A method and system for evaluating and early warning of the resilience of a characteristic double-coupled import energy sea route
By employing a dual-coupling approach based on characteristics and an LSTM-attention mechanism, the problem of quantitative assessment and early warning for imported energy shipping channels was solved. This approach enables precise response and characteristic adaptation to complex disturbance scenarios, thereby improving the accuracy of assessment and the timeliness of response.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TRANSPORT PLANNING & RES INST MINIST OF TRANSPORT
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies are insufficient for quantitative assessment, dynamic weight adjustment, accurate early warning, and closed-loop response of imported energy shipping routes. They are unable to effectively cope with complex disturbance scenarios, ignore differences in recovery characteristics, and result in deviations between assessment results and actual needs, as well as response delays.
By employing a dual-coupling approach based on characteristics, the final dynamic weights of multiple resilience indicators are calculated by collecting multi-source data. Combined with a perturbation intensity prediction model based on LSTM and attention mechanism, a resilience decay function is established to achieve forward-looking early warning and selection of the optimal response plan.
It enables precise resilience assessment and early warning of imported energy shipping routes, improves the quantitative capability of assessment and the accuracy of response, reduces false alarm and missed alarm rates, and supports proactive closed-loop response and feature adaptation.
Smart Images
Figure CN122155580A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of international maritime energy transportation safety technology, and in particular relates to a method and system for assessing and warning the resilience of imported energy maritime transport channels with dual-coupled characteristics. Background Technology
[0002] The resilience of my country's maritime transport routes for imported LNG and crude oil is directly related to the country's energy supply security. However, these routes frequently encounter diverse natural and man-made disturbances in actual operation: for example, the offshore-international artificial canal-nearshore LNG route experiences reduced LNG transport continuity due to canal scheduling delays; the tropical sea-nearshore LNG route is affected by giant waves caused by typhoons, revealing insufficient resilience; the cold-region sea-nearshore crude oil route suffers from significantly increased ship recovery time and weak recovery capabilities due to winter sea ice cover; and the offshore-narrow waterway-nearshore crude oil route experiences transport interruptions due to ship encounters, resulting in energy transport losses. These scenarios demonstrate that different routes exhibit significantly different response characteristics to disturbances, necessitating resilience assessment technologies with differentiated adaptability.
[0003] However, existing resilience assessment techniques have the following shortcomings, making it difficult to meet the actual needs of the aforementioned complex disturbance scenarios: (1) The assessment model is qualitative and lacks quantitative support: Existing technologies mostly use qualitative descriptions such as "high, medium and low" resilience levels, and fail to construct quantifiable assessment indicators for the "delay sensitivity" of LNG transportation and the "interruption sensitivity" of crude oil transportation, making it difficult for the assessment results to effectively guide actual operation and maintenance decisions.
[0004] (2) Static resilience weights ignore channel differences: Existing methods usually use fixed weights (e.g., each accounting for 1 / 3) for each resilience dimension (such as continuity, disturbance resistance, and resilience), failing to dynamically adjust according to the dominant disturbance type of the channel (e.g., the offshore-international artificial canal-nearshore channel is mainly affected by scheduling delays, so the continuity weight needs to be increased; the cold-region nearshore area-nearshore channel is mainly affected by sea ice retention, so the resilience weight needs to be increased), resulting in a significant deviation between the assessment results and the actual resilience requirements.
[0005] (3) Generalized warning thresholds and insufficient accuracy: Existing technologies use a uniform warning standard for all channels (such as triggering a warning when the resilience index RI < 0.5), without considering the differences in sensitivity of different channels to specific disturbances (such as the offshore-international artificial canal-nearshore LNG channel being more sensitive to delays, requiring a warning when RI < 0.65; and the tropical sea-nearshore LNG channel being more sensitive to typhoons, requiring a warning when RI < 0.7), resulting in a high rate of false alarms or missed alarms.
[0006] (4) Lack of resilience correlation and broken closed-loop response: Existing technologies have not established a quantitative functional relationship between disturbance intensity (such as wave height and delay duration) and resilience index, and cannot achieve an active closed-loop response of "disturbance occurrence → resilience assessment → early warning triggering → response execution", resulting in a significant lag in response.
[0007] (5) Ignoring differences in recovery characteristics: Existing assessments do not fully consider the additional recovery characteristics of LNG ships caused by special operations such as maintaining low temperature and adjusting tank pressure, resulting in distorted recovery capacity calculations and failing to truly reflect the actual recovery process.
