A method and system for internet monitoring of offshore electronic fencing

Abstract

The application relates to the technical field of offshore monitoring electronic fences, in particular to an offshore electronic fence internet monitoring method and system, which comprises the following steps: receiving a structured operation intention declaration; obtaining an associated historical behavior record; evaluating the trust level of a ship management party according to the historical behavior record; inquiring whether there is an effective structured operation intention declaration related to the current ship in space-time; if yes, judging whether the judgment result of the deviating behavior belongs to planned deviation or a rule violation according to the trust level of the current ship; if not, judging the nature of the deviating behavior according to an electronic fence basic rule set. The method has the advantages that planned deviation and real rule violation can be intelligently distinguished, the workload of manual verification is significantly reduced, and the monitoring accuracy and efficiency are improved.

Description

Technical Field

[0001] This application relates to the technical field of marine electronic fence monitoring, and more specifically, to a marine electronic fence internet monitoring method and system. Background Technology

[0002] In complex marine environments, such as major international shipping hubs, areas where fishing operations intersect with marine protected areas, or areas where multiple parties jointly develop marine engineering projects, deploying maritime electronic fence systems to monitor numerous and diverse groups of vessels is a crucial means of ensuring navigational order and enforcing regional management regulations. These systems are typically monitored by regional authorities, controlling the geographical boundaries of fishing vessels' operations, passage requirements, speed limits, and other regulations.

[0003] However, situations such as fishing vessels operating near the boundaries of the electronically fenced no-fishing zones with valid permits, research vessels briefly stopping at low speeds within recommended channels for sampling, or cargo ships following economic speed instructions and operating slightly below the minimum speed recommended by the electronic fence, can all be flagged as abnormal or non-compliant by the existing system, generating numerous such "apparent non-compliance" alerts. Monitoring center operators need to invest significant time and effort in manual verification, reviewing vessel navigation plans and operational declarations, and even communicating with vessels or their management to distinguish between genuine violations requiring intervention and planned deviations from legitimate instructions. In an environment with a large number of vessels and frequent activity, this manual verification process is inefficient and prone to delays or errors due to information asymmetry or oversight.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for monitoring electronic fences at sea, which has the advantages of intelligently distinguishing between planned deviations and actual violations, significantly reducing the workload of manual verification, and improving the accuracy and efficiency of monitoring.

[0006] On the one hand, this application provides a method for internet monitoring of marine electronic fences, the method comprising:

[0007] Continuously receive structured operational intent statements submitted by vessels, which include planned operational parameters and specific operational instruction information;

[0008] Obtain historical behavior records associated with the vessel, including historical accuracy records of structured operational intent statements and historical rule compliance records;

[0009] Based on historical behavior records, assess the trust level of the ship management and maintain and update the trust level.

[0010] When the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set, query whether there is an effective structured operation intent declaration that is related to the current vessel in time and space.

[0011] If such deviations exist, the deviation tolerance threshold for the corresponding planned operation parameters is determined based on the current trust level of the vessel. The real-time behavior parameters are compared with the planned operation parameters of the current vessel to obtain the actual degree of deviation. Based on the comparison result between the actual degree of deviation and the deviation tolerance threshold, the determination result of the deviation behavior is whether it belongs to planned deviation or violation.

[0012] If it does not exist, the nature of the deviation behavior is determined according to the basic rule set of the electronic fence.

[0013] By integrating the operational intentions and historical trust levels submitted by vessels, the above-mentioned solution can intelligently distinguish between planned deviations and genuine violations, significantly reducing the workload of manual verification and improving the accuracy and efficiency of monitoring.

[0014] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, determining the corresponding deviation tolerance threshold for planned operation parameters according to the current trust level of the vessel, including:

[0015] Obtain parameter type information for planned tasks;

[0016] Obtain the preset application weights corresponding to the parameter types;

[0017] Based on the current trust level and application weight of the vessel, calculate the deviation tolerance threshold for the planned operation parameters.

[0018] The above scheme further refines the process of determining the deviation tolerance threshold, making it related to specific parameter types and application weights, thereby improving the rationality of the threshold setting.

[0019] Furthermore, this application also proposes that, according to the aforementioned maritime electronic fence internet monitoring method, the trust level includes multiple trust dimension indicators, including the number of rule violations, the trajectory compliance rate of intent declarations, and the historical conflict resolution rate. Based on the current vessel's trust level and application weight, the deviation tolerance threshold for planned operation parameters is calculated as follows:

[0020] Obtain the dimensional indicator scores of multiple trust dimensions for the current vessel;

[0021] Obtain the set of factors influencing the application weights;

[0022] Application weights are generated based on dimensional indicator scores and a set of impact factors.

[0023] Calculate the deviation tolerance threshold based on application weight and trust level.

[0024] The above approach introduces multi-dimensional trust indicators and generates application weights based on these indicators and influencing factors, making the calculation of trust levels and deviation tolerance thresholds more precise and comprehensive.

[0025] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, to calculate a deviation tolerance threshold according to application weight and trust level, including:

[0026] Obtain specific operational instruction information contained in the structured operational intent statement corresponding to the current vessel;

[0027] Identify the sensitivity level or priority of specific job instruction information;

[0028] The deviation tolerance threshold is obtained by jointly calculating based on sensitivity level or priority, multiple trust dimension indicators and application weight.

[0029] By incorporating the sensitivity level or priority of specific work instructions into the calculation of deviation from the tolerance threshold, the threshold setting can better reflect the importance of the instructions and improve the accuracy of the judgment.

[0030] Furthermore, this application also proposes that, based on the aforementioned maritime electronic fence internet monitoring method, a deviation tolerance threshold is obtained by jointly calculating based on sensitivity level or priority, multiple trust dimension indicators, and application weights, including:

[0031] Determine whether there is a directional conflict in the judgment results regarding sensitivity level or priority, trust level, and / or application weight;

[0032] If there is a directional conflict in the judgment results, a preset conflict handling strategy is invoked. The conflict handling strategy includes:

[0033] Quantitatively assess the specific level values ​​of sensitivity level or priority, trust level and / or application weight of the directional conflict association of the judgment results;

[0034] Based on the preset priority relationship between factors, the sensitivity level or priority, trust level and / or application weight corresponding to the directional conflict association of the judgment result are reordered.

[0035] The sensitivity level or priority is reordered based on weights and weighted with multiple trust dimension indicators to obtain the deviation tolerance threshold.

[0036] The above scheme provides a strategy for handling potential conflicts between sensitivity levels, trust levels, and application weights. Through quantitative evaluation and weight reordering, it improves the robustness of judgment in complex situations.

[0037] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, to reorder the weights corresponding to the sensitivity level or priority, trust level, and / or application weight of the directional conflict association in the judgment results, based on a preset priority relationship between factors, including:

[0038] Obtain multiple historical conflict decision records, which include the original specific level values ​​of sensitivity level or priority, trust level and application weight when the historical conflict occurred, the deviation tolerance threshold adopted when the historical conflict occurred, and the nature of the subsequent confirmation of the deviation behavior of the historical conflict.

[0039] Determine whether the priority relationship between the factors corresponding to the historical conflict is inconsistent with the nature of the subsequent confirmation of the deviation behavior of the historical conflict;

[0040] If they do not match, identify the corresponding misjudgment pattern and adjust the priority relationship between the update factors according to the misjudgment pattern.

[0041] The above approach introduces historical conflict decision records to optimize the priority relationship between factors. By identifying misjudgment patterns and adjusting priorities, the system can learn from historical experience and improve the accuracy of future judgments.

[0042] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, the identification of corresponding misjudgment patterns, including:

[0043] The degree of inconsistency between the priority relationship between factors corresponding to each historical conflict decision and the nature of subsequent confirmation of deviation behavior in historical conflicts is quantified to generate a quantified value of inconsistency.

[0044] Records with the same combination of factors for multiple quantifications of discrepancy are grouped together to form a case group;

[0045] Analyze the central tendency of the quantified discrepancies in the case study group;

[0046] If the concentration level value meets the preset concentration threshold, it is determined to be a misjudgment mode.

