.

[0041] In this implementation plan, a unified modeling adaptation scoring mechanism is constructed to comprehensively and quantitatively evaluate standardized ecological monitoring data across three key dimensions: structural complexity, semantic consistency, and temporal integrity. The system can quickly identify the modeling difficulty and access value of different data sources, enabling tiered processing and precise filtering of multi-source heterogeneous data. High-quality data can be directly incorporated into the digital twin construction process, improving modeling efficiency and system stability; medium-quality data is optimized to enhance its usability and reduce potential modeling biases; low-quality data is effectively isolated to prevent erroneous data from interfering with subsequent analysis. This step provides a clear, semantically consistent, and quality-controllable data input foundation for digital twin construction.

[0042] Specifically, the modeling fit score is calculated as follows: The total number of fields, the number of physical unit types used, and the maximum nesting level of the fields in the current ecological monitoring data are summed, and then one is added to the result before taking the logarithm to obtain the structural complexity score of the data. The semantic difference ratio between the data source and the unified data model is calculated, and one is added to this ratio before taking the reciprocal to obtain the semantic fit score. The ratio of the cumulative missing time of the data within the target analysis period to the total analysis time is calculated to obtain the time coverage ratio. The structural complexity score, semantic fit score, and time coverage ratio are then added together to obtain the modeling fit score of the data source.

[0043] The formula for calculating the model fit score is:

[0044]

[0045] In the formula, N represents the number of fields collected from the data source, which originates from monitoring data collection. U represents the number of physical units used in the field, such as mg / L, ℃, m 3 / s is counted as 3. This is derived from unit differences in hydrological / soil / atmospheric data. D represents the maximum field nesting depth in the data structure, derived from remote sensing and unmanned platform data acquisition. δ represents the proportion of fields in the current data source whose semantic expression is inconsistent with the standard fields of the main model, derived from semantic comparison rules, applicable to field comparisons across multiple meteorological or water quality platforms; T g This indicates the length of the time gap (in hours or seconds) in the current data source within the target modeling period, derived from time and sampling information collection (statistics of missing time periods); T t This indicates the total time of the current modeling and analysis period (e.g., 24 hours) and the system's preset analysis window (corresponding to the missing time).

[0046] In this implementation plan, the modeling fit score comprehensively evaluates the modeling difficulty of each ecological data source in three aspects: structural complexity, semantic fit, and temporal integrity. By integrating and calculating factors such as the number of fields, nesting level, unit type, field naming conflict ratio, and time missing ratio, a modeling fit score is generated. This provides a quantitative basis for the system to determine whether the data is suitable for inclusion in the digital twin model, thereby enabling automatic screening and classification of data quality.

[0047] Specifically, the process involves performing a state synchronization assessment on the data to be modeled, calculating the state synchronization score for each ecological entity, and dynamically updating the operational status of the corresponding ecological entity in the digital twin based on the digital twin state results. This includes the following steps:

[0048] By counting the number of fields successfully received by each ecological entity within the current time window, and combining this with the total number of fields that the entity should acquire at the expected collection frequency, the field integrity rate is calculated to measure the information integrity and real-time performance of the entity at the data reception level.

[0049] The modeling adaptation score calculated for the ecological entity in the standard modeling process is obtained, which reflects the complexity of its data structure and the difficulty of standardization. At the same time, the update timestamp of the latest data of the entity is collected and compared with the system's baseline time to obtain the data update delay and quantify the timeliness deviation of its information.

[0050] Based on three indicators—field completeness rate, modeling adaptation score, and data update delay—the state synchronization score of the ecological entity is calculated as a core criterion for measuring its data availability and twin synchronization quality at the current moment.

[0051] The system compares the state synchronization score with the set state thresholds in real time. The state thresholds include a first state threshold and a second state threshold, which are used to classify and determine the state credibility of ecological entities.

[0052] When the state synchronization score is greater than or equal to the first state threshold, the ecological entity is determined to be in a highly consistent state, and its data is considered to be complete, timely, and well-adapted to the model. The system directly synchronizes the entity's state to the digital twin and includes it in the current state view to support subsequent resilience analysis and trend prediction.

