Intelligent cable management method and system based on rule verification and cooperative control

Abstract

The invention relates to the technical field of industrial asset management, and particularly discloses a cable intelligent management method and system based on rule verification and cooperative control, and the method comprises the steps: constructing a multi-dimensional basic configuration system which comprises ship environment parameters, cable basic material attributes, process specifications and user authority; setting a rule verification reference library containing design compliance rules, production process constraints and operation and maintenance health thresholds; a cross-role collaborative authority system is implemented and is used in the whole life cycle of design, production, laying and operation and maintenance of the cable. According to the method, the multi-dimensional basic configuration system covering the ship environment, the material attributes, the process specifications and the user permissions is constructed, and the verification reference library containing the full-process rules is combined, so that the information barriers of the links of design, production, laying and operation and maintenance are broken, mistakes and omissions of artificial data transcription are avoided, and the efficiency is improved. And unified management and compliance control of cable full life cycle data are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial asset management, in particular to a cable intelligent management method and system based on rule checking and collaborative control. BACKGROUND

[0002] As the "nerve network" of modern complex equipment, cables undertake the key tasks of power transmission, signal communication and control instruction issuing, and their reliability is directly related to the safety and normal operation of the entire equipment. In typical scenarios such as ships, the cable network is large in scale and complex in layout, and has been serving in harsh composite environments such as salt mist, vibration, high temperature and strong electromagnetic interference for a long time, which puts forward high requirements for the management of cables.

[0003] At present, the cable management methods commonly used in the industry mainly have the following technical defects:

[0004] The design, production, laying and operation and maintenance of cables are usually managed by different departments using independent software tools (such as CAD software, ERP systems, project management tables) and processes. Data between different stages depends on manual transcription or file import and export, which is not only inefficient but also prone to errors, leading to problems such as disconnection between design parameters and actual production, mismatch between laying records and operation and maintenance needs, etc. This fragmentation of data makes it extremely difficult to trace and analyze the entire life cycle of cables.

[0005] Moreover, traditional cable design, such as path planning and model selection, is mainly based on general design specifications and engineers' personal experience. Although this method can meet the basic safety requirements, it cannot quantitatively analyze and consider local micro-environmental factors (such as vibration frequency, salt mist concentration or electromagnetic field strength in a specific cabin) in the final service of the cable. This makes it difficult to accurately grasp the design margin of the cable, or causes cost waste due to overdesign, or risks premature aging or failure in a specific stress environment due to inadequate consideration. SUMMARY

[0006] The present application aims to at least solve one of the technical problems in the related art. To this end, the purpose of the present application is to propose a cable intelligent management method and system based on rule checking and collaborative control to improve the intelligent level and reliability of cable management.

[0007] To achieve the above-mentioned purpose, the first aspect of the present application proposes a cable intelligent management method based on rule checking and collaborative control, which comprises: S1, a multi-dimensional basic configuration system containing ship environment parameters, cable basic material attributes, process specifications and user permissions is constructed; S2, set a rule verification benchmark library containing design compliance rules, production process constraints and operation health thresholds; S3, implement a cross-role collaborative permission system for data verification, process approval and state locking based on the rule verification benchmark library in the whole life cycle of cable design, production, laying and operation; S4, by collecting historical data and real-time state data of the whole life cycle of the cable, using an artificial intelligence model for analysis and prediction, and feeding back the results to the design, production and operation links, to realize closed-loop optimization.

[0008] To achieve the above purpose, the first aspect of the present application proposes a cable intelligent management system based on rule verification and collaborative control, comprising: A storage module configured to store a multi-dimensional basic configuration system containing ship environmental parameters, cable basic material attributes, process specifications and user permissions, and a rule verification benchmark library containing design compliance rules, production process constraints and operation health thresholds; A data acquisition module including sensors deployed at key positions on the ship, the sensors being configured to acquire environmental data including at least salt mist concentration, vibration acceleration and electromagnetic field intensity in real time; A collaborative control engine connected to the storage module, configured to implement a transaction-based collaborative task management based on the user permissions and the rule verification benchmark library, the collaborative task management encapsulating a group of modification operations with inherent logical association into transactional task packages with independent life cycle states, and automatically analyzing the dependency relationship between the transactional task packages to realize accurate locking of cross-role data verification, process approval and state;

[0009] An intelligent processing engine connected to the storage module and the data acquisition module, the intelligent processing engine being configured to: Perform data pre-verification on the environmental data collected by the sensors to identify and smooth out abnormal values in the data; Based on the pre-verified environmental data, run an environmental interference factor model to evaluate the cable failure probability, and use an artificial intelligence prediction model 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 failure data collected subsequently, the weight coefficients or risk functions of the environmental interference factor model are adaptively and iteratively updated; Run a digital twin model to perform three-dimensional simulation on the path design of the cable to identify physical interference, and recommend a revised path according to the rule verification benchmark library;

[0010] Based on the historical length deviation data recorded in the production process, the design length parameters of the same type of cable in the subsequent batch are automatically corrected.