[0047] The above approach provides a method for quantifying and analyzing the degree of inconsistency in historical conflicts. Through case group analysis and concentration assessment, it is possible to more accurately identify misjudgment patterns.

[0048] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, to analyze the concentration values ​​of the quantified discrepancies in the case group, including:

[0049] The degree of discrepancy in the case group is quantified by statistical analysis to obtain the concentration value.

[0050] The above scheme clarifies the calculation method of the concentration value, ensuring the operability of the misjudged pattern recognition process.

[0051] Furthermore, this application also proposes, based on the aforementioned maritime electronic fence internet monitoring method, to quantify the degree of inconsistency between the priority relationship between factors corresponding to each historical conflict decision and the nature of subsequent confirmation of deviation behaviors in historical conflicts, generating a quantified value of the degree of inconsistency, including:

[0052] Obtain the determination results of deviation behaviors in historical conflicts;

[0053] To obtain the nature of subsequent confirmation of deviations from historical conflicts;

[0054] Based on a pre-defined comparison matrix, the degree of discrepancy is quantified by comparing the differences between the judgment results of the deviation behavior and the nature of the subsequent confirmation of the deviation behavior in historical conflicts.

[0055] The above scheme provides a specific method for quantifying the degree of inconsistency. By comparing the judgment results with the comparison matrix and subsequent confirmation properties, the quantification of the degree of inconsistency becomes more objective and standardized.

[0056] Furthermore, this application also provides a marine electronic fence internet monitoring system, the technical solution of which is as follows:

[0057] include:

[0058] The receiving module is used to continuously receive structured operation intent statements submitted by vessels. The structured operation intent statements include planned operation parameters and specific operation instruction information.

[0059] The information query module is used to obtain historical behavior records associated with the vessel, including historical accuracy records of structured operational intent statements and historical rule compliance records.

[0060] The trust level management module is used to assess the trust level of the ship management party based on historical behavior records, and to maintain and update the trust level.

[0061] The information query module is also used to query whether there is a structured operation intent declaration that is related to the current vessel in time and space when the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set.

[0062] The judgment module is used to determine the deviation tolerance threshold of the corresponding planned operation parameters based on the current trust level of the vessel if deviation exists. It compares the real-time behavior parameters with the current planned operation parameters of the vessel to obtain the actual degree of deviation. Based on the comparison result of the actual degree of deviation and the deviation tolerance threshold, it judges whether the deviation behavior belongs to the planned deviation or the violation behavior.

[0063] The determination module is also used to determine the nature of the deviation behavior based on the basic rule set of the electronic fence if the behavior does not exist.

[0064] The above scheme provides a system for implementing the above method. Through modular design, the system is easy to build and deploy, and can effectively execute the monitoring method.

[0065] As can be seen from the above, the marine electronic fence Internet monitoring method and system provided in this application, by integrating the operational intentions and historical trust levels submitted by vessels, can intelligently distinguish between planned deviations and actual violations, thus solving the problems of low efficiency and difficulty in distinguishing different types of deviations in the prior art through manual verification. It has the advantages of intelligently distinguishing between planned deviations and actual violations by integrating the operational intentions and historical trust levels submitted by vessels, significantly reducing the workload of manual verification, and improving the accuracy and efficiency of monitoring. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating a marine electronic fence internet monitoring method provided in one embodiment of this application.

[0067] Figure 2 This is one of the flowcharts illustrating a marine electronic fence internet monitoring method provided in another embodiment of this application.

[0068] Figure 3 This is a second schematic flowchart of a marine electronic fence internet monitoring method provided for another embodiment of this application.

[0069] Figure 4 This is the third flowchart illustrating a marine electronic fence internet monitoring method, provided as another embodiment of this application.

[0070] Figure 5 This is the fourth flowchart illustrating a marine electronic fence internet monitoring method, provided as another embodiment of this application.

[0071] Figure 6 The fifth flowchart illustrates a method for monitoring an electronic fence at sea, as provided in another embodiment of this application.

[0072] Figure 7 This is a sixth flowchart illustrating a marine electronic fence internet monitoring method, provided as another embodiment of this application.

[0073] Figure 8 This is the seventh flowchart illustrating a marine electronic fence internet monitoring method, provided as another embodiment of this application.

[0074] Figure 9This is the eighth flowchart illustrating a marine electronic fence internet monitoring method, provided as another embodiment of this application.

[0075] Figure 10 A flowchart of a marine electronic fence internet monitoring system provided for another embodiment of this application. Detailed Implementation

[0076] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0077] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0078] Reference Figure 1 In response, this application proposes a method for internet-based electronic fence monitoring at sea, comprising:

[0079] S1000: Continuously receives structured operation intent statements submitted by vessels, which include planned operation parameters and specific operation instruction information;

[0080] S2000: Obtain historical behavior records associated with the vessel, including historical accuracy records of structured operational intent statements and historical rule compliance records;

[0081] S3000: Assess the trust level of the vessel management based on historical behavior records, and maintain and update the trust level;

[0082] S4000: When the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set, query whether there is an effective structured operation intent declaration that is related to the current vessel in time and space.

[0083] S5000: If present, determine the deviation tolerance threshold of the corresponding planned operation parameters based on the current trust level of the vessel, compare the real-time behavior parameters with the current planned operation parameters of the vessel to obtain the actual degree of deviation, and determine whether the deviation behavior is a planned deviation or a violation based on the comparison result between the actual degree of deviation and the deviation tolerance threshold.

[0084] S6000: If it does not exist, the nature of the deviation behavior shall be determined according to the basic rule set of the electronic fence.

[0085] In this embodiment, the continuous reception of structured operational intent declarations submitted by vessels is achieved by a monitoring system that continuously acquires information proactively reported by vessels in a preset format. This can be accomplished through methods such as Internet communication, satellite communication, or shore-based radio communication.

[0086] A structured operational intent statement is an information carrier containing specific fields and data formats. It includes planned operational parameters and specific operational instructions, and can be represented using XML, JSON, or other custom data structures. This standardizes the vessel's intent information, facilitating automated processing by the system. Planned operational parameters refer to the specific values ​​or ranges related to the planned operational activities the vessel intends to perform, such as planned speed, planned course, and coordinates of the planned operational area. Specific operational instructions refer to the specific instructions from the management authority upon which the vessel executes a particular operation.

[0087] Historical behavior records refer to a collection of data reflecting a vessel's activities over a period of time. These records include historical accuracy records of structured operational intent declarations and historical rule compliance records, primarily to provide traceability of vessel behavior. Historical accuracy records refer to the degree of conformity between a vessel's past structured operational intent declarations and its subsequent actual behavior, used to assess the credibility of the vessel's intent declarations. Historical rule compliance records refer to the degree of conformity between a vessel's past behavior and the electronic fence basic rule set or other relevant rules, primarily to assess the vessel's compliance status.

[0088] Assessing the trust level of a vessel's management organization involves evaluating its reliability based on the vessel's historical behavior records. This can be achieved using rule-based scoring models, statistical analysis methods, or machine learning algorithms, primarily to quantify the credibility of the vessel's management organization. The trust level is a quantitative representation of the vessel's trustworthiness and can be categorized into multiple levels, such as high, medium, and low. Maintenance and updates refer to adjusting the assessed trust level based on the vessel's latest behavior. This can be done periodically or through event-triggered updates, primarily to ensure the trust level dynamically reflects the vessel's current performance. Monitoring deviations of real-time vessel behavior parameters from the preset electronic fence rule set involves the system acquiring the vessel's current position, speed, and heading in real-time and comparing them with thresholds or ranges set in the electronic fence rule set. Discrepancies are identified to pinpoint potential abnormal behavior.

[0089] Real-time behavior parameters refer to the actual operating status data of a vessel at a certain moment, such as position, speed, and heading information obtained through AIS, radar, or other sensors. They are mainly used to reflect the current actual behavior of the vessel. The electronic fence basic rule set refers to the set of basic rules set by the regional competent authority to regulate the behavior of vessels. It includes geographical boundaries, speed limits, and waterway requirements. It is mainly used to provide a standard basis for compliance judgment.

