Ship maintenance planning system based on state evaluation

Abstract

The invention relates to the technical field of planning management, in particular to a ship maintenance planning system based on state evaluation, which comprises a ship maintenance demand evaluation module, a maintenance time planning module, a maintenance priority analysis module and a maintenance period adjustment module. By analyzing the ship operation data and the state data of the parts and calculating the health indexes of the parts, the maintenance work can be reasonably arranged, the non-planned shutdown time is reduced, the future idle probability of the ship is predicted through historical data analysis, the maintenance time can be reasonably arranged, the maintenance work and the operation plan of the ship are coordinated and consistent, and the maintenance efficiency is improved. By analyzing the service life and the use scene of the component, the priority ranking of the maintenance work is realized, the key component is ensured to be maintained in time, the operation safety of the ship is enhanced, and by quantitatively evaluating the maintenance effect and adjusting the maintenance period, the maintenance strategy is enabled to better meet the actual operation requirement, and the maintenance efficiency is improved. And the economic efficiency and effect of maintenance are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of planning management, and particularly relates to a ship maintenance planning system based on state evaluation. BACKGROUND

[0002] The technical field of planning management focuses on systematically arranging, organizing and optimizing resources and processes using various tools and methods, aiming to optimize the allocation and utilization of resources to achieve specific business or engineering goals. This field includes production scheduling, equipment maintenance, project management, and resource optimization. In ship maintenance planning, this technical field emphasizes effective planning and management of maintenance activities to ensure the continuity and safety of ship operations. By using data analysis and comprehensive evaluation tools, planning management can not only predict maintenance needs, but also optimize the scheduling and resource allocation of maintenance work, thereby reducing costs and improving efficiency.

[0003] Among them, the ship maintenance planning system is a tool specially designed for ship maintenance and maintenance process, which is used to ensure the operational efficiency and safety of the ship. The main purpose of the system is to ensure the operational efficiency and safety of the ship, reduce unplanned downtime by predicting maintenance needs and reasonably scheduling maintenance work. This system usually includes fault diagnosis, maintenance task priority setting, spare parts management and maintenance personnel scheduling functions. By integrating historical maintenance data and real-time performance monitoring, the ship maintenance planning system can provide data-driven decision support to help ship managers develop more scientific and economic maintenance strategies, enabling more accurate maintenance cycle planning and reducing maintenance costs.

[0004] Traditional maintenance planning systems rely on experience and static maintenance plans, and fail to fully utilize ship operation data and real-time performance monitoring information. This results in maintenance plans that cannot flexibly respond to changes in actual ship operation, increasing the risk of unplanned downtime and affecting the operational efficiency and safety of the ship. For example, in traditional systems, maintenance cycles are fixed and do not take into account the actual wear and tear of components and the use environment, resulting in key components not being able to receive timely maintenance in emergency situations, increasing the probability of accidents. In addition, the lack of effective evaluation of maintenance priority and time planning leads to waste of resources, such as unnecessary maintenance activities consuming a large amount of resources, while more critical components are not able to be timely processed, affecting the long-term health and safety of the ship. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and a ship maintenance planning system based on state evaluation is proposed.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a ship maintenance planning system based on state evaluation, the system comprises: The ship maintenance demand evaluation module extracts state data of components needing maintenance on the ship based on operation data of the ship through AI, evaluates a health index of each component, and calculates an overall maintenance demand index of the ship according to the health index of each component, to obtain ship maintenance demand information; The maintenance time planning module extracts idle data of the ship in a historical period through AI based on the ship maintenance demand information and in combination with historical operation data of the ship, calculates an idle probability in a future time period, and selects a target maintenance time period according to a maintenance personnel scheduling period, to obtain a maintenance time selection result; The maintenance priority analysis module extracts usage life and theoretical life data of the components to be maintained according to maintenance and usage records of the ship through AI based on the maintenance time selection result, calculates a maintenance urgency index of each component in combination with a severity of a usage scenario, and calculates a maintenance priority of each component according to importance of the component in ship operation, to obtain maintenance priority information; The maintenance cycle adjustment module adjusts a maintenance cycle of the ship according to the maintenance priority information, quantitatively evaluates maintenance effect according to a change in the health index of the component before and after maintenance, and adjusts the maintenance cycle of the ship in combination with a target health state of the ship, to obtain maintenance cycle optimization information.