[0011] To achieve the above object, the third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the above-mentioned cable intelligent management method based on rule checking and collaborative control.

[0012] The cable intelligent management method and system based on rule checking and collaborative control according to the embodiments of the present application break the information barriers among design, production, laying and operation, avoid mistakes and omissions in manual data transcription, and realize unified management and compliance control of cable life cycle data by constructing a multi-dimensional basic configuration system covering ship environment, material attributes, process specifications and user permissions, and combining with a verification benchmark library containing full-process rules.

[0013] By means of environment data collection, interference factor model, digital twin and AI analysis and prediction, the local micro-environment of the ship is accurately integrated into the design decision, which avoids premature failure caused by excessive design or insufficient environmental adaptation. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart of the cable intelligent management method based on rule checking and collaborative control provided by the present application; Figure 2 is a structural schematic diagram of the cable intelligent management system based on rule checking and collaborative control provided by the present application; Figure 3 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0015] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0016] The cable intelligent management method, system and electronic device based on rule checking and collaborative control according to the embodiments of the present application are described below with reference to the drawings.

[0017] Embodiment One:

[0018] Figure 1 is a flowchart of a rule verification and collaborative control-based intelligent cable management method according to an embodiment of the present application. The method is mainly applied to the whole process of design, production, laying and operation and maintenance of ship cables, aiming to solve the problems of fragmented links, data errors and omissions, and low efficiency of collaboration in traditional cable management. By establishing a multi-dimensional basic configuration system, setting a rule verification benchmark library, and implementing a cross-role collaborative permission system, unified management and process control of cable life cycle data can be achieved, ensuring the compliance of design and the reliability of operation and maintenance.

[0019] As shown in Figure 1 , the rule verification and collaborative control-based intelligent cable management method comprises the following steps: 1. Construction of multi-dimensional basic configuration system

[0020] The method first needs to construct a multi-dimensional basic configuration system, which includes four dimensions: ship environmental parameters, cable basic material attributes, process specifications and user permissions.

[0021] Ship environmental parameters: refer to the microenvironmental conditions in different cabins or deck areas of the ship, including but not limited to temperature, humidity, salt fog concentration, vibration acceleration and electromagnetic field strength. These parameters directly determine the service environment of the cable.

[0022] Cable basic material attributes: refer to the conductor material (such as copper, aluminum), insulation layer material (such as cross-linked polyethylene, ethylene-propylene rubber), sheath type and shielding structure, etc.

[0023] Process specifications: refer to the standards that must be followed in the design and manufacture of cables, such as IEC60092 Ship Electrical Installations or relevant specifications of China Classification Society (CCS).

[0024] User permissions: refer to the operation range of different roles in the system, including data reading permission, modification permission and approval permission.

[0025] The establishment of this multi-dimensional system enables each configuration parameter of the cable to be associated with the environment, materials and standards, and to be bound with user permissions, thereby ensuring the integrity and consistency of the data.

[0026] 2. Establishment of rule verification benchmark library

[0027] Secondly, the method needs to set up a rule verification benchmark library, which covers three levels of rules:

[0028] Design compliance rules: for example, the rated voltage of the cable should match the target circuit voltage level, and the cable cross section should meet the current carrying capacity requirements.

[0029] Production process constraints: such as minimum value of cable insulation thickness, maximum allowable deviation of conductor stranding pitch.

[0030] Operation and maintenance health threshold: such as when the cable insulation resistance drops below a certain value (e.g. 0.1 MΩ·km), a warning needs to be triggered.

[0031] The establishment of the rule verification reference library ensures that the system can automatically verify compliance when data is entered and operated at each link, reducing the mistakes of manual judgment.

[0032] 3. Implementation of cross-role collaborative permission system

[0033] Then, the method proposes a cross-role collaborative permission system, which is not only an access control tool, but also a dynamic collaboration framework covering the whole process of design, production, laying and operation. This system fundamentally breaks the problem of data isolation and process disconnection between different departments in the traditional mode. The core is:

[0034] Automatic verification of input data and operations based on rule verification reference library;

[0035] In the process of flow transfer, approval and confirmation are carried out according to role permission;

[0036] For sensitive states (such as key design parameters), unauthorized modifications are prevented through state locking mechanism.

[0037] 4. Data acquisition and closed-loop optimization

[0038] Finally, the method collects historical data and real-time state data of the whole life cycle of the cable, uses artificial intelligence model for analysis and prediction, and feeds back the prediction results to the design, production and operation links, realizing closed-loop optimization. This step ensures that the scheme is not only a static rule management, but also can realize dynamic adaptive adjustment.

[0039] Optionally, the cross-role collaborative permission system includes a four-level user permission structure and a three-dimensional collaborative rule system, wherein:

[0040] 1. The four-level user permission structure contains four levels:

[0041] Read-only permission: users can only view cable configuration parameters and cannot modify them.