[0090] The query for the existence of a valid structured operational intent statement that is spatiotemporally related to the current vessel refers to the system searching among the received and valid structured operational intent statements for a statement that is related to the vessel's current position and time when a deviation from the basic rules is detected. This is mainly to determine whether the vessel's deviation behavior was intended to be pre-declared. A valid structured operational intent statement that is spatiotemporally related refers to the existence of a previously submitted structured operational intent statement that is still valid near the time and geographical location where the vessel's deviation behavior occurred. This is mainly to ensure that the intent statement found is related to the actual deviation behavior.

[0091] Determining the deviation tolerance threshold for the corresponding planned operation parameters based on the current trust level of the vessel refers to setting an upper limit on the range within which the vessel's actual behavior deviates from its planned operation parameters, based on the trust level of the vessel management. This is mainly to provide differentiated tolerance for vessels with different trust levels. The deviation tolerance threshold refers to the maximum acceptable degree of deviation of the vessel's actual behavior from its planned operation parameters. It can be a specific value or a range, mainly to define the acceptable range of planned deviations. Comparing the real-time behavior parameters with the current planned operation parameters of the vessel to obtain the actual deviation degree refers to calculating the difference between the vessel's real-time behavior parameters and the corresponding planned operation parameters in its structured declaration of intent. This is mainly to quantify the degree of deviation between the vessel's actual behavior and its planned behavior. The actual deviation degree is the quantitative difference between the vessel's real-time behavior parameters and the planned operation parameters, mainly used for comparison with the deviation tolerance threshold. Determining whether the deviation behavior is a planned deviation or a violation based on the comparison result of the actual deviation degree and the deviation tolerance threshold refers to comparing the calculated actual deviation degree with the deviation tolerance threshold determined according to the trust level. If the actual deviation... If the deviation is less than or equal to the tolerance threshold, it is considered a planned deviation; if it exceeds the tolerance threshold, it is considered a violation. This distinction is primarily to differentiate between different types of deviations. A planned deviation refers to a vessel's behavior that deviates from the basic rules set of the electronic fence but conforms to its pre-declared structured operational intent statement, and the degree of deviation is within the tolerance range determined based on the trust level. This mainly indicates an acceptable and reasonably justified deviation. A violation refers to a vessel's behavior that deviates from the basic rules set of the electronic fence and does not conform to its pre-declared structured operational intent statement, or although it conforms to the intent statement, the degree of deviation exceeds the tolerance range determined based on the trust level. This mainly indicates a non-compliant behavior that requires attention or intervention. Determining the nature of a deviation based on the basic rules set means that when a vessel's behavior deviates from the basic rules set and there is no relevant structured operational intent statement, the deviation is directly judged based on the provisions of the basic rules set to determine whether the deviation constitutes a violation. This is mainly used to handle deviations without pre-declared intent. The nature of the deviation refers to the qualitative judgment of a vessel's deviation from the basic rules set of the electronic fence, including planned deviations or violations. This mainly indicates the final conclusion regarding the compliance of the vessel's behavior.

[0092] By continuously receiving structured operational intent declarations submitted by vessels, the system obtains their planned operational parameters and specific operational instructions, thereby gaining advance knowledge of their operational intentions. Simultaneously, the system acquires and maintains historical behavior records associated with vessels, including the historical accuracy of their intent declarations and their compliance with historical rules. Based on this historical data, the system assesses and dynamically updates the trust level of the vessel's management. When the system detects that a vessel's real-time behavioral parameters deviate from the preset electronic fence basic rule set, it first checks whether there is an effective structured operational intent declaration related to that vessel in time and space. If so, the system does not directly make a simple judgment based on the basic rule set, but instead determines a corresponding deviation tolerance threshold for the planned operational parameters based on the vessel's current trust level. Subsequently, the system compares the vessel's real-time behavioral parameters with its planned operational parameters to calculate the actual degree of deviation. Finally, based on the comparison between the actual degree of deviation and the deviation tolerance threshold, it determines whether the deviation is within the plan or a violation. This approach allows vessels with higher trust levels to deviate from the basic rule set within a certain range to execute their legitimate intentions. If no relevant structured operational intent statement exists, the system directly determines the nature of the deviation based on the basic rule set of the electronic fence, ensuring basic regulatory effectiveness in the absence of intent information. The entire process forms a dynamic, hierarchical judgment mechanism based on intent, history, and trust, enabling more precise identification of the true nature of vessel behavior.

[0093] In some examples of this embodiment, the maritime monitoring system is deployed in a complex sea area with basic speed limit rules. A research vessel plans to conduct low-speed hydrological sampling operations in this area. Its management submits a structured declaration of operational intent to the monitoring system via the internet, which includes planned operational parameters (e.g., operating at 5 knots in a specific area) and specific operational instructions (e.g., performing a hydrological sampling task for a national research project). The monitoring system continuously receives and stores this declaration. The system also maintains the research vessel's historical behavior records. For example, its past declarations of intent for research missions are generally highly consistent with its actual behavior, and its routine navigation, except for research operations, strictly adheres to the basic rules. Therefore, the vessel's trust level is assessed as high. When the research vessel enters the sampling area and reduces its speed to 5 knots, the monitoring system detects that its speed (5 knots) deviates from the minimum speed set by the basic rule set for that area (e.g., 10 knots). The system immediately queries and finds an existing structured declaration of operational intent related to the vessel's current position and time. Because the ship has a high trust level, the system determines a relatively large deviation tolerance threshold based on its trust level, for example, allowing a speed deviation of 3 knots above or below the planned speed (5 knots). The system compares the real-time speed (5 knots) with the planned speed (5 knots), and the actual deviation is 0 knots. Comparing the actual deviation (0 knots) with the deviation tolerance threshold (3 knots), 0 knots is less than 3 knots, therefore the deviation is determined to be an in-plan deviation. The system records this judgment result and may only generate a low-priority alert message, without triggering a high-priority violation alert or mandatory intervention. If the ship does not submit an intent declaration, or its trust level is very low, or the actual speed deviation from the planned speed exceeds the tolerance threshold (e.g., the speed drops to 1 knot), it may be judged as a violation.

[0094] Reference Figure 2 Furthermore, sub-step S5100 of S5000: Based on the current trust level of the vessel, determine the deviation tolerance threshold for the corresponding planned operation parameters, including:

[0095] S5110: Obtain parameter type information for planned operation parameters;

[0096] S5120: Obtain the preset application weights corresponding to the parameter type;

[0097] S5130: Calculate the deviation tolerance threshold for planned operation parameters based on the current trust level and application weight of the vessel.

[0098] Among them, the parameter type information of the planned operation parameters refers to the specific physical quantity or behavioral attribute represented by the planned operation parameters, such as speed, position, heading, draft, cargo type, etc. It can be represented in the form of identifiers, enumeration values ​​or text descriptions, and its purpose is to distinguish the importance and sensitivity of different parameters in terms of monitoring and safety.

[0099] Application weights refer to pre-defined numerical values ​​or levels that reflect the relative importance of different parameter types in determining deviation tolerance thresholds. They can be represented by numerical coefficients, level labels, or weight vectors, and their purpose is to provide a basis for parameter-level importance in subsequent calculations. Calculating the deviation tolerance threshold for planned operation parameters based on the current vessel's trust level and application weights involves using a calculation model or rule to combine the trust level, representing the overall reliability of the vessel, with the application weight, representing the importance of a specific parameter, to arrive at a specific numerical value or range. This value or range limits the maximum allowable deviation of the parameter from the planned value.

[0100] By acquiring parameter type information of planned operation parameters, the specific category of parameters requiring deviation judgment is identified. Based on this parameter type, a corresponding preset application weight is obtained, reflecting the importance or sensitivity of that type of parameter within the overall monitoring system. Subsequently, based on the vessel's trust level and the acquired application weight, the deviation tolerance threshold for the planned operation parameter is calculated. This approach combines the vessel's overall trust level with the importance of specific parameters, ensuring that the determination of the deviation tolerance threshold no longer solely relies on the vessel's trust level but also considers the characteristics of the parameters themselves. For example, for vessels with high trust levels, the deviation tolerance threshold for non-critical parameters (lower application weights) can be appropriately relaxed, but for their critical parameters (higher application weights), even with a high trust level, the deviation tolerance threshold will be relatively strict. Conversely, for vessels with low trust levels, the deviation tolerance threshold will be relatively small regardless of the parameter type, especially for critical parameters. This calculation method, combining vessel trust level and parameter importance, makes the setting of deviation tolerance thresholds more refined and reasonable, enabling a more accurate distinction between planned deviations and actual violations, thereby improving the accuracy of monitoring and judgment.