[0007] The present application improves the method for evaluating the health index of each component, which comprises the following steps: Based on operation data of the ship, state data of components needing maintenance on the ship is extracted through AI, real-time performance data of each component is compared with preset standard health state data, and a performance decline index is calculated through a formula:

[0008] The performance decline index is calculated , wherein, is a real-time performance index, is a performance index of a standard health state, is a weight factor, is a performance decline index, is an index; Based on the performance decline index , a component health index is calculated through a formula:

[0009] The component health index is calculated , and a health state of the component is evaluated according to a size of the component health index , wherein, and are adjustment coefficients, is a component health index, is a performance decline index, is an index. It is the base of the natural logarithm.

[0010] The present invention is improved in that the step of obtaining the ship maintenance demand information is as follows: Based on the component health index Based on the importance of each component in the overall operation of the ship, a weighting factor is assigned to each component. ; Based on the component health index and weighting factors Through the formula:

[0011] Calculate the overall maintenance demand index of ships The overall maintenance demand index of ships The system compares the data with a preset threshold to determine if the vessel requires maintenance, thus obtaining vessel maintenance requirement information. It is the overall maintenance demand index for ships. It is the total number of parts. It is the first Health index of each component It is a weighting factor. It is a constant. It is a sensitivity adjustment parameter. It is the base of the natural logarithm.

[0012] The present invention is improved in that the step of obtaining the maintenance time selection result is as follows: Based on the ship's historical operational data, AI is used to extract the ship's idle data during historical periods, including the ship's navigation, berthing and maintenance records in the past period; Based on historical idle data, using the formula:

[0013] Calculate the probability of a ship being idle in the future time period Based on the probability of ships being idle in the future time period The value, combined with the maintenance personnel's shift schedule, is used to select the target maintenance time period, resulting in the maintenance time selection result. It is the probability that a ship will be idle in the future. It is an activity frequency parameter. Indicates the time unit index. It refers to the length of a future time period.

[0014] The present invention is improved in that the method for calculating the maintenance urgency index of each component is as follows: Based on the ship's maintenance and usage records, AI is used to extract the service life of the parts to be repaired. and theoretical lifespan ; Based on the aforementioned years of use and theoretical lifespan Combined with the severity of the usage scenarios of the components Through the formula:

[0015] Calculate the maintenance emergency index ,in, and It is the adjustment coefficient. The actual service life of the component. This is the theoretical lifespan of the component. Due to the severity of the usage scenario, The emergency repair index for components. It is an index.

[0016] The present invention is improved in that the step of obtaining the maintenance priority information is as follows: Based on the aforementioned maintenance urgency index Based on the function of each component and its impact on the safety of ship operation, the importance of each component in ship operation is assessed, and the importance level of each component is obtained. ; Based on the importance level of each component Through the formula:

[0017] Calculate the repair priority for each component. Based on the maintenance priority of the components The values ​​are used to sort the components by repair priority, thus obtaining repair priority information. It is the first Emergency repair index of each component It is the first The importance level of each component in ship operation It is a stability constant. It is the first Repair priority of each component.

[0018] The present invention is improved in that the method for quantitatively evaluating the maintenance effect is as follows: Based on the aforementioned maintenance priority information, ship maintenance is carried out, and the health index of components before and after maintenance is collected using the formula:

[0019] Calculate the change in health index of each component before and after repair. ,in, The change in the health index. The health index before maintenance. The health index after repair; Based on the change in the health index Through the formula:

[0020] Quantitative values ​​for calculating repair effectiveness ,in, The change in the health index. and For adjustment coefficients, As a quantitative evaluation value for the repair effect, It is the base of the natural logarithm.

[0021] The present invention is improved in that the step of obtaining the maintenance cycle optimization information is as follows: The quantitative value of the repair effect Based on the health index of the repaired parts Through the formula:

[0022] Calculate the average health index of key components. ,in, It is the number of key components. The health index of the repaired parts. This represents the average health index of key components; Based on the average health index of the key components Through the formula:

[0023] Calculate the adjusted maintenance cycle This yields maintenance cycle optimization information, among which... It is the target state of health. This is an adjustment factor used to balance the impact of differences between repair effectiveness and actual health index. This represents the average health index of key components. This is the average quantitative value of the component repair effect. For the current maintenance cycle, This is the adjusted maintenance cycle.