[0053] When the state synchronization score is greater than or equal to the second state threshold and less than the first state threshold, the ecological entity is determined to be in an intermediate transition state and will not be included in the main view for the time being. The system caches the entity's state data in the state buffer, records its state change trend, and periodically performs field completion and time realignment. It will be included in the twin body after its synchronization score meets the update conditions.

[0054] When the state synchronization score is lower than the second state threshold, the ecological entity is determined to be in a low consistency state, and its data is considered to be distorted or invalid. The system will implement a blocking update operation on the entity and mark it as a "low-trust node". At the same time, a data re-collection request will be triggered. If the entity's state synchronization score continues to be low in subsequent cycles, a structural correction or local twin reconstruction process will be further triggered to ensure the accuracy and stability of the overall twin model.

[0055] In this implementation plan, by combining the field completeness rate, modeling adaptation score and data update time delay of the ecological entity, the state synchronization score is calculated to dynamically evaluate the credibility and synchronization quality of each entity at the current moment, ensuring that only high-quality entity states with complete data, good timeliness and strong structural adaptability are accessed in the digital twin.

[0056] This step enables dynamic screening and classification management of the state of ecological entities, ensuring the consistency and timeliness of the digital twin structure, and providing an accurate and reliable input basis for subsequent state view generation and resilience analysis.

[0057] Specifically, the method for calculating the state synchronization score of each ecological entity is as follows: divide the number of valid fields received by each ecological entity within the current time window by one and add the modeling adaptation score of the ecological entity to obtain the data integrity score of the ecological entity under the background of structural complexity; divide the data update delay of the ecological entity by the update time interval to calculate the time synchronization offset ratio, and subtract the time synchronization offset ratio by one to obtain the update timeliness score; multiply the data integrity score and the update timeliness score to obtain the state synchronization score of the ecological entity.

[0058] The formula for calculating the state synchronization score is:

[0059]

[0060] In the formula, Q i (t) represents the real-time observation quality index, derived from the completeness rate of key fields collected for the entity at the current moment, calculated from the number of valid fields received by the data acquisition and preprocessing module; R represents the ecological data modeling evaluation value, indicating structural complexity and standardization difficulty, derived from the previous formula; T i (t) represents the data update time delay, calculated from the difference between the timestamp of the latest collected data and the current system time, derived from the timestamp alignment step of the data acquisition and preprocessing module; T r This represents the optimal reference value for the data update cycle within the current time period. It involves statistically analyzing the data update intervals of all ecological entities within the current time window and selecting the update time of the entity with the smallest data update interval as the optimal reference value.

[0061] In this implementation plan, the formula comprehensively evaluates the state synchronization quality of each ecological entity within the current time window, quantifying its overall performance in terms of data integrity, model adaptation, and update timeliness. By analyzing the relationship between the number of fields received, the model adaptation score, and data latency, a state synchronization score for each entity is calculated. This score serves as a crucial basis for determining whether the entity can be reliably synchronized to the digital twin, ensuring that the system state expressed in the digital twin is authentic, effective, and timely.

[0062] Specifically, based on the modeling adaptation score, state synchronization score, connection strength, and data update frequency of each ecological entity in the state view, the network resilience fluctuation value is calculated. Then, based on the network resilience fluctuation value, key vulnerable nodes and risky paths are identified, and corresponding ecological risk levels and control recommendations are output. The specific steps are as follows:

[0063] By traversing all ecological entities in the status view, the modeling adaptation score of each entity is calculated to reflect its structural complexity and standardization difficulty. Real-time status synchronization score is obtained to measure the reliability and update quality of the data. At the same time, the connection strength of the entity in the current ecological network is extracted to represent its coupling density in the network, as well as the data update frequency per unit time, reflecting the activity of the entity in dynamic monitoring.

[0064] Based on the above four indicators, the resilience fluctuation contribution value of each ecological entity is calculated, which comprehensively reflects the influence weight of the entity in the overall resilience fluctuation of the system, and ensures that the impact of key nodes is accurately quantified.

[0065] The network resilience fluctuation value of the system at the current moment is obtained by weighted averaging of the resilience fluctuation contribution values ​​of all ecological entities. This value serves as the core indicator reflecting the overall operational stability and vulnerability level of the ecological network and is used to monitor the trend of system resilience changes in real time.