[0101] In some examples of this embodiment, it is assumed that it is necessary to determine the deviation tolerance threshold for a vessel's "speed" parameter in its planned operation intention statement. First, the system obtains the parameter type information for this planned operation parameter as "speed". According to preset rules, the application weight corresponding to the "speed" parameter type is set to a high value, such as 0.8, indicating that speed is a key parameter affecting navigation safety and efficiency. Simultaneously, the system queries that the current vessel's trust level is "high". Based on the "high" trust level and the application weight of 0.8, the system calls a preset calculation model to calculate the deviation tolerance threshold. This calculation model can be a function, for example: Deviation Tolerance Threshold = Base Threshold * f (Trust Level) * g (Application Weight), where f (Trust Level) is a function positively correlated with the trust level, and g (Application Weight) is a function negatively correlated with the application weight (because the higher the weight, the lower the tolerance should be). Assuming the base speed deviation threshold is 2 knots, f("high") = 1.2, and g(0.8) = 0.5. The calculated speed deviation tolerance threshold is 2 * 1.2 * 0.5 = 1.2 knots. This means that the maximum allowable deviation of this high-trust-level vessel from its planned speed is 1.2 knots. If the vessel's real-time speed deviates from the planned speed by more than 1.2 knots, it may be considered a violation. In this way, even for high-trust-level vessels, the tolerance for deviations from key parameters is reasonably limited.

[0102] Reference Figure 3 Furthermore, the S5130 includes:

[0103] S5131: Obtain the dimensional indicator scores of multiple trust dimensions for the current vessel;

[0104] S5132: Obtain the set of influencing factors for application weights;

[0105] S5133: Generate application weights based on dimensional indicator scores and the set of influence factors;

[0106] S5134: Calculate the deviation tolerance threshold based on application weight and trust level.

[0107] The trust level includes multiple trust dimension indicators, which are quantitative indicators used to assess the reliability of ships from different aspects. Specifically, they may include the number of rule violations, the trajectory compliance rate of intention declarations, and the historical conflict resolution rate.

[0108] Dimensional indicator scoring refers to the score obtained by quantitatively evaluating each trust dimension indicator, which can be calculated through preset scoring rules or models. The set of influencing factors for application weights refers to the set of various factors that affect the application weights of planned operation parameters, which can include parameter type, current environmental conditions, and characteristics of the operation area. Based on the dimensional indicator scores and the set of influencing factors, generating application weights means calculating the application weight value corresponding to the planned operation parameter according to the vessel's performance in each trust dimension and the specific situation of the influencing factors, which can be achieved through weighted averaging, machine learning model prediction, or rule engine judgment.

[0109] By refining a vessel's trust level into multiple trust dimensions and quantifying these dimensions, a more comprehensive assessment of vessel reliability can be achieved. Simultaneously, by introducing a set of influencing factors for application weights, the generation process of application weights can consider various practical situations such as parameter types and environmental factors, enabling dynamic adjustment and refinement of application weights. It is precisely this combination of multi-dimensional trust assessment and dynamic application weights that allows the final calculated deviation tolerance threshold to more accurately reflect the actual reliability of the vessel and the importance of parameters in a specific context, thereby improving the accuracy of judging vessel behavior deviations. When a vessel's behavior deviates from the basic rules, the system can more effectively distinguish between reasonable planned deviations and genuine violations based on a more accurate deviation tolerance threshold, avoiding misjudgments caused by a single trust assessment or fixed weights, and improving monitoring efficiency and judgment accuracy.

[0110] In some examples of this embodiment, the specific implementation of this application is as follows. Suppose it is necessary to calculate the deviation tolerance threshold of the speed parameter of a specific cargo ship in a certain segment of the route. First, the system obtains the dimensional index scores of multiple trust dimensions for the cargo ship. For example, based on historical records, the cargo ship has few rule violations, corresponding to a dimensional index score of 90; a high trajectory compliance rate for intent declarations, a score of 85; and a high historical conflict resolution rate, a score of 95. Next, the system obtains the set of influencing factors for the application weight. Currently, the weight of the speed parameter needs to be calculated, while also considering environmental factors such as low traffic density and good visibility in the current segment. Then, based on these dimensional index scores (90, 85, 95) and the set of influencing factors (parameter type: speed; environmental factors: low traffic density, good visibility), the system generates the application weight of the speed parameter. For example, through preset rules, a high trust score and good environmental factors result in the application weight of the speed parameter being set to a relatively high value, such as 0.8. Finally, based on the generated application weight (0.8) and the overall trust level of the cargo ship (e.g., a higher trust level derived from a comprehensive dimensional score), the deviation tolerance threshold for the speed parameter is calculated. For example, a specific speed deviation tolerance threshold, such as 0.5 knots, can be obtained through a formula: Threshold = Base Threshold * (1 - Trust Level Coefficient) * (1 - Application Weight), or by consulting a two-dimensional lookup table based on the trust level and application weight.

[0111] Reference Figure 4 Furthermore, S5134 includes:

[0112] S51341: Obtain specific operational instruction information contained in the structured operational intent statement corresponding to the current vessel;

[0113] S51342: Identify the sensitivity level or priority of specific job instruction information;

[0114] S51343: The deviation tolerance threshold is obtained by jointly calculating based on the sensitivity level or priority, multiple trust dimension indicators and application weights.

[0115] Specific operational instructions refer to the instructions explicitly listed in the structured declaration of operational intent, issued by the vessel's management system, and guiding the vessel's specific operations. These may include route instructions, speed instructions, operational area instructions, and specific task instructions. Sensitivity level or priority refers to the assessment of the importance or urgency of specific operational instructions. It can be determined based on factors such as the content of the instruction, the safety risks involved, and the degree of impact on navigation order. For example, it can be divided into high, medium, and low levels, or assigned different priority values.

[0116] This scheme calculates deviation tolerance thresholds more accurately by incorporating the importance of specific operational instructions currently being executed, in addition to considering the vessel's historical trust performance and parameter importance. Specifically, it first obtains specific operational instructions from the vessel's structured operational intent statement, which forms the basis for determining the nature of the current task. Next, it identifies the sensitivity level or priority of these instructions, thereby quantifying their importance. Since different instructions have varying impacts on safety and order, their deviation tolerance during execution should also differ. Based on this, the identified sensitivity level or priority is jointly calculated with multiple trust dimension indicators reflecting the vessel's historical performance and application weights reflecting parameter importance. This joint calculation mechanism ensures that the deviation tolerance threshold is determined not only based on the vessel's general trust level or the general importance of parameters, but also integrates information from three dimensions: "the vessel's condition" (trust dimension indicators), "parameter importance" (application weights), and "urgency / risk of the current task" (sensitivity level or priority). It is precisely because of this multi-dimensional and contextualized joint calculation that the final deviation tolerance threshold can more accurately reflect the actual tolerance level for deviations in ship behavior under the current specific situation, thereby effectively distinguishing between apparent deviations caused by following reasonable specific instructions and real violations.

[0117] In some examples of this embodiment, it is assumed that the vessel is currently executing a work instruction to pass through a specific narrow channel. The system first obtains the structured work intent statement submitted by the vessel and extracts specific work instruction information regarding "passing through the narrow channel." Next, the system identifies the sensitivity level or priority of this instruction. Since passing through a narrow channel places extremely high demands on navigation safety, the system identifies it as a high-sensitivity level. Simultaneously, the system queries the vessel's historical behavior records to obtain scores for multiple trust dimensions, such as a low number of historical rule violations and a high trajectory compliance rate for the intent statement. The system also obtains the application weights corresponding to the planned work parameters (such as track and speed) associated with the work instruction. Finally, based on the identified high sensitivity level, the vessel's high trust dimension score, and the corresponding application weights, the system jointly calculates the deviation tolerance threshold for the vessel during passage through the narrow channel. In this case, even if the vessel has a high historical trust level, due to the high sensitivity level of the work instruction, the calculated deviation tolerance threshold will be set very small; for example, the allowed positional deviation range will be limited to a very small range to ensure navigation safety.