[0008] Therefore, a technical solution is urgently needed to solve the above technical problems. Summary of the Invention
[0009] To address the aforementioned technical problems, this invention proposes a dual-coupled method for assessing and providing early warning of the resilience of imported energy shipping channels, comprising: Collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each resilience indicator; The current resilience index of the target sea lane is calculated based on the resilience index and the final dynamic weight. The time series data of the multi-source data is input into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel; Based on the current resilience index and the future disturbance intensity, the future resilience index is calculated using a resilience decay function, and an early warning is issued based on the future resilience index.
[0010] Furthermore, the multi-source data includes basic data, resilience characteristic data, and disturbance propagation data; The basic data includes: ship position, speed, significant wave height, and sea ice thickness; The resilience data includes: additional adaptation energy consumption caused by disturbance, emergency reserve call-up amount, cabin pressure regulation energy consumption, actual transport volume completed in the channel, and transport volume loss caused by disturbance. The disturbance propagation data includes: disturbance propagation velocity, disturbance influence radius, and channel segment disturbance attenuation coefficient.
[0011] Furthermore, several of the aforementioned resilience indicators include: characteristic-sensitive capacity guarantee rate, dynamic disturbance rejection-characteristic coefficient, unit characteristic recovery efficiency, and disturbance propagation index; The capacity guarantee rate for calculation characteristics-sensitive types includes: in, For characteristic-sensitive transport capacity guarantee rate, This represents the actual volume of freight transported via the channel. This is the transport capacity loss coefficient. This refers to the amount of transport volume lost due to disturbances. For the planned transport volume of the corridor, The additional adaptive energy consumption caused by the disturbance. Total power consumption for channel adaptation; The calculation of dynamic disturbance rejection characteristic coefficients includes: in, For dynamic disturbance rejection - characteristic coefficients, The duration of outages caused by disturbances. The disturbance intensity coefficient is... The total travel time of the passage. The amount of energy loss avoided after taking anti-interference measures. This represents the energy loss without taking any countermeasures. The calculation of unit property recovery efficiency includes: in, To restore efficiency based on unit characteristics, To recover the volume of transport completed during the recovery phase, To restore duration, To adjust energy consumption per unit time during the recovery phase, Plan the sailing time for the passage. To adapt energy consumption to the unit time required for normal transportation; The calculation of the disturbance transmission index includes: in, The disturbance transmission index. For the disturbance propagation velocity, The radius of influence of the disturbance. This is the disturbance attenuation coefficient for the channel segment. This refers to the emergency response time.
[0012] Furthermore, calculating the final dynamic weight of each of the resilience indices includes: Based on historical data of the aforementioned resilience characteristics, the basic weights of each resilience index are calculated using the information entropy method. ; The calculation of the final dynamic weights includes: in, For the first The final dynamic weights of the aforementioned resilience indicators, For the first The correlation correction coefficient of the aforementioned resilience index, For the first The disturbance intensity correction coefficient of the aforementioned toughness index. For the first The characteristic sensitivity correction coefficient of the toughness index.
[0013] Furthermore, calculating the current resilience index of the target sea lane includes: The current resilience index is obtained by multiplying each resilience index by its corresponding final dynamic weight and then adding the results.
[0014] Furthermore, the perturbation intensity prediction model includes: using the model after fusing LSTM with the attention mechanism as the perturbation intensity prediction model.
[0015] Furthermore, based on the current resilience index and the future disturbance intensity, the future resilience index is calculated using the resilience decay function, including: in, For time The additional adaptive energy consumption caused by the disturbance at that time. For time intervals, For time The future resilience index mentioned at that time, For time The current resilience index at that time, The first coefficient of the toughness decay curve. This is the second coefficient of the toughness decay curve. This is the third coefficient of the toughness decay curve. Total power consumption for channel adaptation.
[0016] Furthermore, the early warning based on the future resilience index includes: triggering a forward-looking early warning when the future resilience index is lower than a preset early warning threshold; Multiple response options are selected as a set of candidate response options to improve the future resilience index, and the optimal response option is selected from the set of candidate response options.
[0017] Furthermore, selecting the optimal response from the set of candidate responses includes: in, Select an index for the plan. To address the increase in the future resilience index brought about by the proposed solution. Additional adaptive energy consumption caused by disturbances; Will with the largest Option selection index The corresponding response plan is the optimal response plan.