[0024] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by analyzing ship operation data and component status data, the health index of components is calculated, enabling the rational scheduling of maintenance work. This ensures that the ship receives maintenance at the most suitable time, reducing unplanned downtime. Furthermore, by analyzing historical data, the future idle probability of the ship can be predicted, allowing for the rational scheduling of maintenance time and ensuring that maintenance work is consistent with the ship's operational plan. By analyzing the service life and usage scenarios of components, the priority of maintenance work is achieved, ensuring that critical components receive timely maintenance and enhancing the ship's operational safety. Through quantitative evaluation of maintenance effectiveness, maintenance cycles are adjusted, making maintenance strategies more aligned with actual operational needs and improving the economic efficiency and effectiveness of maintenance. Attached Figure Description

[0025] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the health index assessment of each component in this invention; Figure 3 This is a flowchart illustrating the process of obtaining ship maintenance demand information according to the present invention; Figure 4 This is a flowchart illustrating the process of obtaining maintenance time selection results according to the present invention; Figure 5 This is a flowchart illustrating the calculation of the maintenance urgency index for each component in this invention; Figure 6 This is a flowchart illustrating how the present invention obtains maintenance priority information; Figure 7 This is a flowchart for the quantitative evaluation of repair effectiveness in this invention; Figure 8 This is a flowchart illustrating the process of obtaining maintenance cycle optimization information for this invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0027] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Example

[0028] Please see Figure 1 This invention provides a technical solution: a ship maintenance planning system based on condition assessment, the system comprising: The ship maintenance demand assessment module uses AI to extract the status data of the parts that need maintenance based on the ship's operational data. It compares the deviation value of the current status of each part with the standard health status, assesses the health index of each part, and calculates the overall maintenance demand index of the ship based on the health index of each part to assess the ship's maintenance needs and obtain ship maintenance demand information. The maintenance time planning module is based on ship maintenance demand information and combined with the ship's historical operation data. It uses AI to extract the ship's idle data in historical time periods, calculates the idle probability in future time periods, and selects the target maintenance time period according to the maintenance personnel's shift schedule to obtain the maintenance time selection result. The maintenance priority analysis module, based on the maintenance time selection results and the ship's maintenance and usage records, uses AI to extract the service life and theoretical lifespan data of the parts to be maintained, and combines this with the severity of the usage scenario to calculate the maintenance urgency index of each part. Based on the importance of each part in the ship's operation, it calculates the maintenance priority of each part, sorts the priorities, and obtains maintenance priority information. The maintenance cycle adjustment module performs ship maintenance based on maintenance priority information, and quantitatively evaluates the maintenance effect based on the changes in the health index of components before and after maintenance. Combined with the ship's target health status, it adjusts the ship's maintenance cycle to obtain maintenance cycle optimization information.

[0029] Ship maintenance needs information includes component health ratings and performance degradation rates; maintenance time selection results include selected maintenance dates, estimated maintenance duration, and maintenance shift schedules; maintenance priority information includes component urgency scores, importance levels, and expected recovery benefits; and maintenance cycle optimization information includes adjusted maintenance frequencies, preventative maintenance recommendations, and component replacement cycles.

[0030] Please see Figure 2 The method for assessing the health index of each component is as follows: Based on the ship's operational data, AI is used to extract the status data of components that need maintenance on the ship, and the real-time performance data of each component is compared with preset standard health status data through formulas;

[0031] Computational performance degradation index ,in, It is a real-time performance metric. It is a performance indicator of standard health status. It is a weighting factor. It is a performance degradation index. It is an index; Based on performance degradation index Through the formula:

[0032] Calculate the component health index According to the component health index The size of the component is used to assess its health status. and To adjust the coefficient, For the component health index, It is a performance degradation index. It is an index. It is the base of the natural logarithm.

[0033] formula;

[0034]

[0035] Parameter details and acquisition methods: This refers to real-time performance indicators obtained from components, such as temperature, pressure, and rotational speed. This data is typically acquired in real-time from ship components via sensors.