[0118] Reference Figure 5 Furthermore, step S51343 includes:

[0119] A1: Determine whether there is a directional conflict in the judgment results of sensitivity level or priority, trust level and / or application weight;

[0120] A2: If there is a directional conflict in the judgment results, the preset conflict handling strategy will be invoked. The conflict handling strategy includes:

[0121] A3: Quantitatively assess the specific level values ​​of sensitivity level or priority, trust level and / or application weight of the directional conflict association of the judgment results;

[0122] A4: Based on the preset priority relationship between factors, the sensitivity level or priority, trust level and / or the weight corresponding to the applied weight of the directional conflict association of the judgment result are reordered.

[0123] A5: Based on the weighted reordering, the sensitivity level or priority is weighted and calculated with multiple trust dimension indicators and applied weights to obtain the deviation tolerance threshold.

[0124] Specifically, the conflict in the direction of the judgment result refers to a situation where the factors of sensitivity level or priority, trust level, and / or application weight contradict or are inconsistent in indicating whether deviation from the tolerance threshold should lean towards strictness or leniency. This can be addressed by comparing the threshold directions indicated by each factor (e.g., a high sensitivity level indicates strictness, and a high trust level indicates leniency). The conflict resolution strategy refers to a series of pre-defined rules and calculation steps used to resolve the aforementioned conflict in the direction of the judgment result, which can be implemented using rule-based expert systems or table lookup methods. Quantitative evaluation of specific level values ​​refers to quantifying the qualitative or semi-quantitative factors such as sensitivity level or priority, trust level, and / or application weight. Factors are transformed into numerical values ​​that can be mathematically calculated, which can be achieved using a pre-defined numerical mapping table or function; the pre-defined priority relationship between factors refers to the relative importance order of the influence of each factor (sensitivity level or priority, trust level, application weight) when a conflict occurs in the direction of the judgment result, which can be achieved using a priority list or hierarchical structure; weight reordering refers to adjusting the proportion or influence of sensitivity level or priority, multiple trust dimension indicators and application weights when jointly calculating deviation from the tolerance threshold according to the pre-defined priority relationship between factors, which can be achieved by adjusting the coefficients in the weighted calculation formula or introducing a correction factor.

[0125] By determining whether there are directional conflicts in the judgment results of sensitivity levels or priorities, trust levels, and / or application weights, a targeted conflict resolution mechanism can be initiated. The reason for determining whether a conflict exists is that simple weighted calculations may lead to unreasonable thresholds only when conflicts exist; otherwise, conventional weighted calculations are sufficient. It is precisely because a conflict has been identified that a pre-defined conflict resolution strategy needs to be invoked. This strategy first quantitatively assesses the specific level values ​​of the conflict-related factors. This is to unify factors of different natures into a calculable numerical form, laying the foundation for subsequent weight adjustments. Based on this, and according to the pre-defined priority relationships between factors, the weights corresponding to these conflict-related factors are reordered. This step is the core of conflict resolution. Through pre-defined priority relationships, such as stipulating that sensitivity levels are higher than trust levels, it ensures that when sensitivity level requirements are strict and trust level tends to be lenient, the requirements of the sensitivity level are prioritized, thereby avoiding excessive relaxation of monitoring of sensitive areas or instructions due to high trust levels. Finally, based on the weight reordering, a weighted calculation is performed on the sensitivity level or priority with multiple trust dimension indicators and application weights to obtain the final deviation tolerance threshold. This weighted calculation, with adjusted weights, effectively coordinates the impact of conflicting factors, resulting in a more reasonable and accurate deviation tolerance threshold. Compared to the basic scheme, which only performs joint calculations without considering potential conflicts between factors and their handling, the basic scheme may lead to deviations in threshold settings during conflict scenarios. This scheme, by introducing conflict assessment and a priority-based weight adjustment mechanism, further enhances the robustness and accuracy of threshold determination within the basic scheme's calculation framework. This ensures that the final deviation tolerance threshold more accurately reflects actual risks and management needs, thereby better supporting the accurate assessment of vessel deviation behavior.

[0126] In some examples of this embodiment, specifically, when the system detects that a vessel's real-time behavioral parameters deviate from the basic rule set of the electronic fence, and there is an effective structured job intent statement associated with that vessel, the system determines a deviation tolerance threshold based on the vessel's trust level. In determining the threshold, the system obtains the sensitivity level or priority of a specific job instruction in the intent statement, as well as the vessel's multiple trust dimension index scores and application weights related to the planned job parameter type. The system determines whether there is a directional conflict in the judgment results of the three factors: sensitivity level or priority, trust level, and application weight. For example, if the sensitivity level is very high (indicating a very strict threshold is required), but the vessel's trust level is also high and the application weight is large (both indicating that the threshold can be appropriately relaxed), the system will identify this conflict. If a conflict exists, the system will invoke a preset conflict handling strategy. First, the specific values ​​of high sensitivity level, high trust level, and large application weight are quantitatively evaluated; for example, the sensitivity level is mapped to a value of 3, the trust level score is 95, and the application weight is 0.8. Then, based on a pre-defined priority relationship between factors—for example, setting sensitivity level priority higher than trust level, and trust level priority higher than application weight—the system reorders or adjusts the weights used to calculate the threshold. For instance, even if the trust level and application weight are high, because the sensitivity level has the highest priority and a high value, the system adjusts the weights in the calculation formula so that the impact of a high sensitivity level on the final threshold is far greater than the impact of a high trust level and a large application weight. Finally, based on this reordered weighting method, the system combines the sensitivity level, trust dimension indicator score, and application weight to calculate the final deviation tolerance threshold. This threshold is more stringent than the threshold obtained by simple weighted calculation in conflict-free scenarios, thus better meeting the monitoring requirements of high-sensitivity scenarios.

[0127] Reference Figure 6 Furthermore, step A4 includes:

[0128] A41: Obtain multiple historical conflict decision records, including the original specific level values ​​of sensitivity level or priority, trust level and application weight when the historical conflict occurred, the deviation tolerance threshold adopted when the historical conflict occurred, and the nature of subsequent confirmation of the deviation behavior of the historical conflict.

[0129] A42: Determine whether the priority relationship between factors corresponding to historical conflicts is inconsistent with the nature of subsequent confirmation of deviation behavior in historical conflicts;

[0130] A43: If they do not match, identify the corresponding misjudgment pattern and adjust the priority relationship between the update factors according to the misjudgment pattern.

[0131] Historical conflict decision records refer to the complete record of the decision-making process and results generated by the system when handling historical deviation behavior conflict events. Specifically, this includes the specific values ​​or levels of each factor leading to the conflict (sensitivity level or priority, trust level, application weight) at the time, the deviation tolerance threshold calculated by the system based on the rules and priority relationships at that time, and the true nature of the deviation behavior confirmed afterward through manual review or external information (e.g., whether it was a genuine violation or a reasonable planned deviation). These records can be stored in a database for subsequent analysis and learning. The priority relationship between factors refers to the preset relative importance or order of influence among the factors such as sensitivity level or priority, trust level, and application weight when handling directional conflicts in judgment results. It determines which factor's tendency dominates the final deviation tolerance threshold calculation when the judgment directions of these factors are inconsistent. Misjudgment patterns refer to situations where, due to improper setting of the priority relationship between factors, the system misjudges specific combinations of factor values ​​or specific types of deviation behavior.

[0132] This leads to a recognizable and somewhat regular pattern of erroneous judgments, resulting from repeated decisions that contradict subsequent confirmations of the true nature of the case.

