Ship route compliance assessment system fusing electronic fence and GIS

Abstract

The application discloses a ship channel compliance evaluation system fusing an electronic fence and GIS, and belongs to the field of shipping management, and comprises a rule definition module, a parameter injection module and the like, wherein the rule definition module is used for defining logical atoms and dynamic parameters of channel rules, wherein the logical atom is a condition-conclusion pair containing a parameterized placeholder, and a rule logic source code library is formed; the parameter injection module is used for establishing a real-time parameter injection channel for the parameterized placeholder, listening to messages of a preset data source and capturing values in the messages, broadcasting parameter update events and values, and updating and maintaining a real-time parameter value state table; the system can realize real-time parameter injection according to the parameterized placeholder, accurate search optimization of rule instantiation, and dynamic generation of a compliance judgment program instance, realizes adaptive matching of dynamic characteristics such as time, season, temporality and hydrological dependence of channel rules, and solves the false alarm and missed alarm problems caused by the fact that the traditional fixed electronic fence cannot be updated synchronously.

Description

Technical Field

[0001] This invention relates to the field of shipping management, and more specifically, to a ship and waterway compliance assessment system that integrates electronic fences and GIS. Background Technology

[0002] With the maturity and widespread adoption of Geographic Information System (GIS) technology, electronic fence technology, as a spatial boundary delineation and early warning method, has been widely integrated into vessel navigation compliance assessment systems. By pre-setting geographic polygon fences to delineate permitted, prohibited, or restricted navigation areas within a waterway, and combining this with vessel positioning information, rapid compliance verification is achieved. Existing electronic fences in vessel navigation compliance assessment systems, through pre-collected waterway geographic boundary data, solidify the spatial constraints corresponding to waterway rules into fixed, unchanging polygon geographic ranges. When a vessel's positioning coordinates exceed or enter this polygon range, the system determines it as a violation. While a vessel may be in compliance with regulations and trigger corresponding prompts, the actual navigation rules followed by ships are not a single, fixed spatial constraint. Instead, they are dynamic, characterized by temporality, seasonality, temporality, and hydrological dependence. For example, seasonal water level changes can cause navigation marks to shift dynamically, thereby altering the actual usable boundaries of the waterway. Some waterways may have one-way traffic rules set for specific periods based on shipping flow control needs. Construction zones or no-navigation zones may be temporarily designated for scenarios such as waterway construction and emergency rescue. Under special hydrological conditions such as flood season and dry season, temporary navigation bans or restrictions may be implemented on specific sections of the waterway.

[0003] When navigation rules are adjusted based on time, hydrological conditions, etc., and the electronic fence is not updated accordingly, the system may misjudge the compliance of vessels based on outdated fences, classifying navigation status that complies with the new rules as a violation, thus generating false alarms. When new navigation rules are established, such as temporary no-navigation zones or adjusted channel boundaries, if the electronic fence does not promptly cover the spatial range corresponding to the new rules, the system will be unable to identify vessels' navigation behaviors that violate the new rules, thus generating missed alarms. The aforementioned problems of false alarms and missed alarms seriously reduce the accuracy of vessel navigation compliance assessment results, interfere with the judgment of command and dispatch personnel, and thus affect the effectiveness of maritime traffic command and dispatch, and may even lead to navigation safety risks due to misjudgments. Summary of the Invention

[0004] To address the problems existing in the prior art, the purpose of this invention is to provide a vessel waterway compliance assessment system that integrates electronic fences and GIS. This system can achieve real-time parameter injection based on parameterized placeholders, precise retrieval and optimization of rule instantiation, and dynamic generation of compliance judgment program instances. It enables adaptive matching of waterway rules to dynamic characteristics such as timeliness, seasonality, temporariness, and hydrological dependence, and solves the problems of false alarms and missed alarms caused by the inability to update rules synchronously in traditional fixed electronic fences.

[0005] To solve the above problems, the present invention adopts the following technical solution:

[0006] A vessel navigation channel compliance assessment system integrating electronic fences and GIS includes:

[0007] The rule definition module is used to define the logical atoms and dynamic parameters of the waterway rules. The logical atoms are condition-conclusion pairs containing parameterized placeholders, forming the rule logic source code library.

[0008] The parameter injection module is used to establish a real-time parameter injection channel for parameterized placeholders, listen to messages from preset data sources and capture values ​​in the messages, broadcast parameter update events and values, and update and maintain the real-time parameter value status table.

[0009] The rule instantiation module is used to respond to events such as a ship entering the monitoring area or a parameter status update event. It retrieves relevant logic atoms from the rule logic source code library, substitutes the values ​​in the real-time parameter value status table into its parameterized placeholders, and compiles to generate an executable compliance judgment program instance.

[0010] The compliance simulation module is used to input the ship's status into the program instance, execute logical simulations to calculate logical atoms, and output compliance status labels;

[0011] The conflict arbitration module is used to initiate arbitration when a conflicting compliance status label is generated during logical deduction. It makes a decision based on the predefined rule element priority, obtains the final judgment result, and encapsulates the final judgment result and its derivation chain into a causal package.

[0012] The response generation module is used to synthesize response instructions containing navigation guidance based on the final judgment result and the tracing package. It generates a correction navigation vector corresponding to the navigation guidance based on the pre-stored waterway network topology data and real-time ship density data, and outputs the response instructions.

[0013] Furthermore, the rule definition module also includes:

[0014] The condition factors used for parameterization are extracted from the original waterway rules as rule dimensions, and discretized coordinate axes are established for each rule dimension. The condition part of each rule is parsed into a coordinate region or set of coordinate points in a multi-dimensional coordinate system formed by the coordinate axes, and a coordinate system and coordinate mapping relationship table for rule dimensions are generated.

[0015] Based on the coordinate mapping relationship table, logical atoms are constructed for each rule. The logical atom includes a logical key corresponding to the coordinate region or set of coordinate points, and a logical body that encapsulates the parameterized placeholder condition judgment logic and conclusion, forming a rule logic source code library indexed by logical keys.

[0016] The relative positions and overlaps of the logical bonds of different logical atoms in the rule-based logic source code library are analyzed in the rule-based coordinate system. Based on the predefined association rules, corresponding attractive or repulsive potential fields are established between logical atoms with inclusion or mutual exclusion relationships.

[0017] Furthermore, the parameter injection module also includes:

[0018] Capture the message value corresponding to the parameterized placeholder, generate a data fingerprint containing the source identifier, timestamp sequence number and value digest, and bind the value, data fingerprint, preset confidence weight and logical validity duration as the parameter original unit;

[0019] Based on the original parameter units, update and maintain the dynamic parameter state field; the update includes: performing same-source update or heterogeneous merging based on the source identifier of the data fingerprint, heterogeneous merging is weighted and compared with the current field strength in the state field according to the confidence weight; and performing time decay calculation related to the logical effective duration on all field strengths in the state field.

[0020] When an update causes the field strength of a specific parameter in the dynamic parameter state field to change beyond a preset change threshold, the state field unit corresponding to that parameter will be encapsulated as a parameter state update event and broadcast.

[0021] Furthermore, the rule instantiation module also includes:

[0022] Receive trigger signals for ship entering the monitoring area or parameter status update events, map the trigger signals to the rule dimension coordinate system, and generate corresponding query coordinates;

[0023] Centered on the query coordinates, guided diffusion retrieval is performed in the rule logic source code library based on the inter-atomic potential field to obtain the set of logical atoms that directly cover the query coordinates. Then, related sub-atoms are adsorbed according to the attraction potential field and mutually exclusive atoms are excluded according to the repulsion potential field to form a rule logic subgraph.

