Rule-based verification and collaborative control-based cable intelligent management method and system

Abstract

The application relates to the technical field of industrial asset management, and particularly discloses a cable intelligent management method and system based on rule checking and collaborative control, which comprises the following steps: a multi-dimensional basic configuration system containing ship environment parameters, cable basic material attributes, process specifications and user permissions is constructed; a rule checking benchmark library containing design compliance rules, production process constraints and operation and maintenance health threshold values is set; and a cross-role collaborative permission system is implemented for the whole life cycle of cable design, production, laying and operation and maintenance. Through the construction of the multi-dimensional basic configuration system covering the ship environment, material attributes, process specifications and user permissions, and in combination with the checking benchmark library containing the whole-process rules, the information barriers of the design, production, laying and operation and maintenance links are broken, the mistakes and omissions of manual transcription data are avoided, and the unified management and compliance control of the cable whole-life cycle data are realized.

Description

Technical Field

[0001] This invention relates to the field of industrial asset management technology, and in particular to a cable intelligent management method and system based on rule verification and collaborative control. Background Technology

[0002] As the "neural network" of modern complex equipment, cables undertake the critical tasks of power transmission, signal communication, and control command issuance. Their reliability is directly related to the safety and normal operation of the entire equipment. In typical scenarios such as ships, cable networks are large-scale and intricately laid out, and they operate for extended periods in harsh and complex environments such as salt spray, vibration, high temperatures, and strong electromagnetic interference. This places extremely high demands on cable management.

[0003] Currently, the commonly used cable management methods in the industry have the following main technical shortcomings:

[0004] The design, production, installation, and maintenance of cables are typically managed by different departments using independent software tools (such as CAD software, ERP systems, and project management forms) and processes. Data between different stages often relies on manual transcription or file import / export, which is not only inefficient but also prone to errors, leading to problems such as a disconnect between design parameters and actual production, and a mismatch between installation records and maintenance needs. This data fragmentation makes it exceptionally difficult to trace and comprehensively analyze the entire lifecycle of cables.

[0005] Furthermore, traditional cable design, such as route planning and model selection, primarily relies on general design specifications and engineers' personal experience. While this approach can meet basic safety requirements, it cannot quantify and incorporate into the design considerations the local microenvironmental factors that will ultimately affect the cable's service life (such as vibration frequency, salt spray concentration, or electromagnetic field strength in a specific compartment). This makes it difficult to accurately determine the cable's design margin, potentially leading to cost waste due to over-design or premature aging or even failure under specific stress conditions due to insufficient consideration. Summary of the Invention

[0006] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the purpose of this invention is to propose a cable intelligent management method and system based on rule verification and collaborative control, in order to improve the intelligence level and reliability of cable management.

[0007] To achieve the above objectives, a first aspect of the present invention proposes a cable intelligent management method based on rule verification and collaborative control, the method comprising:

[0008] S1. Construct a multi-dimensional basic configuration system that includes ship environmental parameters, cable basic material properties, process specifications, and user permissions;

[0009] S2. Set up a rule verification benchmark library that includes design compliance rules, production process constraints, and operation and maintenance health thresholds;

[0010] S3. Implement a cross-role collaborative permission system to perform data verification, process approval, and status locking based on the rule verification benchmark library throughout the entire life cycle of cable design, production, laying, and operation and maintenance.

[0011] S4. By collecting historical and real-time status data throughout the cable's entire lifecycle, we use artificial intelligence models to analyze and predict data, and then feed the results back to the design, production, and operation and maintenance processes to achieve closed-loop optimization.

[0012] To achieve the above objectives, a first aspect of the present invention proposes a cable intelligent management system based on rule verification and collaborative control, comprising:

[0013] The storage module is configured to store a multi-dimensional basic configuration system containing ship environmental parameters, cable basic material properties, process specifications and user permissions, as well as a rule verification benchmark library containing design compliance rules, production process constraints and operation and maintenance health thresholds.

[0014] The data acquisition module includes sensors deployed at key locations on the ship, the sensors being configured to acquire environmental data in real time, including at least salt spray concentration, vibration acceleration, and electromagnetic field strength.

[0015] The collaborative control engine, connected to the storage module, is configured to implement a transaction-based collaborative task management system based on the user permissions and rule verification benchmark library. The collaborative task management system encapsulates a set of modification operations with inherent logical relationships into transactional task packages with independent lifecycle states, and automatically resolves the dependencies between the transactional task packages to achieve precise data verification, process approval, and state locking across roles.

[0016] An intelligent processing engine, connected to the storage module and the data acquisition module, is configured to:

[0017] Perform data pre-validation on the environmental data collected by the sensor to identify and smooth out outliers in the data;

[0018] Based on the pre-validated environmental data, an environmental interference factor model is run to assess the cable failure probability, and an artificial intelligence prediction model is used to predict the remaining service life of the cable.

[0019] By continuously comparing the prediction results of the artificial intelligence prediction model with the actual cable fault data collected subsequently, the weight coefficients or risk function of the environmental interference factor model are adaptively and iteratively updated.

[0020] Run a digital twin model to perform a 3D simulation of the cable path design to identify physical interferences, and verify the recommended corrected path based on the rules of the benchmark library;

[0021] Based on historical length deviation data recorded during the production process, the design length parameters of subsequent batches of cables of the same type are automatically corrected.

[0022] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the above-described intelligent cable management method based on rule verification and collaborative control.

[0023] The cable intelligent management method and system based on rule verification and collaborative control in this invention breaks down information barriers between design, production, laying, and operation and maintenance by constructing a multi-dimensional basic configuration system covering ship environment, material properties, process specifications, and user permissions, combined with a verification benchmark library containing full-process rules. This avoids errors and omissions in manually transcribed data and achieves unified management and compliance control of cable lifecycle data. Relying on a cross-role collaborative permission system and transactional task management mechanism, it clarifies multi-role permissions and collaborative rules, solving the problems of ambiguous responsibilities and operational conflicts in traditional collaboration, and ensuring orderly and efficient process approval and status locking.