[0042] Modification permission: users can modify data within a certain range, such as inputting new sensor collection results.

[0043] Approval permission: users can review the modification requests submitted by subordinates and decide whether to pass.

[0044] Management authority: Users can define rule verification benchmark library, modify system settings, and have the highest authority.

[0045] Through this hierarchical division, the scope of operation of different roles can be strictly limited, and unapproved data can be directly adopted.

[0046] Users can be divided into four typical user roles:

[0047] Electrical cabin personnel: mainly responsible for the preliminary design of cables, including selection and path planning.

[0048] Data personnel: responsible for maintaining environmental parameter database and organizing and feeding back operation data.

[0049] Management personnel: responsible for reviewing whether the rule threshold is reasonable and making necessary adjustments.

[0050] Maintenance engineer: responsible for real-time health monitoring and fault handling.

[0051] This role division corresponds to the actual ship cable management workflow, which can ensure that each link has a clear person in charge.

[0052] 2. Three-dimensional collaborative rule system defines cross-role collaboration rules from three dimensions:

[0053] Data sharing rules: define which data can be shared across departments, for example, design parameters are visible to maintenance engineers, but some production process details may be limited to the production department.

[0054] Mutually exclusive operation locking rules: for example, when maintenance personnel are analyzing faults based on the operation data of a batch of cables, the design department cannot modify the original design parameters of the batch at the same time.

[0055] Process flow rules: define the approval path of operations, for example, design modifications must be confirmed by data personnel before entering the production stage.

[0056] Through the three-dimensional collaborative rule system, multi-role orderly and efficient interaction can be achieved in a complex collaborative environment.

[0057] As an example, take a long-distance ship as an example:

[0058] In the design stage, electrical cabin personnel enter cable path planning through the system. The system automatically calls the environmental parameter database to verify whether the selected cable model meets the tolerance requirements of the target cabin.

[0059] In the production stage, data personnel enter the length deviation data collected by the production workshop, and the system verifies it according to the rule verification benchmark library to avoid excessive deviation.

[0060] In the operation and maintenance phase, the operation and maintenance engineer monitors the electromagnetic field intensity of a certain cabin in real time through the sensor. The system triggers rule verification, prompting that the cable in this area may have an early aging risk. After receiving the warning, the manager can adjust the operation and maintenance health threshold and issue a maintenance task.

[0061] Through this closed-loop process, cable management is transformed from passive response to active prevention.

[0062] The permission control logic can be represented by a Boolean function to represent the constraints of user permissions on operations:

[0063] ;

[0064] Where: : user identifier; : operation type (such as read, modify, approve); : permission judgment function. If the system determines , the operation is rejected and a log is recorded.

[0065] The process approval state transition uses a state machine to describe the process:

[0066] ;

[0067] Where: : process state at time , which can take values such as "pending approval", "under review", "passed", and "rejected"; : approval operation; : state transition function. For example, when pending approval, and approved, then passed.

[0068] In this embodiment, the multi-dimensional configuration system ensures data consistency between design, production and operation, the rule verification benchmark library ensures 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 safety. In summary, this embodiment not only solves the fragmentation and inefficiency problems in traditional cable management, but also provides a full-life-cycle closed-loop intelligent cable management method through rule verification and permission control mechanisms.

[0069] Embodiment Two:

[0070] This embodiment can significantly improve the scientificity and initiative of cable operation and maintenance by deploying sensors in key positions of the ship to collect environmental data in real time, building an environmental interference factor model, and combining artificial intelligence prediction algorithms to predict the remaining service life of the cable, especially the cable joint.

[0071] 1. The cable intelligent management method based on rule verification and collaborative control further comprises the step of collecting environmental data through hardware deployment, specifically: deploying sensors in key positions of the ship to collect environmental data in real time, the environmental data at least including salt mist concentration, vibration acceleration, and electromagnetic field intensity; and transmitting the environmental data to the database for real-time health monitoring and long-term state evaluation of the cable.

[0072] During the cable selection and design stage, the system automatically verifies whether the selected cable specifications meet the environmental resistance requirements based on the environmental data of the target laying area, and recommends the optimal cable model.

[0073] Several key points are involved here:

[0074] Sensor deployment: Select key node positions on the ship structure, such as engine room, under deck, communication cabin, power cabin, etc. These areas often have different environmental stresses.

[0075] Environmental data collection: At least includes salt mist concentration (affects insulation aging), vibration acceleration (affects mechanical fatigue), and electromagnetic field intensity (affects signal interference and shielding layer performance).

[0076] Design verification and recommendation: In the design stage, the system matches real-time or historical environmental data with cable material performance data to determine whether the cable meets the use requirements. If not, it automatically recommends a model that meets the conditions.