[0133] By acquiring and analyzing historical conflict decision records, the system compares its historical judgments based on preset priority relationships between factors with the subsequent actual nature of the deviation. When a discrepancy is found between the historical judgment and the actual nature, it indicates that the current priority relationships between factors are biased in handling such specific situations, leading to misjudgment. The system further identifies specific patterns leading to misjudgment, such as whether the sensitivity level is overemphasized while the rationality of the high trust level is ignored, or vice versa. Based on the identified misjudgment patterns, the system can adjust and update the preset priority relationships between factors in a targeted manner. For example, if it finds that the system tends to misjudge reasonable within-plan deviations as violations under a certain pattern, it can appropriately reduce the relative priority of a factor (such as the sensitivity level) that caused the misjudgment, or increase the relative priority of another factor (such as the trust level). Through this feedback and adjustment mechanism based on historical experience, the priority relationships between factors can be continuously optimized to better reflect the actual situation, thereby improving the accuracy of the system's judgments when handling future conflicts and reducing the occurrence of misjudgments. This mechanism of adaptively optimizing judgment rules based on historical data enables the system to more intelligently distinguish between deviations of different natures, improving the overall reliability of monitoring.

[0134] In some examples of this embodiment, specifically, the system can maintain a historical conflict decision record database. When a deviation behavior conflict occurs and its true nature is subsequently confirmed manually or externally, the system stores the key information of this event as a historical conflict decision record in the database, including the sensitivity level or priority at the time, trust level, specific numerical values ​​of application weight, the deviation tolerance threshold calculated by the system, the preliminary judgment result made by the system based on the threshold (planned deviation or violation), and the finally confirmed true nature. The system can periodically or after accumulating a certain number of records, initiate an optimization process. For example, the system iterates through the historical conflict decision records, and for each record, compares the system's judgment result at the time with the subsequently confirmed true nature. If the two are inconsistent, it is marked as a misjudgment. The system can further analyze these misjudgment records, for example, to statistically analyze under which combination of factors (such as high sensitivity level, medium trust level, low application weight) the misjudgment occurs more frequently or the discrepancy is greater. The system can quantify the degree of inconsistency. For example, if the system classifies something as "within-plan" but it is later confirmed as "serious violation," the degree of inconsistency is high; similarly, if the system classifies something as "violation" but it is later confirmed as "reasonable within-plan deviation," the degree of inconsistency is also high. The system can group records with similar combinations of factors and degrees of inconsistency to form case groups. By analyzing the concentration of inconsistency within the case groups—for example, calculating the average degree of inconsistency or statistically analyzing the proportion of records with high degrees of inconsistency—if the concentration exceeds a preset threshold, a misjudgment pattern is identified. For instance, it was found that when the sensitivity level is high and the trust level is low, the system tends to misjudge reasonable deviations as violations. To address this misjudgment pattern, the system can adjust the priority relationship between factors. For example, when dealing with conflicts between high sensitivity and low trust levels, the system can appropriately reduce the influence weight of the sensitivity level or increase the influence weight of the trust level, enabling the system to more accurately weigh these two factors in future conflict resolution and reducing the occurrence of such misjudgments.

[0135] Reference Figure 7 Furthermore, the steps for identifying the corresponding misjudgment patterns include:

[0136] B1: The subsequent behavior of deviating from the historical conflict in relation to the priority relationships between factors corresponding to each historical conflict decision.

[0137] The degree of inconsistency of the confirmed properties is quantified to generate a quantified value of the degree of inconsistency;

[0138] B2: Records with the same combination of factors for multiple quantifications of the degree of discrepancy are grouped together to form a case group;

[0139] B3: Analyze the concentration of the quantitative values ​​of discrepancies in the case study group;

[0140] B4: If the concentration level value meets the preset concentration threshold, it is determined to be a false judgment mode.

[0141] Among them, the degree of discrepancy quantification value refers to the numerical representation of the difference between the deviation behavior judgment result obtained based on the priority relationship between factors at that time in historical conflict decision-making and the subsequent actual confirmed nature of the deviation behavior. It can be calculated using a preset comparison matrix based on the combination of the judgment result (planned deviation or violation) and the subsequent confirmed nature (actual permission or actual violation). For example, a high value is assigned to complete discrepancy (judged as violation but actually permitted), a medium value is assigned to partial discrepancy (judged as planned but actually violated), and a zero value is assigned to complete agreement. Factor combination refers to the combination of specific values ​​or value ranges of sensitivity level or priority, trust level, and applied weight that led to the historical conflict decision. Case group refers to the set formed by aggregating multiple historical conflict decision records with the same factor combination. Concentration value refers to the statistical measure of the dispersion or concentration of multiple degree of discrepancy quantification values ​​in the case group, which can be measured by statistical indicators such as standard deviation, variance, mean absolute deviation, and interquartile range. Concentration threshold is a preset value used to judge whether the degree of discrepancy quantification values ​​in the case group are sufficiently concentrated, thereby determining the existence of a misjudgment pattern.

[0142] When calculating the deviation tolerance threshold based on sensitivity level or priority, trust level, and application weight, if there is a directional conflict in the judgment results, a conflict handling strategy will be invoked. This strategy reorders the weights based on a preset priority relationship between factors. To optimize this priority relationship, the system retrieves historical conflict decision records. These records contain the factor level values ​​at the time of the conflict, the judgment results obtained based on the priority relationship at that time, and the actual nature of the subsequent confirmation of the deviation behavior. To accurately identify misjudgment patterns in historical conflict decisions, the system analyzes each historical conflict decision. First, it quantifies the degree of inconsistency between the priority relationship between factors corresponding to the historical conflict decision (implied in the judgment result at that time) and the subsequent confirmation nature, generating a quantified value of the degree of inconsistency. This quantified value reflects the degree of "error" in the decision at that time. Then, historical records with the same combination of factors (i.e., conflicts occurring under similar sensitivity levels, trust levels, and application weights) are grouped together to form a case group. This is done to analyze whether misjudgments are common under specific conditions. Next, the concentration of these quantified values ​​of the degree of inconsistency in the case group is analyzed. If these quantified values ​​are concentrated (for example, under a certain combination of factors, many historical records show that the judgment results are highly inconsistent with the subsequent confirmed nature), it indicates that under this combination of factors, the current priority relationship is likely to lead to systematic misjudgment. Finally, if the concentration value meets the preset concentration threshold, a misjudgment pattern is determined to exist. After identifying the misjudgment pattern, the system can adjust the priority relationship between factors more accurately based on these patterns, rather than simply adjusting based on a single historical record. This pattern-based adjustment can more effectively reduce the possibility of misjudgment under similar combinations of factors in the future, thereby improving the accuracy of the deviation tolerance threshold calculation, making the system's judgment of ship deviation behavior more accurate, and reducing false alarms and false negatives.

[0143] In some examples of this embodiment, the system records multiple historical conflict decisions. For instance, in a certain case group, all records correspond to the factor combination of "medium sensitivity level, low trust level, and high application weight." For each historical record in this case group, the system calculates a quantified value for the degree of discrepancy. For example, if in a record the system initially judged it as "planned deviation," but it was later confirmed as "violation," a higher quantified value for the degree of discrepancy can be calculated based on a preset comparison matrix, such as a value of 3. If in another record the system initially judged it as "violation," but it was later confirmed as "planned deviation," an even higher quantified value for the degree of discrepancy can be calculated, such as a value of 5. If the judgment and confirmation are consistent, the quantified value is 0. The system collects the quantified values ​​for the degree of discrepancy of all records in this case group, for example, obtaining a set of values ​​{3, 5, 4, 4, 5, 3, 4, 5}. The system calculates the central tendency of this set of values. For example, it calculates its standard deviation. If the standard deviation is low, it indicates that these quantified values ​​for the degree of discrepancy are relatively concentrated, for example, all between 3 and 5. The system compares the calculated standard deviation with a preset concentration threshold. For example, the preset threshold is 1.0. If the calculated standard deviation is 0.8, which is less than the threshold of 1.0, it determines that a misjudgment pattern exists under the factor combination of "medium sensitivity level, low trust level, and high application weight." After identifying the misjudgment pattern, the system can adjust the priority relationship between factors accordingly. For example, under this factor combination, it can reduce the influence of trust level or application weight on the final judgment, or increase the influence of sensitivity level, to reduce the possibility of misjudging actual violations as planned deviations in this situation in the future.

