Wave parameter early warning calculation method and system based on wave floating ball

Abstract

The invention relates to the technical field of wave early warning, in particular to a wave parameter early warning calculation method and system based on a wave floating ball, and the method comprises the steps: obtaining the current wave early warning information; acquiring corresponding multi-source real-time sea condition observation data; obtaining port operation scheduling information corresponding to the wave early warning influence area within the wave early warning coverage time; calculating an early warning conformity score based on the trend and deviation continuity of the early warning difference value to generate a first quantitative evaluation parameter; on the basis of port operation scheduling information, the limiting influence of the wave early warning information on operation execution conditions is evaluated, and a second quantitative evaluation parameter is generated; and based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, in combination with an early warning adjustment logic, determining an adjustment indication demand of the current wave early warning information. The method has the advantages that the accuracy, timeliness and practicability of early warning can be improved according to dynamic evaluation and adjustment of wave early warning information, and therefore the operation efficiency and safety of a port are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wave warning, in particular to a wave parameter warning calculation method and system based on wave floats. BACKGROUND

[0002] In order to ensure the safety of ship navigation in the water area and improve the overall operation efficiency of the port, the port management department usually deploys wave floats at key positions in the port area and along the main approach channel. These wave floats collect wave motion original data in real time through built-in sensors, and periodically transmit the data to the calculation system of the port central monitoring center through a wireless communication network. After receiving the data, the warning module of the port management system is triggered to issue warning instructions to the ships in the port area and the ships planning to enter and exit the port.

[0003] However, the ocean environment has high dynamicity and local variability, for example, sudden local strong gusts, near-shore deformation of far-sea swells, or rapid transit of adjacent water area frontal weather systems, which may cause deviations in the prediction of the changing characteristics of the actual wave conditions. In actual operation, there are cases where the warning does not match the actual sea conditions: first, the risk predicted by the prediction model is too high or the duration is too long, while the actual sea conditions are good, resulting in unnecessary economic costs and reduced efficiency, such as ships being forced to wait for a long time, port operations being interrupted, and cargo turnover efficiency being reduced; second, the given warning level is too low or the risk occurrence time is estimated to be too late, while the actual sea conditions deteriorate rapidly, which may not be fully considered for ships in critical safety conditions or with weak wind and wave resistance, and the ship personnel may weaken their trust and compliance with official warnings when they repeatedly experience significant discrepancies between warnings and actual sea conditions, which may pose a safety hazard. Although the port management department has post-mortem analysis, it cannot meet the immediate adjustment needs during the warning execution period, making it difficult to achieve more accurate, efficient, and safe dynamic management of the port channel.

[0004] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0005] In order to solve the problems of the prior art, the present application provides a wave parameter warning calculation method and system based on wave floats, which can dynamically evaluate and adjust wave warning information, improve the accuracy, timeliness and practicality of the warning, and thus improve the operation efficiency and safety of the port.

[0006] In one aspect, the present application provides a wave parameter warning calculation method based on wave floats, the method comprising: obtaining current wave warning information, the wave warning information including a wave warning influence area, a warning parameter, and a wave warning coverage time; Obtaining multi-source real-time sea state observation data covering the wave warning influence area, the data sources of the multi-source real-time sea state observation data including wave float monitoring data, in-flight ship feedback information and shore-based meteorological telemetry data; Obtaining port operation scheduling information corresponding to the wave warning influence area within the wave warning coverage time, the port operation scheduling information including operation type and corresponding position, planned time and operation tolerance parameter to wave conditions; Comparing the multi-source real-time sea state observation data with the warning parameters to obtain a warning difference, calculating a warning compliance score based on the trend and deviation persistence of the warning difference to generate a first quantitative evaluation parameter; Based on the port operation scheduling information, evaluating the restriction influence of the wave warning information on the operation execution condition, and generating an operation window sensitivity score as a second quantitative evaluation parameter; Based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, combining a preset warning adjustment logic, determining an adjustment instruction requirement of the current wave warning information, the adjustment instruction requirement being whether to perform confirmation, degradation, upgrading, shortening and / or cancellation operation.

[0007] Through the above scheme, the wave warning information can be dynamically evaluated and adjusted according to real-time sea state and port operation demand, improving the accuracy, timeliness and practicality of the warning, thereby improving the port operation efficiency and safety.

[0008] Preferably, the application also proposes that based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, combining a preset warning adjustment logic, determining an adjustment instruction requirement of the current wave warning information, including: Receiving an operation state switching instruction of the current port, the state switching instruction indicating that the current port is in a normal operation, emergency response and / or night operation state; From a preset multi-group warning adjustment logic library, selecting a warning adjustment logic matched with the operation state switching instruction as an adjustment logic rule; Based on the adjustment logic rule, combining the first quantitative evaluation parameter and the second quantitative evaluation parameter, outputting a dynamic adjustment suggestion, and adjusting the adjustment instruction requirement according to the dynamic adjustment suggestion.

[0009] Through the above scheme, the warning adjustment logic can be dynamically adjusted according to the current operation state of the port, making the warning adjustment more flexible and adaptive to actual demand.

[0010] Preferably, the application also proposes that comparing the multi-source real-time sea state observation data with the warning parameters to obtain a warning difference, calculating a warning compliance score based on the trend and deviation persistence of the warning difference to generate a first quantitative evaluation parameter, including: obtain a current quality index of each data source in the multi-source real-time sea state observation data and a geographical or physical correlation index of the data source and a wave warning influence area; calculate a fusion weight of each data source according to the quality index and the geographical or physical correlation index; perform a normalized deviation comparison according to the data source and the corresponding fusion weight, construct a coincidence function, and generate a first quantitative evaluation parameter according to the coincidence function.

[0011] Through the above scheme, the accuracy of the warning coincidence evaluation can be improved by fusing multi-source sea state data and considering data quality and correlation.

[0012] Preferably, the method further comprises: periodically or in response to a trigger, re-obtain the current quality index of each data source and the corresponding geographical or physical correlation index; determine whether a change value of the current quality index of the data source and the corresponding geographical or physical correlation index obtained currently compared with previous data reaches a weight update threshold value; if the change value reaches the weight update threshold value, recalculate and update the fusion weight; otherwise, maintain the original fusion weight.

[0013] Through the above scheme, the fusion weight can be dynamically updated according to the changes of data source quality and correlation, further improving the reliability of data fusion.

[0014] Preferably, the method further comprises: convert the quality index and the geographical or physical correlation index of each data source into a first score value and a second score value, respectively; determine whether the first score value and the second score value satisfy a preset score conflict condition; if the score conflict condition is satisfied, modify and fuse the first score value and the second score value according to a preset conflict resolution mechanism to generate the fusion weight; if the score conflict condition is not satisfied, calculate the first score value and the second score value according to a conventional weighting strategy to generate the fusion weight.

[0015] Through the above scheme, the conflict between the quality and correlation indexes of the data source can be handled, and the rationality of the fusion weight can be ensured.

[0016] Preferably, the method further comprises: The interruption risk value is calculated by comparing the operation tolerance parameter corresponding to the wave condition of the operation type with the current early warning parameter; The operation impact degree caused by the operation interruption estimation is evaluated by combining the corresponding preset time limit level of the operation type, the loading and unloading object type, and the preset safety risk level; The operation window sensitivity score of the corresponding operation type is generated by coupling the interruption risk value and the operation impact degree; The second quantitative evaluation parameter is output by weighting and summarizing the operation window sensitivity scores of each operation type according to the priority of the operation type.

[0017] Through the above scheme, the influence of early warning on port operation can be comprehensively evaluated by considering factors such as operation type, time limit, and risk, and the accuracy of operation window sensitivity evaluation can be improved.

[0018] Preferably, the application further proposes that the operation window sensitivity score of the corresponding operation type is generated by coupling the interruption risk value and the operation impact degree, including: The operation window sensitivity score of the corresponding operation type is generated by functionally combining the interruption risk value and the operation impact degree.

