Operation and maintenance auxiliary decision-making method and system for ship equipment

Abstract

The invention relates to the technical field of ship equipment operation and maintenance, and discloses an auxiliary decision-making method and system for operation and maintenance of ship equipment, and the method comprises the steps: collecting the historical state data of the ship equipment; preprocessing the historical state data to obtain preprocessed historical state data; based on the preprocessed historical state data, performing fault diagnosis by using a pre-trained fault diagnosis model, outputting a fault type diagnosis result, and generating a diagnosis report; making and executing a maintenance plan according to the diagnosis report, and recording maintenance data of the maintenance process; and iteratively updating the fault diagnosis model according to the maintenance data to obtain an optimal fault diagnosis model, and re-diagnosing the fault by using the optimal fault diagnosis model to improve the operation and maintenance auxiliary decision of the ship equipment. And iteratively updating the fault diagnosis model to ensure that the model can adapt to the change of the equipment state. By updating the fault diagnosis model, the prediction accuracy can be improved, so that the operation and maintenance decision is better supported, and the downtime of the equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ship equipment operation and maintenance, and in particular to an auxiliary decision-making method and system for operation and maintenance of ship equipment. Background Art

[0002] With the development of the shipping industry and the advancement of technology, the complexity and automation of ship equipment are constantly improving, which puts higher requirements on the operation and maintenance of equipment. In order to improve the efficiency and accuracy of equipment operation and maintenance, more and more research has begun to focus on how to use machine learning technology for fault prediction and auxiliary decision-making. Existing methods for predicting equipment failures for operation and maintenance based on machine learning models are mainly based on machine learning models of historical data: this type of method uses historical state data to train machine learning models (such as support vector machines, random forests, neural networks, etc.), and predicts future failures by learning the characteristics of equipment in normal and faulty states. Although this method has improved the accuracy of diagnosis to a certain extent, the following problems still exist:

[0003] Due to the limitations of training data, the model may not be able to generalize well to unseen fault scenarios, resulting in reduced prediction accuracy. Especially in the field of ship equipment operation and maintenance, factors such as equipment aging, wear, and environmental changes can cause unforeseen changes in equipment status, which may not appear in the training data, thereby reducing the generalization ability of the model;

[0004] When the prediction accuracy of the model decreases, it may lead to incorrect diagnosis results, which will directly affect the formulation of maintenance plans. Incorrect diagnosis may lead to unnecessary maintenance activities, wasting time and resources; or miss the real fault, causing the equipment failure to worsen or even cause an accident, which will have a certain impact on operation and maintenance decisions. Over time, the status of the equipment will change, and the original training data may no longer be applicable to the current situation. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an operation and maintenance auxiliary decision-making method and system for ship equipment to achieve iterative updating of fault diagnosis models and improve operation and maintenance auxiliary decision-making of ship equipment.

[0006] The present invention provides an auxiliary decision-making method for operation and maintenance of ship equipment, comprising:

[0007] Step S1, collecting historical status data of ship equipment;

[0008] Step S2, preprocessing the historical state data to obtain preprocessed historical state data;

[0009] Step S3, based on the pre-processed historical state data, using the pre-trained fault diagnosis model to perform fault diagnosis, output the fault type diagnosis result, and generate a diagnosis report;

[0010] Step S4, formulate and execute a maintenance plan based on the diagnostic report, and record maintenance data of the maintenance process;

[0011] Step S5, iteratively updating the fault diagnosis model according to the maintenance data to obtain the optimal fault diagnosis model, and re-diagnosing the fault using the optimal fault diagnosis model to improve the auxiliary decision-making of operation and maintenance of ship equipment.

[0012] Furthermore, the training set of the pre-trained fault diagnosis model is: historical status data with fault type labels.

[0013] Further, the fault diagnosis model is iteratively updated according to the maintenance data to obtain the optimal fault diagnosis model, which specifically includes the following steps:

[0014] Step S51, obtaining the fault type output accuracy according to the fault type diagnosis result;

[0015] Step S52, obtaining the correlation between the maintenance plan and the fault type label according to the fault type diagnosis result and the maintenance plan;

[0016] Step S53, obtaining the contribution of the maintenance data to the performance of the fault diagnosis model, and arranging the contribution of each maintenance data from high to low, and selecting the first L maintenance data as effective maintenance data;

[0017] Step S54, defining weight coefficients for the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the maintenance validity data;

[0018] Step S55, calculating the comprehensive score of the fault diagnosis model performance according to the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the effective maintenance data;

[0019] Step S56, adding the effective maintenance data to the training set to repeatedly train the fault diagnosis model, and adjusting the weight coefficient of the fault diagnosis model to obtain the optimal fault diagnosis model.