[0024] Furthermore, the rule instantiation module also includes:

[0025] Analyze the structural features and trigger signal types of the rule logic subgraph, select a compilation strategy accordingly, and perform logical simplification on the rule logic subgraph to obtain an optimized rule logic subgraph;

[0026] Extract the current authority value corresponding to each parameterized placeholder in the optimization rule logic subgraph from the dynamic parameter state field, generate a parameter value snapshot, and compile the optimization rule logic subgraph and parameter value snapshot based on the selected compilation strategy to generate an executable compliance judgment program instance.

[0027] Furthermore, the compliance simulation module also includes:

[0028] Generate a snapshot of the current ship status based on the ship status input interface of the program instance, compare the current ship status snapshot with the ship status snapshot used in the previous simulation, identify the variables that have changed and their associated positions in the program instance, and generate a list of status difference identifiers.

[0029] Maintain the logical path cache corresponding to the program instance. The logical path cache records the logical path triggered by the last simulation. Based on the list of state difference identifiers, start the incremental simulation from the starting point of the logical path cache. Only recalculate the condition judgment nodes associated with the changed variables in the logical path, update the logical path cache and obtain the compliance status label.

[0030] Furthermore, the conflict arbitration module also includes:

[0031] When logical deduction produces mutually exclusive compliance status labels, extract the relevant logical atoms and their derivation chains to construct a conflict topology graph, and identify the conflict root node in the conflict topology graph that leads to the conclusion fork.

[0032] By focusing on the root cause nodes of conflict and combining them with the interatomic potential field, a dynamic correlation network representing the interaction relationship of the potential field is constructed.

[0033] Furthermore, the conflict arbitration module also includes:

[0034] For each conflict root cause node, different adjudication options are simulated in parallel based on the current ship status snapshot, parameter value snapshot, and dynamic correlation network. The decision risk index of each option is calculated to generate an arbitration risk matrix.

[0035] Based on the arbitration risk matrix, the predefined rule element priority, and the attributes of the conflict root node, the final judgment result is obtained through multi-factor adjudication, and the final judgment result, the original derivation chain, and arbitration process information are encapsulated into an enhanced abduction package.

[0036] Furthermore, the response generation module also includes:

[0037] Based on the final judgment results and the enhanced tracing package, the root cause nodes of the conflict and the derivation chain are analyzed to identify the violation constraints and the ship's compliance objectives. Based on the ship's current position and compliance objectives, the reachable waterway subgraph is extracted from the waterway network topology data.

[0038] The violation constraints are mapped to prohibited elements in the reachable channel subgraph. The dynamic repulsion field value is calculated by combining real-time ship density data. A restricted path search algorithm is then executed on the weighted reachable channel subgraph to generate a set of multiple candidate compliant paths.

[0039] Furthermore, the response generation module also includes:

[0040] Establish a multi-objective optimization evaluation system that includes path safety, navigation efficiency, and the estimated level of interference to ship navigation in the waterway network. Calculate and rank the comprehensive evaluation values ​​of each path in the multi-candidate compliant path set to generate a path ranking list.

[0041] The first path in the path sorting list is selected as the primary path. The primary path is then decomposed into an immediate action command, a sequence of intermediate waypoints, and a summary of alternative paths, which are then encapsulated to generate a smart response command package.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] (1) Based on the real-time parameter injection of parameterized placeholders, the precise retrieval optimization of rule instantiation and the dynamic generation of compliance judgment program instances, this solution realizes the adaptive matching of waterway rules to dynamic characteristics such as timeliness, seasonality, temporariness and hydrological dependence, and solves the problem of false alarms and missed alarms caused by the inability of traditional fixed electronic fences to update rules synchronously.

[0044] (2) This scheme filters the authoritative value of parameters by the field strength of the dynamic parameter state field and generates a parameter value snapshot to fix the current parameter state, avoiding logical inconsistencies caused by parameter changes during compilation and deduction, ensuring the accuracy of parameters in the compliance judgment program instance, and improving the reliability of waterway compliance judgment from the basic data level.

[0045] (3) This solution integrates the optimized rule logic subgraph and parameter value snapshot to generate an executable compliance judgment program instance. The program instance integrates complete compliance judgment logic and real-time authoritative parameter data, which can quickly receive ship status input and perform logical deduction, effectively improving the real-time performance and computational efficiency of waterway compliance judgment.

[0046] (4) This solution forms a precise waterway compliance assessment system throughout the entire process, from extracting authoritative parameters and generating accurate program instances to subsequent compliance simulations, conflict arbitration, and intelligent response command generation. This not only improves the effectiveness of water traffic command and dispatch but also reduces the risk to ship navigation safety caused by misjudgment. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0048] Figure 1 This is a diagram showing the data flow direction between the various modules in this invention. Detailed Implementation

[0049] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 The system integrates electronic fences and GIS for evaluating vessel and waterway compliance. It includes a rule definition module for defining the logical atoms and dynamic parameters of waterway rules. The logical atoms are condition-conclusion pairs containing parameterized placeholders, forming a rule logic source code library. The specific operations are as follows:

[0051] The rule definition module is first used to define the logical atoms and dynamic parameters of the waterway rules. The logical atoms, as the basic building blocks of the rules, adopt a condition-conclusion pair structure, and reserve parameterized placeholders in the condition and conclusion parts. These placeholders are used to receive real-time updated parameter values ​​to achieve dynamic adaptation. During the definition process, the rule definition module decomposes and abstracts various waterway rules, breaking down complex waterway compliance requirements into multiple independently operable and combinable logical atoms. Each logical atom corresponds to a single compliance judgment logic in a specific scenario. At the same time, the rule definition module sorts out all parameters in the waterway rules that change with time, hydrology, shipping traffic, and other factors, identifies them as dynamic parameters, and completes attribute definitions, including key information such as the data type, value range, and update frequency of the parameters. All defined logical atoms are stored in a unified format specification to form a rule logic source code library.

[0052] The rule definition module also performs the following steps:

[0053] Step 11: Extract the condition factors for parameterization from the original waterway rules as rule dimensions, and establish discretized coordinate axes for each rule dimension. Analyze the condition part of each rule into a coordinate region or set of coordinate points in a multi-dimensional coordinate system formed by the coordinate axes, generating the rule dimension coordinate system and coordinate mapping table. The specific operations are as follows:

[0054] First, conditional factors suitable for parametric representation are selected from the original waterway rule texts, industry standards, and control requirements. These conditional factors are elements that influence waterway compliance judgments, such as water level values, navigation periods, vessel density thresholds, and navigation mark coordinates. These conditional factors are defined as rule dimensions. For each rule dimension, the rule definition module discretizes it based on its value characteristics, establishing a corresponding discretized coordinate axis. The discretization criteria are determined based on the actual needs of waterway control and data acquisition accuracy, ensuring that each scale on the coordinate axis accurately represents a specific state or numerical range under that dimension. Subsequently, the conditions of each original waterway rule are... The system performs structured analysis to extract the value requirements of each rule dimension contained in the condition part, and transforms them into coordinate regions or sets of coordinate points in a multidimensional coordinate system composed of coordinate axes of all rule dimensions. The coordinate regions correspond to the case where the condition factor has a range of values, and the sets of coordinate points correspond to the case where the condition factor has a fixed value. Based on the above analysis results, the rule definition module generates a rule dimension coordinate system that fully covers all rule dimensions and their corresponding value ranges. At the same time, a coordinate mapping relationship table is established, which records the one-to-one correspondence between each original rule and the coordinate region or set of coordinate points in the multidimensional coordinate system, realizing the association and binding of the original rules with the digital coordinate representation.