[0018] This invention also proposes a dual-coupled resilience assessment and early warning system for imported energy shipping channels, comprising: The final dynamic weight calculation module is used to collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each of the resilience indicators. The resilience index calculation module is used to calculate the current resilience index of the target sea lane based on the resilience index and the final dynamic weight. The module for calculating future disturbance intensity is used to input the time series data of the multi-source data into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel. The early warning module is used to calculate the future resilience index based on the current resilience index and the future disturbance intensity using a resilience decay function, and to issue an early warning based on the future resilience index.
[0019] Compared with the prior art, the present invention has the following advantages and technical effects: First, a three-layer dedicated data system integrating resilience characteristics and disturbance propagation mechanisms was constructed, adding core parameters related to characteristic adaptation and disturbance propagation, thus distinguishing it from the basic data system of existing technologies. On this basis, a four-dimensional quantitative resilience index system was established, and the final dynamic weights were integrated into each index to fill the gap in existing technologies that do not consider the spatial propagation and characteristic differences of disturbances, thereby solving the defect of static weights in existing technologies. Finally, a predictive model that integrates LSTM and an improved attention mechanism adapted to maritime channel disturbance data was proposed. By optimizing the calculation method of attention weights, the limitations of traditional LSTM in this type of data application were overcome. Attached Figure Description
[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation
[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0022] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0023] Example 1 like Figure 1 As shown in the figure, this embodiment proposes a dual-coupled characteristic method for assessing and warning of the resilience of imported energy shipping channels, which specifically includes the following steps: Step S1: Collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each resilience indicator. Specifically, the multi-source data includes basic data, resilience characteristic data, and disturbance propagation data; The basic data includes: ship position, speed, significant wave height, sea ice thickness, etc. The resilience data includes: additional adaptation energy consumption caused by disturbances, emergency reserve call-up amount, cabin pressure regulation energy consumption, actual transport volume completed in the channel, and transport volume loss caused by disturbances, etc. The disturbance propagation data includes: disturbance propagation velocity, disturbance influence radius, and channel segment disturbance attenuation coefficient, etc.
[0024] Specifically, the resilience indicators include: characteristic-sensitive capacity guarantee rate, dynamic disturbance immunity-characteristic coefficient, unit characteristic recovery efficiency, and disturbance propagation index; 1. The calculation of the capacity guarantee rate for characteristic-sensitive transport includes: in, For characteristic-sensitive transport capacity guarantee rate, This represents the actual volume of freight transported via the channel. This is the transport capacity loss coefficient. This refers to the amount of transport volume lost due to disturbances. For the planned transport volume of the corridor, The additional adaptive energy consumption caused by the disturbance. Total power consumption for channel adaptation; This embodiment uses a characteristic-sensitive capacity assurance rate to quantitatively assess the ability of a transportation corridor to maintain its mission under disturbance conditions from two dimensions: "actual capacity - delay impact" and "characteristic adaptation." The core of this approach is to achieve a balance between capacity assurance and characteristic adaptation, specifically tailored to the different characteristics of LNG (sensitive to delays) and crude oil (sensitive to interruptions). Its significance lies in two aspects: First, it overcomes the shortcomings of traditional methods that only focus on capacity while neglecting characteristic adaptation, avoiding excessive consumption of adaptation resources for the sake of unilaterally ensuring capacity. Second, based on the differences in transportation characteristics between LNG carriers and crude oil carriers, it incorporates the actual transportation impact into the assessment through characteristic factors, enabling the results to directly guide operational and maintenance decisions.
[0025] 2. Calculation of dynamic disturbance rejection-characteristic coefficients includes: in, For dynamic disturbance rejection - characteristic coefficients, The duration of outages caused by disturbances. The disturbance intensity coefficient is... The total travel time of the passage. The amount of energy loss avoided after taking anti-interference measures. This represents the energy loss without taking any countermeasures. The dynamic anti-interference-characteristic coefficient in this embodiment is based on the coupled analysis of "outage duration caused by disturbance" and "characteristic benefits of anti-interference measures," quantifying the comprehensive ability of a channel to resist disturbances. The key is to reflect the cost-effectiveness balance between "anti-interference effect" and "characteristic adaptation." Its significance lies in two aspects: First, it breaks through the traditional evaluation framework that only focuses on anti-interference results while neglecting characteristic adaptation, avoiding the decision-making trap of over-investing adaptation resources in simply pursuing anti-interference capabilities; Second, for different channels with different dominant disturbance types, it achieves differentiated evaluation and precise measurement of anti-interference effectiveness by constructing an "outage duration-characteristic adaptation ratio."