[0020] Furthermore, the comprehensive score is:

[0021]

[0022] In the formula, represents the comprehensive score of the fault diagnosis model performance at the tth time point, represents the output accuracy of the fault type at the tth time point, , , Represent the weight coefficients of the fault diagnosis model performance respectively. Specifically, The weight coefficient representing the accuracy of the fault type output, represents the correlation between the maintenance plan and the fault type label at the tth time point, The weight coefficient representing the correlation between the maintenance plan and the fault type label, Represents the contribution of effective maintenance data at the tth time point, The weight coefficient representing the contribution of maintenance effective data.

[0023] Further, the weight coefficient is adjusted, including the following steps:

[0024] Step S561, obtaining a change in the output accuracy of the fault type according to the output accuracy of the fault type after each execution of the maintenance plan and the output accuracy of the fault type after the last execution of the maintenance plan;

[0025] Step S562, obtaining a change in the correlation between the maintenance plan and the fault type label according to the correlation between the maintenance plan and the fault type label after each execution of the maintenance plan and the correlation between the maintenance plan and the fault type label after the last execution of the maintenance plan;

[0026] Step S563, obtaining a contribution degree change amount of the maintenance effective data according to the maintenance effective data obtained after each execution of the maintenance plan and the maintenance effective data obtained after the last execution of the maintenance plan;

[0027] Step S564, calculating an updated value of the weight coefficient according to the output accuracy change, the correlation change, the contribution change, and the comprehensive score;

[0028] Step S565, adjusting the weight coefficient of the fault diagnosis model according to the weight coefficient update value.

[0029] Further, the weight coefficient of the fault diagnosis model is adjusted according to the weight coefficient update value, including:

[0030] Define the update step size of the fault diagnosis model;

[0031] According to the weight coefficient, the weight coefficient update value, and the update step size, the weight coefficient of the fault diagnosis model is adjusted to obtain the optimized weight coefficient;

[0032] The optimal fault diagnosis model is obtained according to the optimized weight coefficient.

[0033] The present invention also provides an operation and maintenance auxiliary decision system for ship equipment, which is used to execute the above-mentioned operation and maintenance auxiliary decision method for ship equipment, and includes the following modules:

[0034] Historical status data collection module: used to collect historical status data of ship equipment;

[0035] Preprocessing module: connected with the historical state data acquisition module, used for preprocessing the historical state data to obtain preprocessed historical state data;

[0036] Diagnostic report acquisition module: connected to the preprocessing module, used to perform fault diagnosis based on preprocessed historical status data using a pretrained fault diagnosis model, output fault type diagnosis results, and generate a diagnostic report;

[0037] Maintenance plan execution module: connected with the diagnosis report acquisition module, used to formulate and execute maintenance plans according to the diagnosis report, and record maintenance data of the maintenance process;

[0038] Optimal fault diagnosis model: connected to the maintenance plan execution module, used to iteratively update the fault diagnosis model according to the maintenance data, obtain the optimal fault diagnosis model, and re-diagnose the fault using the optimal fault diagnosis model.

[0039] The embodiments of the present invention have the following technical effects:

[0040] 1. The present invention uses maintenance effective data to iteratively update the fault diagnosis model to ensure that the model can adapt to changes in equipment status. By updating the fault diagnosis model, the prediction accuracy can be improved, thereby better supporting operation and maintenance decisions, reducing equipment downtime, and improving equipment availability and safety.

[0041] 2. The present invention calculates the comprehensive score of the fault diagnosis model performance according to the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the effective maintenance data, and iteratively updates the model weight coefficient according to the comprehensive score to obtain the optimal fault diagnosis model, so that the model can continuously learn new maintenance data and adapt to changes in equipment status, thereby improving the generalization ability of the model and enabling it to maintain a high level of diagnostic accuracy in the face of unprecedented fault scenarios, take preventive maintenance measures in a timely manner, reduce the probability of equipment failure, extend the service life of the equipment, and improve the availability and safety of the equipment.