[0055] Step 12: Based on the coordinate mapping table, construct logical atoms for each rule. A logical atom includes a logical key corresponding to the coordinate region or set of coordinate points, and a logical body encapsulating parameterized placeholder condition judgment logic and conclusion, forming a rule logic source code library indexed by logical keys. The specific operations are as follows:

[0056] After completing the construction of the rule dimension coordinate system and coordinate mapping table, the rule definition module constructs logical atoms based on this mapping table. For each original channel rule, the rule definition module generates a unique logical key based on its corresponding coordinate region or set of coordinate points in the coordinate mapping table. This logical key accurately identifies the coordinate characteristics of the rule, facilitating rapid retrieval in the rule logic source code library. Simultaneously, a logical body is constructed for each rule, encapsulating the rule's condition judgment logic and conclusion output logic. In the condition judgment logic and conclusion logic, all numerical positions that need to be dynamically adjusted according to real-time data are set as parameterized placeholders, achieving decoupling between the logical body and real-time parameters. The logical key and logical body corresponding to each rule together constitute a complete logical atom. All logical atoms are organized and stored according to the logical key as an index, forming the rule logic source code library. The index structure design of this source code library ensures that after receiving a trigger event, the relevant logical atoms can be quickly located and retrieved based on the coordinate information.

[0057] Step 13: Analyze the relative positions and overlaps of the logical bonds of different logical atoms in the rule-based logic source code library within the rule-based coordinate system. Based on predefined association rules, establish corresponding attractive or repulsive potential fields between logical atoms with inclusion or mutual exclusion relationships. The specific operations are as follows:

[0058] To address the issue of handling association relationships when multiple rules coexist, the rule definition module performs association analysis and potential field construction on all logical atoms in the rule logic source code library. First, it extracts the logical key corresponding to each logical atom. Based on the coordinate definition of the rule dimensional coordinate system, it determines the specific position of each logical key in the multidimensional coordinate system. Then, it analyzes the relative positional relationships and value range overlap relationships between the logical keys of different logical atoms. Relative positional relationships include adjacent, containment, and separation, while value range overlap relationships include partial overlap, complete overlap, and no overlap. Subsequently, based on predefined association rules, it identifies logical atoms with containment relationships. A containment relationship refers to a logical atom whose corresponding coordinate region completely covers the other logical atom. For the coordinate region corresponding to another logical atom, an attractive potential field is established between the two logical atoms. The role of the attractive potential field is to automatically associate the included logical atom when one of the logical atoms is retrieved in the subsequent retrieval process. For logical atoms with mutual exclusion relationships, which means that the coordinate regions corresponding to the two logical atoms have no overlap and their condition judgment logic is contradictory, a repulsive potential field is established between the two logical atoms. The role of the repulsive potential field is to automatically exclude the mutually exclusive logical atoms when one of the logical atoms is retrieved in the subsequent retrieval process, so as to avoid logical conflicts. Through the establishment of attractive and repulsive potential fields, the digital representation of the association relationship between logical atoms is realized.

[0059] In some embodiments of the present invention, a parameter injection module is also included, which is used to establish a real-time parameter injection channel for parameterized placeholders, listen to messages from a preset data source and capture values ​​in the messages, broadcast parameter update events and values, and update and maintain a real-time parameter value status table. The specific operations are as follows:

[0060] The parameter injection module first establishes a dedicated real-time parameter injection channel for each parameterized placeholder preset by the rule definition module. The channel configuration is adapted to the data source type, transmission protocol, and update frequency of the corresponding parameter to ensure the real-time performance and stability of parameter transmission. The parameter injection module also continuously monitors the message transmission of preset data sources, which include nodes that provide dynamic parameters, such as hydrological monitoring stations, navigation mark positioning systems, shipping dispatch centers, and automatic identification systems for ships. During the monitoring process, the module captures the numerical information corresponding to each parameterized placeholder in the message in real time. For the captured values ​​and parameter update status, the parameter injection module broadcasts the parameter update event and the corresponding value immediately. At the same time, the parameter injection module continuously updates and maintains the real-time parameter value status table. This status table records key information such as the current value, update time, and data source corresponding to each parameterized placeholder, providing unified data support for parameter calling and status traceability, and ensuring the consistency and accuracy of parameter use throughout the system.

[0061] The parameter injection module also performs the following steps:

[0062] Step 21: Capture the message value corresponding to the parameterized placeholder, generate a data fingerprint containing the source identifier, timestamp sequence number, and value digest, and bind the value, data fingerprint, preset confidence weight, and logical validity duration into the parameter original unit. The specific operation is as follows:

[0063] First, the system extracts values ​​from the captured messages that precisely match each parameterized placeholder, ensuring a correct one-to-one correspondence between values ​​and placeholders. Then, it generates a data fingerprint corresponding to each value. The data fingerprint consists of three parts: a source identifier, a timestamp sequence number, and a value digest. The source identifier uniquely identifies the data source node of the value, facilitating the differentiation of parameter data from different sources. The timestamp sequence number is generated according to the time sequence of value capture, representing the temporal relationship of parameter updates. The value digest is obtained by performing a specific hash algorithm on the captured value, used to verify whether the value has been tampered with during transmission and processing. After generating the data fingerprint, the parameter injection module binds the captured value, the generated data fingerprint, the preset confidence weight, and the logical validity period to form a parameter raw unit. The confidence weight is preset based on factors such as the reliability of the data source and the stability of the transmission link, used to quantify the credibility of parameter data from different sources. The logical validity period defines the valid usage period of the parameter value; after this period, the value loses its validity. Through the construction of the parameter raw unit, the structured encapsulation of parameter data is achieved.

[0064] Step 22: Update and maintain the dynamic parameter state field based on the original parameter units. The update includes: performing same-source update or heterogeneous-source merging based on the source identifier of the data fingerprint; heterogeneous-source merging is performed by weighting the confidence weight with the current field strength in the state field; and performing a time-lapse calculation related to the logical validity duration on all field strengths in the state field. The specific operations are as follows:

[0065] Based on the constructed parameter original units, the parameter injection module carries out the updating and maintenance of the dynamic parameter state field. The update process mainly includes two steps: same-source update or heterogeneous merging based on data fingerprints, and time-effect decay calculation of field strength. In the dynamic parameter state field, field strength is an indicator used to quantify the credibility and timeliness of a single parameter. In essence, it is a comprehensive representation of the effective value of the parameter in the current system and a quantitative standard for measuring whether the parameter has the qualifications to be a basis for compliance judgment.

[0066] In the same-source update or heterogeneous-source merging stage, the parameter injection module first parses the data fingerprint in the original parameter unit, extracts the source identifier, and determines whether the current original parameter unit and the parameter unit already stored in the dynamic parameter state field belong to the same data source based on the source identifier. If they belong to the same data source, a same-source update operation is performed, directly replacing the corresponding data record in the state field with the new original parameter unit to ensure the timeliness of parameter data from the same source. If they belong to different data sources, a heterogeneous-source merging operation is performed. The merging process uses a weighted comparison based on a preset confidence weight and the current field strength in the state field. To calculate the merged parameter field strength, the following formula is introduced: ;

[0067] The derivation logic of the formula is as follows: heterogeneous source merging needs to consider the credibility of parameters from different sources, i.e., the confidence weight, as well as the current state of the parameter, i.e., the current field strength. The influence of the two is balanced by the weighting coefficient. This is abstracted from the business logic of parameter fusion to ensure that the field strength after merging can objectively reflect the true state of the parameter. In the formula, α represents the combined parameter field strength; α represents the weighting coefficient, with a value range of 0 < α < 1, used to balance the influence of multi-source data and the current field strength. Represents the confidence weight of the i-th heterogeneous parameter original unit; This represents the value of the original unit of the i-th heterogeneous parameter; This represents the current field strength of the parameter in the state field. During heterogeneous source merging, the new field strength is calculated according to the above formula, and the corresponding parameter unit in the state field is updated. In the time-decrease calculation stage, the parameter injection module performs calculations related to the logical validity period for the field strength of all parameters in the dynamic parameter state field. As time goes by, the parameter field strength will gradually decrease according to the preset decay law. When the parameter usage time exceeds the logical validity period, the field strength decays to zero, indicating that the parameter value is invalid, thereby ensuring the validity of the parameters in the state field. Through the continuous execution of the above two stages, the parameter injection module realizes the real-time update and stable maintenance of the dynamic parameter state field.