[0077] 2. In addition, this method also proposes to build an environmental interference factor model based on the collected environmental data to evaluate the failure probability of the cable joint and further predict the remaining service life.

[0078] This model is based on historical data and evaluates the failure probability of the cable joint through the following formula to calculate the environmental interference factor :

[0079] ;

[0080] Where: is the comprehensive environmental interference factor; is the real-time salt mist concentration data; is the real-time vibration acceleration data; Real-time electromagnetic field intensity data; , , are normalized risk functions corresponding to environmental factors, respectively; , , are weight coefficients of each environmental factor, and . The environmental interference factor is input into an artificial intelligence prediction model (such as an LSTM long short-term memory network) to predict the remaining service life of the cable . When the predicted value is lower than a preset threshold, a warning is triggered.

[0081] Among them, the salt mist 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 : refers to the mechanical vibration intensity generated by the equipment or cabin during operation, usually expressed in meters per square second (m / s²). Long-term vibration can cause cable joints to loosen or conductors to fatigue; electromagnetic field intensity : refers to the intensity of the electric field 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 layer overload; remaining useful life RUL: refers to the length of time the cable is expected to operate normally under current conditions.

[0082] And the method further comprises establishing a model parameter dynamic self-correction closed loop and a data pre-check mechanism, specifically:

[0083] Before inputting the real-time salt mist concentration data , real-time vibration acceleration data and real-time electromagnetic field intensity data into the environmental interference factor model, a data pre-checking step is performed to identify, mark and smooth the abnormal values or missing values in the sensor data through time series analysis and statistical outlier detection; data pre-checking refers to the process of anomaly detection and correction before data enters the model, common methods include outlier detection, interpolation completion and time series smoothing;

[0084] And by continuously comparing the prediction results of the model with subsequent collected actual cable failure or maintenance data, the weight coefficients and / or the normalized risk functions are periodically iteratively updated using machine learning algorithms to achieve adaptive optimization of the model.

[0085] Among them, the normalized risk function is to unify the dimensions of different environmental parameters, and a normalization function needs to be constructed, for example:

[0086]

[0087]

[0088]

[0089] Wherein: : The upper limit of the salt spray concentration set by the system; : The upper limit of the vibration acceleration set by the system; : The upper limit of the electromagnetic field intensity set by the system.

[0090] And the life prediction model adopts an exponential decay model as a benchmark, for example:

[0091]

[0092] Wherein: : The theoretical design life of the cable (e.g. 30 years = 360 months); : Environmental impact coefficient; : Comprehensive environmental interference factor. The AI model improves prediction accuracy through adaptive learning of .

[0093] Data pre-check uses the outlier detection formula:

[0094]

[0095] Wherein: : Real-time collection value; : Historical average; : Standard deviation; : Standardized index. When , it is determined as an abnormal value and corrected by interpolation method.

[0096] And the model parameter dynamic self-correction closed loop is specifically shown as follows: the error between the th prediction and actual observation is:

[0097]

[0098] The weight update rule is:

[0099]

[0100] Wherein: : Learning rate; : Error partial derivative of weight. In this way, the system can gradually optimize the parameters.

[0101] For example, taking a long-distance container ship as an example:

[0102] 1. Sensor deployment: Install vibration sensors in the engine room, salt mist sensors under the deck, and electromagnetic field sensors in the communication cabin.

[0103] 2. Data collection: The average vibration acceleration data in the engine room is 3.2 m / s², the salt mist concentration under the deck is 15 mg / m³, and the electromagnetic field intensity in the communication cabin is 40 V / m.

[0104] 3. Environmental interference factor calculation: The system normalizes the above data and calculates the comprehensive factor by substituting the weight coefficient. .

[0105] 4. Life prediction: The system predicts the remaining life of the batch of cables to be 18 months using an AI model, which is below the safety threshold of 24 months, triggering an early warning.

[0106] 5. Feedback optimization: When the actual maintenance personnel find that the cable indeed has insulation damage at 16 months, the system will correct the weight coefficient of the model to improve the accuracy of subsequent predictions.

[0107] As an example, assume that the initial design life of a batch of cables is 24 months.

[0108] The collected data is as follows: salt mist concentration , set the upper limit value , normalize ; vibration acceleration , set the upper limit value , normalize ; electromagnetic field intensity , set the upper limit value , normalize . Weight settings: , , .

[0109] The environmental interference factor is:

[0110]

[0111] Assuming the environmental impact coefficient , the life prediction is:

[0112]

[0113] That is, about 12.4 years. If the pre-set safety threshold is 15 years (180 months), the system will issue a warning.

[0114] ​The embodiment realizes real-time perception of the environment: through sensors, comprehensive acquisition of salt spray, vibration, electromagnetic field and other data, conversion of complex environment into calculable indicators, prediction of cable residual life by using AI model, early discovery of potential hazards, and guarantee of the reliability of input data to avoid the influence of false data on prediction. The closed-loop mechanism continuously corrects the model parameters, the prediction accuracy improves over time, realizes the transformation from experience-driven to data-driven, and improves the scientificity and initiative of ship cable management.