[0042] 3. The fault diagnosis model provided by the present invention can provide operation and maintenance personnel with more accurate and reliable fault diagnosis results, and provide strong data support for the formulation of maintenance plans. By continuously optimizing the performance of the model, the present invention enhances the intelligence of decision support, enabling operation and maintenance personnel to make decisions more confidently. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of an auxiliary decision-making method for operation and maintenance of ship equipment provided by an embodiment of the present invention;

[0045] Figure 2 It is a structural diagram of an operation and maintenance decision-making support method for ship equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0047] Figure 1 1 is a flow chart of a method for auxiliary decision-making for operation and maintenance of ship equipment provided by an embodiment of the present invention. Figure 1 , specifically including:

[0048] Step S1, collecting historical status data of ship equipment.

[0049] Historical status data includes but is not limited to: equipment operating parameters: temperature, pressure, vibration frequency, etc.; operation log: equipment start and stop time, operator operation records, etc.; alarm record: all warnings and error codes issued by the equipment; maintenance record: historical maintenance activity records, including information on replacement parts, maintenance time, etc.; environmental data: seawater temperature, wind speed and other external conditions that affect equipment operation.

[0050] Step S2, preprocessing the historical state data to obtain preprocessed historical state data.

[0051] Preprocessing includes: data cleaning: removing invalid or missing data points; outlier detection: identifying and processing abnormal data points; data standardization / normalization: converting data in different ranges to the same range for easy model processing.

[0052] Step S3, based on the pre-processed historical state data, using the pre-trained fault diagnosis model to perform fault diagnosis, output the fault type diagnosis result, and generate a diagnosis report;

[0053] The pre-trained fault diagnosis model can be a random forest model, a support vector machine model, a neural network model, etc. The training set of the pre-trained fault diagnosis model is: historical state data with fault type labels. The selected model is trained using the training set, and the hyperparameters are adjusted through cross-validation to optimize the model performance to obtain the pre-trained fault diagnosis model.

[0054] Acquire status data from ship equipment in real time, input it into the pre-trained fault diagnosis model, and output a diagnosis report, which includes: fault type, diagnosis results, maintenance suggestions, etc.

[0055] Step S4, formulate and execute a maintenance plan based on the diagnosis report, and record maintenance data of the maintenance process.

[0056] Develop a maintenance plan based on the maintenance recommendations in the diagnostic report and perform actual maintenance work according to the maintenance plan.

[0057] Maintenance data includes: Maintenance records: maintenance date (used to analyze the frequency and pattern of failures), maintenance personnel, maintenance time (reflecting the severity of the failure and the complexity of the repair); Parts replacement records: Part name / model (used to analyze the failure rate and reliability of specific parts), supplier information (used to evaluate the supplier's quality and service level), part status (used to understand the degree of wear and service life of parts); Working hour records: technicians' working hours (reflecting the technicians' skill level and work efficiency), total working hours for the entire maintenance process (used to evaluate the efficiency and cost of the maintenance process).

[0058] Step S5, iteratively updating the fault diagnosis model according to the maintenance data to obtain the optimal fault diagnosis model, and re-diagnosing the fault using the optimal fault diagnosis model to improve the auxiliary decision-making of operation and maintenance of ship equipment.

[0059] The fault diagnosis model is iteratively updated according to the maintenance data to obtain the optimal fault diagnosis model, which specifically includes the following steps:

[0060] Step S51, obtaining the fault type output accuracy according to the fault type diagnosis result.

[0061] The prediction results of the pre-trained fault diagnosis model for the fault types of ship equipment are collected, and the prediction results are compared with the fault type labels that have occurred historically. The number of correctly predicted fault types is counted and divided by the total number of predictions to obtain the fault type output accuracy.

[0062] Step S52: Obtain the correlation between the maintenance plan and the fault type label according to the fault type diagnosis result and the maintenance plan.

[0063] Through the pre-trained fault diagnosis model, the maintenance effect of the ship equipment after maintenance is tracked. Specifically, the real-time status data is used to predict the status of the ship equipment again to determine whether the maintenance plan execution results are successful. The correlation is calculated based on the execution results of the maintenance plan and its fault type label. If the prediction accuracy of the re-prediction is improved compared with the prediction accuracy before maintenance, it is considered that the correlation between the execution results of the maintenance plan and its fault type label is strongly correlated, indicating that the maintenance plan is successfully executed; if the prediction result of the re-prediction is not improved but reduced compared with the prediction accuracy before maintenance, it is considered that the correlation between the execution results of the maintenance plan and its fault type label is weakly correlated, indicating that the maintenance plan has failed.