[0068] Step 23: When the update causes the field strength change of a specific parameter in the dynamic parameter state field to exceed a preset change amplitude threshold, the state field unit containing the parameter is encapsulated as a parameter state update event and broadcast. The specific operation is as follows:

[0069] During monitoring, for each parameter in the state field, the parameter injection module calculates the change in field strength between the updated and unupdated values. The change is represented by the absolute value of the difference between the two values. The calculated change is compared with a preset change threshold, which is pre-set based on factors such as the parameter's impact on waterway compliance determination and system response sensitivity requirements. Different threshold standards can be configured for different parameters. When the change in field strength of a specific parameter exceeds its corresponding preset threshold, it indicates that the parameter's state has changed significantly. This change may affect the accuracy of subsequent compliance determination results. At this time, the parameter injection module completely encapsulates the state field unit corresponding to the parameter into a parameter state update event. The state field unit includes information such as the parameter's current value, field strength, update time, data source, and confidence weight.

[0070] In some embodiments of the present invention, a rule instantiation module is also included, which, in response to a ship entering a monitoring area event or a parameter status update event, retrieves relevant logic atoms from the rule logic source code library, substitutes the values ​​in the real-time parameter value status table into its parameterized placeholders, and compiles to generate an executable compliance determination program instance. The specific operations are as follows:

[0071] The rule instantiation module continuously monitors two types of trigger events: ship entry into the monitoring area and parameter status update events. Both events indicate a change in the scenario or basis for current channel compliance determination, necessitating the regeneration of an adapted determination program. Upon receiving either trigger event, the module retrieves logic atoms highly relevant to the current scenario from the rule logic source code library based on the information carried by the event. The retrieval process relies on the rule-dimensional coordinate system and the potential field relationship between atoms for precise positioning, ensuring that the retrieved logic atoms fully cover the compliance determination requirements of the current scenario. After retrieval, the module extracts the current valid values ​​corresponding to the parameterized placeholders within each logic atom from the real-time parameter value status table and the dynamic parameter status field. These values ​​are then precisely substituted into the placeholders to complete the parameterized filling of the logic atoms. Subsequently, the filled logic atoms undergo syntax parsing and logical integration, transforming them into a compliance determination program instance that can be directly executed by a computer through a preset compilation process. This program instance can receive ship status input, perform logical operations, and output the corresponding compliance determination results. The entire process ensures that the compliance determination program can adapt to scenario changes in real time, guaranteeing the timeliness and accuracy of the determination results.

[0072] The rule instantiation module also performs the following steps:

[0073] Step 31: Receive trigger signals for ship entering the monitoring area or parameter status update events, map the trigger signals to the rule-based coordinate system, and generate corresponding query coordinates. The specific operations are as follows:

[0074] The rule instantiation module first receives trigger signals from events such as a ship entering a monitoring area or a parameter status update event. It then performs structured parsing on these signals, extracting information related to waterway compliance determination. For trigger signals from ship entering a monitoring area events, the parsed content typically includes the ship's current position coordinates, the time of entry into the monitoring area, the ship type, and its navigation status. For trigger signals from parameter status update events, the parsed content typically includes the updated parameter identifier, the updated parameter value, the update time, and confidence information about the data source. Based on the rule dimension coordinate system and coordinate mapping table constructed in step 11, the parsed... The information obtained is mapped one by one to each rule dimension of the coordinate system. Each rule dimension corresponds to an independent coordinate axis. The value in the information that matches the dimension is the coordinate value on that coordinate axis. The coordinate values ​​corresponding to all rule dimensions are combined to generate a multi-dimensional query coordinate. This query coordinate can uniquely represent the scene characteristics corresponding to the current triggering event. Its position in the rule dimension coordinate system directly determines the scope and accuracy of the subsequent retrieval logic atoms. During the mapping process, the coordinate mapping relationship established in step 11 is strictly followed to ensure that each key piece of information can accurately correspond to the corresponding rule dimension, avoiding errors in subsequent retrieval results due to mapping deviations.

[0075] Step 32: Centered on the query coordinates, perform guided diffusion retrieval in the regular logic source code library based on the inter-atomic potential field to obtain the set of logical atoms that directly cover the query coordinates. Then, attract related sub-atoms based on the attractive potential field and exclude mutually exclusive atoms based on the repulsive potential field to form a regular logic subgraph. The specific operations are as follows:

[0076] Using the query coordinates generated in step 31 as the starting point, a guided diffusion retrieval based on the inter-atomic potential field is initiated in the rule logic source code library. During the retrieval process, the specific location of the query coordinates in the rule dimension coordinate system is first determined, and logical atoms whose coordinate regions or sets of coordinate points corresponding to logical keys can directly cover the query coordinates are selected. These atoms constitute the initial set of logical atoms, which are the most core compliance judgment basis in the current scenario. Subsequently, based on the attraction and repulsion potential field relationship established in step 13, the initial atom set is associated with expansion and redundancy elimination: for each atom in the initial atom set, according to its associated attraction potential field, the related key with an inclusion relationship with the atom is automatically retrieved and adsorbed. Connecting atoms, these sub-atoms can supplement and refine the logical details of compliance judgment, ensuring the completeness of the judgment basis; at the same time, based on the repulsive potential field of atomic association, logical atoms that have mutual exclusion relationships with the initial atomic set or associated sub-atoms are automatically identified and excluded. The condition judgment logic of these mutually exclusive atoms contradicts the current scenario or core atoms, and their inclusion will lead to judgment logic conflicts; after association adsorption and mutual exclusion, the remaining logical atoms are organized according to their positional relationship and logical association relationship in the rule dimension coordinate system to form a clear structure and logically consistent rule logic subgraph. There are no logical conflicts between the atoms in the subgraph, and it can fully cover the compliance judgment requirements of the current scenario.

[0077] Step 33: Analyze the structural features and trigger signal types of the regular logic subgraph, select a compilation strategy accordingly, and perform logical simplification on the regular logic subgraph to obtain an optimized regular logic subgraph. The specific operations are as follows:

[0078] Structural feature analysis is performed on the rule logic subgraph formed in step 32, focusing on the number of logical atoms within the subgraph, the types of logical relationships between atoms (e.g., series, parallel, containment), the presence of duplicate condition judgment nodes, and the complexity of the logical paths. Simultaneously, the type of the current trigger signal is identified, distinguishing between a ship entering a monitored area event and a parameter status update event. The compliance judgment requirements for these two types of events differ: ship entering a monitored area events typically require rapid generation of judgment procedures to achieve real-time compliance verification; parameter status update events place greater emphasis on the accuracy of the judgment procedure to ensure the adaptability of the judgment logic after parameter updates. Combining the subgraph structural features and the trigger signal type, a selection is made. The appropriate compilation strategy is adopted. For example, for scenarios with a small number of atoms, simple logical paths, and a ship entry event as the trigger signal, a fast compilation strategy is selected to improve program generation efficiency. For scenarios with a large number of atoms, complex logical paths, and parameter update events as the trigger signal, a precise compilation strategy is selected to ensure the accuracy of program execution. Subsequently, a logic simplification operation is performed on the rule logic subgraph, deleting duplicate condition judgment nodes, merging logically equivalent atoms, and eliminating invalid logical branches. This simplifies the relationships between atoms and reduces the computational load of subsequent compilation and deduction. After simplification, an optimized rule logic subgraph is obtained. This subgraph retains the core compliance judgment logic and has a simpler structure and higher computational efficiency.