[0019] Through the above scheme, the operation window sensitivity score can be generated more finely through function combination.

[0020] Preferably, the application further proposes that the priority adjustment method of each operation type includes: The current operation scheduling state is monitored, and the operation scheduling state is obtained or input by a port operation scheduling system; It is judged whether the key path changes compared with the historical operation scheduling state; If there is a change, a preset first priority model matched with the current scheduling state is selected to reassign the priority of each operation type; If there is no change, a preset second priority model matched with the historical operation scheduling state is used to assign the priority of each operation type.

[0021] Through the above scheme, the priority of the operation type can be dynamically adjusted according to the change of the operation scheduling state, so that the sensitivity evaluation is more suitable for the actual operation situation.

[0022] Preferably, the application further proposes that based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, the adjustment indication demand of the current wave early warning information is determined by combining a preset early warning adjustment logic, including: It is judged whether the adjustment indication demand indicated by the first quantitative evaluation parameter and the second quantitative evaluation parameter exists a preset adjustment conflict condition, and the adjustment conflict condition includes the conflicting adjustment parameters in the first quantitative evaluation parameter and the second quantitative evaluation parameter; When it is determined that the adjustment conflict condition exists, then according to the preset conflict adjustment rule, and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter, an adjustment decision required by the adjustment instruction is determined; When it is determined that the adjustment conflict condition does not exist, then according to the preset general adjustment rule, and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter, the adjustment instruction required by the current wave warning information is determined.

[0023] Through the above scheme, the conflict between the first quantitative evaluation parameter and the second quantitative evaluation parameter can be processed, and the rationality of the final adjustment decision is ensured.

[0024] On the other hand, the present application also provides a wave parameter warning calculation system based on a wave float ball, and the technical points are: The system comprises: A warning information acquisition module is configured to acquire current wave warning information, wherein the wave warning information comprises a wave warning influence area, a warning parameter, and a wave warning coverage time. A sea state data acquisition module is configured to acquire multi-source real-time sea state observation data covering the wave warning influence area, wherein the data sources of the multi-source real-time sea state observation data comprise wave float ball monitoring data, in-transit ship feedback information, and shore-based meteorological telemetry data. An operation information acquisition module is configured to acquire port operation scheduling information corresponding to the wave warning influence area within the wave warning coverage time, wherein the port operation scheduling information comprises an operation type and a corresponding position, a planned time, and an operation tolerance parameter for a wave condition. A consistency evaluation module is configured to compare the multi-source real-time sea state observation data with the warning parameter to obtain a warning difference, calculate a warning compliance score based on a trend and a deviation persistence of the warning difference, and generate a first quantitative evaluation parameter. A sensitivity evaluation module is configured to evaluate the restriction influence of the wave warning information on operation execution conditions based on the port operation scheduling information, and generate an operation window sensitivity score as a second quantitative evaluation parameter. An adjustment decision module is configured to determine an adjustment instruction requirement of the current wave warning information based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, in combination with a preset warning adjustment logic, wherein the adjustment instruction requirement is whether to perform a confirmation, degradation, upgrade, shortening, and / or revocation operation.

[0025] Through the above scheme, a system for implementing the above method is provided, which is convenient for actual deployment and application.

[0026] In summary, the wave parameter early warning calculation method and system based on wave floating ball provided by the application has the advantages that the wave early warning information can be dynamically evaluated and adjusted according to real-time sea conditions and port operation requirements, the accuracy, timeliness and practicability of early warning are improved, and the port operation efficiency and safety are improved. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by one embodiment of the application.

[0028] Figure 2 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0029] Figure 3 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0030] Figure 4 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0031] Figure 5 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0032] Figure 6 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0033] Figure 7 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0034] Figure 8 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0035] Figure 9 A flowchart of a wave parameter early warning calculation method based on a wave floating ball provided by another embodiment of the application.

[0036] Figure 10 A program block diagram of a wave parameter early warning calculation system based on a wave floating ball provided by another embodiment of the application. DETAILED DESCRIPTION

[0037] The technical solutions in the present application will be described clearly and completely in the present application in combination with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0039] Referring to Figure 1 The present application proposes a wave parameter early warning calculation method based on wave floating ball, which comprises: S1000: obtaining current wave early warning information, the wave early warning information including wave early warning influence area, early warning parameter and wave early warning coverage time; S2000: obtaining multi-source real-time sea state observation data covering the wave early warning influence area, the data sources of the multi-source real-time sea state observation data including wave floating ball monitoring data, in-flight ship feedback information and shore-based meteorological telemetry data; S3000: obtaining port operation scheduling information corresponding to the wave early warning influence area within the wave early warning coverage time, the port operation scheduling information including operation type and its corresponding position, planned time and operation tolerance parameter to wave conditions; S4000: comparing the multi-source real-time sea state observation data with the early warning parameter to obtain early warning difference, calculating early warning coincidence score based on the trend and deviation persistence of the early warning difference to generate first quantitative evaluation parameter; S5000: based on the port operation scheduling information, evaluating the limiting influence of the wave early warning information on operation execution conditions, and generating operation window sensitivity score as the second quantitative evaluation parameter; S6000: based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, combining the preset early warning adjustment logic, determining the adjustment indication requirement of the current wave early warning information, whether to perform confirmation, degradation, upgrading, shortening and / or cancellation operation.

[0040] Specifically, in the embodiment, current wave warning information is acquired, which contains a wave warning influence area, warning parameters, and a wave warning coverage time, for the port management department to issue warning instructions according to the prediction results.

[0041] Multi-source real-time sea state observation data refers to real-time sea state data from different sources covering the wave warning influence area, including wave float monitoring data, in-transit ship feedback information, and shore-based meteorological telemetry data. Wave float monitoring data refers to real-time wave parameter data collected and transmitted by wave floats deployed in the water area. Specifically, it can use an internal sensor group to collect raw data and transmit through a wireless communication network, providing fixed-point and continuous wave parameter information. In-transit ship feedback information refers to real-time sea state information obtained and reported by in-transit ships through shipboard equipment or manual observation. Specifically, it can use AIS additional messages, shipboard weather station data transmission, or manual reporting, etc. to obtain, providing actual sea state perception during navigation. Shore-based meteorological telemetry data refers to meteorological and sea state data obtained by shore-based telemetry equipment. Specifically, it can use shore-based radar, high-frequency ground wave radar, video monitoring systems, etc. to obtain, providing more widely covered meteorological background information and nearshore sea state information.

[0042] The embodiments of the present application can cover a wide range and reflect the actual sea state truly by integrating real-time sea state data from different sources, making up for the limitations of a single data source and providing a high-trust data basis for subsequent warning evaluation. Further, port operation scheduling information corresponding to the wave warning influence area within the wave warning coverage time is also acquired. Port operation scheduling information refers to port operation arrangement information corresponding to the wave warning influence area within the wave warning coverage time, including operation type and its corresponding location, planned time, and operation tolerance parameter to wave conditions. By considering port operation information, the influence of the warning on actual operations can be evaluated with high matching degree, reducing unnecessary operation interruption or delay, and improving port operation efficiency. The operation type and its location determine the affected objects; the planned time determines the time range of the influence of the warning on the operation type; and the operation tolerance parameter to wave conditions is strongly related to whether the corresponding operation type can be safely performed.

[0043] The early warning difference refers to the difference between the multi-source real-time sea state observation data and the early warning parameters, which can be represented by numerical difference, percentage difference or difference value based on specific metrics, quantifying the deviation of the early warning from the actual sea state. The trend of the early warning difference refers to the direction of change of the early warning difference over time, which can be calculated by linear regression, moving average or difference analysis, reflecting the direction of change of the deviation. The deviation persistence refers to the length of time or stability of the early warning difference remaining within a certain range or direction, which can be measured by the average deviation, standard deviation or duration threshold within a time window, reflecting the stability of the deviation.