[0064] Step S53, obtaining the contribution of the maintenance data to the performance of the fault diagnosis model, and arranging the contribution of each maintenance data from high to low, and selecting the first L maintenance data as effective maintenance data;

[0065] The contribution degree of the maintenance data to the performance of the fault diagnosis model is obtained. Specifically, a contribution degree index is defined to evaluate the influence of the maintenance data on the performance of the pre-trained fault diagnosis model. In this embodiment, the contribution degree index is preferably the change in the output accuracy of the fault type before and after the maintenance. For each maintenance data, the contribution degree of the contribution degree index to the performance of the pre-trained fault diagnosis model is calculated. Then, the contribution degree of each maintenance data is arranged from high to low, and the first L maintenance data are selected as the effective maintenance data.

[0066] The contribution degree of each maintenance data is the change in the accuracy of the fault type output before and after the maintenance. The larger the change in the accuracy of the fault type output before and after the maintenance, the greater the maintenance data has in improving the model performance after the maintenance, so it is determined as the effective maintenance data.

[0067] Step S54, defining weight coefficients for the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the maintenance validity data.

[0068] Determine the weight coefficient based on business needs, experimental results, expert opinions, etc.

[0069] Step S55, calculating the comprehensive score of the fault diagnosis model performance according to the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the effective maintenance data;

[0070] The composite score is:

[0071]

[0072] In the formula, represents the comprehensive score of the fault diagnosis model performance at the tth time point, represents the output accuracy of the fault type at the tth time point, The weight coefficient representing the performance of the fault diagnosis model, represents the correlation between the maintenance plan and the fault type label at the tth time point, The weight coefficient representing the correlation between the maintenance plan and the fault type label, Represents the contribution of effective maintenance data at the tth time point, The weight coefficient representing the contribution of maintenance effective data.

[0073] Step S56, adding the effective maintenance data to the training set to repeatedly train the fault diagnosis model, and adjusting the weight coefficient to obtain the optimal fault diagnosis model.

[0074] Adjusting the weight coefficient includes the following steps:

[0075] Step S561, obtaining a change in the output accuracy of the fault type according to the output accuracy of the fault type after each execution of the maintenance plan and the output accuracy of the fault type after the last execution of the maintenance plan.

[0076] The change in output accuracy of the fault type is:

[0077]

[0078] in, represents the change in output accuracy of the fault type, n represents the maximum number of repairs, represents the output accuracy of the fault type of the fault diagnosis model at the tth time point after the i-th maintenance, Represents the fault type output accuracy of the fault diagnosis model at time point t after the i-1th (last) maintenance.

[0079] Step S562, obtaining a change in the correlation between the maintenance plan and the fault type label according to the correlation between the maintenance plan and the fault type label after each execution of the maintenance plan and the correlation between the maintenance plan and the fault type label after the last execution of the maintenance plan.

[0080] Change in the correlation between maintenance plan and fault type label:

[0081]

[0082] in, Represents the change in the correlation between the maintenance plan and the fault type label, represents the correlation between the maintenance plan and the fault type label of the fault diagnosis model after the i-th maintenance at the t-th time point, Represents the correlation between the maintenance plan and the fault type label at the tth time point in the fault diagnosis model after the i-1th (last) maintenance.

[0083] Step S563, obtaining the contribution degree change of the maintenance effective data according to the maintenance effective data obtained after each execution of the maintenance plan and the maintenance effective data obtained after the last execution of the maintenance plan.

[0084] Change in contribution of maintenance effective data:

[0085]

[0086] in, Represents the contribution degree change of maintenance effective data, represents the change in contribution of the fault diagnosis model to the effective maintenance data at time point t after the i-th maintenance, Represents the change in the contribution of the fault diagnosis model to the effective maintenance data at the tth time point after the i-1th (last) maintenance.

[0087] Step S564, calculate the updated value of the weight coefficient according to the change in output accuracy, the change in relevance, the change in contribution degree, and the comprehensive score.

[0088] First, the updated value of the weight coefficient is calculated using the output accuracy change, relevance change, contribution change, and comprehensive score:

[0089]

[0090]

[0091]

[0092] In the formula, An updated value of the weight coefficient representing the accuracy of the fault type output; The updated value of the weight coefficient representing the correlation between the maintenance plan and the fault type label, Represents the updated value of the weight coefficient of maintenance effective data.