[0079] Step 34: Extract the current authority value corresponding to each parameterized placeholder in the optimization rule logic subgraph from the dynamic parameter state field, generate a parameter value snapshot, and compile the optimization rule logic subgraph and parameter value snapshot based on the selected compilation strategy to generate an executable compliance judgment program instance. The specific operations are as follows:

[0080] From the dynamic parameter state field maintained in step 22, extract the current authority value corresponding to each parameterized placeholder in the optimization rule logic subgraph. During the extraction process, the corresponding parameter unit in the dynamic parameter state field is retrieved using the identifier of the parameterized placeholder as an index. The parameter value with the highest field strength is selected as the authority value. The field strength comprehensively reflects the confidence and timeliness of the parameter value, ensuring that the extracted value is the most reliable parameter data at present. All extracted authority values ​​are associated with the corresponding parameterized placeholder identifier, extraction time, confidence weight, and other information to generate a parameter value snapshot. The parameter value snapshot can fix the parameter state at the current moment and avoid the need for further processing during subsequent compilation or deduction. Changes in parameter values ​​lead to logical inconsistencies. Based on the compilation strategy selected in step 33, the optimization rule logic subgraph and parameter value snapshot are integrated and compiled: First, the condition judgment logic and conclusion logic of each logical atom in the optimization subgraph are converted into intermediate code that can be recognized by the computer. Then, the authoritative values ​​in the parameter value snapshot are substituted one by one into the parameterized placeholders corresponding to the intermediate code. After replacement, the intermediate code is subjected to syntax verification and logic optimization. Finally, it is compiled to generate a directly executable compliance judgment program instance. This program instance contains complete compliance judgment logic and real-time parameter data. It can receive ship status input and quickly execute logical deduction, and output accurate compliance status labels.

[0081] In some embodiments of the present invention, a compliance deduction module is also included, which is used to input the ship status into the program instance, perform logical deduction to calculate logical atoms, and output a compliance status label. The specific operation is as follows:

[0082] The compliance simulation module continuously receives real-time vessel status data and establishes a data interaction link with the program instance output by the rule instantiation module. Following the input specifications preset by the program instance, it accurately inputs the vessel status data into the program instance's vessel status input interface. Based on its internally encapsulated compliance judgment logic and real-time parameter data, the program instance initiates a logic simulation process. During the simulation, it calculates the conditional judgment logic of each internal logical atom, and executes the corresponding conclusion output calculation based on the matching of the vessel status and parameter values. The simulation process strictly follows the logical structure and calculation order of the program instance to ensure the accuracy of each logical operation. Finally, the module integrates the calculation results of all logical atoms and outputs a unified compliance status label, which clearly represents the compliance attribute of the current vessel navigation status.

[0083] The compliance simulation module also performs the following steps.

[0084] Step 41: Generate a snapshot of the current ship status based on the ship status input interface of the program instance. Compare the current ship status snapshot with the ship status snapshot used in the previous simulation, identify the variables that have changed and their associated positions in the program instance, and generate a list of status difference identifiers. The specific operations are as follows:

[0085] First, the pre-defined ship status input interface of the compliance assessment program instance is identified. This interface predefines all ship status variable types, data formats, and value specifications required for compliance simulation, serving as a unified standard for ship status data input. Based on the interface's definition requirements, the current valid values ​​of all corresponding variables are extracted from real-time ship status data sources such as the Automatic Identification System (AIS) and Positioning System (PIS). These values ​​are then structurally encapsulated according to the interface specifications to generate a current ship status snapshot. This snapshot fixes the ship status data at the current moment, preventing data changes during simulation from distorting the logical operation results. Subsequently, the module retrieves the ship status snapshot used in the previous compliance simulation, which is persistently stored in the system. A variable-by-variable consistency comparison is performed between the current ship status snapshot and the historical ship status snapshot. This comparison covers all ship status variables defined by the input interface, identifying all ship status variables whose values ​​have changed without omission. Simultaneously, relying on the logical structure mapping relationship built into the program instance, the corresponding condition judgment nodes, logical operation links, and other related positions of each changed variable within the program instance's internal logical system are located. The unique identifier of the changed variable, the difference between the old and new values, and the corresponding program instance related position information are systematically organized to generate a structured list of status difference identifiers.

[0086] Step 42: Maintain the logical path cache corresponding to the program instance. The logical path cache records the logical path triggered in the last simulation. Based on the list of state difference identifiers, start the incremental simulation from the starting point of the logical path cache, only recalculate the condition judgment nodes associated with the changed variables in the logical path, update the logical path cache and obtain the compliance status label. The specific operations are as follows:

[0087] Each generated compliance assessment program instance maintains an independent and dedicated logical path cache. This cache completely and accurately records all logical paths actually triggered during the previous compliance simulation, including all condition judgment nodes within the path, the calling order of logical atoms, the calculation conditions of each node, and the final calculation results, serving as the basis for subsequent incremental simulations. After obtaining the state difference identifier list generated in step 41, the full logical simulation operation is no longer performed. Instead, the starting point of the logical path recorded in the logical path cache is used as the starting position for this simulation. Based on the changed variable identifiers and associated position information in the state difference identifier list, all condition judgment nodes logically related to the changed variables are precisely selected from the cached logical paths. The compliance simulation module only performs simulations on these selected associated conditions. The judgment node re-executes logical calculations and condition judgments, and the calculation process strictly follows the logical rules of the program instance. For other nodes that have not established a connection with the changed variables, the previous deduction result recorded in the logical path cache is directly used, which greatly reduces the amount of invalid calculations. In the process of recalculating the relevant condition judgment nodes, the calculation results, judgment status and related logical path information of the corresponding nodes in the logical path cache are updated synchronously to ensure that the logical path cache is always consistent with the current deduction status and to avoid data deviations in subsequent deductions. Finally, based on the overall calculation results of the logical path after incremental deduction, it is integrated according to the conclusion output rules of the program instance to generate and output the compliance status label of this compliance deduction. While ensuring the accuracy of the judgment results, the calculation efficiency of compliance deduction is improved.

[0088] In some embodiments of the present invention, a conflict arbitration module is also included, which is used to initiate arbitration when a conflicting compliance status label is generated by logical deduction, to adjudicate according to the predefined rule element priority, to obtain a final judgment result, and to encapsulate the final judgment result and its derivation chain into a tracing package. The specific operation is as follows:

[0089] The conflict arbitration module continuously receives compliance status labels output by the compliance simulation module and monitors in real time for logical conflicts among the labels. When contradictory compliance status labels are detected in the simulation results, the arbitration process is immediately initiated. The arbitration process is based on the priority of predefined rule elements in the system. The priority of rule elements is pre-set according to factors such as industry standards for waterway control, safety level requirements, and the importance of the applicable scenarios of the rules, clarifying the priority order of different types of waterway rules in case of conflict. Based on the preset priority, multi-level adjudication analysis is carried out to sort out the derivation logic corresponding to the conflict labels, eliminate logical contradictions, and determine the judgment result that meets the control requirements, forming a unique final judgment result. At the same time, the final judgment result and the complete derivation chain corresponding to the result are structurally integrated and packaged into a traceability package in a unified format. The derivation chain contains information such as the logical atoms involved in the judgment, parameter values, and logical operation steps. The traceability package provides a complete logical basis for subsequent result tracing, instruction generation, and problem investigation.