[0036] Performance indicators under healthy conditions are determined based on the manufacturer's specifications or historical performance data.

[0037] Weighting factors reflect the importance of different performance indicators in the overall health assessment. Parameters can be determined through historical data analysis or set by experts based on experience.

[0038] and The coefficients are adjusted based on historical data fitting or preset thresholds to ensure the formula adapts to the needs of different equipment and operating conditions. The coefficients are obtained through data regression analysis to ensure the model's sensitivity and accuracy in responding to health conditions.

[0039] Calculation example: Suppose there is a ship component with three performance indicators: temperature ( ),pressure( ) and rotational speed ( Real-time data is The health status data is The weighting factors are respectively .calculate :

[0040]

[0041]

[0042] Assumption and ,calculate :

[0043]

[0044]

[0045] Calculation results This indicates that the component's health index is 0.56, which can help the ship maintenance team identify components that may need maintenance or replacement in a timely manner.

[0046] Please see Figure 3 The steps for obtaining ship repair needs information are as follows: Based on component health index Based on the importance of each component in the overall operation of the ship, a weighting factor is assigned to each component. ; Based on component health index and weighting factors Through the formula:

[0047] Calculate the overall maintenance demand index of ships The overall maintenance demand index of ships The system compares the data with a preset threshold to determine if the vessel requires maintenance, thus obtaining vessel maintenance requirement information. It is the overall maintenance demand index for ships. It is the total number of parts. It is the first Health index of each component It is a weighting factor. It is a constant. It is a sensitivity adjustment parameter. It is the base of the natural logarithm.

[0048] formula:

[0049] Parameter details and acquisition methods: It is the component health index, calculated through previous steps.

[0050] Weighting factors: These factors are based on the importance of components and their impact on the overall safety and efficiency of the ship, and are derived through expert evaluation or analysis of historical failure data.

[0051] Constant: Set to a small positive value to ensure that the denominator is not zero, thereby avoiding undefined or infinitely large values ​​in the calculation.

[0052] Sensitivity adjustment parameters: determined by analyzing past maintenance data and component failure modes, used to adjust the impact of the health index on the maintenance demand index.

[0053] Calculation example: Suppose there are three components, and their health index The corresponding weighting factors w = {0.3, 0.4, 0.3} are set as constants. Sensitivity adjustment parameters .

[0054] Calculate the overall maintenance demand index : For the first component ( ):

[0055]

[0056]

[0057] For the second component ( ):

[0058]

[0059]

[0060] For the third component ( ):

[0061]

[0062]

[0063] Total Maintenance Demand Index:

[0064] Calculation results This indicates that, considering the current health and importance of all components, the ship's overall maintenance needs are at a moderate level. These figures can help the ship's maintenance team decide whether immediate repairs are necessary or can be postponed.

[0065] Please see Figure 4 The steps to obtain the maintenance time selection result are as follows: Based on the ship's historical operational data, AI is used to extract the ship's idle data during historical periods, including the ship's navigation, berthing and maintenance records in the past period; Based on historical idle data, using the formula:

[0066] Calculate the probability of a ship being idle in the future time period Based on the probability of ships being idle in the future time period The value, combined with the maintenance personnel's shift schedule, is used to select the target maintenance time period, resulting in the maintenance time selection result. It is the probability that a ship will be idle in the future. It is an activity frequency parameter. Indicates the time unit index. It refers to the length of a future time period.

[0067] formula:

[0068] Parameter details and acquisition methods: The length of the timeframe considered for the future is typically based on the vessel's operation and maintenance schedule. For example, if the plan is to find suitable maintenance time within the next three months, then... Set to 90 days.

[0069] Activity frequency parameter, obtained through time series analysis of historical data. This parameter reflects the average frequency of activity occurrence per unit of time and is typically derived from statistical analysis of historical activity data.

[0070] Time unit index, from 1 to , representing each day within a future time period.

[0071] Calculation example: Assume the parameters are as follows: (Considering the next 30 days) (The daily activity frequency is approximately 0.1) Calculation process: Calculate the daily probability of being free : when ,

[0072] when , … until ,

[0073] Assuming the sum is approximately 15, then:

[0074] Calculated idle probability This indicates that the vessel may be idle for 50% of the next 30 days, making it suitable for maintenance.