[0144] Reference Figure 8 Furthermore, B3 includes:

[0145] B31: Quantify the degree of discrepancy in the case group through statistical analysis to obtain the concentration value.

[0146] Statistical analysis refers to the processing and interpretation of data using statistical methods. It can be achieved by calculating descriptive statistics, such as mean, median, mode, variance, standard deviation, quartiles, skewness, kurtosis, etc., or by performing inferential statistical analysis, such as hypothesis testing and regression analysis.

[0147] By statistically analyzing the quantified discrepancy values ​​in the case group, a concentration value reflecting the tightness of their distribution is obtained. In identifying misjudgment patterns, discrepancy quantified values ​​are first generated based on historical conflict decision records, and records with the same combination of factors are grouped into the same case group. To determine whether the case group represents a typical misjudgment pattern, the concentration of the discrepancy quantified values ​​within the case group needs to be assessed. Statistical analysis can calculate indicators such as mean, median, variance, and standard deviation. These indicators quantify the distribution characteristics of the discrepancy quantified values. For example, a smaller variance or standard deviation indicates that the quantified values ​​are relatively concentrated, meaning that the historical conflict decisions in the case group show a high degree of consistency in discrepancy, thus more likely representing a common misjudgment pattern. Conversely, a larger variance or standard deviation indicates that the quantified values ​​are relatively dispersed, possibly meaning that the case group contains multiple different situations, or that the misjudgment pattern is not typical. Through this statistical analysis, the overall characteristics of the case group can be transformed into one or a set of values, providing an objective basis for subsequent judgments on whether a preset concentration threshold is met. This concentration assessment based on statistical analysis, compared to simple aggregation, can more comprehensively and accurately reflect the distribution characteristics of quantified values, thereby improving the accuracy of misjudgment pattern recognition. This method, combined with the steps of clustering records with the same factor combinations for multiple inconsistent quantified values ​​to form case groups, and determining a pattern as a misjudgment if the concentration value meets a preset concentration threshold, forms a complete data analysis-based misjudgment pattern recognition process. This helps the system more effectively learn from historical data and improve the priority relationships between factors.

[0148] In some examples of this embodiment, statistical analysis can be performed on the quantified discrepancy values ​​of a case group. For example, a case group contains 10 historical conflict records with quantified discrepancy values ​​of 0.8, 0.9, 0.7, 0.85, 0.92, 0.78, 0.88, 0.75, 0.81, and 0.89. The mean (approximately 0.826), median (0.84), variance (approximately 0.004), and standard deviation (approximately 0.063) can be calculated. These statistics constitute the concentration value. If the preset concentration threshold is a standard deviation less than 0.1, then the standard deviation of 0.063 for this case group meets the threshold and can be identified as a misjudgment pattern.

[0149] Reference Figure 9 Furthermore, B1 includes:

[0150] B11: Obtain the determination results of deviation behaviors in historical conflicts;

[0151] B12: The nature of subsequent confirmation of deviations from historical conflicts;

[0152] B13: Based on the preset comparison matrix, calculate the quantification value of the degree of non-compliance by comprehensively considering the differences between the judgment results of the deviation behavior and the nature of the subsequent confirmation of the deviation behavior in historical conflicts.

[0153] Specifically, the degree of inconsistency between the priority relationship between factors corresponding to each historical conflict decision and the subsequent confirmation of the nature of the deviation behavior in the historical conflict is quantified. Generating a quantified value of inconsistency means objectively representing the difference between the initial judgment made by the system based on the priority relationship between factors when the historical conflict occurred and the true nature of the ultimately confirmed deviation behavior through a numerical index. Obtaining the judgment result of the deviation behavior in the historical conflict refers to obtaining the initial judgment made by the system on the ship's deviation behavior at the time of the historical conflict based on the rules and priority relationship between factors at that time. This judgment result can be classified as planned deviation or violation. Obtaining the nature of the subsequent confirmation of the deviation behavior in the historical conflict refers to obtaining the final evaluation result of the deviation behavior in the historical conflict. Information can come from manual review, cross-validation of data from other systems, or supplementary information provided by the vessel management. Its nature can include confirmation of planned deviation, confirmation of minor violations, confirmation of serious violations, etc. A pre-defined comparison matrix refers to a pre-set set of mapping relationships. This set defines the quantified value of the degree of discrepancy corresponding to different combinations of the judgment result of the deviation behavior and the nature of the subsequent confirmation of the deviation behavior. It can be implemented in the form of tables, rule sets, or functions. Calculating the quantified value of the degree of discrepancy by comprehensively considering the differences between the judgment result of the deviation behavior and the nature of the subsequent confirmation of historical conflicting deviation behaviors means obtaining the corresponding quantified value of the degree of discrepancy by referring to or applying the mapping relationships defined in the pre-defined comparison matrix based on the obtained judgment result of the deviation behavior and the nature of the subsequent confirmation of the deviation behavior.

[0154] By acquiring the system's initial judgment of deviation behavior at the time of historical conflicts and obtaining the final confirmation of the deviation behavior, the difference between these two results is transformed into a quantified value—the degree of inconsistency—using a pre-set comparison matrix. This quantified value objectively reflects the degree of inconsistency between the system's decision based on the prioritization of factors at that time and the actual situation. In this way, what might have been a vague or qualitative "inconsistency" judgment is transformed into a precise numerical representation. These quantified values ​​can then be used to aggregate and statistically analyze historical conflict records with the same combination of factors, thereby identifying case groups with highly concentrated degrees of inconsistency values, and thus determining them as misjudgment patterns. This quantification method provides a reliable data foundation for subsequent statistical analysis and pattern recognition, making the identification of misjudgment patterns more accurate and objective. This quantification foundation, combined with subsequent case group aggregation and concentration analysis, forms a complete misjudgment pattern recognition mechanism based on quantified data, thereby improving the effectiveness of the entire misjudgment pattern recognition process, and thus more accurately adjusting the priority relationship between factors, improving the accuracy of monitoring.

[0155] In some examples of this embodiment, a comparison matrix can be preset. For example, this matrix can be defined as follows: if the judgment result is "deviation within the plan" and the subsequent confirmation nature is "confirmation of deviation within the plan", then the degree of non-compliance is quantified as 0; if the judgment result is "deviation within the plan" and the subsequent confirmation nature is "confirmation of minor violation", then the degree of non-compliance is quantified as 1; if the judgment result is "deviation within the plan" and the subsequent confirmation nature is "confirmation of serious violation", then the degree of non-compliance is quantified as 3; if the judgment result is "violation" and the subsequent confirmation nature is "confirmation of deviation within the plan", then the degree of non-compliance is quantified as 2; if the judgment result is "violation" and the subsequent confirmation nature is "confirmation of minor violation", then the degree of non-compliance is quantified as 0.5; if the judgment result is "violation" and the subsequent confirmation nature is "confirmation of serious violation", then the degree of non-compliance is quantified as 0. When processing a historical conflict decision record, first obtain the judgment result of the deviation behavior recorded in the record, such as "violation behavior", and then obtain the nature of the subsequent confirmation of the deviation behavior, such as "confirmation of deviation within the plan". Based on the pre-set comparison matrix, the quantitative value corresponding to the judgment result of "violation" and the subsequent confirmation nature of "confirmation of deviation within the plan" is found, and the degree of non-compliance is obtained as a quantitative value of 2. This quantitative value represents the degree of non-compliance of this historical conflict decision.

[0156] Reference Figure 10 Furthermore, this application also provides a marine electronic fence internet monitoring system, comprising:

[0157] The receiving module is used to continuously receive structured operation intent statements submitted by vessels. The structured operation intent statements include planned operation parameters and specific operation instruction information.

[0158] The information query module is used to obtain historical behavior records associated with the vessel, including historical accuracy records of structured operational intent statements and historical rule compliance records.

[0159] The trust level management module is used to assess the trust level of the ship management party based on historical behavior records, and to maintain and update the trust level.

[0160] The information query module is also used to query whether there is a structured operation intent declaration that is related to the current vessel in time and space when the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set.