[0090] The conflict arbitration module also performs the following steps:

[0091] Step 51: When logical deduction generates mutually exclusive compliance status labels, extract the relevant logical atoms and their derivation chains to construct a conflict topology graph, and identify the root cause nodes of the conflict in the conflict topology graph that lead to the conclusion fork. The specific operations are as follows:

[0092] When the compliance simulation module outputs mutually exclusive compliance status labels, the conflict arbitration module immediately initiates conflict root cause localization. First, it comprehensively extracts all logical atoms related to the generation of the conflict label from the compliance simulation's calculation records. Simultaneously, it retrieves the complete derivation chains corresponding to these logical atoms during the simulation process. These derivation chains cover the parameter substitution process of the atoms, the execution steps of the conditional judgment logic, the calculation results of each node, and the logical call relationships between atoms. Based on the extracted logical atoms and derivation chains, a conflict topology graph is constructed according to the logical relationships and simulation order. The conflict topology graph uses logical atoms as nodes and the relationships between atoms as the basis for its structure. Using derivation relationships and conditional association relationships as edges, the entire logical deduction process of conflict label generation is presented intuitively and completely, clearly showing how different derivation paths lead to contradictory conclusions. Subsequently, the conflict arbitration module conducts a systematic logical analysis of the conflict topology graph, tracing the starting nodes and branch nodes of all derivation paths, and focusing on identifying the key nodes in the topology graph that cause the derivation conclusions to fork. These nodes are the root cause nodes of the conflict. The root cause nodes of the conflict are usually the locations where the condition judgment results, parameter value substitutions, or logical associations between atoms of a certain logical atom diverge, which are the causes of compliance status label conflicts.

[0093] Step 52: Focusing on the root cause node of the conflict, and combining the inter-atomic potential field, construct a dynamic correlation network representing the interaction relationship of the potential field. The specific operation is as follows:

[0094] Taking the conflict root node identified in step 51 as the focus of arbitration analysis, the logical atom corresponding to the conflict root node and its position information in the rule-dimensional coordinate system are first located. Then, the attractive and repulsive potential field relationships established between logical atoms in step 13 are retrieved. At the same time, the field strength changes of parameters related to the root node in the dynamic parameter state field are combined to integrate the potential field interaction relationship with the logical association of the conflict root node, and construct a dynamic association network representing the potential field interaction relationship. This network takes the conflict root node as the node, the logical atom with potential field association with the root node as the surrounding node, and the attractive or repulsive potential fields between the nodes as the association links. The attributes of the links are determined by the potential field type, and the weight of the links is jointly represented by the influence strength of the potential field and the field strength value of the relevant parameters. The dynamic association network can accurately reflect the potential field interaction law between the conflict root node and other logical atoms, and the impact of this interaction relationship on the compliance deduction logic. At the same time, the network can be dynamically adjusted with the changes in potential field strength and parameter field strength to ensure that the constructed network is always consistent with the rule state and parameter state of the current system.

[0095] Step 53: For each conflict root cause node and its different adjudication options, perform parallel simulations based on the current ship status snapshot, parameter value snapshot, and dynamic correlation network to calculate the decision risk index for each option and generate an arbitration risk matrix. The specific operations are as follows:

[0096] First, we identify the different arbitration options available to each root cause node during the arbitration process. Each option corresponds to a different logical judgment tendency for that root cause node, and each option represents a logical processing method for resolving the conflict at that node. For all arbitration options, we initiate a parallel simulation process, while simultaneously conducting independent compliance logic deductions for each option. During the deduction process, we strictly substitute real-time data from the current ship status snapshot and parameter value snapshot to ensure that the simulated scenario is highly consistent with the actual ship navigation and waterway parameter status. Furthermore, relying on the dynamic correlation network constructed in step 52, we integrate the potential field interaction relationships between nodes into the logical operation process of the simulation, fully considering the impact of the potential field on the logical atomic calls. The impact of the condition judgment results; after the simulation is completed, the simulation results of each ruling option are subjected to risk quantification analysis, and the decision risk index corresponding to each option is calculated. The calculation of the decision risk index comprehensively considers factors such as the navigation safety risk of the ship after the option is executed, the degree of compliance with the waterway control rules, and the impact on the surrounding shipping environment. The index value intuitively represents the potential risk level of the ruling option; the conflict arbitration module takes all the root cause nodes of the conflict as the row dimension, the ruling options of each node as the column dimension, and the decision risk index corresponding to each option as the matrix element, and integrates them according to the row and column correspondence to generate an arbitration risk matrix. This matrix systematically presents the risk distribution of all ruling options.

[0097] Step 54: Based on the arbitration risk matrix, predefined rule element priorities, and the attributes of the conflict root cause nodes, the final judgment result is obtained through multi-factor adjudication. The final judgment result, the original derivation chain, and arbitration process information are then encapsulated into an enhanced abduction package. The specific operations are as follows:

[0098] The conflict arbitration module initiates a multi-factor adjudication process, using the arbitration risk matrix, predefined rule element priorities, and the attributes of the conflict root cause nodes as adjudication factors for comprehensive judgment. During arbitration, the decision risk index in the arbitration risk matrix is ​​first referenced, prioritizing adjudication options with lower risk indices. Simultaneously, considering the predefined rule element priorities, when different options have similar risk indices, the adjudication bias is determined according to the rule element priorities, prioritizing the adjudication logic corresponding to higher-priority waterway rules. Furthermore, the attributes of the conflict root cause nodes are fully considered. Node attributes are categorized according to their corresponding rule dimensions, with different attributes corresponding to different waterway control focuses. Based on preset attribute weights, adjudication options for nodes with different attributes are weighted to ensure that the adjudication results align with the actual waterway control requirements. Based on actual needs, through comprehensive analysis and weighted judgment of three core factors, logical contradictions are eliminated and a unique final judgment result is determined. Subsequently, the conflict arbitration module initiates the encapsulation of an enhanced tracing package, which structurally integrates the final judgment result, the original conflict derivation chain generated during the compliance deduction phase, and key information from the entire arbitration process. The information from the entire arbitration process includes details of the construction of the conflict topology diagram, characteristics of the dynamic relational network, results of parallel simulation deduction, data generated from the arbitration risk matrix, and the analysis process of multi-factor adjudication. All information is encapsulated according to a unified format specification to form an enhanced tracing package. Compared with the basic tracing package, this package contains more comprehensive judgment and arbitration information, which can not only clearly present the basis for the formation of the final judgment result, but also completely trace the entire process of conflict generation and arbitration resolution.