[0075] Please see Figure 5 The method for calculating the repair urgency index of each component is as follows: Based on the ship's maintenance and usage records, AI is used to extract the service life of the parts to be repaired. and theoretical lifespan ; Based on years of use and theoretical lifespan Combined with the severity of the usage scenarios of the components Through the formula:

[0076] Calculate the maintenance emergency index ,in, and It is the adjustment coefficient. The actual service life of the component. This is the theoretical lifespan of the component. Due to the severity of the usage scenario, The emergency repair index for components. It is an index.

[0077] formula:

[0078] Parameter details and acquisition methods: Component lifespan: obtained directly from the maintenance record database, recording the operating time of each component since installation.

[0079] Theoretical lifespan of a component: The estimated normal service life of a component, provided by the component manufacturer and based on design and test data.

[0080] The severity of the usage scenario is obtained through environmental monitoring, which records the temperature, humidity, and vibration levels of the ship's operating environment, and can be determined by expert rating.

[0081] Environmental impact adjustment coefficient: determined based on the component's sensitivity to environmental factors, it is an empirical set value, usually determined by component testing and historical data analysis.

[0082] Service life deviation adjustment coefficient: an empirically set value used to adjust the impact of the difference between actual service life and theoretical service life on the maintenance emergency index.

[0083] Calculation example: Assume the following parameters: Years (parts have been in use for 10 years) Years (the theoretical lifespan of the component) (Moderate level of environmental severity) (Environmental impact adjustment coefficient) (Service life deviation adjustment coefficient) Calculation process: Calculate the square root part:

[0084] Calculate the logarithmic part:

[0085] Calculate the absolute value and the denominator:

[0086] Integrated calculation of maintenance emergency index :

[0087] Calculated maintenance urgency index This indicates that, considering the component's age, theoretical lifespan, and the severity of the working environment, the component has a moderate to high level of urgency for repair.

[0088] Please see Figure 6 The steps to obtain maintenance priority information are as follows: Based on maintenance urgency index Based on the function of each component and its impact on the safety of ship operation, the importance of each component in ship operation is assessed, and the importance level of each component is obtained. ; Based on the importance level of each component Through the formula:

[0089] Calculate the repair priority for each component. Based on the maintenance priority of the components The values ​​are used to sort the components by repair priority, thus obtaining repair priority information. It is the first Emergency repair index of each component It is the first The importance level of each component in ship operation It is a stability constant. It is the first Repair priority of each component.

[0090] formula:

[0091] Parameter details and acquisition methods: Component repair urgency index: calculated through previous steps.

[0092] Importance level of components in ship operation: The level is assessed based on the component's function and historical maintenance data. The importance assessment of a component is based on its impact on the safety and efficiency of ship operation and can be pre-determined by experts.

[0093] The stability constant is an empirically set value used to ensure the stability of the calculation and prevent the denominator from being zero. It is usually set to a positive number less than 1 to ensure that the denominator is always greater than zero.

[0094] Calculation example: Assume the following parameters: (This indicates the urgency level of a component's repair; a higher value means the component may require repair more quickly.) (This indicates that the component is of moderate importance to the ship's operation.) (Constants used to ensure computational stability) Calculation process: Calculate the square root and logarithmic parts:

[0095]

[0096] Calculate the denominator:

[0097] Integrated calculation of maintenance priority :

[0098] Calculated maintenance priority This indicates that the component should be given priority during maintenance.

[0099] Please see Figure 7 The method for quantitatively evaluating the effectiveness of maintenance is as follows: Based on maintenance priority information, ship maintenance is carried out, and the health index of components before and after maintenance is collected using the following formula:

[0100] Calculate the change in health index of each component before and after repair. ,in, The change in the health index. The health index before maintenance. The health index after repair; Based on changes in health index Through the formula:

[0101] Quantitative values ​​for calculating repair effectiveness ,in, The change in the health index. and For adjustment coefficients, As a quantitative evaluation value for the repair effect, It is the base of the natural logarithm.