[0066] By comparing the currently calculated network resilience fluctuation value with the system's set risk thresholds, which are divided into a first risk threshold, a second risk threshold, and a third risk threshold, the current state of the system is classified and determined to ensure that the response strategies under different risk levels are targeted and flexible.

[0067] When the network resilience fluctuation value is less than the third risk threshold, the system is determined to be in a high resilience state, the overall ecological network is stable, the data quality and structure are good, the system maintains a normal data collection frequency and status view update rhythm, and no additional intervention is required.

[0068] When the network resilience fluctuation value is greater than or equal to the third risk threshold and less than the second risk threshold, the system is determined to have entered a mild fluctuation state. There is local instability or data fluctuation in the ecological entity. The system will appropriately increase the update frequency of the status view, enhance the dynamic monitoring of critical path nodes, and record and track abnormal trends in real time.

[0069] When the network resilience fluctuation value is greater than or equal to the second risk threshold and less than the first risk threshold, the system is determined to be in a state of resilience decay. The system will initiate local intervention strategies, including executing tracking algorithms on key structural links, identifying key nodes that may cause systemic fluctuations, increasing the data collection frequency in related areas, triggering supplementary data collection and data quality correction, and implementing local isolation of abnormal state nodes to prevent their abnormal impact from spreading to the global state view.

[0070] When the network resilience fluctuation value is higher than the first risk threshold, the system is determined to be in an unbalanced state and faces a high level of ecological risk. The system will immediately mark the risk level as "high" and initiate the resilience risk response process, including implementing structural isolation for high-risk areas, initiating data re-sampling requests, generating path intervention suggestions based on the structural connection relationships between entities, and issuing system-level early warning information.

[0071] Meanwhile, the system will extract the top-ranked ecological entities in terms of resilience fluctuation contribution value as key vulnerable nodes in the current cycle. Combining the connection strength and dependency paths between entities, it will identify potential risk propagation links and output a system-level risk propagation path map and regional regulation and intervention suggestions to provide precise support for management decisions and emergency response.

[0072] In this implementation plan, by comprehensively analyzing the modeling adaptability, state synchronization quality, structural connection strength and data activity of ecological entities, the network resilience fluctuation value is dynamically calculated to fully reflect the operational stability and vulnerability level of the ecosystem.

[0073] The system assesses the current state based on resilience fluctuation values, intelligently identifies key vulnerable nodes and risk propagation paths, and dynamically adjusts monitoring strategies and intervention measures according to risk levels to ensure that the ecological network has the ability to accurately perceive, proactively prevent and control, and respond quickly when facing fluctuations and risks.

[0074] Specifically, the method for calculating the network resilience fluctuation value is as follows:

[0075] Divide the modeling adaptation score of each ecological entity by one and add its state synchronization score to represent the modeling difficulty weight of the entity under the dual influence of structural complexity and data reliability.

[0076] Multiplying this weight by the ratio of the entity's connection strength in the ecological network to the maximum connection strength in the system reflects its structural criticality;

[0077] Multiply this by the ratio of the entity's data update frequency in the current time to the highest update frequency in the system to reflect its activity level;

[0078] The summation of the above products of all ecological entities is divided by the total number of entities to obtain the network resilience fluctuation value at the current moment, which is used to assess the overall operational stability and risk fluctuation level of the system.

[0079] The formula for calculating the network resilience fluctuation value is:

[0080]

[0081] In the formula, R i E represents the modeling fit score for the i-th entity, derived from the formula output of the standard modeling module, and represents the structural complexity and standardization difficulty.i (t) represents the state synchronization score of the i-th entity at time t, derived from the formula output of the digital twin building module, representing the reliability and timeliness of the entity's current state; C i (t) represents the entity connection strength of the i-th ecological entity, which is the number of direct dependencies between this entity and other entities; C max The maximum value of entity connection strength for each ecological entity in the current system; F i (t) represents the data update frequency of the i-th ecological entity at time t, reflecting the data activity level of the entity in the current period, used to measure its influence on the fluctuation of the digital twin's state, and comes from the time and sampling information collection part of the data acquisition module; F max This indicates the maximum data update frequency within the current period. It is derived from the sampling frequency and recording density of the data acquisition module.