[0099] In some embodiments of the present invention, a response generation module is further included, which is used to synthesize a response command containing navigation guidance based on the final judgment result and the tracing package, generate a correction navigation vector corresponding to the navigation guidance according to pre-stored waterway network topology data and real-time ship density data, and output the response command. The specific operations are as follows:

[0100] The response generation module continuously receives the final judgment result and enhanced cause-finding package output by the conflict arbitration module. Based on this, and combined with the actual needs of waterway management, it synthesizes a basic response instruction containing clear navigation guidance. The navigation guidance clarifies the direction that the vessel needs to adjust to in its current navigation state and the target requirements for compliant navigation. Simultaneously, it retrieves pre-stored waterway network topology data, which includes basic spatial information such as the spatial connectivity of the waterway, the attributes of navigable sections, and the distribution of waypoints. Real-time vessel density data for each area of ​​the waterway is also acquired. By combining these two types of data through joint spatial and numerical calculations, a corrective navigation vector is generated that precisely matches the navigation guidance in terms of spatial direction and navigation maneuvers. This vector quantitatively represents the adjustment parameters such as position, heading, and speed required for the vessel to achieve compliant navigation. Finally, the module integrates the basic response instruction and the corrective navigation vector, and outputs the response instruction according to the system's preset communication format and instruction specifications. This provides the vessel terminal or the maritime traffic control center with directly usable navigation operation guidelines, realizing the implementation of compliance judgment results into actual navigation management.

[0101] The response generation module also performs the following steps:

[0102] Step 61: Based on the final judgment result and the enhanced tracing package, analyze the root cause nodes and derivation chains of the conflicts to identify the violation constraints and the vessel's compliance objectives. Then, extract the reachable waterway subgraph from the waterway network topology data according to the vessel's current position and compliance objectives. The specific operations are as follows:

[0103] Based on the final judgment result output by the conflict arbitration module and the enhanced tracing package, this paper systematically analyzes the conflict root cause nodes and complete derivation chain stored in the enhanced tracing package. It traces the logical process of forming the final judgment result, accurately identifying the violation constraints that cause the ship's current navigation state to fail to comply with waterway rules from the condition judgment links and rule constraints in the derivation chain. Simultaneously, it clarifies the ship compliance objectives corresponding to the various compliance requirements of the waterway. The violation constraints and compliance objectives together constitute the core boundary and direction for the subsequent generation of navigation guidance. Subsequently, it extracts data from the ship's status snapshot. The system retrieves core location information such as the ship's current spatial position and course of navigation, combined with the clearly defined compliance objectives. It then filters and extracts the complete waterway network topology data stored in the system, based on the spatial connectivity, navigation attribute restrictions, and the ship's accessibility from its current position to the compliance objective. Waterway sections that are impassable or irrelevant to the compliance objective are eliminated, while waterway elements that are passable and point to the compliance objective are retained. This forms an accessible waterway sub-map that is highly adapted to the current ship's navigation scenario. This sub-map provides accurate spatial range and waterway basis for subsequent path search.

[0104] Step 62: Map the violation constraints to prohibited elements in the reachable channel subgraph, calculate the dynamic repulsion field value by combining real-time vessel density data, and execute a restricted path search algorithm on the weighted reachable channel subgraph to generate a set of multiple candidate compliant paths. The specific operations are as follows:

[0105] The violations identified in step 61 are mapped spatially and logically according to the element types of the waterway network topology, transforming them into corresponding prohibited elements in the reachable waterway sub-graph. These prohibited elements, depending on the type of violation constraint, can be represented as specific prohibited navigation sections, prohibited directions, and prohibited depth areas within the reachable waterway sub-graph, thus realizing the concrete transformation of rule constraints into waterway spatial traffic restrictions. Simultaneously, real-time vessel density data for each area of ​​the waterway is acquired. This data reflects the vessel distribution and congestion level of each waterway section. Using vessel density as a quantitative indicator, combined with attributes such as the navigation capacity and spatial scale of the waterway section, each waterway unit in the reachable waterway sub-graph is analyzed. The dynamic repulsion field value is calculated by the fact that the higher the ship density of a waterway unit, the larger the corresponding dynamic repulsion field value, which indicates that the navigation cost and conflict risk in that area are higher. Based on the dynamic repulsion field value and the basic navigation weight of the waterway unit, the reachable waterway subgraph is weighted so that each node and link in the subgraph has a corresponding navigation weight value. Then, a restricted path search algorithm is executed on the weighted reachable waterway subgraph. During the search process, the mapped prohibited elements are strictly avoided. At the same time, the dynamic repulsion field value is taken into account, and waterway links with low repulsion field values ​​and good navigation weights are selected first. Finally, multiple navigation paths that meet the compliance objectives and avoid the violation constraints are generated, forming a set of multiple candidate compliant paths.

[0106] Step 63: Establish a multi-objective optimization evaluation system that includes path safety, navigation efficiency, and the estimated level of interference to ship navigation in the waterway network. Calculate and rank the comprehensive evaluation values ​​of each path in the multi-candidate compliant path set to generate a path ranking list. The specific operations are as follows:

[0107] To address the management and control needs of ship navigation in waterways, a multi-objective optimization evaluation system is established. This system includes three main evaluation dimensions: route safety, navigation efficiency, and the estimated level of interference to other ships in the waterway network. Each dimension is based on waterway management regulations, ship navigation safety standards, and shipping efficiency requirements, with corresponding quantitative evaluation indicators and scoring rules. This allows for an objective evaluation of the multi-dimensional value of each candidate route. To quantify the comprehensive value of each candidate route, a formula for calculating the comprehensive evaluation value is introduced: ;

[0108] This formula is abstracted from the logic of multi-objective decision-making. It needs to take into account the importance of different evaluation dimensions in navigation. The weight coefficients are used to balance the impact of the scores of each dimension on the comprehensive result, so as to ensure that the comprehensive evaluation value can fully reflect the overall quality of the route. In the formula, S represents the comprehensive evaluation value of the route. This represents the preset weight of the j-th evaluation dimension. The weight is preset based on the safety priority principle of waterway management and the demand for shipping efficiency, and satisfies the following conditions: ; This represents the standardized score of the path in the j-th evaluation dimension. For each path in the set of multiple candidate compliant paths, the standardized score of each dimension is calculated according to the quantitative indicators of the multi-objective optimization evaluation system. The scores and the preset weights of the corresponding dimensions are substituted into the formula to complete the calculation of the comprehensive evaluation value. Then, all candidate paths are arranged in order according to the comprehensive evaluation value to generate a path ranking list. The ranking result of the paths in the list intuitively reflects the overall quality of each candidate path.

[0109] Step 64: Select the first path in the path sorting list as the primary path. Decompose the primary path into an immediate action command, a sequence of intermediate waypoints, and a summary of alternative paths. Encapsulate these into a smart response command package. The specific operations are as follows:

[0110] Based on the path ranking list generated in step 63, the first path with the highest comprehensive evaluation value in the list is selected as the primary navigation path for the vessel's current voyage. This path represents the optimal navigation plan, balancing compliance requirements, navigation safety, efficiency, and overall waterway traffic order. Subsequently, the primary navigation path is structurally analyzed and decomposed. Combining the vessel's current navigation status with the spatial orientation of the primary navigation path, immediate action instructions that the vessel must execute are generated. These instructions specify the vessel's operational requirements regarding course adjustment angles, speed increases / decreases, and position offset distances, ensuring immediate executability. Simultaneously, based on the spatial trajectory of the primary navigation path, key spatial points that the vessel must sequentially pass through are extracted, forming an ordered sequence of intermediate waypoints to provide the vessel with continuous navigation guidance. The system guides the navigation process by simultaneously analyzing several candidate routes with the second-highest comprehensive evaluation values ​​from the route ranking list. It extracts information such as the route's direction, key waypoints, and passage advantages to form a concise summary of alternative routes. This provides alternative navigation options for vessels to respond to unforeseen channel conditions. Finally, the response generation module structurally integrates information such as immediate action instructions, intermediate waypoint sequences, alternative route summaries, final judgment results, vessel compliance objectives, and violation constraints. Following communication formats and data specifications recognizable by both the vessel's terminal and the traffic control center, it encapsulates this information into a smart response instruction package. This package contains all the operational guidance and reference information required for compliant navigation and can be directly transmitted to the corresponding receiving terminal, ensuring the integrity and executability of the navigation guidance.