[0044] The early warning compliance score refers to the quantitative indicator calculated based on the trend of the early warning difference and the deviation persistence, which can be calculated by function mapping, rule-based reasoning or machine learning models, quantifying the accuracy and trustworthiness of the early warning. The first quantitative evaluation parameter refers to the early warning compliance score, which is an indicator for quantifying the accuracy and trustworthiness of the early warning, aiming to provide quantitative basis for subsequent early warning adjustment. By comparing the real-time sea state observation data with the early warning parameters, the accuracy of the early warning can be quantified, and the trustworthiness of the early warning can be evaluated more comprehensively by combining the trend and the deviation persistence.

[0045] The operation window sensitivity score refers to the quantitative indicator calculated by evaluating the impact of the wave warning information on the execution conditions of the operation, which can be calculated by a scoring model based on factors such as operation type, wave tolerance, risk level, etc., quantifying the impact of the early warning on the port operation. The second quantitative evaluation parameter refers to the operation window sensitivity score, which is an indicator for quantifying the impact of the early warning on the port operation, aiming to provide a more comprehensive decision basis for subsequent early warning adjustment. By evaluating the impact of the early warning on the execution conditions of the operation, the impact of the early warning on the port operation can be quantified, providing a more comprehensive decision basis for subsequent early warning adjustment. A high operation window sensitivity score indicates that the early warning has a significant impact on the operation, requiring careful adjustment.

[0046] The preset early warning adjustment logic refers to the rules or models used to determine the need for adjustment of the wave warning information, which can be implemented by rule sets based on expert experience, decision trees, fuzzy logic or machine learning models, etc., considering the accuracy of the early warning and the impact on the port operation to determine the adjustment strategy that meets the actual needs. The adjustment indication requirement refers to the adjustment operation needed for the current wave warning information, including whether to confirm, downgrade, upgrade, shorten and / or cancel the operation, making the early warning information more consistent with the current sea state and operation needs. By considering the accuracy of the early warning and the impact on the port operation, and combining the preset adjustment logic, the early warning information can be adjusted based on data and logic. The adjustment indication requirement specifies the adjustment operation to be performed on the early warning information to adapt to the current sea state and operation needs.

[0047] The innovation of the present application is that by combining multi-source real-time sea state observation data with port operation scheduling information, and based on quantitative evaluation results and preset logic, the dynamic adjustment of the issued wave warning information is completed, and the effect of improving the warning accuracy and considering the port operation efficiency is achieved. The scheme of the present application constructs a complete evaluation and judgment mechanism. First, by obtaining the current warning information, multi-source real-time sea state observation data and port operation scheduling information, the system obtains all the inputs required for evaluation. It is due to the integration of real-time sea state data from different sources such as wave floats, ships at sea and shore-based telemetry that the perception of the actual sea state covers a wider and more realistic range. At the same time, by obtaining the port operation scheduling information, the impact of the warning is directly connected with the actual operation demand. Then, by comparing the real-time sea state data with the warning parameters, the authenticity of the warning is numerized (the first quantitative evaluation parameter), and the trend and persistence of the deviation are considered, which improves the trustworthiness of the evaluation. It is through this numerical evaluation that there is a data-based basis for whether the warning still matches. And by evaluating the limiting effect of the warning on the execution conditions of port operations, the possible impact of the warning on port operations is numerized (the second quantitative evaluation parameter). It is through this numerical evaluation of the impact on operations that the cost-effectiveness can be considered when adjusting the warning. Finally, based on the two numerical evaluation parameters and in combination with the preset warning adjustment logic, the system can automatically determine whether the current warning needs to be adjusted and what adjustment operation needs to be performed (confirmation, downgrading, upgrading, shortening or cancellation). It is through this judgment process based on numerical evaluation and preset logic that dynamic and rapid correction of the issued warning information becomes possible, thereby solving the problem of delay in warning adjustment in the prior art.

[0048] In one of the embodiments of the present embodiment, it is assumed that the port management system issues a wave warning for a certain channel, with the warning parameters being that the significant wave height exceeds 2 meters and the duration being 6 hours in the future. The system first acquires the warning information. At the same time, the system receives real-time monitoring data from wave buoys deployed near the channel, receives sea state data from shipboard equipment feedback from ships sailing in the channel, and obtains meteorological telemetry data in the area from shore-based radars. After processing these multi-source data, the current warning deviation is calculated by comparing them with the warning parameters. For example, the real-time significant wave height is 1.8 meters on average, and has been declining in the past hour, with a short deviation duration. Based on this information, the warning compliance score is calculated, for example 0.75, as the first quantitative evaluation parameter. At the same time, the system obtains the port operation scheduling information of the channel in the future 6 hours, for example, a container ship is planned to enter the port for berthing in the period, and its operation tolerance parameter for wave conditions is that the significant wave height does not exceed 1.5 meters; another tugboat plans to carry out the relocation operation, and its operation tolerance parameter for wave conditions is that the significant wave height does not exceed 2.5 meters. Evaluate the impact of the warning on these operations. The container ship entering the port operation will face the risk of interruption, and the tugboat relocation operation has a lower risk. Combined with the priority of the container ship entering the port operation, the operation window sensitivity score is calculated, for example 0.8, as the second quantitative evaluation parameter. Finally, the system inputs the first quantitative evaluation parameter and the second quantitative evaluation parameter into the preset warning adjustment logic. The logic may be set that when the warning compliance score is high and the operation window sensitivity score is high, it is recommended to downgrade or shorten the operation. The system outputs the adjustment indication requirement as downgrade according to the logic.

[0049] Reference Figure 2 Optionally, in another embodiment of the present application, step S6000 comprises: S6100: receiving an operation state switching instruction of the current port, the state switching instruction indicating that the current port is in a normal operation, an emergency response and / or a night operation state; S6200: selecting a warning adjustment logic matched with the operation state switching instruction as an adjustment logic rule from a plurality of preset warning adjustment logic libraries; S6300: based on the adjustment logic rule, combining the first quantitative evaluation parameter and the second quantitative evaluation parameter, outputting a dynamic adjustment suggestion, and adjusting the adjustment indication requirement according to the dynamic adjustment suggestion.

[0050] The operation state switching instruction is a signal or data used to indicate the operation mode or state of the current port to the system. It can be realized by manual input, automatic signal from the port management system, or internal system instruction triggered by time or event, so that the system can perceive the specific operation environment of the port and provide context information for subsequent early warning adjustment strategy selection. The preset multiple sets of early warning adjustment logic library is a storage structure containing multiple different early warning adjustment rule sets. It can be realized by a database, a configuration file set, or a data structure in memory. Its purpose is to store the logic rules for guiding early warning adjustment decisions that are preset for different operation states. The early warning adjustment logic matched with the operation state switching instruction is the corresponding rule set selected from the preset multiple sets of early warning adjustment logic library according to the state indicated by the received operation state switching instruction. It can be realized by searching, indexing, or conditional judgment based on state identifiers. Its purpose is to ensure that the early warning adjustment logic used is adapted to the actual operation state of the port. The adjustment logic rule is a specific early warning adjustment logic set selected for the current early warning adjustment decision. Its purpose is to provide specific judgment criteria and processes to guide how to generate adjustment suggestions based on quantitative evaluation parameters. The dynamic adjustment suggestion is a specific suggestion or instruction about how to adjust the current wave warning information calculated based on the selected adjustment logic rule and quantitative evaluation parameters. It can be realized by text description, adjustment type identifier (such as upgrade or downgrade), or adjustment parameter value (such as shortening time). Its purpose is to provide decision basis for the final adjustment instruction requirement.