[0093] Step S565, adjusting the weight coefficient of the fault diagnosis model according to the weight coefficient update value.

[0094] The weight coefficient of the fault diagnosis model is adjusted according to the updated value of the weight coefficient, including:

[0095] Define the update step size of the fault diagnosis model;

[0096] According to the weight coefficient, the weight coefficient update value, and the update step size, the weight coefficient of the fault diagnosis model is adjusted to obtain the optimized weight coefficient;

[0097]

[0098]

[0099]

[0100] In the formula, The optimized weight coefficient representing the accuracy of the fault type output, The optimization weight coefficient representing the correlation between the maintenance plan and the fault type label, The optimization weight coefficient representing the contribution of effective maintenance data, Represents the update step size of the fault diagnosis model.

[0101] The optimal fault diagnosis model is obtained according to the optimized weight coefficient.

[0102] The optimized weight coefficients are applied to the fault diagnosis model, that is, using , , Replace the corresponding weight coefficients in the fault diagnosis model. Retrain the fault diagnosis model using the optimized weight coefficients to ensure that the fault diagnosis model can adapt to the new weights. After multiple iterations and optimizations, until the model performance no longer improves significantly, it means that the model has achieved the optimal performance, which is the optimal fault diagnosis model.

[0103] The optimal fault diagnosis model is used to re-diagnose the ship equipment to improve the accuracy of diagnosis.

[0104] The optimal fault diagnosis model can more accurately identify the fault type of ship equipment and obtain more accurate diagnostic results, thereby providing more reliable information for maintenance plans, formulating more targeted maintenance plans, and reducing unnecessary maintenance activities. By identifying potential faults at an early stage, the downtime of equipment due to faults can be reduced, thereby improving auxiliary decision-making.

[0105] Figure 2 is a structural diagram of an operation and maintenance auxiliary decision-making method for ship equipment provided by an embodiment of the present invention, referring to Figure 2 This embodiment also provides an operation and maintenance auxiliary decision system for ship equipment, which is used to execute the above-mentioned operation and maintenance auxiliary decision method for ship equipment, and includes the following modules:

[0106] Historical status data collection module: used to collect historical status data of ship equipment;

[0107] Preprocessing module: connected with the historical state data acquisition module, used for preprocessing the historical state data to obtain preprocessed historical state data;

[0108] Diagnostic report acquisition module: connected to the preprocessing module, used to perform fault diagnosis based on preprocessed historical status data using a pretrained fault diagnosis model, output fault type diagnosis results, and generate a diagnostic report;

[0109] Maintenance plan execution module: connected with the diagnosis report acquisition module, used to formulate and execute maintenance plans according to the diagnosis report, and record maintenance data of the maintenance process;

[0110] Optimal fault diagnosis model: connected to the maintenance plan execution module, used to iteratively update the fault diagnosis model according to the maintenance data, obtain the optimal fault diagnosis model, and re-diagnose the fault using the optimal fault diagnosis model.

[0111] This embodiment also provides an electronic device, which includes one or more processors and a memory.

[0112] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0113] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement the operation and maintenance auxiliary decision-making method of a ship equipment of any embodiment of the present application described above and / or other desired functions. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0114] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including early warning information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0115] In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0116] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of a method for auxiliary decision-making for operation and maintenance of ship equipment provided in any embodiment of the present application.

[0117] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0118] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of a method for auxiliary decision-making for operation and maintenance of ship equipment provided in any embodiment of the present application.

[0119] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0120] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0121] It should also be noted that the orientations or positional relationships indicated by the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for auxiliary decision making for operation and maintenance of ship equipment, characterized in that: include: Step S1, collecting historical status data of ship equipment; Step S2, preprocessing the historical state data to obtain preprocessed historical state data; Step S3, based on the pre-processed historical state data, using the pre-trained fault diagnosis model to perform fault diagnosis, output the fault type diagnosis result, and generate a diagnosis report; Step S4, formulate and execute a maintenance plan based on the diagnostic report, and record maintenance data of the maintenance process; Step S5, iteratively updating the fault diagnosis model according to the maintenance data to obtain the optimal fault diagnosis model, and re-diagnosing the fault using the optimal fault diagnosis model to improve the auxiliary decision-making of operation and maintenance of ship equipment.