[0026] 3. The calculation of unit property recovery efficiency includes: in, To restore efficiency based on unit characteristics, To recover the volume of transport completed during the recovery phase, To restore duration, To adjust energy consumption per unit time during the recovery phase, Plan the sailing time for the passage. To adapt energy consumption to the unit time required for normal transportation; In this embodiment, the unit characteristic recovery efficiency focuses on the "capacity recovery speed under unit adaptive energy consumption," quantitatively evaluating the efficiency of the channel in recovering to normal transportation status after disturbance. The core lies in accurately adapting to the differences in recovery characteristics between LNG and crude oil. Its significance is reflected in two aspects: First, it breaks through the limitations of traditional assessments that only focus on recovery speed and ignore characteristic differences, truly reflecting the additional recovery characteristic impact of LNG ships due to special requirements such as maintaining cryogenic temperatures and regulating tank pressure; Second, by constructing the "efficiency ratio between the planning phase and the recovery phase," it intuitively judges the degree of adaptation between the adopted recovery measures and transportation characteristics.
[0027] 4. Calculating the disturbance transmission index includes: in, The disturbance transmission index. For the disturbance propagation velocity, The radius of influence of the disturbance. This is the disturbance attenuation coefficient for the channel segment. This refers to the emergency response time.
[0028] The disturbance propagation index in this embodiment focuses on the propagation process of disturbances within the channel, quantitatively characterizing it from three dimensions: diffusion speed, impact range, and attenuation law. It aims to systematically reveal the spatial propagation characteristics of disturbances, overcoming the shortcomings of existing technologies in this regard. Its significance lies in two aspects: First, it accurately identifies the degree of impact of disturbances on different segments of the channel, addressing the differences in disturbance propagation mechanisms across different channels, thus improving the precision of the assessment; second, it provides core characteristic parameters for disturbance prediction, supporting the achievement of forward-looking early warning capabilities several hours in advance.
[0029] Specifically, calculating the final dynamic weight of each of the resilience indices includes: Based on historical data of the aforementioned resilience characteristics, the basic weights of each resilience index are calculated using the information entropy method. ; The calculation of the final dynamic weights includes: in, For the first The final dynamic weights of the aforementioned resilience indicators, For the first The correlation correction coefficient of the aforementioned resilience index, For the first The disturbance intensity correction coefficient of the aforementioned toughness index. For the first The characteristic sensitivity correction coefficient of the toughness index.
[0030] In this embodiment, the basic weights of the four-dimensional indicators are determined using the information entropy method based on historical data. For example, based on historical data including 12 months of basic data, resilience characteristic data, and disturbance transmission data, the basic weights of each dimension are calculated using information entropy. ( =1~4, corresponding to characteristic-sensitive capacity guarantee rate, dynamic disturbance resistance-characteristic coefficient, unit characteristic recovery efficiency, and disturbance transmission index. The core logic is that if a certain indicator has a large fluctuation range in historical data (i.e., high discriminative power), its information entropy is small, and the corresponding basic weight is... The corresponding values are higher, and vice versa. Taking the "Coastal-International Artificial Canal-Nearshore LNG Channel" as an example, based on a 12-month scheduling delay sample, the basic entropy weights are calculated as follows: (Weight of the capacity guarantee rate for characteristic-sensitive traffic) = 0.28 (Weight of dynamic disturbance rejection characteristic coefficient) = 0.35 (Weight of unit characteristic recovery efficiency) = 0.22, (Weight of the disturbance transmission index) = 0.15.
[0031] Preferably, this embodiment introduces three types of correction coefficients to achieve dynamic adjustment of the weights: Correlation correction coefficient This coefficient characterizes the degree of matching between the four-dimensional index and the dominant channel disturbance type. The higher the correlation level, the better. The larger the value, the better. For example, in the "offshore-international artificial canal-nearshore LNG corridor," the dominant disturbance is "scheduling delay," which has the highest correlation with the "characteristic-sensitive transport volume guarantee rate." Therefore, the value corresponding to the characteristic-sensitive transport volume guarantee rate is... The value is set to 1.0, while the correlation level with the "disturbance transmission index" is relatively low, therefore the value corresponding to the disturbance transmission index is... Set to 0.6.