[0051] The scheme of the present application receives the operation state switching instruction of the current port, so that the system can know the specific operation environment of the port in real time, for example, whether it is in regular operation, emergency response requiring high caution, or night operation with low visibility. Due to the system's awareness of the change in the operation state, the pre-set multiple sets of pre-warning adjustment logic library can be selected to match the current operation state switching instruction to serve as the current adjustment logic rule. This is because different operation states have different requirements and tolerances for wave pre-warning. For example, a more conservative pre-warning strategy may be needed in the emergency response state to ensure safety, while efficiency may be more important in the regular operation state. Due to the selection of the adjustment logic rule that matches the current state, the first quantitative evaluation parameter (reflecting the degree of coincidence between the pre-warning and the actual sea conditions) and the second quantitative evaluation parameter (reflecting the degree of influence of the pre-warning on the port operation) can be combined to output a dynamic adjustment suggestion that is more in line with the current operation reality. Due to the adjustment of the final adjustment instruction requirement (confirmation, downgrading, upgrading, shortening or cancellation) based on this dynamic adjustment suggestion, the adjustment of the pre-warning information is no longer based on fixed and universal logic, but can be dynamically adapted according to the change in the port operation state, thereby improving the flexibility and practicality of the pre-warning adjustment. This way of dynamically selecting adjustment logic according to the port operation state can more finely manage wave pre-warning and better balance the needs of safety and efficiency compared to the method of using only pre-set fixed logic for judgment in the basic scheme, especially in complex and variable port operation environments.

[0052] In one embodiment of the present embodiment, the system receives an operation state switching instruction manually input by the operator of the port dispatch center, such as the operator selecting "emergency response mode" on the system interface. After receiving this instruction, the system accesses the pre-set multiple sets of pre-warning adjustment logic library stored in the database. This logic library may contain independent rule sets for different states such as "regular operation", "emergency response" and "night operation". The system extracts the pre-set "emergency response pre-warning adjustment logic" as the current adjustment logic rule from the library according to the received "emergency response mode" instruction. This "emergency response pre-warning adjustment logic" contains a series of conditional statements, such as "if the pre-warning coincidence score is less than 60 and the operation window sensitivity score is higher than 75, then suggest upgrading the pre-warning level". The system then inputs the first quantitative evaluation parameter (such as a pre-warning coincidence score of 55) and the second quantitative evaluation parameter (such as an operation window sensitivity score of 80) currently calculated into this "emergency response pre-warning adjustment logic" for judgment. According to the above rule, since 55 is less than 60 and 80 is higher than 75, the logic judgment result outputs a dynamic adjustment suggestion of "suggest upgrading the pre-warning level". The system determines the adjustment instruction requirement of the current wave pre-warning information as "upgrading operation" according to this dynamic adjustment suggestion.

[0053] With reference to Figure 3 Optionally, step S4000 comprises: S4100: obtaining the current quality indicators of each data source in the multi-source real-time sea state observation data and the geographical or physical correlation indicators thereof with the wave warning influence area; S4200: calculating the fusion weight of each data source according to the quality indicators and the geographical or physical correlation indicators; S4700: performing normalized deviation comparison according to the data sources and their corresponding fusion weights, and constructing a conformity function to generate the first quantitative evaluation parameter according to the conformity function.

[0054] In this embodiment, the quality indicators refer to the reliability, accuracy, integrity, timeliness and other attributes of the data sources, which can specifically include the signal-to-noise ratio, missing rate, update frequency and the like of the sensor data, and the purpose is to quantify the availability and credibility of the information provided by each data source; the geographical or physical correlation indicators refer to the proximity in space or the correlation in physical process between the monitoring position or observation range of the data source and the wave warning influence area, which can specifically be the distance between the monitored buoy and the warning area, the coverage range of the shore-based radar, the influence degree of the weather station data on the regional wind field and wave field, and the like, and is used to evaluate the representativeness of the data source to the actual sea state of the warning area; the fusion weight refers to the relative importance coefficient given to each data source when the multi-source data is comprehensively utilized, and the weight is calculated based on the quality indicators and the correlation indicators of the data source, and the purpose is to dynamically adjust the contribution proportion of each data source in the overall evaluation according to its reliability and representativeness; the normalized deviation comparison refers to the standardization processing of the differences between the sea state parameters observed by different data sources and the warning parameters, which eliminates the differences in units, dimensions or numerical ranges of different parameters, and can specifically be dividing the deviation value by the threshold or standard deviation of the warning parameter, and the purpose is to enable the deviation values from different data sources to be compared and fused on the same scale; the conformity function is a mathematical model or algorithm, which is used to comprehensively consider the normalized deviation of each data source and the corresponding fusion weight, and calculate a single value to represent the overall conformity degree of the multi-source observation data and the warning parameters, and the function can adopt weighted average, fuzzy logic, machine learning model and the like, and the purpose is to integrate the multi-source heterogeneous evaluation information into a unified quantitative evaluation parameter.

[0055] The embodiment introduces the evaluation of the quality and correlation of multi-source real-time sea state observation data itself, and calculates the fusion weight based thereon, so that when the deviation comparison is performed, the difference between the actual sea state and the warning parameter can be more accurately reflected. Specifically, first, the quality index and the correlation index with the warning area of each data source are obtained, which reflect the reliability and representativeness of the data source. Based on these indexes, the fusion weight is calculated, so that the data source with high quality and high correlation occupies a more important position in the subsequent evaluation. Then, the deviation of each data source and the warning parameter is normalized and weighted fused by using the fusion weight, and the multi-source information is integrated into a unified first quantitative evaluation parameter by constructing a conformity function. Due to the weighted fusion mechanism based on the quality and correlation of the data source, the present scheme can effectively process multi-source heterogeneous data, reduce the interference of low-quality or irrelevant data, and generate more accurate and reliable warning conformity scores, thereby solving the problem of how to effectively fuse multi-source heterogeneous sea state observation data and fully consider the quality and correlation of each data source to improve the accuracy and reliability of the warning conformity score.

[0056] In one embodiment of the present application, the quality indicators of each data source in the multi-source real-time sea state observation data include, for the wave buoy monitoring data, the real-time performance of data transmission, the stability of sensor readings, the completeness of historical data, etc.; the geographical correlation indicators include the straight-line distance between the monitored buoy and the center point of the warning influence area. For the in-transit ship feedback information, the quality indicators include the timeliness of the feedback information, the accuracy level of the ship equipment, the experience evaluation of the feedback personnel, etc.; the geographical correlation indicators include the overlapping degree of the current position of the feedback ship and the warning influence area. For the shore-based meteorological telemetry data, the quality indicators include the calibration status of the equipment, the data coverage range, the data update frequency, etc.; the physical correlation indicators include the influence degree of the telemetry data (such as wind field data) on the wave generation and propagation in the warning area evaluated by a meteorological model. When calculating the fusion weight of each data source according to the quality indicators and the geographical or physical correlation indicators, the quality indicators and the correlation indicators can be quantified as scores from 0 to 100, and then the fusion weight of each data source can be calculated by using weighted average or fuzzy reasoning, for example, weight = w1 * quality score + w2* correlation score, where w1 and w2 are preset weight coefficients. When performing the deviation comparison and constructing the conformity function according to the data sources and their corresponding fusion weights, first, the deviation of the wave parameter (such as the significant wave height) observed by each data source from the warning parameter (such as the warning threshold) is normalized, for example, normalized deviation = (observed value - warning threshold) / warning threshold. Then, the conformity function is constructed, for example, conformity score = Sum (fusion weight i * (1 - |normalized deviation i|)) / Sum (fusion weight i), where i represents different data sources. The first quantitative evaluation parameter is calculated according to the conformity function.

[0057] With reference to Figure 4 Optionally, in another embodiment of the present application, the method further comprises: S4300: periodically or in response to a trigger, reacquire the current quality indicators of each data source and the corresponding geographical or physical correlation indicators; S4400: determine whether the change value of the current quality indicators of each data source and the corresponding geographical or physical correlation indicators compared with the previous data reaches a weight update threshold; S4500: if the change value reaches the weight update threshold, recalculate and update the fusion weight; S4600: otherwise, maintain the original fusion weight.