[0082] In this embodiment, the modeling adaptation score of ecological entity E1 is set to 3.0, the state synchronization score to 0.7, the connection strength to 10, and the data update frequency to 100; the modeling adaptation score of ecological entity E2 is set to 2.5, the state synchronization score to 0.5, the connection strength to 8, and the data update frequency to 80; the modeling adaptation score of ecological entity E3 is set to 4.0, the state synchronization score to 0.9, the connection strength to 12, and the data update frequency to 120; the modeling adaptation score of ecological entity E4 is set to 3.5, the state synchronization score to 0.6, the connection strength to 6, and the data update frequency to 60; and the modeling adaptation score of ecological entity E5 is set to 2.0, the state synchronization score to 0.8, the connection strength to 9, and the data update frequency to 90.

[0083] Table 1 Network Resilience Fluctuation Values

[0084]

[0085] like Figure 3 The image shows a bar chart of network resilience fluctuation values ​​provided in an embodiment of this application, based on Table 1 and... Figure 3As can be seen, ecological entity E3 has the highest network resilience fluctuation value, reflecting its strongest comprehensive impact in dimensions such as modeling adaptability, state synchronization quality, connection strength, and data activity. It is the main influencing node of current network resilience fluctuations, and the system should prioritize monitoring and structural protection of this entity. Ecological entity E1 has the second highest network resilience fluctuation value, indicating that it also has strong structural coupling and data dominance in the ecological network, and should be included in the high-frequency dynamic monitoring range. Ecological entities E2 and E5 have moderate network resilience fluctuation values. Although their impact is relatively weak, they still have a certain fluctuation amplification effect, making them suitable for inclusion in daily monitoring and trend tracking tasks. Ecological entity E4 has the lowest network resilience fluctuation value, indicating that its data quality, connection strength, and update frequency are relatively weak in the current period. The system will reduce its real-time monitoring priority to optimize resource allocation. The bar chart of network resilience fluctuation values ​​intuitively shows the influence weight of each ecological entity on the overall network resilience status. The higher the value, the greater its impact on system resilience fluctuations, and the more it should be included in risk monitoring and structural optimization strategies.

[0086] Reference Figure 2 As shown, the second aspect of this invention provides a network resilience prediction system based on digital twins, including a data acquisition and preprocessing module, a standard modeling module, a digital twin construction module, and a resilience index mapping module. The data acquisition and preprocessing module collects ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and performs standardized preprocessing on the ecological monitoring data. The standard modeling module calculates the modeling fit score based on the standardized preprocessed ecological monitoring data, compares the ecological data modeling evaluation results with a structural template, and marks it as data to be modeled. The digital twin construction module performs state synchronization evaluation on the data to be modeled, calculates the state synchronization score of each ecological entity, and dynamically updates the operating state of the corresponding ecological entity in the digital twin based on the digital twin state results. The resilience index mapping module calculates the network resilience fluctuation value based on the modeling fit score and state synchronization score of each ecological entity, identifies key vulnerable nodes and risk paths based on the fluctuation score results, and outputs the corresponding ecological risk level and control recommendations.

[0087] In this implementation plan, the network resilience prediction system based on digital twins integrates multiple functional modules, including data acquisition and preprocessing, standard modeling, digital twin construction, and resilience index mapping, to achieve accurate perception and dynamic prediction of ecosystem resilience status, comprehensively improving resilience monitoring and risk response capabilities. The data acquisition and preprocessing module ensures the consistency of multi-source ecological monitoring data in structure, semantics, and time. Through field standardization, time alignment, and missing data completion, it provides a high-quality data foundation for subsequent modeling and analysis. The standard modeling module quantifies the data modeling adaptability of each ecological entity based on parameters such as field complexity, semantic matching, and time coverage ratio, selecting high-quality data sources and improving the accuracy and reliability of twin construction. The digital twin construction module, through state synchronization scoring and dynamic updating of entity states, realizes a virtual-real corresponding, real-time evolving digital twin, ensuring that the system can accurately reflect the operational status and structural changes of the ecological network. The resilience index mapping module calculates the system resilience fluctuation value based on the modeling adaptability score, state synchronization score, connection strength, and data update frequency of each ecological entity, accurately identifying vulnerable nodes and risk paths, and outputting risk warnings and control suggestions to support ecological protection and management decisions. Overall, through the synergistic effect of its various modules, the system has achieved standardized processing of ecological monitoring data, dynamic construction of twins, and continuous assessment of resilience status. It has significantly improved the real-time nature of ecosystem resilience perception, the accuracy of risk warning, and the pertinence of regulation strategies, and solved problems such as data heterogeneity, state distortion, and lag in resilience assessment in existing technologies.