2. The operation and maintenance auxiliary decision-making method of ship equipment according to claim 1 is characterized in that: The training set of the pre-trained fault diagnosis model is: historical status data with fault type labels.

3. The operation and maintenance auxiliary decision-making method of ship equipment according to claim 1 is characterized in that: The fault diagnosis model is iteratively updated according to the maintenance data to obtain the optimal fault diagnosis model, which specifically includes the following steps: Step S51, obtaining the fault type output accuracy according to the fault type diagnosis result; Step S52, obtaining the correlation between the maintenance plan and the fault type label according to the fault type diagnosis result and the maintenance plan; Step S53, obtaining the contribution of the maintenance data to the performance of the fault diagnosis model, and arranging the contribution of each maintenance data from high to low, and selecting the first L maintenance data as effective maintenance data; Step S54, defining weight coefficients for the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the maintenance validity data; Step S55, calculating the comprehensive score of the fault diagnosis model performance according to the output accuracy of the fault type, the correlation between the maintenance plan and the fault type label, and the effective maintenance data; Step S56, adding the effective maintenance data to the training set to repeatedly train the fault diagnosis model, and adjusting the weight coefficient of the fault diagnosis model to obtain the optimal fault diagnosis model.

4. The operation and maintenance auxiliary decision-making method for ship equipment according to claim 3 is characterized in that: The composite score is: ； In the formula, represents the comprehensive score of the fault diagnosis model performance at the tth time point, represents the output accuracy of the fault type at the tth time point, , , Represent the weight coefficients of the fault diagnosis model performance respectively. Specifically, The weight coefficient representing the accuracy of the fault type output, represents the correlation between the maintenance plan and the fault type label at the tth time point, The weight coefficient representing the correlation between the maintenance plan and the fault type label, Represents the contribution of effective maintenance data at the tth time point, The weight coefficient representing the contribution of maintenance effective data.

5. The operation and maintenance auxiliary decision-making method for ship equipment according to claim 3 is characterized in that: Adjusting the weight coefficient includes the following steps: Step S561, obtaining a change in the output accuracy of the fault type according to the output accuracy of the fault type after each execution of the maintenance plan and the output accuracy of the fault type after the last execution of the maintenance plan; Step S562, obtaining a change in the correlation between the maintenance plan and the fault type label according to the correlation between the maintenance plan and the fault type label after each execution of the maintenance plan and the correlation between the maintenance plan and the fault type label after the last execution of the maintenance plan; Step S563, obtaining a contribution degree change amount of the maintenance effective data according to the maintenance effective data obtained after each execution of the maintenance plan and the maintenance effective data obtained after the last execution of the maintenance plan; Step S564, calculating an updated value of the weight coefficient according to the output accuracy change, the correlation change, the contribution change, and the comprehensive score; Step S565, adjusting the weight coefficient of the fault diagnosis model according to the weight coefficient update value.

6. The operation and maintenance auxiliary decision-making method for ship equipment according to claim 5 is characterized in that: The weight coefficient of the fault diagnosis model is adjusted according to the updated value of the weight coefficient, including: Define the update step size of the fault diagnosis model; According to the weight coefficient, the weight coefficient update value, and the update step size, the weight coefficient of the fault diagnosis model is adjusted to obtain the optimized weight coefficient; The optimal fault diagnosis model is obtained according to the optimized weight coefficient.

7. An operation and maintenance auxiliary decision system for ship equipment, used to execute an operation and maintenance auxiliary decision method for ship equipment according to any one of claims 1 to 6, characterized in that: Includes the following modules: Historical status data collection module: used to collect historical status data of ship equipment; Preprocessing module: connected with the historical state data acquisition module, used for preprocessing the historical state data to obtain preprocessed historical state data; Diagnostic report acquisition module: connected to the preprocessing module, used to perform fault diagnosis based on preprocessed historical status data using a pretrained fault diagnosis model, output fault type diagnosis results, and generate a diagnostic report; Maintenance plan execution module: connected with the diagnosis report acquisition module, used to formulate and execute maintenance plans according to the diagnosis report, and record maintenance data of the maintenance process; Optimal fault diagnosis model: connected to the maintenance plan execution module, used to iteratively update the fault diagnosis model according to the maintenance data, obtain the optimal fault diagnosis model, and re-diagnose the fault using the optimal fault diagnosis model.

Images