[0058] In the embodiment, the periodically or triggered re-acquisition of the evaluation indicators of the data sources refers to re-acquiring the evaluation indicators of the data sources according to a preset time interval or when a specific event occurs. The weight update threshold refers to a critical value for judging whether the change of the evaluation indicators of the data sources is sufficient to trigger the recalculation of the fusion weights, which avoids unnecessary frequent weight updates. The recalculation and update of the fusion weights refers to recalculating the contribution proportions of the data sources in the data fusion according to the latest acquired quality indicators and the geographical or physical correlation indicators of the data sources, and replacing the original fusion weights with the new calculation results, so as to make the fusion weights reflect the latest state of the data sources.

[0059] In one of the embodiments of the embodiment, the time interval for periodically re-acquiring the evaluation indicators of the data sources can be set to once per hour. The triggered conditions can include but are not limited to that a device failure is reported by a data source, a significant change occurs in the geographical position of a data source, or a sudden increase in the data anomaly rate reported by a data source. The quality indicator can be specifically calculated as the effective data transmission rate of the data source in the past one hour. The geographical or physical correlation indicator can be calculated as the reciprocal of the straight-line distance between the position of the data source and the geometric center of the current warning affected area. The weight update threshold can be set to a change of more than 10% in the quality indicator or a change of more than 20% in the geographical or physical correlation indicator. When recalculating the fusion weights, an algorithm based on the entropy weight method can be used to calculate a new weight vector according to the latest quality indicators and the geographical or physical correlation indicators.

[0060] With reference to Figure 5 Optionally, in another embodiment of the present application, step S4200 includes: S4210: converting the quality indicators and the geographical or physical correlation indicators of the data sources into first score values and second score values respectively; S4220: judging whether the first score values and the second score values satisfy a preset score conflict condition; S4230: if the score conflict condition is satisfied, modifying and fusing the first score values and the second score values according to a preset conflict resolution mechanism to generate the fusion weights; S4240: if the score conflict condition is not satisfied, calculating the first score values and the second score values according to a conventional weighting strategy to generate the fusion weights.

[0061] Wherein, in the embodiment, the first score value and the second score value refer to the values mapped to a unified scoring system by the quality index and the geographical or physical correlation index, which can be realized by linear mapping, piecewise function, fuzzy logic, etc., the purpose being to unify the indexes of different dimensions or value ranges to a comparable scale; the preset score conflict condition refers to the condition that a significant difference or contradiction exists between the quality score and the correlation score, which can be set as a score difference exceeding a preset threshold, or a score combination falling into a preset conflict region; the preset conflict resolution mechanism refers to the fusion method in the case of score conflict, which can be realized by nonlinear weighting, rule-based adjustment, introduction of a third factor for correction, or a more complex fusion algorithm, the purpose being to generate a more reasonable fusion weight in the case of conflict; the revised fusion refers to the adjustment and combination of the original score by the conflict resolution mechanism to obtain the final score for calculating the fusion weight; the conventional weighting strategy refers to the score fusion method in the case of no conflict, which can be realized by simple linear weighting, arithmetic mean, geometric mean, etc.

[0062] By converting the original quality index and the geographical or physical correlation index into scores of a unified dimension, comparison and judgment are facilitated. Then, by the preset condition, it is determined whether the two scores are in conflict. If there is a conflict, it indicates that there is a contradiction between the quality of the data source and its correlation with the warning region, at which time a specially designed conflict resolution mechanism is used to comprehensively consider the two scores to generate a revised fusion weight, avoiding the excessive influence of a certain index on the fusion weight caused by simple weighting. If there is no conflict, it indicates that the quality and correlation indexes are relatively consistent, at which time a conventional weighting strategy can be used to generate the fusion weight, maintaining the simplicity of the calculation. This case-by-case processing method enables fine adjustment in the case of index conflict to generate a fusion weight that is more consistent with the actual situation, thereby improving the accuracy of the subsequent warning compliance score.

[0063] In one embodiment of the present embodiment, specifically, the quality index (e.g., data accuracy, value range 0-100%) can be directly taken as the first score value (0-100 points). The geographic distance (e.g., the distance from the data source to the center of the warning area, value range 0-100 kilometers) is converted into the second score value (0-100 points), for example, using an inverse proportional mapping, a distance of 0 kilometers corresponds to 100 points, a distance of 100 kilometers corresponds to 0 points, and an intermediate distance is linearly mapped to 0-100 points. The preset score conflict condition can be set as: if the first score value is greater than 80 points and the second score value is less than 30 points, or the first score value is less than 30 points and the second score value is greater than 80 points, it is determined that there is a score conflict. If the score conflict condition is met, the first score value and the second score value are modified and fused according to the preset conflict resolution mechanism, for example, if the quality is high and the relevance is low, the fusion score = 0.4 * the first score value + 0.6 * the second score value can be used; if the quality is low and the relevance is high, the fusion score = 0.6 * the first score value + 0.4 * the second score value can be used. If the score conflict condition is not met, the first score value and the second score value are calculated according to the conventional weighting strategy, a fusion weight is generated, for example, using simple average, fusion score = 0.5 * the first score value + 0.5 * the second score value. Finally, the fusion score is normalized to the range of 0-1 as the fusion weight of the data source.

[0064] With reference to Figure 6 Optionally, in another embodiment of the present application, step S5000 comprises: S5100: comparing the operation tolerance parameter of the wave condition corresponding to the operation type with the current warning parameter to calculate an interruption risk value; S5200: combining the corresponding preset time limit level, the type of loading and unloading objects, and the preset safety risk level of the operation type to evaluate the operation impact degree caused by the estimated operation interruption; S5300: coupling the interruption risk value and the operation impact degree to generate an operation window sensitivity score corresponding to the operation type; S5400: weighting and summarizing the operation window sensitivity scores according to the priority of the operation type to output a second quantitative evaluation parameter.

[0065] In the embodiment, the interruption risk value refers to a quantitative representation of the possibility or probability of a specific operation type being interrupted due to exceeding its wave condition operation tolerance parameter under the current wave warning parameter, which can be calculated by the difference, ratio or statistical model based on historical data of the warning parameter and the operation tolerance parameter. The operation impact degree refers to a quantitative evaluation of the potential negative consequences caused by the interruption of a specific operation type due to the wave warning on the overall port operation efficiency, economic benefit, cargo safety and personnel safety, which can be determined by looking up a table, rule-based reasoning or risk assessment model in combination with factors such as pre-set time efficiency level, type of loading and unloading goods, safety risk level, etc. The final comprehensive evaluation value obtained by weighted averaging or weighted summation of the priority of each operation type is the second quantitative evaluation parameter, which is used to measure the overall restriction impact of the current wave warning on the port operation window, which can be calculated by using the weighted average formula or the weighted summation formula.

[0066] By decomposing the impact of wave warning on port operations into two dimensions of possibility of interruption (interruption risk) and consequences of interruption (operation impact degree) for quantitative evaluation, and further coupling them to generate a sensitivity score for a specific operation type, and finally weighting the summary according to the operation priority, a second quantitative evaluation parameter that can fully reflect the restriction impact of the warning on the port operation window is obtained. This makes the evaluation of the impact of the warning no longer stay at the level of whether the operation is affected, but goes deep into the risk level of interruption and the size of the actual loss caused by the interruption. It is precisely because of this detailed quantitative evaluation that the subsequent warning adjustment decision based on this parameter can more accurately balance the demand for safety and efficiency. For example, even if the warning parameter slightly exceeds the tolerance parameter of a certain operation (there is a certain interruption risk), but if the operation impact degree is low (such as non-critical and non-time-sensitive cargo loading and unloading), the operation window sensitivity score of the operation may not be high, prompting that the warning adjustment can appropriately consider relaxing the restrictions; on the contrary, if a certain operation has a high operation impact degree (such as involving dangerous goods or high-value cargo loading and unloading, or critical ship entering and leaving the port) even if the interruption risk is not extremely high, the sensitivity score will be high, prompting that the warning adjustment must prioritize safety. This comprehensive evaluation based on risk and impact, combined with the weight distribution of operation priority, makes the final second quantitative evaluation parameter more real and more comprehensive in reflecting the potential impact of the current warning on the overall port operation, providing a solid data support for the dynamic adjustment of the warning.