[0115] Embodiment three:

[0116] In traditional ship cable design, path layout often relies on manual experience, which is prone to physical interference problems between cables, pipes, equipment and even other cables, leading to post-rework and increasing construction costs. In addition, cable length design usually relies on theoretical calculation and manual estimation, ignoring the deviation data accumulated in the historical production process, resulting in material waste or deficiency.

[0117] To solve the above problems, the embodiment proposes:

[0118] 1. Digital twin three-dimensional simulation model: use the digital twin model to simulate the path design of the cable in three dimensions to pre-identify possible physical interference between the cable and other pipes, equipment or cables, and automatically recommend a corrected path according to the rules verification library.

[0119] Among them, the digital twin model refers to the establishment of a digital copy completely consistent with the actual ship space and equipment in the computer through virtual modeling technology. Three-dimensional simulation refers to simulating the path layout of the cable in this digital twin environment and checking the spatial relationship with other pipes, equipment and cables. Interference identification: when the three-dimensional path of the cable intersects or is too close to other objects, the system marks it as an "interference point". Automatic path correction: the system automatically calculates an alternative path based on the rules verification library (such as minimum bend radius, minimum safety distance) and provides the designer with a choice.

[0120] 2. Based on the historical length deviation data recorded in the production process, automatically correct the design length parameters of the same type of cable in subsequent batches to improve the accuracy of the cutting. The combination of the two realizes intelligent optimization of the cable in the design and production links, reduces rework, improves accuracy and reduces costs.

[0121] Among them, the length deviation data refers to the difference between the actual cutting length and the design length in the production or installation process; the deviation correction mechanism: the system establishes a correction model according to the statistical law of historical data, automatically adjusts the subsequent design parameters. Improve the cutting accuracy: through continuous correction, reduce the material waste or rework caused by inaccurate length.

[0122] For the first point, a practical scenario is presented here. A ship design department is laying the main power cable, and the original path design goes through the pipe area above the engine room. The system simulates in three dimensions through the digital twin model and finds that the distance between the cable and a steam pipe is only 20 mm, while the minimum safety distance required by the rule checking benchmark library is 100 mm. The system immediately generates a warning and automatically calculates an alternative path that bypasses the pipe, recommending it to the designer.

[0123] Among them, it may involve the use of interference detection algorithm and automatic path correction algorithm.

[0124] (1) Interference detection algorithm: Let the cable path be represented by a sequence of spatial points , and the pipes or equipment be represented by a set of geometric bodies . The interference detection formula can be expressed as:

[0125]

[0126] Where: : cable path point; : pipe or equipment geometry; : minimum distance from point to geometry; : global minimum safety distance. If , where is the safety distance specified by the rule library, then it is determined to be an interference.

[0127] (2) Automatic path correction algorithm: Path correction can use a weighted shortest path model:

[0128]

[0129] Where: : total path cost; : path segment length; : number of path bends; : bend penalty coefficient. The system finds the path that minimizes as the recommended solution under the condition of meeting the safety distance and bend radius constraints.

[0130] As an example, assume that the cable path goes through the pipe area, and the minimum distance between the path point and the pipe is:

[0131]

[0132] And the safety threshold . Determine: , there is interference. The system automatically calculates an alternative path:

[0133] The new path length increases ;

[0134] The number of bends increases The path cost is:

[0135]

[0136] If , the original path length , the number of bends , then the new path cost is:

[0137]

[0138] Although the path is slightly longer, it meets the safety requirements, so it is recommended to use.

[0139] For the second point, an actual scenario is presented here. In the production process, historical data shows that there is a +2% deviation in the unloading of a certain type of cable, that is, there are 2 meters of cable left when 100 meters are actually used. The system applies this correction coefficient to subsequent design through statistical analysis. When the new cable order is 500 meters, the system automatically adjusts it to 490 meters, thereby avoiding unnecessary material waste. This may involve the use of a length deviation correction model.

[0140] (3) Length deviation correction model: the design length is , the actual length is , and the historical deviation ratio is , then the corrected design length is:

[0141]

[0142] Among them: , is the historical average deviation ratio.

[0143] As an example, the average deviation rate of a certain type of cable is . The design length of the new order is . After correction:

[0144]

[0145] Result: avoid cable waste.

[0146] This embodiment can find path interference before construction through digital twin three-dimensional simulation, avoiding rework; generate alternative paths that meet the specifications based on algorithms, improving design efficiency. And use historical deviation data to correct the design length, greatly reduce the cost of redundant materials, and improve production controllability. Complementary to the first two embodiments, covering the whole chain optimization from design, production to operation.