[0088] A third aspect of this invention provides a network resilience prediction device based on digital twins, comprising: an ecological monitoring terminal, used to collect various types of ecological environment data such as meteorological, hydrological, water quality, soil, biological activity and remote sensing images in real time within a target monitoring area, to obtain field information, location coordinates, sampling time and device status of each ecological entity, and to upload the collected data to a data processor via wired or wireless means; and a data processor, used to standardize the field format, align the timestamps and convert the units of the received ecological monitoring data, calculate the modeling adaptation score and status synchronization score of each ecological entity, and transmit the score results and processed data to the twin construction module; The digital twin construction unit is used to map ecological entities to corresponding objects in the twin model based on the scoring results and standardized data provided by the data processor. It also updates the operating status and connection relationships of each ecological entity in the twin in real time according to the entity status synchronization score, forming a virtual mapping that evolves synchronously with the real ecosystem. The resilience assessment and early warning unit is used to calculate the overall resilience fluctuation score of the system based on the modeling adaptation score, status synchronization score, connection strength and data update frequency of each ecological entity in the status view. It also judges the system risk level based on the score results, identifies key vulnerable nodes and risk propagation paths, and generates risk early warning information and regulation and intervention suggestions.

[0089] In this implementation plan, the network resilience prediction device based on digital twins integrates ecological monitoring terminals, data processors, digital twin building units, and resilience assessment and early warning units, forming a closed-loop process from data collection, standardized processing, virtual mapping to resilience analysis, which significantly improves the intelligence level of ecosystem resilience monitoring and risk management. The ecological monitoring terminal enables real-time collection of various ecological and environmental data within the target area, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images. This ensures the comprehensiveness and timeliness of data sources, avoiding the problems of isolated multi-source data collection and poor timeliness in existing technologies. The data processor, as the core data calculation module, effectively solves the problem of heterogeneous data fusion through field format standardization, timestamp alignment, and unit conversion. Based on the collected data, it calculates modeling adaptation scores and state synchronization scores, ensuring the data quality and accuracy of subsequent digital twin construction. The digital twin construction unit, relying on the standardized data and scoring results provided by the processor, accurately maps ecological entities to corresponding objects in the twin model. By updating entity states and connections in real time, it achieves synchronous evolution between the virtual and real worlds, breaking through the technical bottlenecks of traditional static modeling and state lag. The resilience assessment and early warning unit dynamically calculates the system's resilience fluctuation score by fusing modeling adaptation scores, state synchronization scores, connection strength, and data update frequency, accurately identifying vulnerable nodes and risk paths, and outputting risk warnings and control suggestions. This significantly enhances the system's responsiveness and decision support effectiveness in ecological risk prevention and resilience management. Overall, this invention, through the organic synergy of the various functional units of the physical device, opens up the entire processing path of ecological monitoring data, realizes dynamic perception, accurate assessment and intelligent response of the ecosystem's resilience status, and solves the problems of data fragmentation, monitoring lag and inaccurate risk identification in the prior art. It has significant technical advantages of strong real-time performance, high accuracy and excellent adaptability.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0091] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A network resilience prediction method based on digital twins, characterized in that, include: S1 collects various types of ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity and remote sensing images, through ecological monitoring equipment, and performs standardized preprocessing on the ecological monitoring data. S2, based on the standardized preprocessed ecological monitoring data, calculate the modeling adaptation score, compare the ecological data modeling evaluation results with the structural template, and mark them as data to be modeled; The specific method for calculating the modeling adaptation score is as follows: The structural complexity score of the current ecological monitoring data is obtained by summing the number of fields, the number of unit types, and the field nesting level, adding one to the result and taking the logarithm. Calculate the ratio of the ecological monitoring data field to the standard field, add one to the ratio and take the reciprocal to obtain the semantic fit score; The time coverage ratio is calculated by comparing the length of time the ecological monitoring data is missing within the target analysis period with the total analysis time. The modeling fit score of the ecological entity is obtained by adding the three results: structural complexity score, semantic fit score, and time coverage ratio. S3, perform state synchronization assessment on the data to be modeled, calculate the state synchronization score of each ecological entity, and dynamically update the operating status of the corresponding ecological entity in the digital twin based on the state results of the digital twin. The specific method for calculating the state synchronization score value of each ecological entity is as follows: The number of valid fields received by each ecological entity within the current time window / (1 + the modeling adaptation score of the ecological entity) = the data integrity score of the ecological entity in the context of structural complexity; Divide the data update delay of the ecological entity by the optimal reference value of the data update cycle in the current time period to calculate the time synchronization offset ratio, and subtract the time synchronization offset ratio from one to obtain the update timeliness score. Multiply the data integrity score and the update timeliness score to obtain the state synchronization score of the ecological entity; S4 calculates the network resilience fluctuation value based on the modeling adaptation score and state synchronization score of each ecological entity, and identifies key vulnerable nodes and risk paths based on the fluctuation score results, and outputs the corresponding ecological risk level and regulation suggestions. The specific steps for calculating the network resilience fluctuation value based on the modeling adaptation score, state synchronization score, connection strength, and data update frequency of each ecological entity are as follows: The resilience fluctuation contribution value of each ecological entity is calculated by statistically analyzing the modeling adaptation score, state synchronization score, entity connection strength, and data update frequency per unit time of all ecological entities. The network resilience fluctuation value of the system at the current moment is obtained by weighted averaging of the resilience fluctuation contributions of each ecological entity.