[0067] In one of the embodiments of the present embodiment, specifically, it is assumed that the port currently has two types of operations affected by the wave warning: large container ship berthing operation (operation type A) and small tugboat shifting operation (operation type B). For operation type A, the wave condition operation tolerance parameter is that the significant wave height is not more than 1.5 meters, and the current warning parameter is that the significant wave height is predicted to be 2.0 meters. After comparison, it is calculated that the interruption risk value is higher, for example, quantified as 0.8. At the same time, the preset time efficiency level of operation type A is high (ship schedule is tight), the type of loading and unloading goods is ordinary container, and the preset safety risk level is high (the ship is large and the operation is complex). Combined with these factors, the degree of operational impact is very high, for example, quantified as 0.9. The interruption risk value 0.8 and the degree of operational impact 0.9 are coupled, for example, by product calculation, to obtain the operation window sensitivity score of operation type A as 0.8 * 0.9 = 0.72. For operation type B, the wave condition operation tolerance parameter is that the significant wave height is not more than 2.5 meters, and the current warning parameter is still 2.0 meters. After comparison, it is calculated that the interruption risk value is lower, for example, quantified as 0.3. At the same time, the preset time efficiency level of operation type B is low, the type of loading and unloading goods is none, and the preset safety risk level is medium. Combined with these factors, the degree of operational impact is lower, for example, quantified as 0.4. The interruption risk value 0.3 and the degree of operational impact 0.4 are coupled, for example, by product calculation, to obtain the operation window sensitivity score of operation type B as 0.3 * 0.4 = 0.12. It is assumed that the priority of operation type A is high (weight 0.7), and the priority of operation type B is medium (weight 0.3). According to the priority of the operation type, the operation window sensitivity scores are weighted and summarized to output the second quantized evaluation parameter, for example, calculated as (0.72 * 0.7) + (0.12 * 0.3) = 0.504 + 0.036 = 0.54. This 0.54 is the second quantized evaluation parameter of the overall operation window restriction impact of the port by the current wave warning, which reflects the comprehensive impact of the warning on the port operation after considering the interruption risk, operational impact and priority of different operations.

[0068] With reference to Figure 7 Optionally, in another embodiment of the present application, step S5300 comprises: S5310: functionally combining the interruption risk value and the degree of operational impact to generate an operation window sensitivity score corresponding to the operation type.

[0069] In the present embodiment, the functional combination refers to the process of mapping two or more input variables into an output variable through a mathematical function, which can be implemented by a linear function, a nonlinear function or a more complex model, and the purpose is to establish a quantitative correlation between the interruption risk and the operational impact, so as to generate a sensitivity score that can comprehensively reflect the influence of the two.

[0070] After the interruption risk value and the operation impact degree are calculated and evaluated, instead of using simple superposition or table lookup, the interruption risk value and the operation impact degree are combined by a preset function model as the input of the function. The function model is constructed according to the actual port operation experience, historical data analysis or expert knowledge, and can capture the interaction relationship between the interruption risk level and the operation impact degree. Through function combination, the data of the two dimensions of the interruption risk value and the operation impact degree are fused into a single operation window sensitivity score with practical significance. The score quantifies the vulnerability and importance of a specific operation type under the current wave warning, and provides basic data for subsequent weighted aggregation according to the priority of the operation type to generate the second quantitative evaluation parameter. This coupling method based on function combination enables the sensitivity score to more accurately reflect the real risks faced by different operation types and their impact on the overall operation of the port, thereby improving the degree of reflecting the actual situation of the second quantitative evaluation parameter, and further optimizing the warning adjustment decision based on the first quantitative evaluation parameter and the second quantitative evaluation parameter.

[0071] In one embodiment of the present embodiment, the interruption risk value and the operation impact degree are combined by a function to generate an operation window sensitivity score corresponding to the operation type. Specifically, a weighted summation method can be used, for example, sensitivity score = w1 * interruption risk value + w2 * operation impact degree, where w1 and w2 are weights that can be preset or dynamically adjusted according to factors such as the importance and timeliness of the operation type. As a specific embodiment, a product method can be used, for example, sensitivity score = interruption risk value * operation impact degree. The following is illustrated by a specific example, and a segmented function can also be used, for example, when the operation impact degree exceeds a certain threshold, the sensitivity score increases rapidly.

[0072] Reference Figure 8 Alternatively, in another embodiment of the present application, the priority adjustment method for each operation type includes: A1: Monitor the current operation scheduling state, which is obtained or input by the port operation scheduling system; A2: Determine whether a critical path change has occurred compared to the historical operation scheduling state; A3: If a change has occurred, select a preset first priority model matching the current scheduling state to redistribute the priority of each operation type; A4: If no change has occurred, use a preset second priority model matching the historical operation scheduling state to distribute the priority of each operation type.

[0073] The operation scheduling state refers to the overall execution of the current port operation, which can be obtained automatically from the port operation scheduling system or manually input. The key path change refers to the change of the key operation sequence that affects the overall operation completion time of the port, which can be determined by the network analysis method based on the operation dependency relationship and duration. The preset first priority model and the preset second priority model refer to the pre-established rule set or algorithm for determining the relative importance of different operation types under a specific operation scheduling state, which can be realized by using the priority allocation strategy based on historical data analysis, expert experience or optimization algorithm.

[0074] By dynamically monitoring the operation scheduling state of the port, the actual situation of the port operation is mastered in real time. By comparing the current state with the historical state, it is determined whether there is a key path change that affects the overall operation efficiency. If the key path changes, it indicates that the bottleneck link or key task of the port operation has shifted, and at this time the preset first priority model matched with the current scheduling state is used to re-allocate the priority of each operation type according to the new operation focus, so as to ensure that important operations have higher weight. If the key path does not change, the preset second priority model matched with the historical scheduling state is used to maintain the stability of the priority allocation. This method of dynamically adjusting the priority of each operation type according to the operation scheduling state provides more accurate input for the subsequent weighted aggregation of the sensitivity scores of each operation window according to the priority. By applying the dynamically adjusted priority to the weighted aggregation process, the second quantitative evaluation parameter generated can more accurately reflect the degree of actual influence of the wave warning on the current port operation, so that the warning adjustment decision based on this parameter is more scientific and reasonable, effectively improving the flexibility and efficiency of the port operation, while taking into account the safety.

[0075] In one embodiment of the present embodiment, the system monitors the current operation scheduling state, such as obtaining the information of the ship berthing, loading and unloading, and moving from the port operation scheduling system. The system compares the current scheduling state with the historical record to analyze whether there is a key path change, such as analyzing the dependency relationship and planned time between operations to identify whether the key operation sequence that affects the overall port throughput efficiency has changed. If it is determined that there is a key path change, such as a sudden situation causing a low-priority operation to become a key link affecting subsequent important operations, the system selects the preset first priority model, which dynamically increases the priority of the key operation type for such key path change scenario. If it is determined that there is no key path change, the current scheduling state is consistent with the historical normal state, and the system uses the preset second priority model, which maintains the priority allocation of each operation type under the normal state.

[0076] Referring to Figure 9 . Alternatively, in another embodiment of the present application, step S6000 comprises: S6100: determining whether a preset adjustment conflict condition exists for the adjustment indication demand of the first quantitative evaluation parameter and the second quantitative evaluation parameter, the adjustment conflict condition comprising conflicting adjustment parameters in the first quantitative evaluation parameter and the second quantitative evaluation parameter; S6200: when it is determined that the adjustment conflict condition exists, determining the adjustment decision for the adjustment indication demand according to a preset conflict adjustment rule and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter; S6300: when it is determined that the adjustment conflict condition does not exist, determining the adjustment indication demand of the current wave warning information according to a preset regular adjustment rule and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter.