[0147] Example Four:

[0148] In the whole life cycle management of ship cables, there are multiple links involved in design, production, laying and operation, each of which is usually responsible by different roles and departments. If there is no unified task management framework, the following problems are likely to occur:

[0149] Data conflict: For example, the design department modifies the cable path, while the production department still produces according to the old version, resulting in waste;

[0150] Task misplacement: The operation department submits an operation check task before the cable is installed, causing process confusion;

[0151] Unclear responsibility: When multiple tasks modify the same data at the same time, it is difficult to trace the responsibility.

[0152] To solve these problems, this embodiment introduces the idea of database transaction, encapsulates cross-role modification operations into transactional task packages, and gives them independent life cycle states. By automatically analyzing the dependency relationship between task packages, the system can ensure the ordered parallel processing and state locking of complex tasks.

[0153] In practical application, the system is dominated by the collaborative control engine for transaction management, combined with the workflow engine (such as CamundaBPM), message system (such as ApacheKafka) and database system (such as OracleExadata or PostgreSQLEnterpriseDB) to realize stable adjustment across departments and links. Specifically:

[0154] 1. A group of modification operations with internal logical association, across multiple cables or systems, is encapsulated into a transactional task package with independent life cycle state. In this embodiment, the transactional task package is an execution unit composed of a group of logically associated modification operations. Its core feature is atomicity: either all succeed or all be revoked. In the practical application of ship cable management, the scenarios of transactional task package are as follows:

[0155] Design modification task: The design department modifies the cable path in SiemensNX software and submits it to the system. The system packages this operation and the attached parameters (such as new length value, bend radius adjustment) into a transactional task package.

[0156] Production preparation task: The production department uses the ERP system (such as SAPS / 4HANA) to prepare the cutting list. This list depends on the latest design parameters, so it is marked by the system as dependent on the "design modification task package".

[0157] Operation plan task: The operation department submits an application for early maintenance of the cable in a certain area. Since the cable model in this area is still being modified, the operation task package is marked as "dependent on waiting" by the system.

[0158] Through the mechanism of transactional task package, the system can avoid inter-link conflicts. For example, if the design task is not submitted, the production task cannot enter the "to be executed" state, thereby avoiding incorrect discharging behavior.

[0159] And the independent life cycle state at least includes: to be executed, in execution, dependent waiting, to be submitted and submitted.

[0160] Take the annual maintenance of a ship as an example:

[0161] When the design department enters a new cable specification, the task package enters "to be executed";

[0162] When the actual parameter is modified, the task enters "in execution";

[0163] If the production department submits a dependent task at this time, the task is locked as "dependent waiting";

[0164] When the design personnel complete the modification and upload the approval, the state changes to "to be submitted";

[0165] After the management department approves, it enters "submitted".

[0166] This state machine type management is similar to the task flow in the project management tool Jira, but its focus is on transactional atomicity.

[0167] 2, and the system automatically analyzes and manages the dependency relationship between different transactional task packages. Only when the life cycle state of the transactional task package as a precondition is submitted, the subsequent transactional task package with a dependent relationship can change from the dependent waiting state to the to-be-executed state, thereby realizing the ordered parallel processing and accurate locking of the state of complex tasks. The dependent relationship means that if the execution of task B is based on the completion of task A, then B is marked as dependent on A. For example, production discharging must depend on design modification submission.

[0168] For example, set the following scenario:

[0169] Scenario one: dependency of design modification and production task

[0170] A ship design department proposes to modify the cable path (task package A). At the same time, the production department has prepared to discharge the cable (task package B). Under the transaction management mechanism, task package B is automatically marked as "dependent waiting", and only after the state of task package A changes to "submitted", task package B can enter the "to be executed" state. In this way, it avoids the waste of production caused by unconfirmed design.

[0171] Scenario two: parallel operation and maintenance tasks

[0172] The operation and maintenance department needs to check the cables in different cabins, and these inspection tasks are independent of each other, so the system encapsulates them as multiple independent task packages and enters the "executing" state in parallel, thereby improving efficiency.

[0173] This embodiment four introduces a transactional task management mechanism, converts complex cable management activities into "task packages", and gives them independent life cycle states. The system automatically analyzes the dependency relationship to ensure that the process progresses in an orderly manner, complementing the previous embodiments one to three, and together forming an intelligent management system for the entire life cycle of the cable.

[0174] Embodiment five:

[0175] This embodiment proposes a cable intelligent management system based on rule verification and collaborative control. Unlike the previous embodiments one to four, this embodiment is not only described from the method level, but also from the system architecture perspective, combined with the specific implementation of hardware and software. In actual engineering environments, especially in ocean-going ships, offshore drilling platforms or large port facilities, the number of cables is large, the path is complex, and the service environment is harsh. Without an efficient management system, problems such as design and construction disconnection, operation and maintenance response lag, and data fragmentation often occur. Therefore, through the close cooperation of the storage module, data acquisition module, collaborative control engine and intelligent processing engine, the system realizes intelligent management of the entire life cycle of the cable from design to operation and maintenance.