[0024] By collecting environmental data, using interference factor models, digital twins, and AI analysis and prediction, the ship's local microenvironment is precisely integrated into design decisions, avoiding premature failures caused by over-design or insufficient environmental adaptation. At the same time, by leveraging full lifecycle data feedback, dynamic optimization of design parameters, early warning of faults, and precise scheduling of operation and maintenance are achieved, ultimately improving the intelligence level and reliability of cable management and ensuring the safe and stable operation of the ship's cable network. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the intelligent cable management method based on rule verification and collaborative control provided by the present invention.

[0026] Figure 2 This is a schematic diagram of the cable intelligent management system based on rule verification and collaborative control provided by the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0029] The cable intelligent management method, system, and electronic device based on rule verification and collaborative control according to embodiments of the present invention are described below with reference to the accompanying drawings.

[0030] Example 1:

[0031] Figure 1 This is a flowchart illustrating an embodiment of the intelligent cable management method based on rule verification and collaborative control according to the present invention. This method is mainly applied to the entire process of design, production, laying, and operation and maintenance of marine cables, aiming to solve problems such as fragmented processes, data errors and omissions, and inefficient collaboration in traditional cable management. By establishing a multi-dimensional basic configuration system, setting up a rule verification benchmark library, and implementing a cross-role collaborative permission system, unified management and process control of cable lifecycle data can be achieved, ensuring design compliance and operational reliability.

[0032] like Figure 1 As shown, the intelligent cable management method based on rule verification and collaborative control includes the following steps:

[0033] 1. Construction of a multi-dimensional basic configuration system

[0034] The method first requires the construction of a multi-dimensional basic configuration system, which includes four dimensions: ship environmental parameters, cable basic material properties, process specifications, and user permissions.

[0035] Ship environmental parameters refer to the microenvironmental conditions in different compartments or deck areas of a ship, including but not limited to temperature, humidity, salt spray concentration, vibration acceleration, and electromagnetic field strength. These parameters directly determine the service environment of the cables.

[0036] Basic material properties of cables: These refer to the conductor material (such as copper or aluminum), insulation material (such as cross-linked polyethylene or ethylene propylene rubber), sheath type, and shielding structure of the cable.

[0037] Process specifications: These refer to the standards that must be followed in the design and manufacture of cables, such as IEC 60092 Marine Electrical Installations or relevant specifications of the China Classification Society (CCS).

[0038] User permissions refer to the scope of operations that different roles in the system have, including data read permissions, modification permissions, and approval permissions.

[0039] The establishment of this multi-dimensional system enables every configuration parameter of the cable to be associated with the environment, materials and standards, and bound to user permissions, thereby ensuring the integrity and consistency of the data.

[0040] 2. Establishment of the rule validation benchmark library

[0041] Secondly, the method requires setting up a rule validation benchmark library, which covers three levels of rules:

[0042] Design compliance rules: For example, the rated voltage of the cable should match the voltage level of the target circuit, and the cable cross-section should meet the current carrying capacity requirements.

[0043] Manufacturing process constraints: such as the minimum value of cable insulation thickness and the maximum allowable deviation of conductor stranding pitch.

[0044] Operation and maintenance health threshold: When the cable insulation resistance drops below a certain value (e.g., 0.1 MΩ·km), an early warning needs to be triggered.

[0045] The establishment of the rule verification benchmark library ensures that the system can automatically perform compliance verification during data entry and operation at each stage, reducing errors from human judgment.

[0046] 3. Implementation of a cross-role collaborative permission system

[0047] Next, this method proposes a cross-role collaborative permission system. This system is not merely an access control tool, but a dynamic collaborative framework covering the entire process of design, production, deployment, and operation. This system fundamentally breaks down the problems of data silos and process disconnects between different departments in the traditional model. Its core lies in:

[0048] Automatic validation of input data and operations is performed based on a rule-based validation benchmark library;

[0049] During the workflow, approval and confirmation are carried out according to role permissions;

[0050] For sensitive states (such as critical design parameters), a state locking mechanism is used to prevent unauthorized modifications.

[0051] 4. Data Acquisition and Closed-Loop Optimization

[0052] Finally, this method collects historical and real-time status data throughout the cable's entire lifecycle, uses artificial intelligence models for analysis and prediction, and feeds the prediction results back to the design, production, and operation and maintenance stages to achieve closed-loop optimization. This step ensures that the solution is not merely static rule management, but also capable of dynamic adaptive adjustment.

[0053] Optionally, the cross-role collaborative permission system includes a four-level user permission structure and a three-dimensional collaborative rule system, wherein:

[0054] 1. The four-level user permission structure consists of four levels:

[0055] Read-only access: Users can only view cable configuration parameters and cannot modify them.

[0056] Modify permissions: Users can modify data within a specific scope, such as inputting new sensor acquisition results.

[0057] Approval authority: Users can review modification requests submitted by subordinates and decide whether to approve them.

[0058] Management permissions: Users can define rules to verify the benchmark library and modify system settings, and have the highest level of privileges.

[0059] This hierarchical division can strictly limit the scope of operations for different roles and prevent unapproved data from being directly adopted.

[0060] Users can be categorized into these four typical user roles:

[0061] Electrical compartment personnel: primarily responsible for the preliminary design of cables, including selection and route planning.

[0062] Data personnel: responsible for maintaining the environmental parameter database and organizing and providing feedback on operational data.

[0063] Managers: Responsible for reviewing whether the rule thresholds are reasonable and making necessary adjustments.