[0077] In the embodiment, the preset adjustment conflict condition refers to a preset inconsistent or contradictory state between the adjustment directions or degrees indicated by the first quantitative evaluation parameter and the second quantitative evaluation parameter when determining the adjustment indication demand of the wave warning information, which can be defined by a condition list or a logical expression, for example, when the first quantitative evaluation parameter indicates “upgrade” and the second quantitative evaluation parameter indicates “downgrade”, an adjustment conflict condition is constituted, and the purpose is to identify the evaluation result combination that needs special processing; the conflicting adjustment parameters refer to the specific adjustment suggestions or values that are contradictory or inconsistent in the first quantitative evaluation parameter and the second quantitative evaluation parameter under the preset adjustment conflict condition, which can be an enumerated value set, for example, “upgrade”, “downgrade”, “maintain”, etc., and the purpose is to clearly show the specific forms of conflict; the preset conflict adjustment rule refers to a rule set for guiding how to comprehensively determine the final adjustment decision from the first quantitative evaluation parameter and the second quantitative evaluation parameter when the adjustment conflict condition exists, which can be implemented by a priority rule, a weighted average rule, a majority rule, or an arbitration rule based on third-party information, and the purpose is to provide a logical framework for processing conflicts and ensure the rationality of the decision; the preset regular adjustment rule refers to a rule set for determining the adjustment indication demand according to the first quantitative evaluation parameter and the second quantitative evaluation parameter when the adjustment conflict condition does not exist, which can be implemented by a simple mapping table, a linear combination, or a nonlinear function, and the purpose is to process the adjustment decision in the non-conflict situation and ensure the efficiency of the decision.

[0078] By introducing the judgment of whether there is an adjustment conflict condition between the first quantitative evaluation parameter and the second quantitative evaluation parameter, the adjustment decision process is divided into two branches of conflict processing and regular processing. When it is judged that there is an adjustment conflict condition, instead of simply applying the regular rule, the adjustment decision is determined according to the preset conflict adjustment rule and in combination with the two quantitative evaluation parameters. It is due to this case-by-case processing mechanism that when facing the complex situation where the evaluation parameters indicate conflict, the decision can be made according to the preset and targeted rule, avoiding the randomness or stagnation of the decision, and ensuring the controllability and rationality of the adjustment process. When it is judged that there is no adjustment conflict condition, the processing is performed according to the preset regular adjustment rule, which ensures the processing efficiency in most non-conflict situations. This way of dynamically selecting the adjustment rule according to the state of the evaluation parameters makes the determination process of the adjustment indication demand of the wave warning information more robust and intelligent, can effectively cope with the complex combination of the evaluation parameters, and thus improves the accuracy and reliability of the warning information adjustment.

[0079] In one embodiment of the present embodiment, specifically, the system first obtains the currently calculated first quantitative evaluation parameter, for example, the warning compliance score is 70 points (full score 100 points), and the second quantitative evaluation parameter, for example, the operation window sensitivity score is 8 points (full score 10 points). The preset adjustment conflict condition can be defined as: when the first quantitative evaluation parameter indicates that the warning accuracy is low (for example, the score is lower than 80 points) and indicates that it needs to be downgraded or canceled, and the second quantitative evaluation parameter indicates that the operation window sensitivity is high (for example, the score is higher than 7 points) and indicates that it needs to maintain or upgrade the warning, then there is an adjustment conflict. The system judges whether the current 70 points and 8 points meet this conflict condition. Assuming that according to the preset logic, 70 points indicate that the warning may be too high (tending to be downgraded), and 8 points indicate that the operation is greatly affected (tending to maintain or upgrade), it is judged that there is an adjustment conflict condition. At this time, the system is according to the preset conflict adjustment rule. The conflict adjustment rule can be set as: when this specific conflict exists, the operation safety is given priority, that is, the suggestion of maintaining or upgrading is given priority, but the factor of insufficient warning accuracy is also considered, and finally the adjustment decision is determined as "maintaining" the current warning, or "shortening" the warning duration. If it is judged that there is no adjustment conflict condition, for example, the first quantitative evaluation parameter is 95 points (indicating high warning accuracy, tending to maintain or upgrade), and the second quantitative evaluation parameter is 2 points (indicating small operation impact, tending to be downgraded or canceled), there is no preset conflict condition. The system is according to the preset regular adjustment rule, for example, the regular rule is set as: when the first parameter indicates high accuracy and the second parameter indicates small impact, it tends to maintain the original warning or slightly downgraded. In combination with 95 points and 2 points, it is determined that the adjustment indication demand of the current wave warning information is "maintaining" or "slightly downgraded".

[0080] Referring toFigure 10 Further, in another embodiment of the present application, a wave parameter early warning calculation system based on wave floating ball, the system comprises: an early warning information acquisition module, configured to acquire current wave early warning information, the wave early warning information including a wave early warning influence area, early warning parameters and wave early warning coverage time; a sea state data acquisition module, configured to acquire multi-source real-time sea state observation data covering the wave early warning influence area, the data sources of the multi-source real-time sea state observation data including wave floating ball monitoring data, in-transit ship feedback information and shore-based meteorological telemetry data; an operation information acquisition module, configured to acquire port operation scheduling information corresponding to the wave early warning influence area within the wave early warning coverage time, the port operation scheduling information including operation types and corresponding positions, planned times and operation tolerance parameters to wave conditions; a consistency evaluation module, configured to compare the multi-source real-time sea state observation data with the early warning parameters to obtain early warning difference values, calculate early warning compliance scores based on trends and deviation persistence of the early warning difference values, and generate a first quantitative evaluation parameter; a sensitivity evaluation module, configured to evaluate the restriction influence of the wave early warning information on operation execution conditions based on the port operation scheduling information, generate an operation window sensitivity score as a second quantitative evaluation parameter; an adjustment decision module, configured to determine adjustment instruction requirements of the current wave early warning information based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, in combination with a preset early warning adjustment logic, the adjustment instruction requirements being whether to perform confirmation, degradation, upgrading, shortening and / or cancellation operations.

[0081] The following will be described in detail. Among them, the early warning information acquisition module refers to a functional unit for acquiring current wave early warning information, which can be a data interface or a data reading program; among them, the sea state data acquisition module refers to a functional unit for acquiring multi-source real-time sea state observation data covering the wave early warning influence area, which can be integrated with multiple data interfaces or sensor network interfaces; among them, the operation information acquisition module refers to a functional unit for acquiring port operation scheduling information corresponding to the wave early warning influence area within the wave early warning coverage time, which can be a database interface or a scheduling system interface; among them, the consistency evaluation module refers to a functional unit for comparing multi-source real-time sea state observation data with early warning parameters to obtain early warning difference, calculating early warning compliance score based on the trend and deviation persistence of the early warning difference, and generating the first quantitative evaluation parameter, which can be a data processing unit or a calculation program; among them, the sensitivity evaluation module refers to a functional unit for evaluating the restriction influence of wave early warning information on operation execution conditions based on port operation scheduling information, and generating operation window sensitivity score as the second quantitative evaluation parameter, which can be a data processing unit or a calculation program; among them, the adjustment decision module refers to a functional unit for determining the adjustment indication requirement of the current wave early warning information based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, and combining the preset early warning adjustment logic, which can be a decision engine or a rule base system.

[0082] Through the above technical solution, the system can automatically and integrally perform a series of functions such as wave parameter early warning information acquisition, multi-source sea state data aggregation, port operation information matching, early warning consistency quantitative evaluation, operation sensitivity comprehensive evaluation, and early warning adjustment intelligent decision-making. This overcomes the limitation that only relying on method definition is difficult to efficiently and stably land, so that the dynamic adjustment process of early warning information is more timely, accurate and systematic. The system can quickly respond and provide adjustment suggestions according to the real-time changes of sea state and port operation demand, so as to maximize the port operation efficiency under the premise of ensuring the safety of ship navigation and operation.