[0176] As shown in Figure 2 , the cable intelligent management system based on rule verification and collaborative control includes:

[0177] 1. A storage module configured to store a multi-dimensional basic configuration system including ship environmental parameters, cable basic material attributes, process specifications and user permissions, and a rule verification benchmark library including design compliance rules, production process constraints and operation and maintenance health thresholds.

[0178] The storage module plays the role of data hub for the entire system, responsible for saving the multi-dimensional basic configuration system and the rule verification benchmark library.

[0179] In actual implementation, this module can use a distributed database architecture. For example, commonly used PostgreSQL enterprise version or Oracle Exadata database system can be used as the underlying storage platform. Such databases can support complex structured data, such as cable material attributes, ship cabin environmental parameters, design specification files and user permission configurations.

[0180] For example, in a certain ocean-going cargo ship project, the "multi-dimensional basic configuration system" saved in the storage module may include the following:

[0181] Ship environmental parameters: e.g. engine room temperature range 40-60℃, deck salt mist concentration long-term average 15mg / m³;

[0182] Cable material properties: e.g. conductor material is copper (type: T2 high purity copper), insulation material is cross-linked polyethylene XLPE (type: Shanghai Shenghua SH-XLPE-90);

[0183] Process specification: e.g. IEC 60092-350 Ships' Electrical Installations and China Classification Society (CCS) cable design specifications shall be followed;

[0184] User permissions: different roles (design engineer, production supervisor, maintenance personnel) have access control configurations in the database.

[0185] One of the remarkable features of this storage module is high reliability. During the long-term voyage of the ship, the network conditions may not be stable, and the system uses dual hot standby or distributed replication to ensure that data can still be safely saved in the case of network interruption, vibration or electromagnetic interference.

[0186] 2. Data acquisition module, including sensors deployed at key locations on the ship, the sensors are configured to collect environmental data including at least salt mist concentration, vibration acceleration and electromagnetic field strength in real time, for real-time collection of key parameters affecting cable life and performance.

[0187] In actual configuration, the following types of sensors can be used:

[0188] Salt mist sensor: e.g. Sensirion SHT85 series, which can work stably in high humidity and high salt environment for a long time.

[0189] Vibration sensor: e.g. PCB Piezotronics 352C33 accelerometer, which can collect vibration data in the engine room and deck area of the ship.

[0190] Electromagnetic field sensor: e.g. Narda ELT-400 electromagnetic field strength meter, suitable for monitoring the pressure on signal cables in a strong electromagnetic interference environment.

[0191] For example, when the ship is performing a voyage task in the South Pacific, the salt mist sensor in the deck area detects an abnormal increase in concentration (from 15mg / m³ to 28mg / m³). The data acquisition module immediately transmits this data to the storage module through the Modbus-TCP protocol and generates an alarm on the system interface. At the same time, the vibration sensor detects that the vibration acceleration in the engine room has been above 4m / s² for a long time. All this information entering the system provides real-time basis for subsequent life prediction and maintenance planning.

[0192] Data collection module not only "upload data", also has edge computing capabilities. Some sensors have built-in microprocessors (such as STM32F4 microcontroller), which can perform preliminary data preprocessing locally, such as denoising and outlier identification, to reduce the computational burden on the central system.

[0193] 3. A collaborative control engine connected to the storage module, configured to check the rule verification library based on the user permissions, and to implement a transaction-based collaborative task management. The collaborative task management encapsulates a group of modification operations with inherent logical association into a transactional task package with independent life cycle state, and automatically analyzes the dependency relationship between the transactional task packages, to realize accurate locking of cross-role data verification, process approval and state.

[0194] The collaborative control engine is the "dispatching brain" of the entire system, which combines user permissions and rule verification library to implement cross-role transactional task management.

[0195] In actual application scenarios, the management of ship cables involves multiple departments:

[0196] The design department submits a path modification plan;

[0197] The production department prepares the material according to the modification plan;

[0198] The maintenance department proposes a maintenance task;

[0199] The management department audits and issues the final decision.

[0200] Under traditional circumstances, the operations of these departments are often scattered in different software and files, which are prone to conflicts. The collaborative control engine in this system uses a mechanism similar to the enterprise-level workflow engine CamundaBPM to encapsulate these operations as transactional task packages.

[0201] For example: when the design department proposes a cable model replacement application, the system will generate a "task package" with a status of "to be executed";

[0202] If the production department submits a material preparation application before the design is approved, the task package will be marked as "dependent waiting";

[0203] Only when the design modification task package enters the "submitted" state, can the production department's material preparation task continue to be executed.

[0204] This transactional management is similar to the transaction concept in databases, either all operations succeed together, or all are rolled back. This not only guarantees the logical order between tasks, but also avoids conflicts in cross-department collaboration.