[0064] Operations Engineer: Responsible for real-time health monitoring and fault handling.

[0065] This role division corresponds to the actual workflow of ship cable management, ensuring that there is a clear person responsible for each step.

[0066] 2. The three-dimensional collaborative rule system defines the rules for cross-role collaboration from three dimensions:

[0067] Data sharing rules: Define which data can be shared across departments. For example, design parameters may be visible to operations engineers, but some production process details may be limited to the production department.

[0068] Mutual exclusion operation locking rules: For example, when maintenance personnel are performing fault analysis based on a batch of cable operation data, the design department cannot simultaneously modify the original design parameters of that batch.

[0069] Workflow rules: Define the approval path for operations. For example, design modifications must be confirmed by data personnel before they can enter the production stage.

[0070] Through a three-dimensional collaborative rule system, orderly and efficient interaction among multiple roles can be achieved in complex collaborative environments.

[0071] As an example, let's take an ocean-going vessel as an example:

[0072] During the design phase, electrical compartment personnel input cable route plans into the system. The system automatically accesses the environmental parameter database to verify whether the selected cable type meets the tolerance requirements of the target compartment.

[0073] During the production phase, data personnel input the length deviation data collected in the production workshop, and the system verifies it against the benchmark library according to the rules to avoid deviations exceeding the standard.

[0074] During the operation and maintenance phase, maintenance engineers can use sensors to monitor in real time whether the electromagnetic field strength in a certain compartment has increased. This triggers a rule check, indicating a potential risk of premature aging of cables in that area. Upon receiving the warning, management personnel can adjust the operational health thresholds and assign maintenance tasks.

[0075] Through this closed-loop process, cable management has shifted from a reactive approach to a proactive prevention approach.

[0076] Access control logic can be represented using Boolean functions to show the constraints of user permissions on operations:

[0077] ;

[0078] in: User identifier; Operation type (e.g., read, modify, approve); : Permission determination function. If the system determines... If the operation fails, the operation will be rejected and logged.

[0079] The process approval status transition uses a state machine to describe the process:

[0080] ;

[0081] in: :time The process status can be "Pending Approval", "Under Approval", "Approved", or "Rejected". Approval process; : State transition function. For example, when Pending approval, and If approved, then Approved.

[0082] In this embodiment, the multi-dimensional configuration system ensures data consistency between design, production, and operation and maintenance; the rule verification benchmark library guarantees that all operations comply with industry standards and health thresholds; and the cross-role collaborative permission system avoids permission ambiguity and data conflicts, ensuring process security. In summary, this embodiment not only solves the fragmentation and inefficiency problems in traditional cable management but also provides a closed-loop intelligent cable management method throughout its entire lifecycle through rule verification and permission control mechanisms.

[0083] Example 2:

[0084] This embodiment significantly improves the scientific rigor and proactiveness of cable maintenance by deploying sensors at key locations on the ship to collect environmental data in real time, constructing an environmental interference factor model, and combining it with artificial intelligence prediction algorithms to predict the remaining service life of cables, especially cable joints. Furthermore, through data pre-verification and dynamic closed-loop optimization, model parameters are continuously corrected, gradually improving prediction accuracy.

[0085] 1. The intelligent cable management method based on rule verification and collaborative control also includes the step of collecting environmental data through hardware deployment. Specifically, sensors are deployed at key locations on the ship to collect environmental data in real time. The environmental data includes at least salt spray concentration, vibration acceleration, and electromagnetic field strength. The environmental data is then transmitted to a database for real-time health monitoring and long-term condition assessment of the cables.

[0086] During the cable selection and design phase, the system automatically verifies whether the specifications of the candidate cables meet the environmental tolerance requirements based on the environmental data of the target laying area, and recommends the optimal cable model.

[0087] This involves several key points:

[0088] Sensor deployment: Select key node locations on the ship's structure, such as the engine room, below deck, communication compartment, and power compartment. These areas often have different environmental stresses.

[0089] Environmental data collection: including at least salt spray concentration (affecting insulation aging), vibration acceleration (affecting mechanical fatigue), and electromagnetic field strength (affecting signal interference and shielding performance).

[0090] Design Verification and Recommendation: During the design phase, the system matches real-time or historical environmental data with cable material performance data to determine if the cable meets the usage requirements. If not, it automatically recommends a suitable model.

[0091] 2. In addition, this method proposes to construct an environmental interference factor model based on the collected environmental data, assess the failure probability of cable joints, and further predict the remaining service life.

[0092] Based on historical data, this model assesses the failure probability of cable joints using the following formula, thereby calculating the environmental interference factor. :

[0093] ;

[0094] in: For comprehensive environmental interference factors; This provides real-time salt spray concentration data. This provides real-time vibration acceleration data. This provides real-time electromagnetic field intensity data. , , These are the normalized risk functions for the corresponding environmental factors; , , Here are the weighting coefficients for each environmental factor, and The environmental interference factors As input, artificial intelligence prediction models (such as LSTM long short-term memory networks) are used to estimate the remaining life of the cable. Make predictions. When the predicted value is lower than a preset threshold, trigger an alert.

[0095] Among them, salt spray concentration : Refers to the concentration of salt particles such as sodium chloride in the air, usually expressed in milligrams per cubic meter (mg / m³), which has a strong corrosive effect on cable insulation and metal sheath; vibration acceleration. Electromagnetic field strength refers to the intensity of mechanical vibration generated by equipment or compartments during operation, usually expressed in meters per second square (m / s²). Long-term vibration can lead to loose cable joints or conductor fatigue; Electromagnetic field strength refers to the intensity of an electric or magnetic field in space, usually expressed in volts per meter (V / m) or amperes per meter (A / m). Strong electromagnetic interference can cause signal cable distortion or shielding overload; Remaining service life (RUL) refers to the length of time a cable is expected to continue operating normally under current conditions.