[0161] The judgment module is used to determine the deviation tolerance threshold of the corresponding planned operation parameters based on the current trust level of the vessel if deviation exists. It compares the real-time behavior parameters with the current planned operation parameters of the vessel to obtain the actual degree of deviation. Based on the comparison result of the actual degree of deviation and the deviation tolerance threshold, it judges whether the deviation behavior belongs to the planned deviation or the violation behavior.

[0162] The determination module is also used to determine the nature of the deviation behavior based on the basic rule set of the electronic fence if the behavior does not exist.

[0163] The receiving module is a unit for receiving external input information, which can be implemented using a network communication interface, a data acquisition interface, or a message queue interface. The information query module is a unit for retrieving data from storage media or external sources, which can be implemented using a database access interface, a file system interface, or a remote data service interface. The trust level management module is a unit for processing and updating the logic related to the ship's trust level, which can be implemented using a computational processing unit, an algorithm execution unit, or a state machine management unit. The decision module is a unit for making logical judgments and decisions based on input information, which can be implemented using a logical judgment unit, a rule engine, or a decision tree execution unit.

[0164] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for internet-based electronic fence monitoring at sea, characterized in that, The method includes: Continuously receive structured operation intent statements submitted by vessels, which include planned operation parameters and specific operation instruction information; Obtain historical behavior records associated with the vessel, including historical accuracy records of the structured operational intent statement and historical rule compliance records; Based on the historical behavior records, assess the trust level of the ship management and maintain and update the trust level. When the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set, query whether there is an effective structured operation intent declaration that is related to the current vessel in time and space. If such deviation exists, the deviation tolerance threshold of the corresponding planned operation parameter is determined according to the trust level of the current vessel. The real-time behavior parameter is compared with the planned operation parameter of the current vessel to obtain the actual degree of deviation. Based on the comparison result of the actual degree of deviation and the deviation tolerance threshold, the determination result of the deviation behavior is judged to be either a deviation within the plan or a violation. If it does not exist, then the nature of the deviation behavior is determined according to the electronic fence basic rule set; The step of determining the deviation tolerance threshold of the corresponding planned operation parameters based on the current trust level of the vessel includes: Obtain the parameter type information of the planned operation parameters; Obtain the preset application weight corresponding to the parameter type; Based on the current vessel's trust level and application weight, calculate the deviation tolerance threshold for the planned operation parameters; The trust level includes multiple trust dimension indicators, including the number of rule violations, the trajectory compliance rate of intent declarations, and the historical conflict resolution rate. The calculation of the deviation tolerance threshold for the planned operation parameters based on the current vessel's trust level and the application weight includes: Obtain the dimensional index scores of multiple trust dimension indicators for the current vessel; Obtain the set of influencing factors for the application weights; The application weights are generated based on the dimensional indicator scores and the set of influencing factors. The deviation tolerance threshold is calculated based on the application weight and the trust level.

2. The marine electronic fence internet monitoring method according to claim 1, characterized in that, The step of calculating the deviation tolerance threshold based on the application weight and the trust level includes: Obtain the specific operational instruction information contained in the structured operational intent statement corresponding to the current vessel; Identify the sensitivity level or priority of the specific job instruction information; The deviation tolerance threshold is obtained by jointly calculating the sensitivity level or priority, multiple trust dimension indicators, and the application weight.

3. The marine electronic fence internet monitoring method according to claim 2, characterized in that, The step of calculating the deviation tolerance threshold based on the sensitivity level or priority, multiple trust dimension indicators, and the application weight includes: Determine whether there is a directional conflict in the determination results of the sensitivity level or priority, the trust level, and / or the application weight; If a directional conflict exists in the judgment results, a preset conflict resolution strategy is invoked, which includes: Quantitatively evaluate the specific level values ​​of the sensitivity level or priority, the trust level and / or the application weight associated with the directional conflict of the judgment result; Based on the preset priority relationship between factors, the sensitivity level or priority, the trust level and / or the weight corresponding to the application weight of the directional conflict association of the judgment result are reordered. The deviation tolerance threshold is obtained by reordering the sensitivity level or priority according to the weights and weighting it with multiple trust dimension indicators and the application weights.

4. The marine electronic fence internet monitoring method according to claim 3, characterized in that, The reordering of the weights corresponding to the sensitivity level or priority, the trust level, and / or the application weight in the directional conflict association of the judgment result, based on the preset priority relationship between factors, includes: Obtain multiple historical conflict decision records, which include the original specific level values ​​of the sensitivity level or priority, the trust level, and the application weight when the historical conflict occurred, the deviation tolerance threshold adopted when the historical conflict occurred, and the nature of the subsequent confirmation of the deviation behavior of the historical conflict. Determine whether the priority relationship between the factors corresponding to the historical conflict is inconsistent with the nature of the subsequent confirmation of the deviation behavior of the historical conflict; If they do not match, identify the corresponding misjudgment pattern and adjust and update the priority relationship between the factors according to the misjudgment pattern.

5. The marine electronic fence internet monitoring method according to claim 4, characterized in that, The identification of the corresponding misjudgment pattern includes: The degree of inconsistency between the priority relationship between the factors corresponding to each historical conflict decision and the nature of the subsequent confirmation of the deviation behavior of the historical conflict is quantified to generate a quantified value of inconsistency. Records with the same combination of factors for multiple quantifications of the degree of discrepancy are grouped together to form a case group; Analyze the degree of concentration of the quantified discrepancies in the case group; If the concentration level value meets the preset concentration threshold, it is determined to be the misjudgment mode.

6. The marine electronic fence internet monitoring method according to claim 5, characterized in that, The analysis of the concentration of the quantified discrepancies in the case group includes: The degree of discrepancy in the case group was statistically analyzed to obtain the concentration value.

7. The marine electronic fence internet monitoring method according to claim 5, characterized in that, The process of quantifying the degree of inconsistency between the priority relationship between the factors corresponding to each historical conflict decision and the nature of the subsequent confirmation of the deviation behavior of the historical conflict, generating a quantified value of the degree of inconsistency, includes: Obtain the determination result of the deviation behavior of the historical conflict; To obtain the nature of subsequent confirmation of the deviation behavior of the historical conflict; Based on a preset comparison matrix, the degree of discrepancy is quantified by combining the differences between the determination results of the deviation behavior and the nature of the subsequent confirmation of the deviation behavior in the historical conflict.

8. A marine electronic fence internet monitoring system, characterized in that, include: The receiving module is used to continuously receive structured operation intent statements submitted by vessels, which include planned operation parameters and specific operation instruction information; The information query module is used to obtain historical behavior records associated with the vessel, including historical accuracy records of the structured operation intent statement and historical rule compliance records. The trust level management module is used to assess the trust level of the ship management party based on the historical behavior records, and to maintain and update the trust level. The information query module is also used to query whether there is an effective structured operation intent declaration that is related to the current vessel in time and space when the real-time behavior parameters of the current vessel deviate from the preset electronic fence basic rule set. The determination module is used to determine the deviation tolerance threshold of the corresponding planned operation parameters based on the trust level of the current vessel if the deviation exists, compare the real-time behavior parameters with the planned operation parameters of the current vessel to obtain the actual degree of deviation, and determine whether the deviation behavior belongs to planned deviation or violation based on the comparison result of the actual degree of deviation and the deviation tolerance threshold. The determination module is further configured to, if the behavior does not exist, determine the nature of the deviation behavior based on the electronic fence basic rule set. The determination module is also used to obtain parameter type information of the planned operation parameters; Obtain the preset application weight corresponding to the parameter type; Based on the current vessel's trust level and application weight, calculate the deviation tolerance threshold for the planned operation parameters; The trust level includes multiple trust dimension indicators, including the number of rule violations, the trajectory compliance rate of intent declarations, and the historical conflict resolution rate. The calculation of the deviation tolerance threshold for the planned operation parameters based on the current vessel's trust level and the application weight includes: Obtain the dimensional index scores of multiple trust dimension indicators for the current vessel; Obtain the set of influencing factors for the application weights; The application weights are generated based on the dimensional indicator scores and the set of influencing factors. The deviation tolerance threshold is calculated based on the application weight and the trust level.

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