2. The network resilience prediction method based on digital twins according to claim 1, characterized in that: The specific steps for collecting ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images, through ecological monitoring equipment, and performing standardized preprocessing on the ecological monitoring data are as follows: Meteorological data, including temperature, humidity, wind speed, wind direction, atmospheric pressure, and precipitation, are collected through weather stations. Hydrological and water quality data, including water level, flow velocity, pH value, dissolved oxygen concentration, conductivity, and ammonia nitrogen concentration, were collected through hydrological and water quality monitoring stations. Soil data on moisture, temperature, and salinity were collected using geological sensors. Ecological monitoring data, including species activity frequency, behavioral tags, and estimated biodensity, are collected using infrared cameras, acoustic monitors, and biosensors. Remote sensing image data on changes in surface temperature, vegetation index, and water area were collected using remote sensing satellites and drones. All collected data are subjected to standardized preprocessing, which includes field format standardization, timestamp alignment, missing value imputation, and unit conversion. Collect the number of valid fields received for each ecological entity within the current time window, entity connection strength, data update frequency per unit time, data update delay, and field nesting level; and record the collected ecological monitoring data fields, standard fields, the length of time the ecological monitoring data is missing during the collection process, and the total analysis time.

3. The network resilience prediction method based on digital twins according to claim 1, characterized in that: The specific steps for calculating the modeling fit score based on the standardized preprocessed ecological monitoring data, comparing the ecological data modeling evaluation results with the structural template, and marking them as data to be modeled are as follows: The number of fields, the nesting level of fields, and the number of unit types are extracted from the standardized ecological monitoring data, and the field structure complexity is calculated. The matching degree between the ecological monitoring data fields and the standard fields is calculated, and the semantic fit score is obtained. The length of time missing ecological monitoring data and the total analysis time were statistically analyzed, and the time coverage ratio was calculated. The modeling fit score is obtained by combining the field structure complexity, semantic conflict ratio, and time coverage ratio. Real-time comparison of modeling fit score with modeling fit threshold, which includes a first fit threshold and a second fit threshold: When the modeling adaptation score is less than the first adaptation threshold, it is judged as high-quality data with simple structure, consistent semantics, and complete time, and is directly included in the unified description model and enters the digital twin construction process. When the modeling fit score is greater than or equal to the first fit threshold and less than the second fit threshold, it is judged as medium quality data with some structural complexity or semantic conflict. After performing field mapping optimization, unit normalization and missing completion on the data, it is decided whether to include it in the Siamese model. When the modeling fit score is greater than or equal to the second fit threshold, it is judged as low-quality data with complex structure, disordered field naming or serious time missing, and marked as data to be corrected, and is not included in the current twin construction process.