[0096] Furthermore, this method also includes establishing a dynamic self-correcting closed loop for model parameters and a data pre-validation mechanism, specifically:

[0097] The real-time salt spray concentration data Real-time vibration acceleration data and real-time electromagnetic field strength data Before inputting the environmental interference factor model, a data pre-validation step is performed to identify, mark, and smooth out outliers or missing values ​​in the sensor data through time series analysis and statistical outlier detection. Data pre-validation refers to the process of anomaly detection and correction before the data enters the model. Common methods include outlier detection, interpolation completion, and time series smoothing.

[0098] Furthermore, by continuously comparing the prediction results of the model with subsequently collected actual cable fault or maintenance data, machine learning algorithms are used to adjust the weighting coefficients. and / or the normalized risk function Periodic iterative updates are performed to achieve adaptive optimization of the model.

[0099] The normalized risk function is used to unify the dimensions of different environmental parameters. A normalization function needs to be constructed, for example:

[0100]

[0101]

[0102]

[0103] in: The upper limit of salt spray concentration set by the system; The upper limit of vibration acceleration set by the system; The upper limit of electromagnetic field strength set by the system.

[0104] The lifetime prediction model uses the exponential decay model as a benchmark, for example:

[0105]

[0106] in: The theoretical design life of the cable (e.g., 30 years = 360 months). Environmental impact coefficient; : Comprehensive environmental interference factors. The AI ​​model analyzes... Adaptive learning improves prediction accuracy.

[0107] Data pre-validation utilizes outlier detection formulas:

[0108]

[0109] in: Real-time collected values; Historical average; Standard deviation; Standardized indicators. When When the value is out of the question, it is identified as an outlier and corrected using interpolation.

[0110] The specific manifestation of the model parameter dynamic self-correction closed loop is as follows: In the first... The error between the prediction and the actual observation is:

[0111]

[0112] The weight update rule is as follows:

[0113]

[0114] in: Learning rate; The partial derivative of the error with respect to the weights. This allows the system to gradually optimize the parameters.

[0115] For example, consider an ocean-going container ship:

[0116] 1. Sensor deployment: Install vibration sensors in the cabin, salt spray sensors below the deck, and electromagnetic field sensors in the communications bay.

[0117] 2. Data Acquisition: The average vibration acceleration data of the engine room is 3.2 m / s², the salt spray concentration below the deck is 15 mg / m³, and the electromagnetic field strength of the communication cabin is 40 V / m.

[0118] 3. Environmental interference factor calculation: The system normalizes the above data and substitutes it into the weighting coefficients for calculation to obtain the comprehensive factor. .

[0119] 4. Lifetime prediction: The system uses an AI model to predict that the remaining lifetime of this batch of cables is 18 months, which is lower than the safety threshold of 24 months, triggering an early warning.

[0120] 5. Feedback Optimization: When the actual maintenance personnel discover that the cable has insulation damage after 16 months, the system will adjust the weight coefficients of the model to improve the accuracy of subsequent predictions.

[0121] As an example, suppose a batch of cables has an initial design life. Months;

[0122] The collected data are as follows: Salt spray concentration Set an upper limit value Normalization Vibration acceleration Set an upper limit value Normalization Electromagnetic field strength Set an upper limit value Normalization Weight settings: , , .

[0123] Environmental interference factors are:

[0124]

[0125] Set the environmental impact factor The lifespan prediction is as follows:

[0126]

[0127] That is, approximately 12.4 years. If the preset safety threshold is 15 years (180 months), the system will issue a warning.

[0128] This embodiment achieves real-time environmental sensing: by comprehensively acquiring data such as salt spray, vibration, and electromagnetic fields through sensors, the complex environment is transformed into calculable indicators. An AI model is then used to predict the remaining lifespan of cables, identifying potential hazards in advance and ensuring the reliability of input data to prevent erroneous data from affecting predictions. Furthermore, a closed-loop mechanism continuously corrects model parameters, improving prediction accuracy over time. This achieves a shift from experience-driven to data-driven approaches, enhancing the scientific rigor and proactiveness of ship cable management.

[0129] Example 3:

[0130] In traditional ship cable design, route layout often relies on manual experience, which can easily lead to physical interference between cables and pipes, equipment, or even other cables, resulting in rework and increased construction costs. Furthermore, cable length design typically depends on theoretical calculations and manual estimations, ignoring historical deviation data accumulated during production, thus leading to material waste or shortages.

[0131] To address the aforementioned issues, this embodiment proposes the following:

[0132] 1. Digital twin 3D simulation model: The digital twin model is used to perform 3D simulation of the cable path design in order to identify potential physical interference between the cable and other pipes, equipment or cables in advance, and automatically recommend corrected paths according to the rule verification benchmark library.

[0133] The digital twin model refers to a digital copy of the actual ship's space and equipment created in a computer using virtual modeling technology. 3D simulation refers to simulating the cable path layout within this digital twin environment and examining its spatial relationships with other pipes, equipment, and cables. Interference identification: When the 3D path of a cable intersects or is excessively close to other objects, the system marks it as an "interference point." Automatic path correction: The system automatically calculates alternative paths based on a rule-based benchmark library (such as minimum bending radius and minimum safety clearance) and provides options for designers.

[0134] 2. Based on historical length deviation data recorded during the production process, the design length parameters of subsequent batches of cables of the same type are automatically corrected to improve cutting accuracy. The combination of these two methods enables intelligent optimization of cable design and production, reducing rework, improving precision, and lowering costs.