[0032] Disturbance intensity correction factor This coefficient is calculated based on the ratio of real-time disturbance intensity to the channel tolerance threshold, aiming to allow the weights to be dynamically adjusted as the disturbance risk changes in real time. The calculation formula is: =1 + 0.2 × (current disturbance intensity / tolerance threshold). For example, when a tropical ocean-nearshore LNG channel encounters a typhoon, the real-time effective wave height is... =6m, channel tolerance threshold is =8m, then the dynamic disturbance rejection characteristic coefficient of =1+0.2×(6 / 8)=1.15.
[0033] Characteristic sensitivity correction coefficient This coefficient is calculated based on the ratio of the sensitivity of a specific indicator to the average sensitivity, and is used to reflect the degree of influence of the characteristics corresponding to each indicator. The calculation formula is: =1 + 0.1 × (Indicator characteristic sensitivity / Average sensitivity). For example, in an LNG channel, the unit characteristic recovery efficiency... Its characteristic sensitivity is significantly higher than other indicators, then The value is 1.2.
[0034] Step S2: Calculate the current resilience index of the target sea lane based on the resilience index and the final dynamic weight; Specifically, calculating the current resilience index of the target sea lane includes: The current resilience index is obtained by multiplying each resilience index by its corresponding final dynamic weight and then adding the results.
[0035] Step S3: Input the time series data of the multi-source data into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel; Specifically, the perturbation intensity prediction model includes: using the model that integrates LSTM and attention mechanism as the perturbation intensity prediction model.
[0036] Preferably, the disturbance intensity prediction model adopts a four-layer serial architecture, consisting of an input layer, a feature extraction layer (based on an LSTM network), an attention enhancement layer (introducing an improved attention mechanism), and an output layer, forming a complete prediction chain. The input data is the multi-source data mentioned above, and the output is the predicted disturbance intensity value of each segment of the channel in the next 24 hours.
[0037] The functions of each layer are as follows: 1. Input Layer: Responsible for receiving and standardizing multi-source data. The sampling interval is 1 hour, and the input sequence length is fixed at 72 hours (i.e., historical observations from the previous 3 days). After standardization, the output is a feature vector. ,in For a moment Time series data from multiple sources.
[0038] Feature extraction layer (LSTM network): Employs a three-layer stacked Long Short-Term Memory network to extract temporal correlation features of perturbations from the input sequence (e.g., typhoon intensity evolution trends, delay accumulation patterns). Each LSTM layer consists of a forget gate, an input gate, and an output gate. Through a gating mechanism, it achieves selective memorization and forgetting of historical features, effectively avoiding gradient vanishing or exploding problems, and adapting to the long-term temporal characteristics of maritime channel perturbation data.
[0039] Attention Enhancement Layer (Improved Attention Mechanism): This layer introduces an attention weight allocation mechanism based on the features output by the LSTM, focusing on enhancing historical moment features that significantly impact future disturbances (such as typhoon path turning points and sudden changes in scheduling delays). This addresses the problem of insufficient attention to key features in traditional LSTM, thereby improving prediction accuracy. This layer represents an improvement over the basic LSTM model.
[0040] Output layer: Employing a fully connected structure, it maps the attention-enhanced feature vectors to predicted perturbation intensity values for the next 24 hours, with the output sequence being... ,in, For the future The predicted values of disturbance intensity for each segment of the channel (such as significant wave height, sea ice thickness, and delay duration).
[0041] The core function formula is shown below: 1. Core formulas for LSTM networks (per LSTM layer): Forgotten Gate: ,in It is the sigmoid activation function. Here is the forget gate weight matrix. This is the output of the hidden layer from the previous time step. This is a bias term used to control the proportion of cell state forgotten from the previous time step.
[0042] Input Gate: , ,in, The input gate activation value, Candidate cell state, This is the weight matrix. This is a bias term used to control the input ratio of features at the current time step.
[0043] Cell status update: ,in, For dot product operation, For a moment The cellular state.
[0044] Output gate: , ,in, For a moment The output gate activation value, For a moment The hidden layer output is the LSTM feature extraction result.