4. The network resilience prediction method based on digital twins according to claim 1, characterized in that: The specific steps for performing state synchronization assessment on the data to be modeled, calculating the state synchronization score of each ecological entity, and dynamically updating the operating state of the corresponding ecological entity in the digital twin based on the digital twin state results are as follows: The field completeness rate is calculated by counting the number of valid fields received by each ecological entity within the current time window; the modeling adaptation score and data update delay of the ecological entity are obtained; the data update delay of all ecological entities within the current time window is counted, and the data update delay of the ecological entity with the smallest data update delay is selected as the optimal reference value for the data update cycle within the current time period. Calculate the state synchronization score of the ecological entity based on the field completeness rate, modeling adaptation score, and data update latency. Real-time comparison of status synchronization score value and status threshold, the status threshold is divided into first status threshold and second status threshold; When the state synchronization score is greater than or equal to the first state threshold, the current ecological entity is in a highly consistent state, and is considered to have complete data, good timeliness, and good modeling adaptation. The state is directly written into the digital twin and included in the current state view for subsequent resilience analysis and prediction. When the state synchronization score is greater than or equal to the second state threshold and less than the first state threshold, the current ecological entity is in an intermediate transition state and should be delayed and not written to the main view for the time being. The current state is put into a buffer, the trend of state changes is recorded, and the missing fields are periodically attempted to be filled in and the timestamps are realigned. The data is then written to the twin after the update conditions are met. When the state synchronization score is less than the second state threshold, the current ecological entity is in a low consistency state. The entity is considered distorted and untrustworthy, and updates are blocked and marked as abnormal. The system will mark the entity as a "low-trust node" and initiate a data supplementation request. If low scores are achieved for multiple consecutive periods, a structural correction or reconstruction process will be triggered.

5. The network resilience prediction method based on digital twins according to claim 1, characterized in that: The specific steps for identifying key vulnerable nodes and risk paths based on the volatility score results, and outputting the corresponding ecological risk level and regulation recommendations, are as follows: By comparing the network resilience fluctuation value with the risk threshold, the current system status is determined. The risk threshold is divided into a first risk threshold, a second risk threshold, and a third risk threshold. When the network resilience fluctuation value is less than the third risk threshold, the system is judged to be in a high resilience state, and the normal monitoring frequency and view update rhythm are maintained. When the network resilience fluctuation value is greater than or equal to the third risk threshold and less than the second risk threshold, the system is judged to have entered a mild fluctuation state. The update frequency of the status view is increased, and high-frequency collection is performed on critical path nodes to record the evolution trend. When the network resilience fluctuation value is greater than or equal to the second risk threshold and less than the first risk threshold, the system is judged to be in a state of resilience decay. Local intervention is carried out, the structure chain tracing algorithm is activated, key nodes are identified, the sampling frequency of the relevant area is increased and data supplementation is triggered; abnormal state nodes are locally isolated and their impact on the global state is suspended. When the network resilience fluctuation value exceeds the first risk threshold, the system is judged to be in an unbalanced state. The system will mark the risk level as "high" and initiate the resilience risk response process, including local structural isolation, data re-sampling requests, path intervention suggestions, and early warning output. At the same time, the ecological entities with the highest resilience contribution values ​​are extracted as key vulnerable nodes, and the risk propagation path is identified based on the connection relationship between entities, outputting a system-level risk map and regulation and intervention suggestions.

6. The network resilience prediction method based on digital twins according to claim 1, characterized in that: The specific method for calculating the network resilience fluctuation value is as follows: The modeling adaptation score of each ecological entity / (1 + the state synchronization score of each ecological entity) = the modeling difficulty weight of the entity under the dual influence of structural complexity and data reliability. Multiplying this weight by the ratio of the connection strength of the ecological entity in the ecological network to the maximum connection strength among all ecological entities reflects its structural criticality. Multiply this by the ratio of the data update frequency of the ecological entity in the current time to the highest data update frequency among all ecological entities to reflect its activity level; The summation of the products of all ecological entities is divided by the total number of entities to obtain the network resilience fluctuation value at the current moment, which is used to assess the overall operational stability and risk fluctuation level of the system.