[0135] Among them, length deviation data refers to the difference between the actual cutting length and the design length during production or installation; deviation correction mechanism: the system establishes a correction model based on the statistical patterns of historical data and automatically adjusts subsequent design parameters. Improve cutting accuracy: through continuous correction, material waste or rework caused by inaccurate length is reduced.

[0136] Regarding the first point, a practical scenario is presented here. When laying the main power cable, the original route design of a ship's design department passed through the piping area above the engine room. The system, through a digital twin model and 3D simulation, discovered that the distance between the cable and a steam pipe was only 20 millimeters, while the minimum safe distance required by the rule verification benchmark library was 100 millimeters. The system immediately generated a warning and automatically calculated an alternative route to bypass the pipe, recommending it to the designers.

[0137] This may involve the use of interference detection algorithms and automatic path correction algorithms.

[0138] (1) Interference detection algorithm: Suppose that the cable path consists of a set of spatial point sequences This indicates that the pipes or equipment are composed of geometric shapes. The interferometric detection formula can be expressed as:

[0139]

[0140] in: Cable path point; : Pipe or equipment geometry; The minimum distance from a point to a geometric object; : Global minimum safe distance. If ,in If the distance is within the safe distance specified by the rule base, it is considered interference.

[0141] (2) Automatic path correction algorithm: Path correction can adopt the weighted shortest path model:

[0142]

[0143] in: Total path cost; : Path segment length; Number of path bends; : Bending penalty coefficient. The system seeks to minimize the bending radius while satisfying safety clearance and bending radius constraints. The shortest path is recommended.

[0144] As an example, suppose the cable path passes through a conduit area, and the minimum distance between the path point and the conduit is:

[0145]

[0146] And safety threshold . determination: Interference exists. The system automatically calculates an alternative path:

[0147] The new path length increases ;

[0148] Increased number of bends The path cost is:

[0149]

[0150] like Original path length , bending Then the cost of the new path is:

[0151]

[0152] Although the path is slightly longer, it meets the safety requirements, so it is recommended.

[0153] Regarding the second point, a practical scenario is presented here. During production, historical data shows that a certain type of cable generally has a +2% deviation during cutting, meaning that for every 100 meters of cable, 2 meters remain unused. The system, through statistical analysis, applies this correction factor to subsequent designs. When a new cable order is for 500 meters, the system automatically adjusts it to 490 meters, thus avoiding unnecessary material waste. This may involve using a length deviation correction model.

[0154] (3) Length deviation correction model: The design length is The actual length is The historical deviation ratio is The corrected design length is:

[0155]

[0156] in: , which is the historical average deviation ratio.

[0157] As an example, the average deviation rate of a certain type of cable New order design length Corrected version:

[0158]

[0159] Result: Avoided Cable waste.

[0160] This embodiment utilizes digital twin 3D simulation to detect path interference before construction, avoiding rework. It also generates compliant alternative paths based on algorithms, improving design efficiency. Furthermore, it uses historical deviation data to correct design lengths, significantly reducing redundant material costs and improving production controllability. This complements the aforementioned embodiments one and two, covering the entire optimization chain from design and production to operation and maintenance.

[0161] Example 4:

[0162] The lifecycle management of marine cables involves multiple stages, including design, production, installation, and operation and maintenance, each typically handled by different roles and departments. Without a unified task management framework, problems can easily arise.

[0163] Data conflicts: For example, the design department changes the cable route, but the production department still uses the old version to cut materials, resulting in waste;

[0164] Task misalignment: The operations and maintenance department submitted an operations and maintenance inspection task before the cable installation was completed, resulting in process confusion;

[0165] Unclear responsibility: When multiple tasks modify the same data at the same time, it is difficult to trace responsibility.

[0166] To address these issues, this embodiment introduces a database transaction-like approach, encapsulating cross-role modification operations into transactional task packages and assigning them independent lifecycle states. By automatically resolving dependencies between task packages, the system can ensure the orderly parallel processing and state locking of complex tasks.

[0167] In practical applications, the system's transaction management is led by a collaborative control engine, combined with a workflow engine (such as Camunda BPM), a messaging system (such as Apache Kafka), and a database system (such as Oracle Exadata or PostgreSQL EnterpriseDB) to achieve stable scheduling across departments and processes. Specifically:

[0168] 1. A set of logically related modification operations spanning multiple cables or systems is encapsulated into a transactional task package with an independent lifecycle state. In this embodiment, a transactional task package is an execution unit composed of a set of logically related modification operations. Its core characteristic is atomicity: either all succeed or all are undone. In practical applications of ship cable management, the scenarios for transactional task packages are as follows:

[0169] Design modification task: The design department modified the cable path in Siemens NX software and submitted it to the system. The system packages this operation and accompanying parameters (such as new length values ​​and bend radius adjustments) into a transactional task package.

[0170] Production preparation task: The production department uses an ERP system (such as SAPS / 4HANA) to prepare a bill of materials. This bill of materials depends on the latest design parameters and is therefore marked by the system as dependent on the "design modification task package".

[0171] Operations and Maintenance Plan Task: The operations and maintenance department submits a request for advance maintenance of cables in a certain area. Because the cable model for that area is still under modification, the operations and maintenance task package is marked as "dependent and waiting" by the system.

[0172] By using transactional task packages, the system can avoid conflicts between different stages. For example, if a design task is not submitted, a production task cannot enter the "pending execution" state, thus preventing erroneous material handling.

[0173] The independent lifecycle states include at least: pending execution, in execution, dependency waiting, pending commit, and committed.

[0174] Taking annual ship maintenance as an example:

[0175] Once the design department enters the new cable specifications, the task package enters the "pending execution" stage.

[0176] When the parameters are actually modified, the task enters the "Execution" state;

[0177] If the production department submits a dependent task at this time, the task will be locked as "dependency waiting";

[0178] Once the designer completes the modifications and uploads them for approval, the status changes to "pending submission".