[0045] 2. Core formula of the improved attention mechanism: Attention weight calculation: ,in, Here is the attention weight matrix. For attention bias, For attention vectors, For matrix transpose, For a moment The attention weights of the LSTM output features, with the sum of all weights being 1.
[0046] Features after attention enhancement: ,in, The final feature vector used for prediction amplifies the influence of key temporal features.
[0047] 3. Output layer prediction formula: ,in This is the output layer weight matrix. For output layer bias terms, For the future The predicted values of the disturbance intensity of each segment of the channel are transformed from feature vectors to predicted values through linear mapping.
[0048] Step S4: Calculate the future resilience index using the resilience decay function based on the current resilience index and the future disturbance intensity, and issue an early warning based on the future resilience index.
[0049] Specifically, the calculation of the future resilience index using the resilience decay function, based on the current resilience index and the future disturbance intensity, includes: in, For time The additional adaptive energy consumption caused by the disturbance at that time. For time intervals, For time The future resilience index mentioned at that time, For time The current resilience index at that time, The first coefficient of the toughness decay curve, This is the second coefficient of the toughness decay curve. This is the third coefficient of the toughness decay curve. Total power consumption for channel adaptation.
[0050] Taking typhoon disturbances in tropical waters-nearshore LNG channels as an example, the above calculations of future resilience indices are applied. Explanation: in, For time The future resilience index mentioned at that time, For time The current resilience index at that time, The additional adaptive energy consumption caused by the disturbance 24 hours later (this value is a prediction) has resilience decay curve coefficients of 0.08, 0.005, and 0.03 (derived through linear regression analysis based on historical typhoon disturbance data of the tropical sea-nearshore LNG channel).
[0051] Specifically, issuing early warnings based on the future resilience index includes: triggering a forward-looking early warning when the future resilience index is lower than a preset early warning threshold; For example, the future resilience index for the next 24 hours Compared with the preset warning threshold, when An alert is triggered when the temperature falls below a preset warning threshold. For example, currently... If the forecast indicates that the temperature will drop to 0.68 within 24 hours, falling below the Level 1 threshold of 0.7 for tropical coastal areas and nearshore channels, a Level 1 warning will be triggered ahead of schedule. Simultaneously, a warning urgency grading mechanism will be introduced, based on... The warning is categorized based on its decay rate: if it decays by 0.02 per hour, it is classified as an "emergency forward-looking warning"; if it decays by 0.01 per hour, it is classified as a "concern forward-looking warning". This classification aims to improve the targeting and precision of the warning response.
[0052] Multiple response options are selected as a set of candidate response options to improve the future resilience index, and the optimal response option is selected from the set of candidate response options.
[0053] Specifically, selecting the optimal response from the set of candidate responses includes: in, Select an index for the plan. To address the increase in the future resilience index brought about by the proposed solution. Additional adaptive energy consumption caused by disturbances; Will with the largest Option selection index The corresponding optimal solutions are shown in the table below.
[0054] Example 2 like Figure 2 As shown, this embodiment proposes a dual-coupled resilience assessment and early warning system for imported energy shipping channels, specifically including the following modules: The final dynamic weight calculation module is used to collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each of the resilience indicators. The resilience index calculation module is used to calculate the current resilience index of the target sea lane based on the resilience index and the final dynamic weight. The module for calculating future disturbance intensity is used to input the time series data of the multi-source data into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel. The early warning module is used to calculate the future resilience index based on the current resilience index and the future disturbance intensity using a resilience decay function, and to issue an early warning based on the future resilience index.
[0055] Since the system technical solution of this embodiment 2 is based on the technical solution of embodiment 1, it will not be described again.
[0056] The above are merely preferred embodiments 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.
Claims
1. A method for assessing and providing early warning of the resilience of imported energy shipping channels with dual-coupled characteristics, characterized in that, include: Collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each resilience indicator. The current resilience index of the target sea lane is calculated based on the resilience index and the final dynamic weight. The time series data of the multi-source data is input into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel; Based on the current resilience index and the future disturbance intensity, the future resilience index is calculated using a resilience decay function, and an early warning is issued based on the future resilience index.
2. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 1, characterized in that, The multi-source data includes basic data, resilience characteristic data, and disturbance propagation data; The basic data includes: ship position, speed, significant wave height, and sea ice thickness; The resilience data includes: additional adaptation energy consumption caused by disturbance, emergency reserve call-up amount, cabin pressure regulation energy consumption, actual transport volume completed in the channel, and transport volume loss caused by disturbance. The disturbance propagation data includes: disturbance propagation velocity, disturbance influence radius, and channel segment disturbance attenuation coefficient.
3. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 1, characterized in that, Several of the aforementioned resilience indicators include: characteristic-sensitive capacity guarantee rate, dynamic disturbance immunity-characteristic coefficient, unit characteristic recovery efficiency, and disturbance propagation index; The capacity guarantee rate for calculation characteristics-sensitive traffic includes: in, For characteristic-sensitive transport capacity guarantee rate, This represents the actual volume of transport completed by the channel. This is the transport capacity loss coefficient. This refers to the amount of transport volume lost due to disturbances. For the planned transport volume of the corridor, The additional adaptive energy consumption caused by the disturbance. Total power consumption for channel adaptation; The calculation of dynamic disturbance rejection characteristic coefficients includes: in, For dynamic disturbance rejection - characteristic coefficients, The duration of outages caused by disturbances. The disturbance intensity coefficient is... The total travel time of the passage. The amount of energy loss avoided after taking anti-interference measures. This represents the energy loss without taking any countermeasures. The calculation of unit property recovery efficiency includes: in, To restore efficiency based on unit characteristics, To recover the volume of transport completed during the recovery phase, To restore duration, To adjust energy consumption per unit time during the recovery phase, Plan the sailing time for the passage. To adapt energy consumption to the unit time of normal transportation; The calculation of the disturbance transmission index includes: in, The disturbance transmission index. For the disturbance propagation velocity, The radius of influence of the disturbance. This is the disturbance attenuation coefficient for the channel segment. This refers to the emergency response time.
4. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 2, characterized in that, Calculating the final dynamic weight for each of the resilience indices includes: Based on historical data of the aforementioned resilience characteristics, the basic weights of each resilience index are calculated using the information entropy method. ; The calculation of the final dynamic weights includes: in, For the first The final dynamic weights of the aforementioned resilience indicators, For the first The correlation correction coefficient of the aforementioned resilience index, For the first The disturbance intensity correction coefficient of the aforementioned toughness index. For the first The characteristic sensitivity correction coefficient of the toughness index.
5. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 4, characterized in that, Calculating the current resilience index of the target sea lane includes: The current resilience index is obtained by multiplying each resilience index by its corresponding final dynamic weight and then adding the results.
6. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 1, characterized in that, The perturbation intensity prediction model includes: a model that integrates LSTM and attention mechanism as the perturbation intensity prediction model.
7. The method for assessing and providing early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 1, characterized in that, The calculation of the future resilience index using the resilience decay function, based on the current resilience index and the future disturbance intensity, includes: in, For time The additional adaptive energy consumption caused by the disturbance at that time. For time intervals, For time The future resilience index mentioned at that time, For time The current resilience index at that time, The first coefficient of the toughness decay curve. This is the second coefficient of the toughness decay curve. This is the third coefficient of the toughness decay curve. Total power consumption for channel adaptation.
8. The method for assessing and providing early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 1, characterized in that, Early warning based on the future resilience index includes: triggering a forward-looking early warning when the future resilience index is lower than a preset early warning threshold; Multiple response options are selected as a set of candidate response options to improve the future resilience index, and the optimal response option is selected from the set of candidate response options.
9. The method for assessing and early warning of the resilience of imported energy shipping channels with dual-coupled characteristics as described in claim 8, characterized in that, Selecting the optimal response from the set of candidate responses includes: in, Select an index for the plan. To address the increase in the future resilience index brought about by the proposed solution. Additional adaptive energy consumption caused by disturbances; Will with the largest Option selection index The corresponding response plan is the optimal response plan.
10. A dual-coupled system for assessing and warning the resilience of imported energy shipping channels, characterized in that, include: The final dynamic weight calculation module is used to collect multi-source data of the target sea lane, calculate multiple resilience indicators, and calculate the final dynamic weight of each of the resilience indicators. The resilience index calculation module is used to calculate the current resilience index of the target sea lane based on the resilience index and the final dynamic weight. The module for calculating future disturbance intensity is used to input the time series data of the multi-source data into the disturbance intensity prediction model to predict the future disturbance intensity of the target shipping channel. The early warning module is used to calculate the future resilience index based on the current resilience index and the future disturbance intensity using a resilience decay function, and to issue an early warning based on the future resilience index.