[0083] The above only describes the embodiments of the present application and does not limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A wave parameter early warning calculation method based on a wave buoy, characterized in that: The method comprises: Obtaining current wave warning information, wherein the wave warning information includes the wave warning impact area, warning parameters, and wave warning coverage time; Acquire multi-source real-time sea condition observation data covering the wave warning impact area, where the data sources of the multi-source real-time sea condition observation data include wave buoy monitoring data, feedback information from ships at sea, and shore-based meteorological telemetry data; Obtaining port operation scheduling information corresponding to the wave warning impact area within the wave warning coverage time, the port operation scheduling information including the operation type and its corresponding location, planned time, and operation tolerance parameters for wave conditions; Comparing the multi-source real-time sea state observation data with the warning parameter to obtain a warning difference, and calculating a warning compliance score based on the trend and deviation persistence of the warning difference to generate a first quantitative evaluation parameter; Based on the port operation scheduling information, evaluating the restrictive impact of the wave warning information on the operation execution conditions, and generating an operation window sensitivity score as a second quantitative evaluation parameter; Based on the first quantitative evaluation parameter and the second quantitative evaluation parameter, combined with the preset warning adjustment logic, the adjustment indication requirement of the current wave warning information is determined, and the adjustment indication requirement is whether to confirm, downgrade, upgrade, shorten and / or cancel operations.

2. The wave parameter early warning calculation method based on wave buoy according to claim 1 is characterized in that: The determining, based on the first quantitative evaluation parameter and the second quantitative evaluation parameter and in combination with a preset warning adjustment logic, an adjustment indication requirement of the current wave warning information includes: receiving an operation state switching instruction of a current port, wherein the state switching instruction indicates that the current port is in a normal operation, emergency response and / or night operation operation state; Selecting the warning adjustment logic that matches the operation state switching instruction from a preset plurality of warning adjustment logic libraries as the adjustment logic rule; Based on the adjustment logic rule, the first quantitative evaluation parameter and the second quantitative evaluation parameter are combined to output a dynamic adjustment suggestion, and the adjustment indication requirement is adjusted according to the dynamic adjustment suggestion.

3. The wave parameter early warning calculation method based on wave buoy according to claim 1 is characterized in that: The step of comparing the multi-source real-time sea state observation data with the warning parameter to obtain a warning difference, and calculating a warning compliance score based on the trend and deviation persistence of the warning difference to generate a first quantitative evaluation parameter includes: Obtaining a current quality index of each of the data sources in the multi-source real-time sea state observation data and an index of its geographical or physical relevance to the wave warning impact area; Calculating a fusion weight of each of the data sources according to the quality indicator and the geographical or physical relevance indicator; According to the data source and the corresponding fusion weight, a normalized deviation comparison is performed, and a conformity function is constructed, and the first quantitative evaluation parameter is generated according to the conformity function.

4. The wave parameter early warning calculation method based on wave buoy according to claim 3 is characterized in that: The method further comprises: Re-acquiring the current quality indicator and the corresponding geographic or physical relevance indicator of each data source periodically or in response to a trigger; Determine whether a change in the quality index of the currently acquired data source and the corresponding geographic or physical relevance index compared to the previous data reaches a weight update threshold; If the change value reaches the weight update threshold, recalculate and update the fusion weight; Otherwise, the original fusion weight is maintained.

5. The wave parameter early warning calculation method based on wave buoy according to claim 3 is characterized in that: Calculating the fusion weight of each of the data sources according to the quality indicator and the geographical or physical correlation indicator includes: Converting the quality indicator and the geographical or physical relevance indicator of each of the data sources into a first scoring value and a second scoring value respectively; Determining whether the first rating value and the second rating value satisfy a preset rating conflict condition; If the scoring conflict condition is met, the first scoring value and the second scoring value are corrected and merged according to a preset conflict resolution mechanism to generate the fusion weight; If the scoring conflict condition is not met, the first scoring value and the second scoring value are calculated according to a conventional weighting strategy to generate the fusion weight.

6. The wave parameter early warning calculation method based on wave buoy according to claim 1 is characterized in that: The step of evaluating the restrictive impact of the wave warning information on the operation execution conditions based on the port operation scheduling information and generating an operation window sensitivity score as a second quantitative evaluation parameter includes: Calculating an interruption risk value by comparing the operation tolerance parameter of the wave condition corresponding to the operation type with the current warning parameter; Assess the operational impact of the estimated interruption of the operation based on the preset timeliness level, loading and unloading type, and preset safety risk level corresponding to the operation type; Coupling the interruption risk value with the operational impact degree to generate an operation window sensitivity score corresponding to the operation type; The sensitivity scores of the job windows are weighted and aggregated according to the priority of the job type, and the second quantitative evaluation parameter is output.

7. The wave parameter early warning calculation method based on wave buoy according to claim 6 is characterized in that: The coupling of the interruption risk value and the operational impact degree to generate an operation window sensitivity score corresponding to the operation type includes: The interruption risk value and the operational impact degree are functionally combined to generate an operation window sensitivity score corresponding to the operation type.

8. The wave parameter early warning calculation method based on wave buoy according to claim 6 is characterized in that: The priority adjustment method for each of the job types includes: Monitoring the current operation scheduling status, which is obtained from or input into the port operation scheduling system; Determine whether a critical path change occurs in the current job scheduling state compared with the historical job scheduling state; If there is a change, a preset first priority model matching the current scheduling state is selected to reallocate the priorities of the various job types; If there is no change, the priority of each of the job types is assigned using a preset second priority model that matches the historical job scheduling status.

9. The wave parameter early warning calculation method based on wave buoy according to claim 1 is characterized in that: The determining, based on the first quantitative evaluation parameter and the second quantitative evaluation parameter and in combination with a preset warning adjustment logic, an adjustment indication requirement of the current wave warning information includes: determining whether a preset adjustment conflict condition exists between the first quantitative evaluation parameter and the second quantitative evaluation parameter for the adjustment indication requirement indication, the adjustment conflict condition including a conflicting adjustment parameter between the first quantitative evaluation parameter and the second quantitative evaluation parameter; When it is determined that the adjustment conflict condition exists, determining an adjustment decision for the adjustment indication requirement according to a preset conflict adjustment rule and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter; When it is determined that the adjustment conflict condition does not exist, the adjustment indication requirement of the current wave warning information is determined according to a preset conventional adjustment rule and in combination with the first quantitative evaluation parameter and the second quantitative evaluation parameter.

10. A wave parameter early warning calculation system based on a wave buoy, characterized in that: The system comprises: A warning information acquisition module is used to obtain current wave warning information, wherein the wave warning information includes the wave warning impact area, warning parameters and wave warning coverage time; A sea state data acquisition module is used to acquire multi-source real-time sea state observation data covering the wave warning impact area, the data sources of the multi-source real-time sea state observation data including wave buoy monitoring data, feedback information from ships at sea, and shore-based meteorological telemetry data; an operation information acquisition module, configured to acquire port operation scheduling information corresponding to the wave warning impact area within the wave warning coverage period, wherein the port operation scheduling information includes the operation type and its corresponding location, planned time, and operation tolerance parameters for wave conditions; a consistency assessment module, configured to compare the multi-source real-time sea state observation data with the warning parameter to obtain a warning difference, and calculate a warning compliance score based on the trend and deviation persistence of the warning difference to generate a first quantitative assessment parameter; a sensitivity assessment module, configured to assess the restrictive impact of the wave warning information on the operation execution conditions based on the port operation scheduling information, and generate an operation window sensitivity score as a second quantitative assessment parameter; An adjustment decision module is used to determine, based on the first quantitative evaluation parameter and the second quantitative evaluation parameter and in combination with a preset warning adjustment logic, an adjustment indication requirement for the current wave warning information, wherein the adjustment indication requirement is whether to confirm, downgrade, upgrade, shorten and / or cancel an operation.

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