[0179] After approval by the management department, the application will be marked as "Submitted".

[0180] This state machine-based management is similar to task flow in the project management tool Jira, but its focus is on transactional atomicity.

[0181] 2. The system automatically parses and manages the dependencies between different transactional task packages. Only when the lifecycle state of the transactional task package that serves as a prerequisite is "committed" can subsequent transactional task packages with dependencies transition from a waiting state to an execution state. This achieves ordered parallel processing of complex tasks and precise state locking. A dependency relationship means that if the execution of task B depends on the completion of task A, then B is marked as dependent on A. For example, production material cutting depends on the submission of design modifications.

[0182] For example, consider the following scenario:

[0183] Scenario 1: Dependency between design modifications and production tasks

[0184] A ship design department proposed modifying the cable path (Task Package A). Meanwhile, the production department was preparing to cut the cables for that batch (Task Package B). Under the transaction management mechanism, Task Package B was automatically marked as "dependency awaiting" until Task Package A's status changed to "committed," at which point Task Package B could enter the "pending execution" state. This avoids production waste caused by the design not being finalized.

[0185] Scenario 2: Parallel Operation and Maintenance Tasks

[0186] The operations and maintenance department needs to inspect the cables in different compartments. These inspection tasks are independent of each other, so the system will encapsulate them into multiple independent task packages and put them into the "running" state in parallel to improve efficiency.

[0187] This fourth embodiment introduces a transactional task management mechanism, transforming complex cable management activities into "task packages" and assigning them independent lifecycle states. The system automatically resolves dependencies, ensuring the orderly progress of the process. This complements the aforementioned embodiments one to three, together forming an intelligent management system for the entire cable lifecycle.

[0188] Example 5:

[0189] This embodiment proposes a cable intelligent management system based on rule verification and collaborative control. Unlike the aforementioned embodiments one to four, this embodiment does not only describe the methodological aspects but also focuses on the system architecture, combining specific hardware and software implementations. In real-world engineering environments, especially in ocean-going vessels, offshore drilling platforms, or large port facilities, cables are numerous, have complex routes, and operate in harsh environments. Without an efficient management system, problems such as disconnect between design and construction, delayed operation and maintenance response, and data fragmentation often occur. Therefore, this system achieves intelligent management of the entire cable lifecycle, from design to operation and maintenance, through the close collaboration of storage modules, data acquisition modules, collaborative control engines, and intelligent processing engines.

[0190] like Figure 2 As shown, the cable intelligent management system based on rule verification and collaborative control includes:

[0191] 1. Storage module: configured to store a multi-dimensional basic configuration system containing ship environmental parameters, cable basic material attributes, process specifications and user permissions, as well as a rule verification benchmark library containing design compliance rules, production process constraints and operation and maintenance health thresholds.

[0192] The storage module serves as the data hub of the entire system, responsible for storing the multi-dimensional basic configuration system and rule verification benchmark library.

[0193] In practical implementation, this module can employ a distributed database architecture. For example, commonly used database systems such as PostgreSQL Enterprise Edition or Oracle Exadata can serve as the underlying storage platform. These databases can support complex structured data, such as cable material properties, ship cabin environmental parameters, design specifications, and user permission configurations.

[0194] For example, in a certain ocean-going freighter project, the "multi-dimensional basic configuration system" stored in the storage module may include the following:

[0195] Ship environmental parameters: such as engine room temperature range of 40–60℃, and long-term average deck salt spray concentration of 15mg / m³;

[0196] Cable material properties: For example, the conductor material is copper (model: T2 high purity copper), and the insulation material is cross-linked polyethylene XLPE (model: Shanghai Shenghua SH-XLPE-90).

[0197] Process specifications: If necessary, comply with IEC 60092-350 Marine Electrical Installations and the cable design specifications of China Classification Society (CCS);

[0198] User permissions: Access control configuration for different roles (design engineer, production manager, operations and maintenance personnel) in the database.

[0199] A notable feature of this storage module is its high reliability. During long-term voyages, network conditions may be unstable. The system employs dual-machine hot standby or distributed replication to ensure that data can be safely stored even in the event of network outages, vibrations, or electromagnetic interference.

[0200] 2. Data acquisition module, including sensors deployed at key locations on the ship, wherein the sensors are configured to acquire environmental data in real time, including at least salt spray concentration, vibration acceleration and electromagnetic field strength, for real-time acquisition of key parameters affecting cable life and performance.

[0201] In practical configurations, the following types of sensors can be used:

[0202] Salt spray sensors, such as the Sensirion SHT85 series, can operate stably for a long time in high humidity and high salt environments.

[0203] Vibration sensors, such as the PCBPiezotronics 352C33 accelerometer, can collect vibration data in the ship's engine room and deck areas.

[0204] Electromagnetic field sensors, such as the NardaELT-400 electromagnetic field strength meter, are suitable for monitoring the stress on signal cables in environments with strong electromagnetic interference.

[0205] For example, when the ship was sailing in the South Pacific, the salt spray sensor in the deck area detected an abnormally high concentration (from 15 mg / m³ to 28 mg / m³). The data acquisition module immediately transmitted this data to the storage module via the Modbus-TCP protocol and generated an alarm on the system interface. Simultaneously, the vibration sensor detected vibration acceleration exceeding 4 m / s² for an extended period in the engine room. All this information, once entered into the system, provides real-time data for subsequent lifespan prediction and maintenance planning.

[0206] The data acquisition module does more than just "upload data"; it also possesses edge computing capabilities. Some sensors have built-in microprocessors (such as the STM32F4 microcontroller) that can perform preliminary data preprocessing locally, such as noise reduction and outlier identification, thereby reducing the computational burden on the central system.