[0111] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A vessel and waterway compliance assessment system integrating electronic fences and GIS, characterized in that, include: The rule definition module is used to define the logical atoms and dynamic parameters of the waterway rules. The logical atoms are condition-conclusion pairs containing parameterized placeholders, forming the rule logic source code library. The parameter injection module is used to establish a real-time parameter injection channel for parameterized placeholders, listen to messages from preset data sources and capture values ​​in the messages, broadcast parameter update events and values, and update and maintain the real-time parameter value status table. The rule instantiation module is used to respond to events such as a ship entering the monitoring area or a parameter status update event. It retrieves relevant logic atoms from the rule logic source code library, substitutes the values ​​in the real-time parameter value status table into its parameterized placeholders, and compiles to generate an executable compliance judgment program instance. The compliance simulation module is used to input the ship's status into the program instance, execute logical simulations to calculate logical atoms, and output compliance status labels; The conflict arbitration module is used to initiate arbitration when a conflicting compliance status label is generated during logical deduction. It makes a decision based on the predefined rule element priority, obtains the final judgment result, and encapsulates the final judgment result and its derivation chain into a causal package. The response generation module is used to synthesize response instructions containing navigation guidance based on the final judgment result and the tracing package. It generates a correction navigation vector corresponding to the navigation guidance based on the pre-stored waterway network topology data and real-time ship density data, and outputs the response instructions.

2. The ship navigation channel compliance assessment system integrating electronic fences and GIS as described in claim 1, characterized in that, The rule definition module also includes: The condition factors used for parameterization are extracted from the original waterway rules as rule dimensions, and discretized coordinate axes are established for each rule dimension. The condition part of each rule is parsed into a coordinate region or set of coordinate points in a multi-dimensional coordinate system formed by the coordinate axes, and a coordinate system and coordinate mapping relationship table for rule dimensions are generated. Based on the coordinate mapping relationship table, logical atoms are constructed for each rule. The logical atom includes a logical key corresponding to the coordinate region or set of coordinate points, and a logical body that encapsulates the parameterized placeholder condition judgment logic and conclusion, forming a rule logic source code library indexed by logical keys. The relative positions and overlaps of the logical bonds of different logical atoms in the rule-based logic source code library are analyzed in the rule-based coordinate system. Based on the predefined association rules, corresponding attractive or repulsive potential fields are established between logical atoms with inclusion or mutual exclusion relationships.

3. The ship navigation channel compliance assessment system integrating electronic fences and GIS as described in claim 2, characterized in that, The parameter injection module also includes: Capture the message value corresponding to the parameterized placeholder, generate a data fingerprint containing the source identifier, timestamp sequence number and value digest, and bind the value, data fingerprint, preset confidence weight and logical validity duration as the parameter original unit; Based on the original parameter units, update and maintain the dynamic parameter state field; the update includes: performing same-source update or heterogeneous merging based on the source identifier of the data fingerprint, heterogeneous merging is weighted and compared with the current field strength in the state field according to the confidence weight; and performing time decay calculation related to the logical effective duration on all field strengths in the state field. When an update causes the field strength of a specific parameter in the dynamic parameter state field to change beyond a preset change threshold, the state field unit corresponding to that parameter will be encapsulated as a parameter state update event and broadcast.

4. The ship navigation channel compliance assessment system integrating electronic fences and GIS as described in claim 3, characterized in that, The rule instantiation module also includes: Receive trigger signals for ship entering the monitoring area or parameter status update events, map the trigger signals to the rule dimension coordinate system, and generate corresponding query coordinates; Centered on the query coordinates, guided diffusion retrieval is performed in the rule logic source code library based on the inter-atomic potential field to obtain the set of logical atoms that directly cover the query coordinates. Then, related sub-atoms are adsorbed according to the attraction potential field and mutually exclusive atoms are excluded according to the repulsion potential field to form a rule logic subgraph.

5. The ship navigation channel compliance assessment system integrating electronic fences and GIS according to claim 4, characterized in that, The rule instantiation module also includes: Analyze the structural features and trigger signal types of the rule logic subgraph, select a compilation strategy accordingly, and perform logical simplification on the rule logic subgraph to obtain an optimized rule logic subgraph; Extract the current authority value corresponding to each parameterized placeholder in the optimization rule logic subgraph from the dynamic parameter state field, generate a parameter value snapshot, and compile the optimization rule logic subgraph and parameter value snapshot based on the selected compilation strategy to generate an executable compliance judgment program instance.

6. The ship navigation channel compliance assessment system integrating electronic fences and GIS according to claim 5, characterized in that, The compliance simulation module also includes: Generate a snapshot of the current ship status based on the ship status input interface of the program instance, compare the current ship status snapshot with the ship status snapshot used in the previous simulation, identify the variables that have changed and their associated positions in the program instance, and generate a list of status difference identifiers. Maintain the logical path cache corresponding to the program instance. The logical path cache records the logical path triggered by the last simulation. Based on the list of state difference identifiers, start the incremental simulation from the starting point of the logical path cache. Only recalculate the condition judgment nodes associated with the changed variables in the logical path, update the logical path cache and obtain the compliance status label.

7. The ship and waterway compliance assessment system integrating electronic fences and GIS according to claim 6, characterized in that, The conflict arbitration module also includes: When logical deduction produces mutually exclusive compliance status labels, extract the relevant logical atoms and their derivation chains to construct a conflict topology graph, and identify the conflict root node in the conflict topology graph that leads to the conclusion fork. By focusing on the root cause nodes of conflict and combining them with the interatomic potential field, a dynamic correlation network representing the interaction relationship of the potential field is constructed.

8. The ship navigation channel compliance assessment system integrating electronic fences and GIS according to claim 7, characterized in that, The conflict arbitration module also includes: For each conflict root cause node, different adjudication options are simulated in parallel based on the current ship status snapshot, parameter value snapshot, and dynamic correlation network. The decision risk index of each option is calculated to generate an arbitration risk matrix. Based on the arbitration risk matrix, the predefined rule element priority, and the attributes of the conflict root node, the final judgment result is obtained through multi-factor adjudication, and the final judgment result, the original derivation chain, and arbitration process information are encapsulated into an enhanced abduction package.

9. The ship navigation channel compliance assessment system integrating electronic fences and GIS according to claim 8, characterized in that, The response generation module also includes: Based on the final judgment results and the enhanced tracing package, the root cause nodes of the conflict and the derivation chain are analyzed to identify the violation constraints and the ship's compliance objectives. Based on the ship's current position and compliance objectives, the reachable waterway subgraph is extracted from the waterway network topology data. The violation constraints are mapped to prohibited elements in the reachable channel subgraph. The dynamic repulsion field value is calculated by combining real-time ship density data. A restricted path search algorithm is then executed on the weighted reachable channel subgraph to generate a set of multiple candidate compliant paths.

10. The ship navigation channel compliance assessment system integrating electronic fences and GIS according to claim 9, characterized in that, The response generation module also includes: Establish a multi-objective optimization evaluation system that includes path safety, navigation efficiency, and the estimated level of interference to ship navigation in the waterway network. Calculate and rank the comprehensive evaluation values ​​of each path in the multi-candidate compliant path set to generate a path ranking list. The first path in the path sorting list is selected as the primary path. The primary path is then decomposed into an immediate action command, a sequence of intermediate waypoints, and a summary of alternative paths, which are then encapsulated to generate a smart response command package.

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