[0102] formula:

[0103] Parameter details and acquisition methods: Post-repair health index and pre-repair health index The health index is obtained by measuring the component's performance before and after maintenance through the component's performance monitoring system.

[0104] and The parameters are adjustment coefficients that allow the model to adapt more flexibly to different changes in maintenance outcomes. The parameter settings are based on historical data analysis and experimental data, aiming to best reflect the sensitivity and threshold of maintenance improvements.

[0105] Calculation example: Assume the following parameters: (Indicates the health index before maintenance) (Indicates the health index after repair) (Used to enhance the model's sensitivity to changes in maintenance outcomes) (Thresholds used to adjust the model) Calculation process: calculate :

[0106] Calculate the denominator and combine the formulas:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Calculated quantitative maintenance effect This indicates that the improvement in health index brought about by maintenance is relatively small.

[0115] Please see Figure 8 The steps for obtaining maintenance cycle optimization information are as follows: Quantitative value of repair effect Based on the health index of the repaired parts Through the formula:

[0116] Calculate the average health index of key components. ,in, It is the number of key components. The health index of the repaired parts. This represents the average health index of key components; Based on the average health index of key components Through the formula:

[0117] Calculate the adjusted maintenance cycle This yields maintenance cycle optimization information, among which... It is the target state of health. This is an adjustment factor used to balance the impact of differences between repair effectiveness and actual health index. This represents the average health index of key components. This is the average quantitative value of the component repair effect. For the current maintenance cycle, This is the adjusted maintenance cycle.

[0118] formula:

[0119] Parameter details and acquisition methods: Current maintenance cycle: This is existing data obtained directly from the ship's maintenance record system.

[0120] The average health index of all critical components after repair is calculated by adding up the health indices after repair and then dividing by the number of components. .

[0121] The target health status of a vessel is typically set by the vessel's operations and maintenance team based on the vessel's performance requirements and safety standards.

[0122] The average quantitative value of the repair effect of all components, calculated by adding the repair effect values ​​of each component and dividing by the number of components. The conclusion is as follows.

[0123] : Adjustment coefficient, used to balance the sensitivity of maintenance cycle adjustments, is set based on historical data analysis and predictive models.

[0124] Calculation example: Assume the following parameters: Months (current maintenance cycle) (Average health index of all critical components after repair) (Target health status of the ship) (Average quantitative value of repair effectiveness for all components) (Adjustment coefficient) Calculation process: Calculate the difference between the target health status and the average health index:

[0125] Calculate the adjustment factor:

[0126] Calculate the new maintenance cycle:

[0127] The calculated new maintenance cycle is By appropriately shortening maintenance cycles over several months, the health of components can be optimized.

[0128] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A ship maintenance planning system based on condition assessment, characterized in that, The system includes: The ship maintenance demand assessment module uses AI to extract the status data of the parts on the ship that need maintenance based on the ship's operational data, assesses the health index of each part, and calculates the overall maintenance demand index of the ship based on the health index of each part, thus obtaining the ship maintenance demand information. The maintenance time planning module, based on the ship maintenance demand information and combined with the ship's historical operation data, uses AI to extract the ship's idle data in historical time periods, calculates the idle probability in future time periods, and selects the target maintenance time period according to the maintenance personnel's shift schedule to obtain the maintenance time selection result. Based on the maintenance time selection results, the maintenance priority analysis module extracts the service life and theoretical life data of the components to be maintained through AI according to the ship's maintenance and usage records. It also calculates the maintenance urgency index of each component by combining the severity of the usage scenario and the maintenance priority of each component according to its importance in the ship's operation, thus obtaining maintenance priority information. The maintenance cycle adjustment module performs ship maintenance based on the maintenance priority information, and quantitatively evaluates the maintenance effect based on the changes in the health index of components before and after maintenance. Combined with the target health status of the ship, it adjusts the ship's maintenance cycle to obtain maintenance cycle optimization information.

2. The ship maintenance planning system based on condition assessment according to claim 1, characterized in that, The method for assessing the health index of each component is as follows: Based on the ship's operational data, AI is used to extract the status data of components that need maintenance on the ship, and the real-time performance data of each component is compared with preset standard health status data through formulas; ； Computational performance degradation index ,in, It is a real-time performance metric. It is a performance indicator of standard health status. It is a weighting factor. It is a performance degradation index. It is an index; Based on the performance degradation index Through the formula: ； Calculate the component health index According to the component health index The size of the component is used to assess its health status. and To adjust the coefficient, For component health index, It is a performance degradation index. It is an index, and it is the base of the natural logarithm.