[0205] In actual use, the collaborative control engine often needs to have high concurrent processing capability. For example, in a ship annual maintenance, the operation and maintenance department may submit cable inspection applications for dozens of cabins at the same time. The engine processes these requests in parallel through a task queue mechanism (such as a Kafka distributed message system) to ensure rapid response.

[0206] 4. An intelligent processing engine connected to the storage module and the data acquisition module, the intelligent processing engine being configured to:

[0207] performing data pre-verification on the environmental data collected by the sensor to identify and smooth abnormal values in the data;

[0208] based on the pre-verified environmental data, running an environmental interference factor model to assess the cable failure probability and using an artificial intelligence prediction model to predict the remaining service life of the cable;

[0209] by continuously comparing the prediction results of the artificial intelligence prediction model with the actual cable failure data collected subsequently, adaptively updating the weight coefficients or risk functions of the environmental interference factor model;

[0210] running a digital twin model to perform three-dimensional simulation on the cable path design to identify physical interference and recommend a revised path according to the rule verification benchmark library;

[0211] based on the historical length deviation data recorded during the production process, automatically correcting the design length parameters of the same type of cable in subsequent batches.

[0212] For example, taking a 50,000-ton bulk carrier as an example, the system's actual running process is as follows:

[0213] Design stage: The designer calls the storage module through the client to retrieve the environmental parameters of the target cabin (such as high temperature and strong vibration in the engine room), and the system automatically verifies whether the selected cable model meets the specifications. If not, the intelligent processing engine calculates an alternative path through digital twin simulation and recommends a more suitable cable model.

[0214] Production stage: The production department uploads the material release record, which is synchronized in real time by the data acquisition module. The system identifies that a certain type of cable has an average length remaining of 2% in the past five batches, and the intelligent processing engine corrects the subsequent design parameters accordingly.

[0215] Laying stage: When the ship enters the wharf for cable laying, the collaborative control engine locks the design parameters according to the transaction task management to prevent the operation and maintenance department from arbitrarily changing the configuration during the construction process.

[0216] Operation and maintenance link: During the ship's voyage at sea, the sensor continuously uploads salt spray, vibration and electromagnetic field data. After analysis by the intelligent processing engine, it is found that the environmental stress in a certain area is significantly higher than the average level, and the remaining life is predicted to be shortened to 16 months. The system immediately generates a maintenance task package and submits it to the management department for approval.

[0217] Management link: The management personnel check the prediction report and simulation results through the system interface on the shore side, and approve the early maintenance task. The entire closed loop is automatically tracked and archived by the system to ensure traceability in the future.

[0218] Example five comprehensively describes the implementation path of the cable intelligent management system based on rule verification and collaborative control from the system perspective. Through in-depth description of the storage module, data acquisition module, collaborative control engine and intelligent processing engine, and combined with actual products, it fully proves the landability and value of the system in engineering practice. Its advantage is that it can not only meet the real-time monitoring needs in complex environments, but also provide data-driven optimization basis for design and production, thereby greatly improving the intelligence and reliability level of cable management.

[0219] Example six:

[0220] Corresponding to the above embodiment, the application also provides an electronic device.

[0221] As Figure 3 shown is a structural schematic diagram of an electronic device in the application, the electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 can also include a transceiver 204. It should be noted that in actual application, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the application.

[0222] The processor 201 can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor 201 can also be a combination of computing functions, such as one or more microprocessor combinations, DSP and microprocessor combinations, etc.

[0223] The bus 202 can include a path for transmitting information between the above-mentioned components. The bus 202 can be a PCI bus or an EISA bus, etc. The bus 202 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0224] The memory 203 is configured to store a computer program corresponding to the rule-based verification and cooperative control method of the cable intelligent management method of the embodiments of the present application, and the computer program is controlled and executed by the processor 201. The processor 201 is configured to execute the computer program stored in the memory 203 to realize the content shown in the foregoing method embodiments.

[0225] The electronic device 200 includes, but is not limited to, a mobile terminal such as a notebook computer, a PAD (tablet computer), and the like, and a fixed terminal such as a desktop computer and the like. Figure 3 The electronic device 200 shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0226] 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 list of executable instructions for implementing logic functions, and can be specifically embodied 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 system including a processor or other system that can fetch and execute instructions from the instruction execution system, apparatus or device. For the present specification, the "computer readable medium" can be any device that can contain, store, industrial asset management, propagate or transmit programs for use by or in conjunction with the instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or device. More specific examples (non-exhaustive list) of computer readable medium include the following: electrical connections having one or more wires (electronic devices), portable computer disk boxes (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), optical fiber devices, and portable compact disc read-only memories (CD ROMs). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optical scanning of the paper or other medium, followed by editing, interpretation or necessary processing, if necessary, in other suitable ways, and then stored in the computer memory.

[0227] It should be understood that various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0228] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0229] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise explicitly specified.

[0230] Although the embodiments of the present application have been shown and described above, it should be understood that the above-described embodiments are exemplary and cannot be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

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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