[0207] 3. A collaborative control engine, connected to the storage module, is configured to implement a transaction-based collaborative task management system based on the user permissions and rule verification benchmark library. The collaborative task management system encapsulates a set of modification operations with inherent logical relationships into transactional task packages with independent lifecycle states, and automatically resolves the dependencies between the transactional task packages to achieve precise data verification, process approval, and state locking across roles.

[0208] The collaborative control engine is the "scheduling brain" of the entire system. It combines user permissions and rule verification benchmarks to achieve cross-role transactional task management.

[0209] In practical applications, the management of ship cables involves multiple departments:

[0210] The design department submitted a path modification proposal;

[0211] The production department prepares to cut materials according to the revised plan;

[0212] The maintenance department proposed the maintenance task;

[0213] The management department reviews and issues the final decision.

[0214] Traditionally, the operations of these departments are often scattered across different software and files, making them prone to conflicts. The collaborative control engine in this system adopts a mechanism similar to the enterprise-level workflow engine CamundaBPM, encapsulating these operations into transactional task packages.

[0215] For example, when the design department submits a request to change the cable type, the system will generate a "task package" with a status of "pending execution".

[0216] If the production department submits a material application before the design is approved, the task package will be marked as "dependency pending".

[0217] The production department's material cutting task can only continue to be executed after the design modification task package enters the "submitted" status.

[0218] This transactional management is similar to the concept of transactions in databases: either all operations succeed together, or all are rolled back. This ensures the logical order of tasks while avoiding conflicts in cross-departmental collaboration.

[0219] In practical applications, collaborative control engines often need to have high concurrency processing capabilities. For example, during an annual ship maintenance, the operations and maintenance department may submit cable inspection requests for dozens of compartments simultaneously. The engine processes these requests in parallel through a task queuing mechanism (such as the Kafka distributed messaging system) to ensure rapid response.

[0220] 4. An intelligent processing engine, connected to the storage module and the data acquisition module, wherein the intelligent processing engine is configured to:

[0221] Perform data pre-validation on the environmental data collected by the sensor to identify and smooth out outliers in the data;

[0222] Based on the pre-validated environmental data, an environmental interference factor model is run to assess the cable failure probability, and an artificial intelligence prediction model is used to predict the remaining service life of the cable.

[0223] By continuously comparing the prediction results of the artificial intelligence prediction model with the actual cable fault data collected subsequently, the weight coefficients or risk function of the environmental interference factor model are adaptively and iteratively updated.

[0224] Run a digital twin model to perform a 3D simulation of the cable path design to identify physical interferences, and verify the recommended corrected path based on the rules of the benchmark library;

[0225] Based on historical length deviation data recorded during the production process, the design length parameters of subsequent batches of cables of the same type are automatically corrected.

[0226] For example, taking a 50,000-ton bulk carrier as an example, the actual operation process of the system is as follows:

[0227] Design Phase: Designers access the storage module via the client to retrieve environmental parameters of the target compartment (e.g., high cabin temperature and strong vibration). The system automatically verifies whether the selected cable model meets the specifications. If not, the intelligent processing engine uses digital twin simulation to calculate alternative paths and recommends more suitable cable models.

[0228] In the production process: the production department uploads material unloading records, and the data acquisition module synchronizes them in real time. The system identifies that a certain type of cable has an average of 2% excess length in the past five batches, and the intelligent processing engine adjusts subsequent design parameters accordingly.

[0229] Cable laying phase: When the ship enters the dock for cable laying, the collaborative control engine locks the design parameters according to the task management to prevent the operation and maintenance department from arbitrarily changing the configuration during the construction process.

[0230] In the operation and maintenance phase: While the ship is at sea, sensors continuously upload data on salt spray, vibration, and electromagnetic fields. After analysis, the intelligent processing engine detects that the environmental stress in a certain area is significantly higher than average, predicting that the remaining lifespan is shortened to 16 months. The system immediately generates a maintenance task package and submits it to the management department for approval.

[0231] Management phase: Onshore managers view forecast reports and simulation results through the system interface and approve advance maintenance tasks. The entire closed loop is automatically tracked and archived by the system to ensure subsequent traceability.

[0232] This fifth embodiment comprehensively illustrates the implementation path of a cable intelligent management system based on rule verification and collaborative control from a system perspective. Through in-depth descriptions of the storage module, data acquisition module, collaborative control engine, and intelligent processing engine, and combined with actual products, it fully demonstrates the system's feasibility and value in engineering practice. Its advantages lie in its ability to meet real-time monitoring needs in complex environments while providing data-driven optimization for design and production, thereby significantly improving the intelligence and reliability of cable management.

[0233] Example 6:

[0234] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0235] like Figure 3 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one unit, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of the present invention.

[0236] Processor 201 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 201 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0237] Bus 202 may include a path for transmitting information between the aforementioned components. Bus 202 may be a PCI bus or an EISA bus, etc. Bus 202 may be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0238] The memory 203 stores a computer program corresponding to the cable intelligent management method based on rule verification and collaborative control according to the above embodiments of the present invention. This computer program is controlled and executed by the processor 201. The processor 201 executes the computer program stored in the memory 203 to implement the content shown in the aforementioned method embodiments.