3. The ship maintenance planning system based on condition assessment according to claim 2, characterized in that, The steps for obtaining the ship maintenance demand information are as follows: Based on the component health index Based on the importance of each component in the overall operation of the ship, a weighting factor is assigned to each component. ; Based on the component health index and weighting factors Through the formula: ； Calculate the overall maintenance demand index of ships The overall maintenance demand index of ships The system compares the data with a preset threshold to determine if the vessel requires maintenance, thus obtaining vessel maintenance requirement information. It is the overall maintenance demand index for ships. It is the total number of parts. It is the first Health index of each component It is a weighting factor. It is a constant. It is a sensitivity adjustment parameter. It is the base of the natural logarithm.

4. The ship maintenance planning system based on condition assessment according to claim 1, characterized in that, The steps for obtaining the maintenance time selection result are as follows: Based on the ship's historical operational data, AI is used to extract the ship's idle data during historical periods, including the ship's navigation, berthing and maintenance records in the past period; Based on historical idle data, using the formula: ； Calculate the probability of a ship being idle in the future time period Based on the probability of ships being idle in the future time period The value, combined with the maintenance personnel's shift schedule, is used to select the target maintenance time period, resulting in the maintenance time selection result. It is the probability that a ship will be idle in the future. It is an activity frequency parameter. Indicates the time unit index. It refers to the length of a future time period.

5. The ship maintenance planning system based on condition assessment according to claim 1, characterized in that, The method for calculating the maintenance urgency index of each component is as follows: Based on the ship's maintenance and usage records, AI is used to extract the service life of the parts to be repaired. and theoretical lifespan ; Based on the aforementioned years of use and theoretical lifespan Combined with the severity of the usage scenarios of the components Through the formula: ； Calculate the maintenance emergency index ,in, and It is the adjustment coefficient. The actual service life of the component. For the theoretical lifespan of the component, Due to the severity of the usage scenario, The emergency repair index for components. It is an index.

6. The ship maintenance planning system based on condition assessment according to claim 5, characterized in that, The steps for obtaining the maintenance priority information are as follows: Based on the aforementioned maintenance urgency index Based on the function of each component and its impact on the safety of ship operation, the importance of each component in ship operation is assessed, and the importance level of each component is obtained. ; Based on the importance level of each component Through the formula: ； Calculate the repair priority for each component. Based on the maintenance priority of the components The values ​​are used to sort the components by repair priority, thus obtaining repair priority information. It is the first Emergency repair index of each component It is the first The importance level of each component in ship operation It is a stability constant. It is the first Repair priority of each component.

7. The ship maintenance planning system based on condition assessment according to claim 1, characterized in that, The method for quantitatively evaluating the effectiveness of maintenance is as follows: Based on the aforementioned maintenance priority information, ship maintenance is carried out, and the health index of components before and after maintenance is collected using the formula: ； Calculate the change in health index of each component before and after repair. ,in, The change in the health index. The health index before maintenance. The health index after repair; Based on the change in the health index Through the formula: ； Calculate the quantitative value of maintenance effect ,in, The change in the health index. and For adjustment coefficients, As a quantitative evaluation value for the repair effect, It is the base of the natural logarithm.

8. The ship maintenance planning system based on condition assessment according to claim 7, characterized in that, The steps for obtaining the maintenance cycle optimization information are as follows: The quantitative value of the repair effect Based on the health index of the repaired parts Through the formula: ； Calculate the average health index of key components. ,in, It is the number of key components. The health index of the repaired parts. This represents the average health index of key components; Based on the average health index of the key components Through the formula: ； Calculate the adjusted maintenance cycle This yields maintenance cycle optimization information, among which... It is the target state of health. This is an adjustment factor used to balance the impact of differences between repair effectiveness and actual health index. This represents the average health index of key components. This is the average quantitative value of the component repair effect. For the current maintenance cycle, This is the adjusted maintenance cycle.

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