7. A network resilience prediction system based on digital twins, characterized in that: It includes a data acquisition and preprocessing module, a standard modeling module, a digital twin construction module, and a resilience index mapping module, among which: The data acquisition and preprocessing module is used to collect ecological monitoring data, including meteorological, hydrological, water quality, soil, biological activity and remote sensing images, through ecological monitoring equipment, and to perform standardized preprocessing on the ecological monitoring data. The standard modeling module is used to calculate the modeling fit score based on the standardized preprocessed ecological monitoring data, and compare the ecological data modeling evaluation results with the structural template, marking them as data to be modeled; The specific method for calculating the modeling adaptation score is as follows: The structural complexity score of the current ecological monitoring data is obtained by summing the number of fields, the number of unit types, and the field nesting level, adding one to the result and taking the logarithm. Calculate the ratio of the ecological monitoring data field to the standard field, add one to the ratio and take the reciprocal to obtain the semantic fit score; The time coverage ratio is calculated by comparing the length of time the ecological monitoring data is missing within the target analysis period with the total analysis time. The modeling fit score of the ecological entity is obtained by adding the three results: structural complexity score, semantic fit score, and time coverage ratio. The digital twin construction module is used to perform state synchronization assessment on the data to be modeled, calculate the state synchronization score of each ecological entity, and dynamically update the running status of the corresponding ecological entity in the digital twin based on the digital twin state results. The specific method for calculating the state synchronization score value of each ecological entity is as follows: The number of valid fields received by each ecological entity within the current time window / (1 + the modeling adaptation score of the ecological entity) = the data integrity score of the ecological entity in the context of structural complexity; Divide the data update delay of the ecological entity by the optimal reference value of the data update cycle in the current time period to calculate the time synchronization offset ratio, and subtract the time synchronization offset ratio from one to obtain the update timeliness score. Multiply the data integrity score and the update timeliness score to obtain the state synchronization score of the ecological entity; The resilience index mapping module is used to calculate the network resilience fluctuation value based on the modeling adaptation score and state synchronization score of each ecological entity, and to identify key vulnerable nodes and risk paths based on the fluctuation score results, and output the corresponding ecological risk level and regulation suggestions. The specific steps for calculating the network resilience fluctuation value based on the modeling adaptation score, state synchronization score, connection strength, and data update frequency of each ecological entity are as follows: The resilience fluctuation contribution value of each ecological entity is calculated by statistically analyzing the modeling adaptation score, state synchronization score, entity connection strength, and data update frequency per unit time of all ecological entities. The network resilience fluctuation value of the system at the current moment is obtained by weighted averaging of the resilience fluctuation contributions of each ecological entity.

8. A network resilience prediction device based on digital twins, used to implement the network resilience prediction method based on digital twins as described in any one of claims 1-6, characterized in that: include: Ecological monitoring terminals are used to collect various types of ecological and environmental data in real time within the target monitoring area, including meteorological, hydrological, water quality, soil, biological activity, and remote sensing images. They can obtain field information, location coordinates, sampling time, and equipment status of each ecological entity and upload the collected data to the data processor via wired or wireless means. The data processor is used to standardize the field format, align the timestamps and convert the units of the received ecological monitoring data, calculate the modeling adaptation score and state synchronization score of each ecological entity, and transmit the score results and the processed data to the twin construction module. The digital twin construction unit is used to map ecological entities to corresponding objects in the twin model based on the scoring results and standardized data provided by the data processor, and to update the operating status and connection relationship of each ecological entity in the twin in real time according to the entity status and synchronous scoring value, forming a virtual mapping that evolves synchronously with the real ecosystem. The resilience assessment and early warning unit is used to calculate the overall resilience fluctuation score of the system based on the modeling adaptation score, state synchronization score, connection strength and data update frequency of each ecological entity in the state view, and to determine the system risk level based on the score results, identify key vulnerable nodes and risk propagation paths, and generate risk early warning information and regulation and intervention suggestions.

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