[0239] Among them, electronic devices 200 include, but are not limited to: mobile terminals such as laptops and tablets, as well as fixed terminals such as desktop computers. Figure 3 The electronic device 200 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0240] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, manage, distribute, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0241] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0242] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0243] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0244] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A cable intelligent management method based on rule verification and collaborative control, characterized in that, The method includes: S1. Construct a multi-dimensional basic configuration system that includes ship environmental parameters, cable basic material properties, process specifications, and user permissions; S2. Set up a rule verification benchmark library that includes design compliance rules, production process constraints, and operation and maintenance health thresholds; S3. Implement a cross-role collaborative permission system to perform data verification, process approval, and status locking based on the rule verification benchmark library throughout the entire life cycle of cable design, production, laying, and operation and maintenance. S4. By collecting historical and real-time status data throughout the cable's entire lifecycle, we use artificial intelligence models to analyze and predict data, and then feed the results back to the design, production, and operation and maintenance processes to achieve closed-loop optimization.

2. The method according to claim 1, characterized in that, The cross-role collaborative permission system in step S3 includes: A four-level user permission structure is established, which defines the access and operation permissions of different user roles to data; A three-dimensional collaborative rule system is established, which defines the rules for cross-role data sharing, mutual exclusion operation locking, and process flow in collaborative tasks.

3. The method according to claim 2, characterized in that, The user roles include at least: electrical compartment personnel responsible for cable design, data personnel responsible for maintaining basic parameters and processing feedback data, management personnel responsible for reviewing and adjusting thresholds, and maintenance engineers responsible for health monitoring and fault handling.

4. The method according to claim 1, characterized in that, The method also includes the step of collecting environmental data through hardware deployment, specifically: deploying sensors at key locations on the ship to collect environmental data in real time, the environmental data including at least salt spray concentration, vibration acceleration and electromagnetic field strength; and transmitting the environmental data to a database for real-time health monitoring and long-term condition assessment of the cable; During the cable selection and design phase, the system automatically verifies whether the specifications of the candidate cables meet the environmental tolerance requirements based on the environmental data of the target laying area, and recommends the optimal cable model.

5. The method according to claim 1, characterized in that, The method also includes the step of constructing an environmental interference factor model, which, based on historical data, evaluates the failure probability of cable joint locations using the following formula to calculate the environmental interference factor. : ； in: For comprehensive environmental interference factors; This provides real-time salt spray concentration data. This provides real-time vibration acceleration data. This provides real-time electromagnetic field intensity data. , , These are the normalized risk functions for the corresponding environmental factors; , , Here are the weighting coefficients for each environmental factor, and ; The environmental interference factors As input, an artificial intelligence prediction model is used to predict the remaining service life (RUL) of the cable, and an early warning is triggered when the predicted service life is lower than a preset safety threshold.

6. The method according to claim 1, characterized in that, Step S4 further includes: using a digital twin model to perform a three-dimensional simulation of the cable path design in order to pre-identify possible physical interferences between the cable and other pipes, equipment or cables, and automatically recommend corrected paths based on the rule verification benchmark library.

7. The method according to claim 1, characterized in that, Step S4 further includes: automatically correcting the design length parameters of subsequent batches of cables of the same type based on historical length deviation data recorded during the production process, so as to improve the accuracy of material cutting.

8. The method according to claim 2, characterized in that, The cross-role collaborative permission system is also implemented through a transaction-based collaborative task management mechanism, specifically as follows: A set of modification operations that are logically related and span multiple cables or systems are encapsulated into a transactional task package with an independent lifecycle state, which includes at least: pending execution, in execution, dependent waiting, pending commit, and committed. The system automatically parses and manages the dependencies between different transactional task packages. Only when the lifecycle state of the transactional task package that serves as a prerequisite is committed can the subsequent transactional task packages with dependencies transition from the dependency waiting state to the pending execution state, thereby achieving orderly parallel processing of complex tasks and precise state locking.

9. The method according to claim 5, characterized in that, This method also includes establishing a dynamic self-correcting closed loop for model parameters and a data pre-validation mechanism, specifically: The real-time salt spray concentration data Real-time vibration acceleration data and real-time electromagnetic field strength data Before inputting the environmental interference factor model, a data pre-validation step is performed to identify, mark, and smooth out outliers or missing values ​​in the sensor data through time series analysis and statistical outlier detection. Furthermore, by continuously comparing the prediction results of the model with subsequently collected actual cable fault or maintenance data, machine learning algorithms are used to adjust the weighting coefficients. and / or the normalized risk function Periodic iterative updates are performed to achieve adaptive optimization of the model.

10. A cable intelligent management system based on rule verification and collaborative control, characterized in that, include: The storage module is configured to store a multi-dimensional basic configuration system containing ship environmental parameters, cable basic material properties, process specifications and user permissions, as well as a rule verification benchmark library containing design compliance rules, production process constraints and operation and maintenance health thresholds. The data acquisition module includes sensors deployed at key locations on the ship, the sensors being configured to acquire environmental data in real time, including at least salt spray concentration, vibration acceleration, and electromagnetic field strength. The collaborative control engine, connected to the storage module, is configured to implement a transaction-based collaborative task management system based on the user permissions and rule verification benchmark library. The collaborative task management system encapsulates a set of modification operations with inherent logical relationships into transactional task packages with independent lifecycle states, and automatically resolves the dependencies between the transactional task packages to achieve precise data verification, process approval, and state locking across roles. An intelligent processing engine, connected to the storage module and the data acquisition module, is configured to: Perform data pre-validation on the environmental data collected by the sensor to identify and smooth out outliers in the data; Based on the pre-validated environmental data, an environmental interference factor model is run to assess the cable failure probability, and an artificial intelligence prediction model is used to predict the remaining service life of the cable. By continuously comparing the prediction results of the artificial intelligence prediction model with the actual cable fault data collected subsequently, the weight coefficients or risk function of the environmental interference factor model are adaptively and iteratively updated. Run a digital twin model to perform a 3D simulation of the cable path design to identify physical interferences, and verify the recommended corrected path based on the rules of the benchmark library; Based on historical length deviation data recorded during the production process, the design length parameters of subsequent batches of cables of the same type are automatically corrected.

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